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Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China

Author

Listed:
  • Jinping Zhang

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Qiuru Lu

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Li Guan

    (College of Applied Sciences, Beijing University of Technology, Beijing 100124, China)

  • Xiaoying Wang

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

Abstract

This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating M o r a n ’ s I and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based on the provincial panel data (1998–2016) of mainland China (data source: China Statistical Yearbook, Statistical Yearbook of provinces), the findings indicate that level of economic development and industrial structure have a significant role in promoting energy efficient. Resource endowment, government intervention and energy efficiency show a negative correlation. However, the negative effect of government intervention is weakened with the increase of energy efficiency. Lastly, we compare the results of SQAR with that of ordinary spatial autoregression (SAR). The empirical result shows that the SQAR model is superior to SAR model in influencing factors analysis of energy efficiency.

Suggested Citation

  • Jinping Zhang & Qiuru Lu & Li Guan & Xiaoying Wang, 2021. "Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China," Energies, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:504-:d:483154
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    References listed on IDEAS

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    1. Yang Liu & Ruochan Xiong & Shigong Lv & Da Gao, 2022. "The Impact of Digital Finance on Green Total Factor Energy Efficiency: Evidence at China’s City Level," Energies, MDPI, vol. 15(15), pages 1-17, July.
    2. Li, Mengna & Pan, Xiongfeng & Yuan, Sai, 2022. "Do the national industrial relocation demonstration zones have higher regional energy efficiency?," Applied Energy, Elsevier, vol. 306(PA).

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