Research on the green total factor productivity and its influencing factors based on system GMM model


The entropy method is used to calculate pollution comprehensive index reflecting provincial environmental pollution level. On this basis, the Malmquist productivity index is used to study the regional green total factor productivity (GTFP) in China, and the system generalized moment method is used to explore the influencing factors of GTFP. Firstly, the results show that the provinces with lower environmental pollution comprehensive index are mainly in the western and eastern regions, while the provinces with higher pollution comprehensive index are mainly distributed in the central inland areas. Secondly, GTFP shows an N-type upward trend, which is basically consistent with the trend of total factor productivity. The overall GTFP in the east is on the rise, which is promoted by technical efficiency. The GTFP in the central and west has declined, and the main factor restricting their improvement is technological retrogression. The expansion of production scale in the east and west can contribute to the improvement of GTFP. The central region is at a stage where the scale of returns is not economic, and it is more important for promoting technological progress. Lastly, the industrial structure can significantly inhibit the increase of GTFP, however, the energy consumption structure, FDI and pollution control investment have a significant role in improving GTFP.

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  1. Bond SR (2002) Dynamic panel data models: a guide to micro data methods and practice. Port Econ J 1(2):141–162

    Article  Google Scholar 

  2. Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and the measurement of input, output and productivity. Econ J Econ Soc 50(6):1393–1414

    MATH  Google Scholar 

  3. Fare R, Grosskopf S, Lindgren B et al (1992) Productivity changes in Swedish pharmacies 1980–1989: a non-parametric Malmquist approach. J Product Anal 3(1):81–97

    Google Scholar 

  4. Golove WH, Schipper LJ (1997) Restraining carbon emissions: measuring energy use and efficiency in the USA. Energy Policy 25(7):803–812

    Article  Google Scholar 

  5. Halkos GE, Tzeremes NG (2013) Economic growth and environmental efficiency: evidence from US regions. Econ Lett 120(1):48–52

    Article  Google Scholar 

  6. Han BL, Ouyang ZY, Wang WJ (2018) The relationship between regional industrial organizing levels and ecological economic efficiency. J Clean Prod 171:857–866

    Article  Google Scholar 

  7. Iftikhar Y, He WJ, Wang ZH (2016) Energy and CO2 emissions efficiency of major economies: a non-parametric analysis. J Clean Prod 139:779–787

    Article  Google Scholar 

  8. Kumar S (2006) Environmentally sensitive productivity growth: a global analysis using Malmquist–Luenberger index. Ecol Econ 56(2):280–293

    Article  Google Scholar 

  9. Liao H, Du YF, Huang ZM et al (2016) Measuring energy economic efficiency: a mathematical programming approach. Appl Energy 179:479–487

    Article  Google Scholar 

  10. Malmquist S (1953) Index numbers and indifference surfaces. Trabajos de Estadistica 4(2):209–242

    MathSciNet  Article  Google Scholar 

  11. Ren WH, Ji JY, Chen L et al (2018) Evaluation of China’s marine economic efficiency under environmental constraints-an empirical analysis of China’s eleven coastal regions. J Clean Prod 184:806–814

    Article  Google Scholar 

  12. Song ML, Wang SH (2014) DEA decomposition of China’s environmental efficiency based on search algorithm. Appl Math Comput 247:562–572

    MathSciNet  MATH  Google Scholar 

  13. Tang QS, Guo XW, Sun Y et al (2007) Ecological conversion efficiency and its influencers in twelve species of fish in the Yellow Sea Ecosystem. J Mar Syst 67(3–4):282–291

    Article  Google Scholar 

  14. Tao XP, Wang P, Zhu BZ (2016) Provincial green economic efficiency of China: a non-separable input–output SBM approach. Appl Energy 171:58–66

    Article  Google Scholar 

  15. Wang K, Wei YM (2014) China’s regional industrial energy efficiency and carbon emissions abatement costs. Appl Energy 130(1):617–631

    Google Scholar 

  16. Wapner P (2011) Civil society and the emergent green economy. Rev Policy Res 28(5):525–530

    Article  Google Scholar 

  17. Woodward RT, Bishop RC (1995) Efficiency, sustainability and global warming. Ecol Econ 14(2):101–111

    Article  Google Scholar 

  18. Xie HL, Chen QR, Wang W et al (2018) Analyzing the green efficiency of arable land use in China. Technol Forecast Soc Change 133:15–28

    Article  Google Scholar 

  19. Yao X, Zhou HC, Zhang AZ et al (2015) Regional energy efficiency, carbon emission performance and technology gaps in China: a meta-frontier non-radial directional distance function analysis. Energy Policy 84:142–154

    Article  Google Scholar 

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Ren, Y. Research on the green total factor productivity and its influencing factors based on system GMM model. J Ambient Intell Human Comput 11, 3497–3508 (2020).

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  • Pollution comprehensive index
  • GTFP
  • Influencing factors
  • Malmquist index
  • System GMM