The Rise of AI-Enabled Startups in Creating a Low-Carbon Built Environment
低炭素建築環境を創出するAI対応スタートアップの台頭 (AI 翻訳)
F. Pacheco-Torgal
🤖 gxceed AI 要約
日本語
本論文は、人工知能（AI）が建築環境の脱炭素化と気候レジリエンスに果たす役割を体系的にレビューする。設計段階での生成デザイン、運用時の予知保全やデジタルツイン、廃棄物管理など、建物ライフサイクル全体でのAI応用をマッピングし、エネルギー効率向上、材料循環、レジリエンス強化の可能性を示す。AIの効果はデータ品質、相互運用性、規制枠組み、人材育成に依存し、循環経済戦略と連携することで最大化されると論じる。
English
This paper systematically reviews the role of AI in decarbonizing and enhancing resilience of the built environment. It maps AI applications across the building lifecycle—including generative design, predictive maintenance, digital twins, and waste analytics—showing how AI can reduce operational energy, optimize material use, and support reuse/recycling. The paper argues that AI's effectiveness depends on data, interoperability, regulation, and workforce, and is maximized when integrated with circular economy strategies.
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
日本の建設・不動産セクターは、2025年の改正省エネ法や2030年目標に向けたZEB普及など、厳しい脱炭素規制に直面している。本論文のAI活用事例は、日本の中小建設事業者や不動産テック企業にとって即応用可能なツールを提示し、SSBJ開示におけるスコープ3（建設資材）の削減にも寄与しうる。
In the global GX context
Globally, buildings account for nearly 40% of energy-related CO2 emissions, and AI-driven efficiency, material optimization, and resilience are increasingly recognized in TCFD/ISSB climate risk assessments. This paper provides a structured synthesis that informs corporate disclosure strategies and policy alignment under CSRD and SEC rules.
👥 読者別の含意
🔬研究者:Provides a structured review of AI applications across the building lifecycle, identifying research gaps in data interoperability and regulatory alignment.
🏢実務担当者:Offers concrete AI tools (generative design, predictive maintenance, waste analytics) for reducing operational energy and embodied carbon in buildings.
🏛政策担当者:Highlights need for regulatory frameworks that support AI integration, data sharing, and circular economy incentives in building codes.
📄 Abstract（原文）
The accelerating climate emergency places the built environment under increasing pressure as both a major source of greenhouse gas emissions and a system highly vulnerable to climate impacts. Buildings contribute substantially to global operational energy use and embodied carbon, while much of the existing stock remains poorly adapted to changing climatic conditions. This paper examines the role of artificial intelligence (AI) in improving energy efficiency, enabling circular material flows, and enhancing resilience across the building lifecycle. Based on a structured synthesis of recent peer-reviewed literature, institutional reports, and documented case examples, the study maps AI applications in design, construction, operation, and end-of-life stages, including generative design, predictive maintenance, digital twins, and construction and demolition waste analytics. The analysis shows how AI can reduce operational energy demand, optimize material use, and support reuse and recycling strategies, while enabling new software-driven business models in the building sector. The paper argues that AI’s effectiveness depends on data availability, interoperability, regulatory alignment, and workforce capabilities, and that its benefits are maximized when integrated with circular economy strategies and supportive policy and financial frameworks. This integrated perspective highlights pathways for reducing emissions and improving the resilience of the built environment under climate stress.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/buildings16030632first seen 2026-06-29 06:53:00
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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。