IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v95y2024ics0038012124001460.html
   My bibliography  Save this article

Hybrid model for evaluating the transformation of China’s resource-based cities

Author

Listed:
  • Pu, Song
  • Xia, Chang

Abstract

This paper constructs a new and proposes a novel hybrid model combining multi-criteria decision making (MCDM) model and variability for multi-periods as well as the approaches to select the best specific hybrid model. A case study based on 17 RBCs in three provinces of southwest of China for 2012–2020 indicates that the transformation efficiency of most RBCs is between 0.400 and 0.600. More specifically, 12 out of 17 RBCs have a positive variability direction with the biggest variability value less than 0.039. Sichuan has an obvious downward trend with a decreased rate of 9.13%, while Yunnan and Guizhou increase by 10.50% and 6.43%, respectively. The transformation efficiency of mature cities is the worst, while the only recession city is the best. The transformation efficiency of RBCs has the highest correlation with the freight volume, other indicators including unemployment rate, ratio of GDP growth, fiscal revenue, average annual population and unemployment rate are high correlation with the transformation efficiency.

Suggested Citation

  • Pu, Song & Xia, Chang, 2024. "Hybrid model for evaluating the transformation of China’s resource-based cities," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  • Handle: RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124001460
    DOI: 10.1016/j.seps.2024.101947
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012124001460
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.seps.2024.101947?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.

    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:eee:soceps:v:95:y:2024:i:c:s0038012124001460. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.