From 0c4aac5be55ab08a87cab1a271e4a88d6f404382 Mon Sep 17 00:00:00 2001
From: romainsacchi <romain@Romains-iMac.psi.ch>
Date: Fri, 3 May 2024 17:19:16 +0200
Subject: [PATCH 1/6] Update example

---
 .../metals-checkpoint.ipynb                   |   306 -
 dev/.ipynb_checkpoints/pivot-checkpoint.ipynb | 11963 ----------------
 pathways/data/activities_classifications.yaml |  4913 ++++++-
 pathways/pathways.py                          |     2 +-
 4 files changed, 4913 insertions(+), 12271 deletions(-)
 delete mode 100644 dev/.ipynb_checkpoints/metals-checkpoint.ipynb
 delete mode 100644 dev/.ipynb_checkpoints/pivot-checkpoint.ipynb

diff --git a/dev/.ipynb_checkpoints/metals-checkpoint.ipynb b/dev/.ipynb_checkpoints/metals-checkpoint.ipynb
deleted file mode 100644
index 84bbf0b..0000000
--- a/dev/.ipynb_checkpoints/metals-checkpoint.ipynb
+++ /dev/null
@@ -1,306 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c9c8dc83-f048-488b-a8c3-a6c2e212a49e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "arr = xr.open_dataarray(\"results_image_SSP2_metals.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "861fedb1-f9cc-4605-ae9e-fe7cbb1fb26c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Coordinates:\n",
-       "  * year             (year) int32 2005 2006 2007 2008 ... 2097 2098 2099 2100\n",
-       "  * variable         (variable) object 'Biomass CHP' ... 'Wind Onshore'\n",
-       "  * region           (region) object 'BRA' 'CAN' 'CEU' ... 'USA' 'WAF' 'WEU'\n",
-       "  * impact_category  (impact_category) object 'RELICS - metals extraction - A...\n",
-       "  * act_category     (act_category) object 'Manufacture of basic chemicals' ....\n",
-       "  * scenario         (scenario) object 'SSP2-RCP19'\n",
-       "  * model            (model) object 'image'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr.coords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0030853e-aa98-4a28-81c0-98c5eb75c798",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>year</th>\n",
-       "      <th>act_category</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>RELICS - metals extraction - Aluminium</td>\n",
-       "      <td>Biomass CHP</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>RELICS - metals extraction - Aluminium</td>\n",
-       "      <td>Biomass CHP CCS</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>RELICS - metals extraction - Aluminium</td>\n",
-       "      <td>Biomass IGCC</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>RELICS - metals extraction - Aluminium</td>\n",
-       "      <td>Biomass IGCC CCS</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>RELICS - metals extraction - Aluminium</td>\n",
-       "      <td>Biomass ST</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   model    scenario region                         impact_category  \\\n",
-       "0  image  SSP2-RCP19    BRA  RELICS - metals extraction - Aluminium   \n",
-       "1  image  SSP2-RCP19    BRA  RELICS - metals extraction - Aluminium   \n",
-       "2  image  SSP2-RCP19    BRA  RELICS - metals extraction - Aluminium   \n",
-       "3  image  SSP2-RCP19    BRA  RELICS - metals extraction - Aluminium   \n",
-       "4  image  SSP2-RCP19    BRA  RELICS - metals extraction - Aluminium   \n",
-       "\n",
-       "           variable  year                    act_category  value  \n",
-       "0       Biomass CHP  2005  Manufacture of basic chemicals    NaN  \n",
-       "1   Biomass CHP CCS  2005  Manufacture of basic chemicals    NaN  \n",
-       "2      Biomass IGCC  2005  Manufacture of basic chemicals    NaN  \n",
-       "3  Biomass IGCC CCS  2005  Manufacture of basic chemicals    NaN  \n",
-       "4        Biomass ST  2005  Manufacture of basic chemicals    NaN  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
-    "    \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
-    "])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "523aa0b7-5f9f-46da-9366-14a6d0631f1f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "704704"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = [2005, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100]\n",
-    "df = df.loc[df[\"year\"].isin(years)]\n",
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ab9d7898-bea5-42a8-b1ae-79bce25533a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"impact_category\"] = df[\"impact_category\"].str.replace(\"RELICS - metals extraction - \", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "48494068-488f-4682-87ca-cf79768417a8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"value\"] /= 1000 # kg to tons"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "6d17f96f-9c08-4770-9712-caf4cf4da41d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.loc[~df[\"value\"].isnull()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "5f8ec078-301a-4e3d-ac99-699b64065867",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "129695"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "d1080e41-2ab2-4e15-81c4-410879f422b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"500\"\n",
-       "            src=\"pivottable_all_metals.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x1294aeee0>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pivottablejs import pivot_ui\n",
-    "from IPython.display import HTML\n",
-    "pivot_ui(df, outfile_path='pivottable_all_metals.html')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e270b389-27b8-4c09-a396-2162982e60eb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb b/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb
deleted file mode 100644
index 1bef064..0000000
--- a/dev/.ipynb_checkpoints/pivot-checkpoint.ipynb
+++ /dev/null
@@ -1,11963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "18916cee-310e-4785-b496-988d59883636",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "eabc5614-5c90-4bb7-b229-f12ced2f6f04",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr = xr.open_dataarray(\"results_image_SSP2-RCP19.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "05aa3629-f29f-42e2-8f5b-9c717f6649dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = arr.to_dataframe(\"x\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "499da85e-e3a1-4e40-93d6-358b9ae40209",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df=df.loc[df[\"x\"]>0, \"x\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "de978966-a3a9-4022-bf48-d0a59b7577ca",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df = df.unstack()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "f53b633b-fa91-4c6d-a2fd-90c80cf0784f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>act_category</th>\n",
-       "      <th>Carbon capture</th>\n",
-       "      <th>Collection of nonhazardous waste</th>\n",
-       "      <th>Construction of other civil engineering projects</th>\n",
-       "      <th>Construction of roads and railways</th>\n",
-       "      <th>Construction of utility projects</th>\n",
-       "      <th>Electric power generation transmission and distribution</th>\n",
-       "      <th>Extraction of crude petroleum</th>\n",
-       "      <th>Extraction of crude petroleum and natural gas</th>\n",
-       "      <th>Extraction of natural gas</th>\n",
-       "      <th>Extraction of salt</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Site preparation</th>\n",
-       "      <th>Steam and air conditioning supply</th>\n",
-       "      <th>Support activities for crop production</th>\n",
-       "      <th>Support activities for petroleum and natural gas extraction</th>\n",
-       "      <th>Transport via pipeline</th>\n",
-       "      <th>Treatment and coating of metals machining</th>\n",
-       "      <th>Treatment and disposal of hazardous waste</th>\n",
-       "      <th>Treatment and disposal of nonhazardous waste</th>\n",
-       "      <th>Water collection treatment and supply</th>\n",
-       "      <th>other</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>year</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"5\" valign=\"top\">image</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">SSP2-RCP19</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">2020</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">BRA</th>\n",
-       "      <th rowspan=\"5\" valign=\"top\">EF v3.1 - acidification - accumulated exceedance (AE)</th>\n",
-       "      <th>Heating_Residential (water heating)_Liquid fossil</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.360020e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Industry_Chemical (high value)_Liquid fossil</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.935812e+08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>513907.043401</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.860549e+06</td>\n",
-       "      <td>8.043030e+05</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.392382e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Industry_Food and Tobacco_Electricity</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.990820e+07</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>580945.064255</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.033499e+06</td>\n",
-       "      <td>1.410038e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.098854e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Industry_Food and Tobacco_Solid biomass</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.225494e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>298447.144647</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.431459e+05</td>\n",
-       "      <td>7.628308e+07</td>\n",
-       "      <td>3.823759e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.803603e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Industry_Food processing_Electricity</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.990820e+07</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>580945.064255</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.033499e+06</td>\n",
-       "      <td>1.410038e+06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.098854e+05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 69 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "act_category                                                                                                                       Carbon capture  \\\n",
-       "model scenario   year region impact_category                                    variable                                                            \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil             NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                  NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                         NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                       NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                          NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Collection of nonhazardous waste  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                              \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                               NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                           NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                         NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                            NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Construction of other civil engineering projects  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                              \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                               NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                           NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                         NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                                            NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Construction of roads and railways  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                 NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                      NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                             NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                           NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                              NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Construction of utility projects  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                              \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                               NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                           NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                         NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                            NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Electric power generation transmission and distribution  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                                     \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                                NaN         \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                            4.935812e+08         \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                   1.990820e+07         \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                 1.225494e+06         \n",
-       "                                                                                Industry_Food processing_Electricity                                                    1.990820e+07         \n",
-       "\n",
-       "act_category                                                                                                                       Extraction of crude petroleum  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                           \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                            NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                 NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                        NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                      NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                         NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Extraction of crude petroleum and natural gas  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                           \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                            NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                       513907.043401   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                              580945.064255   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                            298447.144647   \n",
-       "                                                                                Industry_Food processing_Electricity                                               580945.064255   \n",
-       "\n",
-       "act_category                                                                                                                       Extraction of natural gas  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                       \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                        NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                             NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                  NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                     NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Extraction of salt  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                 NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                      NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                             NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                           NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                              NaN   \n",
-       "\n",
-       "act_category                                                                                                                       ...  \\\n",
-       "model scenario   year region impact_category                                    variable                                           ...   \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil  ...   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil       ...   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity              ...   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass            ...   \n",
-       "                                                                                Industry_Food processing_Electricity               ...   \n",
-       "\n",
-       "act_category                                                                                                                       Site preparation  \\\n",
-       "model scenario   year region impact_category                                    variable                                                              \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil               NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil           1.860549e+06   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                  2.033499e+06   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                7.431459e+05   \n",
-       "                                                                                Industry_Food processing_Electricity                   2.033499e+06   \n",
-       "\n",
-       "act_category                                                                                                                       Steam and air conditioning supply  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                               \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                            8.043030e+05   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                   1.410038e+06   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                 7.628308e+07   \n",
-       "                                                                                Industry_Food processing_Electricity                                    1.410038e+06   \n",
-       "\n",
-       "act_category                                                                                                                       Support activities for crop production  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                    \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                     NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                          NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                 NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                      3.823759e+06   \n",
-       "                                                                                Industry_Food processing_Electricity                                                  NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Support activities for petroleum and natural gas extraction  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                                         \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                                NaN             \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                                     NaN             \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                            NaN             \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                          NaN             \n",
-       "                                                                                Industry_Food processing_Electricity                                                             NaN             \n",
-       "\n",
-       "act_category                                                                                                                       Transport via pipeline  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                    \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                     NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                          NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                 NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                               NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                  NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Treatment and coating of metals machining  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                       \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                        NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                             NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                  NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                                     NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Treatment and disposal of hazardous waste  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                       \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                        NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                             NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                    NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                  NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                                     NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Treatment and disposal of nonhazardous waste  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                          \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                           NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                                NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                       NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                                     NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                                        NaN   \n",
-       "\n",
-       "act_category                                                                                                                       Water collection treatment and supply  \\\n",
-       "model scenario   year region impact_category                                    variable                                                                                   \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil                                    NaN   \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil                                         NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Electricity                                                NaN   \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass                                              NaN   \n",
-       "                                                                                Industry_Food processing_Electricity                                                 NaN   \n",
-       "\n",
-       "act_category                                                                                                                              other  \n",
-       "model scenario   year region impact_category                                    variable                                                         \n",
-       "image SSP2-RCP19 2020 BRA    EF v3.1 - acidification - accumulated exceedanc... Heating_Residential (water heating)_Liquid fossil  5.360020e+05  \n",
-       "                                                                                Industry_Chemical (high value)_Liquid fossil       1.392382e+06  \n",
-       "                                                                                Industry_Food and Tobacco_Electricity              9.098854e+05  \n",
-       "                                                                                Industry_Food and Tobacco_Solid biomass            8.803603e+05  \n",
-       "                                                                                Industry_Food processing_Electricity               9.098854e+05  \n",
-       "\n",
-       "[5 rows x 69 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "40301255-6a18-4968-857a-2a46ecaac165",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df=df.reset_index().melt(id_vars=[\n",
-    "    \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "60c8b5e2-431f-461b-b0b8-023ed5d57412",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "68699919"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "d7e240c6-78b7-4e0b-b238-72ca5f6f7471",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13627431"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = [2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055, 2060, 2065, 2070, 2075, 2080, 2085, 2090, 2095, 2100]\n",
-    "df_short = df.loc[df[\"year\"].isin(years)]\n",
-    "len(df_short)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "9bb4ebbb-1516-44f1-9858-0fbebe23ac62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "930220"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_short = df_short.loc[~df_short[\"value\"].isnull()]\n",
-    "len(df_short)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f33230d2-44fe-4c47-a3b0-eecbf3fd311c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "93289b77-0a65-4213-becc-5c9ee28d8730",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>year</th>\n",
-       "      <th>act_category</th>\n",
-       "      <th>value</th>\n",
-       "      <th>Main sector</th>\n",
-       "      <th>Sub sector</th>\n",
-       "      <th>Energy carrier</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>188323</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>CAN</td>\n",
-       "      <td>EF v3.1 - acidification - accumulated exceedan...</td>\n",
-       "      <td>Heating_Residential (water heating)_Electricity</td>\n",
-       "      <td>2035</td>\n",
-       "      <td>Carbon capture</td>\n",
-       "      <td>2.375128e+06</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Residential (water heating)</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>188327</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>CAN</td>\n",
-       "      <td>EF v3.1 - acidification - accumulated exceedan...</td>\n",
-       "      <td>Industry_Pulp and Paper_Electricity</td>\n",
-       "      <td>2035</td>\n",
-       "      <td>Carbon capture</td>\n",
-       "      <td>2.669263e+06</td>\n",
-       "      <td>Industry</td>\n",
-       "      <td>Pulp and Paper</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>188533</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>CAN</td>\n",
-       "      <td>EF v3.1 - particulate matter formation - impac...</td>\n",
-       "      <td>Heating_Residential (water heating)_Electricity</td>\n",
-       "      <td>2035</td>\n",
-       "      <td>Carbon capture</td>\n",
-       "      <td>1.651579e+01</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Residential (water heating)</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>188537</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>CAN</td>\n",
-       "      <td>EF v3.1 - particulate matter formation - impac...</td>\n",
-       "      <td>Industry_Pulp and Paper_Electricity</td>\n",
-       "      <td>2035</td>\n",
-       "      <td>Carbon capture</td>\n",
-       "      <td>1.856110e+01</td>\n",
-       "      <td>Industry</td>\n",
-       "      <td>Pulp and Paper</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190269</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>EAF</td>\n",
-       "      <td>EF v3.1 - acidification - accumulated exceedan...</td>\n",
-       "      <td>Industry_Food and Tobacco_Electricity</td>\n",
-       "      <td>2035</td>\n",
-       "      <td>Carbon capture</td>\n",
-       "      <td>9.791550e+05</td>\n",
-       "      <td>Industry</td>\n",
-       "      <td>Food and Tobacco</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        model    scenario region  \\\n",
-       "188323  image  SSP2-RCP19    CAN   \n",
-       "188327  image  SSP2-RCP19    CAN   \n",
-       "188533  image  SSP2-RCP19    CAN   \n",
-       "188537  image  SSP2-RCP19    CAN   \n",
-       "190269  image  SSP2-RCP19    EAF   \n",
-       "\n",
-       "                                          impact_category  \\\n",
-       "188323  EF v3.1 - acidification - accumulated exceedan...   \n",
-       "188327  EF v3.1 - acidification - accumulated exceedan...   \n",
-       "188533  EF v3.1 - particulate matter formation - impac...   \n",
-       "188537  EF v3.1 - particulate matter formation - impac...   \n",
-       "190269  EF v3.1 - acidification - accumulated exceedan...   \n",
-       "\n",
-       "                                               variable  year    act_category  \\\n",
-       "188323  Heating_Residential (water heating)_Electricity  2035  Carbon capture   \n",
-       "188327              Industry_Pulp and Paper_Electricity  2035  Carbon capture   \n",
-       "188533  Heating_Residential (water heating)_Electricity  2035  Carbon capture   \n",
-       "188537              Industry_Pulp and Paper_Electricity  2035  Carbon capture   \n",
-       "190269            Industry_Food and Tobacco_Electricity  2035  Carbon capture   \n",
-       "\n",
-       "               value Main sector                   Sub sector Energy carrier  \n",
-       "188323  2.375128e+06     Heating  Residential (water heating)    Electricity  \n",
-       "188327  2.669263e+06    Industry               Pulp and Paper    Electricity  \n",
-       "188533  1.651579e+01     Heating  Residential (water heating)    Electricity  \n",
-       "188537  1.856110e+01    Industry               Pulp and Paper    Electricity  \n",
-       "190269  9.791550e+05    Industry             Food and Tobacco    Electricity  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_short.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "1f2a611d-607f-41e9-b551-14f007d0a207",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df_short.loc[df_short[\"value\"]<0, \"value\"] = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6a129543-43d1-4ac1-9550-8349517e5c81",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"500\"\n",
-       "            src=\"IMAGE_SSP2_RCP19.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x12ee7bc10>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pivottablejs import pivot_ui\n",
-    "from IPython.display import HTML\n",
-    "pivot_ui(df_short.loc[~df_short[\"value\"].isnull()], outfile_path='IMAGE_SSP2_RCP19.html')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c2b2a738-eadd-4484-af93-fa4ef448819f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e09d7255-3959-4375-83c9-9daba005535f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99aa311e-ba9b-4fd0-9fcb-198834964e25",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "85c8b131-4863-4fce-b15a-bc8885b10cb6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ec0e303-72e9-4c05-9737-7946a2fd6e2f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "994dd3fb-79a0-4da6-a3fd-7b9f51c7f21e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "09ecc66d-f028-4c60-a8a1-3f80d27c4821",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathways import Pathways"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b3b37d5c-8f65-4fb3-8f1b-7bbc05327081",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Invalid datapackage: Descriptor validation error: {'path': 'mapping/mapping.yaml', 'profile': 'data-resource', 'name': 'mapping', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/89\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n",
-      "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/89/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n"
-     ]
-    }
-   ],
-   "source": [
-    "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2/datapackage.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "2f3bf87a-5922-41b6-92e5-0a5e55500e8e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calculating LCA results for image...\n",
-      "--- Calculating LCA results for SSP2-Base...\n",
-      "------ Calculating LCA results for 2005...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "0% [##########################] 100% | ETA: 00:00:00\n",
-      "Total time elapsed: 00:17:59\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "------ Calculating LCA results for 2010...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "0% [######################    ] 100% | ETA: 00:02:45\n",
-      "KeyboardInterrupt\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "p.calculate(\n",
-    "    methods=[\n",
-    "        'EF v3.1 - acidification - accumulated exceedance (AE)',\n",
-    "        'EF v3.1 - climate change - global warming potential (GWP100)',\n",
-    "        'EF v3.1 - ecotoxicity: freshwater - comparative toxic unit for ecosystems (CTUe)',\n",
-    "        'EF v3.1 - energy resources: non-renewable - abiotic depletion potential (ADP): fossil fuels',\n",
-    "        'EF v3.1 - eutrophication: freshwater - fraction of nutrients reaching freshwater end compartment (P)',\n",
-    "        #'EF v3.1 - eutrophication: marine - fraction of nutrients reaching marine end compartment (N)',\n",
-    "        #'EF v3.1 - eutrophication: terrestrial - accumulated exceedance (AE)',\n",
-    "        #'EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)',\n",
-    "        #'EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)',\n",
-    "        #'EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235',\n",
-    "        #'EF v3.1 - land use - soil quality index',\n",
-    "        'EF v3.1 - material resources: metals/minerals - abiotic depletion potential (ADP): elements (ultimate reserves)',\n",
-    "        'EF v3.1 - ozone depletion - ozone depletion potential (ODP)',\n",
-    "        'EF v3.1 - particulate matter formation - impact on human health',\n",
-    "        #'EF v3.1 - photochemical oxidant formation: human health - tropospheric ozone concentration increase',\n",
-    "        'EF v3.1 - water use - user deprivation potential (deprivation-weighted water consumption)',\n",
-    "        'RELICS - metals extraction - Aluminium',\n",
-    "        'RELICS - metals extraction - Cobalt',\n",
-    "        'RELICS - metals extraction - Copper',\n",
-    "        'RELICS - metals extraction - Graphite',\n",
-    "        'RELICS - metals extraction - Lithium',\n",
-    "        'RELICS - metals extraction - Molybdenum',\n",
-    "        'RELICS - metals extraction - Neodymium',\n",
-    "        'RELICS - metals extraction - Nickel',\n",
-    "        'RELICS - metals extraction - Platinum',\n",
-    "        'RELICS - metals extraction - Vanadium',\n",
-    "        'RELICS - metals extraction - Zinc',\n",
-    "    ],\n",
-    "    regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
-    "    scenarios=[\n",
-    "        \"SSP2-Base\",\n",
-    "        #\"SSP2-RCP19\",\n",
-    "    ],\n",
-    "    variables=[\n",
-    "        v for v in p.scenarios.coords[\"variables\"].values\n",
-    "        if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
-    "    ],\n",
-    "    years=[2005, 2010, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
-    "    characterization=True,\n",
-    "    data_type=np.float32,\n",
-    "    multiprocessing=False,\n",
-    "    demand_cutoff=0.1,\n",
-    ")\n",
-    "arr = p.display_results()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "83d7622b-18db-429b-9ec5-453244db74bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p.scenarios"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a63727ad-455f-4d7d-80fb-086678cbf901",
-   "metadata": {},
-   "outputs": [
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-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; (model: 0, scenario: 0, year: 0, region: 0,\n",
-       "                           impact_category: 0, variable: 0, act_category: 0)&gt;\n",
-       "array([], shape=(0, 0, 0, 0, 0, 0, 0), dtype=float64)\n",
-       "Coordinates:\n",
-       "  * model            (model) object \n",
-       "  * scenario         (scenario) object \n",
-       "  * year             (year) int64 \n",
-       "  * region           (region) object \n",
-       "  * impact_category  (impact_category) object \n",
-       "  * variable         (variable) float64 \n",
-       "  * act_category     (act_category) object </pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>model</span>: 0</li><li><span class='xr-has-index'>scenario</span>: 0</li><li><span class='xr-has-index'>year</span>: 0</li><li><span class='xr-has-index'>region</span>: 0</li><li><span class='xr-has-index'>impact_category</span>: 0</li><li><span class='xr-has-index'>variable</span>: 0</li><li><span class='xr-has-index'>act_category</span>: 0</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9260d193-faaf-46fa-91d3-df90a266b455' class='xr-array-in' type='checkbox' checked><label for='section-9260d193-faaf-46fa-91d3-df90a266b455' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span></span></div><div class='xr-array-data'><pre>array([], shape=(0, 0, 0, 0, 0, 0, 0), dtype=float64)</pre></div></div></li><li class='xr-section-item'><input id='section-0b0babbe-6252-42ee-b00d-b9613426e59c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b0babbe-6252-42ee-b00d-b9613426e59c' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-4258a7c1-385f-4a91-b724-384799d2028e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4258a7c1-385f-4a91-b724-384799d2028e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-113c9209-c4be-4c7e-9a7a-1064bd1c902a' class='xr-var-data-in' type='checkbox'><label for='data-113c9209-c4be-4c7e-9a7a-1064bd1c902a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scenario</span></div><div class='xr-var-dims'>(scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-8cac74ca-065c-4f4c-8585-ec54f8410e1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8cac74ca-065c-4f4c-8585-ec54f8410e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-802e15a4-8595-4864-a4d8-862d568aba0d' class='xr-var-data-in' type='checkbox'><label for='data-802e15a4-8595-4864-a4d8-862d568aba0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'></div><input id='attrs-38de9260-c8d5-47ab-8e29-45f011452224' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38de9260-c8d5-47ab-8e29-45f011452224' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c7f383-8320-4778-87fa-ce7783371f75' class='xr-var-data-in' type='checkbox'><label for='data-99c7f383-8320-4778-87fa-ce7783371f75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-655c1dfe-cd24-43ad-ad33-17c41cc7d218' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-655c1dfe-cd24-43ad-ad33-17c41cc7d218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f1af02-f5f7-41c2-ac26-23fa928f5861' class='xr-var-data-in' type='checkbox'><label for='data-b6f1af02-f5f7-41c2-ac26-23fa928f5861' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>impact_category</span></div><div class='xr-var-dims'>(impact_category)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-f13f7307-17cd-4cfd-a7c6-ce65f44ab291' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f13f7307-17cd-4cfd-a7c6-ce65f44ab291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4905237f-5bfa-4ab9-adfd-83b32d3ac1e4' class='xr-var-data-in' type='checkbox'><label for='data-4905237f-5bfa-4ab9-adfd-83b32d3ac1e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'></div><input id='attrs-c881cf7d-2473-49c2-b79b-8ade135b5dc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c881cf7d-2473-49c2-b79b-8ade135b5dc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fb21623-70fe-4ffb-ba9e-956f61e5974f' class='xr-var-data-in' type='checkbox'><label for='data-5fb21623-70fe-4ffb-ba9e-956f61e5974f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=float64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>act_category</span></div><div class='xr-var-dims'>(act_category)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-234d1202-750f-4bc2-bed2-07c5ac0afa32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-234d1202-750f-4bc2-bed2-07c5ac0afa32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7e5c1a-6763-431f-911b-5c53eb8b865e' class='xr-var-data-in' type='checkbox'><label for='data-db7e5c1a-6763-431f-911b-5c53eb8b865e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56a40fcd-5b57-4488-a570-183064a73511' class='xr-section-summary-in' type='checkbox'  ><label for='section-56a40fcd-5b57-4488-a570-183064a73511' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7346c56e-0cc7-4edd-af93-cd2f15a31f8f' class='xr-index-data-in' type='checkbox'/><label for='index-7346c56e-0cc7-4edd-af93-cd2f15a31f8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>scenario</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b44dc4d2-679e-41e3-80f9-8c2fca4afad9' class='xr-index-data-in' type='checkbox'/><label for='index-b44dc4d2-679e-41e3-80f9-8c2fca4afad9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;object&#x27;, name=&#x27;scenario&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f70f5691-4d8d-4527-b1fc-488bb8dc6290' class='xr-index-data-in' type='checkbox'/><label for='index-f70f5691-4d8d-4527-b1fc-488bb8dc6290' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67741457-63d1-4755-a199-904f9eab9c2b' class='xr-index-data-in' type='checkbox'/><label for='index-67741457-63d1-4755-a199-904f9eab9c2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;object&#x27;, name=&#x27;region&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>impact_category</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0fc1a36-62ae-41b2-95a2-53a9954d2a46' class='xr-index-data-in' type='checkbox'/><label for='index-a0fc1a36-62ae-41b2-95a2-53a9954d2a46' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;object&#x27;, name=&#x27;impact_category&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>variable</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cc370b1f-055a-4f1a-a2d8-e942083fb090' class='xr-index-data-in' type='checkbox'/><label for='index-cc370b1f-055a-4f1a-a2d8-e942083fb090' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;float64&#x27;, name=&#x27;variable&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>act_category</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b8354bc-3910-404e-9653-a2326a452e2d' class='xr-index-data-in' type='checkbox'/><label for='index-9b8354bc-3910-404e-9653-a2326a452e2d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([], dtype=&#x27;object&#x27;, name=&#x27;act_category&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-590e553d-5344-46ad-a7bf-ef3490d032ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-590e553d-5344-46ad-a7bf-ef3490d032ca' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' (model: 0, scenario: 0, year: 0, region: 0,\n",
-       "                           impact_category: 0, variable: 0, act_category: 0)>\n",
-       "array([], shape=(0, 0, 0, 0, 0, 0, 0), dtype=float64)\n",
-       "Coordinates:\n",
-       "  * model            (model) object \n",
-       "  * scenario         (scenario) object \n",
-       "  * year             (year) int64 \n",
-       "  * region           (region) object \n",
-       "  * impact_category  (impact_category) object \n",
-       "  * variable         (variable) float64 \n",
-       "  * act_category     (act_category) object "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "55c1ae5b-f165-4b45-84b4-f46c8b859fdc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.to_netcdf(\"results_image_SSP2.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0078f7fc-a267-4eab-b28d-d943133cd2c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'x'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mreset_index()\u001b[38;5;241m.\u001b[39mmelt(id_vars\u001b[38;5;241m=\u001b[39m[\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscenario\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregion\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimpact_category\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      3\u001b[0m ])\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:3895\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_single_key:\n\u001b[1;32m   3894\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 3895\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_multilevel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3896\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   3897\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:3953\u001b[0m, in \u001b[0;36mDataFrame._getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3951\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_multilevel\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[1;32m   3952\u001b[0m     \u001b[38;5;66;03m# self.columns is a MultiIndex\u001b[39;00m\n\u001b[0;32m-> 3953\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3954\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, (\u001b[38;5;28mslice\u001b[39m, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m   3955\u001b[0m         new_columns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns[loc]\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2925\u001b[0m, in \u001b[0;36mMultiIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2922\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m mask\n\u001b[1;32m   2924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 2925\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_level_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2926\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _maybe_to_slice(loc)\n\u001b[1;32m   2928\u001b[0m keylen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(key)\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/indexes/multi.py:3303\u001b[0m, in \u001b[0;36mMultiIndex._get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   3299\u001b[0m     end \u001b[38;5;241m=\u001b[39m algos\u001b[38;5;241m.\u001b[39msearchsorted(level_codes, idx, side\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mright\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;241m==\u001b[39m end:\n\u001b[1;32m   3302\u001b[0m     \u001b[38;5;66;03m# The label is present in self.levels[level] but unused:\u001b[39;00m\n\u001b[0;32m-> 3303\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m   3304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mslice\u001b[39m(start, end)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'x'"
-     ]
-    }
-   ],
-   "source": [
-    "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
-    "    \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "42f0f2c3-2faf-42a6-adcf-b13d4942d2cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df[[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMain sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSub sector\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEnergy carrier\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvariable\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:136\u001b[0m, in \u001b[0;36mforbid_nonstring_types.<locals>._forbid_nonstring_types.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    131\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use .str.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with values of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    133\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minferred dtype \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inferred_dtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    134\u001b[0m     )\n\u001b[1;32m    135\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 136\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:916\u001b[0m, in \u001b[0;36mStringMethods.split\u001b[0;34m(self, pat, n, expand, regex)\u001b[0m\n\u001b[1;32m    914\u001b[0m     regex \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39marray\u001b[38;5;241m.\u001b[39m_str_split(pat, n, expand, regex)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturns_string\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexpand\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/strings/accessor.py:400\u001b[0m, in \u001b[0;36mStringMethods._wrap_result\u001b[0;34m(self, result, name, expand, fill_value, returns_string, returns_bool)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m expand:\n\u001b[1;32m    399\u001b[0m     cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor_expanddim\n\u001b[0;32m--> 400\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mcons\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    401\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;66;03m# Must be a Series\u001b[39;00m\n\u001b[1;32m    403\u001b[0m     cons \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_orig\u001b[38;5;241m.\u001b[39m_constructor\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/frame.py:809\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    808\u001b[0m         columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[0;32m--> 809\u001b[0m     arrays, columns, index \u001b[38;5;241m=\u001b[39m \u001b[43mnested_data_to_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    810\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# error: Argument 3 to \"nested_data_to_arrays\" has incompatible\u001b[39;49;00m\n\u001b[1;32m    811\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# type \"Optional[Collection[Any]]\"; expected \"Optional[Index]\"\u001b[39;49;00m\n\u001b[1;32m    812\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    813\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    814\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m    815\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    816\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    817\u001b[0m     mgr \u001b[38;5;241m=\u001b[39m arrays_to_mgr(\n\u001b[1;32m    818\u001b[0m         arrays,\n\u001b[1;32m    819\u001b[0m         columns,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    822\u001b[0m         typ\u001b[38;5;241m=\u001b[39mmanager,\n\u001b[1;32m    823\u001b[0m     )\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:520\u001b[0m, in \u001b[0;36mnested_data_to_arrays\u001b[0;34m(data, columns, index, dtype)\u001b[0m\n\u001b[1;32m    517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named_tuple(data[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;129;01mand\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    518\u001b[0m     columns \u001b[38;5;241m=\u001b[39m ensure_index(data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m_fields)\n\u001b[0;32m--> 520\u001b[0m arrays, columns \u001b[38;5;241m=\u001b[39m \u001b[43mto_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    521\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[1;32m    523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:835\u001b[0m, in \u001b[0;36mto_arrays\u001b[0;34m(data, columns, dtype)\u001b[0m\n\u001b[1;32m    832\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arrays, columns\n\u001b[1;32m    834\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 835\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[43m_list_to_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    836\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data[\u001b[38;5;241m0\u001b[39m], abc\u001b[38;5;241m.\u001b[39mMapping):\n\u001b[1;32m    837\u001b[0m     arr, columns \u001b[38;5;241m=\u001b[39m _list_of_dict_to_arrays(data, columns)\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/core/internals/construction.py:856\u001b[0m, in \u001b[0;36m_list_to_arrays\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    853\u001b[0m     content \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39mto_object_array_tuples(data)\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;66;03m# list of lists\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m     content \u001b[38;5;241m=\u001b[39m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_object_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m content\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
-    "    \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
-    "])\n",
-    "df[['Main sector', 'Sub sector', 'Energy carrier']] = df['variable'].str.split('_', expand=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "67b9d6b0-ed89-44b3-9bf1-b11aa20e1b84",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Invalid datapackage: Descriptor validation error: {'path': 'mapping/mapping.yaml', 'profile': 'data-resource', 'name': 'mapping', 'format': 'yaml', 'mediatype': 'text/yaml', 'encoding': 'utf-8'} is not valid under any of the given schemas at \"resources/89\" in descriptor and at \"properties/resources/items/oneOf\" in profile\n",
-      "Invalid datapackage: Descriptor validation error: 'data-resource' is not one of ['tabular-data-resource'] at \"resources/89/profile\" in descriptor and at \"properties/resources/items/properties/profile/enum\" in profile\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pathways import Pathways\n",
-    "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2/datapackage.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "db6729e1-78a9-467f-a8cb-7fa272bb2734",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Calculating LCA results for image...\n",
-      "--- Calculating LCA results for SSP2-Base...\n",
-      "------ Calculating LCA results for 2020...\n",
-      "------ Calculating LCA results for 2030...\n",
-      "------ Calculating LCA results for 2040...\n",
-      "------ Calculating LCA results for 2050...\n",
-      "------ Calculating LCA results for 2060...\n",
-      "------ Calculating LCA results for 2070...\n",
-      "------ Calculating LCA results for 2080...\n",
-      "------ Calculating LCA results for 2090...\n",
-      "------ Calculating LCA results for 2100...\n",
-      "--- Calculating LCA results for SSP2-RCP19...\n",
-      "------ Calculating LCA results for 2020...\n",
-      "------ Calculating LCA results for 2030...\n",
-      "------ Calculating LCA results for 2040...\n",
-      "------ Calculating LCA results for 2050...\n",
-      "------ Calculating LCA results for 2060...\n",
-      "------ Calculating LCA results for 2070...\n",
-      "------ Calculating LCA results for 2080...\n",
-      "------ Calculating LCA results for 2090...\n",
-      "------ Calculating LCA results for 2100...\n"
-     ]
-    }
-   ],
-   "source": [
-    "metals = [\n",
-    "    #\"Aluminium\",\n",
-    "    #\"Cadmium\",\n",
-    "    \"Cobalt\",\n",
-    "    \"Graphite\",\n",
-    "    #\"Iridium\",\n",
-    "    #\"Iron\",\n",
-    "    #\"Lanthanum\",\n",
-    "    #\"Lead\",\n",
-    "    \"Lithium\",\n",
-    "    #\"Magnesium\",\n",
-    "    #\"Manganese\",\n",
-    "    \"Molybdenum\",\n",
-    "    \"Neodymium\",\n",
-    "    \"Nickel\",\n",
-    "    #\"Palladium\",\n",
-    "    \"Platinum\",\n",
-    "    #\"Tin\",\n",
-    "    #\"Silver\",\n",
-    "    \"Titanium\",\n",
-    "    #\"Uranium\",\n",
-    "    \"Vanadium\",\n",
-    "    \"Zinc\",\n",
-    "]\n",
-    "\n",
-    "p.calculate(\n",
-    "    regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
-    "    #regions=[\"World\",],\n",
-    "    scenarios=[\n",
-    "        \"SSP2-Base\",\n",
-    "        \"SSP2-RCP19\",\n",
-    "    ],\n",
-    "    #regions=[\"WEU\",],\n",
-    "    years=[2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
-    "    variables=[\n",
-    "        v for v in p.scenarios.coords[\"variables\"].values\n",
-    "        if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
-    "    ],\n",
-    "    characterization=False,\n",
-    "    flows=[(metal, \"natural resource\", \"in ground\", \"kilogram\") for metal in metals]\n",
-    "    #flows=[(\"Carbon dioxide, fossil\", \"air\", \"unspecified\", \"kilogram\"),]\n",
-    ")\n",
-    "arr = p.display_results(cutoff=0.01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ee0e2805-adf5-43c4-92af-8e7c28f82bf5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "\n",
-    "arr = xr.open_dataarray(\"results_image_SSP2_metals2.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4b1f9ec1-4bba-4d79-90d0-2273b4ff5697",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.to_netcdf(\"results_image_SSP2_metals2.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a14f36a5-eab7-4510-a01b-a4bd048fe777",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>year</th>\n",
-       "      <th>act_category</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-Base</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt - natural resource - in ground - kilogram</td>\n",
-       "      <td>Heating_Commercial (space heating)_Electricity</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-Base</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt - natural resource - in ground - kilogram</td>\n",
-       "      <td>Heating_Commercial (space heating)_Liquid fossil</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-Base</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt - natural resource - in ground - kilogram</td>\n",
-       "      <td>Heating_Commercial (space heating)_Natural gas</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-Base</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt - natural resource - in ground - kilogram</td>\n",
-       "      <td>Heating_Commercial (space heating)_Solid biomass</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-Base</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt - natural resource - in ground - kilogram</td>\n",
-       "      <td>Heating_Commercial (space heating)_Solid coal</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   model   scenario region                                   impact_category  \\\n",
-       "0  image  SSP2-Base    BRA  Cobalt - natural resource - in ground - kilogram   \n",
-       "1  image  SSP2-Base    BRA  Cobalt - natural resource - in ground - kilogram   \n",
-       "2  image  SSP2-Base    BRA  Cobalt - natural resource - in ground - kilogram   \n",
-       "3  image  SSP2-Base    BRA  Cobalt - natural resource - in ground - kilogram   \n",
-       "4  image  SSP2-Base    BRA  Cobalt - natural resource - in ground - kilogram   \n",
-       "\n",
-       "                                           variable  year  \\\n",
-       "0    Heating_Commercial (space heating)_Electricity  2020   \n",
-       "1  Heating_Commercial (space heating)_Liquid fossil  2020   \n",
-       "2    Heating_Commercial (space heating)_Natural gas  2020   \n",
-       "3  Heating_Commercial (space heating)_Solid biomass  2020   \n",
-       "4     Heating_Commercial (space heating)_Solid coal  2020   \n",
-       "\n",
-       "                     act_category  value  \n",
-       "0  Manufacture of basic chemicals    NaN  \n",
-       "1  Manufacture of basic chemicals    NaN  \n",
-       "2  Manufacture of basic chemicals    NaN  \n",
-       "3  Manufacture of basic chemicals    NaN  \n",
-       "4  Manufacture of basic chemicals    NaN  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = arr.to_dataframe(\"x\").unstack()[\"x\"].reset_index().melt(id_vars=[\n",
-    "    \"model\", \"scenario\", \"region\", \"impact_category\", \"variable\", \"year\"\n",
-    "])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9f451db5-7bcb-4b02-b8f6-76c3e260a3ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1906632"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = [2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100]\n",
-    "df_short = df.loc[df[\"year\"].isin(years)]\n",
-    "len(df_short)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d91c5cba-2b06-4237-96d5-c09a8752df8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n",
-      "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n",
-      "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1532667233.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_short[['Main sector', 'Sub sector', 'Energy carrier']] = df_short['variable'].str.split('_', expand=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8a317d76-b749-462d-946a-af43d4dd3728",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/3107786552.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_short[\"impact_category\"] = df_short[\"impact_category\"].str.replace(\" - natural resource - in ground - kilogram\", \"\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_short[\"impact_category\"] = df_short[\"impact_category\"].str.replace(\" - natural resource - in ground - kilogram\", \"\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e02b9327-29db-4280-9401-121fb6eb8f54",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/cn/pys1j9rn0y7djkhv3hfdtrs00000gn/T/ipykernel_16102/1133324581.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df_short[\"value\"] /= 1000\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_short[\"value\"] /= 1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a078dea5-fd2e-48aa-83cf-5dbc42ae802b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Cobalt', 'Lithium', 'Nickel', 'Platinum', 'Titanium', 'Vanadium',\n",
-       "       'Zinc'], dtype=object)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_short[\"impact_category\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e24ef9b7-094b-4b63-a725-ac570045e1fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "371076"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df_short.loc[~df_short[\"value\"].isnull()])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0b4be8e2-bf2b-4e75-978e-9933acedeada",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8b0b8d60-9048-45be-be52-3ea3b32305d1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"100%\"\n",
-       "            height=\"500\"\n",
-       "            src=\"pivottable_all_metals.html\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x139743b20>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from pivottablejs import pivot_ui\n",
-    "from IPython.display import HTML\n",
-    "pivot_ui(df_short.loc[~df_short[\"value\"].isnull()], outfile_path='pivottable_all_metals.html')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "75baf250-be1a-4123-995b-2189d7198a47",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "ename": "DataPackageException",
-     "evalue": "Unable to load JSON at \"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\"",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mMissingSchema\u001b[0m                             Traceback (most recent call last)",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/helpers.py:55\u001b[0m, in \u001b[0;36mretrieve_descriptor\u001b[0;34m(descriptor)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m     req \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_descriptor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m     req\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/api.py:73\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \n\u001b[1;32m     65\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mget\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/sessions.py:575\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    563\u001b[0m req \u001b[38;5;241m=\u001b[39m Request(\n\u001b[1;32m    564\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod\u001b[38;5;241m.\u001b[39mupper(),\n\u001b[1;32m    565\u001b[0m     url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    573\u001b[0m     hooks\u001b[38;5;241m=\u001b[39mhooks,\n\u001b[1;32m    574\u001b[0m )\n\u001b[0;32m--> 575\u001b[0m prep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    577\u001b[0m proxies \u001b[38;5;241m=\u001b[39m proxies \u001b[38;5;129;01mor\u001b[39;00m {}\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/sessions.py:486\u001b[0m, in \u001b[0;36mSession.prepare_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    485\u001b[0m p \u001b[38;5;241m=\u001b[39m PreparedRequest()\n\u001b[0;32m--> 486\u001b[0m \u001b[43mp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    487\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    488\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    489\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    491\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    492\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_class\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCaseInsensitiveDict\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    495\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    496\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_setting\u001b[49m\u001b[43m(\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerged_cookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    498\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhooks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmerge_hooks\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhooks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhooks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    499\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m p\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/models.py:368\u001b[0m, in \u001b[0;36mPreparedRequest.prepare\u001b[0;34m(self, method, url, headers, files, data, params, auth, cookies, hooks, json)\u001b[0m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_method(method)\n\u001b[0;32m--> 368\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_headers(headers)\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/requests/models.py:439\u001b[0m, in \u001b[0;36mPreparedRequest.prepare_url\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m scheme:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MissingSchema(\n\u001b[1;32m    440\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid URL \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: No scheme supplied. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    441\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPerhaps you meant https://\u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    442\u001b[0m     )\n\u001b[1;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m host:\n",
-      "\u001b[0;31mMissingSchema\u001b[0m: Invalid URL '/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json': No scheme supplied. Perhaps you meant https:///Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json?",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mDataPackageException\u001b[0m                      Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43mPathways\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatapackage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:327\u001b[0m, in \u001b[0;36mPathways.__init__\u001b[0;34m(self, datapackage)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, datapackage):\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatapackage \u001b[38;5;241m=\u001b[39m datapackage\n\u001b[0;32m--> 327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m validate_datapackage(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_datapackage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    328\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmapping \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_mapping()\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmapping\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_final_energy_mapping())\n",
