statement of need

example/example.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# Example\n",
-    "Author: [Romain Sacchi]([email protected])\n",
+    "Author: [Romain Sacchi]([email protected]), [Alvaro Hahn]([email protected])\n",
     "\n",
     "``pathways``allows calculating LCA impacts of a product or system along a time axis, combining time series of demand with scenario-based LCA databases.\n"
    ]
@@ -24,13 +24,13 @@
    "id": "3589debc-dfa2-48f7-96ef-4b8ab1724f92",
    "metadata": {},
    "source": [
-    "This notebook presents a mock case to illustrate the use of `pathways`. The diagram below introduces the production system proposed.\n",
+    "This notebook presents a mock case to illustrate the use of `pathways`. The diagram below introduces the proposed production system.\n",
     "\n",
-    "The goal of this exercise is be to calculate the environmental impact (both direct and indirect) associated to satisfying the demand over time (2020-2050) for **product A** under two different future scenarios.\n",
+    "The goal of this exercise is to calculate the environmental impact (both direct and indirect) associated with meeting the demand over time (2020-2050) for **product A** under two different future scenarios.\n",
     "\n",
-    "For each timestep, we present the technosphere and biosphere matrices. As to the LCA conventions, the technosphere matrix displays the different activities in columns, and the different products in rows. Meanwhile, positive values represent an output from a certain activity and negative values inputs. Hence, as an example, in 2020: *activity A*, to produce 1 unit of *product A*, demands 0.8 units of *product B* and directly emits 1.5 units of CO2. At the same time, *activity B* consumes 0.2 units of *product E* and emits 0.2 units of CO2 to produce 1 unit of *product B*. [...]\n",
+    "We present the technosphere and biosphere matrices at each timestep. According to LCA conventions, the technosphere matrix lists the different activities in columns, and the different products in rows. Positive values indicate outputs from an activity, while negative values indicate inputs. For example, in 2020: *activity A*, to produce 1 unit of *product A*, demands 0.8 units of *product B* and directly emits 1.5 units of CO2. Concurrently, *activity B* consumes 0.2 units of *product E* and emits 0.2 units of CO2 to produce 1 unit of *product B*. [...]\n",
     "\n",
-    "Per each timestep, we can identify different changes in the technosphere exchanges and emissions intensities caused by changes in the system"
+    "For each timestep, we can identify different changes in the technosphere exchanges and emissions intensities caused by changes in the system."
    ]
   },
   {

