Low-Carbon Construction Strategies through BIM-LCA Integration: A Multi-Case Evaluation across Building Types
BIM-LCA統合による低炭素建設戦略：建物タイプ横断の複数ケース評価 (AI 翻訳)
Aydın Oğuz, Osman Hansu
🤖 gxceed AI 要約
日本語
本研究はBIMとLCAの統合フレームワークを3種類の建物（商業オフィス、住宅高層、産業倉庫）に適用し、最大30%の体化炭素削減と36%の運用炭素削減を達成した。AI/MLを将来の自動化の鍵と位置づけ、ソフトウェア相互運用性や地域LCAデータ不足などの導入障壁を指摘。政策提言として地域LCAデータベース整備と財政的インセンティブを提案している。
English
This study applies an integrated BIM-LCA framework to three building types—commercial offices, residential high-rises, and industrial warehouses—achieving up to 30% embodied carbon reductions and 36% operational carbon savings. It identifies AI/ML as key enablers for future automation and highlights adoption barriers such as software interoperability and lack of regional LCA datasets. Policy recommendations include developing localized LCA databases and financial incentives.
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
日本でも建設分野の脱炭素が急務であり、BIM-LCA統合はSSBJの開示要件にも応える可能性がある。ただし本論文は特定地域に焦点を当てていないため、日本の実務に直接適用するには国内データベースの整備が課題。
In the global GX context
Globally, this paper demonstrates scalable carbon reductions through BIM-LCA integration, aligning with net-zero targets. It identifies key barriers like software interoperability and lack of regional LCA datasets, which are relevant for ISSB and other disclosure frameworks.
👥 読者別の含意
🔬研究者:Provides empirical evidence of BIM-LCA effectiveness across building types, with future AI integration potential.
🏢実務担当者:Offers a framework for reducing embodied and operational carbon in construction projects, with policy recommendations for incentives and localized databases.
🏛政策担当者:Highlights the need for localized LCA databases and financial incentives to accelerate BIM-LCA adoption in construction.
📄 Abstract（原文）
The construction industry accounts for approximately 31% of global carbon emissions, underscoring the need for integrated digital strategies to reduce environmental impacts. This study investigates the potential of Building Information Modeling (BIM) and Life Cycle Assessment (LCA) integration across three distinct building types—commercial offices, residential high-rises, and industrial warehouses—spanning diverse climatic contexts. By embedding real-time environmental feedback into early design processes, the BIM-LCA framework achieved embodied carbon reductions of up to 30% and operational carbon savings of up to 36%, outperforming conventional project workflows. The study highlights the scalability of this method while identifying critical adoption barriers, including software interoperability and the lack of region-specific LCA datasets, particularly for small and medium-sized enterprises (SMEs). Importantly, it introduces artificial intelligence (AI) and machine learning as key enablers for automating material selection and optimizing energy performance, offering a pathway toward adaptive, low-carbon design automation. Policy recommendations include the development of localized LCA databases and financial incentives for BIM-LCA adoption. The findings contribute to advancing net-zero targets and align with the Paris Agreement and SDG 13. Future research should focus on AI-integrated BIM-LCA platforms to enhance predictive modeling and real-time decision-making across the building lifecycle.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.31466/kfbd.1622286first seen 2026-06-29 06:26:33
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