-      "File \u001b[0;32m~/GitHub/pathways/pathways/pathways.py:351\u001b[0m, in \u001b[0;36mPathways.read_datapackage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_datapackage\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataPackage:\n\u001b[1;32m    344\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Read the datapackage.json file.\u001b[39;00m\n\u001b[1;32m    345\u001b[0m \n\u001b[1;32m    346\u001b[0m \u001b[38;5;124;03m    Returns\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[38;5;124;03m        The datapackage as a dictionary.\u001b[39;00m\n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 351\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataPackage\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatapackage\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/package.py:102\u001b[0m, in \u001b[0;36mPackage.__init__\u001b[0;34m(self, descriptor, base_path, strict, unsafe, storage, schema, default_base_path, **options)\u001b[0m\n\u001b[1;32m     99\u001b[0m         descriptor[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresources\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mappend({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m'\u001b[39m: bucket})\n\u001b[1;32m    101\u001b[0m \u001b[38;5;66;03m# Process descriptor\u001b[39;00m\n\u001b[0;32m--> 102\u001b[0m descriptor \u001b[38;5;241m=\u001b[39m \u001b[43mhelpers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mretrieve_descriptor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdescriptor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    103\u001b[0m descriptor \u001b[38;5;241m=\u001b[39m helpers\u001b[38;5;241m.\u001b[39mdereference_package_descriptor(descriptor, base_path)\n\u001b[1;32m    105\u001b[0m \u001b[38;5;66;03m# Handle deprecated resource.path/url\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/datapackage/helpers.py:62\u001b[0m, in \u001b[0;36mretrieve_descriptor\u001b[0;34m(descriptor)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mIOError\u001b[39;00m, requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mRequestException) \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m     61\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnable to load JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m descriptor\n\u001b[0;32m---> 62\u001b[0m     \u001b[43msix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_from\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexceptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataPackageException\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;66;03m# Python2 doesn't have json.JSONDecodeError (use ValueErorr)\u001b[39;00m\n\u001b[1;32m     65\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnable to parse JSON at \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (descriptor, error)\n",
-      "File \u001b[0;32m<string>:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
-      "\u001b[0;31mDataPackageException\u001b[0m: Unable to load JSON at \"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\""
-     ]
-    }
-   ],
-   "source": [
-    "p = Pathways(datapackage=\"/Users/romain/GitHub/premise/dev/image-SSP2-RCP19/datapackage.json\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dcd996a6-3ba8-480a-9ea7-24aced9c2fc2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p.calculate(\n",
-    "    methods=[\n",
-    "        'IPCC 2021 - climate change - GWP 100a, incl. H',\n",
-    "        'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - renewable energy resources',\n",
-    "        'EN15804 - inventory indicators ISO21930 - Cumulative Energy Demand - non-renewable energy resources',\n",
-    "        'EN15804 - inventory indicators ISO21930 - use of net fresh water',\n",
-    "        'EF v3.1 EN15804 - particulate matter formation - impact on human health',\n",
-    "    ],\n",
-    "    regions=[r for r in p.scenarios.coords[\"region\"].values if r!=\"World\"],\n",
-    "    #regions=[\"World\",],\n",
-    "    scenarios=[\n",
-    "        #\"SSP2-Base\",\n",
-    "        \"SSP2-RCP19\",\n",
-    "    ],\n",
-    "    years=[2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100],\n",
-    "    variables=[\n",
-    "        v for v in p.scenarios.coords[\"variables\"].values\n",
-    "        if any(i in v for i in [\"Industry\", \"Transport\", \"Heating\"])\n",
-    "    ],\n",
-    "    #variables=[\"Transport_Freight_(train)_Liquid_fossil\"],\n",
-    "    #variables = [\"Electricity\", ],\n",
-    "    characterization=True\n",
-    ")\n",
-    "arr = p.display_results(cutoff=0.01)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2685e409-e694-436c-81f4-55eb86889685",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.to_netcdf(\"results_image_SSP2.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "fd21f031-f036-47e9-b378-c4593d356334",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Coordinates:\n",
-       "  * model            (model) object 'image'\n",
-       "  * scenario         (scenario) object 'SSP2-RCP19'\n",
-       "  * region           (region) object 'BRA' 'CAN' 'CEU' ... 'USA' 'WAF' 'WEU'\n",
-       "  * impact_category  (impact_category) object 'Cobalt - natural resource - in...\n",
-       "  * variable         (variable) object 'Heating_Commercial (space heating)_El...\n",
-       "  * act_category     (act_category) object 'Manufacture of basic chemicals' ....\n",
-       "  * year             (year) int64 2020 2021 2022 2023 ... 2097 2098 2099 2100"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr.coords"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3e6b3af7-0d61-4719-9401-0ee62bbbc872",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.values[arr.values < 0] = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "43312db1-d957-402d-a936-99846e1f2768",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"results_metals_rcp19.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "975fb2b2-3111-4ccd-ae65-0481fac975b1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2918916"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "9ca4a0e7-8ea6-48da-bd5d-054dec8ebaee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>model</th>\n",
-       "      <th>scenario</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>year</th>\n",
-       "      <th>act_category</th>\n",
-       "      <th>value</th>\n",
-       "      <th>Main sector</th>\n",
-       "      <th>Sub sector</th>\n",
-       "      <th>Energy carrier</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt</td>\n",
-       "      <td>Heating_Commercial (space heating)_Electricity</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Commercial (space heating)</td>\n",
-       "      <td>Electricity</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt</td>\n",
-       "      <td>Heating_Commercial (space heating)_Liquid fossil</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Commercial (space heating)</td>\n",
-       "      <td>Liquid fossil</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt</td>\n",
-       "      <td>Heating_Commercial (space heating)_Natural gas</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Commercial (space heating)</td>\n",
-       "      <td>Natural gas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt</td>\n",
-       "      <td>Heating_Commercial (space heating)_Solid biomass</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Commercial (space heating)</td>\n",
-       "      <td>Solid biomass</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>image</td>\n",
-       "      <td>SSP2-RCP19</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Cobalt</td>\n",
-       "      <td>Heating_Commercial (space heating)_Solid coal</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>Manufacture of basic chemicals</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Heating</td>\n",
-       "      <td>Commercial (space heating)</td>\n",
-       "      <td>Solid coal</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   model    scenario region impact_category  \\\n",
-       "0  image  SSP2-RCP19    BRA          Cobalt   \n",
-       "1  image  SSP2-RCP19    BRA          Cobalt   \n",
-       "2  image  SSP2-RCP19    BRA          Cobalt   \n",
-       "3  image  SSP2-RCP19    BRA          Cobalt   \n",
-       "4  image  SSP2-RCP19    BRA          Cobalt   \n",
-       "\n",
-       "                                           variable  year  \\\n",
-       "0    Heating_Commercial (space heating)_Electricity  2020   \n",
-       "1  Heating_Commercial (space heating)_Liquid fossil  2020   \n",
-       "2    Heating_Commercial (space heating)_Natural gas  2020   \n",
-       "3  Heating_Commercial (space heating)_Solid biomass  2020   \n",
-       "4     Heating_Commercial (space heating)_Solid coal  2020   \n",
-       "\n",
-       "                     act_category  value Main sector  \\\n",
-       "0  Manufacture of basic chemicals    NaN     Heating   \n",
-       "1  Manufacture of basic chemicals    NaN     Heating   \n",
-       "2  Manufacture of basic chemicals    NaN     Heating   \n",
-       "3  Manufacture of basic chemicals    NaN     Heating   \n",
-       "4  Manufacture of basic chemicals    NaN     Heating   \n",
-       "\n",
-       "                   Sub sector Energy carrier  \n",
-       "0  Commercial (space heating)    Electricity  \n",
-       "1  Commercial (space heating)  Liquid fossil  \n",
-       "2  Commercial (space heating)    Natural gas  \n",
-       "3  Commercial (space heating)  Solid biomass  \n",
-       "4  Commercial (space heating)     Solid coal  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
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-   "execution_count": 54,
-   "id": "8b959999-68c6-436a-9653-066f3a371f68",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; (model: 1, pathway: 1, variables: 6)&gt;\n",
-       "array([[[0.5404288940429688, 0.31976019287109375, 0.0, 0.03776106,\n",
-       "         0.5397548160000001, 0.0]]], dtype=object)\n",
-       "Coordinates:\n",
-       "  * pathway    (pathway) object &#x27;SSP2-RCP19&#x27;\n",
-       "    region     &lt;U3 &#x27;CHN&#x27;\n",
-       "    year       int64 2050\n",
-       "  * model      (model) &lt;U5 &#x27;image&#x27;\n",
-       "  * variables  (variables) &lt;U49 &#x27;Heating_Commercial (space heating)_Electrici...\n",
-       "Attributes:\n",
-       "    units:    {&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Heating_Commercial (space heating...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>model</span>: 1</li><li><span class='xr-has-index'>pathway</span>: 1</li><li><span class='xr-has-index'>variables</span>: 6</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ef67bde2-047c-4fde-ab39-c564f74ed6ec' class='xr-array-in' type='checkbox' checked><label for='section-ef67bde2-047c-4fde-ab39-c564f74ed6ec' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.5404288940429688 0.31976019287109375 0.0 ... 0.5397548160000001 0.0</span></div><div class='xr-array-data'><pre>array([[[0.5404288940429688, 0.31976019287109375, 0.0, 0.03776106,\n",
-       "         0.5397548160000001, 0.0]]], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-9d454ca5-4840-4015-b6fe-12ffe2a041e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d454ca5-4840-4015-b6fe-12ffe2a041e1' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pathway</span></div><div class='xr-var-dims'>(pathway)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-RCP19&#x27;</div><input id='attrs-3c43024b-00b0-44b7-9dd6-cabea4df3b86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c43024b-00b0-44b7-9dd6-cabea4df3b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89f319e1-1534-46ed-b7b6-dd781f4d80e3' class='xr-var-data-in' type='checkbox'><label for='data-89f319e1-1534-46ed-b7b6-dd781f4d80e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP2-RCP19&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;CHN&#x27;</div><input id='attrs-cd1898e4-6e8e-44e2-a076-f5c4d3bcabc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd1898e4-6e8e-44e2-a076-f5c4d3bcabc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d586a3b-816a-4cbc-a185-5d2cd5a05fe4' class='xr-var-data-in' type='checkbox'><label for='data-9d586a3b-816a-4cbc-a185-5d2cd5a05fe4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;CHN&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2050</div><input id='attrs-8c2cda85-d9d3-49f0-afaa-e1b394375544' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c2cda85-d9d3-49f0-afaa-e1b394375544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55c46d4a-f3f1-4af4-9072-bd653aca37d0' class='xr-var-data-in' type='checkbox'><label for='data-55c46d4a-f3f1-4af4-9072-bd653aca37d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2050)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-3c1e5cbf-ff0a-4985-91f2-040401f2383e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c1e5cbf-ff0a-4985-91f2-040401f2383e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790645d3-b9a3-4738-8ac9-fee5812cbada' class='xr-var-data-in' type='checkbox'><label for='data-790645d3-b9a3-4738-8ac9-fee5812cbada' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;image&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variables</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Heating_Commercial (space heati...</div><input id='attrs-30212f12-44af-4699-bf9a-885c30d44498' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30212f12-44af-4699-bf9a-885c30d44498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-956c4514-bf8d-4af3-8fbe-887529cdfc2d' class='xr-var-data-in' type='checkbox'><label for='data-956c4514-bf8d-4af3-8fbe-887529cdfc2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heating_Commercial (space heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential (space heating)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical (ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical (high value)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential (water heating)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight (Intl. Shipping)_Electricity&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22921c4e-f415-4889-aa4d-b717f5059a76' class='xr-section-summary-in' type='checkbox'  ><label for='section-22921c4e-f415-4889-aa4d-b717f5059a76' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pathway</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ac13d84-5cb5-4ee1-aab4-82acd038b417' class='xr-index-data-in' type='checkbox'/><label for='index-6ac13d84-5cb5-4ee1-aab4-82acd038b417' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SSP2-RCP19&#x27;], dtype=&#x27;object&#x27;, name=&#x27;pathway&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fba9aca2-8373-4e3f-9d4c-bad41e951841' class='xr-index-data-in' type='checkbox'/><label for='index-fba9aca2-8373-4e3f-9d4c-bad41e951841' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;image&#x27;], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>variables</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21d7cb12-e4c7-429f-b366-403f23aeafd9' class='xr-index-data-in' type='checkbox'/><label for='index-21d7cb12-e4c7-429f-b366-403f23aeafd9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Heating_Commercial (space heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential (space heating)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical (ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical (high value)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential (water heating)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight (Intl. Shipping)_Electricity&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;variables&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88af2b03-098a-49da-ae0b-c4773d82da1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-88af2b03-098a-49da-ae0b-c4773d82da1e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>{&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Heating_Commercial (space heating)_Solid coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial (space heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial (space heating)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial (space heating)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial (space heating)_Solid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (space heating)_Solid coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (space heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (space heating)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (space heating)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (space heating)_Solid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Liquid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Other&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Solid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (ammonia)_Solid coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Liquid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Solid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical (high value)_Solid coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (water heating)_Solid coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (water heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (water heating)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (water heating)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential (water heating)_Solid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight (Intl. Shipping)_Liquid biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight (Intl. Shipping)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight (Intl. Shipping)_Natural gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight (Intl. Shipping)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight (Intl. Shipping)_Liquid fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass crops - purpose grown&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass - residual&#x27;: &#x27;EJ/yr&#x27;, &#x27;steel - secondary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;steel - primary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;biodiesel, oil, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;biodiesel, oil&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Hydro&#x27;: &#x27;EJ/yr&#x27;, &#x27;Nuclear&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Geothermal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar CSP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Centralized&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Residential&#x27;: &#x27;EJ/yr&#x27;, &#x27;Storage, Flow Battery&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Onshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Offshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, electrolysis&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, solar&#x27;: &#x27;EJ/yr&#x27;}</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' (model: 1, pathway: 1, variables: 6)>\n",
-       "array([[[0.5404288940429688, 0.31976019287109375, 0.0, 0.03776106,\n",
-       "         0.5397548160000001, 0.0]]], dtype=object)\n",
-       "Coordinates:\n",
-       "  * pathway    (pathway) object 'SSP2-RCP19'\n",
-       "    region     <U3 'CHN'\n",
-       "    year       int64 2050\n",
-       "  * model      (model) <U5 'image'\n",
-       "  * variables  (variables) <U49 'Heating_Commercial (space heating)_Electrici...\n",
-       "Attributes:\n",
-       "    units:    {'dac_solvent': 'Mt CO2/yr', 'Heating_Commercial (space heating..."
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.scenarios.sel(\n",
-    "    variables=[v for v in p.scenarios.coords[\"variables\"].values\n",
-    "              if \"electricity\" in v.lower()\n",
-    "              ],\n",
-    "    year=2050,\n",
-    "    region=\"CHN\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1de1d7a2-6a41-4255-9639-6745ef981ac9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='year'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "arr.sel(\n",
-    "    model=\"image\",\n",
-    "    scenario=\"SSP2-RCP19\",\n",
-    "    impact_category=\"Vanadium - natural resource - in ground - kilogram\"\n",
-    ").sum(dim=[\"act_category\", \"region\"]).to_dataframe(\"x\").unstack()[\"x\"].plot(kind=\"area\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7af282bf-198f-48fb-867b-55cd63c9b925",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"results_image_SSP2-RCP19.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "6b010325-585c-401e-9e12-4f6b1238020e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Updated YAML file saved to updated_yaml_file.yaml\n"
-     ]
-    }
-   ],
-   "source": [
-    "import yaml\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Replace 'your_yaml_file.yaml' with the path to your YAML file\n",
-    "yaml_file_path = '/Users/romain/GitHub/pathways/pathways/data/activities_classifications.yaml'\n",
-    "\n",
-    "# Replace 'your_excel_file.xlsx' with the path to your Excel file\n",
-    "excel_file_path = '/Users/romain/GitHub/pathways/dev/missing_classifications.xlsx'\n",
-    "\n",
-    "# Load the YAML file\n",
-    "with open(yaml_file_path, 'r') as file:\n",
-    "    yaml_content = yaml.load(file, Loader=yaml.FullLoader)\n",
-    "\n",
-    "# Load the Excel file\n",
-    "excel_content = pd.read_excel(excel_file_path)\n",
-    "\n",
-    "# Convert Excel data to the same tuple format as YAML keys\n",
-    "excel_tuples = [\n",
-    "    ((row['name'], row['reference product'], row['unit'], row['location']), row['classification'])\n",
-    "    for index, row in excel_content.iterrows()\n",
-    "]\n",
-    "\n",
-    "# Update the YAML dictionary with the new classifications from the Excel file\n",
-    "for csv_key, classification in excel_tuples:\n",
-    "    yaml_content[csv_key] = classification\n",
-    "\n",
-    "# Save the updated dictionary to a new YAML file\n",
-    "# Replace 'updated_yaml_file.yaml' with the path where you want to save the updated file\n",
-    "updated_yaml_file_path = 'updated_yaml_file.yaml'\n",
-    "\n",
-    "with open(updated_yaml_file_path, 'w') as file:\n",
-    "    yaml.dump(yaml_content, file)\n",
-    "\n",
-    "print(f'Updated YAML file saved to {updated_yaml_file_path}')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "22ad62d5-d7d4-47b8-8177-6d5d702cb16f",
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "\n",
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-       "  grid-column: 2;\n",
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-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; (model: 1, pathway: 1, variables: 5, year: 2)&gt;\n",
-       "array([[[[0.0, 4.667243],\n",
-       "         [0.0, 0.0],\n",
-       "         [0.0, 0.0],\n",
-       "         [1.3415830078800094e-17, 0.069667796875],\n",
-       "         [7.440479, 2.801207]]]], dtype=object)\n",
-       "Coordinates:\n",
-       "  * pathway    (pathway) object &#x27;SSP2-RCP19&#x27;\n",
-       "    region     &lt;U5 &#x27;World&#x27;\n",
-       "  * year       (year) int64 2005 2050\n",
-       "  * model      (model) &lt;U5 &#x27;image&#x27;\n",
-       "  * variables  (variables) &lt;U49 &#x27;Transport_Freight_(Intl._Shipping)_Liquid_bi...\n",
-       "Attributes:\n",
-       "    units:    {&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Heating_Commercial_(space_heating...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>model</span>: 1</li><li><span class='xr-has-index'>pathway</span>: 1</li><li><span class='xr-has-index'>variables</span>: 5</li><li><span class='xr-has-index'>year</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed11efff-f363-4ae9-b870-1d202706eb72' class='xr-array-in' type='checkbox' checked><label for='section-ed11efff-f363-4ae9-b870-1d202706eb72' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 4.667243 0.0 0.0 ... 0.069667796875 7.440479 2.801207</span></div><div class='xr-array-data'><pre>array([[[[0.0, 4.667243],\n",
-       "         [0.0, 0.0],\n",
-       "         [0.0, 0.0],\n",
-       "         [1.3415830078800094e-17, 0.069667796875],\n",
-       "         [7.440479, 2.801207]]]], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-ce1c3ba4-3272-4bfd-a8cd-d294026a62c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce1c3ba4-3272-4bfd-a8cd-d294026a62c4' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pathway</span></div><div class='xr-var-dims'>(pathway)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-RCP19&#x27;</div><input id='attrs-1c0d9b12-a114-4fdb-a056-cacff04614d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c0d9b12-a114-4fdb-a056-cacff04614d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-710645d2-3afb-4647-9855-b4878021cedb' class='xr-var-data-in' type='checkbox'><label for='data-710645d2-3afb-4647-9855-b4878021cedb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP2-RCP19&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-50d25339-80dd-472e-8a33-cd7733cc60ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50d25339-80dd-472e-8a33-cd7733cc60ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eefcfc80-7440-4bad-94af-1a6d6d4c8b04' class='xr-var-data-in' type='checkbox'><label for='data-eefcfc80-7440-4bad-94af-1a6d6d4c8b04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;World&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2005 2050</div><input id='attrs-1f6d8f15-232e-436c-a985-c043d710c3f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f6d8f15-232e-436c-a985-c043d710c3f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12b9c616-535d-48a2-841f-e428c9c24950' class='xr-var-data-in' type='checkbox'><label for='data-12b9c616-535d-48a2-841f-e428c9c24950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2005, 2050])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-b7b089a1-25b7-4e9d-a343-2337e3dd74f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7b089a1-25b7-4e9d-a343-2337e3dd74f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-935a66d5-4b57-47df-87c9-0ae78358f264' class='xr-var-data-in' type='checkbox'><label for='data-935a66d5-4b57-47df-87c9-0ae78358f264' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;image&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variables</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Transport_Freight_(Intl._Shippi...</div><input id='attrs-48ab08d0-715e-4ffe-9b70-c0998f82162b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48ab08d0-715e-4ffe-9b70-c0998f82162b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-200a23f8-498d-486c-9d2b-cae02f318838' class='xr-var-data-in' type='checkbox'><label for='data-200a23f8-498d-486c-9d2b-cae02f318838' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3a8bd33-ac36-4c8a-a1c0-e6189598d773' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3a8bd33-ac36-4c8a-a1c0-e6189598d773' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pathway</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5248284-07ea-4ab4-b38e-4681cb2e2872' class='xr-index-data-in' type='checkbox'/><label for='index-d5248284-07ea-4ab4-b38e-4681cb2e2872' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SSP2-RCP19&#x27;], dtype=&#x27;object&#x27;, name=&#x27;pathway&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ecd5f4c3-e523-413b-9f33-7f9aa2c3b2ed' class='xr-index-data-in' type='checkbox'/><label for='index-ecd5f4c3-e523-413b-9f33-7f9aa2c3b2ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2005, 2050], dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-212f391b-6440-413d-985a-62df373a302f' class='xr-index-data-in' type='checkbox'/><label for='index-212f391b-6440-413d-985a-62df373a302f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;image&#x27;], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>variables</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9084fe7-d413-4f64-b333-ce1c7ccf20a3' class='xr-index-data-in' type='checkbox'/><label for='index-f9084fe7-d413-4f64-b333-ce1c7ccf20a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;variables&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-128b09e0-5855-4492-a039-9bec6519f140' class='xr-section-summary-in' type='checkbox'  checked><label for='section-128b09e0-5855-4492-a039-9bec6519f140' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>{&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Heating_Commercial_(space_heating)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial_(space_heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial_(space_heating)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial_(space_heating)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Commercial_(space_heating)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(space_heating)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(space_heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(space_heating)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(space_heating)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(space_heating)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Other&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(water_heating)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(water_heating)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(water_heating)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(water_heating)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Heating_Residential_(water_heating)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass crops - purpose grown&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass - residual&#x27;: &#x27;EJ/yr&#x27;, &#x27;steel - secondary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;steel - primary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;biodiesel, oil, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;biodiesel, oil&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Hydro&#x27;: &#x27;EJ/yr&#x27;, &#x27;Nuclear&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Geothermal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar CSP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Centralized&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Residential&#x27;: &#x27;EJ/yr&#x27;, &#x27;Storage, Flow Battery&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Onshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Offshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, electrolysis&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, solar&#x27;: &#x27;EJ/yr&#x27;}</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' (model: 1, pathway: 1, variables: 5, year: 2)>\n",
-       "array([[[[0.0, 4.667243],\n",
-       "         [0.0, 0.0],\n",
-       "         [0.0, 0.0],\n",
-       "         [1.3415830078800094e-17, 0.069667796875],\n",
-       "         [7.440479, 2.801207]]]], dtype=object)\n",
-       "Coordinates:\n",
-       "  * pathway    (pathway) object 'SSP2-RCP19'\n",
-       "    region     <U5 'World'\n",
-       "  * year       (year) int64 2005 2050\n",
-       "  * model      (model) <U5 'image'\n",
-       "  * variables  (variables) <U49 'Transport_Freight_(Intl._Shipping)_Liquid_bi...\n",
-       "Attributes:\n",
-       "    units:    {'dac_solvent': 'Mt CO2/yr', 'Heating_Commercial_(space_heating..."
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.scenarios.sel(\n",
-    "    variables=[x for x in p.scenarios.coords[\"variables\"].values if x.startswith(\"Transport\")],\n",
-    "    region=\"World\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "37a03201-e21b-4323-9eb1-82e772bcc67b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('x', 'Heating_Commercial_(space_heating)_Electricity'),\n",
-       " ('x', 'Heating_Residential_(space_heating)_Electricity'),\n",
-       " ('x', 'Heating_Residential_(water_heating)_Electricity'),\n",
-       " ('x', 'Industry_Chemical_(ammonia)_Electricity'),\n",
-       " ('x', 'Industry_Chemical_(high_value)_Electricity'),\n",
-       " ('x', 'Transport_Freight_(Intl._Shipping)_Electricity')]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cols = [\n",
-    "    c for c in arr.sel(act_category=\"other\").to_dataframe(\"x\").unstack().reset_index().columns\n",
-    "    if \"electricity\" in c[1].lower() and c[0] == \"x\"\n",
-    "]\n",
-    "cols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "768c3ca0-837c-4f4f-ba0e-9062684b1243",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "  padding-right: 10px;\n",
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-       "\n",
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-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; (model: 1, scenario: 1, year: 46, region: 1,\n",
-       "                           impact_category: 1, variable: 33)&gt;\n",
-       "array([[[[[[5.08234160e+10, 7.87668540e+10, 8.05574141e+10, ...,\n",
-       "            0.00000000e+00, 7.00791453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09058254e+10, 7.75201896e+10, 7.91272103e+10, ...,\n",
-       "            1.28381793e+09, 6.90118953e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09882348e+10, 7.62735252e+10, 7.76970065e+10, ...,\n",
-       "            2.56763586e+09, 6.79446453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         ...,\n",
-       "\n",
-       "\n",
-       "         [[[5.43670204e+10, 2.51602843e+10, 1.90586510e+10, ...,\n",
-       "            5.52041711e+10, 2.41873958e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.44494298e+10, 2.39136199e+10, 1.76284472e+10, ...,\n",
-       "            5.64879890e+10, 2.31201458e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.45318392e+10, 2.26669555e+10, 1.61982434e+10, ...,\n",
-       "            5.77718069e+10, 2.20528958e+10, 0.00000000e+00]]]]]])\n",
-       "Coordinates:\n",
-       "  * model            (model) object &#x27;image&#x27;\n",
-       "  * scenario         (scenario) object &#x27;SSP2-RCP19&#x27;\n",
-       "  * region           (region) object &#x27;World&#x27;\n",
-       "  * impact_category  (impact_category) object &#x27;IPCC 2021 - climate change - G...\n",
-       "  * variable         (variable) object &#x27;Heating_Commercial_(space_heating)_El...\n",
-       "    act_category     &lt;U5 &#x27;other&#x27;\n",
-       "  * year             (year) int64 2005 2006 2007 2008 ... 2047 2048 2049 2050</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>model</span>: 1</li><li><span class='xr-has-index'>scenario</span>: 1</li><li><span class='xr-has-index'>year</span>: 46</li><li><span class='xr-has-index'>region</span>: 1</li><li><span class='xr-has-index'>impact_category</span>: 1</li><li><span class='xr-has-index'>variable</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2ba07aca-4bd1-4f99-9eb5-fc303638f0bd' class='xr-array-in' type='checkbox' checked><label for='section-2ba07aca-4bd1-4f99-9eb5-fc303638f0bd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.082e+10 7.877e+10 8.056e+10 0.0 0.0 ... 0.0 5.777e+10 2.205e+10 0.0</span></div><div class='xr-array-data'><pre>array([[[[[[5.08234160e+10, 7.87668540e+10, 8.05574141e+10, ...,\n",
-       "            0.00000000e+00, 7.00791453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09058254e+10, 7.75201896e+10, 7.91272103e+10, ...,\n",
-       "            1.28381793e+09, 6.90118953e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09882348e+10, 7.62735252e+10, 7.76970065e+10, ...,\n",
-       "            2.56763586e+09, 6.79446453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         ...,\n",
-       "\n",
-       "\n",
-       "         [[[5.43670204e+10, 2.51602843e+10, 1.90586510e+10, ...,\n",
-       "            5.52041711e+10, 2.41873958e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.44494298e+10, 2.39136199e+10, 1.76284472e+10, ...,\n",
-       "            5.64879890e+10, 2.31201458e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.45318392e+10, 2.26669555e+10, 1.61982434e+10, ...,\n",
-       "            5.77718069e+10, 2.20528958e+10, 0.00000000e+00]]]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8c13f468-9566-49ba-84e0-a1a74abdae14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c13f468-9566-49ba-84e0-a1a74abdae14' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-51ae1bdc-fc68-4b74-9028-ac9cf9ce9639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51ae1bdc-fc68-4b74-9028-ac9cf9ce9639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b1b4c7a-6839-4ac8-9cdf-6905c99cb73c' class='xr-var-data-in' type='checkbox'><label for='data-9b1b4c7a-6839-4ac8-9cdf-6905c99cb73c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;image&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>scenario</span></div><div class='xr-var-dims'>(scenario)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-RCP19&#x27;</div><input id='attrs-aed385d7-8904-41d5-94d5-b8dcd235d2a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aed385d7-8904-41d5-94d5-b8dcd235d2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee9d098-9e87-49fb-8b23-214109b3c7d9' class='xr-var-data-in' type='checkbox'><label for='data-aee9d098-9e87-49fb-8b23-214109b3c7d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP2-RCP19&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-a2587cea-a176-4e98-8edc-0b1940409799' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2587cea-a176-4e98-8edc-0b1940409799' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b44a1fab-e55d-4c4a-873b-ce423dcc7aee' class='xr-var-data-in' type='checkbox'><label for='data-b44a1fab-e55d-4c4a-873b-ce423dcc7aee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;World&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>impact_category</span></div><div class='xr-var-dims'>(impact_category)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;IPCC 2021 - climate change - GW...</div><input id='attrs-a2b23e5b-711c-4995-b1c7-f79e86ab20cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2b23e5b-711c-4995-b1c7-f79e86ab20cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c73330e-24c2-4630-ba1d-a2010b70536d' class='xr-var-data-in' type='checkbox'><label for='data-7c73330e-24c2-4630-ba1d-a2010b70536d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IPCC 2021 - climate change - GWP 100a, incl. H&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Heating_Commercial_(space_heati...</div><input id='attrs-96ca68ef-8691-40db-a1fc-01828374077d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96ca68ef-8691-40db-a1fc-01828374077d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59302766-4227-4e6d-8c40-109fea64708f' class='xr-var-data-in' type='checkbox'><label for='data-59302766-4227-4e6d-8c40-109fea64708f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Heating_Commercial_(space_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Natural_gas&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Other&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Solid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Solid_coal&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Natural_gas&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Solid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Solid_coal&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>act_category</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;other&#x27;</div><input id='attrs-e9bd76be-1a8b-46eb-ba38-50e7a9e257d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9bd76be-1a8b-46eb-ba38-50e7a9e257d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aae8b657-ca9d-4a03-bb34-394a9e70c58c' class='xr-var-data-in' type='checkbox'><label for='data-aae8b657-ca9d-4a03-bb34-394a9e70c58c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;other&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2005 2006 2007 ... 2048 2049 2050</div><input id='attrs-588ebdfe-20c3-4bfd-8445-c83ae397a067' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-588ebdfe-20c3-4bfd-8445-c83ae397a067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbfde170-c254-4e98-ac5b-bbe74f8de5d2' class='xr-var-data-in' type='checkbox'><label for='data-fbfde170-c254-4e98-ac5b-bbe74f8de5d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,\n",
-       "       2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028,\n",
-       "       2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040,\n",
-       "       2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-91f35163-4a16-4288-9e74-59891c56d5fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-91f35163-4a16-4288-9e74-59891c56d5fd' class='xr-section-summary' >Indexes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>model</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c112a830-60f6-4e72-adbb-c2dd6539446b' class='xr-index-data-in' type='checkbox'/><label for='index-c112a830-60f6-4e72-adbb-c2dd6539446b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;image&#x27;], dtype=&#x27;object&#x27;, name=&#x27;model&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>scenario</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aac25546-97a7-4587-9a4c-0aaece26dd8e' class='xr-index-data-in' type='checkbox'/><label for='index-aac25546-97a7-4587-9a4c-0aaece26dd8e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SSP2-RCP19&#x27;], dtype=&#x27;object&#x27;, name=&#x27;scenario&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8c4646e5-c646-4897-bcfa-c62e8c3b2fd8' class='xr-index-data-in' type='checkbox'/><label for='index-8c4646e5-c646-4897-bcfa-c62e8c3b2fd8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;World&#x27;], dtype=&#x27;object&#x27;, name=&#x27;region&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>impact_category</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-491c6960-fc1b-431a-8a50-7578ff85dd3c' class='xr-index-data-in' type='checkbox'/><label for='index-491c6960-fc1b-431a-8a50-7578ff85dd3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IPCC 2021 - climate change - GWP 100a, incl. H&#x27;], dtype=&#x27;object&#x27;, name=&#x27;impact_category&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>variable</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6194a1fa-fbde-4b52-bf8b-d51df901f728' class='xr-index-data-in' type='checkbox'/><label for='index-6194a1fa-fbde-4b52-bf8b-d51df901f728' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Heating_Commercial_(space_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Commercial_(space_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Residential_(space_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Electricity&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Natural_gas&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Solid_biomass&#x27;,\n",
-       "       &#x27;Heating_Residential_(water_heating)_Solid_coal&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Natural_gas&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Other&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Solid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(ammonia)_Solid_coal&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Natural_gas&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Solid_biomass&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Solid_coal&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;variable&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9330a340-be0b-4ac6-a83d-a11dbebc08e7' class='xr-index-data-in' type='checkbox'/><label for='index-9330a340-be0b-4ac6-a83d-a11dbebc08e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,\n",
-       "       2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028,\n",
-       "       2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040,\n",
-       "       2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdc2a425-7ea7-4b05-84fd-e04be604c690' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cdc2a425-7ea7-4b05-84fd-e04be604c690' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' (model: 1, scenario: 1, year: 46, region: 1,\n",
-       "                           impact_category: 1, variable: 33)>\n",
-       "array([[[[[[5.08234160e+10, 7.87668540e+10, 8.05574141e+10, ...,\n",
-       "            0.00000000e+00, 7.00791453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09058254e+10, 7.75201896e+10, 7.91272103e+10, ...,\n",
-       "            1.28381793e+09, 6.90118953e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.09882348e+10, 7.62735252e+10, 7.76970065e+10, ...,\n",
-       "            2.56763586e+09, 6.79446453e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         ...,\n",
-       "\n",
-       "\n",
-       "         [[[5.43670204e+10, 2.51602843e+10, 1.90586510e+10, ...,\n",
-       "            5.52041711e+10, 2.41873958e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.44494298e+10, 2.39136199e+10, 1.76284472e+10, ...,\n",
-       "            5.64879890e+10, 2.31201458e+10, 0.00000000e+00]]],\n",
-       "\n",
-       "\n",
-       "         [[[5.45318392e+10, 2.26669555e+10, 1.61982434e+10, ...,\n",
-       "            5.77718069e+10, 2.20528958e+10, 0.00000000e+00]]]]]])\n",
-       "Coordinates:\n",
-       "  * model            (model) object 'image'\n",
-       "  * scenario         (scenario) object 'SSP2-RCP19'\n",
-       "  * region           (region) object 'World'\n",
-       "  * impact_category  (impact_category) object 'IPCC 2021 - climate change - G...\n",
-       "  * variable         (variable) object 'Heating_Commercial_(space_heating)_El...\n",
-       "    act_category     <U5 'other'\n",
-       "  * year             (year) int64 2005 2006 2007 2008 ... 2047 2048 2049 2050"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr.sel(act_category=\"other\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "eaf03dda-afd6-4fca-9e43-04432663895c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'dataset': {'name': 'market group for electricity, low voltage',\n",
-       "  'reference product': 'electricity, low voltage',\n",
-       "  'unit': 'kilowatt hour'},\n",
-       " 'scenario variable': 'Final Energy|Transportation|Passenger|Electricity'}"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.mapping[\"Transport_Passenger_Electricity\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b6ab0429-a364-4f97-9501-3cc18c309074",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"6\" halign=\"left\">x</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>variable</th>\n",
-       "      <th>Heating_Commercial_(space_heating)_Electricity</th>\n",
-       "      <th>Heating_Residential_(space_heating)_Electricity</th>\n",
-       "      <th>Heating_Residential_(water_heating)_Electricity</th>\n",
-       "      <th>Industry_Chemical_(ammonia)_Electricity</th>\n",
-       "      <th>Industry_Chemical_(high_value)_Electricity</th>\n",
-       "      <th>Transport_Freight_(Intl._Shipping)_Electricity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.082342e+10</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>5.090583e+10</td>\n",
-       "      <td>8.160550e+08</td>\n",
-       "      <td>9.375265e+08</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.098823e+10</td>\n",
-       "      <td>1.632110e+09</td>\n",
-       "      <td>1.875053e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.107064e+10</td>\n",
-       "      <td>2.448165e+09</td>\n",
-       "      <td>2.812579e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.115305e+10</td>\n",
-       "      <td>3.264220e+09</td>\n",
-       "      <td>3.750106e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5.123546e+10</td>\n",
-       "      <td>4.080275e+09</td>\n",
-       "      <td>4.687632e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5.131787e+10</td>\n",
-       "      <td>4.896330e+09</td>\n",
-       "      <td>5.625159e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5.140028e+10</td>\n",
-       "      <td>5.712385e+09</td>\n",
-       "      <td>6.562685e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>5.148269e+10</td>\n",
-       "      <td>6.528440e+09</td>\n",
-       "      <td>7.500212e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>5.156510e+10</td>\n",
-       "      <td>7.344495e+09</td>\n",
-       "      <td>8.437738e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>5.164751e+10</td>\n",
-       "      <td>8.160550e+09</td>\n",
-       "      <td>9.375265e+09</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>5.172992e+10</td>\n",
-       "      <td>8.976605e+09</td>\n",
-       "      <td>1.031279e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>5.181233e+10</td>\n",
-       "      <td>9.792660e+09</td>\n",
-       "      <td>1.125032e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>5.189474e+10</td>\n",
-       "      <td>1.060872e+10</td>\n",
-       "      <td>1.218784e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>5.197715e+10</td>\n",
-       "      <td>1.142477e+10</td>\n",
-       "      <td>1.312537e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>5.205956e+10</td>\n",
-       "      <td>1.224083e+10</td>\n",
-       "      <td>1.406290e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>5.214197e+10</td>\n",
-       "      <td>1.305688e+10</td>\n",
-       "      <td>1.500042e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>5.222438e+10</td>\n",
-       "      <td>1.387294e+10</td>\n",
-       "      <td>1.593795e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>5.230679e+10</td>\n",
-       "      <td>1.468899e+10</td>\n",
-       "      <td>1.687548e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>5.238919e+10</td>\n",
-       "      <td>1.550505e+10</td>\n",
-       "      <td>1.781300e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>5.247160e+10</td>\n",
-       "      <td>1.632110e+10</td>\n",
-       "      <td>1.875053e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>5.255401e+10</td>\n",
-       "      <td>1.713716e+10</td>\n",
-       "      <td>1.968806e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>5.263642e+10</td>\n",
-       "      <td>1.795321e+10</td>\n",
-       "      <td>2.062558e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>5.271883e+10</td>\n",
-       "      <td>1.876927e+10</td>\n",
-       "      <td>2.156311e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>5.280124e+10</td>\n",
-       "      <td>1.958532e+10</td>\n",
-       "      <td>2.250064e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>5.288365e+10</td>\n",
-       "      <td>2.040138e+10</td>\n",
-       "      <td>2.343816e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>5.296606e+10</td>\n",
-       "      <td>2.121743e+10</td>\n",
-       "      <td>2.437569e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>5.304847e+10</td>\n",
-       "      <td>2.203349e+10</td>\n",
-       "      <td>2.531321e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.313088e+10</td>\n",
-       "      <td>2.284954e+10</td>\n",
-       "      <td>2.625074e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.321329e+10</td>\n",
-       "      <td>2.366560e+10</td>\n",
-       "      <td>2.718827e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.329570e+10</td>\n",
-       "      <td>2.448165e+10</td>\n",
-       "      <td>2.812579e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.337811e+10</td>\n",
-       "      <td>2.529771e+10</td>\n",
-       "      <td>2.906332e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.346052e+10</td>\n",
-       "      <td>2.611376e+10</td>\n",
-       "      <td>3.000085e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.354293e+10</td>\n",
-       "      <td>2.692982e+10</td>\n",
-       "      <td>3.093837e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>5.362534e+10</td>\n",
-       "      <td>2.774587e+10</td>\n",
-       "      <td>3.187590e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5.370775e+10</td>\n",
-       "      <td>2.856193e+10</td>\n",
-       "      <td>3.281343e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>5.379015e+10</td>\n",
-       "      <td>2.937798e+10</td>\n",
-       "      <td>3.375095e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5.387256e+10</td>\n",
-       "      <td>3.019404e+10</td>\n",
-       "      <td>3.468848e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>5.395497e+10</td>\n",
-       "      <td>3.101009e+10</td>\n",
-       "      <td>3.562601e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>5.403738e+10</td>\n",
-       "      <td>3.182615e+10</td>\n",
-       "      <td>3.656353e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>5.411979e+10</td>\n",
-       "      <td>3.264220e+10</td>\n",
-       "      <td>3.750106e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>5.420220e+10</td>\n",
-       "      <td>3.345826e+10</td>\n",
-       "      <td>3.843858e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>5.428461e+10</td>\n",
-       "      <td>3.427431e+10</td>\n",
-       "      <td>3.937611e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>5.436702e+10</td>\n",
-       "      <td>3.509037e+10</td>\n",
-       "      <td>4.031364e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>5.444943e+10</td>\n",
-       "      <td>3.590642e+10</td>\n",
-       "      <td>4.125116e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>5.453184e+10</td>\n",
-       "      <td>3.672248e+10</td>\n",
-       "      <td>4.218869e+10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                      x  \\\n",
-       "variable Heating_Commercial_(space_heating)_Electricity   \n",
-       "0                                          5.082342e+10   \n",
-       "1                                          5.090583e+10   \n",
-       "2                                          5.098823e+10   \n",
-       "3                                          5.107064e+10   \n",
-       "4                                          5.115305e+10   \n",
-       "5                                          5.123546e+10   \n",
-       "6                                          5.131787e+10   \n",
-       "7                                          5.140028e+10   \n",
-       "8                                          5.148269e+10   \n",
-       "9                                          5.156510e+10   \n",
-       "10                                         5.164751e+10   \n",
-       "11                                         5.172992e+10   \n",
-       "12                                         5.181233e+10   \n",
-       "13                                         5.189474e+10   \n",
-       "14                                         5.197715e+10   \n",
-       "15                                         5.205956e+10   \n",
-       "16                                         5.214197e+10   \n",
-       "17                                         5.222438e+10   \n",
-       "18                                         5.230679e+10   \n",
-       "19                                         5.238919e+10   \n",
-       "20                                         5.247160e+10   \n",
-       "21                                         5.255401e+10   \n",
-       "22                                         5.263642e+10   \n",
-       "23                                         5.271883e+10   \n",
-       "24                                         5.280124e+10   \n",
-       "25                                         5.288365e+10   \n",
-       "26                                         5.296606e+10   \n",
-       "27                                         5.304847e+10   \n",
-       "28                                         5.313088e+10   \n",
-       "29                                         5.321329e+10   \n",
-       "30                                         5.329570e+10   \n",
-       "31                                         5.337811e+10   \n",
-       "32                                         5.346052e+10   \n",
-       "33                                         5.354293e+10   \n",
-       "34                                         5.362534e+10   \n",
-       "35                                         5.370775e+10   \n",
-       "36                                         5.379015e+10   \n",
-       "37                                         5.387256e+10   \n",
-       "38                                         5.395497e+10   \n",
-       "39                                         5.403738e+10   \n",
-       "40                                         5.411979e+10   \n",
-       "41                                         5.420220e+10   \n",
-       "42                                         5.428461e+10   \n",
-       "43                                         5.436702e+10   \n",
-       "44                                         5.444943e+10   \n",
-       "45                                         5.453184e+10   \n",
-       "\n",
-       "                                                          \\\n",
-       "variable Heating_Residential_(space_heating)_Electricity   \n",
-       "0                                           0.000000e+00   \n",
-       "1                                           8.160550e+08   \n",
-       "2                                           1.632110e+09   \n",
-       "3                                           2.448165e+09   \n",
-       "4                                           3.264220e+09   \n",
-       "5                                           4.080275e+09   \n",
-       "6                                           4.896330e+09   \n",
-       "7                                           5.712385e+09   \n",
-       "8                                           6.528440e+09   \n",
-       "9                                           7.344495e+09   \n",
-       "10                                          8.160550e+09   \n",
-       "11                                          8.976605e+09   \n",
-       "12                                          9.792660e+09   \n",
-       "13                                          1.060872e+10   \n",
-       "14                                          1.142477e+10   \n",
-       "15                                          1.224083e+10   \n",
-       "16                                          1.305688e+10   \n",
-       "17                                          1.387294e+10   \n",
-       "18                                          1.468899e+10   \n",
-       "19                                          1.550505e+10   \n",
-       "20                                          1.632110e+10   \n",
-       "21                                          1.713716e+10   \n",
-       "22                                          1.795321e+10   \n",
-       "23                                          1.876927e+10   \n",
-       "24                                          1.958532e+10   \n",
-       "25                                          2.040138e+10   \n",
-       "26                                          2.121743e+10   \n",
-       "27                                          2.203349e+10   \n",
-       "28                                          2.284954e+10   \n",
-       "29                                          2.366560e+10   \n",
-       "30                                          2.448165e+10   \n",
-       "31                                          2.529771e+10   \n",
-       "32                                          2.611376e+10   \n",
-       "33                                          2.692982e+10   \n",
-       "34                                          2.774587e+10   \n",
-       "35                                          2.856193e+10   \n",
-       "36                                          2.937798e+10   \n",
-       "37                                          3.019404e+10   \n",
-       "38                                          3.101009e+10   \n",
-       "39                                          3.182615e+10   \n",
-       "40                                          3.264220e+10   \n",
-       "41                                          3.345826e+10   \n",
-       "42                                          3.427431e+10   \n",
-       "43                                          3.509037e+10   \n",
-       "44                                          3.590642e+10   \n",
-       "45                                          3.672248e+10   \n",
-       "\n",
-       "                                                          \\\n",
-       "variable Heating_Residential_(water_heating)_Electricity   \n",
-       "0                                           0.000000e+00   \n",
-       "1                                           9.375265e+08   \n",
-       "2                                           1.875053e+09   \n",
-       "3                                           2.812579e+09   \n",
-       "4                                           3.750106e+09   \n",
-       "5                                           4.687632e+09   \n",
-       "6                                           5.625159e+09   \n",
-       "7                                           6.562685e+09   \n",
-       "8                                           7.500212e+09   \n",
-       "9                                           8.437738e+09   \n",
-       "10                                          9.375265e+09   \n",
-       "11                                          1.031279e+10   \n",
-       "12                                          1.125032e+10   \n",
-       "13                                          1.218784e+10   \n",
-       "14                                          1.312537e+10   \n",
-       "15                                          1.406290e+10   \n",
-       "16                                          1.500042e+10   \n",
-       "17                                          1.593795e+10   \n",
-       "18                                          1.687548e+10   \n",
-       "19                                          1.781300e+10   \n",
-       "20                                          1.875053e+10   \n",
-       "21                                          1.968806e+10   \n",
-       "22                                          2.062558e+10   \n",
-       "23                                          2.156311e+10   \n",
-       "24                                          2.250064e+10   \n",
-       "25                                          2.343816e+10   \n",
-       "26                                          2.437569e+10   \n",
-       "27                                          2.531321e+10   \n",
-       "28                                          2.625074e+10   \n",
-       "29                                          2.718827e+10   \n",
-       "30                                          2.812579e+10   \n",
-       "31                                          2.906332e+10   \n",
-       "32                                          3.000085e+10   \n",
-       "33                                          3.093837e+10   \n",
-       "34                                          3.187590e+10   \n",
-       "35                                          3.281343e+10   \n",
-       "36                                          3.375095e+10   \n",
-       "37                                          3.468848e+10   \n",
-       "38                                          3.562601e+10   \n",
-       "39                                          3.656353e+10   \n",
-       "40                                          3.750106e+10   \n",
-       "41                                          3.843858e+10   \n",
-       "42                                          3.937611e+10   \n",
-       "43                                          4.031364e+10   \n",
-       "44                                          4.125116e+10   \n",
-       "45                                          4.218869e+10   \n",
-       "\n",
-       "                                                  \\\n",
-       "variable Industry_Chemical_(ammonia)_Electricity   \n",
-       "0                                            0.0   \n",
-       "1                                            0.0   \n",
-       "2                                            0.0   \n",
-       "3                                            0.0   \n",
-       "4                                            0.0   \n",
-       "5                                            0.0   \n",
-       "6                                            0.0   \n",
-       "7                                            0.0   \n",
-       "8                                            0.0   \n",
-       "9                                            0.0   \n",
-       "10                                           0.0   \n",
-       "11                                           0.0   \n",
-       "12                                           0.0   \n",
-       "13                                           0.0   \n",
-       "14                                           0.0   \n",
-       "15                                           0.0   \n",
-       "16                                           0.0   \n",
-       "17                                           0.0   \n",
-       "18                                           0.0   \n",
-       "19                                           0.0   \n",
-       "20                                           0.0   \n",
-       "21                                           0.0   \n",
-       "22                                           0.0   \n",
-       "23                                           0.0   \n",
-       "24                                           0.0   \n",
-       "25                                           0.0   \n",
-       "26                                           0.0   \n",
-       "27                                           0.0   \n",
-       "28                                           0.0   \n",
-       "29                                           0.0   \n",
-       "30                                           0.0   \n",
-       "31                                           0.0   \n",
-       "32                                           0.0   \n",
-       "33                                           0.0   \n",
-       "34                                           0.0   \n",
-       "35                                           0.0   \n",
-       "36                                           0.0   \n",
-       "37                                           0.0   \n",
-       "38                                           0.0   \n",
-       "39                                           0.0   \n",
-       "40                                           0.0   \n",
-       "41                                           0.0   \n",
-       "42                                           0.0   \n",
-       "43                                           0.0   \n",
-       "44                                           0.0   \n",
-       "45                                           0.0   \n",
-       "\n",
-       "                                                     \\\n",
-       "variable Industry_Chemical_(high_value)_Electricity   \n",
-       "0                                               0.0   \n",
-       "1                                               0.0   \n",
-       "2                                               0.0   \n",
-       "3                                               0.0   \n",
-       "4                                               0.0   \n",
-       "5                                               0.0   \n",
-       "6                                               0.0   \n",
-       "7                                               0.0   \n",
-       "8                                               0.0   \n",
-       "9                                               0.0   \n",
-       "10                                              0.0   \n",
-       "11                                              0.0   \n",
-       "12                                              0.0   \n",
-       "13                                              0.0   \n",
-       "14                                              0.0   \n",
-       "15                                              0.0   \n",
-       "16                                              0.0   \n",
-       "17                                              0.0   \n",
-       "18                                              0.0   \n",
-       "19                                              0.0   \n",
-       "20                                              0.0   \n",
-       "21                                              0.0   \n",
-       "22                                              0.0   \n",
-       "23                                              0.0   \n",
-       "24                                              0.0   \n",
-       "25                                              0.0   \n",
-       "26                                              0.0   \n",
-       "27                                              0.0   \n",
-       "28                                              0.0   \n",
-       "29                                              0.0   \n",
-       "30                                              0.0   \n",
-       "31                                              0.0   \n",
-       "32                                              0.0   \n",
-       "33                                              0.0   \n",
-       "34                                              0.0   \n",
-       "35                                              0.0   \n",
-       "36                                              0.0   \n",
