Drivers and Multi-Scenario Projections of Life Cycle Carbon Emissions from China’s Construction Industry

Qiangsheng Li, Renfu Jia (), Qianhui Du, Buhan Wang, Anqi Xu, Xiaoxia Zhu and Yi Wei
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Qiangsheng Li: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Renfu Jia: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Qianhui Du: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Buhan Wang: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Anqi Xu: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Xiaoxia Zhu: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China
Yi Wei: School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, China

Sustainability, 2025, vol. 17, issue 9, 1-23

Abstract: Life cycle carbon emissions from the construction industry (CE) have a profound impact on China’s “dual carbon” goals, with significant emissions posing severe challenges to the environment. In this paper, four prediction models were trained and compared, and the optimal model, the Genetic Algorithm Optimized BP Neural Network (GA-BP), was finally selected for multi-scenario prediction of CE. Firstly, this study performs a comprehensive accounting and indicator analysis of CE over its entire life cycle. In addition, this paper further conducts a spatial differentiation analysis of CE. Subsequently, parameter analysis was conducted using an improved STIRPAT model, followed by LMDI factor decomposition based on this model. Finally, the model performance was verified using three evaluation metrics: the coefficient of determination ( R 2 ), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicate that (1) in the carbon emission impact assessment, CE reached a peak of 42.52 t per capita annually and 8.90 t CO 2 /m 2 per unit area; (2) the year-end resident population has the greatest influence on CE, with other related variables also contributing positively; and (3) the GA-BP model outperforms other models, with R 2 increasing from 0.0435 to 0.0981, MAE reducing from 63% to 76%, and MAPE decreasing from 23% to 68%.

Keywords: life cycle carbon emissions; ArcGIS; GA-BP model; STIRPAT-LMDI model; low-carbon transition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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