부산대학교 도서관

To facilitate rational regional emission targets and enhance nationwide emission reduction efforts, this study systematically examines carbon emission spatial correlations. Using social network analysis (SNA), we investigated the China Carbon Emission Spatial Correlation Network (CCESCN) from 2011 to 2020. The network's structure gradually evolved with strong stability. Spatial associations loosened, and correlations reduced over time. Jiangsu and Shandong had strong carbon spillover effects, while Shanghai, Zhejiang, Beijing, and Tianjin received emissions from other regions. Jiangsu, Shanghai, Shandong, Anhui, and Zhejiang played core roles, while Jiangsu, Shanghai, Guangdong, and Beijing acted as intermediaries. Different levels of regions are interacting more and regional integration is increasing. Regions were grouped into four functionally different blocks. Industry proportion and urbanization influenced sending relationships, while openness, industry proportion, energy efficiency, and urbanization affected receiving relationships. Geographic, information, transportation, and innovation distances also played roles in CCESCN relationships.