-       "37                                              0.0   \n",
-       "38                                              0.0   \n",
-       "39                                              0.0   \n",
-       "40                                              0.0   \n",
-       "41                                              0.0   \n",
-       "42                                              0.0   \n",
-       "43                                              0.0   \n",
-       "44                                              0.0   \n",
-       "45                                              0.0   \n",
-       "\n",
-       "                                                         \n",
-       "variable Transport_Freight_(Intl._Shipping)_Electricity  \n",
-       "0                                                   0.0  \n",
-       "1                                                   0.0  \n",
-       "2                                                   0.0  \n",
-       "3                                                   0.0  \n",
-       "4                                                   0.0  \n",
-       "5                                                   0.0  \n",
-       "6                                                   0.0  \n",
-       "7                                                   0.0  \n",
-       "8                                                   0.0  \n",
-       "9                                                   0.0  \n",
-       "10                                                  0.0  \n",
-       "11                                                  0.0  \n",
-       "12                                                  0.0  \n",
-       "13                                                  0.0  \n",
-       "14                                                  0.0  \n",
-       "15                                                  0.0  \n",
-       "16                                                  0.0  \n",
-       "17                                                  0.0  \n",
-       "18                                                  0.0  \n",
-       "19                                                  0.0  \n",
-       "20                                                  0.0  \n",
-       "21                                                  0.0  \n",
-       "22                                                  0.0  \n",
-       "23                                                  0.0  \n",
-       "24                                                  0.0  \n",
-       "25                                                  0.0  \n",
-       "26                                                  0.0  \n",
-       "27                                                  0.0  \n",
-       "28                                                  0.0  \n",
-       "29                                                  0.0  \n",
-       "30                                                  0.0  \n",
-       "31                                                  0.0  \n",
-       "32                                                  0.0  \n",
-       "33                                                  0.0  \n",
-       "34                                                  0.0  \n",
-       "35                                                  0.0  \n",
-       "36                                                  0.0  \n",
-       "37                                                  0.0  \n",
-       "38                                                  0.0  \n",
-       "39                                                  0.0  \n",
-       "40                                                  0.0  \n",
-       "41                                                  0.0  \n",
-       "42                                                  0.0  \n",
-       "43                                                  0.0  \n",
-       "44                                                  0.0  \n",
-       "45                                                  0.0  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = arr.sel(act_category=\"other\").to_dataframe(\"x\").unstack().reset_index()\n",
-    "df.loc[:, cols]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "3f5bed4e-1d8d-4635-b0e4-bb414fe155e0",
-   "metadata": {},
-   "outputs": [
-    {
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-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; (variables: 14, year: 2)&gt;\n",
-       "array([[0.0, 0.0],\n",
-       "       [0.0, 0.27399100800000004],\n",
-       "       [1327.5010559999998, 5211.887103999999],\n",
-       "       [1.327501056, 5.2118871040000005],\n",
-       "       [1401.7859839999999, 662.85088],\n",
-       "       [1595.011968, 4739.643904],\n",
-       "       [2718.476032, 9365.069824],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.0, 0.4390741875],\n",
-       "       [1.4624859392642975e-07, 2.5065459999999997],\n",
-       "       [3.525719046592712e-07, 2.0155689999999997],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.280205, 2.791153],\n",
-       "       [0.7107158124999999, 18.028119999999998]], dtype=object)\n",
-       "Coordinates:\n",
-       "    pathway    &lt;U10 &#x27;SSP2-RCP19&#x27;\n",
-       "    region     &lt;U5 &#x27;World&#x27;\n",
-       "  * year       (year) int64 2005 2050\n",
-       "    model      &lt;U5 &#x27;image&#x27;\n",
-       "  * variables  (variables) &lt;U49 &#x27;Industry_Chemical_(ammonia)_Electricity&#x27; ......\n",
-       "Attributes:\n",
-       "    units:    {&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Electri...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>variables</span>: 14</li><li><span class='xr-has-index'>year</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a1f09f4d-e464-4a5e-ba29-949dd45fee20' class='xr-array-in' type='checkbox' checked><label for='section-a1f09f4d-e464-4a5e-ba29-949dd45fee20' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 ... 2.791153 0.7107158124999999 18.028119999999998</span></div><div class='xr-array-data'><pre>array([[0.0, 0.0],\n",
-       "       [0.0, 0.27399100800000004],\n",
-       "       [1327.5010559999998, 5211.887103999999],\n",
-       "       [1.327501056, 5.2118871040000005],\n",
-       "       [1401.7859839999999, 662.85088],\n",
-       "       [1595.011968, 4739.643904],\n",
-       "       [2718.476032, 9365.069824],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.0, 0.4390741875],\n",
-       "       [1.4624859392642975e-07, 2.5065459999999997],\n",
-       "       [3.525719046592712e-07, 2.0155689999999997],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.280205, 2.791153],\n",
-       "       [0.7107158124999999, 18.028119999999998]], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-f3c3f7ab-4876-492a-b6c6-dd9016431c83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f3c3f7ab-4876-492a-b6c6-dd9016431c83' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pathway</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-RCP19&#x27;</div><input id='attrs-a48d9ba6-0367-4c19-9307-ed01d70f3560' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a48d9ba6-0367-4c19-9307-ed01d70f3560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71478d03-e120-4f49-9251-11d746df11d7' class='xr-var-data-in' type='checkbox'><label for='data-71478d03-e120-4f49-9251-11d746df11d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;SSP2-RCP19&#x27;, dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-5bc2ccbf-5697-4c39-93fa-3db7ae2d2416' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bc2ccbf-5697-4c39-93fa-3db7ae2d2416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74f786aa-62a4-4e3d-9017-3a0dbc45742a' class='xr-var-data-in' type='checkbox'><label for='data-74f786aa-62a4-4e3d-9017-3a0dbc45742a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;World&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2005 2050</div><input id='attrs-7921b16f-d7a2-4971-82e4-75283f04a7fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7921b16f-d7a2-4971-82e4-75283f04a7fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98e7e2d6-774c-4970-ba6e-33be370a8974' class='xr-var-data-in' type='checkbox'><label for='data-98e7e2d6-774c-4970-ba6e-33be370a8974' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2005, 2050])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-a720a31e-8c8a-479b-84ce-bb0868f95a3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a720a31e-8c8a-479b-84ce-bb0868f95a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb21d65b-4c1a-4ec3-bc6c-f76a37b585b9' class='xr-var-data-in' type='checkbox'><label for='data-cb21d65b-4c1a-4ec3-bc6c-f76a37b585b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;image&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variables</span></div><div class='xr-var-dims'>(variables)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Industry_Chemical_(ammonia)_Ele...</div><input id='attrs-2cfc91f5-658f-4130-a7ea-5022947a9608' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cfc91f5-658f-4130-a7ea-5022947a9608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c7eb8d-6633-4da6-8974-84fd08a3422c' class='xr-var-data-in' type='checkbox'><label for='data-b1c7eb8d-6633-4da6-8974-84fd08a3422c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Industry_Chemical_(ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Food_and_Tobacco_Electricity&#x27;,\n",
-       "       &#x27;Industry_Food_processing_Electricity&#x27;,\n",
-       "       &#x27;Industry_Non-metallic_minerals_Electricity&#x27;,\n",
-       "       &#x27;Industry_Pulp_and_Paper_Electricity&#x27;, &#x27;Industry_Steel_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(air)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(heavy_truck)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(med._Truck)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(nat._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(train)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Passenger_Electricity&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2c40020-5686-4fa5-b701-f450b6f5e549' class='xr-section-summary-in' type='checkbox'  ><label for='section-f2c40020-5686-4fa5-b701-f450b6f5e549' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac614a49-e234-473e-9154-4eb862e93597' class='xr-index-data-in' type='checkbox'/><label for='index-ac614a49-e234-473e-9154-4eb862e93597' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2005, 2050], dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>variables</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1d210e40-dd6e-4b43-8282-2443a9f451d8' class='xr-index-data-in' type='checkbox'/><label for='index-1d210e40-dd6e-4b43-8282-2443a9f451d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Industry_Chemical_(ammonia)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Chemical_(high_value)_Electricity&#x27;,\n",
-       "       &#x27;Industry_Food_and_Tobacco_Electricity&#x27;,\n",
-       "       &#x27;Industry_Food_processing_Electricity&#x27;,\n",
-       "       &#x27;Industry_Non-metallic_minerals_Electricity&#x27;,\n",
-       "       &#x27;Industry_Pulp_and_Paper_Electricity&#x27;, &#x27;Industry_Steel_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(air)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(heavy_truck)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(med._Truck)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(nat._Shipping)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Freight_(train)_Electricity&#x27;,\n",
-       "       &#x27;Transport_Passenger_Electricity&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;variables&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49721939-1a5c-4ba2-acfd-a778049cf419' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49721939-1a5c-4ba2-acfd-a778049cf419' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>{&#x27;dac_solvent&#x27;: &#x27;Mt CO2/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Electricity&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Natural_gas&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Heat&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Hydrogen&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Liquid_fossil&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Solid_biomass&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Food_and_Tobacco_Solid_coal&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Electricity&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Natural_gas&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Heat&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Hydrogen&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Liquid_fossil&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Solid_biomass&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Non-metallic_minerals_Solid_coal&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Electricity&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Natural_gas&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Heat&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Hydrogen&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Liquid_fossil&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Solid_biomass&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Pulp_and_Paper_Solid_coal&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Electricity&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Natural_gas&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Heat&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Hydrogen&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Liquid_coal&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Solid_biomass&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Steel_Solid_coal&#x27;: &#x27;PJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Other&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(ammonia)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Chemical_(high_value)_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Heat&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Solid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Industry_Food_processing_Solid_coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(air)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(air)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(air)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(air)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(air)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(heavy_truck)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(heavy_truck)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(heavy_truck)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(heavy_truck)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(heavy_truck)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(Intl._Shipping)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(med._Truck)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(med._Truck)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(med._Truck)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(med._Truck)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(med._Truck)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(nat._Shipping)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(nat._Shipping)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(nat._Shipping)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(nat._Shipping)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(nat._Shipping)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(train)_Liquid_biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(train)_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(train)_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(train)_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Freight_(train)_Liquid_fossil&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Passenger_Electricity&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Passenger_Natural_gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Passenger_Hydrogen&#x27;: &#x27;EJ/yr&#x27;, &#x27;Transport_Passenger_Liquid&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass crops - purpose grown&#x27;: &#x27;EJ/yr&#x27;, &#x27;biomass - residual&#x27;: &#x27;EJ/yr&#x27;, &#x27;steel - secondary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;steel - primary&#x27;: &#x27;Mtonne/yr&#x27;, &#x27;biodiesel, oil, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;biodiesel, oil&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, grain&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, sugar&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;bioethanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel, synthetic, from wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, grass&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;methanol, wood&#x27;: &#x27;EJ/yr&#x27;, &#x27;diesel&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Biomass CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal PC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal IGCC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Coal CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Gas CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Hydro&#x27;: &#x27;EJ/yr&#x27;, &#x27;Nuclear&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil ST&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CC&#x27;: &#x27;EJ/yr&#x27;, &#x27;Oil CHP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Geothermal&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar CSP&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Centralized&#x27;: &#x27;EJ/yr&#x27;, &#x27;Solar PV Residential&#x27;: &#x27;EJ/yr&#x27;, &#x27;Storage, Flow Battery&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Onshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;Wind Offshore&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, biomass&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, coal&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, electrolysis&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas, with CCS&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, nat. gas&#x27;: &#x27;EJ/yr&#x27;, &#x27;hydrogen, solar&#x27;: &#x27;EJ/yr&#x27;}</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' (variables: 14, year: 2)>\n",
-       "array([[0.0, 0.0],\n",
-       "       [0.0, 0.27399100800000004],\n",
-       "       [1327.5010559999998, 5211.887103999999],\n",
-       "       [1.327501056, 5.2118871040000005],\n",
-       "       [1401.7859839999999, 662.85088],\n",
-       "       [1595.011968, 4739.643904],\n",
-       "       [2718.476032, 9365.069824],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.0, 0.4390741875],\n",
-       "       [1.4624859392642975e-07, 2.5065459999999997],\n",
-       "       [3.525719046592712e-07, 2.0155689999999997],\n",
-       "       [0.0, 0.0],\n",
-       "       [0.280205, 2.791153],\n",
-       "       [0.7107158124999999, 18.028119999999998]], dtype=object)\n",
-       "Coordinates:\n",
-       "    pathway    <U10 'SSP2-RCP19'\n",
-       "    region     <U5 'World'\n",
-       "  * year       (year) int64 2005 2050\n",
-       "    model      <U5 'image'\n",
-       "  * variables  (variables) <U49 'Industry_Chemical_(ammonia)_Electricity' ......\n",
-       "Attributes:\n",
-       "    units:    {'dac_solvent': 'Mt CO2/yr', 'Industry_Food_and_Tobacco_Electri..."
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.scenarios.sel(\n",
-    "    model=\"image\",\n",
-    "    pathway=\"SSP2-RCP19\",\n",
-    "    region=\"World\",\n",
-    "    variables=[c[1] for c in cols]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "05d7d6f4-64a4-461c-8119-27f8b699646b",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "When stacked is True, each column must be either all positive or all negative. Column 'other' contains both positive and negative values",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43marea\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_core.py:1031\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1028\u001b[0m             label_name \u001b[38;5;241m=\u001b[39m label_kw \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m   1029\u001b[0m             data\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m label_name\n\u001b[0;32m-> 1031\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_backend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m         kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft_ax\u001b[39m\u001b[38;5;124m\"\u001b[39m, ax)\n\u001b[1;32m     70\u001b[0m plot_obj \u001b[38;5;241m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m \u001b[43mplot_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m plot_obj\u001b[38;5;241m.\u001b[39mdraw()\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj\u001b[38;5;241m.\u001b[39mresult\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:453\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compute_plot_data()\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_subplots()\n\u001b[0;32m--> 453\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_table()\n\u001b[1;32m    455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_legend()\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1392\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1389\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mark_right_label(label, index\u001b[38;5;241m=\u001b[39mi)\n\u001b[1;32m   1390\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m-> 1392\u001b[0m newlines \u001b[38;5;241m=\u001b[39m \u001b[43mplotf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1393\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1394\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1395\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1396\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1397\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolumn_num\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1398\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstacking_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstacking_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1399\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_errorbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_errorbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1400\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1401\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1402\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[38;5;241m0\u001b[39m], label)\n\u001b[1;32m   1404\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ts_plot():\n\u001b[1;32m   1405\u001b[0m     \u001b[38;5;66;03m# reset of xlim should be used for ts data\u001b[39;00m\n\u001b[1;32m   1406\u001b[0m     \u001b[38;5;66;03m# TODO: GH28021, should find a way to change view limit on xaxis\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1566\u001b[0m, in \u001b[0;36mAreaPlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, is_errorbar, **kwds)\u001b[0m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m column_num \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1565\u001b[0m     \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_initialize_stacker(ax, stacking_id, \u001b[38;5;28mlen\u001b[39m(y))\n\u001b[0;32m-> 1566\u001b[0m y_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_stacked_values\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstacking_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1568\u001b[0m \u001b[38;5;66;03m# need to remove label, because subplots uses mpl legend as it is\u001b[39;00m\n\u001b[1;32m   1569\u001b[0m line_kwds \u001b[38;5;241m=\u001b[39m kwds\u001b[38;5;241m.\u001b[39mcopy()\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1475\u001b[0m, in \u001b[0;36mLinePlot._get_stacked_values\u001b[0;34m(cls, ax, stacking_id, values, label)\u001b[0m\n\u001b[1;32m   1472\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m (values \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m   1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39m_stacker_neg_prior[stacking_id] \u001b[38;5;241m+\u001b[39m values\n\u001b[0;32m-> 1475\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1476\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhen stacked is True, each column must be either \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1477\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall positive or all negative. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1478\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumn \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m contains both positive and negative values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1479\u001b[0m )\n",
-      "\u001b[0;31mValueError\u001b[0m: When stacked is True, each column must be either all positive or all negative. Column 'other' contains both positive and negative values"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "arr.to_dataframe(\"val\").unstack()[\"val\"].plot(kind=\"area\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "37856379-82ba-4077-afa6-60b55b14f3cf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import yaml\n",
-    "fp=\"/Users/romain/GitHub/pathways/pathways/data/activities_classifications.yaml\"\n",
-    "with open(fp, 'rb') as fp:\n",
-    "    cat = yaml.full_load(fp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "06b93390-275a-4f5d-ae48-f157713c5f44",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{('1,1-difluoroethane production, HFC-152a',\n",
-       "  '1,1-difluoroethane, HFC-152a',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('1,1-difluoroethane production, HFC-152a',\n",
-       "  '1,1-difluoroethane, HFC-152a',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic chemicals',\n",
-       " ('1,1-dimethylcyclopentane to generic market for solvent, organic',\n",
-       "  'solvent, organic',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of chemicals and chemical products',\n",
-       " ('1-methoxy-2-propanol production',\n",
-       "  '1-methoxy-2-propanol',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('1-methylcyclopropene production',\n",
-       "  '1-methylcyclopropene',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('1-methylcyclopropene production',\n",
-       "  'sodium chloride, powder',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('1-naphthylacetic acid production',\n",
-       "  '1-naphthylacetic acid',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('1-propanol production',\n",
-       "  '1-propanol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('1-propanol production',\n",
-       "  '1-propanol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2,3-dimethylbutan to generic market for solvent, organic',\n",
-       "  'solvent, organic',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of chemicals and chemical products',\n",
-       " ('2,4-di-tert-butylphenol production',\n",
-       "  '2,4-di-tert-butylphenol',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dichlorophenol production',\n",
-       "  '2,4-dichlorophenol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dichlorophenol production',\n",
-       "  '2,4-dichlorophenol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dichlorotoluene production',\n",
-       "  '2,4-dichlorotoluene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dichlorotoluene production',\n",
-       "  '2,4-dichlorotoluene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dinitrotoluene production',\n",
-       "  '2,4-dinitrotoluene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2,4-dinitrotoluene production',\n",
-       "  '2,4-dinitrotoluene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2,5-dimethylhexane-2,5-dihydroperoxide production',\n",
-       "  '2,5-dimethylhexane-2,5-dihydroperoxide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('2,6-di-tert-butylphenol production',\n",
-       "  '2,6-di-tert-butylphenol',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('2-butanol production by hydration of butene',\n",
-       "  '2-butanol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2-butanol production by hydration of butene',\n",
-       "  '2-butanol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2-butanol production by hydration of butene',\n",
-       "  'butane',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2-butanol production by hydration of butene',\n",
-       "  'butane',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2-methyl-2-butanol production',\n",
-       "  '2-methyl-2-butanol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2-methyl-2-butanol production',\n",
-       "  '2-methyl-2-butanol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2-methylpentane to generic market for solvent, organic',\n",
-       "  'solvent, organic',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of chemicals and chemical products',\n",
-       " ('2-nitroaniline production',\n",
-       "  '2-nitroaniline',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2-nitroaniline production',\n",
-       "  '2-nitroaniline',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('2-pyridinol production',\n",
-       "  '2-pyridinol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('2-pyridinol production',\n",
-       "  '2-pyridinol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('3-methyl-1-butyl acetate production',\n",
-       "  '3-methyl-1-butyl acetate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('3-methyl-1-butyl acetate production',\n",
-       "  '3-methyl-1-butyl acetate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('3-methylpyridine production',\n",
-       "  '3-methylpyridine',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('3-methylpyridine production',\n",
-       "  '3-methylpyridine',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('4-methyl-2-pentanone production',\n",
-       "  '4-methyl-2-pentanone',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('4-methyl-2-pentanone production',\n",
-       "  '4-methyl-2-pentanone',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('4-tert-butylbenzaldehyde production',\n",
-       "  '4-tert-butylbenzaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('4-tert-butylbenzaldehyde production',\n",
-       "  '4-tert-butylbenzaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('4-tert-butyltoluene production',\n",
-       "  '4-tert-butyltoluene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('4-tert-butyltoluene production',\n",
-       "  '4-tert-butyltoluene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('6-benzyladenine production',\n",
-       "  '6-benzyladenine',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('Brown-Schlesinger process',\n",
-       "  'sodium methoxide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('Brown-Schlesinger process',\n",
-       "  'sodium tetrahydridoborate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'CO'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'Europe without Switzerland'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'PE'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of refined petroleum products',\n",
-       " ('C3 hydrocarbon production, mixture, petroleum refinery operation',\n",
-       "  'C3 hydrocarbon mixture',\n",
-       "  'kilogram',\n",
-       "  'ZA'): 'Manufacture of refined petroleum products',\n",
-       " ('DTPA production',\n",
-       "  'DTPA, diethylenetriaminepentaacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('DTPA production',\n",
-       "  'DTPA, diethylenetriaminepentaacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " (\"Distiller's Dried Grains with Solubles to generic market for protein feed\",\n",
-       "  'energy feed, gross',\n",
-       "  'megajoule',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " (\"Distiller's Dried Grains with Solubles to generic market for protein feed\",\n",
-       "  'protein feed, 100% crude',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('EDTA production',\n",
-       "  'EDTA, ethylenediaminetetraacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('EDTA production',\n",
-       "  'EDTA, ethylenediaminetetraacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('EUR-flat pallet production',\n",
-       "  'EUR-flat pallet',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of wooden containers',\n",
-       " ('EUR-flat pallet production',\n",
-       "  'EUR-flat pallet',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of wooden containers',\n",
-       " ('MOX fuel element production, for light water reactor',\n",
-       "  'MOX fuel element, for light water reactor',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('MOX fuel element production, for light water reactor',\n",
-       "  'MOX fuel element, for light water reactor',\n",
-       "  'kilogram',\n",
-       "  'UCTE'): 'Manufacture of basic chemicals',\n",
-       " ('Mannheim process',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('Mannheim process',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('Mannheim process',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('Mannheim process',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('N,N-dimethylformamide production',\n",
-       "  'N,N-dimethylformamide',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('N,N-dimethylformamide production',\n",
-       "  'N,N-dimethylformamide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('N-methyl-2-pyrrolidone production',\n",
-       "  'N-methyl-2-pyrrolidone',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('N-methyl-2-pyrrolidone production',\n",
-       "  'N-methyl-2-pyrrolidone',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NCA hydroxide production, for Li-ion battery',\n",
-       "  'NCA hydroxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NCA hydroxide production, for Li-ion battery',\n",
-       "  'NCA hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NCA hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NCA hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NCA oxide production, for Li-ion battery',\n",
-       "  'NCA oxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NCA oxide production, for Li-ion battery',\n",
-       "  'NCA oxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 hydroxide production, for Li-ion battery',\n",
-       "  'NMC111 hydroxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 hydroxide production, for Li-ion battery',\n",
-       "  'NMC111 hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 oxide production, for Li-ion battery',\n",
-       "  'NMC111 oxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC111 oxide production, for Li-ion battery',\n",
-       "  'NMC111 oxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 hydroxide production, for Li-ion battery',\n",
-       "  'NMC811 hydroxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 hydroxide production, for Li-ion battery',\n",
-       "  'NMC811 hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 hydroxide production, for Li-ion battery',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 oxide production, for Li-ion battery',\n",
-       "  'NMC811 oxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('NMC811 oxide production, for Li-ion battery',\n",
-       "  'NMC811 oxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('NPK (15-15-15) fertiliser production',\n",
-       "  'NPK (15-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (15-15-15) fertiliser production',\n",
-       "  'NPK (15-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (15-15-15) fertiliser production',\n",
-       "  'NPK (15-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (15-15-15) fertiliser production',\n",
-       "  'NPK (15-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (26-15-15) fertiliser production',\n",
-       "  'NPK (26-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (26-15-15) fertiliser production',\n",
-       "  'NPK (26-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (26-15-15) fertiliser production',\n",
-       "  'NPK (26-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('NPK (26-15-15) fertiliser production',\n",
-       "  'NPK (26-15-15) fertiliser',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('Sohio process',\n",
-       "  'acetonitrile',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('Sohio process',\n",
-       "  'acetonitrile',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('Sohio process',\n",
-       "  'acrylonitrile',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('Sohio process',\n",
-       "  'acrylonitrile',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('Sohio process',\n",
-       "  'hydrogen cyanide',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('Sohio process',\n",
-       "  'hydrogen cyanide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('[sulfonyl]urea-compound production',\n",
-       "  '[sulfonyl]urea-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('[sulfonyl]urea-compound production',\n",
-       "  '[sulfonyl]urea-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('[thio]carbamate-compound production',\n",
-       "  '[thio]carbamate-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('[thio]carbamate-compound production',\n",
-       "  '[thio]carbamate-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('absorption chiller production, 100kW',\n",
-       "  'absorption chiller, 100kW',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('absorption chiller production, 100kW',\n",
-       "  'absorption chiller, 100kW',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('acetaldehyde oxidation',\n",
-       "  'acetic acid, without water, in 98% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetaldehyde oxidation',\n",
-       "  'acetic acid, without water, in 98% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetaldehyde oxidation',\n",
-       "  'acetic anhydride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetaldehyde oxidation',\n",
-       "  'acetic anhydride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetaldehyde production',\n",
-       "  'acetaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetaldehyde production',\n",
-       "  'acetaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetamide-anillide-compound production, unspecified',\n",
-       "  'acetamide-anillide-compound, unspecified',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('acetamide-anillide-compound production, unspecified',\n",
-       "  'acetamide-anillide-compound, unspecified',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('acetanilide production',\n",
-       "  'acetanilide',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetanilide production',\n",
-       "  'acetanilide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetic acid production, product in 98% solution state',\n",
-       "  'acetic acid, without water, in 98% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetic acid production, product in 98% solution state',\n",
-       "  'acetic acid, without water, in 98% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetic anhydride production, ketene route',\n",
-       "  'acetic anhydride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetic anhydride production, ketene route',\n",
-       "  'acetic anhydride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetoacetic acid production',\n",
-       "  'acetoacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetoacetic acid production',\n",
-       "  'acetoacetic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetone cyanohydrin production',\n",
-       "  'acetone cyanohydrin',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('acetone cyanohydrin production',\n",
-       "  'acetone cyanohydrin',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('acetone production, from isopropanol',\n",
-       "  'acetone, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetone production, from isopropanol',\n",
-       "  'acetone, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetonitrile to generic market for solvent for chromatography analysis',\n",
-       "  'solvent for chromatography analysis',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acetyl chloride production',\n",
-       "  'acetyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetyl chloride production',\n",
-       "  'acetyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acetylene production',\n",
-       "  'acetylene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acetylene production',\n",
-       "  'acetylene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aclonifen production',\n",
-       "  'aclonifen',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('aclonifen production',\n",
-       "  'aclonifen',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('acrolein production',\n",
-       "  'acrolein',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acrolein production',\n",
-       "  'acrolein',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acrylic acid production',\n",
-       "  'acrylic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('acrylic acid production',\n",
-       "  'acrylic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('acrylic binder production, with water, in 54% solution state',\n",
-       "  'acrylic binder, with water, in 54% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic binder production, with water, in 54% solution state',\n",
-       "  'acrylic binder, with water, in 54% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic dispersion production, with water, in 58% solution',\n",
-       "  'acrylic dispersion, with water, in 58% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic dispersion production, with water, in 58% solution',\n",
-       "  'acrylic dispersion, with water, in 58% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic filler production',\n",
-       "  'acrylic filler',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic filler production',\n",
-       "  'acrylic filler',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic varnish production, with water, in 53% solution state',\n",
-       "  'acrylic varnish, with water, in 53% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylic varnish production, with water, in 53% solution state',\n",
-       "  'acrylic varnish, with water, in 53% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('acrylonitrile-butadiene-styrene copolymer production',\n",
-       "  'acrylonitrile-butadiene-styrene copolymer',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('acrylonitrile-butadiene-styrene copolymer production',\n",
-       "  'acrylonitrile-butadiene-styrene copolymer',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('activated bentonite production',\n",
-       "  'activated bentonite',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Manufacture of basic chemicals',\n",
-       " ('activated bentonite production',\n",
-       "  'activated bentonite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('activated carbon production, granular from hard coal',\n",
-       "  'activated carbon, granular',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other chemical products nec',\n",
-       " ('activated carbon production, granular from hard coal',\n",
-       "  'activated carbon, granular',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other chemical products nec',\n",
-       " ('activated silica production',\n",
-       "  'activated silica',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('activated silica production',\n",
-       "  'sodium sulfate, anhydrite',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('adhesive mortar production',\n",
-       "  'adhesive mortar',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of cement lime and plaster',\n",
-       " ('adhesive mortar production',\n",
-       "  'adhesive mortar',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('adhesive production, for metal',\n",
-       "  'adhesive, for metal',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Manufacture of other chemical products nec',\n",
-       " ('adhesive production, for metal',\n",
-       "  'adhesive, for metal',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other chemical products nec',\n",
-       " ('adipic acid production',\n",
-       "  'adipic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('adipic acid production',\n",
-       "  'adipic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('adipic acid production',\n",
-       "  'succinic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('adipic acid production',\n",
-       "  'succinic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('adiponitrile production',\n",
-       "  'adiponitrile',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('adiponitrile production',\n",
-       "  'adiponitrile',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('agricultural machinery production, tillage',\n",
-       "  'agricultural machinery, tillage',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('agricultural machinery production, tillage',\n",
-       "  'agricultural machinery, tillage',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('agricultural machinery production, unspecified',\n",
-       "  'agricultural machinery, unspecified',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('agricultural machinery production, unspecified',\n",
-       "  'agricultural machinery, unspecified',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('agricultural trailer production',\n",
-       "  'agricultural trailer',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('agricultural trailer production',\n",
-       "  'agricultural trailer',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('air compressor production, screw-type compressor, 300kW',\n",
-       "  'air compressor, screw-type compressor, 300kW',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air compressor production, screw-type compressor, 300kW',\n",
-       "  'air compressor, screw-type compressor, 300kW',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air compressor production, screw-type compressor, 4kW',\n",
-       "  'air compressor, screw-type compressor, 4kW',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air compressor production, screw-type compressor, 4kW',\n",
-       "  'air compressor, screw-type compressor, 4kW',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air distribution terminal panel production, steel, 120 m3/h',\n",
-       "  'air distribution terminal panel, steel, 120 m3/h',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air distribution terminal panel production, steel, 120 m3/h',\n",
-       "  'air distribution terminal panel, steel, 120 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, central unit, 600 m3/h',\n",
-       "  'air filter, central unit, 600 m3/h',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, central unit, 600 m3/h',\n",
-       "  'air filter, central unit, 600 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, decentralized unit, 180-250 m3/h',\n",
-       "  'air filter, decentralized unit, 180-250 m3/h',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, decentralized unit, 180-250 m3/h',\n",
-       "  'air filter, decentralized unit, 180-250 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, decentralized unit, 250 m3/h',\n",
-       "  'air filter, decentralized unit, 250 m3/h',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, decentralized unit, 250 m3/h',\n",
-       "  'air filter, decentralized unit, 250 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, in exhaust air valve',\n",
-       "  'air filter, in exhaust air valve',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air filter production, in exhaust air valve',\n",
-       "  'air filter, in exhaust air valve',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('air input/output unit production, heat and power co-generation unit, 160kW electrical',\n",
-       "  'air input/output unit, heat and power co-generation unit, 160kW electrical',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air input/output unit production, heat and power co-generation unit, 160kW electrical',\n",
-       "  'air input/output unit, heat and power co-generation unit, 160kW electrical',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other pumps compressors taps and valves',\n",
-       " ('air separation facility construction',\n",
-       "  'air separation facility',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of other civil engineering projects',\n",
-       " ('air separation facility construction',\n",
-       "  'air separation facility',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('air separation, cryogenic',\n",
-       "  'argon, crude, liquid',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'argon, crude, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'argon, crude, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'nitrogen, liquid',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'nitrogen, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'nitrogen, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'oxygen, liquid',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'oxygen, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, cryogenic',\n",
-       "  'oxygen, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'krypton, gaseous',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'krypton, gaseous',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'krypton, gaseous',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'xenon, gaseous',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'xenon, gaseous',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('air separation, xenon krypton purification',\n",
-       "  'xenon, gaseous',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aircraft production, belly-freight aircraft, long haul',\n",
-       "  'aircraft, belly freight, long haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, belly-freight aircraft, medium haul',\n",
-       "  'aircraft, belly freight, medium haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, belly-freight aircraft, short haul',\n",
-       "  'aircraft, belly freight, short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, belly-freight aircraft, very short haul',\n",
-       "  'aircraft, belly freight, very short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, dedicated freight aircraft, long haul',\n",
-       "  'aircraft, dedicated freight, long haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, dedicated freight aircraft, medium haul',\n",
-       "  'aircraft, dedicated freight, medium haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, dedicated freight aircraft, short haul',\n",
-       "  'aircraft, dedicated freight, short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, dedicated freight aircraft, very short haul',\n",
-       "  'aircraft, dedicated freight, very short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, passenger aircraft, long haul',\n",
-       "  'aircraft, passenger, long haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, passenger aircraft, medium haul',\n",
-       "  'aircraft, passenger, medium haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, passenger aircraft, short haul',\n",
-       "  'aircraft, passenger, short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('aircraft production, passenger aircraft, very short haul',\n",
-       "  'aircraft, passenger, very short haul',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of air and spacecraft and related machinery',\n",
-       " ('airport construction',\n",
-       "  'airport',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of roads and railways',\n",
-       " ('airport construction',\n",
-       "  'airport',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of roads and railways',\n",
-       " ('alfalfa-grass mixture production, Swiss integrated production',\n",
-       "  'alfalfa-grass mixture, Swiss integrated production',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of other nonperennial crops',\n",
-       " ('alfalfa/grass silage production',\n",
-       "  'alfalfa-grass silage',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Growing of other nonperennial crops',\n",
-       " ('alfalfa/grass silage production',\n",
-       "  'alfalfa-grass silage',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of other nonperennial crops',\n",
-       " ('alfalfa/grass silage production',\n",
-       "  'alfalfa-grass silage',\n",
-       "  'kilogram',\n",
-       "  'ZA'): 'Growing of other nonperennial crops',\n",
-       " ('alkyd paint production, white, solvent-based, product in 60% solution state',\n",
-       "  'alkyd paint, white, without solvent, in 60% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyd paint production, white, solvent-based, product in 60% solution state',\n",
-       "  'alkyd paint, white, without solvent, in 60% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyd paint production, white, water-based, product in 60% solution state',\n",
-       "  'alkyd paint, white, without water, in 60% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyd paint production, white, water-based, product in 60% solution state',\n",
-       "  'alkyd paint, white, without water, in 60% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyd resin production, long oil, product in 70% white spirit solution state',\n",
-       "  'alkyd resin, long oil, without solvent, in 70% white spirit solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyd resin production, long oil, product in 70% white spirit solution state',\n",
-       "  'alkyd resin, long oil, without solvent, in 70% white spirit solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of paints varnishes and similar coatings printing ink and mas',\n",
-       " ('alkyl sulphate (C12-14) production',\n",
-       "  'alkyl sulphate (C12-14)',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylbenzene production, linear',\n",
-       "  'alkylbenzene, linear',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylbenzene production, linear',\n",
-       "  'alkylbenzene, linear',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylbenzene production, linear',\n",
-       "  'alkylbenzene, linear',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylbenzene sulfonate production, linear, petrochemical',\n",
-       "  'alkylbenzene sulfonate, linear, petrochemical',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylbenzene sulfonate production, linear, petrochemical',\n",
-       "  'alkylbenzene sulfonate, linear, petrochemical',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('alkylketene dimer sizing agent production, for paper production',\n",
-       "  'alkylketene dimer sizing agent, for paper production',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('alkylketene dimer sizing agent production, for paper production',\n",
-       "  'alkylketene dimer sizing agent, for paper production',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'allyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'allyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'dichloropropene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'dichloropropene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('allyl chloride production, reaction of propylene and chlorine',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('almond production',\n",
-       "  'almond',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Growing of other tree and bush fruits and nuts',\n",
-       " ('almond production',\n",
-       "  'almond',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of other tree and bush fruits and nuts',\n",
-       " ('almond production',\n",
-       "  'almond',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Growing of other tree and bush fruits and nuts',\n",
-       " ('alpha-naphthol production',\n",
-       "  'alpha-naphthol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('alpha-naphthol production',\n",
-       "  'alpha-naphthol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('alpha-picoline production',\n",
-       "  'alpha-picoline',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('alpha-picoline production',\n",
-       "  'alpha-picoline',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium alloy production, AlLi',\n",
-       "  'aluminium alloy, AlLi',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, AlLi',\n",
-       "  'aluminium alloy, AlLi',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, AlLi',\n",
-       "  'copper, cathode',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, AlLi',\n",
-       "  'copper, cathode',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, AlMg3',\n",
-       "  'aluminium alloy, AlMg3',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, AlMg3',\n",
-       "  'aluminium alloy, AlMg3',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, Metallic Matrix Composite',\n",
-       "  'aluminium alloy, metal matrix composite',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, Metallic Matrix Composite',\n",
-       "  'aluminium alloy, metal matrix composite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, Metallic Matrix Composite',\n",
-       "  'copper, cathode',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium alloy production, Metallic Matrix Composite',\n",
-       "  'copper, cathode',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium around steel bi-metal stranded cable production, 3x3.67mm external diameter wire',\n",
-       "  'aluminium around steel bi-metal stranded cable, 3x3.67mm external diameter wire',\n",
-       "  'meter',\n",
-       "  'CA-QC'): 'Manufacture of other fabricated metal products nec',\n",
-       " ('aluminium around steel bi-metal stranded cable production, 3x3.67mm external diameter wire',\n",
-       "  'aluminium around steel bi-metal stranded cable, 3x3.67mm external diameter wire',\n",
-       "  'meter',\n",
-       "  'RoW'): 'Manufacture of other fabricated metal products nec',\n",
-       " ('aluminium around steel bi-metal wire production, 3.67 mm external diameter',\n",
-       "  'aluminium around steel bi-metal wire, 3.67mm external diameter',\n",
-       "  'meter',\n",
-       "  'CA-QC'): 'Manufacture of other fabricated metal products nec',\n",
-       " ('aluminium around steel bi-metal wire production, 3.67 mm external diameter',\n",
-       "  'aluminium around steel bi-metal wire, 3.67mm external diameter',\n",
-       "  'meter',\n",
-       "  'RoW'): 'Manufacture of other fabricated metal products nec',\n",
-       " ('aluminium casting facility construction',\n",
-       "  'aluminium casting facility',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('aluminium casting facility construction',\n",
-       "  'aluminium casting facility',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('aluminium chloride production',\n",
-       "  'aluminium chloride',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium collector foil production, for Li-ion battery',\n",
-       "  'aluminium collector foil, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of other electrical equipment',\n",
-       " ('aluminium drilling, computer numerical controlled',\n",
-       "  'aluminium removed by drilling, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium drilling, computer numerical controlled',\n",
-       "  'aluminium removed by drilling, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium drilling, conventional',\n",
-       "  'aluminium removed by drilling, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium drilling, conventional',\n",
-       "  'aluminium removed by drilling, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium electrolysis facility construction',\n",
-       "  'aluminium electrolysis facility',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('aluminium electrolysis facility construction',\n",
-       "  'aluminium electrolysis facility',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('aluminium fluoride production',\n",
-       "  'aluminium fluoride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium fluoride production',\n",
-       "  'aluminium fluoride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide factory construction',\n",
-       "  'aluminium hydroxide factory',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('aluminium hydroxide factory construction',\n",
-       "  'aluminium hydroxide factory',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium hydroxide production',\n",
-       "  'aluminium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium ingot, primary, to aluminium, cast alloy market',\n",
-       "  'aluminium, cast alloy',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium ingot, primary, to aluminium, wrought alloy market',\n",
-       "  'aluminium, wrought alloy',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium melting furnace production',\n",
-       "  'aluminium melting furnace',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('aluminium melting furnace production',\n",
-       "  'aluminium melting furnace',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('aluminium milling, average',\n",
-       "  'aluminium removed by milling, average',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, average',\n",
-       "  'aluminium removed by milling, average',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, dressing',\n",
-       "  'aluminium removed by milling, dressing',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, dressing',\n",
-       "  'aluminium removed by milling, dressing',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, large parts',\n",
-       "  'aluminium removed by milling, large parts',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, large parts',\n",
-       "  'aluminium removed by milling, large parts',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, small parts',\n",
-       "  'aluminium removed by milling, small parts',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium milling, small parts',\n",
-       "  'aluminium removed by milling, small parts',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium oxide factory construction',\n",
-       "  'aluminium oxide factory',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('aluminium oxide factory construction',\n",
-       "  'aluminium oxide factory',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide production',\n",
-       "  'aluminium oxide, non-metallurgical',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide, metallurgical, import from Northern America',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide, metallurgical, import from Rest of Europe',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide, metallurgical, import from South America',\n",
-       "  'aluminium oxide, metallurgical',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium oxide, non-metallurgical to generic market for refractory material, acid',\n",
-       "  'refractory material, acid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of refractory products',\n",
-       " ('aluminium oxide, non-metallurgical to generic market for refractory material, acid',\n",
-       "  'refractory material, acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of refractory products',\n",
-       " ('aluminium production, primary, cast alloy slab from continuous casting',\n",
-       "  'aluminium, primary, cast alloy slab from continuous casting',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, cast alloy slab from continuous casting',\n",
-       "  'aluminium, primary, cast alloy slab from continuous casting',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'CA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Africa'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Gulf Cooperation Council'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, ingot',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, Söderberg',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, Söderberg',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, Söderberg',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, Söderberg',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, Söderberg',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'CA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Africa'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Gulf Cooperation Council'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium production, primary, liquid, prebake',\n",
-       "  'aluminium, primary, liquid',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium sulfate production, powder',\n",
-       "  'aluminium sulfate, powder',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium sulfate production, powder',\n",
-       "  'aluminium sulfate, powder',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium sulfate production, without water, in 4.33% aluminium solution state',\n",
-       "  'aluminium sulfate, without water, in 4.33% aluminium solution state',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium sulfate production, without water, in 4.33% aluminium solution state',\n",
-       "  'aluminium sulfate, without water, in 4.33% aluminium solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('aluminium turning, average, computer numerical controlled',\n",
-       "  'aluminium removed by turning, average, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, average, computer numerical controlled',\n",
-       "  'aluminium removed by turning, average, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, average, conventional',\n",
-       "  'aluminium removed by turning, average, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, average, conventional',\n",
-       "  'aluminium removed by turning, average, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily dressing, computer numerical controlled',\n",
-       "  'aluminium removed by turning, primarily dressing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily dressing, computer numerical controlled',\n",
-       "  'aluminium removed by turning, primarily dressing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily dressing, conventional',\n",
-       "  'aluminium removed by turning, primarily dressing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily dressing, conventional',\n",
-       "  'aluminium removed by turning, primarily dressing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily roughing, computer numerical controlled',\n",
-       "  'aluminium removed by turning, primarily roughing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily roughing, computer numerical controlled',\n",
-       "  'aluminium removed by turning, primarily roughing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily roughing, conventional',\n",
-       "  'aluminium removed by turning, primarily roughing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium turning, primarily roughing, conventional',\n",
-       "  'aluminium removed by turning, primarily roughing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('aluminium, ingot, primary, import from Africa',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Africa',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Asia (excluding China)',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Asia (excluding China)',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from EU27 & EFTA',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Middle East (Gulf cooperation Council)',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Middle East (Gulf cooperation Council)',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Northern America',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Oceania',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Oceania',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Rest of Europe',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from Rest of Europe',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from South America',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from South America',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('aluminium, ingot, primary, import from unspecified',\n",
