Spatial network structure characteristics of green total factor productivity in transportation and its influencing factors: Evidence from China

Abstract

Improving green total factor productivity (GTFP) is an effective way to achieve efficient use of resources and low-carbon development in the transportation industry. Accurately grasping the spatial associated structure and its influencing factors of China’s transportation GTFP is of great significance for promoting coordinated regional development. This study used the DEA-Malmquist model to measure China’s provincial transportation GTFP from 2006 to 2019. The spatial associated matrix is constructed by the modified gravity model, and the social network analysis (SNA) method is used to analyze the structural characteristics and influencing factors of the GTFP spatial associated network. It is found that: 1) The tightness of the spatial associated network of China’s transportation GTFP increased year by year, and the hierarchical spatial structure was gradually broken. 2) There are significant differences in the status of various regions in the spatial network. Among them, Shanghai plays the role of “leader” and “core participant”, with the highest point centrality and eigenvector centrality; Jiangxi and Guangdong play the role of “intermediary” and “bridge”; However, Jilin, Qinghai, Ningxia, and other regions have a weak influence on the spatial correlation. 3) Spatial aggregation analysis shows that block I has a strong correlation with other regions, while the spatial correlation level of the other three plates is relatively poor. 4) QAP analysis shows that province adjacency, per capita GDP, and technological innovation have a significant positive impact on the spatial correlation. Therefore, the Chinese government should increase the level of informatization and create a regional coordinated development mechanism to optimize the overall development pattern of the transportation industry.