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 &amp; 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 <b>Supporting Information.pdf:</b><b> </b>This is a copy of Supporting Information which is also available at the journal. It is included here for completeness.The folder <b>Figures</b> 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 <b>Step 4 - LCI model</b> contains:<b>Foreground.xlsx:</b> spreadsheet file with the life cycle inventory of the foreground system which can readily be imported in Brightway2.The folder <b>Step 5 - Supply chain contribution analyses</b> contains:<b>ReCiPe 2016 E - midpoint-to-endpoint.xlsx:</b> spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Egalitarian versions of ReCiPe 2016<b>ReCiPe 2016 H - midpoint-to-endpoint.xlsx: </b>spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Hierarchist versions of ReCiPe 2016<b>ReCiPe 2016 I - midpoint-to-endpoint.xlsx:</b> spreadsheet file with the results for the midpoint-to-endpoint contribution analyses using the Individualist versions of ReCiPe 2016<b>jupyter_notebook.ipynb:</b> Jupyter notebook file for reproducing the contribution analysis<b>jupyter_notebook.html:</b>.HTML version of the Jupyter notebook file. The folder <b>Step 8 - Learning curves for major contributors</b> contains:<b>learning_curve_data.xlsx:</b> contains all raw data used to generate the learnign curves<b>input_data_learning_curve_regressions.xlsx:</b> contains a cleaned up version of the raw data for use in regression analyses<b>empirical_learning_curve.xlsx:</b> contains data for the empirical learning curve by Louwen et al. 2016 (https://doi.org/10.1038/ncomms13728)<b>cumulative_installed_capacity_projections.xlsx:</b> contains projections for the cumulative installed PV capacity in 2050.<b>learning_curves_coefficients.xlsx:</b> contains the learning curve coefficients as represented in Figure 3 of the main text<b>convert_learning_curve_data_to_LCI_data.xlsx:</b> 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 <b>Step 9 - Future database for background system</b> contains:<b>Apply_premise_2.0.2_to_ecoinvent_3.9.1.ipynb:</b> a Jupyter notebook file with the code that was used to apply premise 2.0.2 to ecoinvent 3.9.1.<b>Apply_premise_2.0.2_to_ecoinvent_3.9.1.html:</b> an HTML version of the jupyter notebook file for easier consultation.The folder <b>Step 12 - Scenario files for foreground system</b> 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 <b>Step 13 - Scenario LCA</b> 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 <b>Step 15 - Interpret results</b> contains:<b>00_Functions_2025_01_06.R: </b>script file that defines base funtion for the sensitivity assessments<b>01_LearningCurves_2025_01_06.R: </b>script file that creates the process-specific learning curves in Figure 3 of the main text.<b>02_OATSensitivityAnalysis_2025_01_06.R: </b>script file to perform one-at-a-time sensitivity analyses on the results for the IMAGE scenarios.<b>03_OATSensitivityAnalysis_IEA_2025_01_06.R: </b>script file to perform one-at-a-time sensitivity analyses on the results for the IEA scenarios.<b>04_SpearmanRankMCMC_2025_01_06.R: </b>script file to create spearman rank plots<b>05_EmpiricalLearningCurvePlot_2025_01_06.R: </b>script file that creates the empirical learning curves and violin plots in Figure 4 of the main text.Folder <b>Data: </b>contains 5 .XLSX files with the raw data used by in the .R scripts to make the plots.Folder <b>Output: </b>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.