Code and data for reproducing figures in Pathak et al., "Commonalities and differences in national pathways toward net-zero emissions"

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides reproducible code and data for national net-zero pathway analysis, enabling further research and comparison.

🏢実務担当者:Offers a framework for understanding national decarbonization pathways, useful for corporate transition planning and scenario analysis.

🏛政策担当者:Supports evidence-based policy by illustrating commonalities and differences in national net-zero strategies, informing international cooperation.

This repository contains the code required to reproduce the analysis and figures for a national scenario analysis. The workflow is implemented in  R  and organized around a single project structure with a central execution script. The data directory contains reference datasets, including IPCC AR6 data, effort-sharing data, historical CO₂ emissions (IEA-EDGAR), and World Bank income classifications. IPCC AR6 scenario data v1.1 are downloaded from  https://data.ene.iiasa.ac.at/ar6  (AR6 Scenarios Database hosted by IIASA, release v1.1). Place the following files inside the  data/ref_data/ folder from AR6 database: AR6_Scenarios_Database_World_v1.1.csv AR6_Scenarios_Database_R5_regions_v1.1.csv AR6_Scenarios_Database_R10_regions_v1.1 AR6_Scenarios_Database_metadata_indicators_v1.1.xlsx Historical CO2 emissions database (IEA-EDGAR CO2) is downloaded from https://edgar.jrc.ec.europa.eu/dataset_ghg80 . Place the following file inside the data/ref_data/ folder: IEA_EDGAR_CO2_1970_2022.xlsx Economic classification of countries is based on the World Bank's classification which is downloaded from https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups Effort sharing allocations of global carbon budget is based on the calculation method used by (Fujimori, S. et al, 2026), DOI: https://doi.org/10.1038/s43247-026-03208-5 National scenario data used in the analysis is available at https://zenodo.org/records/21662719 . After obtaining the scenario data, name the IAMC format data file as scen_data.csv and place it inside data/scen_data/ folder. All figures and results can be generated by running the main script after setting up the project environment. R version 4.3.1 was used to perform statistical analysis. The analysis relies on the following R packages: tidyverse, readr, readxl, ggpubr, patchwork, RColorBrewer, sf, rnaturalearth, broom, here To reproduce the analysis: Open the  .Rproj  file in RStudio Install the required packages  Place scenario database inside  data/scen_data/ Run the main script:  prog/main.R All outputs (figures) will be saved automatically in:  output/figures/

• Zenodo https://zenodo.org/records/21974998first seen 2026-08-18 04:11:36