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Modelling energy efficiency in China: a fixed-effects panel stochastic frontier approach

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  • Feng Song
  • Yihua Yu

Abstract

This paper combines energy demand modelling with stochastic frontier analysis to investigate the changing trends, variations and determinants of energy efficiency for 27 Chinese provinces over the period 1995 to 2014. An aggregate ‘frontier’ energy demand function and an efficiency function are estimated simultaneously. We obtained several findings. First, the energy intensity is not a particularly good indicator of energy efficiency. Second, the energy efficiency levels for all the provinces improved during the sample period, but the current efficiency levels are still low, implying great potential for energy saving. In addition, the energy efficiency gap among the provinces seems to have widened over the past 20 years, as the variance has increased by almost three times. Finally, technological progress driven by new investment and the development of market mechanisms are two important drivers of energy efficiency improvement.

Suggested Citation

  • Feng Song & Yihua Yu, 2018. "Modelling energy efficiency in China: a fixed-effects panel stochastic frontier approach," Economic and Political Studies, Taylor & Francis Journals, vol. 6(2), pages 158-175, April.
    • Handle: RePEc:taf:repsxx:v:6:y:2018:i:2:p:158-175
    DOI: 10.1080/20954816.2018.1463479
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    Cited by:

    1. Siliang Yi & Chuyuan Zou, 2023. "Assessing Transformation Practices in China under Energy and Environmental Policy Goals: A Green Design Perspective," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
    2. Liu, Fengqin & Sim, Jae-yeon & Sun, Huaping & Edziah, Bless Kofi & Adom, Philip Kofi & Song, Shunfeng, 2023. "Assessing the role of economic globalization on energy efficiency: Evidence from a global perspective," China Economic Review, Elsevier, vol. 77(C).
    3. Hiroyuki Taguchi & Aktamov Asomiddin, 2022. "Energy-Use Inefficiency and Policy Governance in Central Asian Countries," Energies, MDPI, vol. 15(4), pages 1-15, February.
    4. Kexin Li & Jianxu Liu & Yuting Xue & Sanzidur Rahman & Songsak Sriboonchitta, 2022. "Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-17, July.
    5. Tingting Xiao & Zhong Liu, 2023. "Air Pollution and Enterprise Energy Efficiency: Evidence from Energy-Intensive Manufacturing Industries in China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    6. Huaping Sun & Bless Kofi Edziah & Xiaoqian Song & Anthony Kwaku Kporsu & Farhad Taghizadeh-Hesary, 2020. "Estimating Persistent and Transient Energy Efficiency in Belt and Road Countries: A Stochastic Frontier Analysis," Energies, MDPI, vol. 13(15), pages 1-19, July.
    7. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung, 2022. "Predicting monthly biofuel production using a hybrid ensemble forecasting methodology," International Journal of Forecasting, Elsevier, vol. 38(1), pages 3-20.

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