Clarifying the spatial correlation network (SCN) evolution characteristics and influencing factors of industrial carbon emission efficiency
Clarifying the spatial correlation network (SCN) evolution characteristics and influencing factors of industrial carbon emission efficiency (ICEE) is of great significance for improving ICEE, achieving the ''dual carbon policy'' goals, and promoting coordinated regional development. This research examines 30 Chinese provinces and cities, utilizing Social Network Analysis (SNA) to explore the spatial network evolution of industrial carbon emission efficiency (CICEE) between 2011 and 2022.The QAP model explores the factors driving network formation. The findings are as follows:1. CICEE shows an overall upward trend, with high-value regions mainly concentrated in coastal areas, displaying obvious SCN characteristics. 2. The network's node roles have shifted notably, transitioning from bidirectional outflow plates (BOP) to main inflow (IP) and outflow plates (OP). The CICEE network's spatial correlation has strengthened, with connections increasing from 235 to 244 and network density rising from 0.2701 to 0.2805. The network structure is gradually evolving toward a stable state, although hierarchical patterns remain prominent. Regions such as Beijing, Shanghai, and Jiangsu have consistently occupied core positions within the network, and spatial correlation relationships primarily exist among major regions.3. The formation of the SCN of CICEE is significantly influenced by factors such as industrial structure, openness to the outside world, industrialization level, economic development level, transportation infrastructure, and spatial adjacency relationships. Based on the research findings, this study proposes regional cooperation strategies for low-carbon management. The results provide references for improving CICEE and facilitating collaborative emission reductions among regions.
