Factors influencing carbon emissions from China's electricity industry: Analysis using the combination of LMDI and K-means clustering

Carbon emissions from the electricity industry (CEEI) account for more than 40% of China's total emissions. This paper examines the influential factors of China's CEEI at both national and provincial level and explores targeted provincial strategies, which are critical for China to control its CEEI effectively and to achieve its carbon peaking aim. First, this study quantifies the contributions of nine factors influencing China's CEEI increase using Logistic Mean Divided Index (LMDI) decomposition. The results show that economic growth is the dominant driver, while power consumption intensity, energy intensity of thermal power generation (TPG) and power mix are the main inhibitors. After stepping into the new era in 2012, in general, the evolutions of all the 4 main factors aided CEEI control. Second, according to the recent status of the main factors, we classify 30 provinces into 4 groups with K-means clustering. And then, based on the characteristics of each group, the paper puts forward provincial targeted recommendations to address the rebound of CEEI since 2017 and to promote the low-carbon transformation of China's electricity industry. This study confirms that it is a promising direction for LMDI model to combine with cluster analysis and proposes a basic flow for this combination: LMDI -> main influencing factors -> clustering variables -> cluster analysis -> targeted strategies, which will conduce to deepen LMDI applications.