Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data

Shaojian Wang, Chuanglin Fang and Yang Wang

Renewable and Sustainable Energy Reviews, 2016, vol. 55, issue C, 505-515

Abstract: This paper examines carbon dioxide (CO2) emissions from the perspective of energy consumption, detailing an empirical investigation into the spatiotemporal variations and impact factors of energy-related CO2 emissions in China. The study, which is based on a provincial panel data set for the period 1995–2011, used an extended STIRPAT model, which was in turn examined using System-Generalized Method of Moments (Sys-GMM) regression. Results indicate that while per capita CO2 emissions in China were characterized by conspicuous regional imbalances during the period studied, regional inequality and spatial autocorrelation (agglomeration) both decreased gradually between 1995 and 2011, and the pattern evolutions of emissions evidenced a clear path dependency effect. The urbanization level was found to be the most important driving impact factor of CO2 emissions, followed by economic level and industry proportion. Conversely, tertiary industry proportion constituted the main inhibiting factor among the negative influencing factors, which also included technology level, energy consumption structure, energy intensity, and tertiary industry proportion. Importantly, the study revealed that the CO2 Kuznets Curve (CKC), which describes the relation between CO2 emissions and economic growth, in fact took the form of N-shape in the medium- and long-term, rather than the classical inverted-U shape of the environmental Kuznets Curve (EKC). Specifically, an additional inflection appeared after the U-shape relationship between economic growth and CO2 emissions, indicating the emergence of a relink phase between the two variables. The findings of this study have important implications for policy makers and urban planners: alongside steps to improve the technology level, accelerate the development of tertiary industry, and boost recycling and renewable energies, the optimization of a country’s energy structure that can in fact reduce reliance on fossil energy resources and constitute an effective measure to reduce CO2 emissions.

Keywords: CO2 emissions; Spatiotemporal variations; STIRPAT model; Sys-GMM regression; China (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (61)

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DOI: 10.1016/j.rser.2015.10.140

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