Original Supporting Information to "Learning Curves in Prospective Life Cycle Assessment"

This folder contains all the supporting information to "Learning Curves in Prospective Life Cycle Assessment" in its original state at the time of publication in Environmental Science & Technology. For any updates or corrections to this original data, consult the respective folder.The supporting information is further organized into folders. The main folder contains Supporting Information.pdf: This is a copy of Supporting Information which is also available at the journal. It is included here for completeness.The folder Figures contains:21 .PDF files for figures in the main text and Supporting Information.pdf36 .JPG/.PNG files for figures in the main text and Supporting Information.pdf1 .xlsx file for the underlying data of Figure S4.15 .txt files with the codes required to recreate the Sankey Diagrams with SankeyMATIC.The folder Step 4 - LCI model contains:Foreground.xlsx: spreadsheet file with the life cycle inventory of the foreground system which can readily be imported in Brightway2.The folder Step 5 - Supply chain contribution analyses contains:ReCiPe 2016 E - midpoint-to-endpoint.xlsx: spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Egalitarian versions of ReCiPe 2016ReCiPe 2016 H - midpoint-to-endpoint.xlsx: spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Hierarchist versions of ReCiPe 2016ReCiPe 2016 I - midpoint-to-endpoint.xlsx: spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Individualist versions of ReCiPe 2016jupyter_notebook.ipynb: Jupyter notebook file for reproducing the contribution analysisjupyter_notebook.html:.HTML version of the Jupyter notebook file. The folder Step 8 - Learning curves for major contributors contains:learning_curve_data.xlsx: contains all raw data used to generate the learnign curvesinput_data_learning_curve_regressions.xlsx: contains a cleaned up version of the raw data for use in regression analysesempirical_learning_curve.xlsx: contains data for the empirical learning curve by Louwen et al. 2016 (https://doi.org/10.1038/ncomms13728)cumulative_installed_capacity_projections.xlsx: contains projections for the cumulative installed PV capacity in 2050.learning_curves_coefficients.xlsx: contains the learning curve coefficients as represented in Figure 3 of the main textconvert_learning_curve_data_to_LCI_data.xlsx: takes the data sampled from the learning curves with Monte Carlo simulations and converts it to a format that can be used in scenario LCAThe folder Step 9 - Future database for background system contains:Apply_premise_2.0.2_to_ecoinvent_3.9.1.ipynb: a Jupyter notebook file with the code that was used to apply premise 2.0.2 to ecoinvent 3.9.1.Apply_premise_2.0.2_to_ecoinvent_3.9.1.html: an HTML version of the jupyter notebook file for easier consultation.The folder Step 12 - Scenario files for foreground system contains:7 .XLSX spreadsheet files starting with "deterministic_" containing values for the deterministic scenarios, where the part after "f_"specifies the foreground scenario and the part after "b_" specifies the background scenario. 5 .XLSX spreadsheet files starting with "monte_carlo_" containing values for the monte carlo scenarios, where the part after "f_"specifies the foreground scenario and the part after "b_" specifies the background scenario. The folder Step 13 - Scenario LCA contains:7 .XLSX spreadsheet files starting with "deterministic_" containing values for the deterministic impact assessment results, where the part after "f_"specifies the foreground scenario and the part after "b_" specifies the background scenario. 5 .XLSX spreadsheet files starting with "monte_carlo_" containing values for the monte carlo impact assessment results, where the part after "f_"specifies the foreground scenario and the part after "b_" specifies the background scenario. The folder Step 15 - Interpret results contains:00_Functions_2025_01_06.R: script file that defines base funtion for the sensitivity assessments01_LearningCurves_2025_01_06.R: script file that creates the process-specific learning curves in Figure 3 of the main text.02_OATSensitivityAnalysis_2025_01_06.R: script file to perform one-at-a-time sensitivity analyses on the results for the IMAGE scenarios.03_OATSensitivityAnalysis_IEA_2025_01_06.R: script file to perform one-at-a-time sensitivity analyses on the results for the IEA scenarios.04_SpearmanRankMCMC_2025_01_06.R: script file to create spearman rank plots05_EmpiricalLearningCurvePlot_2025_01_06.R: script file that creates the empirical learning curves and violin plots in Figure 4 of the main text.Folder Data: contains 5 .XLSX files with the raw data used by in the .R scripts to make the plots.Folder Output: contains 35 .xlsx files with results as well as a folder containing 56 .PDF files with spearman rank plots obtained as output from the .R scripts.