-       "  'aluminium, primary, ingot',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, North America'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('amidosulfuron production',\n",
-       "  'amidosulfuron',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('amination of chlorosilane',\n",
-       "  'ammonium chloride',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('amination of chlorosilane',\n",
-       "  'hexamethyldisilazane',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('amine oxide production',\n",
-       "  'amine oxide',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('amine oxide production',\n",
-       "  'amine oxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of soap and detergents cleaning and polishing preparations pe',\n",
-       " ('ammonia production, partial oxidation, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, partial oxidation, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, partial oxidation, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, partial oxidation, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RAF'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER w/o RU'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RLA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RU'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'SAS'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'ammonia, anhydrous, liquid',\n",
-       "  'kilogram',\n",
-       "  'UN-SEASIA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RER w/o RU'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'carbon dioxide, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'steam, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RER w/o RU'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonia production, steam reforming, liquid',\n",
-       "  'steam, in chemical industry',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium bicarbonate production',\n",
-       "  'ammonium bicarbonate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium bicarbonate production',\n",
-       "  'ammonium bicarbonate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium carbonate production',\n",
-       "  'ammonium carbonate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium carbonate production',\n",
-       "  'ammonium carbonate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium chloride production',\n",
-       "  'ammonium chloride',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate phosphate production',\n",
-       "  'ammonium nitrate phosphate',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate phosphate production',\n",
-       "  'ammonium nitrate phosphate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate phosphate production',\n",
-       "  'ammonium nitrate phosphate',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate phosphate production',\n",
-       "  'ammonium nitrate phosphate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate production',\n",
-       "  'ammonium nitrate',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate production',\n",
-       "  'ammonium nitrate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate production',\n",
-       "  'ammonium nitrate',\n",
-       "  'kilogram',\n",
-       "  'RNA'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrate production',\n",
-       "  'ammonium nitrate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium nitrite production',\n",
-       "  'ammonium nitrite',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('ammonium nitrite production',\n",
-       "  'ammonium nitrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('ammonium paratungstate production, ion exchange',\n",
-       "  'ammonium paratungstate',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('ammonium paratungstate production, solvent extraction',\n",
-       "  'ammonium paratungstate',\n",
-       "  'kilogram',\n",
-       "  'AT'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('ammonium paratungstate production, solvent extraction',\n",
-       "  'ammonium paratungstate',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('ammonium paratungstate production, solvent extraction',\n",
-       "  'ammonium paratungstate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('ammonium paratungstate production, solvent extraction',\n",
-       "  'ammonium paratungstate',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('ammonium sulfate production',\n",
-       "  'ammonium sulfate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium sulfate production',\n",
-       "  'ammonium sulfate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of fertilizers and nitrogen compounds',\n",
-       " ('ammonium thiocyanate production',\n",
-       "  'ammonium thiocyanate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('anaerobic digestion of manure',\n",
-       "  'biogas',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('anaerobic digestion of manure',\n",
-       "  'biogas',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('anaerobic digestion plant construction, agricultural',\n",
-       "  'anaerobic digestion plant, agricultural',\n",
-       "  'unit',\n",
-       "  'CH'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, agricultural',\n",
-       "  'anaerobic digestion plant, agricultural',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, agriculture, with methane recovery',\n",
-       "  'anaerobic digestion plant, agriculture, with methane recovery',\n",
-       "  'unit',\n",
-       "  'CH'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, agriculture, with methane recovery',\n",
-       "  'anaerobic digestion plant, agriculture, with methane recovery',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, for biowaste',\n",
-       "  'anaerobic digestion plant, for biowaste',\n",
-       "  'unit',\n",
-       "  'CH'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, for biowaste',\n",
-       "  'anaerobic digestion plant, for biowaste',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, for sewage sludge',\n",
-       "  'anaerobic digestion plant, for sewage sludge',\n",
-       "  'unit',\n",
-       "  'CH'): 'Construction of other civil engineering projects',\n",
-       " ('anaerobic digestion plant construction, for sewage sludge',\n",
-       "  'anaerobic digestion plant, for sewage sludge',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('anchovy, capture by steel purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy by-catch, fresh',\n",
-       "  'kilogram',\n",
-       "  'ES'): 'Marine fishing',\n",
-       " ('anchovy, capture by steel purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy by-catch, fresh',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Marine fishing',\n",
-       " ('anchovy, capture by steel purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy, fresh',\n",
-       "  'kilogram',\n",
-       "  'ES'): 'Marine fishing',\n",
-       " ('anchovy, capture by steel purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy, fresh',\n",
-       "  'kilogram',\n",
-       "  'PE'): 'Marine fishing',\n",
-       " ('anchovy, capture by steel purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy, fresh',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Marine fishing',\n",
-       " ('anchovy, capture by wooden purse seiner and landing whole, fresh',\n",
-       "  'landed anchovy, fresh',\n",
-       "  'kilogram',\n",
-       "  'PE'): 'Marine fishing',\n",
-       " ('anhydrite floor production',\n",
-       "  'anhydrite floor',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('anhydrite floor production',\n",
-       "  'anhydrite floor',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('anhydrite production',\n",
-       "  'anhydrite',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of cement lime and plaster',\n",
-       " ('anhydrite production',\n",
-       "  'anhydrite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('anhydrite production, burned',\n",
-       "  'anhydrite, burned',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of cement lime and plaster',\n",
-       " ('anhydrite production, burned',\n",
-       "  'anhydrite, burned',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('aniline production',\n",
-       "  'aniline',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('aniline production',\n",
-       "  'aniline',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('anionic resin production',\n",
-       "  'anionic resin',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('anionic resin production',\n",
-       "  'anionic resin',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('anode factory construction',\n",
-       "  'anode factory',\n",
-       "  'unit',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('anode factory construction',\n",
-       "  'anode factory',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('anode production, for metal electrolysis',\n",
-       "  'anode, for metal electrolysis',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, for metal electrolysis',\n",
-       "  'anode, for metal electrolysis',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, graphite, for Li-ion battery',\n",
-       "  'anode, graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode production, graphite, for Li-ion battery',\n",
-       "  'anode, graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Africa'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Gulf Cooperation Council'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, paste, for aluminium electrolysis',\n",
-       "  'anode, paste, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'CA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Africa'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Asia, without China and GCC'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, EU27 & EFTA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Gulf Cooperation Council'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, Russia & RER w/o EU27 & EFTA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'IAI Area, South America'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, prebake, for aluminium electrolysis',\n",
-       "  'anode, prebake, for aluminium electrolysis',\n",
-       "  'kilogram',\n",
-       "  'UN-OCEANIA'): 'Manufacture of other electrical equipment',\n",
-       " ('anode production, silicon coated graphite, NCA, for Li-ion battery',\n",
-       "  'anode, silicon coated graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode production, silicon coated graphite, NCA, for Li-ion battery',\n",
-       "  'anode, silicon coated graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode production, silicon coated graphite, NMC811, for Li-ion battery',\n",
-       "  'anode, silicon coated graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode production, silicon coated graphite, NMC811, for Li-ion battery',\n",
-       "  'anode, silicon coated graphite, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('anode refinery construction',\n",
-       "  'anode refinery',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('anode refinery construction',\n",
-       "  'anode refinery',\n",
-       "  'unit',\n",
-       "  'SE'): 'Construction of buildings',\n",
-       " ('anodising, aluminium sheet',\n",
-       "  'anodising, aluminium sheet',\n",
-       "  'square meter',\n",
-       "  'CA-QC'): 'Treatment and coating of metals machining',\n",
-       " ('anodising, aluminium sheet',\n",
-       "  'anodising, aluminium sheet',\n",
-       "  'square meter',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('anodising, aluminium sheet',\n",
-       "  'anodising, aluminium sheet',\n",
-       "  'square meter',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('anthranilic acid production',\n",
-       "  'anthranilic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('anthranilic acid production',\n",
-       "  'anthranilic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('anthraquinone production',\n",
-       "  'anthraquinone',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('anthraquinone production',\n",
-       "  'anthraquinone',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('anti-reflex-coating, etching, solar glass',\n",
-       "  'anti-reflex-coating, etching, solar glass',\n",
-       "  'square meter',\n",
-       "  'DK'): 'Manufacture of glass and glass products',\n",
-       " ('anti-reflex-coating, etching, solar glass',\n",
-       "  'anti-reflex-coating, etching, solar glass',\n",
-       "  'square meter',\n",
-       "  'RoW'): 'Manufacture of glass and glass products',\n",
-       " ('antimony production',\n",
-       "  'antimony',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('antimony production',\n",
-       "  'antimony',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'CL'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'IT'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apple production',\n",
-       "  'apple',\n",
-       "  'kilogram',\n",
-       "  'ZA'): 'Growing of pome fruits and stone fruits',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'BR'): 'Support activities for crop production',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'CA-QC'): 'Support activities for crop production',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'CH'): 'Support activities for crop production',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'Canada without Quebec'): 'Support activities for crop production',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'RoW'): 'Support activities for crop production',\n",
-       " ('application of plant protection product, by field sprayer',\n",
-       "  'application of plant protection product, by field sprayer',\n",
-       "  'hectare',\n",
-       "  'ZA'): 'Support activities for crop production',\n",
-       " ('apricot production',\n",
-       "  'apricot',\n",
-       "  'kilogram',\n",
-       "  'ES'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apricot production',\n",
-       "  'apricot',\n",
-       "  'kilogram',\n",
-       "  'FR'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apricot production',\n",
-       "  'apricot',\n",
-       "  'kilogram',\n",
-       "  'IT'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apricot production',\n",
-       "  'apricot',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of pome fruits and stone fruits',\n",
-       " ('apricot production',\n",
-       "  'apricot',\n",
-       "  'kilogram',\n",
-       "  'TR'): 'Growing of pome fruits and stone fruits',\n",
-       " ('argon production, liquid',\n",
-       "  'argon, liquid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('argon production, liquid',\n",
-       "  'argon, liquid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('argon to generic market for inert gas for discharge lamps',\n",
-       "  'inert gas for discharge lamps',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('arsine production',\n",
-       "  'arsine',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('asbestos production, crysotile type',\n",
-       "  'asbestos, crysotile type',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Other mining and quarrying nec',\n",
-       " ('ascorbic acid production',\n",
-       "  'ascorbic acid',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pharmaceuticals medicinal chemical and botanical products',\n",
-       " ('ascorbic acid production',\n",
-       "  'ascorbic acid',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pharmaceuticals medicinal chemical and botanical products',\n",
-       " ('asparagus seedling production, for planting',\n",
-       "  'asparagus seedling, for planting',\n",
-       "  'unit',\n",
-       "  'FR'): 'Plant propagation',\n",
-       " ('asparagus seedling production, for planting',\n",
-       "  'asparagus seedling, for planting',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Plant propagation',\n",
-       " ('assembly of generator and motor, auxilliaries and energy use, for mini CHP plant',\n",
-       "  'assembly of generator and motor, auxilliaries and energy use, for mini CHP plant',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of engines and turbines except aircraft vehicle and cycle eng',\n",
-       " ('assembly of generator and motor, auxilliaries and energy use, for mini CHP plant',\n",
-       "  'assembly of generator and motor, auxilliaries and energy use, for mini CHP plant',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of engines and turbines except aircraft vehicle and cycle eng',\n",
-       " ('assembly of generator and motor, auxilliaries and energy use, heat and power co-generation unit, 160kW electrical',\n",
-       "  'assembly of generator and motor, auxilliaries and energy use, heat and power co-generation unit, 160kW electrical',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of engines and turbines except aircraft vehicle and cycle eng',\n",
-       " ('assembly of generator and motor, auxilliaries and energy use, heat and power co-generation unit, 160kW electrical',\n",
-       "  'assembly of generator and motor, auxilliaries and energy use, heat and power co-generation unit, 160kW electrical',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of engines and turbines except aircraft vehicle and cycle eng',\n",
-       " ('assembly of liquid crystal display, auxilliaries and energy use',\n",
-       "  'assembly of liquid crystal display, auxilliaries and energy use',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of computers and peripheral equipment',\n",
-       " ('atrazine production',\n",
-       "  'atrazine',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('atrazine production',\n",
-       "  'atrazine',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('aubergine production, in heated greenhouse',\n",
-       "  'aubergine',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('aubergine production, open field',\n",
-       "  'aubergine',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('aubergine production, open field',\n",
-       "  'aubergine',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('autoclaved aerated concrete block production',\n",
-       "  'autoclaved aerated concrete block',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('autoclaved aerated concrete block production',\n",
-       "  'autoclaved aerated concrete block',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('autoclaved aerated concrete block production',\n",
-       "  'autoclaved aerated concrete block',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('autoclaved aerated concrete block production',\n",
-       "  'hard coal ash',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('autoclaved aerated concrete block production',\n",
-       "  'hard coal ash',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of articles of concrete cement and plaster',\n",
-       " ('auxiliary heating unit production, electric, 5kW',\n",
-       "  'auxiliary heating unit, electric, 5kW',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('auxiliary heating unit production, electric, 5kW',\n",
-       "  'auxiliary heating unit, electric, 5kW',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('avocado production',\n",
-       "  'avocado',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Growing of tropical and subtropical fruits',\n",
-       " ('azodicarbonamide production',\n",
-       "  'azodicarbonamide',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('azodicarbonamide production',\n",
-       "  'azodicarbonamide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('backlight production, for liquid crystal display',\n",
-       "  'backlight, for liquid crystal display',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of computers and peripheral equipment',\n",
-       " ('bale loading',\n",
-       "  'bale loading',\n",
-       "  'unit',\n",
-       "  'CA-QC'): 'Postharvest crop activities',\n",
-       " ('bale loading', 'bale loading', 'unit', 'CH'): 'Postharvest crop activities',\n",
-       " ('bale loading',\n",
-       "  'bale loading',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Postharvest crop activities',\n",
-       " ('baling', 'baling', 'unit', 'CA-QC'): 'Postharvest crop activities',\n",
-       " ('baling', 'baling', 'unit', 'CH'): 'Postharvest crop activities',\n",
-       " ('baling', 'baling', 'unit', 'RoW'): 'Postharvest crop activities',\n",
-       " ('bamboo forestry, sustainable forest management',\n",
-       "  'bamboo culm',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Gathering of nonwood forest products',\n",
-       " ('bamboo forestry, sustainable forest management',\n",
-       "  'bamboo culm',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Gathering of nonwood forest products',\n",
-       " ('bamboo forestry, sustainable forest management',\n",
-       "  'bamboo culm',\n",
-       "  'kilogram',\n",
-       "  'CO'): 'Gathering of nonwood forest products',\n",
-       " ('bamboo forestry, sustainable forest management',\n",
-       "  'bamboo culm',\n",
-       "  'kilogram',\n",
-       "  'PH'): 'Gathering of nonwood forest products',\n",
-       " ('bamboo forestry, sustainable forest management',\n",
-       "  'bamboo culm',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Gathering of nonwood forest products',\n",
-       " ('bamboo pole production',\n",
-       "  'bamboo pole',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Manufacture of products of wood cork straw and plaiting materials',\n",
-       " ('bamboo pole production',\n",
-       "  'bamboo pole',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of products of wood cork straw and plaiting materials',\n",
-       " ('bamboo pole production',\n",
-       "  'bamboo pole',\n",
-       "  'kilogram',\n",
-       "  'CO'): 'Manufacture of products of wood cork straw and plaiting materials',\n",
-       " ('bamboo pole production',\n",
-       "  'bamboo pole',\n",
-       "  'kilogram',\n",
-       "  'PH'): 'Manufacture of products of wood cork straw and plaiting materials',\n",
-       " ('bamboo pole production',\n",
-       "  'bamboo pole',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of products of wood cork straw and plaiting materials',\n",
-       " ('banana production',\n",
-       "  'banana',\n",
-       "  'kilogram',\n",
-       "  'CO'): 'Growing of tropical and subtropical fruits',\n",
-       " ('banana production',\n",
-       "  'banana',\n",
-       "  'kilogram',\n",
-       "  'CR'): 'Growing of tropical and subtropical fruits',\n",
-       " ('banana production',\n",
-       "  'banana',\n",
-       "  'kilogram',\n",
-       "  'EC'): 'Growing of tropical and subtropical fruits',\n",
-       " ('banana production',\n",
-       "  'banana',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Growing of tropical and subtropical fruits',\n",
-       " ('banana production',\n",
-       "  'banana',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of tropical and subtropical fruits',\n",
-       " ('barge production',\n",
-       "  'barge',\n",
-       "  'unit',\n",
-       "  'RER'): 'Building of ships and floating structures',\n",
-       " ('barge production',\n",
-       "  'barge',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Building of ships and floating structures',\n",
-       " ('barge tanker production',\n",
-       "  'barge tanker',\n",
-       "  'unit',\n",
-       "  'RER'): 'Building of ships and floating structures',\n",
-       " ('barge tanker production',\n",
-       "  'barge tanker',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Building of ships and floating structures',\n",
-       " ('barite production',\n",
-       "  'barite',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Mining of other nonferrous metal ores',\n",
-       " ('barite production',\n",
-       "  'barite',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Mining of other nonferrous metal ores',\n",
-       " ('barite production',\n",
-       "  'barite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Mining of other nonferrous metal ores',\n",
-       " ('barium carbonate production',\n",
-       "  'barium carbonate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('barium hydroxide production',\n",
-       "  'barium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('barium oxide production',\n",
-       "  'barium oxide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('barium sulfide production',\n",
-       "  'barium sulfide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('bark chips production, hardwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, hardwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, hardwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, hardwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, softwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, softwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, softwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips production, softwood, at sawmill',\n",
-       "  'bark chips, wet, measured as dry mass',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual hardwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual hardwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual hardwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual hardwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual softwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual softwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual softwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('bark chips, wet, measured as dry mass to generic market for residual hardwood, wet',\n",
-       "  'residual softwood, wet',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('barley grain to generic market for energy feed',\n",
-       "  'energy feed, gross',\n",
-       "  'megajoule',\n",
-       "  'GLO'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain to generic market for energy feed',\n",
-       "  'protein feed, 100% crude',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain, feed production',\n",
-       "  'barley grain, feed',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain, feed production',\n",
-       "  'barley grain, feed',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain, feed production, Swiss integrated production',\n",
-       "  'barley grain, feed, Swiss integrated production',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain, feed production, organic',\n",
-       "  'barley grain, feed, organic',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley grain, feed production, organic',\n",
-       "  'barley grain, feed, organic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of prepared animal feeds',\n",
-       " ('barley production',\n",
-       "  'barley grain',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production',\n",
-       "  'barley grain',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production',\n",
-       "  'barley grain',\n",
-       "  'kilogram',\n",
-       "  'ES'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production',\n",
-       "  'barley grain',\n",
-       "  'kilogram',\n",
-       "  'FR'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production',\n",
-       "  'barley grain',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, Swiss integrated production, extensive',\n",
-       "  'barley grain, Swiss integrated production',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, Swiss integrated production, extensive',\n",
-       "  'straw',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, Swiss integrated production, intensive',\n",
-       "  'barley grain, Swiss integrated production',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, Swiss integrated production, intensive',\n",
-       "  'straw',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, organic',\n",
-       "  'barley grain, organic',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, organic',\n",
-       "  'barley grain, organic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, organic',\n",
-       "  'straw, organic',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley production, organic',\n",
-       "  'straw, organic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of cereals except rice leguminous crops and oil seeds',\n",
-       " ('barley seed production, Swiss integrated production, for sowing',\n",
-       "  'barley seed, Swiss integrated production, for sowing',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Seed processing for propagation',\n",
-       " ('barley seed production, for sowing',\n",
-       "  'barley seed, for sowing',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Seed processing for propagation',\n",
-       " ('barley seed production, organic, for sowing',\n",
-       "  'barley seed, organic, for sowing',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Seed processing for propagation',\n",
-       " ('barley seed production, organic, for sowing',\n",
-       "  'barley seed, organic, for sowing',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Seed processing for propagation',\n",
-       " ('basalt quarry operation',\n",
-       "  'basalt',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Quarrying of stone sand and clay',\n",
-       " ('basalt quarry operation',\n",
-       "  'basalt',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Quarrying of stone sand and clay',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'CO'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'Europe without Switzerland'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'PE'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of refined petroleum products',\n",
-       " ('base oil production, petroleum refinery operation',\n",
-       "  'base oil',\n",
-       "  'kilogram',\n",
-       "  'ZA'): 'Manufacture of refined petroleum products',\n",
-       " ('base plaster production',\n",
-       "  'base plaster',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of cement lime and plaster',\n",
-       " ('base plaster production',\n",
-       "  'base plaster',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace dust to market for basic oxygen furnace dust, for recovery',\n",
-       "  'basic oxygen furnace dust',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace dust to market for basic oxygen furnace dust, for recovery',\n",
-       "  'basic oxygen furnace dust',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace dust, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace dust, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace secondary metallurgy slag to market for basic oxygen furnace secondary metallurgy slag for recovery',\n",
-       "  'basic oxygen furnace secondary metallurgy slag',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace secondary metallurgy slag to market for basic oxygen furnace secondary metallurgy slag for recovery',\n",
-       "  'basic oxygen furnace secondary metallurgy slag',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace secondary metallurgy slag, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace secondary metallurgy slag, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace slag to market for basic oxygen furnace slag, for recovery',\n",
-       "  'basic oxygen furnace slag',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace slag to market for basic oxygen furnace slag, for recovery',\n",
-       "  'basic oxygen furnace slag',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace slag, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace slag, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace sludge to market for basic oxygen furnace sludge, for recovery',\n",
-       "  'basic oxygen furnace sludge',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace sludge to market for basic oxygen furnace sludge, for recovery',\n",
-       "  'basic oxygen furnace sludge',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('basic oxygen furnace sludge, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('basic oxygen furnace sludge, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('batch dyeing, fibre, cotton',\n",
-       "  'batch dyeing, fibre, cotton',\n",
-       "  'kilogram',\n",
-       "  'BD'): 'Finishing of textiles',\n",
-       " ('batch dyeing, fibre, cotton',\n",
-       "  'batch dyeing, fibre, cotton',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Finishing of textiles',\n",
-       " ('batch dyeing, fibre, cotton',\n",
-       "  'batch dyeing, fibre, cotton',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Finishing of textiles',\n",
-       " ('batter wheat mix production',\n",
-       "  'wheat flour mix',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of grain mill products',\n",
-       " ('batter wheat mix production',\n",
-       "  'wheat flour mix',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of grain mill products',\n",
-       " ('battery cell production, Li-ion, LFP',\n",
-       "  'battery cell, Li-ion, LFP',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, LFP',\n",
-       "  'battery cell, Li-ion, LFP',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, LiMn2O4',\n",
-       "  'battery cell, Li-ion, LiMn2O4',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, LiMn2O4',\n",
-       "  'battery cell, Li-ion, LiMn2O4',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NCA',\n",
-       "  'battery cell, Li-ion, NCA',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NCA',\n",
-       "  'battery cell, Li-ion, NCA',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NMC111',\n",
-       "  'battery cell, Li-ion, NMC111',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NMC111',\n",
-       "  'battery cell, Li-ion, NMC111',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NMC811',\n",
-       "  'battery cell, Li-ion, NMC811',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery cell production, Li-ion, NMC811',\n",
-       "  'battery cell, Li-ion, NMC811',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery management system production, for Li-ion battery',\n",
-       "  'battery management system, for Li-ion battery',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery module packaging production, Li-ion',\n",
-       "  'battery module packaging, Li-ion',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery module packaging production, Li-ion',\n",
-       "  'battery module packaging, Li-ion',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, LFP, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, LFP, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, LFP, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, LFP, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, LiMn2O4, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, LiMn2O4, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NCA, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NCA, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NCA, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NCA, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NMC111, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NMC111, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NMC111, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NMC111, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NMC811, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NMC811, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, Li-ion, NMC811, rechargeable, prismatic',\n",
-       "  'battery, Li-ion, NMC811, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, NaCl, rechargeable',\n",
-       "  'battery, NaCl',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, NiMH, rechargeable, prismatic',\n",
-       "  'battery, NiMH, rechargeable, prismatic',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, lead acid, rechargeable, stationary',\n",
-       "  'battery, lead acid, rechargeable, stationary',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery production, lead acid, rechargeable, stationary',\n",
-       "  'battery, lead acid, rechargeable, stationary',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery separator production',\n",
-       "  'battery separator',\n",
-       "  'kilogram',\n",
-       "  'CN'): 'Manufacture of batteries and accumulators',\n",
-       " ('battery separator production',\n",
-       "  'battery separator',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of batteries and accumulators',\n",
-       " ('bauxite mine operation',\n",
-       "  'bauxite',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Mining of other nonferrous metal ores',\n",
-       " ('beam, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('beam, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, beam, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('beef cattle production on pasture',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'ZA'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and feedlot',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and feedlot',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and feedlot',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and feedlot',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and proteic supplement',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and proteic supplement',\n",
-       "  'cattle for slaughtering, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and proteic supplement',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Raising of cattle and buffaloes',\n",
-       " ('beef cattle production on pasture and proteic supplement',\n",
-       "  'weaned heifers, live weight',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Raising of cattle and buffaloes',\n",
-       " ('beet seed production, Swiss integrated production, for sowing',\n",
-       "  'beet seed, Swiss integrated production, for sowing',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Seed processing for propagation',\n",
-       " ('beet sugar production',\n",
-       "  'molasses, from sugar beet',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of sugar',\n",
-       " ('beet sugar production',\n",
-       "  'molasses, from sugar beet',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of sugar',\n",
-       " ('beet sugar production',\n",
-       "  'sugar beet pulp',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of sugar',\n",
-       " ('beet sugar production',\n",
-       "  'sugar beet pulp',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of sugar',\n",
-       " ('beet sugar production',\n",
-       "  'sugar, from sugar beet',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of sugar',\n",
-       " ('beet sugar production',\n",
-       "  'sugar, from sugar beet',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of sugar',\n",
-       " ('bell pepper production, in heated greenhouse',\n",
-       "  'bell pepper',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Growing of spices aromatic drug and pharmaceutical crops',\n",
-       " ('bell pepper production, open field',\n",
-       "  'bell pepper',\n",
-       "  'kilogram',\n",
-       "  'IN-MH'): 'Growing of spices aromatic drug and pharmaceutical crops',\n",
-       " ('bell pepper production, open field',\n",
-       "  'bell pepper',\n",
-       "  'kilogram',\n",
-       "  'IN-UP'): 'Growing of spices aromatic drug and pharmaceutical crops',\n",
-       " ('bell pepper production, open field',\n",
-       "  'bell pepper',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of spices aromatic drug and pharmaceutical crops',\n",
-       " ('bentonite quarry construction',\n",
-       "  'bentonite quarry infrastructure',\n",
-       "  'unit',\n",
-       "  'DE'): 'Construction of other civil engineering projects',\n",
-       " ('bentonite quarry construction',\n",
-       "  'bentonite quarry infrastructure',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('bentonite quarry operation',\n",
-       "  'bentonite',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Quarrying of stone sand and clay',\n",
-       " ('bentonite quarry operation',\n",
-       "  'bentonite',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Quarrying of stone sand and clay',\n",
-       " ('benzal chloride production',\n",
-       "  'benzal chloride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzal chloride production',\n",
-       "  'benzal chloride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzaldehyde production',\n",
-       "  'benzaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzaldehyde production',\n",
-       "  'benzaldehyde',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzaldehyde-2-sulfonic acid production',\n",
-       "  'benzaldehyde-2-sulfonic acid',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'hydrochloric acid, without water, in 30% solution state',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'monochlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'monochlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'o-dichlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'o-dichlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'p-dichlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzene chlorination',\n",
-       "  'p-dichlorobenzene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzene production',\n",
-       "  'benzene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of refined petroleum products',\n",
-       " ('benzene production',\n",
-       "  'benzene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of refined petroleum products',\n",
-       " ('benzimidazole-compound production',\n",
-       "  'benzimidazole-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzimidazole-compound production',\n",
-       "  'benzimidazole-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzo[thia]diazole-compound production',\n",
-       "  'benzo[thia]diazole-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzo[thia]diazole-compound production',\n",
-       "  'benzo[thia]diazole-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzoic-compound production',\n",
-       "  'benzoic-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzoic-compound production',\n",
-       "  'benzoic-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('benzyl alcohol production',\n",
-       "  'benzyl alcohol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzyl alcohol production',\n",
-       "  'benzyl alcohol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('benzyl chloride production',\n",
-       "  'benzyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('benzyl chloride production',\n",
-       "  'benzyl chloride',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('beryllium hydroxide production',\n",
-       "  'beryllium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('beryllium hydroxide production',\n",
-       "  'beryllium hydroxide',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('beryllium production',\n",
-       "  'beryllium',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('beryllium production',\n",
-       "  'beryllium',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('beverage carton converting',\n",
-       "  'beverage carton converting',\n",
-       "  'square meter',\n",
-       "  'GLO'): 'Manufacture of corrugated paper and paperboard and of containers of paper a',\n",
-       " ('bicycle production',\n",
-       "  'bicycle',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of bicycles and invalid carriages',\n",
-       " ('bicycle production',\n",
-       "  'bicycle',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of bicycles and invalid carriages',\n",
-       " ('biogas production from grass',\n",
-       "  'biogas, from grass',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('biogas production from grass',\n",
-       "  'grass fibre',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('biogas production from grass',\n",
-       "  'protein feed, 100% crude',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('biogas purification to biomethane by amino washing',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biogas purification to biomethane by amino washing',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biogas purification to biomethane by membrane technique',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biogas purification to biomethane by membrane technique',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biogas purification to biomethane by pressure swing adsorption',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biogas purification to biomethane by pressure swing adsorption',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane pressure reduction, from high to low pressure',\n",
-       "  'biomethane, low pressure',\n",
-       "  'megajoule',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane pressure reduction, from high to low pressure',\n",
-       "  'biomethane, low pressure',\n",
-       "  'megajoule',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure from synthetic gas, wood, fixed bed technology',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure from synthetic gas, wood, fixed bed technology',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure from synthetic gas, wood, fluidised technology',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure from synthetic gas, wood, fluidised technology',\n",
-       "  'biomethane, high pressure',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure, vehicle grade',\n",
-       "  'biomethane, high pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, high pressure, vehicle grade',\n",
-       "  'biomethane, high pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, low pressure, vehicle grade',\n",
-       "  'biomethane, low pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, low pressure, vehicle grade',\n",
-       "  'biomethane, low pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, medium pressure, vehicle grade',\n",
-       "  'biomethane, medium pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane production, medium pressure, vehicle grade',\n",
-       "  'biomethane, medium pressure, vehicle grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of gas distribution of gaseous fuels through mains',\n",
-       " ('biomethane, low pressure burned in micro gas turbine 100kWe',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in micro gas turbine 100kWe',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in micro gas turbine 100kWe',\n",
-       "  'heat, central or small-scale, other than natural gas',\n",
-       "  'megajoule',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in micro gas turbine 100kWe',\n",
-       "  'heat, central or small-scale, other than natural gas',\n",
-       "  'megajoule',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in polymer electrolyte membrane fuel cell 2kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in polymer electrolyte membrane fuel cell 2kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in polymer electrolyte membrane fuel cell 2kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in polymer electrolyte membrane fuel cell 2kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell 125kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell 125kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell 125kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell 125kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell, with micro gas turbine, 180kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell, with micro gas turbine, 180kWe, future',\n",
-       "  'electricity, low voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell, with micro gas turbine, 180kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'CH'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('biomethane, low pressure burned in solid oxide fuel cell, with micro gas turbine, 180kWe, future',\n",
-       "  'heat, future',\n",
-       "  'megajoule',\n",
-       "  'RoW'): 'Electricity gas steam and air conditioning supply',\n",
-       " ('bipyridylium-compound production',\n",
-       "  'bipyridylium-compound',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('bipyridylium-compound production',\n",
-       "  'bipyridylium-compound',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of pesticides and other agrochemical products',\n",
-       " ('bisphenol A epoxy based vinyl ester resin production',\n",
-       "  'bisphenol A epoxy based vinyl ester resin',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('bisphenol A epoxy based vinyl ester resin production',\n",
-       "  'bisphenol A epoxy based vinyl ester resin',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('bisphenol A production, powder',\n",
-       "  'bisphenol A, powder',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('bisphenol A production, powder',\n",
-       "  'bisphenol A, powder',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('bitumen adhesive compound production, cold',\n",
-       "  'bitumen adhesive compound, cold',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen adhesive compound production, cold',\n",
-       "  'bitumen adhesive compound, cold',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen adhesive compound production, hot',\n",
-       "  'bitumen adhesive compound, hot',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen adhesive compound production, hot',\n",
-       "  'bitumen adhesive compound, hot',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production',\n",
-       "  'bitumen seal',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production',\n",
-       "  'bitumen seal',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, Alu80',\n",
-       "  'bitumen seal, Alu80',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, Alu80',\n",
-       "  'bitumen seal, Alu80',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, V60',\n",
-       "  'bitumen seal, V60',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, V60',\n",
-       "  'bitumen seal, V60',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, V60',\n",
-       "  'bitumen seal, V60',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, VA4',\n",
-       "  'bitumen seal, VA4',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, VA4',\n",
-       "  'bitumen seal, VA4',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, polymer EP4 flame retardant',\n",
-       "  'bitumen seal, polymer EP4 flame retardant',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, polymer EP4 flame retardant',\n",
-       "  'bitumen seal, polymer EP4 flame retardant',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bitumen seal production, polymer EP4 flame retardant',\n",
-       "  'bitumen seal, polymer EP4 flame retardant',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('blast furnace dust to market for blast furnace dust, for recovery',\n",
-       "  'blast furnace dust',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('blast furnace dust to market for blast furnace dust, for recovery',\n",
-       "  'blast furnace dust',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('blast furnace dust, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('blast furnace dust, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('blast furnace production',\n",
-       "  'blast furnace',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('blast furnace production',\n",
-       "  'blast furnace',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('blast furnace sludge to market for blast furnace sludge, for recovery',\n",
-       "  'blast furnace sludge',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('blast furnace sludge to market for blast furnace sludge, for recovery',\n",
-       "  'blast furnace sludge',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and disposal of nonhazardous waste',\n",
-       " ('blast furnace sludge, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of cement lime and plaster',\n",
-       " ('blast furnace sludge, for recovery to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of cement lime and plaster',\n",
-       " ('blast oxygen furnace converter production',\n",
-       "  'blast oxygen furnace converter',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('blast oxygen furnace converter production',\n",
-       "  'blast oxygen furnace converter',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of ovens furnaces and furnace burners',\n",
-       " ('blasting', 'blasting', 'kilogram', 'RER'): 'Site preparation',\n",
-       " ('blasting', 'blasting', 'kilogram', 'RoW'): 'Site preparation',\n",
-       " ('bleaching and dyeing, yarn',\n",
-       "  'bleaching and dyeing, yarn',\n",
-       "  'kilogram',\n",
-       "  'BD'): 'Finishing of textiles',\n",
-       " ('bleaching and dyeing, yarn',\n",
-       "  'bleaching and dyeing, yarn',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Finishing of textiles',\n",
-       " ('bleaching and dyeing, yarn',\n",
-       "  'bleaching and dyeing, yarn',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Finishing of textiles',\n",
-       " ('bleaching, textile',\n",
-       "  'bleaching, textile',\n",
-       "  'kilogram',\n",
-       "  'BD'): 'Finishing of textiles',\n",
-       " ('bleaching, textile',\n",
-       "  'bleaching, textile',\n",
-       "  'kilogram',\n",
-       "  'IN'): 'Finishing of textiles',\n",
-       " ('bleaching, textile',\n",
-       "  'bleaching, textile',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Finishing of textiles',\n",
-       " ('blow moulding',\n",
-       "  'blow moulding',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of plastics products',\n",
-       " ('blow moulding',\n",
-       "  'blow moulding',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of plastics products',\n",
-       " ('blow moulding',\n",
-       "  'blow moulding',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics products',\n",
-       " ('blower and heat exchange unit production, Avent E 97',\n",
-       "  'blower and heat exchange unit, Avent E 97',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, Avent E 97',\n",
-       "  'blower and heat exchange unit, Avent E 97',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, GE 250 RH',\n",
-       "  'blower and heat exchange unit, GE 250 RH',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, GE 250 RH',\n",
-       "  'blower and heat exchange unit, GE 250 RH',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, KWL 250',\n",
-       "  'blower and heat exchange unit, KWL 250',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, KWL 250',\n",
-       "  'blower and heat exchange unit, KWL 250',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, KWLC 1200',\n",
-       "  'blower and heat exchange unit, KWLC 1200',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, KWLC 1200',\n",
-       "  'blower and heat exchange unit, KWLC 1200',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, Storkair G 90',\n",
-       "  'blower and heat exchange unit, Storkair G 90',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, Storkair G 90',\n",
-       "  'blower and heat exchange unit, Storkair G 90',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, Twl-700',\n",
-       "  'blower and heat exchange unit, Twl-700',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, Twl-700',\n",
-       "  'blower and heat exchange unit, Twl-700',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, central, 600-1200 m3/h',\n",
-       "  'blower and heat exchange unit, central, 600-1200 m3/h',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, central, 600-1200 m3/h',\n",
-       "  'blower and heat exchange unit, central, 600-1200 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, decentralized, 180-250 m3/h',\n",
-       "  'blower and heat exchange unit, decentralized, 180-250 m3/h',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('blower and heat exchange unit production, decentralized, 180-250 m3/h',\n",
-       "  'blower and heat exchange unit, decentralized, 180-250 m3/h',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('board, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, hardwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, hardwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, air drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=10%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=10%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CA-QC'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'CH'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'Europe without Switzerland'): 'Sawmilling and planing of wood',\n",
-       " ('board, softwood, raw, kiln drying to u=20%',\n",
-       "  'sawnwood, board, softwood, raw, dried (u=20%)',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Sawmilling and planing of wood',\n",
-       " ('borax production, anhydrous, powder',\n",
-       "  'borax, anhydrous, powder',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('borax production, anhydrous, powder',\n",
-       "  'borax, anhydrous, powder',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('borehole heat exchanger production, 150m',\n",
-       "  'borehole heat exchanger, 150m',\n",
-       "  'unit',\n",
-       "  'CH'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('borehole heat exchanger production, 150m',\n",
-       "  'borehole heat exchanger, 150m',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of other generalpurpose machinery',\n",
-       " ('boric acid production, anhydrous, powder',\n",
-       "  'boric acid, anhydrous, powder',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Mining of chemical and fertilizer minerals',\n",
-       " ('boric acid production, anhydrous, powder',\n",
-       "  'boric acid, anhydrous, powder',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Mining of chemical and fertilizer minerals',\n",
-       " ('boric oxide production',\n",
-       "  'boric oxide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('boron carbide production',\n",
-       "  'boron carbide',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('boron trifluoride production',\n",
-       "  'boron trifluoride',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('brass drilling, computer numerical controlled',\n",
-       "  'brass removed by drilling, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass drilling, computer numerical controlled',\n",
-       "  'brass removed by drilling, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass drilling, conventional',\n",
-       "  'brass removed by drilling, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass drilling, conventional',\n",
-       "  'brass removed by drilling, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass production',\n",
-       "  'brass',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('brass production',\n",
-       "  'brass',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('brass turning, average, computer numerical controlled',\n",
-       "  'brass removed by turning, average, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, average, computer numerical controlled',\n",
-       "  'brass removed by turning, average, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, average, conventional',\n",
-       "  'brass removed by turning, average, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, average, conventional',\n",
-       "  'brass removed by turning, average, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily dressing, computer numerical controlled',\n",
-       "  'brass removed by turning, primarily dressing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily dressing, computer numerical controlled',\n",
-       "  'brass removed by turning, primarily dressing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily dressing, conventional',\n",
-       "  'brass removed by turning, primarily dressing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily dressing, conventional',\n",
-       "  'brass removed by turning, primarily dressing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily roughing, computer numerical controlled',\n",
-       "  'brass removed by turning, primarily roughing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily roughing, computer numerical controlled',\n",
-       "  'brass removed by turning, primarily roughing, computer numerical controlled',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily roughing, conventional',\n",
-       "  'brass removed by turning, primarily roughing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Treatment and coating of metals machining',\n",
-       " ('brass turning, primarily roughing, conventional',\n",
-       "  'brass removed by turning, primarily roughing, conventional',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Treatment and coating of metals machining',\n",
-       " ('brazing solder production, cadmium free',\n",
-       "  'brazing solder, cadmium free',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of other chemical products nec',\n",
-       " ('brazing solder production, cadmium free',\n",
-       "  'brazing solder, cadmium free',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other chemical products nec',\n",
-       " ('breadcrumbs production',\n",
-       "  'breadcrumbs',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of grain mill products',\n",
-       " ('breadcrumbs production',\n",
-       "  'breadcrumbs',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of grain mill products',\n",
-       " ('brick production facility construction',\n",
-       "  'brick production facility',\n",
-       "  'unit',\n",
-       "  'CA-QC'): 'Construction of other civil engineering projects',\n",
-       " ('brick production facility construction',\n",
-       "  'brick production facility',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Construction of other civil engineering projects',\n",
-       " ('broccoli production',\n",
-       "  'broccoli',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('bromine production',\n",
-       "  'bromine',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('bromine production',\n",
-       "  'bromine',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('bromopropane production',\n",
-       "  'bromopropane',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('bromopropane production',\n",
-       "  'bromopropane',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('bromoxynil production',\n",
-       "  'bromoxynil',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('bronze production',\n",
-       "  'bronze',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('bronze production',\n",
-       "  'bronze',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('bronze scrap, post-consumer to generic market for bronze',\n",
-       "  'bronze',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Materials recovery',\n",
-       " ('building construction, budget hotel',\n",
-       "  'building, budget hotel',\n",
-       "  'unit',\n",
-       "  'BR'): 'Construction of buildings',\n",
-       " ('building construction, budget hotel',\n",
-       "  'building, budget hotel',\n",
-       "  'unit',\n",
-       "  'PE'): 'Construction of buildings',\n",
-       " ('building construction, hall',\n",
-       "  'building, hall',\n",
-       "  'square meter',\n",
-       "  'CH'): 'Construction of buildings',\n",
-       " ('building construction, hall',\n",
-       "  'building, hall',\n",
-       "  'square meter',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('building construction, hall, steel construction',\n",
-       "  'building, hall, steel construction',\n",
-       "  'square meter',\n",
-       "  'CH'): 'Construction of buildings',\n",
-       " ('building construction, hall, steel construction',\n",
-       "  'building, hall, steel construction',\n",
-       "  'square meter',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('building construction, hall, wood construction',\n",
-       "  'building, hall, wood construction',\n",
-       "  'square meter',\n",
-       "  'CH'): 'Construction of buildings',\n",
-       " ('building construction, hall, wood construction',\n",
-       "  'building, hall, wood construction',\n",
-       "  'square meter',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('building construction, hostel',\n",
-       "  'building, hostel',\n",
-       "  'unit',\n",
-       "  'BR'): 'Construction of buildings',\n",
-       " ('building construction, hostel',\n",
-       "  'building, hostel',\n",
-       "  'unit',\n",
-       "  'PE'): 'Construction of buildings',\n",
-       " ('building construction, luxury hotel',\n",
-       "  'building, luxury hotel',\n",
-       "  'unit',\n",
-       "  'BR'): 'Construction of buildings',\n",
-       " ('building construction, luxury hotel',\n",
-       "  'building, luxury hotel',\n",
-       "  'unit',\n",
-       "  'PE'): 'Construction of buildings',\n",
-       " ('building construction, multi-storey',\n",
-       "  'building, multi-storey',\n",
-       "  'cubic meter',\n",
-       "  'RER'): 'Construction of buildings',\n",
-       " ('building construction, multi-storey',\n",
-       "  'building, multi-storey',\n",
-       "  'cubic meter',\n",
-       "  'RoW'): 'Construction of buildings',\n",
-       " ('building construction, upmarket hotel',\n",
-       "  'building, upmarket hotel',\n",
-       "  'unit',\n",
-       "  'PE'): 'Construction of buildings',\n",
-       " ('building machine production',\n",
-       "  'building machine',\n",
-       "  'unit',\n",
-       "  'RER'): 'Manufacture of machinery for mining quarrying and construction',\n",
-       " ('building machine production',\n",
-       "  'building machine',\n",
-       "  'unit',\n",
-       "  'RoW'): 'Manufacture of machinery for mining quarrying and construction',\n",
-       " ('building operation, budget hotel',\n",
-       "  'building operation, budget hotel',\n",
-       "  'guest night',\n",
-       "  'BR'): 'Short term accommodation activities',\n",
-       " ('building operation, budget hotel',\n",
-       "  'building operation, budget hotel',\n",
-       "  'guest night',\n",
-       "  'PE'): 'Short term accommodation activities',\n",
-       " ('building operation, hostel',\n",
-       "  'building operation, hostel',\n",
-       "  'guest night',\n",
-       "  'BR'): 'Short term accommodation activities',\n",
-       " ('building operation, hostel',\n",
-       "  'building operation, hostel',\n",
-       "  'guest night',\n",
-       "  'PE'): 'Short term accommodation activities',\n",
-       " ('building operation, luxury hotel',\n",
-       "  'building operation, luxury hotel',\n",
-       "  'guest night',\n",
-       "  'BR'): 'Short term accommodation activities',\n",
-       " ('building operation, luxury hotel',\n",
-       "  'building operation, luxury hotel',\n",
-       "  'guest night',\n",
-       "  'PE'): 'Short term accommodation activities',\n",
-       " ('building operation, upmarket hotel',\n",
-       "  'building operation, upmarket hotel',\n",
-       "  'guest night',\n",
-       "  'PE'): 'Short term accommodation activities',\n",
-       " ('bulk carrier production, for dry goods',\n",
-       "  'bulk carrier, for dry goods',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Building of ships and floating structures',\n",
-       " ('bulk lead-zinc concentrate to generic markets for zinc concentrate and lead concentrate',\n",
-       "  'lead concentrate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Mining of other nonferrous metal ores',\n",
-       " ('bulk lead-zinc concentrate to generic markets for zinc concentrate and lead concentrate',\n",
-       "  'zinc concentrate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Mining of other nonferrous metal ores',\n",
-       " ('burnt shale production',\n",
-       "  'burnt shale',\n",
-       "  'kilogram',\n",
