Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: Combining two-layer LMDI method with spectral clustering

Abstract

Understanding the CO2 (carbon dioxide) emissions mechanisms in each province is important to reduce China's CO2 emissions and achieve carbon reduction targets. This paper combines two-layer LMDI (logarithmic mean divisia index) method and spectral clustering to analyze industrial CO2 emission drivers in 30 provinces of China and proposes carbon reduction policies. First, each province's industrial CO2 emission factors are decomposed into energy structure, energy intensity, industrialization level, per capita GDP and total population; energy intensity and per capita GDP play a dominant role in suppressing and promoting industrial CO2 emissions, respectively. The industrialization level has gradually developed from the eastern coastal areas to the north and the central and western regions, especially Inner Mongolia, Jilin, Henan and Sichuan. The impact mechanisms of the provinces on China's CO2 emissions are very different from temporal and spatial angle. Second, this paper optimizes spectral clustering though geodesic distance and utilizes it to cluster 30 provinces in China. Finally, based on the characteristics of CO2 emissions and clustering results, more targeted carbon emission reduction strategies are proposed. Therefore, the formulation of carbon emission reduction policies should not only consider the surface characteristics of CO2 emissions, but also explore the root causes of CO2 emissions.