Citation :

Riza Suwondo, Religiana Hendarti, Militia Keintjem, Chee Fui Wong, "Explainable Machine Learning for Policy-Driven Carbon Emission Reduction in Building Projects," International Journal of Civil Engineering, vol. 13, no. 4, pp. 134-143, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I4P110

Abstract

The construction industry is a major contributor to global carbon emissions; therefore, finding a way to reduce emissions to meet climate-mitigation goals is vital. The challenges that impede emissions reductions are numerous and reliant on implemented policies. This study proposes an explainable machine learning framework to predict and interpret carbon emission reduction performance in building projects by integrating project characteristics, policy intervention indicators, certification levels, lifecycle information, and emission-related variables. Three machine learning models were created-Ridge Regression, Random Forest, and Gradient Boosting-and tested on a split of the data to assess their performance. To explain emissions reductions, the models were evaluated for predictive performance using a set of statistical measures and the explainable machine learning metric, Shapley Additive Explanations (SHAP). The models demonstrated robust predictive performance, with a coefficient of determination of over 0.81 for each model, and all models had similar performance despite using different statistical techniques. Shapley values attributed most of the focus to high-level green certifications and policy measures, whereas project size had little influence on reducing emissions. The results reinforce the idea that policies have the largest impact on emissions reductions in the building sector. The suggested explainable framework provides clarity and relevant insights into policies that aid evidence-based decision-making and strategic planning for decarbonising the building sector.

Keywords

Carbon emission reduction, Explainable machine learning, Building sector decarbonisation, Policy intervention analysis, Shapley Additive Explanations (SHAP).

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