Revealing environmental, social, and governance-driven conflict risk in global energy transition minerals supply
世界のエネルギー転換鉱物供給における環境・社会・ガバナンス主導の紛争リスクの解明 (AI 翻訳)
Yongguang Zhu, Jinning Zhu, Xiaoyang Lin, Zhewei Yu, Shiquan Dou, Penghong Cheng, Gang Liu, Deyi Xu
🤖 gxceed AI 要約
日本語
再生可能エネルギー移行に重要なリチウムやコバルトなどの鉱物の供給チェーンにおけるESGリスクを、機械学習を用いて評価し予測する枠組みを提案。中東・北アフリカ・南アジアでリスクが高く、タングステンが最大、プラチナが最小のリスクを示し、地域・鉱物ごとの対策と国際協力の必要性を強調する。
English
This paper introduces a framework using machine learning to evaluate ESG-driven conflict risks in the supply of critical minerals for the energy transition. Analyzing 112,766 conflict events, it finds high risks in the Middle East, North Africa, and South Asia, with tungsten showing the highest and platinum the lowest risk, highlighting the need for targeted mitigation and international cooperation.
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
日本のエネルギー転換は輸入鉱物に依存しており、本論文は供給途絶リスクの評価手法を提供。ESGデューデリジェンスやサプライチェーン多様化の重要性を示唆する。
In the global GX context
This paper offers a novel framework linking ESG risks to conflict in critical mineral supply chains, directly supporting TCFD/ISSB-aligned disclosure on supply chain resilience. It provides global evidence to inform transition finance and corporate due diligence strategies.
👥 読者別の含意
🔬研究者:Provides a reproducible ML framework for ESG risk prediction in mineral supply chains, advancing methods in sustainability analytics.
🏢実務担当者:Corporate sustainability teams can use the framework to assess and mitigate conflict risks in critical mineral sourcing.
🏛政策担当者:Highlights the need for international cooperation and dimension-specific policies to secure supply chains for the energy transition.
📄 Abstract（原文）
The global transition to renewable energy hinges on critical minerals like lithium, cobalt, platinum, antimony, and tungsten, but their extraction and supply chains are imperiled by environmental, social, and governance risks that undermine stability and sustainability. Here, we introduce a novel framework for environmental, social, and governance risks evaluation, fusing a multi-dimensional indicator system with advanced machine learning models. Drawing on conflict events alongside diverse socio-economic and environmental indicators, we predict environmental, social, and governance risks at a global scale. Results uncover marked spatial heterogeneity, with elevated risks in the Middle East, North Africa, and South Asia associated with governance weaknesses and environmental pressures. Tungsten exhibits the highest overall environmental, social, and governance risks, while platinum the lowest. Our findings underscore the need for targeted, dimension-specific mitigation strategies and enhanced international cooperation to bolster sustainable governance and secure critical mineral supplies for a resilient energy transition. Tungsten shows highest and platinum lowest environmental social and governance risk, with hotspots in Middle East North Africa and South Asia, predicted using multi-dimensional indicators and machine learning from 112766 conflict events.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1038/s43247-026-03689-4first seen 2026-05-31 05:27:21 · last seen 2026-06-03 05:06:07
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