Abstract: As a vital pillar of the tourism industry, the tourism transportation sector plays a crucial role in supporting the achievement of the ''dual carbon" goals, making it essential to explore its green and sustainable development. This study first calculated the carbon rebound effect in the tourism transportation sector across 30 Chinese provinces (autonomous regions and municipalities) from 2003 to 2022. Then it employed a combination of spatial kernel density and spatial Markov chain models to investigate its spatiotemporal evolution characteristics, and applied a random forest model to examine its influencing factors and mechanisms. The results indicated that the carbon rebound effect of the tourism transportation sector in neighboring provinces exerted a significant spatial spillover impact on the future carbon rebound level of the focal province. Furthermore, as time progressed, the carbon rebound effect exhibited a dynamic clustering trend while continuously decreasing. The state transitions of the carbon rebound effect in the provincial tourism transportation sector demonstrated distinct path dependence, yet leapfrog transitions still occurred, with transition probabilities of strong and weak rebound reaching 0.333 and 0.309, respectively. Regarding influencing factors, improvements in transportation accessibility and technological innovation exerted a positive effect on the carbon rebound effect; when these indicators exceeded thresholds of 400 and 1.2, respectively, the carbon rebound effect became negative. Conversely, economic development level, environmental regulatory intensity, and the degree of tourism industry agglomeration exerted negative influences. Therefore, a cooperative mechanism for reducing carbon emissions in interprovincial tourism transportation and a differentiated management system can be established. By optimizing the layout based on multiple factors, this approach will provide empirical support for promoting the low-carbon transition of regional tourism transportation.