Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method. (15th October 2022) - Huo, Tengfei; Cao, Ruijiao; Xia, Nini; Hu, Xuan; Cai, Weiguang; Liu, Bingsheng
Abstract: Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions. Graphical abstract: Image 1 Highlights: Adopting the SNA method to explore the spatial network structure of China's building carbon emissions. Using the QAP method to determine the factors affecting the spatial correlation network of building carbon emissions. The relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang occupy a central position in the network. Spatial adjacency, economic development, energy intensity and industrial structure significantly affected spatial network.