AI for Climate Risk Assessment and Ethical Portfolio Management
気候リスク評価と倫理的ポートフォリオ管理のためのAI (AI 翻訳)
Deepak Gupta, Ziyodullayev Sodiq, Matkarimov Mansur, Khayrulla Urozboev, Elshod Alimardonov, Aripov Aziz, Berdiyev Anvar Abduraxmanovich
🤖 gxceed AI 要約
日本語
本論文は、人工知能（AI）が気候リスク評価と倫理的ポートフォリオ管理にどのように活用されるかを包括的に分析する。機械学習による気候リスクモデリング、深層学習によるESG評価の精度向上、自然言語処理による気候関連開示の抽出、予測モデルによる移行リスク評価などを検討。さらに、AIによるポートフォリオ最適化や気候ストレステスト、炭素リスクの価格付けに関する実証的証拠を提示し、アルゴリズムの透明性やデータバイアスなどの倫理的課題にも言及している。
English
This chapter examines the convergence of AI and climate finance, exploring machine learning for climate risk modeling, deep learning for ESG rating accuracy, NLP for climate disclosure extraction, and predictive models for transition risks. It synthesizes evidence on AI-driven portfolio optimization with ethical constraints, climate stress testing, and carbon risk pricing, while addressing algorithmic transparency and data bias. The analysis offers actionable insights for academics, practitioners, and policymakers in responsible AI for climate-conscious investing.
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
日本ではSSBJ基準に対応したESG評価や気候関連開示の高度化が急務となっている。本論文のAI活用フレームワークは、投資家向け開示や統合報告書作成、ポートフォリオの気候リスク管理において実践的示唆を提供する。
In the global GX context
With global frameworks like TCFD, ISSB, and CSRD demanding robust climate risk quantification, this paper shows how AI can enhance ESG ratings, emissions prediction, and portfolio optimization. It bridges the gap between AI innovation and sustainable finance, relevant for regulators and financial institutions implementing climate stress tests and transition finance strategies.
👥 読者別の含意
🔬研究者:Provides a comprehensive literature synthesis on AI applications in climate finance, identifying key methods and ethical challenges for future research.
🏢実務担当者:Offers actionable insights on integrating AI into ESG assessment and portfolio management, including tools for carbon risk pricing and disclosure analysis.
🏛政策担当者:Highlights the need for algorithmic transparency and data governance in AI-driven climate finance, informing regulatory frameworks and taxonomies.
📄 Abstract（原文）
Artificial intelligence (AI) is revolutionizing climate risk assessment and ethical portfolio management by enabling sophisticated analysis of environmental, social, and governance (ESG) factors. This chapter examines the convergence of AI technologies with climate finance, exploring machine learning applications in climate risk modeling, carbon emissions prediction, and sustainable investment strategies. We analyze how deep learning algorithms enhance ESG rating accuracy, natural language processing extracts climate-related disclosures, and predictive models assess transition risks. The chapter synthesizes recent empirical evidence on AI-driven portfolio optimization incorporating ethical constraints, climate stress testing methodologies, and the pricing of carbon risk in financial markets. Key findings demonstrate that AI significantly improves climate risk quantification while raising critical questions about algorithmic transparency, data bias, and ethical governance. The integration of neuroscience insights with environmental ethics provides novel frameworks for mindful finance, where AI serves as a tool for both financial performance and planetary stewardship. This comprehensive analysis offers academics, practitioners, and policymakers actionable insights for implementing responsible AI in climate-conscious investment decision-making.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4018/979-8-3373-8064-3.ch012first seen 2026-06-10 04:44:56
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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。