AI-Driven Corporate Climate Risk Decision Systems for Global Enterprises: The CADS Architecture, Evidence, Governance, and Research Agenda
グローバル企業のためのAI駆動型気候リスク意思決定システム：CADSアーキテクチャ、エビデンス、ガバナンス、研究アジェンダ (AI 翻訳)
Abhinav Mahajan
🤖 gxceed AI 要約
日本語
本論文は、企業の気候リスクを多領域の意思決定問題と捉え、AIを活用した意思決定支援のための統合的フレームワーク「CADS（Climate AI Decision System）」を提案する。物理リスク・移行リスク・GHG測定・サプライチェーン等を横断し、エビデンスに基づいたガバナンスと監査可能な設計を重視する。既存研究の体系的なレビューに基づき、信頼できる気候AIのための研究アジェンダを提示する。
English
This paper frames corporate climate risk as a multi-domain decision problem and proposes the Climate AI Decision System (CADS) architecture that integrates AI for evidence-based management. It synthesizes literature on climate finance, ML for climate, and NLP for disclosures, emphasizing governance controls, audit trails, and regulatory classification. The paper provides a research agenda for trustworthy AI-enabled climate-risk management.
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基準の策定や有報での気候関連開示が進んでおり、本論文のCADSアーキテクチャは、企業がAIを用いて開示データの品質管理やリスク評価を行う際の設計指針として有用である。特にScope 3の計測・検証におけるAI活用のガバナンス要件は、実務上の示唆に富む。
In the global GX context
Globally, with ISSB standards and CSRD driving climate disclosure, this paper offers a systematic framework for enterprises to deploy AI in climate-risk decision making. The emphasis on evidence-grounded workflows and assurance-ready audit trails directly addresses regulatory demands for transparent and reliable climate risk management.
👥 読者別の含意
🔬研究者:Provides a comprehensive research agenda and integrative review for AI-enabled climate risk management, identifying key evidence gaps and methodological challenges.
🏢実務担当者:Offers the CADS architecture as a blueprint for designing AI-driven climate risk systems, including governance controls and audit trails for compliance.
🏛政策担当者:Highlights regulatory classification and model-risk controls needed to ensure trustworthy AI in climate risk, informing standard-setting and oversight.
📄 Abstract（原文）
Corporate climate risk has become a multi-domain decision problem rather than a sustainability-reporting exercise. Global enterprises must connect physical hazards, transition pathways, greenhouse-gas measurement, supply-chain exposure, capital allocation, insurance, credit, operations, and disclosure into accountable management decisions. Artificial intelligence (AI) can improve climate-risk decision making by extracting information from unstructured disclosures, fusing geospatial and enterprise data, supporting scenario analysis, detecting emissions anomalies, and enabling evidence-grounded workflows. Yet the empirical literature also shows that climate signals are noisy, disclosure is uneven, emissions estimates are method-dependent, and generative models can produce unsupported claims. This integrative structured review synthesizes evidence from climate finance, corporate climate management, machine learning for climate, climate-related natural language processing, measurement-reporting-verification systems, and emerging AI and sustainability regulation. The paper contributes the Climate AI Decision System (CADS) architecture, an enterprise evidence map, governance controls, and a research agenda for AI-enabled climate-risk management. It argues that trustworthy climate AI requires explicit decision rights, source-grounded evidence, model-risk controls, Scope 3 and measurement-reporting-verification traceability, regulatory classification, human escalation, and assurance-ready audit trails.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.2139/ssrn.6704278first seen 2026-07-09 04:30:55
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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。