Climate Intelligence Integration
気候インテリジェンス統合 (AI 翻訳)
Harshal Gavali, Apurva Jangle, Asiya Attar, Pranil Gawande, Anuradha Yenkikar
🤖 gxceed AI 要約
日本語
本論文は、気候変動対策における人工知能（AI）の活用を体系的にレビューし、気候モデリング、説明可能AI、規制コンプライアンスの3分野を統合する気候インテリジェンス統合モデル（CIIM）を提案する。CIIMは、AIモデリングと持続可能性指標を組み合わせたガバナンス統合型社会技術アーキテクチャであり、気候意思決定支援のための包括的な枠組みを提供する。
English
This paper systematically reviews the application of AI in climate change mitigation and adaptation, covering climate modeling, explainable AI, and regulatory compliance. It proposes the Climate Intelligence Integration Model (CIIM), a governance-embedded socio-technological architecture that integrates AI modeling with sustainability metrics for climate decision support.
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や統合報告書への対応が進む中、本論文のCIIMモデルは企業の気候ガバナンスと開示実務にAIを組み込む際の理論的基盤となる可能性がある。特に規制コンプライアンスとAIの統合は、日本の金融庁や経産省の政策方向性とも合致する。
In the global GX context
With the rise of ISSB and CSRD globally, this paper's integrated governance framework offers a blueprint for embedding AI into climate disclosure and transition planning. The emphasis on regulatory compliance and explainable AI addresses key challenges in auditable climate reporting and accountability.
👥 読者別の含意
🔬研究者:Provides a structured literature review and a new integrated model (CIIM) that can guide future research on AI for climate governance.
🏢実務担当者:Offers a framework for corporate sustainability teams to design AI-driven climate decision support systems that align with regulatory requirements.
🏛政策担当者:Highlights the need for governance-embedded AI systems in climate regulation; the CIIM model could inform policy on AI use in mandatory climate disclosures.
📄 Abstract（原文）
Artificial intelligence (AI) is becoming acknowledged as an engine of climate mitigation and adaptation plans. As the magnitude and frequency of climate risks increase, classical approaches to modelling climate cannot deal with the increasing high-dimensionality complexity of environmental data or provide real-time decision support in respect to climate-regulatory framework.It explores the application of advanced machine learning architectures, carbon intelligence systems, and governance frameworks to climate decision infrastructure. This chapter performs a systematic review of the literature under three subject headings: climate modelling, explainable AI and regulatory compliance, which helps (1) identifying significant gaps in accountability (2) the embedding sustainable development goals into AI-driven climate systems. This chapter introduces the Climate Intelligence Integration Model (CIIM), governance-embedded socio-technological architecture that incorporates AI modeling with regulatory compliance and sustainability metrics into a comprehensive climate governance approach.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4018/979-8-2600-2020-3.ch007first seen 2026-06-07 04:45:09
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