Research on Spatial Network Correlation and Influencing Factors of Information Entropy of Carbon Emission Structure in China

Abstract

Based on the dissipative structure theory, the temporal and spatial evolution process of China’s carbon emission structure during the period of 2005–2020 is evaluated by using information entropy. The spatial correlation of information entropy of China’s carbon emission structure is measured by social network analysis , and the spatial correlation characteristics and influencing factors of information entropy of China’s carbon emission structure are discussed. The results show that the following: 1) The spatial network structure has stability and multiple overlapping additives, and the number of spatial relationships increases from 180 in 2005 to 231 in 2020. 2) According to the results of cluster sector model analysis, each province belongs to four different functional sectors respectively. The first is the “net benefit sector”, which is composed of economically developed regions such as Beijing, Shanghai, and Tianjin. The second is the “broker sector”, which includes provinces with strong economic growth vitality, such as Zhejiang, Fujian, and Guangdong. Regarding the third sector, it is the “two-way spillover sector”, which is composed of Henan, Hubei, and other fast-growing provinces in the central region. The next is the “net spillover sector”, which is composed of central and western provinces with rich resources but backward economy, such as Xinjiang, Inner Mongolia, and Shanxi. 3) The empirical results of the QAP model show that geographical adjacency, urban population, energy consumption, and R and D investment have an impact on the spatial correlation of information entropy of China’s carbon emission structure. Moreover, strengthening the spatial network correlation can promote the improvement of the carbon emission structure and be helpful to realize carbon neutrality and low-carbon sustainable development.