[Spatial Heterogeneity of Carbon Emissions from Construction Projects Based on XGBoost-SHAP].

Huan Jing Ke Xue

Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang'an University, Xi'an 710064, China.

Published: July 2025


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Article Abstract

To make the carbon emission reduction of highway construction more effective, this study focuses on the spatial heterogeneity of carbon emission in the process of highway construction. Based on the seven key indicators of carbon emission impacts of the type of structure, bridge-to-tunnel ratio, design gradient, route length, fill volume, excavation volume, and cement consumption screened out from the 40 segment samples of highway project A in Guangdong Province, we trained and validated the XGBoost carbon emission prediction model and constructed the SHAP algorithm to explain the impacts, total feature contributions, and feature interaction effects of the features of these 40 road segments. The SHAP algorithm was constructed to explain the spatial heterogeneity of carbon emissions of these 40 road sections, and the influence of road section features on carbon emissions, total feature contribution, and feature interaction effect were investigated. The results showed that the increase in cement consumption contributed the most to the nonlinear increase of carbon emission, and the route length, excavation volume, and bridge-to-tunnel ratio also contributed significantly to the carbon emission. The analysis of hot and cold spots revealed that the carbon emission tended to be higher in the road sections with a gradient higher than 2.5% and with complex topography, and there exists an agglomeration effect. The XGBoost-SHAP model explained the spatial distribution of carbon emission more clearly than the geographically weighted regression model (GWR); the model captured the characteristics and their influencing factors and also the spatial distribution and effects on carbon emission. The XGBoost-SHAP model could explain the spatial distribution of carbon emissions and its influencing factors more clearly than GWR and performed better in capturing the key carbon sources and understanding the spatial distribution of carbon emissions. Based on the above findings, this study proposes a comprehensive strategy for carbon emission reduction in highway construction and maintenance to promote the sustainable development of highway construction.

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http://dx.doi.org/10.13227/j.hjkx.202406040DOI Listing

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