TY - JOUR
T1 - Spatiotemporal variations in the carbon emissions in China’s provincial construction industries considering structural paths
AU - Chen, Jindao
AU - Bao, Zhikang
AU - Yan, Hang
AU - Li, Shengping
AU - Peng, Xu
AU - You, Jianmin
AU - Zhang, Lihai
PY - 2025/7/12
Y1 - 2025/7/12
N2 - The construction industry contributes considerably to China’s carbon emissions. To reduce the construction carbon emissions (CCEs) in China, understanding the spatiotemporal variations in provincial CCEs becomes essential. However, existing literature fails to consider the variations in emission distribution especially by structural paths. Thus, this study aims to comprehensively explore the spatiotemporal variations in China’s provincial CCEs from 2012 to 2017 using the multi-regional input-output analysis, structural path analysis, and Moran index. The results show that Jiangsu, Hebei, and Zhejiang were among the largest five CCEs in 2012 and 2017, whereas Fujian and Shanghai were among the lowest five intensities. Nonlocal contributions constituted over 30% in most regions with increasing proportions during the period. The nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) dominated the provincial CCEs. Local c13, c14, and c24 contributed substantially in most regions, while Hebei c14 and Henan c13 were the important nonlocal regional industries in numerous regions. The consumption of local c13 and c14 by the construction industry (c27), namely “Local c13→c27” and “local c14→c27”, were generally among the top ten structural paths of CCEs in most regions, while “Hebei c14→c27” and “Henan c13→c27” were the critical nonlocal paths in many regions. The intensities of provincial CCEs showed a significant positive global spatial autocorrelation in 2012 and 2017, where the central and western regions generally belonged to the High-High cluster. The findings could help policymakers appropriately implement region-specific measures for mitigating China’s CCEs.
AB - The construction industry contributes considerably to China’s carbon emissions. To reduce the construction carbon emissions (CCEs) in China, understanding the spatiotemporal variations in provincial CCEs becomes essential. However, existing literature fails to consider the variations in emission distribution especially by structural paths. Thus, this study aims to comprehensively explore the spatiotemporal variations in China’s provincial CCEs from 2012 to 2017 using the multi-regional input-output analysis, structural path analysis, and Moran index. The results show that Jiangsu, Hebei, and Zhejiang were among the largest five CCEs in 2012 and 2017, whereas Fujian and Shanghai were among the lowest five intensities. Nonlocal contributions constituted over 30% in most regions with increasing proportions during the period. The nonmetallic mineral products industry (c13), metal smelting and pressing industry (c14), and electricity industry (c24) dominated the provincial CCEs. Local c13, c14, and c24 contributed substantially in most regions, while Hebei c14 and Henan c13 were the important nonlocal regional industries in numerous regions. The consumption of local c13 and c14 by the construction industry (c27), namely “Local c13→c27” and “local c14→c27”, were generally among the top ten structural paths of CCEs in most regions, while “Hebei c14→c27” and “Henan c13→c27” were the critical nonlocal paths in many regions. The intensities of provincial CCEs showed a significant positive global spatial autocorrelation in 2012 and 2017, where the central and western regions generally belonged to the High-High cluster. The findings could help policymakers appropriately implement region-specific measures for mitigating China’s CCEs.
KW - Carbon emissions
KW - Construction industry
KW - Spatiotemporal heterogeneity
KW - Input-output analysis
KW - Sustainable construction
UR - https://www.scopus.com/pages/publications/105010639263
U2 - 10.1007/s10668-025-06524-6
DO - 10.1007/s10668-025-06524-6
M3 - Article
SN - 1387-585X
JO - Environment, Development and Sustainability
JF - Environment, Development and Sustainability
ER -