Decomposition and Decoupling Analysis of Transportation Carbon Emissions in China Using the Generalized Divisia Index Method

The transportation sector is crucial for achieving China’s “dual carbon” strategic goals, yet its emission drivers and decoupling mechanisms exhibit significant provincial heterogeneity that remains underexplored. Existing studies predominantly rely on the LMDI method, which suffers from limitations in handling multiple absolute indicators, and rarely quantify the policy-driven decoupling effort. To address these gaps, this study employs the generalized Divisia index method to decompose transportation carbon emissions across thirty Chinese provinces from 2005 to 2022. Furthermore, we innovatively integrate the Tapio decoupling model with a novel decoupling effort model to assess both the decoupling state and the effectiveness of emission reduction policies. Our key findings reveal that: (1) economic output scale was the primary driver of emission growth, while output carbon intensity was the dominant mitigation factor; (2) driving mechanisms varied considerably across provinces, with 83% of provinces primarily driven by economic scale expansion; (3) the national decoupling state improved from weak to strong decoupling, with 53% of provinces achieving decoupling advancement; and (4) intensity effects were the core driver enabling decoupling efforts, while scale effects represented the primary inhibiting factor. This study provides a robust analytical framework and empirical evidence for formulating differentiated decarbonization strategies across Chinese provinces.