-       "  'DE'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('burnt shale production',\n",
-       "  'burnt shale',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('burnt shale production',\n",
-       "  'electricity, medium voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'DE'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('burnt shale production',\n",
-       "  'electricity, medium voltage',\n",
-       "  'kilowatt hour',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('bus production', 'bus', 'unit', 'RER'): 'Manufacture of motor vehicles',\n",
-       " ('bus production', 'bus', 'unit', 'RoW'): 'Manufacture of motor vehicles',\n",
-       " ('butadiene production',\n",
-       "  'butadiene',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('butadiene production',\n",
-       "  'butadiene',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of plastics and synthetic rubber in primary forms',\n",
-       " ('butane-1,4-diol production',\n",
-       "  'butane-1,4-diol',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('butane-1,4-diol production',\n",
-       "  'butane-1,4-diol',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('butene production, mixed',\n",
-       "  'butene, mixed',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of refined petroleum products',\n",
-       " ('butene production, mixed',\n",
-       "  'butene, mixed',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of refined petroleum products',\n",
-       " ('butter production, from cow milk',\n",
-       "  'butter, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('butter production, from cow milk',\n",
-       "  'buttermilk, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('butter production, from cow milk',\n",
-       "  'skimmed milk, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('butter production, from cream, from cow milk',\n",
-       "  'butter, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('butter production, from cream, from cow milk',\n",
-       "  'buttermilk, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('butter production, from cream, from cow milk',\n",
-       "  'skimmed milk, from cow milk',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of dairy products',\n",
-       " ('buttermilk, from cow milk to generic market for protein feed',\n",
-       "  'energy feed, gross',\n",
-       "  'megajoule',\n",
-       "  'GLO'): 'Manufacture of prepared animal feeds',\n",
-       " ('buttermilk, from cow milk to generic market for protein feed',\n",
-       "  'protein feed, 100% crude',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of prepared animal feeds',\n",
-       " ('butyl acetate production',\n",
-       "  'butyl acetate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('butyl acetate production',\n",
-       "  'butyl acetate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('butyl acrylate production',\n",
-       "  'butyl acrylate',\n",
-       "  'kilogram',\n",
-       "  'RER'): 'Manufacture of basic chemicals',\n",
-       " ('butyl acrylate production',\n",
-       "  'butyl acrylate',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('butyldiglycol acetate production',\n",
-       "  'butyldiglycol acetate',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of basic chemicals',\n",
-       " ('cabbage red production',\n",
-       "  'cabbage red',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('cabbage white production',\n",
-       "  'cabbage white',\n",
-       "  'kilogram',\n",
-       "  'IN-MH'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('cabbage white production',\n",
-       "  'cabbage white',\n",
-       "  'kilogram',\n",
-       "  'IN-UP'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('cabbage white production',\n",
-       "  'cabbage white',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Growing of vegetables and melons roots and tubers',\n",
-       " ('cable production, connector for computer, without plugs',\n",
-       "  'cable, connector for computer, without plugs',\n",
-       "  'meter',\n",
-       "  'GLO'): 'Manufacture of electronic components and boards',\n",
-       " ('cable production, data cable in infrastructure',\n",
-       "  'cable, data cable in infrastructure',\n",
-       "  'meter',\n",
-       "  'GLO'): 'Manufacture of electronic components and boards',\n",
-       " ('cable production, network cable, category 5, without plugs',\n",
-       "  'cable, network cable, category 5, without plugs',\n",
-       "  'meter',\n",
-       "  'GLO'): 'Manufacture of electronic components and boards',\n",
-       " ('cable production, printer cable, without plugs',\n",
-       "  'cable, printer cable, without plugs',\n",
-       "  'meter',\n",
-       "  'GLO'): 'Manufacture of electronic components and boards',\n",
-       " ('cable production, ribbon cable, 20-pin, with plugs',\n",
-       "  'cable, ribbon cable, 20-pin, with plugs',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of electronic components and boards',\n",
-       " ('cable production, three-conductor cable',\n",
-       "  'cable, three-conductor cable',\n",
-       "  'meter',\n",
-       "  'GLO'): 'Manufacture of other electronic and electric wires and cables',\n",
-       " ('cable production, unspecified',\n",
-       "  'cable, unspecified',\n",
-       "  'kilogram',\n",
-       "  'GLO'): 'Manufacture of other electronic and electric wires and cables',\n",
-       " ('cable yarder production, trailer-mounted',\n",
-       "  'mobile cable yarder, trailer-mounted',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('cable yarder production, truck-mounted',\n",
-       "  'mobile cable yarder, truck-mounted, incl. processor',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('cable yarder with sled winch production',\n",
-       "  'cable yarder with sled winch',\n",
-       "  'unit',\n",
-       "  'GLO'): 'Manufacture of agricultural and forestry machinery',\n",
-       " ('cadmium chloride production, semiconductor-grade',\n",
-       "  'cadmium chloride, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium chloride production, semiconductor-grade',\n",
-       "  'cadmium chloride, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium production, primary',\n",
-       "  'cadmium',\n",
-       "  'kilogram',\n",
-       "  'CA-QC'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('cadmium production, primary',\n",
-       "  'cadmium',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('cadmium production, semiconductor-grade',\n",
-       "  'cadmium, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium production, semiconductor-grade',\n",
-       "  'cadmium, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium sludge from zinc electrolysis stockpiling',\n",
-       "  'cadmium sludge from zinc electrolysis stockpiling',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('cadmium sludge from zinc electrolysis stockpiling',\n",
-       "  'cadmium sludge from zinc electrolysis stockpiling',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic precious and other nonferrous metals',\n",
-       " ('cadmium sulfide production, semiconductor-grade',\n",
-       "  'cadmium sulfide, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium sulfide production, semiconductor-grade',\n",
-       "  'cadmium sulfide, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium telluride production, semiconductor-grade',\n",
-       "  'cadmium telluride, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of basic chemicals',\n",
-       " ('cadmium telluride production, semiconductor-grade',\n",
-       "  'cadmium telluride, semiconductor-grade',\n",
-       "  'kilogram',\n",
-       "  'US'): 'Manufacture of basic chemicals',\n",
-       " ('calcareous marl production',\n",
-       "  'calcareous marl',\n",
-       "  'kilogram',\n",
-       "  'CH'): 'Quarrying of stone sand and clay',\n",
-       " ('calcareous marl production',\n",
-       "  'calcareous marl',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Quarrying of stone sand and clay',\n",
-       " ('calcined clay production',\n",
-       "  'calcined clay',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('calcined clay production',\n",
-       "  'calcined clay',\n",
-       "  'kilogram',\n",
-       "  'RoW'): 'Manufacture of other nonmetallic mineral products nec',\n",
-       " ('calcined clay to generic market for supplementary cementitious materials',\n",
-       "  'supplementary cementitious materials',\n",
-       "  'kilogram',\n",
-       "  'BR'): 'Manufacture of cement lime and plaster',\n",
-       " ...}"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cat"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "498002a5-8a84-4631-a717-4838a5648d7b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<defs>\n",
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-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
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-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
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-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;value&#x27; ()&gt;\n",
-       "array(5.31157551e+11)\n",
-       "Coordinates:\n",
-       "    region   &lt;U5 &#x27;World&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'value'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5b47b198-9026-40f9-84ba-62bb865b019d' class='xr-array-in' type='checkbox' checked><label for='section-5b47b198-9026-40f9-84ba-62bb865b019d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.312e+11</span></div><div class='xr-array-data'><pre>array(5.31157551e+11)</pre></div></div></li><li class='xr-section-item'><input id='section-de72b041-3dc6-440c-afed-034e846e636e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de72b041-3dc6-440c-afed-034e846e636e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-d2a0e5fa-7e9e-4292-88da-9b5c5c64deba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d2a0e5fa-7e9e-4292-88da-9b5c5c64deba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95d4187-3527-43ab-a807-9f13f90b7679' class='xr-var-data-in' type='checkbox'><label for='data-b95d4187-3527-43ab-a807-9f13f90b7679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;World&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ddbbdfc-def3-41cf-8d64-15e63be01cbd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ddbbdfc-def3-41cf-8d64-15e63be01cbd' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a77c6089-bfcf-4aa4-8c45-eb3b446e98a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a77c6089-bfcf-4aa4-8c45-eb3b446e98a1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'value' ()>\n",
-       "array(5.31157551e+11)\n",
-       "Coordinates:\n",
-       "    region   <U5 'World'"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr.sel(\n",
-    "    region=\"World\",\n",
-    ").sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "afeae598-b68e-4a45-8c73-d2683d6f49b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "df = p.scenarios.sel(region=\"World\", model=\"image\", pathway=\"SSP2-Base\").to_dataframe(\"val\").unstack()[\"val\"]\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "df.T.plot(kind=\"area\", stacked=True,\n",
-    "       #color=color_palette, \n",
-    "       figsize=(7, 5))\n",
-    "plt.ylim(0, 400)\n",
-    "plt.legend(ncols=2)\n",
-    "plt.title(\"Global electricity production mix\")\n",
-    "plt.ylabel(\"Exajoules\")\n",
-    "plt.savefig(\"electricity_mix_Base.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d98804ae-a2c8-4bdd-ae08-107275e19123",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2fe82f93-9877-4688-bd2e-04c0f2362ee0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr_human_health = arr.copy()\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)\")] *= 1.15E+01\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)\")] *= 2.70E+00\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - particulate matter formation - impact on human health\")] *= 2.14E+01\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235\")] *= 2.10E-08"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "02743d93-004c-4170-a07c-fe512434e3c4",
-   "metadata": {},
-   "outputs": [
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-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (year: 15)&gt;\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
-       "Coordinates:\n",
-       "    impact_category  &lt;U60 &#x27;EF v3.1 - climate change - global warming potentia...\n",
-       "  * year             (year) int64 2005 2010 2015 2020 ... 2070 2080 2090 2100\n",
-       "    region           &lt;U5 &#x27;World&#x27;\n",
-       "    model            &lt;U5 &#x27;image&#x27;\n",
-       "    scenario         &lt;U9 &#x27;SSP2-Base&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-65ee455e-8f02-4039-ba33-ec9105c1415c' class='xr-array-in' type='checkbox' checked><label for='section-65ee455e-8f02-4039-ba33-ec9105c1415c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])</pre></div></div></li><li class='xr-section-item'><input id='section-9735e7ad-2b7c-482c-84eb-32902b75b58f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9735e7ad-2b7c-482c-84eb-32902b75b58f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>impact_category</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U60</div><div class='xr-var-preview xr-preview'>&#x27;EF v3.1 - climate change - glob...</div><input id='attrs-1093a553-9a7a-4d3f-bb86-6d497c599b4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1093a553-9a7a-4d3f-bb86-6d497c599b4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-631c9395-0b11-413e-bd13-f91e8a370411' class='xr-var-data-in' type='checkbox'><label for='data-631c9395-0b11-413e-bd13-f91e8a370411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;EF v3.1 - climate change - global warming potential (GWP100)&#x27;,\n",
-       "      dtype=&#x27;&lt;U60&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2005 2010 2015 ... 2080 2090 2100</div><input id='attrs-8d89e26e-648c-451c-aa09-a2651787047b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d89e26e-648c-451c-aa09-a2651787047b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f888002a-ad5f-42b7-8f69-df6b217d61d1' class='xr-var-data-in' type='checkbox'><label for='data-f888002a-ad5f-42b7-8f69-df6b217d61d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060, 2070,\n",
-       "       2080, 2090, 2100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-23db79ce-edb3-44cf-9d58-654570e3e729' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23db79ce-edb3-44cf-9d58-654570e3e729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56d0a35f-2972-4cef-8178-c0c1bdb38e9b' class='xr-var-data-in' type='checkbox'><label for='data-56d0a35f-2972-4cef-8178-c0c1bdb38e9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;World&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-8d98291e-df5a-4b56-bcfa-63572b91e24c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d98291e-df5a-4b56-bcfa-63572b91e24c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9327a6e1-82ed-47be-b674-47ed15063363' class='xr-var-data-in' type='checkbox'><label for='data-9327a6e1-82ed-47be-b674-47ed15063363' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;image&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-Base&#x27;</div><input id='attrs-c7b50863-cbbe-4a86-bcaa-f1c61f6e9c5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7b50863-cbbe-4a86-bcaa-f1c61f6e9c5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2a582a1-73cd-4a5d-ba2a-61c227f36e94' class='xr-var-data-in' type='checkbox'><label for='data-a2a582a1-73cd-4a5d-ba2a-61c227f36e94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;SSP2-Base&#x27;, dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd5cf800-8c2e-49c6-8c2d-cbab40b004b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd5cf800-8c2e-49c6-8c2d-cbab40b004b3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50a78c00-8818-421e-8091-34e598b1df5f' class='xr-index-data-in' type='checkbox'/><label for='index-50a78c00-8818-421e-8091-34e598b1df5f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060,\n",
-       "            2070, 2080, 2090, 2100],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dba6b36e-e0d9-473d-873f-dd42cdf8da44' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dba6b36e-e0d9-473d-873f-dd42cdf8da44' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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-       "<xarray.DataArray (year: 15)>\n",
-       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
-       "Coordinates:\n",
-       "    impact_category  <U60 'EF v3.1 - climate change - global warming potentia...\n",
-       "  * year             (year) int64 2005 2010 2015 2020 ... 2070 2080 2090 2100\n",
-       "    region           <U5 'World'\n",
-       "    model            <U5 'image'\n",
-       "    scenario         <U9 'SSP2-Base'"
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-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.lca_results.sel(\n",
-    "        model=\"image\",\n",
-    "        scenario=\"SSP2-Base\",\n",
-    "        region=\"World\",\n",
-    "        impact_category=\"EF v3.1 - climate change - global warming potential (GWP100)\"\n",
-    "    ).sum(dim=[\"act_category\", \"variable\"])/1e3/1e9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8752e004-0122-49b2-b889-6c84d79271d5",
-   "metadata": {},
-   "outputs": [
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-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (year: 15)&gt;\n",
-       "array([23.61215941, 24.78051733, 28.21516367, 35.00152945, 36.99087931,\n",
-       "       39.8899597 , 42.72311544, 45.61040504, 48.32952551, 47.18497341,\n",
-       "       36.3159623 , 28.77312683, 21.90898907, 22.29171039, 24.79193269])\n",
-       "Coordinates:\n",
-       "    impact_category  &lt;U60 &#x27;EF v3.1 - climate change - global warming potentia...\n",
-       "  * year             (year) int64 2005 2010 2015 2020 ... 2070 2080 2090 2100\n",
-       "    region           &lt;U5 &#x27;World&#x27;\n",
-       "    model            &lt;U5 &#x27;image&#x27;\n",
-       "    scenario         &lt;U9 &#x27;SSP2-Base&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a1ea7a8e-9a08-4e36-a6dc-e75833bb902e' class='xr-array-in' type='checkbox' checked><label for='section-a1ea7a8e-9a08-4e36-a6dc-e75833bb902e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>23.61 24.78 28.22 35.0 36.99 39.89 ... 36.32 28.77 21.91 22.29 24.79</span></div><div class='xr-array-data'><pre>array([23.61215941, 24.78051733, 28.21516367, 35.00152945, 36.99087931,\n",
-       "       39.8899597 , 42.72311544, 45.61040504, 48.32952551, 47.18497341,\n",
-       "       36.3159623 , 28.77312683, 21.90898907, 22.29171039, 24.79193269])</pre></div></div></li><li class='xr-section-item'><input id='section-9dde653b-6282-4fcd-8580-703bada1cb12' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9dde653b-6282-4fcd-8580-703bada1cb12' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>impact_category</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U60</div><div class='xr-var-preview xr-preview'>&#x27;EF v3.1 - climate change - glob...</div><input id='attrs-c8381dd6-e95b-4178-8d2b-a21727dc46b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8381dd6-e95b-4178-8d2b-a21727dc46b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39322a9c-df20-4a08-84b3-e54b2231809b' class='xr-var-data-in' type='checkbox'><label for='data-39322a9c-df20-4a08-84b3-e54b2231809b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;EF v3.1 - climate change - global warming potential (GWP100)&#x27;,\n",
-       "      dtype=&#x27;&lt;U60&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2005 2010 2015 ... 2080 2090 2100</div><input id='attrs-d9a5cf8a-96ed-4fa7-a476-7dc2acabb22d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9a5cf8a-96ed-4fa7-a476-7dc2acabb22d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31e78edd-b5b5-4fb9-ba6d-6dc8425ddfb9' class='xr-var-data-in' type='checkbox'><label for='data-31e78edd-b5b5-4fb9-ba6d-6dc8425ddfb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060, 2070,\n",
-       "       2080, 2090, 2100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>region</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;World&#x27;</div><input id='attrs-ecd39daa-8b9c-4be6-b81c-19e234a3792d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecd39daa-8b9c-4be6-b81c-19e234a3792d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eeb2b15-6e81-43f7-ba96-5526c46c3c36' class='xr-var-data-in' type='checkbox'><label for='data-6eeb2b15-6e81-43f7-ba96-5526c46c3c36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;World&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;image&#x27;</div><input id='attrs-7668896f-1688-4616-bb92-6fc22ccfe7a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7668896f-1688-4616-bb92-6fc22ccfe7a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca0af900-3afb-4c93-82b8-5c94c328b9ee' class='xr-var-data-in' type='checkbox'><label for='data-ca0af900-3afb-4c93-82b8-5c94c328b9ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;image&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;SSP2-Base&#x27;</div><input id='attrs-abe01649-5a99-47d9-80c8-03a7ee2b5051' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abe01649-5a99-47d9-80c8-03a7ee2b5051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aad3725e-f37b-4058-8acb-506bfa5123dd' class='xr-var-data-in' type='checkbox'><label for='data-aad3725e-f37b-4058-8acb-506bfa5123dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;SSP2-Base&#x27;, dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cebf4353-a49d-4026-a47c-dd67b78fcd25' class='xr-section-summary-in' type='checkbox'  ><label for='section-cebf4353-a49d-4026-a47c-dd67b78fcd25' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-92e0bd3c-50f8-4cde-bbe9-bd288a6a2e5b' class='xr-index-data-in' type='checkbox'/><label for='index-92e0bd3c-50f8-4cde-bbe9-bd288a6a2e5b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060,\n",
-       "            2070, 2080, 2090, 2100],\n",
-       "           dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2192673-bb87-429f-a1d0-1720e380e0c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d2192673-bb87-429f-a1d0-1720e380e0c6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (year: 15)>\n",
-       "array([23.61215941, 24.78051733, 28.21516367, 35.00152945, 36.99087931,\n",
-       "       39.8899597 , 42.72311544, 45.61040504, 48.32952551, 47.18497341,\n",
-       "       36.3159623 , 28.77312683, 21.90898907, 22.29171039, 24.79193269])\n",
-       "Coordinates:\n",
-       "    impact_category  <U60 'EF v3.1 - climate change - global warming potentia...\n",
-       "  * year             (year) int64 2005 2010 2015 2020 ... 2070 2080 2090 2100\n",
-       "    region           <U5 'World'\n",
-       "    model            <U5 'image'\n",
-       "    scenario         <U9 'SSP2-Base'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.lca_results.sel(\n",
-    "        model=\"image\",\n",
-    "        scenario=\"SSP2-Base\",\n",
-    "        region=\"World\",\n",
-    "        impact_category=\"EF v3.1 - climate change - global warming potential (GWP100)\"\n",
-    "    ).sum(dim=[\"act_category\", \"variable\"])/1e3/1e9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "62db073f-6ebc-4fb2-b9a8-1e97af4a216f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "all_elec = np.array([\n",
-    "    9284.128746,\n",
-    "    10822.55546,\n",
-    "    11751.26338,\n",
-    "    13154.49659,\n",
-    "    13879.48497,\n",
-    "    15324.33017,\n",
-    "    16971.71386,\n",
-    "    18973.94467,\n",
-    "    20680.4471,\n",
-    "    20869.20293,\n",
-    "    18843.73411,\n",
-    "    19044.38083,\n",
-    "    19493.99837,\n",
-    "    19398.71915,\n",
-    "    19882.06689,\n",
-    "]) * 1e9\n",
-    "\n",
-    "coal = np.array([\n",
-    "    7647.148928,\n",
-    "    8917.222898,\n",
-    "    9664.471945,\n",
-    "    11233.23258,\n",
-    "    12174.27898,\n",
-    "    13310.45063,\n",
-    "    14582.37004,\n",
-    "    16350.9504,\n",
-    "    17842.07757,\n",
-    "    17458.07759,\n",
-    "    13901.3199,\n",
-    "    12525.36265,\n",
-    "    11777.23066,\n",
-    "    11047.29605,\n",
-    "    12894.74249\n",
-    "]) * 1e9\n",
-    "\n",
-    "years = [2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060, 2070, 2080, 2090, 2100]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f238404e-eef3-4844-b08f-ec5d5bd3f205",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'years' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[9], line 14\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      2\u001b[0m ax \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m      3\u001b[0m     arr\u001b[38;5;241m.\u001b[39msel(\n\u001b[1;32m      4\u001b[0m         model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     11\u001b[0m     )\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregion\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     12\u001b[0m )\u001b[38;5;241m.\u001b[39mto_dataframe(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39munstack()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marea\u001b[39m\u001b[38;5;124m\"\u001b[39m, stacked\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m---> 14\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43myears\u001b[49m, all_elec, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, c\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend(ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'years' is not defined"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "ax = (\n",
-    "    arr.sel(\n",
-    "        model=\"image\",\n",
-    "        scenario=\"SSP2-RCP19\",\n",
-    "        region=[r for r in arr.region.values if r!=\"World\"],\n",
-    "        #region=[\"World\",],\n",
-    "        #variable=[v for v in arr.coords[\"variable\"].values if v!=\"Electricity\"],\n",
-    "        #variable=[\"Electricity\"],\n",
-    "        #impact_category=\"EF v3.1 - climate change - global warming potential (GWP100)\",\n",
-    "    ).sum(dim=[\"region\", \"variable\"])\n",
-    ").to_dataframe(\"val\").unstack()[\"val\"].plot(kind=\"area\", stacked=True, figsize=(10, 5))\n",
-    "\n",
-    "ax.plot(years, all_elec, linewidth=5, c=\"y\", linestyle=\"-.\")\n",
-    "\n",
-    "\n",
-    "plt.legend(ncols=3, fontsize=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "a7bcdb3e-6d91-461e-a2dc-de6984a4c9e6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x1700 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "arr_human_health = arr.copy()\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)\")] *= 1.15E+01\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)\")] *= 2.70E+00\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - particulate matter formation - impact on human health\")] *= 2.14E+01\n",
-    "arr_human_health.loc[dict(impact_category=\"EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235\")] *= 2.10E-08\n",
-    "\n",
-    "scenario=\"SSP2-Base\"\n",
-    "\n",
-    "# Fill NaNs with 0 (optional, depending on how you want to handle NaNs)\n",
-    "df = arr.sel(model=\"image\", scenario=scenario).to_dataframe(\"val\").unstack()[\"val\"].reset_index()\n",
-    "df_base = arr.sel(model=\"image\", scenario=\"SSP2-Base\").to_dataframe(\"val\").unstack()[\"val\"].reset_index()\n",
-    "\n",
-    "d_titles = {\n",
-    "    'Cumulative Energy Demand (CED) - total - energy content (HHV)': \"Cumulative Energy Demand\",\n",
-    "    'IPCC 2021 - climate change - GWP 100a, incl. H': \"climate change, 100y\",\n",
-    "    'EN15804 - inventory indicators ISO21930 - use of net fresh water': \"use of net fresh water\",\n",
-    "    'selected LCI results, additional - air - particulates': \"air - particulates\",\n",
-    "    'selected LCI results - resource - land occupation': \"land occupation\",\n",
-    "    'EF v3.1 - climate change - global warming potential (GWP100)': \"climate change\",\n",
-    "    'EF v3.1 - ozone depletion - ozone depletion potential (ODP)': \"ozone depletion\",\n",
-    "    'EF v3.1 - photochemical oxidant formation: human health - tropospheric ozone concentration increase': \"Photochemical ozone formation\",\n",
-    "    'EF v3.1 - eutrophication: freshwater - fraction of nutrients reaching freshwater end compartment (P)': \"freshwater eutrophication\",\n",
-    "    'EF v3.1 - eutrophication: marine - fraction of nutrients reaching marine end compartment (N)': \"marine eutrophication\",\n",
-    "    'EF v3.1 - acidification - accumulated exceedance (AE)': \"ocean acidification\",\n",
-    "    'EF v3.1 - eutrophication: terrestrial - accumulated exceedance (AE)': \"terrestrial eutrophication\",\n",
-    "    'selected LCI results - resource - land occupation': \"land use\",\n",
-    "    'EN15804 - inventory indicators ISO21930 - use of net fresh water': \"use of freshwater\"\n",
-    "}\n",
-    "\n",
-    "d_titles.update(\n",
-    "  {v[\"method\"]: k for k, v in planetary_boundaries.items()}\n",
-    ")\n",
-    "\n",
-    "d_units = {\n",
-    "    'Cumulative Energy Demand (CED) - total - energy content (HHV)': \"MJ primary\",\n",
-    "    'IPCC 2021 - climate change - GWP 100a, incl. H': \"kg CO$_2$-eq.\",\n",
-    "    'EN15804 - inventory indicators ISO21930 - use of net fresh water': \"m$^3$\",\n",
-    "    'selected LCI results, additional - air - particulates': \"kg\",\n",
-    "    'selected LCI results - resource - land occupation': \"m$^2$-year\",\n",
-    "}\n",
-    "d_units.update(\n",
-    "    {v[\"method\"]: v[\"unit\"] for k, v in planetary_boundaries.items()}\n",
-    ")\n",
-    "\n",
-    "# Define your categories\n",
-    "impact_categories = df['impact_category'].unique()\n",
-    "\n",
-    "\n",
-    "fig, axs = plt.subplots(4, 3, figsize=(15, 17))\n",
-    "axs = axs.ravel()  # flatten the array to iterate over it\n",
-    "\n",
-    "skip_cats = [\n",
-    "    \"EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)\",\n",
-    "    \"EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)\",\n",
-    "    \"EF v3.1 - particulate matter formation - impact on human health\",\n",
-    "    \"EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235\",\n",
-    "]\n",
-    "\n",
-    "impact_categories = [i for i in impact_categories if i not in skip_cats]\n",
-    "\n",
-    "for ax, impact_category in zip(axs, impact_categories):\n",
-    "    if impact_category in skip_cats:\n",
-    "        continue\n",
-    "    \n",
-    "    ax.axhline(planetary_boundaries[d_titles[impact_category]][\"value\"], 0, 6, linewidth=5, color = 'r', linestyle=\":\", label=\"Planetary boundary\")\n",
-    "    \n",
-    "    df_ = df[df[\"impact_category\"] == impact_category]\n",
-    "    df_ = df_.set_index(\"year\")\n",
-    "    \n",
-    "    df_ = df_.fillna(0)\n",
-    "    \n",
-    "    # Calculate total impact for each row\n",
-    "    df_['total'] = df_.loc[:, \"Carbon capture\":\"Treatment and disposal of nonhazardous waste\"].select_dtypes(include=[np.number]).sum(axis=1)\n",
-    "\n",
-    "    # Calculate proportion for each numeric column\n",
-    "    proportions = df_.loc[:, \"Carbon capture\": \"Treatment and disposal of nonhazardous waste\"].divide(df_['total'], axis=0)\n",
-    "\n",
-    "    # Identify columns where all values are less than 1% of the total\n",
-    "    cols_to_move = proportions.columns[(proportions.sum() < 0.01)]\n",
-    "\n",
-    "    # Add these values to 'other' column\n",
-    "    df_['other'] += df_.loc[:, cols_to_move].sum(axis=1)\n",
-    "\n",
-    "    # Drop these columns\n",
-    "    df_ = df_.drop(columns=cols_to_move)\n",
-    "\n",
-    "    # Drop the 'total' column as it's not needed anymore\n",
-    "    df_ = df_.drop(columns='total')\n",
-    "\n",
-    "    df_.plot(kind=\"area\", stacked=True, ax=ax)\n",
-    "    \n",
-    "    ax.set_title(d_titles[impact_category])\n",
-    "    ax.set_ylabel(d_units.get(impact_category))\n",
-    "    \n",
-    "    # Set the title for each subplot\n",
-    "    #ax.legend(ncols=4)\n",
-    "    ax.legend(fontsize=7, loc=\"upper center\", ncols=1)\n",
-    "    \n",
-    "\n",
-    "# Fill NaNs with 0 (optional, depending on how you want to handle NaNs)\n",
-    "df = arr_human_health.sel(model=\"image\", scenario=scenario, impact_category=skip_cats).sum(dim=\"act_category\").to_dataframe(\"val\").unstack()[\"val\"].reset_index()\n",
-    "df = df.groupby([\"region\", \"year\", \"impact_category\"]).sum().unstack()[\"Electricity\"]\n",
-    "df = df.rename(columns={\n",
-    "    'EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)': 'Human toxicity, cancer',\n",
-    "    'EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)': 'Human toxicity, non-cancer',\n",
-    "    'EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235': 'Ionising radiation',\n",
-    "    'EF v3.1 - particulate matter formation - impact on human health': 'Particulate matter'\n",
-    "})\n",
-    "df.cumsum().plot(kind=\"area\", stacked=True, ax=axs[11])\n",
-    "# Create a pivot table where index is 'year', columns are 'act_category', and values are 'WEU'\n",
-    "#pivot = df.pivot_table(values='World', index='year', columns='impact_category', fill_value=0)\n",
-    "\n",
-    "\n",
-    "# Create the stackplot\n",
-    "#axs[11].stackplot(pivot.index, pivot.T.cumsum(1), labels=pivot.columns)\n",
-    "\n",
-    "axs[11].set_title(\"Disability-adjusted Life Years (cumul.)\")\n",
-    "axs[11].set_ylabel(\"DALY (cumulative)\")\n",
-    "\n",
-    "#df = arr_human_health.sel(model=\"image\", scenario=scenario, impact_category=skip_cats).sum(dim=\"act_category\").to_dataframe(\"val\").unstack()[\"val\"].reset_index()    \n",
-    "# Create a pivot table where index is 'year', columns are 'act_category', and values are 'WEU'\n",
-    "#pivot = df.pivot_table(values='World', index='year', columns='impact_category', fill_value=0)\n",
-    "#pivot = pivot.rename(columns={\n",
-    "#    'EF v3.1 - human toxicity: carcinogenic - comparative toxic unit for human (CTUh)': 'Human toxicity, cancer',\n",
-    "#    'EF v3.1 - human toxicity: non-carcinogenic - comparative toxic unit for human (CTUh)': 'Human toxicity, non-cancer',\n",
-    "#    'EF v3.1 - ionising radiation: human health - human exposure efficiency relative to u235': 'Ionising radiation',\n",
-    "#    'EF v3.1 - particulate matter formation - impact on human health': 'Particulate matter'\n",
-    "#})\n",
-    "#axs[11].plot(pivot.index, pivot.T.cumsum(1).sum(0), linewidth=3, c=\"orange\", linestyle=\"dotted\", label=\"SSP2-Base\")\n",
-    "\n",
-    "#axs[11].legend(fontsize=7, loc=\"upper center\")\n",
-    "\n",
-    "# Automatically adjust the subplot layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Display the figure\n",
-    "plt.savefig(\"results_RCP26.png\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "d05a95b3-10c0-45fe-9940-1c01e3a787e1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import bw2io"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "id": "23176f16-1fff-41c7-b981-bedd92af3c67",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0mbw2io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbw2setup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
-       "\u001b[0;31mSource:\u001b[0m   \n",
-       "\u001b[0;32mdef\u001b[0m \u001b[0mbw2setup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0;34m\"biosphere3\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatabases\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Biosphere database already present!!! No setup is needed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Creating default biosphere\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mcreate_default_biosphere3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Creating default LCIA methods\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mcreate_default_lcia_methods\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Creating core data migrations\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mcreate_core_migrations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      /opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/bw2io/__init__.py\n",
-       "\u001b[0;31mType:\u001b[0m      function"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bw2io.bw2setup??"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "8765bbd1-3f8b-46b0-913f-f17155ea844a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Carbon capture                                             False\n",
-       "Electric power generation transmission and distribution    False\n",
-       "Extraction of crude petroleum                              False\n",
-       "Extraction of crude petroleum and natural gas              False\n",
-       "Extraction of natural gas                                  False\n",
-       "                                                           ...  \n",
-       "2096                                                       False\n",
-       "2097                                                       False\n",
-       "2098                                                       False\n",
-       "2099                                                       False\n",
-       "2100                                                       False\n",
-       "Length: 123, dtype: bool"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(df_.loc[:, \"Carbon capture\": \"Treatment and disposal of nonhazardous waste\"] / df_.loc[:, 'total'] < 0.01).all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "2e134d13-4318-4462-a6df-0dc24132fc64",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>act_category</th>\n",
-       "      <th>region</th>\n",
-       "      <th>impact_category</th>\n",
-       "      <th>variable</th>\n",
-       "      <th>Carbon capture</th>\n",
-       "      <th>Electric power generation transmission and distribution</th>\n",
-       "      <th>Extraction of crude petroleum</th>\n",
-       "      <th>Extraction of crude petroleum and natural gas</th>\n",
-       "      <th>Extraction of natural gas</th>\n",
-       "      <th>Freight rail transport</th>\n",
-       "      <th>Freight transport by road</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Mining of uranium and thorium ores</th>\n",
-       "      <th>Sawmilling and planing of wood</th>\n",
-       "      <th>Sea and coastal freight water transport</th>\n",
-       "      <th>Sewerage</th>\n",
-       "      <th>Site preparation</th>\n",
-       "      <th>Steam and air conditioning supply</th>\n",
-       "      <th>Transport via pipeline</th>\n",
-       "      <th>Treatment and disposal of hazardous waste</th>\n",
-       "      <th>Treatment and disposal of nonhazardous waste</th>\n",
-       "      <th>other</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2005</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.655673e+12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.155451e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2006</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.730669e+12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.198789e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2007</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.805665e+12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.242128e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2008</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.880660e+12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.285467e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.955656e+12</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.328806e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2096</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.178951e+13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.749870e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2097</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.191999e+13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.910556e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2098</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.205047e+13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.071243e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2099</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.218095e+13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.231929e+11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2100</th>\n",
-       "      <td>World</td>\n",
-       "      <td>EF v3.1 - water use - user deprivation potenti...</td>\n",
-       "      <td>Electricity</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.231143e+13</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.392615e+11</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>96 rows × 31 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "act_category region                                    impact_category  \\\n",
-       "year                                                                     \n",
-       "2005          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2006          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2007          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2008          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2009          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "...             ...                                                ...   \n",
-       "2096          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2097          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2098          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2099          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "2100          World  EF v3.1 - water use - user deprivation potenti...   \n",
-       "\n",
-       "act_category     variable  Carbon capture  \\\n",
-       "year                                        \n",
-       "2005          Electricity             NaN   \n",
-       "2006          Electricity             NaN   \n",
-       "2007          Electricity             NaN   \n",
-       "2008          Electricity             NaN   \n",
-       "2009          Electricity             NaN   \n",
-       "...                   ...             ...   \n",
-       "2096          Electricity             NaN   \n",
-       "2097          Electricity             NaN   \n",
-       "2098          Electricity             NaN   \n",
-       "2099          Electricity             NaN   \n",
-       "2100          Electricity             NaN   \n",
-       "\n",
-       "act_category  Electric power generation transmission and distribution  \\\n",
-       "year                                                                    \n",
-       "2005                                               3.655673e+12         \n",
-       "2006                                               3.730669e+12         \n",
-       "2007                                               3.805665e+12         \n",
-       "2008                                               3.880660e+12         \n",
-       "2009                                               3.955656e+12         \n",
-       "...                                                         ...         \n",
-       "2096                                               1.178951e+13         \n",
-       "2097                                               1.191999e+13         \n",
-       "2098                                               1.205047e+13         \n",
-       "2099                                               1.218095e+13         \n",
-       "2100                                               1.231143e+13         \n",
-       "\n",
-       "act_category  Extraction of crude petroleum  \\\n",
-       "year                                          \n",
-       "2005                                    NaN   \n",
-       "2006                                    NaN   \n",
-       "2007                                    NaN   \n",
-       "2008                                    NaN   \n",
-       "2009                                    NaN   \n",
-       "...                                     ...   \n",
-       "2096                                    NaN   \n",
-       "2097                                    NaN   \n",
-       "2098                                    NaN   \n",
-       "2099                                    NaN   \n",
-       "2100                                    NaN   \n",
-       "\n",
-       "act_category  Extraction of crude petroleum and natural gas  \\\n",
-       "year                                                          \n",
-       "2005                                                    NaN   \n",
-       "2006                                                    NaN   \n",
-       "2007                                                    NaN   \n",
-       "2008                                                    NaN   \n",
-       "2009                                                    NaN   \n",
-       "...                                                     ...   \n",
-       "2096                                                    NaN   \n",
-       "2097                                                    NaN   \n",
-       "2098                                                    NaN   \n",
-       "2099                                                    NaN   \n",
-       "2100                                                    NaN   \n",
-       "\n",
-       "act_category  Extraction of natural gas  Freight rail transport  \\\n",
-       "year                                                              \n",
-       "2005                                NaN                     NaN   \n",
-       "2006                                NaN                     NaN   \n",
-       "2007                                NaN                     NaN   \n",
-       "2008                                NaN                     NaN   \n",
-       "2009                                NaN                     NaN   \n",
-       "...                                 ...                     ...   \n",
-       "2096                                NaN                     NaN   \n",
-       "2097                                NaN                     NaN   \n",
-       "2098                                NaN                     NaN   \n",
-       "2099                                NaN                     NaN   \n",
-       "2100                                NaN                     NaN   \n",
-       "\n",
-       "act_category  Freight transport by road  ...  \\\n",
-       "year                                     ...   \n",
-       "2005                                NaN  ...   \n",
-       "2006                                NaN  ...   \n",
-       "2007                                NaN  ...   \n",
-       "2008                                NaN  ...   \n",
-       "2009                                NaN  ...   \n",
-       "...                                 ...  ...   \n",
-       "2096                                NaN  ...   \n",
-       "2097                                NaN  ...   \n",
-       "2098                                NaN  ...   \n",
-       "2099                                NaN  ...   \n",
-       "2100                                NaN  ...   \n",
-       "\n",
-       "act_category  Mining of uranium and thorium ores  \\\n",
-       "year                                               \n",
-       "2005                                         NaN   \n",
-       "2006                                         NaN   \n",
-       "2007                                         NaN   \n",
-       "2008                                         NaN   \n",
-       "2009                                         NaN   \n",
-       "...                                          ...   \n",
-       "2096                                         NaN   \n",
-       "2097                                         NaN   \n",
-       "2098                                         NaN   \n",
-       "2099                                         NaN   \n",
-       "2100                                         NaN   \n",
-       "\n",
-       "act_category  Sawmilling and planing of wood  \\\n",
-       "year                                           \n",
-       "2005                                     NaN   \n",
-       "2006                                     NaN   \n",
-       "2007                                     NaN   \n",
-       "2008                                     NaN   \n",
-       "2009                                     NaN   \n",
-       "...                                      ...   \n",
-       "2096                                     NaN   \n",
-       "2097                                     NaN   \n",
-       "2098                                     NaN   \n",
-       "2099                                     NaN   \n",
-       "2100                                     NaN   \n",
-       "\n",
-       "act_category  Sea and coastal freight water transport  Sewerage  \\\n",
-       "year                                                              \n",
-       "2005                                              NaN       NaN   \n",
-       "2006                                              NaN       NaN   \n",
-       "2007                                              NaN       NaN   \n",
-       "2008                                              NaN       NaN   \n",
-       "2009                                              NaN       NaN   \n",
-       "...                                               ...       ...   \n",
-       "2096                                              NaN       NaN   \n",
-       "2097                                              NaN       NaN   \n",
-       "2098                                              NaN       NaN   \n",
-       "2099                                              NaN       NaN   \n",
-       "2100                                              NaN       NaN   \n",
-       "\n",
-       "act_category  Site preparation  Steam and air conditioning supply  \\\n",
-       "year                                                                \n",
-       "2005                       NaN                                NaN   \n",
-       "2006                       NaN                                NaN   \n",
-       "2007                       NaN                                NaN   \n",
-       "2008                       NaN                                NaN   \n",
-       "2009                       NaN                                NaN   \n",
-       "...                        ...                                ...   \n",
-       "2096                       NaN                                NaN   \n",
-       "2097                       NaN                                NaN   \n",
-       "2098                       NaN                                NaN   \n",
-       "2099                       NaN                                NaN   \n",
-       "2100                       NaN                                NaN   \n",
-       "\n",
-       "act_category  Transport via pipeline  \\\n",
-       "year                                   \n",
-       "2005                             NaN   \n",
-       "2006                             NaN   \n",
-       "2007                             NaN   \n",
-       "2008                             NaN   \n",
-       "2009                             NaN   \n",
-       "...                              ...   \n",
-       "2096                             NaN   \n",
-       "2097                             NaN   \n",
-       "2098                             NaN   \n",
-       "2099                             NaN   \n",
-       "2100                             NaN   \n",
-       "\n",
-       "act_category  Treatment and disposal of hazardous waste  \\\n",
-       "year                                                      \n",
-       "2005                                                NaN   \n",
-       "2006                                                NaN   \n",
-       "2007                                                NaN   \n",
-       "2008                                                NaN   \n",
-       "2009                                                NaN   \n",
-       "...                                                 ...   \n",
-       "2096                                                NaN   \n",
-       "2097                                                NaN   \n",
-       "2098                                                NaN   \n",
-       "2099                                                NaN   \n",
-       "2100                                                NaN   \n",
-       "\n",
-       "act_category  Treatment and disposal of nonhazardous waste         other  \n",
-       "year                                                                      \n",
-       "2005                                                   NaN  1.155451e+11  \n",
-       "2006                                                   NaN  1.198789e+11  \n",
-       "2007                                                   NaN  1.242128e+11  \n",
-       "2008                                                   NaN  1.285467e+11  \n",
-       "2009                                                   NaN  1.328806e+11  \n",
-       "...                                                    ...           ...  \n",
-       "2096                                                   NaN  4.749870e+11  \n",
-       "2097                                                   NaN  4.910556e+11  \n",
-       "2098                                                   NaN  5.071243e+11  \n",
-       "2099                                                   NaN  5.231929e+11  \n",
-       "2100                                                   NaN  5.392615e+11  \n",
-       "\n",
-       "[96 rows x 31 columns]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d5b9dfaf-c744-4c4e-bdf7-c4ec0dd3a4f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import brightway2 as bw\n",
-    "bw.projects.set_current(\"ei39\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "f562abfa-1490-40b3-8c1b-cbac92c17283",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = []\n",
-    "\n",
-    "for method in bw.methods:\n",
-    "    m = bw.Method(method)\n",
-    "    res.append({\"unit\": m.metadata[\"unit\"], \"name\": m.name, \"exchanges\": [{\"name\": bw.get_activity(x)[\"name\"], \"categories\": list(bw.get_activity(x)[\"categories\"]), \"amount\": y} for x, y in m.load()]})\n",
-    "    #for cf in m.load():\n",
-    "    #    r\n",
-    "    #    print(cf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "7f4a57c3-dfb6-4b8e-bee6-99480a3bb496",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "with open('data.json', 'w') as f:\n",
-    "    json.dump(res, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2edce65c-9a6c-40ab-867d-f4a18d0dd36a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/opt/homebrew/Caskroom/miniforge/base/envs/ab/lib/python3.9/site-packages/bw2io/__init__.py'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bw2io.__file__\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "425bf7a6-4011-42d8-baab-78d23f6c728e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate import interpn\n",
-    "\n",
-    "c = interpn(\n",
-    "    ([2005, 2015, 2025, 2045, 2100], range(0, 35073), range(0, 35073)),\n",
-    "    np.nan_to_num(p.lca_A.sel(model=\"image\", scenario=\"SSP2-Base\").values),\n",
-    "    (2008, range(0, 35073), range(0, 35073))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d7c414e0-769b-4437-9df2-48aea3511685",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "np.nan_to_num(c).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ce7094aa-b975-4703-a113-25fb41862cdd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "df = pd.read_excel(\"sample/scenario_data/scenario_data2.xlsx\")\n",
-    "#df.head()\n",
-    "df.columns = [\"model\", \"pathway\", \"region\", \"variables\", \"unit\", 2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2060, 2070, 2080, 2090,2100]\n",
-    "#df.head()\n",
-    "df = df.melt(\n",
-    "       id_vars=[\"model\", \"pathway\", \"region\", \"variables\", \"unit\",]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0df30ddf-4804-4b36-b2a5-96fde54814db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.columns = [\"model\", \"pathway\", \"region\", \"variable\", \"unit\", \"year\", \"value\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b64209e0-8d52-4d0c-ad07-d5607a33cc2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"scenario_data2.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "89ccbece-24cc-4391-9466-80f6df511257",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Object `df.to_csv` not found.\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.to_csv?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "82d82fd0-a8f3-4577-b248-216bb9fce0d7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['GW', 'Mt CO2/yr', '-', 'GJ/tNH3', 'MJ/Tkm', 'kg CO2/GJ-prim',\n",
-       "       'Gg BCyr', 'Mt BC/yr', 'Tg CH4/yr', 'Mt CH4/yr', 'MtCO2/yr',\n",
-       "       'Tg C/yr', 'Mt CO/yr', 'Mt CO2-equiv/yr', 'Tg N/yr', 'kt N2O/yr',\n",
-       "       'Mt NH3/yr', 'Tg Nyr', 'Mt NO2/yr', 'Mt NOx/yr', 'Gg OC/yr',\n",
-       "       'Mt OC/yr', 'Tg S/yr', 'Mt SO2/yr', 'Tg VOCyr', 'Mt VOC/yr',\n",
-       "       'Mt/year', '°C days', 'GJ/cap', 'GJ/m2', 'GJ', 'kJ/m2/HDD',\n",
-       "       'bn tkm/yr', 'bn pkm/yr', 'tN/yr', 'tP2O5/yr', 'EJ/yr', 'PJ/yr',\n",
-       "       'billion US$2010/yr OR local currency', 'million ha', 'Ha/GJ-Prim',\n",
-       "       'years', 'million', 'US$2010/GJ', 'Mtonne/yr', 'Mt/yr', '°C',\n",
-       "       'billion US$2010/yr'], dtype=object)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"unit\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "c5449334-b7de-4580-b08c-f517ea5c7556",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import bw2io, bw2data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "215c7839-110b-430b-9e47-daa15151dcea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bw2data.projects.set_current(\"ei39\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "803572fe-5e58-4c32-a631-f63a117ed762",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "relics = [m for m in bw2data.methods if \"relics\" in m[0].lower()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "fcf25559-06f3-41d5-b93a-b63b224434c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list_m = []\n",
-    "for relic in relics:\n",
-    "    m = bw2data.Method(relic)\n",
-    "    new_m = {}\n",
-    "    new_m[\"unit\"] = m.metadata[\"unit\"]\n",
-    "    new_m[\"name\"] = m.name\n",
-    "    new_m[\"exchanges\"] = [\n",
-    "        {\n",
-    "            \"name\": bw2data.get_activity(cf[0])[\"name\"],\n",
-    "            \"categories\": bw2data.get_activity(cf[0])[\"categories\"],\n",
-    "            \"amount\": cf[1]\n",
-    "        }\n",
-    "        for cf in m.load()\n",
-    "    ]\n",
-    "    list_m.append(new_m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "7f4fbe84-d572-491e-967f-1b54ad63cc62",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'unit': 'kg aluminium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Aluminium'),\n",
-       "  'exchanges': [{'name': 'Aluminium, 24% in bauxite, 11% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Aluminium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg antimony',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Antimony'),\n",
-       "  'exchanges': [{'name': 'Antimony',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg beryllium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Beryllium'),\n",
-       "  'exchanges': [{'name': 'Beryllium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg boron',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Boron'),\n",
-       "  'exchanges': [{'name': 'Boron',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg brass',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Brass'),\n",
-       "  'exchanges': []},\n",
-       " {'unit': 'kg cadmium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Cadmium'),\n",
-       "  'exchanges': [{'name': 'Cadmium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Cadmium, 0.30% in sulfide, Cd 0.18%, Pb, Zn, Ag, In',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg cerium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Cerium'),\n",
-       "  'exchanges': [{'name': 'Cerium, 24% in bastnasite, 2.4% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Cerium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg chromium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Chromium'),\n",
-       "  'exchanges': [{'name': 'Chromium, 25.5% in chromite, 11.6% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Chromium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg cobalt',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Cobalt'),\n",
-       "  'exchanges': [{'name': 'Cobalt',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Cobalt, Co 5.0E-2%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg copper',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Copper'),\n",
-       "  'exchanges': [{'name': 'Copper, 1.42% in sulfide, Cu 0.81% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 0.97% in sulfide, Cu 0.36% and Mo 4.1E-2% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 1.18% in sulfide, Cu 0.39% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 1.13% in sulfide, Cu 0.76% and Ni 0.76% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 2.19% in sulfide, Cu 1.83% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, Cu 6.8E-1%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 0.99% in sulfide, Cu 0.36% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 0.59% in sulfide, Cu 0.22% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 0.52% in sulfide, Cu 0.27% and Mo 8.2E-3% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, Cu 0.2%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, 1.25% in sulfide, Cu 0.24% and Zn 0,1% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Copper, Cu 0.38%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg dysprosium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Dysprosium'),\n",
-       "  'exchanges': [{'name': 'Dysprosium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg erbium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Erbium'),\n",
-       "  'exchanges': [{'name': 'Terbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Ytterbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Erbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg europium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Europium'),\n",
-       "  'exchanges': [{'name': 'Europium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Europium, 0.06% in bastnasite, 0.006% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg gadolinium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Gadolinium'),\n",
-       "  'exchanges': [{'name': 'Gadolinium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gadolinium, 0.15% in bastnasite, 0.015% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg gallium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Gallium'),\n",
-       "  'exchanges': [{'name': 'Gallium, 0.014% in bauxite',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gallium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg germanium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Germanium'),\n",
-       "  'exchanges': [{'name': 'Germanium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg gold',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Gold'),\n",
-       "  'exchanges': [{'name': 'Gold, Au 1.0E-7%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 1.4E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 6.8E-4%, Ag 1.5E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 2.1E-4%, Ag 2.1E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 1.1E-4%, Ag 4.2E-3%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 5.4E-4%, Ag 1.5E-5%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 4.3E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 1.3E-4%, Ag 4.6E-5%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 4.9E-5%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 7.1E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 1.8E-4%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 6.7E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 9.7E-5%, Ag 7.6E-5%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Gold, Au 9.7E-4%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg graphite',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Graphite'),\n",
-       "  'exchanges': [{'name': 'Graphite',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg hafnium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Hafnium'),\n",
-       "  'exchanges': [{'name': 'Hafnium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg indium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Indium'),\n",
-       "  'exchanges': [{'name': 'Indium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Indium, 0.005% in sulfide, In 0.003%, Pb, Zn, Ag, Cd',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg iridium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Iridium'),\n",
-       "  'exchanges': [{'name': 'Iridium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg lanthanum',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Lanthanum'),\n",
-       "  'exchanges': [{'name': 'Lanthanum',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Lanthanum, 7.2% in bastnasite, 0.72% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg lead',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Lead'),\n",
-       "  'exchanges': [{'name': 'Lead, Pb 3.6E-1%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Lead',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Lead, Pb 0.014%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Lead, 5.0% in sulfide, Pb 3.0%, Zn, Ag, Cd, In',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg lithium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Lithium'),\n",
-       "  'exchanges': [{'name': 'Lithium, 0.15% in brine',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Lithium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg magnesium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Magnesium'),\n",
-       "  'exchanges': [{'name': 'Magnesium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg manganese',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Manganese'),\n",
-       "  'exchanges': [{'name': 'Manganese',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Manganese, 35.7% in sedimentary deposit, 14.2% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg molybdenum',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Molybdenum'),\n",
-       "  'exchanges': [{'name': 'Molybdenum, 0.025% in sulfide, Mo 8.2E-3% and Cu 0.39% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.022% in sulfide, Mo 8.2E-3% and Cu 0.22% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.022% in sulfide, Mo 8.2E-3% and Cu 0.36% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.016% in sulfide, Mo 8.2E-3% and Cu 0.27% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.014% in sulfide, Mo 8.2E-3% and Cu 0.81% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.11% in sulfide, Mo 4.1E-2% and Cu 0.36% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Molybdenum, 0.010% in sulfide, Mo 8.2E-3% and Cu 1.83% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg neodymium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Neodymium'),\n",
-       "  'exchanges': [{'name': 'Neodymium, 4% in bastnasite, 0.4% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Neodymium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg nickel',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Nickel'),\n",
-       "  'exchanges': [{'name': 'Nickel, 1.13% in sulfide, Ni 0.76% and Cu 0.76% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Nickel',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Nickel, 1.98% in silicates, 1.04% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Nickel, Ni 2.5E+0%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg niobium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Niobium'),\n",
