National Scenario Analysis

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_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

Scenario data used in the analysis is available from the authors upon request. After obtaining the scenario data file, please place it inside data/scen_data/ folder without changing the file name.

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.

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Getting Started

To reproduce the analysis:

1. Clone or download this repository
2. Open the .Rproj file in RStudio
3. Install the required packages (see below)
4. Download the required reference datasets and place them inside data/ref_data folder
5. Place national scenario database inside data/scen_data folder
6. Run the main script:

source("prog/main.R")

All outputs (figures) will be saved automatically in:

output/figures/

Repository Structure

The project uses a structured directory layout managed via the here package for reproducible file paths:

├── prog/
│   ├── main.R                   # Entry point for running the full workflow and main figures
│   ├── sup_plots.R              # Supplementary figures
│   ├── config.R                 # Path configuration and global settings
│   ├── region_mapping.R         # Region and country mapping definitions
│   ├── load_data.R              # Data loading functions
│   ├── process_data.R           # Data cleaning and preprocessing steps
│   ├── unit_correction_table.R  # Unit harmonization and correction logic
│   └── functions.R              # Custom helper and plotting functions
│
├── data/
│   ├── ref_data/                # Reference datasets
│   └── scen_data/               # Scenario data inputs and processed files
│
└── output/
    └── figures/
       ├── main/                # Main manuscript figures
       └── supplementary/       # Supplementary figures

Required Packages

The analysis relies on the following R packages:

tidyverse
readr
readxl
ggpubr
patchwork
RColorBrewer
sf
rnaturalearth
broom
here

Install all dependencies with:

install.packages(c(
  "tidyverse", "readr", "ggpubr", "readxl", "RColorBrewer",
  "broom", "patchwork", "sf", "rnaturalearth", "here"
))