This paper examines the CO₂ emissions from energy consumption in China’s transport sector, conducting an empirical investigation into the spatial distribution characteristics and influencing factors of transport CO₂ emissions. This study, which is based on province-level panel data covering the 30 provincial regions during the period 2001-2016, used the methods of exploratory spatial data analysis (ESDA) and the extended STIRPAT model (examined by the method of system-generalized method of moments (Sys-GMM) regression). The results indicated that the amount of CO₂ emissions in China’s transport sector has increased steadily during the observation period, but there was a noticeable disparity across the provinces and regions. From the perspective of spatial dimension, the spatial agglomeration characteristics of provincial transport CO₂ emissions tended to be strengthened, and the pattern evolutions of spatial distribution presented a path-dependence effect to some extent. The scale of population was found to be the most important influencing factor of transport CO₂ emissions, and followed by the per-capita GDP. Further, the improvement of energy efficiency was the key factor to controlling transport CO₂ emissions. Compared to freight transportation, passenger transportation was more important in transport CO₂ emissions reduction due to its lower efficiency of energy utilization and rapid growth. Meanwhile, electrification played an important inhibitory effect on transport CO₂ emissions because of its high fuel efficiency and less pollution. Importantly, we could not support the existence of the environmental Kuznets curve (EKC) hypothesis in China’s transport sector during the observation period, which describes the relationship between the environmental pressures and economic development. These findings contain some meaningful implications for policy makers: confirm the priority transport CO₂ emissions reduction areas, improve transport energy efficiency, strengthen passenger transportation decarburization policy, and highlight the model shift of fuel consumption.