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

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:

1. Open the .Rproj file in RStudio
2. Install the required packages 
3. Place scenario database inside data/scen_data/
4. Run the main script: prog/main.R
5. All outputs (figures) will be saved automatically in: output/figures/