paper.bib

@@ -12,18 +12,6 @@ @article{Sacchi:2022
 year = {2022}
 }
 
-@article{Wernet:2016,
-author = {Wernet, G. and Bauer, C. and Steubing, B. and Reinhard, J. and Moreno-Ruiz, E. and Weidema, B.},
-doi = {10.1007/s11367-016-1087-8},
-journal = {The International Journal of Life Cycle Assessment},
-number = {9},
-pages = {1218--1230},
-title = {{The ecoinvent database version 3 (part I): overview and methodology.}},
-url = {http://link.springer.com/10.1007/s11367-016-1087-8},
-volume = {21},
-year = {2016}
-}
-
 @article{Mutel:2017,
 abstract = {Brightway is an open source framework for Life Cycle Assessment (LCA) calculations in Python. The combination of a modular structure, the expressiveness and interactivity of Python and in particular Jupyter notebooks, and tuned calculation pathways allows for new research directions in Life Cycle Assessment. Brightway has been used in papers on meta-analysis of many inventory datasets (Wernet et al. 2011), regionalized LCA (Mutel, Pfister, and Hellweg 2011), and sensitivity analysis (Mutel, Baan, and Hellweg 2013). Brightway consists of three main modules: Brightway2-data (Mutel 2012c) manages how data is stored and accessed; Brightway2-calc (Mutel 2012b) does static and Monte Carlo calculations; and Brightway2-IO (Mutel 2015c) handles the import and export of LCA data from various sources. In addition to these libraries, helper libraries provide documentation and application examples (Mutel 2012a), support for parameterized inventories (Mutel 2015b), and a format for LCA data in arrays (Mutel 2013). A web page (Mutel 2016), documentation (Mutel 2015a), and a development blog (Mutel 2014) are also available.},
 author = {Mutel, Chris},
@@ -34,4 +22,18 @@ @article{Mutel:2017
 title = {{Brightway: An open source framework for Life Cycle Assessment}},
 volume = {2},
 year = {2017}
+}
+
+@article{Riahi:2017,
+abstract ={This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO2 emissions of the baseline scenarios range from about 25 GtCO2 to more than 120 GtCO2 per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m2 that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).}
+author = {Riahi, Keywan and van Vuuren, Detlef P. and Kriegler, Elmar and Edmonds, Jae and O'Neill, Brian C. and Fujimori, Shinichiro and Bauer, Nico and Calvin, Katherine and Dellink, Rob and Fricko, Oliver and Lutz, Wolfgang and Popp, Alexander and Cuaresma, Jesus Crespo and KC, Samir and Leimbach, Marian and Jiang, Leiwen and Kram, Tom and Rao, Shilpa and Emmerling, Johannes and Ebi, Kristie and Hasegawa, Tomoko and Havlik, Petr and Humpen{\"{o}}der, Florian and Da Silva, Lara Aleluia and Smith, Steve and Stehfest, Elke and Bosetti, Valentina and Eom, Jiyong and Gernaat, David and Masui, Toshihiko and Rogelj, Joeri and Strefler, Jessica and Drouet, Laurent and Krey, Volker and Luderer, Gunnar and Harmsen, Mathijs and Takahashi, Kiyoshi and Baumstark, Lavinia and Doelman, Jonathan C. and Kainuma, Mikiko and Klimont, Zbigniew and Marangoni, Giacomo and Lotze-Campen, Hermann and Obersteiner, Michael and Tabeau, Andrzej and Tavoni, Massimo},
+doi = {10.1016/j.gloenvcha.2016.05.009},
+issn = {09593780},
+journal = {Global Environmental Change},
+month = {1},
+pages = {153--168},
+title = {{The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview}},
+volume = {42},
+url = {https://www.sciencedirect.com/science/article/pii/S0959378016300681},
+year = {2017},
 }

paper.md

@@ -1,6 +1,5 @@
 ---
-title: '`pathways`: a Life Cycle Assessment (LCA) approach to characterizing the environmental impacts of transition 
-scenarios'
+title: '`pathways`: enhancing environmental impact assessments of transition scenarios through Life Cycle Assessment (LCA)
 tags:
   - Python
   - life cycle assessment
@@ -15,12 +14,12 @@ authors:
   - name: Romain Sacchi
     orcid: 0000-0003-1440-0905
     affiliation: 1
-  - name: Alvaro J. Hahn Menacho
+  - name: Alvaro J. Hahn-Menacho
     orcid: 0000-0003-2592-2186
     affiliation: 1
 
 affiliations:
- - name: Paul Scherrer Institute, Villigen, Switzerland
+ - name: Paul Scherrer Institute, Laboratory for Energy Systems Analysis, 5232 Villigen, Switzerland
    index: 1
 
 date: 03 May 2024
@@ -36,19 +35,53 @@ provides a more detailed and transparent view of the environmental impacts of a
 between producers and consumers (as an 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, but can be used with any IAM scenarios 
-and LCA databases. It reads a scenario and a corresponding set of scenario-based LCA matrices and calculates the 
+`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.
+
+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.
+
 # Description
 
+1. What pathways does
+
+![Workflow for characterizing the environmental impacts of transition scenarios using `pathways`.\label{fig:workflow}](assets/diagram_1.png)
+
+
+2. Figure of the workflow
+
 [@Sacchi:2022]
 
+# Usage
+
 # Impact
 
-# Conclusion and outlook
+
+
+# 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
 
 # Acknowledgements