Against the backdrop of global low-carbon transformation and China’s “dual-carbon” target, the logistics industry has become a key sector for carbon emission reduction due to its high energy consumption and significant spatial correlation characteristics. Taking 30 provinces in China from 2010 to 2022 as research samples, this paper uses the Super-SBM model to measure the carbon emission efficiency of the logistics industry, constructs a spatial correlation network based on the modified gravity model, and explores the structural characteristics and evolutionary logic of the network through social network analysis (SNA). Furthermore, the quadratic assignment procedure (QAP) regression is employed to reveal the driving factors of the spatial correlation effect. The results show that China’s logistics carbon emission efficiency presents an obvious “east-high west-low, south-high north-low” spatial pattern with significant positive agglomeration and stable spillover effects. A nationwide connected spatial correlation network has formed, with high connectivity, increasing density, and enhanced stability. Eastern coastal provinces are core nodes, central provinces act as bridges, and western provinces are at the network edge. The block model divides the network into net benefit, net spillover, and broker plates, revealing a spillover pattern from the edge to the core. QAP regression shows that energy intensity, transportation structure, economic level, policy support, and population size are negatively correlated with spatial correlation, and narrowing regional differences strengthens network connections.
Liu et al. (Wed,) studied this question.