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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