Artificial Intelligence in Climate and Sustainable Finance: A Blessing or a Curse?
気候変動と持続可能な金融における人工知能：祝福か呪いか？ (AI 翻訳)
Filippo di Pietro, Pilar Giráldez‐Puig, P. Palos-Sanchez, Zbigniew Korzeb
🤖 gxceed AI 要約
日本語
本論文は、気候関連の金融課題に対する機械学習技術の応用を体系的文献レビューと計量書誌分析で調査。排出予測、グリーン投資、サステナビリティ報告でのAI採用拡大を示す一方、データ品質、解釈可能性、アルゴリズムバイアスの課題を指摘。AIが持続可能な移行を促進するには、堅牢なガバナンスと規制監督が必要と結論。
English
This study uses a systematic literature review and bibliometric analysis to examine machine learning applications in climate-related financial challenges. It finds growing AI adoption in emissions forecasting, green investment, and sustainability reporting, while highlighting issues of data quality, interpretability, and algorithmic bias. The authors conclude that robust governance and regulatory oversight are needed for AI to enable a sustainable transition.
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
日本でもESG情報開示やTCFD対応でAI活用が進むが、本レビューはデータ品質やバイアスなど実務上の課題を整理。日本の企業・投資家がAI導入時のガバナンス体制を検討する際の参考となる。
In the global GX context
As AI adoption accelerates in climate finance and ESG analysis globally, this review provides a structured overview of opportunities and challenges. It underscores the need for governance frameworks—relevant for ISSB, CSRD, and SEC rule-making on AI use in disclosures.
👥 読者別の含意
🔬研究者:Provides a comprehensive mapping of AI applications in climate finance, identifying research gaps and future directions.
🏢実務担当者:Highlights practical AI use cases for emissions forecasting and ESG reporting, along with data and interpretability pitfalls.
🏛政策担当者:Emphasizes the need for regulatory oversight and ethical guidelines to avoid algorithmic bias and ensure AI supports sustainable finance.
📄 Abstract（原文）
While there are concerns regarding the sustainability of artificial intelligence (AI), it is a potential ally in the transition toward a greener future. It offers advanced tools for data analysis; risk modeling; and environmental, social, and governance (ESG) assessment. This study provides a systematic literature review and bibliometric analysis on how machine learning techniques are being applied to address climate‐related financial challenges. The results reveal growing adoption of AI in areas such as emissions forecasting, green investment, and sustainability reporting. Based on the findings, there are several ongoing challenges related to data quality, interpretability, and algorithmic bias. While AI can enable a green transition, there are financial and ethical concerns that need to be addressed with robust governance, regulatory oversight, and institutional awareness to ensure that AI acts as a catalyst for financial stability and sustainable development.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1111/joes.70075first seen 2026-05-06 00:31:29
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。