Temporal-spatial analysis of transportation CO₂ emissions in China: Clustering and policy recommendations

Reducing transportation-related carbon dioxide (CO₂) emissions in China poses significant challenges due to the sector's growth potential and variations among provinces and transportation modes. This study utilizes the bottom-up approach and the Logarithmic Mean Divisia Index (LMDI) decomposition method to calculate transportation CO₂ emissions and explores the temporal-spatial differences across Chinese provinces. The results reveal that national transportation CO₂ emissions increased by 50.14% from 2010 to 2019, and emissions from private cars present the fastest growth among all transportation modes by 254% over the decade. Spatially, higher emissions are found in eastern provinces, and neighboring provinces notably distinguish from each other in terms of the emission proportion of different modes and the factor analysis from LMDI. Regarding the heterogeneity of the spatial emission characteristics, a cluster-based evaluation method is proposed for the 31 provinces according to the emission structure and the LMDI decomposition. Four clusters are derived, each featuring varied emission distribution and driving factors. Correspondingly, policy recommendations are proposed to address the characteristics of each cluster, such as controlling car ownership, promoting integrated transport modes, improving fuel economy, and electrifying urban transportation services. The cluster-based analysis method can provide more specific suggestions to province targeting its emission characteristics rather than its location, which is one of the major contributions of this study.