Identifying spatial relations of industrial carbon emissions among provinces of China: evidence from unsupervised clustering algorithms.

Reducing the total carbon emissions of modern industry is of great significance for China to achieve the carbon peak mission. The MD-SNA spatial correlation measure methodology was innovatively proposed in this paper, which was based on the clustering algorithm of similarity measure. Furthermore, the social network analysis (SNA) method was incorporated to explore the spatial relationship of provincial industrial carbon emissions. The GINI coefficient, Theil index (GE0), and mean of logarithmic deviation (GE1) were used to measure the regional differences of China's industrial carbon emissions. More specifically, we adopted a combined tactic of spatial difference and spatial correlation frameworks. The primary objective of the proposed methodology is to empirically investigate the structural characteristics and spatial relations of different provinces. The results of the case study are as follows. First, the regional industrial carbon emission intensity was unbalanced, among which energy-rich provinces and eastern developed provinces were relatively strong. Second, Beijing, Shandong, Shaanxi, Henan, Sichuan, and Xinjiang were located at the center of the spatial network of industrial carbon emissions. Third, our work clarified the node attributes and different functions of provinces. More than half of the core provinces belonged to the primary beneficial block, which was in the central position of spatial correlation network. The conclusion can help policymakers clarify the overall industrial sector spatial pattern and provinces' roles and functions.