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Understanding the CO 2 (carbon dioxide) emissions mechanisms in each province is important to reduce China's CO 2 emissions and achieve carbon reduction targets. This paper combines two-layer LMDI (logarithmic mean divisia index) method and spectral clustering to analyze industrial CO 2 emission drivers in 30 provinces of China and proposes carbon reduction policies. First, each province's industrial CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 emissions, but also explore the root causes of CO 2 emissions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In China, carbon dioxide (CO 2 ) emissions have been in a state of constant growth, and CO 2 emission reduction is a considerable challenge for China. The paper uses the IPAT-E (I = Human Impact, P = Population, A = Affluence, T = Technology, E = Energy) model to determine and classify the influencing factors of CO 2 emissions. Based on the provincial panel data, the paper used the partial least squares (PLS) method to establish a structural equation model (SEM), and analyzes the path coefficient between CO 2 emissions and its influencing factors. The paper contains 16 indicators from 2001 to 2016. Empirical results show that affluence, technology and energy have a significant positive impact on CO 2 emissions at the national level. And the affluence factor under the influence of population factor has the greatest impact. From a regional perspective, affluence factor has a significant positive impact on CO 2 emissions in the Northeast, East, Northwest, Southwest, and Central South China, with the largest impact on the Southwest. Technology factor has a small positive impact on East, Northwest and Southwest China. Energy factor has a significant positive impact on the Northeast, North, East China, Northwest and Central South China, with the greatest impact on North China. Affluence and technology factors have a negative impact on CO 2 emissions in North China. In addition, the population has a greater impact on affluence, with minimal impact on the Southwest. This study provides a comprehensive and accurate indicator estimation framework for CO 2 emissions. And the paper provides theoretical support for the government to formulate policies., (Copyright © 2019 Elsevier B.V. All rights reserved.)