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The impact of energy consumption on carbon emissions intensity in China: evidence from a dynamic panel quantile regression model

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Listed:
  • Jian Hou
  • Shenyang Yang
  • Guoliang Fan
  • Hongxia Xu

Abstract

China, being the largest global emitter of carbon, faces significant challenges in mitigating carbon emissions from energy consumption. The empirical findings reveal that the impact of energy consumption intensity and energy consumption structure on carbon emissions intensity is positively correlated with quantile levels and shows heterogeneous effects in different regions. Employing an energy consumption structure as a threshold variable to examine the impact of energy consumption intensity on carbon emissions intensity, we identify a significant single threshold effect in both the full sample and the western sample. Based on empirical research findings, this paper proposes specific policy recommendations.

Suggested Citation

  • Jian Hou & Shenyang Yang & Guoliang Fan & Hongxia Xu, 2024. "The impact of energy consumption on carbon emissions intensity in China: evidence from a dynamic panel quantile regression model," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 19, pages 268-288.
    • Handle: RePEc:oup:ijlctc:v:19:y:2024:i::p:268-288.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctad127
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    1. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    2. Myung Hwan Seo & Sueyoul Kim & Young-Joo Kim, 2019. "Estimation of dynamic panel threshold model using Stata," Stata Journal, StataCorp LP, vol. 19(3), pages 685-697, September.
    3. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    4. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    5. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    6. Harding, Matthew & Lamarche, Carlos, 2009. "A quantile regression approach for estimating panel data models using instrumental variables," Economics Letters, Elsevier, vol. 104(3), pages 133-135, September.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    8. Wintoki, M. Babajide & Linck, James S. & Netter, Jeffry M., 2012. "Endogeneity and the dynamics of internal corporate governance," Journal of Financial Economics, Elsevier, vol. 105(3), pages 581-606.
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