Green Intelligence Digital Twins: Climate-Resilient, Carbon-Aware Infrastructure
グリーンインテリジェンスデジタルツイン：気候レジリエントでカーボンアウェアなインフラ (AI 翻訳)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
本書は、気候変動と炭素制約下でのインフラ運用を支える「グリーンインテリジェンスデジタルツイン」の枠組みを提示する。不確実性の定量化、因果推論、信頼性のある機械学習、ライフサイクル工学を統合し、リスク登録や炭素会計プロトコル、監査証跡などのガバナンス成果を生み出す設計手法を提案。南アジア、欧州、アフリカ、米州での適用を想定している。
English
This book proposes a framework for 'Green Intelligence Digital Twins' - computational models that integrate uncertainty quantification, causal inference, trustworthy machine learning, and lifecycle engineering to support climate-resilient, carbon-aware infrastructure. It emphasizes evidential and governance architecture producing risk registers, carbon accounting protocols, and audit trails. Applications span South Asia, Europe, Africa, and the Americas.
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基準や有報での気候関連開示が進む中、本フレームワークはインフラ分野でのカーボンアカウンティングとレジリエンス評価を統合する設計思想を提供する。特に、監査証跡やガバナンス出力を重視する点は、日本の実務家にとって参考になる。
In the global GX context
This framework aligns with global trends such as ISSB standards and TCFD recommendations by providing a structured approach to carbon accounting and resilience for infrastructure assets. It offers a governance-ready model relevant to jurisdictions implementing climate disclosure requirements.
👥 読者別の含意
🔬研究者:Provides a comprehensive research design for integrating AI, causal inference, and lifecycle engineering into climate-resilient infrastructure modeling.
🏢実務担当者:Offers practical guidance on building digital twins that produce audit-ready carbon accounting and risk registers for infrastructure assets.
🏛政策担当者:Highlights how digital twins can support policy evaluation and institutional accountability for climate-resilient infrastructure investment.
📄 Abstract（原文）
Abstract Green intelligence digital twins are computational counterparts of physical infrastructure that bind observation, mechanistic knowledge, and decision rules into a continuously updated model of assets and systems operating under climate volatility and carbon constraints. This manuscript argues that the central challenge is not merely to predict failure or optimise performance, but to establish an evidential and governance architecture in which resilience claims remain defensible across time, jurisdictions, and shifting socio-technical conditions. Digital twins are treated as research designs: they specify assumptions about physics, behaviour, and measurement; they define uncertainty budgets and evaluation logic; and they yield governance-ready outputs such as risk registers, carbon accounting protocols, audit trails, and incident learning loops. Across five chapters, the book moves from foundations in uncertainty and research design to statistical explanation and causal inference, then to trustworthy machine learning for prediction and anomaly discovery, followed by scalable engineering and reproducible lifecycle practices, and concluding with sectoral applications and policy integration. The result is a publisher-ready framework for researchers, practitioners, and policymakers seeking climate-resilient, carbon-aware infrastructure strategies that are technically rigorous and institutionally accountable across South Asia, Europe, Africa, and the Americas. Keywords digital twins, green intelligence, climate resilience, carbon accounting, infrastructure risk, uncertainty quantification, causal inference, anomaly detection, sensor fusion, trustworthy machine learning, lifecycle engineering, reproducibility, MLOps, governance, policy evaluation, decision support, systems modeling
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.62311/nesx/rb-978-81-997377-7-8first seen 2026-06-29 04:55:00
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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。