Characterizing the spatial correlation network structure and impact mechanism of carbon emission efficiency: Evidence from China's transportation sector

Numerous countries and regions are actively seeking to reduce carbon emissions through policy guidance and technological innovation. In this process, balancing economic development with environmental protection and achieving synergistic carbon reduction between regions pose challenges for policymakers and the academic community alike. This study analyzes data from 30 provinces in China over the period from 2005 to 2020, employing the SBM-DEA, block model, and the Exponential Random Graph Models (ERGM) to explore the spatial association network structure characteristics of carbon emission efficiency and its driving factors. The findings indicate that: the carbon emission efficiency of the transportation industry is generally on an upward trend, with the eastern region having the highest carbon emission efficiency; the spatial association network exhibits a "dense in the east, sparse in the west" pattern; the block model demonstrates clear inter-regional carbon emission transfer behaviors; the result of ERGM shows that factors such as the level of economic development and population density significantly affect the network structure. The macro-micro individual analysis framework for the carbon emission efficiency network fills the theoretical gap in the context of the digital economy, providing a scientific basis and decision-making reference for policymakers when formulating and optimizing carbon reduction policies, which holds significant theoretical and practical value.