-       "  'exchanges': [{'name': 'Niobium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg palladium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Palladium'),\n",
-       "  'exchanges': [{'name': 'Palladium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Palladium, Pd 1.6E-6%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg phosphorous',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Phosphorous'),\n",
-       "  'exchanges': []},\n",
-       " {'unit': 'kg platinum',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Platinum'),\n",
-       "  'exchanges': [{'name': 'Platinum, Pt 4.7E-7%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Platinum',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg potassium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Potassium'),\n",
-       "  'exchanges': [{'name': 'Potassium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg praseodymium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Praseodymium'),\n",
-       "  'exchanges': [{'name': 'Praseodymium, 0.42% in bastnasite, 0.042% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Praseodymium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg rhenium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Rhenium'),\n",
-       "  'exchanges': [{'name': 'Rhenium, in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Rhenium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg rhodium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Rhodium'),\n",
-       "  'exchanges': [{'name': 'Rhodium, Rh 1.6E-7%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Rhodium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg ruthenium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Ruthenium'),\n",
-       "  'exchanges': [{'name': 'Ruthenium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg samarium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Samarium'),\n",
-       "  'exchanges': [{'name': 'Samarium, 0.3% in bastnasite, 0.03% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Samarium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg scandium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Scandium'),\n",
-       "  'exchanges': [{'name': 'Scandium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg selenium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Selenium'),\n",
-       "  'exchanges': [{'name': 'Selenium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg silicon',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Silicon'),\n",
-       "  'exchanges': [{'name': 'Silicon',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg silver',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Silver'),\n",
-       "  'exchanges': [{'name': 'Silver, Ag 5.4E-3%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, 0.007% in sulfide, Ag 0.004%, Pb, Zn, Cd, In',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 7.6E-5%, Au 9.7E-5%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 9.7E-4%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, 0.01% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 4.6E-5%, Au 1.3E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 1.5E-4%, Au 6.8E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 1.5E-5%, Au 5.4E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 1.8E-6%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 4.2E-3%, Au 1.1E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, Ag 2.1E-4%, Au 2.1E-4%, in ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Silver, 3.2ppm in sulfide, Ag 1.2ppm, Cu and Te, in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg sulfur',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Sulfur'),\n",
-       "  'exchanges': [{'name': 'Sulfur',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg strontium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Strontium'),\n",
-       "  'exchanges': [{'name': 'Strontium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg tantalum',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Tantalum'),\n",
-       "  'exchanges': [{'name': 'Tantalum',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Tantalum, 81.9% in tantalite, 1.6E-4% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg tellurium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Tellurium'),\n",
-       "  'exchanges': [{'name': 'Tellurium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Tellurium, 0.5ppm in sulfide, Te 0.2ppm, Cu and Ag, in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg terbium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Terbium'),\n",
-       "  'exchanges': [{'name': 'Ytterbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Terbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg tin',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Tin'),\n",
-       "  'exchanges': [{'name': 'Platinum',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Astatine',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Protactinium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Tin, 79% in cassiterite, 0.1% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Tin',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Platinum, Pt 4.7E-7%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Actinium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg titanium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Titanium'),\n",
-       "  'exchanges': [{'name': 'Titanium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg tungsten',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Tungsten'),\n",
-       "  'exchanges': [{'name': 'Tungsten',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg vanadium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Vanadium'),\n",
-       "  'exchanges': [{'name': 'Vanadium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg ytterbium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Ytterbium'),\n",
-       "  'exchanges': [{'name': 'Ytterbium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg yttrium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Yttrium'),\n",
-       "  'exchanges': [{'name': 'Yttrium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg zinc',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Zinc'),\n",
-       "  'exchanges': [{'name': 'Zinc, 9.0% in sulfide, Zn 5.3%, Pb, Ag, Cd, In',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Zinc',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Zinc, Zn 0.63%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Zinc, Zn 3.1%, in mixed ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]},\n",
-       " {'unit': 'kg zirconium',\n",
-       "  'name': ('RELICS', 'metals extraction', 'Zirconium'),\n",
-       "  'exchanges': [{'name': 'Zirconium, 50% in zircon, 0.39% in crude ore',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0},\n",
-       "   {'name': 'Zirconium',\n",
-       "    'categories': ('natural resource', 'in ground'),\n",
-       "    'amount': 1.0}]}]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3a40b9fb-40a7-4076-9173-ad4fb6c2db8b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pathways/data/activities_classifications.yaml b/pathways/data/activities_classifications.yaml
index e0c0fe6..ca2a0e7 100644
--- a/pathways/data/activities_classifications.yaml
+++ b/pathways/data/activities_classifications.yaml
@@ -62900,4 +62900,4915 @@
 - market for hydrogen (SPS)
 - hydrogen
 - kilogram
-: Manufacture of refined petroleum products
\ No newline at end of file
+: Manufacture of refined petroleum products
+? !!python/tuple
+- Heavy duty truck, compressed gas, 32t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 32t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 32t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 32t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 40t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 40t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 40t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 40t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 60t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 60t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 60t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, compressed gas, 60t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel hybrid, 32t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel hybrid, 40t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel hybrid, 60t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 32t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 32t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 32t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 32t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 40t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 40t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 40t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 40t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 60t gross weight, EURO-III, long haul
+- Heavy duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 60t gross weight, EURO-IV, long haul
+- Heavy duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 60t gross weight, EURO-V, long haul
+- Heavy duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, diesel, 60t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, plugin diesel hybrid, 32t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, plugin diesel hybrid, 40t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Heavy duty truck, plugin diesel hybrid, 60t gross weight, EURO-VI, long haul
+- Heavy duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 3.5t gross weight, EURO-III, long haul
+- Light duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 3.5t gross weight, EURO-IV, long haul
+- Light duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 3.5t gross weight, EURO-V, long haul
+- Light duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 3.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 7.5t gross weight, EURO-III, long haul
+- Light duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 7.5t gross weight, EURO-IV, long haul
+- Light duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 7.5t gross weight, EURO-V, long haul
+- Light duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, compressed gas, 7.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel hybrid, 3.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel hybrid, 7.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 3.5t gross weight, EURO-III, long haul
+- Light duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 3.5t gross weight, EURO-IV, long haul
+- Light duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 3.5t gross weight, EURO-V, long haul
+- Light duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 3.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 7.5t gross weight, EURO-III, long haul
+- Light duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 7.5t gross weight, EURO-IV, long haul
+- Light duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 7.5t gross weight, EURO-V, long haul
+- Light duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, diesel, 7.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, plugin diesel hybrid, 3.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Light duty truck, plugin diesel hybrid, 7.5t gross weight, EURO-VI, long haul
+- Light duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 18t gross weight, EURO-III, long haul
+- Medium duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 18t gross weight, EURO-IV, long haul
+- Medium duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 18t gross weight, EURO-V, long haul
+- Medium duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 18t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 26t gross weight, EURO-III, long haul
+- Medium duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 26t gross weight, EURO-IV, long haul
+- Medium duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 26t gross weight, EURO-V, long haul
+- Medium duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, compressed gas, 26t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel hybrid, 18t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel hybrid, 26t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 18t gross weight, EURO-III, long haul
+- Medium duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 18t gross weight, EURO-IV, long haul
+- Medium duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 18t gross weight, EURO-V, long haul
+- Medium duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 18t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 26t gross weight, EURO-III, long haul
+- Medium duty truck, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 26t gross weight, EURO-IV, long haul
+- Medium duty truck, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 26t gross weight, EURO-V, long haul
+- Medium duty truck, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, diesel, 26t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, plugin diesel hybrid, 18t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Medium duty truck, plugin diesel hybrid, 26t gross weight, EURO-VI, long haul
+- Medium duty truck, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Moped, gasoline, <4kW, EURO-3
+- Moped, gasoline, <4kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Moped, gasoline, <4kW, EURO-4
+- Moped, gasoline, <4kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Moped, gasoline, <4kW, EURO-5
+- Moped, gasoline, <4kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 11-35kW, EURO-3
+- Motorbike, gasoline, 11-35kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 11-35kW, EURO-4
+- Motorbike, gasoline, 11-35kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 11-35kW, EURO-5
+- Motorbike, gasoline, 11-35kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 4-11kW, EURO-3
+- Motorbike, gasoline, 4-11kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 4-11kW, EURO-4
+- Motorbike, gasoline, 4-11kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, 4-11kW, EURO-5
+- Motorbike, gasoline, 4-11kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, >35kW, EURO-3
+- Motorbike, gasoline, >35kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, >35kW, EURO-4
+- Motorbike, gasoline, >35kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Motorbike, gasoline, >35kW, EURO-5
+- Motorbike, gasoline, >35kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- NMC622 hydroxide
+- NMC622 hydroxide
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- NMC622 oxide
+- NMC622 oxide
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck coach bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck coach bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck coach bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m double deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck coach bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck coach bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck coach bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 13m single deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 18m articulated urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 18m articulated urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 18m articulated urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 18m articulated urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 9m midibus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 9m midibus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 9m midibus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, compressed gas, 9m midibus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 13m double deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 13m double deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 13m single deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 13m single deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 18m articulated urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel hybrid, 9m midibus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck coach bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck coach bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck coach bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m double deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck coach bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck coach bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck coach bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck coach bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 13m single deck urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 18m articulated urban bus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 18m articulated urban bus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 18m articulated urban bus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 18m articulated urban bus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 9m midibus, EURO-III
+- Passenger bus, EURO-III
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 9m midibus, EURO-IV
+- Passenger bus, EURO-IV
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 9m midibus, EURO-V
+- Passenger bus, EURO-V
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger bus, diesel, 9m midibus, EURO-VI
+- Passenger bus, EURO-VI
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Lower medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Lower medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Lower medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Mini, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Mini, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Mini, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Small, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Small, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Small, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Van, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Van, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Van, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, compressed gas, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel hybrid, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Lower medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Lower medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Lower medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Mini, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Mini, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Mini, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Small, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Small, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Small, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Van, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Van, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Van, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, diesel, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline hybrid, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Lower medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Lower medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Lower medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium SUV, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium SUV, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium SUV, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Mini, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Mini, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Mini, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Small, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Small, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Small, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Van, EURO-2
+- Passenger car, EURO-2
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Van, EURO-3
+- Passenger car, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Van, EURO-4
+- Passenger car, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, gasoline, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin diesel hybrid, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Large SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Large SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Large, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Large, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Lower medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Lower medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Medium SUV, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Medium SUV, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Medium, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Medium, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Mini, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Mini, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Small, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Small, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Van, EURO-6ab
+- Passenger car, EURO-6ab
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Passenger car, plugin gasoline hybrid, Van, EURO-6d
+- Passenger car, EURO-6d
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, 4-11kW, EURO-3
+- Scooter, gasoline, 4-11kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, 4-11kW, EURO-4
+- Scooter, gasoline, 4-11kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, 4-11kW, EURO-5
+- Scooter, gasoline, 4-11kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, <4kW, EURO-3
+- Scooter, gasoline, <4kW, EURO-3
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, <4kW, EURO-4
+- Scooter, gasoline, <4kW, EURO-4
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- Scooter, gasoline, <4kW, EURO-5
+- Scooter, gasoline, <4kW, EURO-5
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- battery cell assembly, Li-S
+- battery cell
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, Li-O2
+- battery cell
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, Li-S
+- battery cell, LiS
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, Li-ion, LTO
+- battery cell, Li-ion, LTO
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, Li-ion, NMC622
+- battery cell, Li-ion, NMC622
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, SIB, with NMMT cathode and fossil-HC anode
+- battery cell
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, SIB, with NVPF cathode and fossil-HC anode
+- battery cell
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- battery cell production, SIB, with NaPBA cathode and fossil-HC anode
+- battery cell
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- bicycle, conventional, urban
+- bicycle, conventional, urban
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<25 km/h), LFP battery
+- bicycle, electric (<25 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<25 km/h), NCA battery
+- bicycle, electric (<25 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<25 km/h), NMC battery
+- bicycle, electric (<25 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<45 km/h), LFP battery
+- bicycle, electric (<45 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<45 km/h), NCA battery
+- bicycle, electric (<45 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric (<45 km/h), NMC battery
+- bicycle, electric (<45 km/h)
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric, cargo bike, LFP battery
+- bicycle, electric, cargo bike
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric, cargo bike, NCA battery
+- bicycle, electric, cargo bike
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- bicycle, electric, cargo bike, NMC battery
+- bicycle, electric, cargo bike
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- buffer tank
+- buffer tank
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- cables
+- cables
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- catenary masts
+- catenary masts
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- catenary system
+- catenary system
+- kilometer
+: Manufacture of motor vehicles
+? !!python/tuple
+- cathode, NMC622, for Li-ion battery
+- cathode, NMC622, for Li-ion battery
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- charging station, 100W
+- charging station, 100W
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- charging station, 3kW
+- charging station, 3kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- charging station, 500W
+- charging station, 500W
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- compressed gas, burned in heavy duty vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- compressed gas, burned in passenger car vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- compressed gas, burned in passenger car vehicle_FE_light_duty_truck_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- compressed gas, burned in single deck coach or urban bus
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- compressed gas, burned in single deck coach or urban bus_FE_coach_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in heavy duty vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in passenger car vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in passenger car vehicle_FE_light_duty_truck_diesel
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in passenger car vehicle_FE_other_transport_diesel
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in single deck coach or urban bus
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- diesel, burned in single deck coach or urban bus_FE_coach_diesel
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- distribution sheds
+- distribution sheds
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- electric bicycle production, without battery and motor
+- electric bicycle, without battery and motor
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- electricity production, at co-generation biowaste-fired power plant, post, pipeline
+  200km, storage 1000m_waste incineration, non-fossil (industrial, CCS)
+- electricity, from biowaste, municipal incineration, energy allocation
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, at co-generation plastic waste-fired power plant, post,
+  pipeline 200km, storage 1000m_waste incineration, fossil (industrial, CCS)
+- electricity, from plastic waste mixture, municipal incineration, energy allocation
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, from biowaste, municipal incineration, energy allocation_waste
+  incineration, non-fossil (industrial)
+- electricity, from biowaste, municipal incineration, energy allocation
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, from incineration of plastic waste mixture, energy allocation_waste
+  incineration, fossil (industrial)
+- electricity, from incineration of plastic waste mixture, energy allocation
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, photovoltaic, commercial_pv (industry)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, photovoltaic, commercial_pv (services)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity production, photovoltaic, residential_pv residential (single family)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity supply, from stationary 19.5 kWh NMC battery_battery (multi family)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity supply, high voltage, from vanadium-redox flow battery system_battery
+  (industry)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity supply, high voltage, from vanadium-redox flow battery system_battery
+  (medium)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity supply, high voltage, from vanadium-redox flow battery system_battery
+  (services)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, residential, by conversion of hydrogen using fuel cell, PEM, allocated
+  by exergy, distributed by pipeline, produced by Electrolysis, PEM using electricity
+  from grid_hydrogen fuel cell (industrial CHP)
+- electricity, from residential heating system
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, residential, by conversion of hydrogen using fuel cell, PEM, allocated
+  by exergy, distributed by pipeline, produced by Electrolysis, PEM using electricity
+  from grid_hydrogen fuel cell (services CHP)
+- electricity, from residential heating system
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in battery electric motorcycle
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in heavy duty vehicle
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial electric boiler
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial electric boiler_FE_services_electric_boiler
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial electric processes
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial electric processes_FE_services_FE_other_electric
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial heat pump
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in industrial heat pump_FE_services_heat_pump
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in passenger car vehicle
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in passenger car vehicle_FE_light_duty_truck_electricity
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in passenger car vehicle_FE_other_transport_electric
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in residential electric appliances
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in residential electric boiler
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in residential electric boiler_FE_residential_water_heating_electric_boiler
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in residential heat pump
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in residential heat pump_FE_residential_water_heating_heat_pump
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in single deck coach or urban bus
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in single deck coach or urban bus_FE_coach_electricity
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in single deck trolleybus
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- electricity, used in tram
+- electricity, low voltage
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- fuel supply for gasoline vehicles
+- gasoline blend
+- kilogram
+: Manufacture of refined petroleum products
+? !!python/tuple
+- fuel tank assembly, compressed hydrogen gas, 700bar
+- fuel tank, compressed hydrogen gas, 700bar
+- kilogram
+: Manufacture of motor vehicles
+? !!python/tuple
+- fuel tank assembly, compressed natural gas, 200 bar
+- fuel tank, compressed natural gas, 200 bar
+- kilogram
+: Manufacture of motor vehicles
+? !!python/tuple
+- hard coal, burned in hard coal residential boiler 5-15kW
+- heat
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- hard coal, burned in hard coal residential boiler 5-15kW_FE_residential_water_heating_coal
+- heat
+- megajoule
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 160kW electrical, Jakobsberg_gas (multi
+  family CHP fuel cell)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 160kW electrical, Jakobsberg_gas (multi
+  family CHP)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 160kW electrical, Jakobsberg_gas (services
+  CHP fuel cell)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 160kW electrical, Jakobsberg_gas (services
+  CHP)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 160kW electrical, Jakobsberg_gas (single
+  family CHP fuel cell)
+- electricity, low voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 1MW electrical, lean burn_gas (CHP fuel
+  cell reformer)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 1MW electrical, lean burn_gas (CHP industry)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, natural gas, 1MW electrical, lean burn_gas (fuel cell
+  DH)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat and power co-generation, oil_oil (industry CHP)
+- electricity, high voltage
+- kilowatt hour
+: Electric power generation transmission and distribution
+? !!python/tuple
+- heat, from nuclear power plant
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial biogas-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial fuel cell CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial fuel cell CHP_FE_services_CHP_fuel_cell
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial gas-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial gas-fired CHP with CCS
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial gas-fired CHP_FE_services_CHP_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial hydrogen-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial hydrogen-fired CHP_FE_services_CHP_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial non-renewable waste-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial non-renewable waste-fired CHP with CCS
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial oil-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial wood-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial wood-fired CHP with CCS
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site industrial wood-fired CHP_FE_services_CHP_wood
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site residential fuel cell CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site residential gas-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heat, from on-site residential wood-fired CHP
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- heavy duty truck, battery electric, NMC-622 battery, 32t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy duty truck, battery electric, NMC-622 battery, 40t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy duty truck, battery electric, NMC-622 battery, 60t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy duty truck, fuel cell electric, 32t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy duty truck, fuel cell electric, 40t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy duty truck, fuel cell electric, 60t gross weight, long haul
+- heavy duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- heavy fuel oil, burned in industrial furnace 1MW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen burned, in hydrogen boiler
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen burned, in hydrogen boiler_FE_residential_space_heating_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen burned, in hydrogen boiler_FE_residential_water_heating_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen burned, in hydrogen boiler_FE_services_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen production, gaseous, 30 bar, from PEM electrolysis, from grid electricity_hydrogen,
+  PEM, large (domestic)
+- hydrogen, gaseous, 30 bar
+- kilogram
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in heavy duty vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in hydrogen motorcycle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in passenger car vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in passenger car vehicle_FE_light_duty_truck_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in passenger car vehicle_FE_other_transport_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in single deck coach or urban bus
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- hydrogen, used in single deck coach or urban bus_FE_coach_hydrogen
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- kick Scooter, electric, <1kW, LFP battery
+- kick Scooter, electric, <1kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- kick Scooter, electric, <1kW, NCA battery
+- kick Scooter, electric, <1kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- kick Scooter, electric, <1kW, NMC battery
+- kick Scooter, electric, <1kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- light duty truck, battery electric, NMC-622 battery, 3.5t gross weight, long haul
+- light duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- light duty truck, battery electric, NMC-622 battery, 7.5t gross weight, long haul
+- light duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- light duty truck, fuel cell electric, 3.5t gross weight, long haul
+- light duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- light duty truck, fuel cell electric, 7.5t gross weight, long haul
+- light duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- light fuel oil, burned in industrial furnace 1MW_FE_services_light_fuel_oil
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- maintenance, electric bicycle, without battery
+- maintenance, electric bicycle, without battery
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- market for battery cell, Li-ion, LTO
+- battery cell, Li-ion, LTO
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- market for district heating (SPS)
+- heat, district or industrial, other than natural gas
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for buses (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for cars (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for coaches (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for heavy duty trucks (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for light duty trucks (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for motorcycles (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for energy carriers for other transport (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for forest residue_wood (landscape maintenance)
+- forest residue
+- kilogram
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for forest residue_wood (residues)
+- forest residue
+- kilogram
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for process heat and space heating in industry (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for process heat in industry (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for space and water heating in residential (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for space heating in industry (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for space heating in residential (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for space heating in services (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- market for water heating in residential (SPS)
+- heat
+- megajoule
+: Manufacture of refined petroleum products
+? !!python/tuple
+- medium duty truck, battery electric, NMC-622 battery, 18t gross weight, long haul
+- medium duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- medium duty truck, battery electric, NMC-622 battery, 26t gross weight, long haul
+- medium duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- medium duty truck, fuel cell electric, 18t gross weight, long haul
+- medium duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- medium duty truck, fuel cell electric, 26t gross weight, long haul
+- medium duty truck
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 11-35kW, LFP battery
+- motorbike, electric, 11-35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 11-35kW, NCA battery
+- motorbike, electric, 11-35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 11-35kW, NMC battery
+- motorbike, electric, 11-35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 4-11kW, LFP battery
+- motorbike, electric, 4-11kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 4-11kW, NCA battery
+- motorbike, electric, 4-11kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, 4-11kW, NMC battery
+- motorbike, electric, 4-11kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, <4kW, LFP battery
+- motorbike, electric, <4kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, <4kW, NCA battery
+- motorbike, electric, <4kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, <4kW, NMC battery
+- motorbike, electric, <4kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, >35kW, LFP battery
+- motorbike, electric, >35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, >35kW, NCA battery
+- motorbike, electric, >35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- motorbike, electric, >35kW, NMC battery
+- motorbike, electric, >35kW
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- natural gas, burned in industrial furnace >100kW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- natural gas, burned in industrial furnace >100kW_FE_services_natural_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- natural gas, burned in residential natural gas boiler <100kW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- natural gas, burned in residential natural gas boiler <100kW_FE_residential_space_heating_heat_pump_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- natural gas, burned in residential natural gas boiler <100kW_FE_residential_water_heating_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- natural gas, burned in residential natural gas boiler <100kW_FE_residential_water_heating_heat_pump_gas
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- non-renewable waste, burned in industrial furnace
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- overhead lines
+- overhead lines
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - battery-equipped trolleybus, LTO battery, 13m
+  single deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - battery-equipped trolleybus, LTO battery, 18m
+  articulated urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - opportunity charging, LTO battery, 13m double
+  deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - opportunity charging, LTO battery, 13m single
+  deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - opportunity charging, LTO battery, 18m articulated
+  urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - opportunity charging, LTO battery, 9m midibus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - overnight charging, NMC-622 battery, 13m double
+  deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - overnight charging, NMC-622 battery, 13m single
+  deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - overnight charging, NMC-622 battery, 18m articulated
+  urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, battery electric - overnight charging, NMC-622 battery, 9m midibus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 13m double deck coach bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 13m double deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 13m single deck coach bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 13m single deck urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 18m articulated urban bus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger bus, fuel cell electric, 9m midibus
+- passenger bus
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Large
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Large SUV
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Lower medium
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Medium
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Medium SUV
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Micro
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Mini
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Small
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, battery electric, NMC-622 battery, Van
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Large
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Large SUV
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Lower medium
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Medium
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Medium SUV
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Mini
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Small
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- passenger car, fuel cell electric, Van
+- passenger car
+- unit
+: Manufacture of motor vehicles
+? !!python/tuple
+- pellet, burned in pellet industrial furnace 300kW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- pellet, burned in pellet industrial furnace 300kW_FE_services_pellet
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- pellet, burned in residential pellet stove 5-15kW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- pellet, burned in residential pellet stove 5-15kW_FE_residential_water_heating_pellet
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- petrol, burned in motorcycle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- petrol, burned in passenger car vehicle
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- petrol, burned in passenger car vehicle_FE_heavy_duty_truck_gasoline
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- petrol, burned in passenger car vehicle_FE_light_duty_truck_gasoline
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- petrol, burned in passenger car vehicle_FE_other_transport_gasoline
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- renewable waste, burned in industrial furnace
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- scooter, electric, 4-11kW, LFP battery
+- scooter, electric, 4-11kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- scooter, electric, 4-11kW, NCA battery
+- scooter, electric, 4-11kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- scooter, electric, 4-11kW, NMC battery
+- scooter, electric, 4-11kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- scooter, electric, <4kW, LFP battery
+- scooter, electric, <4kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- scooter, electric, <4kW, NCA battery
+- scooter, electric, <4kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- scooter, electric, <4kW, NMC battery
+- scooter, electric, <4kW
+- unit
+: Other passenger land transport
+? !!python/tuple
+- selective coating, sputtering
+- selective coating, sputtering
+- square meter
+: Other passenger land transport
+? !!python/tuple
+- semi-rigid roadside barriers
+- semi-rigid roadside barriers
+- unit
+: Other passenger land transport
+? !!python/tuple
+- solar energy, from industrial hot water tank
+- heat
+- megajoule
+: Other passenger land transport
+? !!python/tuple
+- solar energy, from industrial hot water tank_FE_services_solar
+- heat
+- megajoule
+: Other passenger land transport
+? !!python/tuple
+- solar energy, from residential hot water tank
+- heat
+- megajoule
+: Other passenger land transport
+? !!python/tuple
+- solar energy, from residential hot water tank_FE_residential_water_heating_solar
+- heat
+- megajoule
+: Other passenger land transport
+? !!python/tuple
+- synthetic diesel import, from neighboring countries
+- diesel, synthetic
+- kilogram
+: Other passenger land transport
+? !!python/tuple
+- synthetic natural gas import, from neighboring countries
+- natural gas, synthetic
+- kilogram
+: Other passenger land transport
+? !!python/tuple
+- tram, electric
+- tram, electric
+- unit
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, conventional, urban
+- transport, Bicycle, conventional, urban
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<25 km/h), LFP battery
+- transport, Bicycle, electric (<25 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<25 km/h), NCA battery
+- transport, Bicycle, electric (<25 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<25 km/h), NMC battery
+- transport, Bicycle, electric (<25 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<45 km/h), LFP battery
+- transport, Bicycle, electric (<45 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<45 km/h), NCA battery
+- transport, Bicycle, electric (<45 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric (<45 km/h), NMC battery
+- transport, Bicycle, electric (<45 km/h)
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric, cargo bike, LFP battery
+- transport, Bicycle, electric, cargo bike
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric, cargo bike, NCA battery
+- transport, Bicycle, electric, cargo bike
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Bicycle, electric, cargo bike, NMC battery
+- transport, Bicycle, electric, cargo bike
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Kick Scooter, electric, <1kW, LFP battery
+- transport, Kick Scooter, electric, <1kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Kick Scooter, electric, <1kW, NCA battery
+- transport, Kick Scooter, electric, <1kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Kick Scooter, electric, <1kW, NMC battery
+- transport, Kick Scooter, electric, <1kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Moped, gasoline, <4kW, EURO-3
+- transport, Moped, gasoline, <4kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Moped, gasoline, <4kW, EURO-4
+- transport, Moped, gasoline, <4kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Moped, gasoline, <4kW, EURO-5
+- transport, Moped, gasoline, <4kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 11-35kW, LFP battery
+- transport, Motorbike, electric, 11-35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 11-35kW, NCA battery
+- transport, Motorbike, electric, 11-35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 11-35kW, NMC battery
+- transport, Motorbike, electric, 11-35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 4-11kW, LFP battery
+- transport, Motorbike, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 4-11kW, NCA battery
+- transport, Motorbike, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, 4-11kW, NMC battery
+- transport, Motorbike, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, <4kW, LFP battery
+- transport, Motorbike, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, <4kW, NCA battery
+- transport, Motorbike, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, <4kW, NMC battery
+- transport, Motorbike, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, >35kW, LFP battery
+- transport, Motorbike, electric, >35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, >35kW, NCA battery
+- transport, Motorbike, electric, >35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, electric, >35kW, NMC battery
+- transport, Motorbike, electric, >35kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 11-35kW, EURO-3
+- transport, Motorbike, gasoline, 11-35kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 11-35kW, EURO-4
+- transport, Motorbike, gasoline, 11-35kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 11-35kW, EURO-5
+- transport, Motorbike, gasoline, 11-35kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 4-11kW, EURO-3
+- transport, Motorbike, gasoline, 4-11kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 4-11kW, EURO-4
+- transport, Motorbike, gasoline, 4-11kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, 4-11kW, EURO-5
+- transport, Motorbike, gasoline, 4-11kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, >35kW, EURO-3
+- transport, Motorbike, gasoline, >35kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, >35kW, EURO-4
+- transport, Motorbike, gasoline, >35kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Motorbike, gasoline, >35kW, EURO-5
+- transport, Motorbike, gasoline, >35kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, 4-11kW, LFP battery
+- transport, Scooter, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, 4-11kW, NCA battery
+- transport, Scooter, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, 4-11kW, NMC battery
+- transport, Scooter, electric, 4-11kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, <4kW, LFP battery
+- transport, Scooter, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, <4kW, NCA battery
+- transport, Scooter, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, electric, <4kW, NMC battery
+- transport, Scooter, electric, <4kW
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, 4-11kW, EURO-3
+- transport, Scooter, gasoline, 4-11kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, 4-11kW, EURO-4
+- transport, Scooter, gasoline, 4-11kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, 4-11kW, EURO-5
+- transport, Scooter, gasoline, 4-11kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, <4kW, EURO-3
+- transport, Scooter, gasoline, <4kW, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, <4kW, EURO-4
+- transport, Scooter, gasoline, <4kW, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Scooter, gasoline, <4kW, EURO-5
+- transport, Scooter, gasoline, <4kW, EURO-5
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, Tram, electric
+- transport, Tram, electric
+- person-kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 18t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 26t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 3.5t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 32t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 40t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 60t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, battery electric, NMC-622 battery, 7.5t gross weight,
+  long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 18t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 18t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 18t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 18t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 26t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 26t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 26t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 26t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 3.5t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 3.5t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 3.5t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 3.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 32t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 32t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 32t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 32t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 40t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 40t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 40t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 40t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 60t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 60t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 60t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 60t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 7.5t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 7.5t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 7.5t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, compressed gas, 7.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 18t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 26t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 3.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 32t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 40t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 60t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel hybrid, 7.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 18t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 18t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 18t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 18t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 26t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 26t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 26t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 26t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 3.5t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 3.5t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 3.5t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 3.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 32t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 32t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 32t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 32t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 40t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 40t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 40t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 40t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 60t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 60t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 60t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 60t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 7.5t gross weight, EURO-III, long haul
+- transport, freight, lorry, EURO-III
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 7.5t gross weight, EURO-IV, long haul
+- transport, freight, lorry, EURO-IV
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 7.5t gross weight, EURO-V, long haul
+- transport, freight, lorry, EURO-V
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, diesel, 7.5t gross weight, EURO-VI, long haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 18t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 26t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 3.5t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 32t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 40t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 60t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, fuel cell electric, 7.5t gross weight, long haul
+- transport, freight, lorry
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 18t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 26t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 3.5t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 32t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 40t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 60t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, freight, lorry, plugin diesel hybrid, 7.5t gross weight, EURO-VI, long
+  haul
+- transport, freight, lorry, EURO-VI
+- ton kilometer
+: Freight transport by road
+? !!python/tuple
+- transport, passenger bus, battery electric - battery-equipped trolleybus, LTO battery,
+  13m single deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - battery-equipped trolleybus, LTO battery,
+  18m articulated urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - opportunity charging, LTO battery,
+  13m double deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - opportunity charging, LTO battery,
+  13m single deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - opportunity charging, LTO battery,
+  18m articulated urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - opportunity charging, LTO battery,
+  9m midibus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - overnight charging, NMC-622 battery,
+  13m double deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - overnight charging, NMC-622 battery,
+  13m single deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - overnight charging, NMC-622 battery,
+  18m articulated urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, battery electric - overnight charging, NMC-622 battery,
+  9m midibus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck coach bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck coach bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck coach bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m double deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck coach bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck coach bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck coach bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 13m single deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 18m articulated urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 18m articulated urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 18m articulated urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 18m articulated urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 9m midibus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 9m midibus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 9m midibus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, compressed gas, 9m midibus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 13m double deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 13m double deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 13m single deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 13m single deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 18m articulated urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel hybrid, 9m midibus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck coach bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck coach bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck coach bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m double deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck coach bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck coach bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck coach bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck coach bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 13m single deck urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 18m articulated urban bus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 18m articulated urban bus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 18m articulated urban bus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 18m articulated urban bus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 9m midibus, EURO-III
+- transport, passenger bus, EURO-III
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 9m midibus, EURO-IV
+- transport, passenger bus, EURO-IV
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 9m midibus, EURO-V
+- transport, passenger bus, EURO-V
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, diesel, 9m midibus, EURO-VI
+- transport, passenger bus, EURO-VI
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 13m double deck coach bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 13m double deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 13m single deck coach bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 13m single deck urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 18m articulated urban bus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger bus, fuel cell electric, 9m midibus
+- transport, passenger bus
+- person kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Large
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Large SUV
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Lower medium
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Medium
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Medium SUV
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Micro
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Mini
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Small
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, battery electric, NMC-622 battery, Van
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Lower medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Lower medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Lower medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Mini, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Mini, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Mini, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Small, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Small, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Small, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Van, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Van, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Van, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, compressed gas, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel hybrid, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Lower medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Lower medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Lower medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Mini, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Mini, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Mini, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Small, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Small, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Small, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Van, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Van, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Van, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, diesel, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Large
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Large SUV
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Lower medium
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Medium
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Medium SUV
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Mini
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Small
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, fuel cell electric, Van
+- transport, passenger car
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline hybrid, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Lower medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Lower medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Lower medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium SUV, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium SUV, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium SUV, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Mini, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Mini, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Mini, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Small, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Small, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Small, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Van, EURO-2
+- transport, passenger car, EURO-2
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Van, EURO-3
+- transport, passenger car, EURO-3
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Van, EURO-4
+- transport, passenger car, EURO-4
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, gasoline, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin diesel hybrid, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Large SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Large SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Large, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Large, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Lower medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Lower medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Medium SUV, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Medium SUV, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Medium, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Medium, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Mini, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Mini, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Small, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Small, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Van, EURO-6
+- transport, passenger car, EURO-6
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- transport, passenger car, plugin gasoline hybrid, Van, EURO-6ab
+- transport, passenger car, EURO-6ab
+- kilometer
+: Other passenger land transport
+? !!python/tuple
+- wood, burned in residential wood stove 5-15kW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- wood, burned in residential wood stove 5-15kW_FE_residential_water_heating_wood
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- wood, burned in wood industrial furnace 1-10MW
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- wood, burned in wood industrial furnace 1-10MW_FE_services_wood
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- market for battery cell, Li-ion, NMC622
+- battery cell, Li-ion, NMC622
+- kilogram
+: Manufacture of batteries and accumulators
+? !!python/tuple
+- light fuel oil, burned in residential 10kW boiler_FE_residential_water_heating_light_fuel_oil
+- heat
+- megajoule
+: Steam and air conditioning supply
+? !!python/tuple
+- light fuel oil, burned in residential 10kW boiler
+- heat
+- megajoule
+: Steam and air conditioning supply
diff --git a/pathways/pathways.py b/pathways/pathways.py
index 0cee4c9..d72f138 100644
--- a/pathways/pathways.py
+++ b/pathways/pathways.py
@@ -175,7 +175,7 @@ def create_dict_with_specific_model(
         mapping_dataframe = pd.read_excel(
             DATA_DIR / "final_energy_mapping.xlsx",
         )
-        model = self.data.descriptor["scenarios"][0]["name"].split(" - ")[0]
+        model = self.data.descriptor["scenarios"][0].split(" - ")[0]
 
