惑星レジリエンスのための地球システムAI、クライメートテックモデリング、持続可能エネルギーインテリジェンス
Earth-System AI, ClimateTech Modelling and Sustainable Energy Intelligence for Planetary Resilience (原題)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
地球システムAI、気候テックモデリング、持続可能エネルギーインテリジェンスを統合する研究アーキテクチャを提示。不確実性を考慮した研究設計、物理誘導ML、MLOps、セクター戦略（エネルギー、都市、金融等）を扱い、ドメインシフトやデータ主権、制度学習に注目。地域適応可能な意思決定枠組みを提供。
English
This monograph presents an integrated research architecture for planetary resilience, combining Earth-system AI, ClimateTech modeling, and sustainable energy intelligence. It covers uncertainty-aware design, physics-guided ML, MLOps, and sectoral strategies for energy, cities, and finance, emphasizing domain shift, data sovereignty, and institutional learning. Offers regionally adaptable decision frameworks.
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、気候変動適応計画やGX推進に資するAI活用が期待される。本枠組みは、自治体や企業のレジリエンス評価、エネルギーシステム最適化への応用可能性を示唆。SSBJ開示における気候リスク分析にも接続可能。
In the global GX context
Globally, this framework aligns with ISSB/TCFD climate risk disclosure needs by providing AI-driven analytical methods for resilience assessment. It supports transition finance and climate adaptation strategies, offering a holistic approach for policymakers and practitioners.
👥 読者別の含意
🔬研究者:Provides an integrated framework for interdisciplinary climate-AI research, highlighting methodological gaps and future directions.
🏢実務担当者:Offers operational guidance for building ClimateTech systems and energy optimization, useful for corporate sustainability teams.
🏛政策担当者:Informs governance of long-horizon transitions and climate adaptation, relevant for national and regional policy design.
📄 Abstract（原文）
Abstract: Earth-system artificial intelligence, ClimateTech modelling and sustainable energy intelligence are increasingly interdependent fields, yet they are often developed as separate technical programmes. This monograph presents an integrated research architecture for planetary resilience that connects Earth-system observation, causal explanation, predictive learning, scalable data engineering, energy-system optimisation and public governance. It treats resilience not as a single outcome but as the capacity of coupled ecological, infrastructural, economic and institutional systems to anticipate disruption, absorb stress, adapt operating rules and transform development pathways. Across five chapters, the manuscript establishes foundations for uncertainty-aware research design; develops statistical and causal approaches for spatiotemporal climate evidence; advances physics-guided and trustworthy machine learning; specifies reproducible big-data and MLOps lifecycles; and translates analytical capability into sectoral strategies for energy, cities, food systems, ecosystems, industry and finance. Mathematical formulations, evaluation metrics, workflow protocols, governance models and regionally adaptable decision frameworks are embedded throughout. Particular attention is given to model validity under domain shift, distributional consequences, data sovereignty, cyber-physical reliability and institutional learning. The resulting framework supports researchers designing interdisciplinary programmes, practitioners building operational ClimateTech systems, and policymakers governing long-horizon transitions across South Asia, Europe, Africa and the Americas. Keywords Earth-system AI, ClimateTech, planetary resilience, sustainable energy intelligence, climate modelling, causal inference, spatiotemporal statistics, physics-guided machine learning, uncertainty quantification, domain generalization, trustworthy AI, climate data engineering, geospatial analytics, digital twins, MLOps, renewable energy systems, grid flexibility, climate adaptation, transition governance, climate risk, policy analytics, data sovereignty, carbon-aware computing, resilience metrics
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62311/nesx/rb4jy-978-81-688921-6-3first seen 2026-08-24 04:50:43
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