Datasets_for_the_manuscript_submitted_to_GIScience_Remote_Sensing_254770186

Mitigating carbon emissions requires coordinated actions across jurisdictions, as emissions often exhibit strong spatial spillovers and interdependencies. Understanding how emissions cluster spatially is therefore essential for designing collaborative mitigation strategies. However, existing studies on the spatial clustering of carbon emissions remain limited by subjective parameter settings and insufficient exploration of heterogenous driving factors across clusters. Here, we integrate the percolation theory in physics with a spatial-temporal clustering algorithm to objectively delineate clusters of transport-related carbon emissions (TCE) for 323 Chinese cities in 2019. Building on six identified spatial clusters at the regional level, we employ random forest models and interpretable machine learning techniques to examine the effects of various factors on TCE. The results further uncover regional heterogeneity in emission determinants: nighttime light intensity is predominant in the northwest, population size in the northeast, and industrial added value in central and coastal regions. Higher daily temperatures are generally associated with increased TCE. The green coverage rate of built-up areas is negatively correlated with TCE in the northwest but exhibits the opposite relationship in the central south and southwest. This study contributes to the methodological frontier by introducing a percolation-based spatial clustering framework for emission analysis, which can be generalizable to broader contexts. Furthermore, it provides actionable insights for fostering cross-city coordination in emission mitigation, advancing China's decarbonization targets and sustainable transport goals.