Integrated Machine Learning-Based Material Quantity Estimation and Carbon Footprint Assessment for Circular Construction

The construction sector is a major consumer of raw materials and a significant source of greenhouse gas emissions, necessitating data-driven approaches to support circular economy implementation and sustainable project management. This study develops an integrated framework combining machine learning-based material stock prediction, carbon footprint assessment, and Environmental, Social, and Governance (ESG) performance evaluation for construction projects. A dataset of 128 residential buildings was compiled from official use-permit documentation. After dimensionality reduction using variance filtering and Spearman correlation analysis, 25 regression algorithms were evaluated to estimate quantities of concrete, reinforcement, and brick products. The K-Nearest Neighbor (KNN) Regressor achieved the best predictive performance, with mean absolute percentage errors of 10.64% for concrete, 10.23% for reinforcement, and 16.05% for brick products. Predicted material quantities were used to calculate CO2 emissions across materialization, demolition, and disposal phases under linear and circular scenarios. The results indicate that circular economy implementation significantly reduces total emissions, particularly for concrete, with reductions of up to 97% under idealized full-substitution conditions, representing an upper-bound estimate. ESG assessment using the Delphi method identified environmental indicators as the most significant sustainability dimension. The proposed framework enables early-stage emission estimation and supports informed decision-making toward low-carbon and resource-efficient construction practices.