Scaling climate finance without structural reform undermines just energy transitions in developing countries
構造改革なしでの気候資金拡大は発展途上国の公正なエネルギー移行を損なう (AI 翻訳)
Lin Yang, Simin Huang, Shumeng Xiao, Jing Meng, Rujia Lan
🤖 gxceed AI 要約
日本語
本研究は、気候資金の拡大が構造改革なしでは発展途上国の公正なエネルギー移行を阻害する可能性を分析した。ランダムフォレストとSHAPを用いてパネルデータを解析し、補完的な改革の重要性を示す。本リポジトリは図表の再現コードを提供する。
English
This repository provides code to reproduce analyses examining how scaling climate finance without structural reform can hinder just energy transitions in developing countries. Using random forest and SHAP, the study highlights the need for complementary reforms. The code enables full reproducibility of figures.
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
日本の気候資金拠出（JICAやGCF経由）の効果を高めるには、構造改革支援の併用が不可欠であることを示唆。日本の国際協力政策に示唆を与える。
In the global GX context
This research underscores that climate finance alone is insufficient for just transitions, reinforcing global calls for integrated policy and institutional reforms. Relevant for international climate finance architecture and development bank strategies.
👥 読者別の含意
🔬研究者:Provides a reproducible analytical framework for studying climate finance and just transitions.
🏢実務担当者:Highlights the need to pair financial scaling with structural reform in project design.
🏛政策担当者:Emphasizes that climate finance effectiveness depends on complementary governance and policy reforms.
📄 Abstract（原文）
This deposit provides scripts to reproduce figures and supplementary analyses for the study on whether scaling climate finance without complementary structural reform can undermine just energy transitions in developing countries. **Contents**- Main figure scripts: `fig2.py`–`fig7.py`- Supplementary scripts: `SI figures/` **Methods (high level)**- Panel data read from Excel (`.xlsx`)- Random Forest regressors with hyperparameter search- Interpretation via SHAP and partial dependence displays (and related robustness exercises in the SI script) **How to use**1. Install Python dependencies (e.g., `numpy`, `pandas`, `matplotlib`, `scikit-learn`, `shap`, `openpyxl`; `seaborn` for the robustness script).2. Provide the manuscript dataset as `.xlsx` and update file paths inside the scripts (several scripts contain machine-specific absolute paths from the original working environment). **Note on reproducibility**The repository is research code exported from interactive workflows; users must adjust input paths and optional environment variables (e.g., temporary folders for parallel backends) to match their system. **Related publication**If available, cite the peer-reviewed article using its DOI (see “Related identifiers” below).
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.19657353first seen 2026-05-15 18:11:19
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。