Code and data for reproducing figures in Pathak et al., "Commonalities and differences in national pathways toward net-zero emissions"
Pathakらの論文「ネットゼロ排出への国家経路の共通点と相違点」の図を再現するためのコードとデータ (AI 翻訳)
Anuj Pathak
🤖 gxceed AI 要約
日本語
本リポジトリは、ネットゼロ排出に向けた国家経路の分析を再現するためのコードとデータを提供する。IPCC AR6シナリオデータ、IEA-EDGARの歴史的CO2排出量、世界銀行の所得分類などを用い、Rで実装されたワークフローにより図表を生成する。研究者が各国の排出経路を比較・検証するための基盤となる。
English
This repository contains code and data to reproduce the analysis and figures from Pathak et al. on national net-zero pathways. It uses IPCC AR6 scenarios, IEA-EDGAR historical CO2 emissions, World Bank income classifications, and effort-sharing allocations. The workflow in R enables researchers to validate and extend the national scenario analysis.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のネットゼロ目標（2050年カーボンニュートラル）の達成経路を評価する際に、本リポジトリの枠組みを適用可能。特に、努力分担（effort sharing）の手法を参考に、日本の排出削減目標の公平性や実現可能性を国際比較できる。
In the global GX context
This work enables reproducible analysis of national pathways to net-zero, directly supporting global climate policy assessment. It leverages authoritative datasets (IPCC AR6, IEA, World Bank) and facilitates cross-country comparisons, which is essential for tracking progress under the Paris Agreement and informing NDC updates.
👥 読者別の含意
🔬研究者:Provides a fully reproducible workflow for national net-zero scenario analysis, enabling validation and extension of the original study.
🏢実務担当者:Offers a ready-to-use framework for corporate or institutional scenario analysis, but requires R proficiency and data access.
📄 Abstract（原文）
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. 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/
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20280095first seen 2026-05-20 04:12:54 · last seen 2026-05-20 04:14:28
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