AUTHOR=Ma Xin , Guo Fuli , Wang Wenbin , Gao Yuxin 

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

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.871332

DOI=10.3389/fenvs.2022.871332

ISSN=2296-665X

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 (SNA), and the spatial correlation characteristics and influencing factors of information entropy of China's carbon emission structure are discussed. The results show that: (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 QAP model show that geographical adjacency, urban population, energy consumption and R & 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 carbon emission structure and helpful to realize carbon neutrality and low-carbon sustainable development.