人工知能・フィンテック・グリーン投資：持続可能な金融意思決定におけるAI駆動型気候リスク評価の役割

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AI×気候金融の研究アジェンダと未解決の認識論的リスクを整理した分類枠組みを提供する。

🏢実務担当者:AI気候分析ツール導入時のデータ品質・説明可能性・AIウォッシング回避のチェックリストとして活用できる。

🏛政策担当者:AI活用型気候開示・ESG評価に対する規制設計と、新興国アクセス格差への政策的対応の論点を提示する。

Artificial Intelligence (AI), financial technology (fintech) and the global sustainability agenda have created a new analytical infrastructure for analyzing climate-related financial risk. The combination of financial technology (fintech), artificial intelligence (AI) and the global sustainability agenda has led to the development of a new analytical infrastructure for the assessment of climate-related financial risk. Financial institutions have embraced machine learning, natural language processing, and geospatial analytics derived from satellite data to measure physical and transition climate risks at a scale and speed that would otherwise be impossible using traditional actuarial and econometric approaches as regulators step up to mandate climate disclosures and investors look for reliable ways to invest trillions of dollars towards decarbonization. This paper reviews the existing literature, regulatory frameworks, and industry practices related to climate-risk assessment tools that can be used for sustainable financial decision making between 2022 and 2026. It establishes a taxonomy of AI applications in physical-risk modelling, transition-risk analysis and scenario analysis, greenwashing identification, credit underwriting and robo-advisory portfolio construction, and maps these applications to an evolving regulatory framework that includes the Task Force on Climate-related Financial Disclosures (TCFD), the International Sustainability Standards Board (ISSB), the European Union's Corporate Sustainability Reporting Directive (CSRD) and the Network for Greening the Financial System (NGFS). Based on data from both developed and emerging markets, the paper proposes that AI is a valuable extension of analytical capacity for sustainable finance, but it also raises new epistemic risks, including from the low-quality or biased climate data used to create AI models, from persistent differences between the different ESG ratings enhanced by AI, from the lack of explainability of the models and the risk of regulatory non-accountability, from the rise of “AI-washing” as a second-order greenwashing risk, from the lack of access to advanced climate-analytics infrastructure in emerging markets, and from the unexamined carbon footprint of the AI systems. The paper ends with a governance design for the responsible use of AI in climate finance and priority fields for further research.

• semanticscholar https://doi.org/10.54938/ijemdss.2026.05.5.824first seen 2026-10-03 05:16:42 · last seen 2026-10-07 05:18:32