Abstract: Cities serve as the primary hubs for human activities, and the successful realization of China’s “Dual Carbon” goals critically hinges on the effective reduction of carbon emissions in urban areas. However, due to the lack of detailed disaggregated data on energy consumption by source, urban carbon emission accounting has emerged as a crucial research area. This study, based on an enhanced Bayesian classification algorithm, leverages provincial-level energy consumption data from 2005 to 2019. It combines this data with various multi-dimensional attributes, including socioeconomic indicators, to determine carbon intensity types. The approach involves training on optimized attributes corresponding to provincial-level carbon intensity and then downscaling them to identify carbon intensity types at the city level. Comparative analysis with data from the carbon emission assessment database system (CEADs) and traditional methods highlights the advantages of the proposed feature-optimized Bayesian classification method. Furthermore, this method unveils the carbon intensity evolution of 282 major Chinese cities from 2005 to 2019, revealing a notable shift from high to low carbon intensity in the majority of cities. Notably, significant disparities persist in carbon intensity types and improvement trends between cities in the northern and southern regions. In the future, special attention should be paid to carbon intensity reduction efforts in resource-rich cities in central and western China. Additionally, the feature-optimized Bayesian classification method proposed in this study exhibits strong scalability, holding promise for applications at smaller scales, including county-level carbon intensity assessments.

Key words: feature optimization, Bayesian classification algorithm, urban carbon intensity, evolution