Unraveling the Spatial Network Topology and Clustering Patterns of Green Transportation Development

This study investigates the spatial association network structure of Green Transportation Development (GTD) in China to support coordinated regional development. Based on panel data from 30 major Chinese cities over the period 2011–2020, an entropy weighting method is used to evaluate urban GTD levels, while social network analysis (SNA) and the Quadratic Assignment Procedure (QAP) are employed to identify the spatial network topology, clustering patterns, and driving factors of GTD. The results show that GTD exhibits significant intercity spatial associations. The overall network structure is relatively stable and exhibits a loose hierarchical pattern, with network density fluctuating between 0.232 and 0.277. Shanghai, Yinchuan, and Nanjing play prominent roles in the core–periphery structure. Block modelling further classifies the network into four functional groups: “net spillover,” “bilateral spillover,” “net benefit,” and “broker” blocks. In 2020, the network contained 214 association ties, of which 176 were inter-block ties, indicating evident cross-block spillover effects but relatively weak intra-block communication. The QAP regression results further reveal that geographical distance inhibits network formation, whereas differences in economic development and transport-related employment promote intercity GTD associations; differences in technological innovation exert a negative effect. These findings suggest that policymakers should reduce administrative barriers, formulate differentiated GTD policies, strengthen regional linkages, and promote intercity cooperation based on complementary advantages to improve the overall performance of GTD.