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Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model

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  • Zhao, Mingxuan
  • Lv, Lianhong
  • Wu, Jing
  • Wang, Shen
  • Zhang, Nan
  • Bai, Zihan
  • Luo, Hong

Abstract

To promote the coordinated governance of atmospheric pollutants and greenhouse gases (GHGs), and to meet the emission goals of carbon peak and carbon neutrality, China should optimize the spatial pattern of its coal consumption. The present study employs the global Malmquist–Luenberger index to calculate the total factor productivity (TFP) of high coal-consuming industries in different provinces in China. Taking TFP as the independent variable, provincial coal consumption (PCC) as the dependent variable, and urbanization rate (UR) and regional gross domestic product (GDP) as the control variables, we establish a dynamic spatial Durbin model of TFP and PCC. We also use spatial effects decomposition to analyze the spatial influence relation between TFP and PCC. The results show a fluctuating upward trend of TFP in China overall, but the TFP in the eastern region is higher than that in the central and western provinces. The direct effects show a significant U-shaped relation between TFP and PCC, where PCC trends down with an increase in TFP. The indirect effects show an inverted U-shaped relation between local TFP and PCC in neighboring provinces. Finally, according to the estimated results, we propose some policy recommendations for China's government to control coal consumption.

Suggested Citation

  • Zhao, Mingxuan & Lv, Lianhong & Wu, Jing & Wang, Shen & Zhang, Nan & Bai, Zihan & Luo, Hong, 2022. "Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008209
    DOI: 10.1016/j.energy.2022.123917
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