LAUSR

Abstract The modified gravity model is combined with Social Network Analysis (SNA) in this work and used to investigate 2004-2017 provincial Industrial Carbon Emissions (ICE) of China. Specifically, by constructing spatial correlation network, the main contribution is here finding out its influencing factors with the help of Quality Assurance Procedure (QAP) of SNA. Results of case study are as follows. (1) The complex spatial correlations exist in the stable overall network structure for ICE of China’s 30 provinces. (2) The obtained spatial correlation network (2017) in which Guangdong, Shandong, Henan, Hubei and Xinjiang are central can be divided into four regional blocks (from which spatial correlation effect is exhibited for entire 30 selected provinces and spatial spillover effect is revealed for several resourceful western provinces). (3) Spatial adjacency relations, innovation intensity and degree of openness (which can significantly enhance the degree of spatial correlation) are the prominent influencing factors for the proposed spatial correlation network. The major achievement is that the spatial correlations of carbon emissions of industry of China are contribute to creative construction of a collaborative carbon emission reduction mechanism for policy making and provincial green development of the developing countries.