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Examining the links among economic growth, energy consumption, and CO 2 emission with linear and nonlinear causality tests

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  • Kefan Wang
  • Bangzhu Zhu
  • Ping Wang
  • Yi-Ming Wei

Abstract

Understanding the causal relationships among economic growth, energy consumption, and CO 2 emission is important for formulating energy conservation and emission reduction policies. In this paper, we explore the causal relationships among economic growth, energy consumption, and CO 2 emission in China during 1978–2012 by using both the linear and nonlinear causality tests. The obtained results show that the links examined by the linear and nonlinear causality tests are not all same. Both linear and nonlinear causality tests indicate a unidirectional causality from CO 2 emission to GDP and a bi-directional causality between energy consumption and CO 2 emission. Linear causality test indicates a unidirectional causality from energy consumption to GDP, while nonlinear causality test indicates a unidirectional causality from GDP to energy consumption. Finally, policy recommendations are proposed for achieving the target of coordinated, sustainable development of China. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Kefan Wang & Bangzhu Zhu & Ping Wang & Yi-Ming Wei, 2016. "Examining the links among economic growth, energy consumption, and CO 2 emission with linear and nonlinear causality tests," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1147-1159, March.
    • Handle: RePEc:spr:nathaz:v:81:y:2016:i:2:p:1147-1159
    DOI: 10.1007/s11069-015-2124-9
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    2. Wei-Hua Qu & Ling Xu & Guo-Hua Qu & Zhi-Jun Yan & Jian-Xiu Wang, 2017. "The impact of energy consumption on environment and public health in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(2), pages 675-697, June.
    3. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
    4. Zheng Fang & Marcin Wolski, 2021. "Human capital, energy and economic growth in China: evidence from multivariate nonlinear Granger causality tests," Empirical Economics, Springer, vol. 60(2), pages 607-632, February.
    5. Li, Wei & Sun, Wen & Li, Guomin & Jin, Baihui & Wu, Wen & Cui, Pengfei & Zhao, Guohao, 2018. "Transmission mechanism between energy prices and carbon emissions using geographically weighted regression," Energy Policy, Elsevier, vol. 115(C), pages 434-442.
    6. Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2019. "Time frequency relationship between energy consumption, economic growth and environmental degradation in the United States: Evidence from transportation sector," Energy, Elsevier, vol. 173(C), pages 706-720.

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