IDEAS home Printed from https://ideas.repec.org/a/prg/jnlpep/v2022y2022i1id791p25-57.html
   My bibliography  Save this article

Efficiency Measurement and Inefficiency Environmental Factors of China's Green Economy

Author

Listed:
  • Xiaoli Qin
  • Jingzheng Wang
  • Yiping Liu

Abstract

This paper uses the projection pursuit method (PP method) to construct a comprehensive output indicator and uses the heterogeneous stochastic frontier model (HSFM model) to calculate China's green economy efficiency and analyse effects of environmental factors on the inefficiency fluctuation of green economy. Conclusions are drawn as follows: (1) The average value of China's green economy efficiency is generally low, and a regional heterogeneity of green economy efficiency is obvious. (2) For the overall inefficiency fluctuation of China's green economy, openness has a significant inhibitory effect; the industrialization level and technological level have a certain inhibitory effect, but their importance is weaker than that of openness; fiscal decentralization has an insignificant effect. Since 2001, changes in unit openness and the unit industrialization level have had a strengthened restraining effect on the inefficiency fluctuations of China's green economy, and the change in the unit technology level has had a small and stable inhibitory effect on China's green economy's inefficiency fluctuation. (3) Openness and the industrialization level have had a significant inhibitory effect on the inefficiency fluctuation of China's regional green economy in the Eastern region more than in the Central and Western regions; the technological level has had a certain inhibitory effect in the Central and Western regions, but its influence is lower than that of openness. The inhibitory effect of unit change in openness and the industrialization level on the inefficiency fluctuation of green economy in the Central region is greater than that in the Eastern region. The inhibitory effect of unit change in the technological level on the inefficiency fluctuation of green economy in the Western region is obviously greater than that in the Central and Eastern regions. These conclusions can provide a mathematical basis for a reform of China's green economy efficiency.

Suggested Citation

  • Xiaoli Qin & Jingzheng Wang & Yiping Liu, 2022. "Efficiency Measurement and Inefficiency Environmental Factors of China's Green Economy," Prague Economic Papers, Prague University of Economics and Business, vol. 2022(1), pages 25-57.
  • Handle: RePEc:prg:jnlpep:v:2022:y:2022:i:1:id:791:p:25-57
    DOI: 10.18267/j.pep.791
    as

    Download full text from publisher

    File URL: http://pep.vse.cz/doi/10.18267/j.pep.791.html
    Download Restriction: free of charge

    File URL: http://pep.vse.cz/doi/10.18267/j.pep.791.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.18267/j.pep.791?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiaoxue Liu & Fuzhen Cao & Shuangshuang Fan, 2022. "Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods," IJERPH, MDPI, vol. 19(18), pages 1-27, September.

    More about this item

    Keywords

    Green economy; efficiency; inefficiency fluctuation; projection pursuit method (PP method); heterogeneous stochastic frontier model (HSFM model);
    All these keywords.

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prg:jnlpep:v:2022:y:2022:i:1:id:791:p:25-57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stanislav Vojir (email available below). General contact details of provider: https://edirc.repec.org/data/uevsecz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.