         return create_dict_with_specific_model(mapping_dataframe, model)
 

From 6e009122374e4f1bd99f8a6989c88df1714aae5e Mon Sep 17 00:00:00 2001
From: romainsacchi <r_s@me.com>
Date: Fri, 3 May 2024 22:02:54 +0200
Subject: [PATCH 2/6] EODC

---
 paper.md | 44 +++++++++++++++++++++++---------------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/paper.md b/paper.md
index 0cac9b4..9c2e045 100644
--- a/paper.md
+++ b/paper.md
@@ -32,34 +32,34 @@ bibliography: paper.bib
 `pathways` is a Python package that characterizes the environmental impacts of products, sectors or transition scenarios
 over time using Life Cycle Assessment (LCA). Compared to traditional scenario results from energy models, `pathways`
 provides a more detailed and transparent view of the environmental impacts of a scenario by resolving supply chains 
-between producers and consumers (as an LCA does). Hence, direct and indirect emissions are accounted for, and 
+between producers and consumers (as LCA does). Hence, direct and indirect emissions are accounted for, and 
 double-counting issues are partially resolved.
 
-`pathways` is initially designed to work with data packages produced by `premise` [@Sacchi:2022], but can be used with any Integrated
-Assessment Model (IAM) scenarios  and LCA databases. It reads a scenario and a corresponding set of scenario-based LCA matrices and calculates the 
-environmental impacts of the scenario (or a subset of it) over time.
+`pathways` is initially designed to work with data packages produced by `premise` [@Sacchi:2022], but can be used with 
+any Integrated Assessment Model (IAM) scenarios and LCA databases. It reads a scenario and a corresponding set of 
+scenario-based LCA matrices and calculates the environmental impacts of the scenario (or a subset of it) over time.
 
