A study on the measurement and influencing factors of carbon emissions in China's construction sector

• Published in: Building and environment 2023-02, Vol.229, p.109912, Article 109912
• Main Authors: Sun, Yuhao, Hao, Shengyue, Long, Xiaofeng
• Format: Article
• Language: English
• Subjects: Carbon emissions; China; Construction sector; Influencing factors
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2022.109912

China plays a significant role in global sustainable development. The construction sector is not solely the backbone of China's economy, but also one of the most carbon-emitting industries. Consequently, it is exceedingly urgent to clarify the current status of carbon emissions of the construction sector (CECS) and seek useful strategies to reduce them. Based on the panel data from 30 Chinese provinces from 2005 to 2019, this study first used the Theil index, GIS techniques and the Moran's I index to depict the spatiotemporal evolution, and then utilized the Spatial Durbin Model (SDM) to analyze the effects of its influencing factors. The findings demonstrated that: (1) annual CECS generally grew at an average annual rate of 9.70%, but the growth rate slowed yearly, while carbon intensity declined at an average rate of 4.65% per year; (2) the CECS exhibited regional heterogeneity in both provinces and regions; (3) there was a significant positive spatial autocorrelation of CECS since 2008, but the spatial agglomeration it embodied diminished after 2016; (4) population size, economic level, technological innovation, government support, foreign trade, environmental regulation, and financial development may influence CECS, but different factors' direct and indirect effects differ. These results could provide empirical scientific evidence for local governments to formulate appropriate emission-reduction policies for the construction sector. •We calculate the provincial CECS from 2005 to 2019.•The distribution of CECS shows remarkable spatial disparities.•The spatial autocorrelation of CECS has been significant since 2008.•We reveal the direct and indirect effects of different influencing factors.