Integrating Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies to Automate CO2 Emission Calculations and Support Low-Carbon Building Design: A Systematic Literature Review
人工知能（AI）とビル情報モデリング（BIM）技術の統合によるCO2排出量計算の自動化と低炭素建築設計の支援：系統的文献レビュー (AI 翻訳)
Kálita Cristina Araújo, Ana Carolina Fernandes Maciel, Bruno B. F. da Costa
🤖 gxceed AI 要約
日本語
本レビューはPRISMAプロトコルに基づき、BIMとAIの統合によるCO2排出量自動計算が低炭素建築設計を支援できるかを検討した。2021～2025年の文献2567件から85件を抽出し、Core研究（BIM+CO2+AI）とBase研究に分類。60%が炭素定量化を行うが、設計代替案の提案・比較・最適化に活用したのは39%にとどまった。統合の標準化・相互運用性・検証・トレーサビリティの向上が課題。
English
This systematic review (PRISMA-based) examines whether automating CO2 emission calculation with AI in BIM can support low-carbon building design. From 2567 records (2021-2025), 85 studies were classified as Core (BIM+CO2+AI) or Base. 60% quantify carbon, but only 39% use it to propose/compare/optimize design alternatives. Needs standardization, interoperability, validation, traceability, and operational integration.
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
日本では建築物の脱炭素化に向けてBIMと連動したCO2算定の自動化が注目されるが、本レビューは国際的な統合の現状と課題を整理しており、日本建設業界のゼロ・エネルギー・ビル（ZEB）やライフサイクルアセスメント（LCA）実務に示唆を与える。SSBJの情報開示基準策定にも参考となる可能性がある。
In the global GX context
Globally, the AECO sector faces pressure from ISSB/TCFD on embodied and operational carbon disclosure. This review consolidates evidence on how AI+BIM integration for automated carbon calculation can support low-carbon design, highlighting standardization gaps and limited decision-use. Relevant for firms aligning with net-zero building mandates and for disclosure standard-setters.
👥 読者別の含意
🔬研究者:Researchers in building LCA and AI can use this review to identify gaps in BIM+CO2+AI integration and prioritize future work on interoperability and validation.
🏢実務担当者:Building design firms can learn about current tools and limitations for automating carbon calculations, informing decisions on adopting BIM-based carbon analysis software.
🏛政策担当者:Regulators developing minimum carbon performance standards or disclosure rules for buildings can reference the need for standardized, verifiable carbon metrics in BIM workflows.
📄 Abstract（原文）
The decarbonization of the Architecture, Engineering, Construction, and Operation (AECO) sector has increased the need to incorporate carbon metrics into design decision-making. This article presents a Systematic Literature Review (SLR), based on the PRISMA protocol, to investigate whether the automation of CO2 emission calculation combined with artificial intelligence has been used to support lower-impact design decisions in BIM-based building design. Searches were conducted in the Scopus, Web of Science, and ScienceDirect databases, considering articles published between 2021 and 2025, resulting in 2567 records. After duplicate removal and successive screening stages, 85 studies composed the final sample, classified into Core studies (BIM + CO2 + AI) and Base studies (BIM + AI, BIM + CO2, BIM + AI + Sustainability, and AI + CO2). The results indicate the predominance of partial integrations and limited representation of Core studies. Although 60% of the studies quantify carbon, only 39% use this quantification to propose, compare, or optimize design alternatives. The findings suggest that BIM + CO2 + AI integration has potential to support low-carbon building design but still requires greater standardization, interoperability, validation, traceability, and operational integration.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/civileng7020038first seen 2026-06-19 04:55:03
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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。