Project-Level Embodied Carbon Prediction Across Building Design Stages Using a Machine Learning Framework
機械学習フレームワークを用いた設計段階別のプロジェクトレベル体化炭素予測 (AI 翻訳)
Zihang Wang, Ling Zhang, Mengmeng Pu
🤖 gxceed AI 要約
日本語
本研究は、78プロジェクト・426棟のデータを用いて、設計段階ごとに建築プロジェクトの体化炭素排出量を予測する機械学習フレームワークを構築した。残差補正付き重み付きアンサンブルモデルが最高精度（R2=0.949）を示し、SHAP分析により主要因が設計段階で変化することが明らかになった。低炭素設計の意思決定を定量的に支援する手法である。
English
This study develops a machine learning framework to predict project-level embodied carbon emissions in buildings across design stages, using data from 78 projects (426 buildings). The residual-corrected weighted ensemble model achieved the best accuracy (R2=0.949) at the construction drawing stage. SHAP analysis revealed a stage-dependent shift in key drivers, from area indicators to material quantities. The framework provides interpretable, stage-specific quantitative support for low-carbon building design.
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政策が進む中、体化炭素の算定・予測は重要な課題である。設計初期段階からの簡易予測は、省エネ法やGX関連の排出削減計画、またサプライチェーン排出量の算定にも貢献できる。
In the global GX context
Globally, embodied carbon is gaining attention in building standards and ESG disclosure frameworks. This ML approach enables early-stage prediction, supporting design decisions and carbon accounting in line with ISSB/TCFD and construction sector decarbonization goals.
👥 読者別の含意
🔬研究者:設計段階に応じた体化炭素予測の精度向上と、解釈可能なAI手法（SHAP）を適用した事例として活用できる。
🏢実務担当者:設計・建設企業のサステナビリティ担当者は、初期設計段階での簡易炭素予測により低炭素オプションの比較や顧客への排出報告に活用できる。
🏛政策担当者:建築セクターの体化炭素算定を迅速化する手法として、規制・基準策定に参考にできる。
📄 Abstract（原文）
Rapid and reliable prediction of embodied carbon emissions is essential for supporting sustainable design decision-making and reducing the environmental impacts of building engineering projects. However, existing studies have mainly focused on single buildings, with limited attention to project-level prediction and variations in information availability across design stages. To address this gap, this study developed a machine learning framework for project-level embodied carbon prediction based on a dataset of 78 projects involving 426 individual buildings. Using project attributes, scale indicators, structural characteristics, material quantities, and construction-related information, nine machine learning models were developed for the schematic design stage and the construction drawing design stage. Two residual-corrected weighted ensemble models were further introduced to improve predictive performance. The results show that the Extra Trees–KNN residual-corrected weighted ensemble model achieved the best performance at the construction drawing design stage, with a test-set R2 of 0.949. SHAP analysis further revealed a stage-dependent shift in dominant drivers: gross floor area and land area dominated at the schematic design stage, whereas concrete and reinforcement quantities became the leading predictors at the construction drawing design stage. The proposed framework provides interpretable and stage-specific quantitative support for low-carbon design decision-making, thereby facilitating embodied carbon reduction and the transition toward a more sustainable built environment.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18157723first seen 2026-08-02 06:25:32
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