<p>Tourism transportation, a major source of energy consumption and carbon emissions in the tourism industry, is critical for carbon reduction under China’s “dual carbon” goals. Geospatial analysis can reveal the spatial correlation network and dynamic changes of tourism transportation carbon emissions. This study combined with big data analysis estimates carbon emissions from tourism transportation across 31 provinces in mainland China from 1999 to 2021 using a bottom-up approach. By analyzing spatiotemporal evolution and decoupling effects, the data research identifies significant regional disparities, with central and eastern coastal areas exhibiting the highest emissions and western and northwestern regions the lowest. The study reveals evolving spatial correlations among provinces, forming high-high and low-low clusters. A key finding is that, from the numerical point of view, while overall emissions have increased, a strong decoupling between tourism transportation carbon emissions and economic growth remains unachieved, with most regions in a weak decoupling state. The innovation of this work lies in the use of data science to detail examination of regional clustering and decoupling patterns, providing new insights into the spatial dynamics of carbon emissions in tourism transportation. Research combining geographic spatial characteristics can not only more accurately identify the spatiotemporal evolution trend and decoupling effect of tourism transportation carbon emissions, but also provide an important basis for formulating scientific and reasonable emission reduction paths. Based on these data findings, the study proposes targeted emission reduction pathways, emphasizing strategic leadership, policy support, and regional coordination, which are critical for achieving China’s “dual carbon” goals.</p>