[Spatiotemporal Dynamics and Driving Mechanism of China's Transport Carbon Emission Correlation Network]

Understanding the spatiotemporal dependence characteristics， patterns， and driving mechanisms of the interprovincial transportation carbon emission network in China is of paramount importance for advancing interprovincial transport carbon reduction and fostering regional high-quality emission reduction coordination. Based on the quadratic assignment procedure （QAP）， social network analysis （SNA）， and exploratory spatiotemporal data analysis （ESTDA）， this research was combined with the multiple regression quadratic assignment procedure （MRQAP） model to explore the spatio-temporal dynamic evolution interaction characteristics and driving mechanism of China's transport carbon emission correlation network from 2003 to 2021. The results indicated that： ① The structure and intensity of China's transportation carbon emission correlation network from 2003 to 2021 exhibited a high degree of similarity， demonstrating a "time inertia" in which the future correlation pattern was significantly influenced by historical correlation trends. ② The transportation carbon emission correlation network showed distinct spatial connection preferences， with pronounced spatial heterogeneity and increasingly evident clustering patterns. Core provinces such as Shandong， Jiangsu， Guangdong， and Shanghai exhibited a dominant role in this network. ③ The lock-in effect and transition inertia of transport carbon emissions were prominent in the spatio-temporal interaction dimension. The inter-provincial cooperative relationship was as high as 84.6% during the study period， but the spatio-temporal competition relationship between southwest and northern provinces was prominent. ④ The driving mechanism of the transport carbon emission correlation network presented the characteristics of "structure-lock-spatiotemporal dependence-diversity of individual attributes，" in which the economic difference matrix and spatio-temporal interaction network had the most significant positive influence， and the industrial difference matrix and transport structure difference matrix had the most prominent negative influence. Therefore， it is suggested that provinces should promote the optimization and coordinated development of regional transport carbon reduction targets from three aspects： inter-regional coordination governance， differentiated carbon reduction policies， and transportation network layout.