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Quantifying the Decision-Making of PPPs in China by the Entropy-Weighted Pareto Front: A URT Case from Guizhou

Author

Listed:
  • Feiran Liu

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Jun Liu

    (State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

  • Xuedong Yan

    (MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China)

Abstract

In recent years, value for money (VFM) evaluations using the Public Sector Comparator (PSC) method have gradually been adopted by governments worldwide in the field of public investment, and used as decision-making tools for public–private partnership (PPP) projects. However, there has been little research concerned with such emerging decision-making questions with VFM. This paper proposed a quantitative decision-making method of entropy-weighted Pareto front applied in the specific context of Chinese PPPs, with a case study of an Urban Transit Railway (URT) PPP project from Guizhou province. Factor analysis was used to extract the qualitative indicators of VFM evaluation, associate them with the quantitative characteristics of the project, and thus help public sector decision-makers choose the proper quantitative decision variables.

Suggested Citation

  • Feiran Liu & Jun Liu & Xuedong Yan, 2018. "Quantifying the Decision-Making of PPPs in China by the Entropy-Weighted Pareto Front: A URT Case from Guizhou," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1753-:d:149201
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    Cited by:

    1. Bisola Beatrice Oguejiofor & Ngozi Samuel Uzougbo & Abosede Olusade Kolade & Ahmed Raji & Chibuike Daraojimba, 2023. "Review of Successful Global Public-Private Partnerships: Extracting key Strategies for Effective U.S. Financial Collaborations," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(8), pages 312-331, August.

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