不確実性下のエネルギー計画と脱炭素化に向けたドメイン知識活用AIの応用
Employing Domain-Informed AI for Energy Planning and Decarbonization under Uncertainty (原題)
Renee Obringer, Mir Matin, Kaveh Madani
🤖 gxceed AI 要約
日本語
本政策ブリーフは、気候変動下で再生可能エネルギー拡大と電力網の安定化を両立するため、ドメイン知識を組み込んだAIが長期インフラ計画をどう補完できるかを論じる。歴史的気象データ依存の従来計画の限界を指摘し、気候科学とAIを統合した適応的規制やモデル透明性、AIインフラの再エネ稼働などの政策提言を示す。
English
This policy brief examines how domain-informed AI can complement traditional long-term energy infrastructure planning under climate uncertainty. It argues that stationary historical weather data and rigid models expose public investments to physical and fiscal risk, and recommends adaptive regulation, model transparency, and renewable-powered AI infrastructure to safeguard decarbonization.
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
日本のGX推進では再エネ大量導入と系統安定化、データセンター電力需要増への対応が急務であり、AIを活用した適応的計画手法はSSBJ開示や企業の気候リスク管理にも示唆を与える。
In the global GX context
Amid global TCFD/ISSB disclosure and transition finance, this brief links AI governance to physical climate risk and stranded-asset avoidance, offering a framework for integrating forward-looking climate data into infrastructure planning.
👥 読者別の含意
🔬研究者:AIと気候科学の融合による不確実性下のエネルギー計画手法に関心を持つ研究者に有用。
🏢実務担当者:再エネ調達や気候リスク評価を進める企業のサステナビリティ担当者に、適応的計画とAI活用の視点を提供。
🏛政策担当者:電力インフラ規制やAIガバナンス、脱炭素政策の設計に携わる政策担当者に具体的提言を提示。
📄 Abstract（原文）
Renewable energy expansion is crucial for climate change mitigation, but electricity grids face mounting stress from surging power demand—driven by electric vehicles, artificial intelligence, and data centers—and heightened susceptibility to changing climate conditions. Traditional long-term infrastructure planning relies heavily on stationary historical weather data and inflexible models, exposing public investments to significant physical and fiscal risks. This policy brief explores how domain-informed artificial intelligence can serve as a powerful complement to existing tools, providing forward-looking insights into climate impacts on renewable integration. By synthesizing peer-reviewed climate science with applied AI, the framework delivers actionable safeguards for capacity planning. Key recommendations include mandating adaptive regulatory frameworks, institutionalizing interdisciplinary expertise across interdependent infrastructure sectors, integrating domain-informed climate emulators into workflows, enforcing strict model transparency to ensure operational safety, and mandating that high-power AI infrastructure operate primarily on verifiable renewable energy. Implementing these measures secures AI governance, protects physical assets from stranded investments, and accelerates long-term decarbonization targets.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.53328/inr26rro001first seen 2026-09-12 06:09:00 · last seen 2026-09-15 05:30:05
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