[Driving Mechanisms and Spatial Pattern Heterogeneity of Transportation Carbon Emissions in Economically Homogeneous...

Under the impetus of the "dual carbon" strategic goals， traffic， as a typical high-energy-consumption and high-emission industry， has seen its regional differences in carbon emissions become a focal point of attention. Existing research predominantly focuses on the urban scale or single urban agglomerations， lacking systematic comparative analysis of transportation carbon emission differences between urban agglomerations with similar economic development levels but different locational structures. In light of this， this study selects the Yangtze River Delta and the Guangdong-Fujian-Zhejiang urban agglomerations as representatives， constructing a "time-space-mechanism" three-dimensional analytical framework. From the perspective of economic homogeneity， it explores the evolutionary characteristics of their transportation carbon emissions and the spatial heterogeneity of driving mechanisms. Firstly， based on the standard deviation ellipse （SDE） method， the spatiotemporal pattern evolution of transportation carbon emissions in the two urban agglomerations from 2010 to 2022 was identified. Secondly， the logarithmic mean Divisia index （LMDI） decomposition method was used to quantify the contributions of multidimensional driving factors， and the multiscale geographically weighted regression （MGWR） model was introduced to reveal their spatial heterogeneity. The study found that： ① Economic development level and unit cargo economic efficiency were the main promoting factors in both regions， while energy intensity and transportation intensity generally inhibited carbon emission growth. ② The center of carbon emissions in the Yangtze River Delta urban agglomeration was concentrated in Wuxi， Jiangsu， and the center in the Guangdong-Fujian-Zhejiang urban agglomeration remained stable in Longyan， Fujian. ③ The impact of energy structure and energy intensity on carbon emissions tended to be localized and refined in geographical space. This study provides empirical support and a spatial decision-making basis for formulating regionally differentiated emission reduction strategies among different urban agglomerations.