Spatial correlation network of carbon emissions in city clusters from the perspective of social network analysis: Empirical data from China’s Jing-Jin-Ji region

The Jing-Jin-Ji city cluster represents northern China’s largest urban concentration and a major industrial center, where carbon emission reduction presents significant challenges. Using social network analysis (SNA), this study examines the spatial correlation structure of carbon emissions across the region from 2013 to 2022. Results show a network with modest but growing density, strong connectivity, notable resilience and efficiency, and a pronounced hierarchical structure. The spatial correlation follows a “core-periphery” pattern, with Beijing, Tianjin, Shijiazhuang, and Tangshan forming the core. Blockmodel analysis further identifies four functional blocks: net beneficial, net spillover, bidirectional spillover, and broker. These findings clarify the spatial dynamics of regional carbon emissions and can help policymakers design more targeted and effective collaborative reduction strategies by accounting for network structure and the specific roles of individual cities. • SNA method applied to Jing-Jin-Ji city cluster carbon emissions. • Spatial network density is low but showed an increasing trend. • Carbon networks exhibit a clear "core-periphery" structure. • Blockmodel identifies four distinct functional carbon sectors. • COVID-19 pandemic caused a temporary decline in network density.