[Analysis of Provincial Carbon Emission Driving Mechanisms Based on the LMDI and K-means Clustering Method]

Analyzing the driving mechanisms behind provincial carbon emissions is crucial to formulating appropriate carbon reduction policies， which is vital for achieving China's "carbon peaking and carbon neutrality" goals. This study employed the LMDI method to examine the influences of six key factors （population size， economic development， industrial structure， energy intensity， energy structure， and carbon emission coefficient） on carbon emissions across 30 regions in China from 2010 to 2021. By using the contribution rate of each driving factor to changes in carbon emissions as the clustering variable， the K-means clustering method was used to categorize the 30 regions into five groups. This facilitated identifying the similarities and differences in carbon emission driving mechanisms across various regions. The results of the study follow： ① For most regions， economic development and population growth are the primary drivers of carbon emission increases， while energy intensity and industrial structure are important factors in carbon emission reductions. ②The driving factors of carbon emissions vary significantly between the Twelfth and Thirteenth Five-Year Plan periods， with the growth in both the amount and rate of carbon emissions being notably lower in the former period. ③ Importantly， the driving mechanisms of carbon emissions differ greatly across the five region types identified. The first and fifth types of regions face greater challenges in achieving carbon emission peak goals， whereas the second and third types are better positioned to attain these objectives. Based on the characteristics of the different region types and representative provinces and cities， targeted carbon reduction policies are proposed.