 # Statement of need
 
-IAMs, frequently based on Shared Socioeconomic Pathways (SSPs), offer cost-optimized projections of future scenarios, 
-highlighting, for example, the necessary changes in regional electricity mixes and different means of transport to meet
-global warming objectives [@Riahi:2017]. This scenario analysis exercise enables us to predict future system changes 
-and their effects on the environmental performance of different technologies along the different supply chains. In this context,
-prospective Life Cycle Assessment (pLCA) emerges as a unique tool to provide a robust evaluation of the environmental 
-performance of both existing and anticipated production systems. At the methodological level, [@Sacchi:2022] has recently 
-laid the foundations for extending present-day process-based life-cycle inventory into the future using the output 
-from IAMs. Meanwhile, most efforts in pLCA have been centred around improving the ability to forecast future life cycle
-inventories accurately.
+IAMs, frequently based on Shared Socioeconomic Pathways (SSPs), offer cost-optimized projections of future scenarios. 
+These scenarios highlight, for example, the necessary changes in regional electricity mixes and different means of 
+transport to meet global warming mitigation objectives [@Riahi:2017]. This scenario analysis exercise enables us to 
+consider future system changes and their effects on the environmental performance of different technologies 
+along the different supply chains. In this context, prospective Life Cycle Assessment (pLCA) emerges as a unique tool 
+to provide a robust evaluation of the environmental performance of both existing and anticipated production systems. 
+At the methodological level, [@Sacchi:2022] has recently laid the foundations for extending present-day process-based 
+life-cycle inventory into the future using the output from IAMs. Meanwhile, most efforts in pLCA have been centred 
+around improving the ability to forecast future life cycle inventories accurately.
 
 At this juncture, performing an LCA of the transition scenarios using the updated life cycle inventories at each time step
 uncaps excellent potential to improve the sustainability assessment of these scenarios. LCA would expand the 
-conventional focus on GHG emissions to broader environmental impacts, such as land use, water consumption, and toxicity while considering direct 
-and indirect emissions. However, running LCAs of the transition scenarios provided by IAMs -or energy system models - at
-the system level remains challenging. Mainly because of the computational expense of running LCAs for each time step and
-region of each scenario and the methodological complexity of consistently defining the functional unit of the LCA based
-on the IAMs outputs while dealing with issues such as double-counting. `pathways`, using the LCA framework `brightway2` [@Mutel:2017]
-and building on `premise`, offers a solution to these challenges by providing a tool to evaluate the environmental impacts 
-of transition scenarios systematically.
+conventional focus on GHG emissions to broader environmental impacts, such as land use, water consumption, and toxicity 
+while considering direct and indirect emissions. However, running LCAs of the transition scenarios provided by IAMs -
+or energy system models - at the system level remains challenging. Mainly because of the computational expense of running 
+LCAs for each time step and region of each scenario and the methodological complexity of consistently defining the 
+functional unit of the LCA based on the IAMs outputs while dealing with issues such as double-counting. 
+`pathways`, using the LCA framework `brightway2` [@Mutel:2017] and building on `premise`, offers a solution to these 
+challenges by providing a tool to evaluate the environmental impacts of transition scenarios systematically.
 
 # Description
 
@@ -86,6 +86,8 @@ of transition scenarios systematically.
 # Acknowledgements
 
 The authors gratefully acknowledge the financial support from the Swiss State Secretariat for Education, Research and 
-Innovation (SERI), under the Horizon Europe project PRISMA (grant agreement no. 101081604).
+Innovation (SERI), under the Horizon Europe project PRISMA (grant agreement no. 101081604). The authors also thank the
+Swiss Federal Office of Energy (SFOE) for the support in the development of the `premise` and `pathways` tools through 
+the SWEET-SURE program.
 
 # References

From a416b8001e237300c29b3011358002a6cbbed972 Mon Sep 17 00:00:00 2001
From: romainsacchi <r_s@me.com>
Date: Sat, 4 May 2024 07:47:57 +0200
Subject: [PATCH 3/6] EODC

---
 paper.md | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/paper.md b/paper.md
index 9c2e045..84328db 100644
--- a/paper.md
+++ b/paper.md
@@ -29,15 +29,17 @@ bibliography: paper.bib
 
 # Summary
 
-`pathways` is a Python package that characterizes the environmental impacts of products, sectors or transition scenarios
-over time using Life Cycle Assessment (LCA). Compared to traditional scenario results from energy models, `pathways`
-provides a more detailed and transparent view of the environmental impacts of a scenario by resolving supply chains 
-between producers and consumers (as LCA does). Hence, direct and indirect emissions are accounted for, and 
-double-counting issues are partially resolved.
-
-`pathways` is initially designed to work with data packages produced by `premise` [@Sacchi:2022], but can be used with 
-any Integrated Assessment Model (IAM) scenarios and LCA databases. It reads a scenario and a corresponding set of 
-scenario-based LCA matrices and calculates the environmental impacts of the scenario (or a subset of it) over time.
+
+`pathways` is a Python package that conducts Life Cycle Assessment (LCA) to evaluate 
+the environmental impacts of products, sectors, or transition scenarios over time. 
+Unlike most energy (ESM) or integrated assessment models (IAM), `pathways` offers 
+a clearer view on impacts caused by a scenario by considering supply chain relations 
+between producers and consumers, thereby addressing direct and indirect emissions. 
+Unlike the reported emissions in ESM and IAM scenarios, which focus primarily on operation,
+`pathways` allows reporting the environmental impacts of infrastructure build-up 
+and decommissioning. Finally, scenarios can be characterized across a wide range of 
+indicators which are usually not included in ESM or IAM: land use, water consumption,
+toxicity impacts, etc.
 
 # Statement of need
 

From 0f7ef78c0ab535f5117593215fea436c38cd97c5 Mon Sep 17 00:00:00 2001
From: romainsacchi <r_s@me.com>
Date: Sat, 4 May 2024 17:56:58 +0200
Subject: [PATCH 4/6] EODC

---
 paper.bib | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 paper.md  | 65 ++++++++++++++++++++++++++++++-------------
 2 files changed, 128 insertions(+), 20 deletions(-)

diff --git a/paper.bib b/paper.bib
index ae5b177..ef8922d 100644
--- a/paper.bib
+++ b/paper.bib
@@ -36,4 +36,85 @@ @article{Riahi:2017
 volume = {42},
 url = {https://www.sciencedirect.com/science/article/pii/S0959378016300681},
 year = {2017}
-}
\ No newline at end of file
+}
+
+@article{Bisinella:2021,
+abstract = {Purpose: Future scenarios and life cycle assessment (LCA) are powerful tools that can provide early sustainability assessments of novel products, technologies and systems. The combination of the two methods involves practical and conceptual challenges, but formal guidance and consensus on a rigorous approach are currently missing. This study provides a comprehensive overview of how different topic areas use future scenarios and LCA in order to identify useful methods and approaches, and to provide overall recommendations. Methods: This study carried out a systematic literature review that involved searching for peer-reviewed articles on Web of Science, Scopus and Science Direct, utilising a rigorous set of keywords for future scenarios and for LCA. We identified 514 suitable peer-reviewed articles that were systematically analysed according to pre-defined sets of characteristics for the combined modelling of future scenarios and LCA. Results and discussion: The numbers of studies combining future scenarios and LCA increase every year and in all of the 15 topic areas identified. This combination is highly complex, due to different sequences in the modelling between future scenarios and LCA, the use of additional models and topic area-specific challenges. We identify and classify studies according to three archetypal modelling sequences: input, output and hybrid. More than 100 studies provide methods and approaches for combining future scenarios and LCA, but existing recommendations are specific to topic areas and for modelling sequences, and consensus is still missing. The efficacy of many studies is hampered by lack of quality. Only half of the articles complied with the LCA ISO standards, and only one quarter demonstrated consistent knowledge of future scenario theory. We observed inconsistent use of terminology and a considerable lack of clarity in the descriptions of methodological choices, assumptions and time frames. Conclusions and Recommendations: The combined use of future scenarios and LCA requires formal guidance, in order to increase clarity and communicability. Guidance should provide unambiguous definitions, identify minimum quality requirements and produce mandatory descriptions of modelling choices. The goal and scope of future scenarios and LCA should be in accordance, and quality should be ensured both for the future scenarios and the LCA. In particular, future scenarios should always be developed contextually, to ensure effective assessment of the problem at hand. Guidance should also allow for maintaining current modelling complexity and topic area differences. We provide recommendations from the reference literature on terminology, future scenario development and the combined use of future scenarios and LCA that may already constitute preliminary guidance in the field. Information collected and recommendations provided will assist in a more balanced development of the combined use of future scenarios and LCA in view of the urgent challenges of sustainable development.},
+author = {Bisinella, V. and Christensen, T. H. and Astrup, T. F.},
+doi = {10.1007/s11367-021-01954-6},
+issn = {16147502},
+journal = {International Journal of Life Cycle Assessment},
+keywords = {Archetypes,Ex-ante,Foresight,Future scenarios,LCA,Life cycle assessment,Prospective},
+number = {11},
+pages = {2143--2170},
+title = {{Future scenarios and life cycle assessment: systematic review and recommendations}},
+volume = {26},
+year = {2021}
+}
+
+@article{MendozaBeltran:2018,
+author = {{Mendoza Beltran}, Angelica and Cox, Brian and Mutel, Chris and van Vuuren, Detlef and Vivanco, David Font and Deetman, Sebastiaan and Edelenbosch, Oreane and Guin{\'{e}}e, Jeroen and Tukker, Arnold},
+doi = {https://doi.org/10.1111/jiec.12825},
+journal = {Journal of Industrial Ecology},
+mendeley-groups = {Carculator},
+title = {{When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment}},
+year = {2018}
+}
+
+@article{Xu:2020,
+abstract = {Coupling life cycle assessment (LCA) and energy systems models (ESM) is a suitable approach to assess energy systems from both life cycle and energy systems perspectives. However, methodological challenges need to be taken into account due to differences between both modeling approaches considering system boundaries, databases, and different levels of detail of their input data. This paper brings these challenges into discussion and introduces the Environmental Assessment Framework for Energy System Analysis (EAFESA), which enables to identify life cycle based non-climate environmental impacts of energy scenarios consistently. EAFESA is applied to analyze potential future decarbonized European electricity systems with a focus on flexibility options using ELTRAMOD as an example of an ESM to test the conceptual approach of combining ESM and LCA. The application confirms the importance and benefits of “integrated thinking” proposed by EAFESA, which allows minimizing the pitfalls of combining both models comprehensively. At the same time, EAFESA has the potential to bring awareness of issues not discussed among policy-makers. One example is the insight that the decarbonized electricity system will be accompanied by increased metal demand and urban land occupation.},
+author = {Xu, Lei and Fuss, Maryegli and Poganietz, Witold Roger and Jochem, Patrick and Schreiber, Steffi and Zoephel, Christoph and Brown, Nils},
+doi = {10.1016/j.jclepro.2019.118614},
+issn = {09596526},
+journal = {Journal of Cleaner Production},
+keywords = {Energy scenarios,Energy system modeling,Life cycle assessment,Prospective analysis},
+title = {{An Environmental Assessment Framework for Energy System Analysis (EAFESA): The method and its application to the European energy system transformation}},
+volume = {243},
+year = {2020}
+}
+
+@article{Pehl:2017,
+abstract = {Both fossil-fuel and non-fossil-fuel power technologies induce life-cycle greenhouse gas emissions, mainly due to their embodied energy requirements for construction and operation, and upstream CH4 emissions. Here, we integrate prospective life-cycle assessment with global integrated energy-economy-land-use-climate modelling to explore life-cycle emissions of future low-carbon power supply systems and implications for technology choice. Future per-unit life-cycle emissions differ substantially across technologies. For a climate protection scenario, we project life-cycle emissions from fossil fuel carbon capture and sequestration plants of 78-110 gCO2eq kWh-1, compared with 3.5-12 gCO2eq kWh-1 for nuclear, wind and solar power for 2050. Life-cycle emissions from hydropower and bioenergy are substantial ($\sim$100 gCO2eq kWh-1), but highly uncertain. We find that cumulative emissions attributable to upscaling low-carbon power other than hydropower are small compared with direct sectoral fossil fuel emissions and the total carbon budget. Fully considering life-cycle greenhouse gas emissions has only modest effects on the scale and structure of power production in cost-optimal mitigation scenarios.},
+author = {Pehl, Michaja and Arvesen, Anders and Humpen{\"{o}}der, Florian and Popp, Alexander and Hertwich, Edgar G. and Luderer, Gunnar},
+doi = {10.1038/s41560-017-0032-9},
+issn = {20587546},
+journal = {Nature Energy},
+number = {12},
+pages = {939--945},
+title = {{Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling}},
+volume = {2},
+year = {2017}
+}
+
+@article{Rauner:2017,
+abstract = {Making the global energy system more sustainable has emerged as a major societal concern and policy objective. This transition comes with various challenges and opportunities for a sustainable evolution affecting most of the UN's Sustainable Development Goals. We therefore propose broadening the current metrics for sustainability in the energy system modeling field by using industrial ecology techniques to account for a conclusive set of indicators. This is pursued by including a life cycle based sustainability assessment into an energy system model considering all relevant products and processes of the global supply chain. We identify three pronounced features: (i) the low-hanging fruit of impact mitigation requiring manageable economic effort; (ii) embodied emissions of renewables cause increasing spatial redistribution of impact from direct emissions, the place of burning fuel, to indirect emissions, the location of the energy infrastructure production; (iii) certain impact categories, in which more overall sustainable systems perform worse than the cost minimal system, require a closer look. In essence, this study makes the case for future energy system modeling to include the increasingly important global supply chain and broaden the metrics of sustainability further than cost and climate change relevant emissions.},
+author = {Rauner, Sebastian and Budzinski, Maik},
+doi = {10.1088/1748-9326/aa914d},
+issn = {17489326},
+journal = {Environmental Research Letters},
+keywords = {co-benefits,energy system modeling,hybrid modeling,life cycle assessment,multi-objective,sustainability},
+number = {12},
+title = {{Holistic energy system modeling combining multi-objective optimization and life cycle assessment}},
+volume = {12},
+year = {2017}
+}
+
+@article{Gibon:2015,
+abstract = {Climate change mitigation demands large-scale technological change on a global level and, if successfully implemented, will significantly affect how products and services are produced and consumed. In order to anticipate the life cycle environmental impacts of products under climate mitigation scenarios, we present the modeling framework of an integrated hybrid life cycle assessment model covering nine world regions. Life cycle assessment databases and multiregional input-output tables are adapted using forecasted changes in technology and resources up to 2050 under a 2 °C scenario. We call the result of this modeling "technology hybridized environmental-economic model with integrated scenarios" (THEMIS). As a case study, we apply THEMIS in an integrated environmental assessment of concentrating solar power. Life-cycle greenhouse gas emissions for this plant range from 33 to 95 g CO2 eq./kWh across different world regions in 2010, falling to 30-87 g CO2 eq./kWh in 2050. Using regional life cycle data yields insightful results. More generally, these results also highlight the need for systematic life cycle frameworks that capture the actual consequences and feedback effects of large-scale policies in the long term.},
+author = {Gibon, Thomas and Wood, Richard and Arvesen, Anders and Bergesen, Joseph D. and Suh, Sangwon and Hertwich, Edgar G.},
+doi = {10.1021/acs.est.5b01558},
+issn = {15205851},
+journal = {Environmental Science and Technology},
+number = {18},
+pages = {11218--11226},
+pmid = {26308384},
+title = {{A Methodology for Integrated, Multiregional Life Cycle Assessment Scenarios under Large-Scale Technological Change}},
+volume = {49},
+year = {2015}
+}
+
+
+
+
+
+
diff --git a/paper.md b/paper.md
index 84328db..603d41e 100644
--- a/paper.md
+++ b/paper.md
@@ -43,25 +43,52 @@ toxicity impacts, etc.
 
 # Statement of need
 
-IAMs, frequently based on Shared Socioeconomic Pathways (SSPs), offer cost-optimized projections of future scenarios. 
-These scenarios highlight, for example, the necessary changes in regional electricity mixes and different means of 
-transport to meet global warming mitigation objectives [@Riahi:2017]. This scenario analysis exercise enables us to 
-consider future system changes and their effects on the environmental performance of different technologies 
-along the different supply chains. In this context, prospective Life Cycle Assessment (pLCA) emerges as a unique tool 
-to provide a robust evaluation of the environmental performance of both existing and anticipated production systems. 
-At the methodological level, [@Sacchi:2022] has recently laid the foundations for extending present-day process-based 
-life-cycle inventory into the future using the output from IAMs. Meanwhile, most efforts in pLCA have been centred 
-around improving the ability to forecast future life cycle inventories accurately.
-
-At this juncture, performing an LCA of the transition scenarios using the updated life cycle inventories at each time step
-uncaps excellent potential to improve the sustainability assessment of these scenarios. LCA would expand the 
-conventional focus on GHG emissions to broader environmental impacts, such as land use, water consumption, and toxicity 
-while considering direct and indirect emissions. However, running LCAs of the transition scenarios provided by IAMs -
-or energy system models - at the system level remains challenging. Mainly because of the computational expense of running 
-LCAs for each time step and region of each scenario and the methodological complexity of consistently defining the 
-functional unit of the LCA based on the IAMs outputs while dealing with issues such as double-counting. 
-`pathways`, using the LCA framework `brightway2` [@Mutel:2017] and building on `premise`, offers a solution to these 
-challenges by providing a tool to evaluate the environmental impacts of transition scenarios systematically.
+Most IAMs and ESMs project future energy supply optimized for cost under a given 
+greenhouse gas emissions trajectory. These scenarios outline changes 
+required in regional energy mixes to achieve global warming mitigation goals 
+[@Riahi:2017]. By analyzing these scenarios, we can assess how future system 
+changes will affect the environmental performance of various technologies across 
+supply chains. 
+
+Prospective LCA (pLCA) emerges as a valuable tool for evaluating 
+the environmental performance of both existing and emerging production systems.
+The body of literature applying scenario-based pLCA to emerging technologies has 
+flourished in the past decade -- see literature review of [@Bisinella:2021].
+
+Extending present-day process-based life-cycle inventories into the future using 
+IAM outputs lays the methodological groundwork for pLCA. Such approach was 
+initially started with the work of [@MendozaBeltran:2018], and more recently 
+formalized with the Python library `premise` [@Sacchi:2022]. 
+
+However, efforts in pLCA have primarily focused on improving the accuracy of 
+forecasting future life cycle inventories. Performing scenario-wide LCAs
+with life cycle inventories adjusted to each time step of the scenario has 
+significant potential to enhance sustainability assessments. This approach broadens 
+the focus beyond greenhouse gas emissions to encompass broader environmental 
+impacts like land use, water consumption, and toxicity, accounting for both 
+direct and indirect emissions. Nonetheless, conducting system-wide LCA remains 
+challenging due to computational costs and methodological complexities, such as 
+defining the functional unit based on IAM outputs and addressing issues like 
+double-counting. 
+
+Several studies have attempted to address the challenges of coupling
+ESM/IAM with LCA, with notable contributions from [@Gibon:2015], [@Rauner:2017] and
+[@Pehl:2017], who quantified the outputs of an ESM or IAM scenario, 
+with a hybrid-LCA framework. The comprehensive and ambitious framework EAFESA 
+developed by Xu and colleagues [@Xu:2020], which aimed at a bidirectional coupling
+between ESM and LCA is also worth mentioning. However, these studies have
+focused on specific sectors or technologies, and have not yet been generalized
+to a broader range of scenarios and indicators. Also, to the authors' knowledge,
+their implementation has not been made available to the broader scientific community.
+
+To tackle these challenges, the open-source library `pathways` leverages the 
+LCA framework `brightway2` [@Mutel:2017] and offers a systematic tool for 
+evaluating the environmental impacts of energy transition scenarios. `pathways` is
+designed to work with data packages containing LCA matrices which have been
+adjusted to each time step of the ESM/IAM scenario. The library calculates the 
+environmental impacts of the scenario (or a subset of it) over time, 
+providing a more detailed and transparent view of the environmental impacts implied
+by the scenario.
 
 # Description
 

From 0c7257c5afbaccc8876d4a779f9cff046efc4d60 Mon Sep 17 00:00:00 2001
From: romainsacchi <r_s@me.com>
Date: Sat, 4 May 2024 20:06:25 +0200
Subject: [PATCH 5/6] EODC

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diff --git a/paper.md b/paper.md
index 603d41e..9469860 100644
--- a/paper.md
+++ b/paper.md
@@ -1,5 +1,5 @@
 ---
-title: '`pathways`: enhancing environmental impact assessments of transition scenarios through Life Cycle Assessment (LCA)'
+title: '`pathways`: sustainable energy transition scenarios'
 tags:
   - Python
   - life cycle assessment
@@ -19,7 +19,7 @@ authors:
     affiliation: 1
 
 affiliations:
- - name: Paul Scherrer Institute, Laboratory for Energy Systems Analysis, 5232 Villigen, Switzerland
+ - name: Laboratory for Energy Systems Analysis, Paul Scherrer Institute, 5232 Villigen, Switzerland
    index: 1
 
 date: 03 May 2024
@@ -43,63 +43,71 @@ toxicity impacts, etc.
 
 # Statement of need
 
-Most IAMs and ESMs project future energy supply optimized for cost under a given 
-greenhouse gas emissions trajectory. These scenarios outline changes 
-required in regional energy mixes to achieve global warming mitigation goals 
-[@Riahi:2017]. By analyzing these scenarios, we can assess how future system 
-changes will affect the environmental performance of various technologies across 
-supply chains. 
-
-Prospective LCA (pLCA) emerges as a valuable tool for evaluating 
-the environmental performance of both existing and emerging production systems.
-The body of literature applying scenario-based pLCA to emerging technologies has 
-flourished in the past decade -- see literature review of [@Bisinella:2021].
-
-Extending present-day process-based life-cycle inventories into the future using 
-IAM outputs lays the methodological groundwork for pLCA. Such approach was 
-initially started with the work of [@MendozaBeltran:2018], and more recently 
-formalized with the Python library `premise` [@Sacchi:2022]. 
-
-However, efforts in pLCA have primarily focused on improving the accuracy of 
-forecasting future life cycle inventories. Performing scenario-wide LCAs
-with life cycle inventories adjusted to each time step of the scenario has 
-significant potential to enhance sustainability assessments. This approach broadens 
-the focus beyond greenhouse gas emissions to encompass broader environmental 
-impacts like land use, water consumption, and toxicity, accounting for both 
-direct and indirect emissions. Nonetheless, conducting system-wide LCA remains 
-challenging due to computational costs and methodological complexities, such as 
-defining the functional unit based on IAM outputs and addressing issues like 
-double-counting. 
-
-Several studies have attempted to address the challenges of coupling
-ESM/IAM with LCA, with notable contributions from [@Gibon:2015], [@Rauner:2017] and
-[@Pehl:2017], who quantified the outputs of an ESM or IAM scenario, 
-with a hybrid-LCA framework. The comprehensive and ambitious framework EAFESA 
-developed by Xu and colleagues [@Xu:2020], which aimed at a bidirectional coupling
-between ESM and LCA is also worth mentioning. However, these studies have
-focused on specific sectors or technologies, and have not yet been generalized
-to a broader range of scenarios and indicators. Also, to the authors' knowledge,
-their implementation has not been made available to the broader scientific community.
-
-To tackle these challenges, the open-source library `pathways` leverages the 
-LCA framework `brightway2` [@Mutel:2017] and offers a systematic tool for 
-evaluating the environmental impacts of energy transition scenarios. `pathways` is
-designed to work with data packages containing LCA matrices which have been
-adjusted to each time step of the ESM/IAM scenario. The library calculates the 
-environmental impacts of the scenario (or a subset of it) over time, 
-providing a more detailed and transparent view of the environmental impacts implied
-by the scenario.
+Most IAMs and ESMs project cost-optimized future energy supplies within 
+specified greenhouse gas emissions trajectories, outlining changes needed 
+in regional energy mixes for global warming mitigation [@Riahi:2017]. 
+Prospective Life Cycle Assessment (pLCA) is crucial for evaluating the 
+environmental performance of existing and emerging production systems, with 
+a growing body of literature in scenario-based pLCA for emerging technologies 
+[@Bisinella:2021].
+
+Extending present-day life-cycle inventories into the future using IAM outputs, 
+initially explored by [@MendozaBeltran:2018] and formalized by the Python library 
+`premise` [@Sacchi:2022], forms the methodological basis for pLCA. Efforts in pLCA 
+focus on improving forecasting accuracy. Performing scenario-wide LCAs with 
+adjusted life cycle inventories at each time step has potential to enhance 
+sustainability assessments, broadening focus beyond greenhouse gas emissions 
+to include broader environmental impacts like land use, water consumption, 
+and toxicity, addressing both direct and indirect emissions. However, system-wide 
+LCA remains challenging due to computational costs and methodological 
+complexities, such as defining functional units based on IAM outputs and 
+resolving double-counting issues.
+
+Several studies characterize energy scenarios with LCA, including 
+[@Gibon:2015], [@Rauner:2017] and [@Pehl:2017], who quantified ESM or 
+IAM scenario outputs using a hybrid-LCA framework. There is also the work of
+[@Xu:2020], who developed the ambitious EAFESA framework aiming for 
+bidirectional coupling between ESM and LCA. Yet, these studies focused 
+on specific sectors or technologies and haven't yet generalized to broader 
+scenarios and indicators, nor made their implementations widely available.
+
+To address these challenges, the open-source library `pathways` utilizes the 
+LCA framework `brightway` [@Mutel:2017] to systematically evaluate 
+environmental impacts of energy transition scenarios. `pathways` works with 
+data packages containing LCA matrices adjusted to each time step of the 
+ESM/IAM scenario, providing detailed and transparent insights into 
+scenario environmental impacts. `pathways` works particularly well with
+data packages produced by `premise`, but can be used with any IAM/ESM scenarios
+and LCA databases. Using LCA matrices which have been modified to reflect
+the scenario's time-dependent technology mixes ensures a consistent and coherent
+characterization of said scenario.
 
-# Description
-
-1. What pathways does
-
-![Workflow for characterizing the environmental impacts of transition scenarios using `pathways`.\label{fig:workflow}](assets/diagram_1.png)
 
+# Description
 
-2. Figure of the workflow
-
-[@Sacchi:2022]
+`pathways` reads a data package containing scenario data, mapping information,
+and LCA matrices. The data package should be a zip file containing the following
+files:
+
+- `datapackage.json`: a JSON file describing the contents of the data package
+- a `mapping` folder containing a `mapping.yaml` file that describes the mapping
+  between the IAM scenario and the LCA databases
+- an `inventories` folder containing the LCA matrices as CSV files
+- a `scenario_data` folder containing the scenario data as CSV files
+
+`pathways` reads teh scenario data files (1 in Figure 1), and iterates, 
+for each time step and region, through technologies with a non-null
+production volume. For each technology, `pathways` retrieves the corresponding
+LCI dataset by looking it up in teh mapping file (2 in Figure 1). The lookup
+indicates `pathways` which LCA matrices to fetch from the data package (3 in Figure 1).
+The LCa matrices are loaded in `bw2calc` (the LCA calculation module of `brightway`)
+and multiplied by the production volume (see 4 in Figure 1). Some post-processing
+is done on the inventory matrices (e.g., Monte Carlo iterations, dealing with
+double accounting, etc., see 5 in Figure 1) before the results are aggregated and saved in a
+dataframe (6 in Figure 1). Impacts are broken down per technology, region, time step,
+geographical origin of impact, life-cycle stage and impact assessment method.
+
+![pathways workflow.\label{fig:workflow}](assets/workflow_diagram.png)
 
 # Usage
 
@@ -109,14 +117,20 @@ by the scenario.
 
 # Conclusion
 
-1. pathways offers a tool to systematically evaluate the environmental impacts of transition scenarios, considering the 
-   full supply chain of products and services in a dynamic way where the results of the scenario are integrated within the LCA database at each timestep
+`pathways` is a tool that evaluates the environmental impacts of transition 
+scenarios over time using time-adjusted and scenario-based LCA matrices. This
+approach allows for characterizing the environmental impacts of a scenario
+across a wide range of indicators, including land use, water consumption,
+toxicity impacts, etc. It also allows to attribute supply chain emissions
+to the final energy carriers, thus providing a more detailed and transparent
+view of the environmental impacts of a scenario.
 
 # Acknowledgements
 
-The authors gratefully acknowledge the financial support from the Swiss State Secretariat for Education, Research and 
-Innovation (SERI), under the Horizon Europe project PRISMA (grant agreement no. 101081604). The authors also thank the
-Swiss Federal Office of Energy (SFOE) for the support in the development of the `premise` and `pathways` tools through 
-the SWEET-SURE program.
+The authors gratefully acknowledge the financial support from the Swiss State 
+Secretariat for Education, Research and Innovation (SERI), under the Horizon 
+Europe project PRISMA (grant agreement no. 101081604). The authors also thank the
+Swiss Federal Office of Energy (SFOE) for the support in the development of the 
+`premise` and `pathways` tools through the SWEET-SURE program.
 
 # References

From 2cd206ae59f5bca4409ea917b5450f3ab3601705 Mon Sep 17 00:00:00 2001
From: romainsacchi <r_s@me.com>
Date: Sat, 4 May 2024 20:08:26 +0200
Subject: [PATCH 6/6] EODC

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