Exploring spatial network and drivers of transportation carbon emissions in China

This study examines the spatial correlation structure and driving mechanisms of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Provincial emissions are estimated using reclassified energy consumption data. A spatial correlation network is constructed using a modified gravity model and analyzed with Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP). Results show rapid growth followed by gradual decline, with a spatial gradient of ‘East > West > Central > Northeast’ and expanding high-emission zones. The network exhibits significant spillover effects but remains structurally sparse, characterized by a core–periphery structure and a ‘Matthew effect’ dominated by core provinces. Four functional roles—net spillover, intermediary, bidirectional spillover, and net beneficiary—indicate heterogeneous interprovincial interactions. QAP results indicate that technological innovation, transportation intensity, and economic development promote network linkages, whereas geographic distance, industrial structure differences, and population density disparities hinder the formation of such linkages.