Project-Level Embodied Carbon Prediction Across Building Design Stages Using a Machine Learning Framework

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.