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A nonparametric analysis of energy environmental Kuznets Curve in Chinese Provinces

Muhammad Shahbaz (), Muhammad Shafiullah, Usman Khalid and Malin Song

Energy Economics, 2020, vol. 89, issue C

Abstract: Energy resources are an important material foundation for the survival and development of human society, and the relationship between energy and economy is interactive and complementary. This paper analyzes the energy consumption–economic growth nexus in Chinese provinces using novel and recent nonparametric time-series as well as panel data empirical approaches. The dataset covers 30 provinces over the period of 1980–2018. The empirical analysis indicates the presence of a nonlinear functional form and smooth structural changes in most of the provinces. The nonparametric empirical analysis validates the presence of a nonlinear unit root problem in energy consumption and economic growth, and nonlinear cointegration between the variables. Additionally, the nonparametric panel cointegration test reports evidence of convergence in energy consumption and economic growth patterns across the provinces. The nonparametric regression analysis finds economic growth to have a positive effect, on average, on energy consumption in all provinces, except for Beijing. Further, the energy environmental Kuznets curve exists between economic growth and energy consumption in 20 out of 30 Chinese provinces. The Granger causality analysis reveals the presence of a mixed causal relationship between economic growth and energy consumption. The empirical findings have important implications for Chinese authorities in planning for improving energy efficiency, decoupling between economic growth and energy consumption, and reducing the environmental footprint of provinces.

Keywords: Energy consumption; Economic growth; China; Nonparametric analysis (search for similar items in EconPapers)
JEL-codes: Q43 R11 C14 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:89:y:2020:i:c:s0140988320301547

DOI: 10.1016/j.eneco.2020.104814

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