IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0283199.html
   My bibliography  Save this article

Variation and internal-external driving forces of grey water footprint efficiency in China’s Yellow River Basin

Author

Listed:
  • Yun Li
  • Yu Liu
  • Lihua Yang
  • Tianbo Fu

Abstract

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China’s Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

Suggested Citation

  • Yun Li & Yu Liu & Lihua Yang & Tianbo Fu, 2023. "Variation and internal-external driving forces of grey water footprint efficiency in China’s Yellow River Basin," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0283199
    DOI: 10.1371/journal.pone.0283199
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283199
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0283199&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0283199?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
    ---><---

    References listed on IDEAS

    as
    1. Alvaredo, Facundo, 2011. "A note on the relationship between top income shares and the Gini coefficient," Economics Letters, Elsevier, vol. 110(3), pages 274-277, March.
    2. Wang, Shaoping & Li, Ang & Wen, Kuangyu & Wu, Ximing, 2020. "Robust kernels for kernel density estimation," Economics Letters, Elsevier, vol. 191(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Makdissi & Myra Yazbeck, 2012. "On the Measurement of Indignation," Working Papers 1213E, University of Ottawa, Department of Economics.
    2. Pablo García S. & Camilo Pérez N., 2017. "Desigualdad, inflación, ciclos y crisis en Chile," Estudios de Economia, University of Chile, Department of Economics, vol. 44(2 Year 20), pages 185-221, December.
    3. Onrubia Fernández, Jorge & Picos, Fidel & Rodado, María del Carmen, 2019. "Shifting tax burden to top income earners: What is the best way to reduce inequality?," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-31.
    4. Torregrosa-Hetland, Sara, 2016. "Sticky Income Inequality In The Spanish Transition (1973-1990)," Revista de Historia Económica / Journal of Iberian and Latin American Economic History, Cambridge University Press, vol. 34(1), pages 39-80, March.
    5. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    6. Sean Higgins & Nora Lustig & Andrea Vigorito, 2018. "The Rich Underreport their Income: Assessing Bias in Inequality Estimates and Correction Methods using Linked Survey and Tax Data," Working Papers 1808, Tulane University, Department of Economics.
    7. Aaberge, Rolf & Atkinson, Anthony B. & Modalsli, Jørgen, 2020. "Estimating long-run income inequality from mixed tabular data: Empirical evidence from Norway, 1875–2017," Journal of Public Economics, Elsevier, vol. 187(C).
    8. Rodríguez Weber, Javier, 2015. "Estimación de desigualdad de ingreso y otras variables relacionadas para Chile entre 1860 y 1970. Metodología y resultados obtenidos [Income inequality estimates for Chile between 1860 and 1970. Me," MPRA Paper 68400, University Library of Munich, Germany.
    9. Javier Cortés Orihuela & Juan D. Díaz & Pablo Gutiérrez Cubillos & Pablo A. Troncoso & Gabriel I. Villarroel, 2024. "Intergenerational earnings mobility in Chile: the tale of the upper tail," Empirical Economics, Springer, vol. 67(5), pages 2411-2447, November.
    10. A. B. Atkinson & Stephen P. Jenkins, 2020. "A Different Perspective on the Evolution of UK Income Inequality," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(2), pages 253-266, June.
    11. Facundo Alveredo & Juliana Londoño Vélez, 2013. "High incomes and personal taxation in a developing economy: Colombia 1993-2010," Commitment to Equity (CEQ) Working Paper Series 12, Tulane University, Department of Economics.
    12. Shantayanan Devarajan & Elena Ianchovichina, 2018. "A Broken Social Contract, Not High Inequality, Led to the Arab Spring," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(s1), pages 5-25, October.
    13. Walter Bossert & Conchita D’Ambrosio & Kohei Kamaga, 2021. "Extreme Values, Means, and Inequality Measurement," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(3), pages 564-590, September.
    14. Pedro Herculano Guimarães Ferreira de Souza & Marcelo Medeiros, 2017. "The concentration of income at the top in Brazil, 2006-2014," Working Papers 163, International Policy Centre for Inclusive Growth.
    15. Carrera Jorge & Montes-Rojas Gabriel & Panigo Demian & Toledo Fernando, 2024. "Income Inequality and External Wealth of Nations," Journal of Globalization and Development, De Gruyter, vol. 15(1), pages 47-62.
    16. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2022. "The weight of the rich: improving surveys using tax data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 119-150, March.
    17. François Bourguignon, 2018. "Simple adjustments of observed distributions for missing income and missing people," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(2), pages 171-188, June.
    18. Frank Cowell & Emmanuel Flachaire, 2021. "Inequality Measurement: Methods and Data," Post-Print hal-03589066, HAL.
    19. Adriaan Van Velthoven & Jakob De Haan & Jan-Egbert Sturm, 2019. "Finance, income inequality and income redistribution," Applied Economics Letters, Taylor & Francis Journals, vol. 26(14), pages 1202-1209, August.
    20. Burkhauser, Richard V. & Herault, Nicolas & Jenkins, Stephen P. & Wilkins, Roger, 2016. "What Has Been Happening to UK Income Inequality since the Mid-1990s? Answers from Reconciled and Combined Household Survey and Tax Return Data," IZA Discussion Papers 9718, Institute of Labor Economics (IZA).

    More about this item

    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:plo:pone00:0283199. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.