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Evaluation of PPP-ABS investment environment based on combined weighting of level difference maximization and TOPSIS method

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  • Lijun Zhang
  • Junwen Feng
  • Bo Feng

Abstract

Asset-backed securitization (ABS) is currently used to refinance public-private partnership (PPP) projects in the infrastructure field. To stimulate the investors’ enthusiasm, this study evaluated the investment environment of PPP projects asset-backed securitization (PPP-ABS). Firstly, we established a PPP-ABS investment environment evaluation indicator system based on the literature review and the practice of PPP-ABS. Then, the optimal weights of each indicator were determined by the combined weighting of level difference maximization method, where the subjective weights were determined by the AHP method, and the objective weights were determined by the entropy method. Finally, we evaluated the PPP-ABS investment environment from 2015 to 2022 with the technique for order preference by similarity to ideal solution (TOPSIS) method. The final valuation results are consistent with the actual situation. The results showed that the PPP-ABS investment environment exhibits a stable and upward trend. Under the overall guidance of the government, the approval process, information disclosure and supervisory systems have continued to improve, the number of ABS products issued has continued to grow, and the overall market risk is controllable. However, some problems still need to be solved and improved, including inadequate accounting and tax systems, insufficient liquidity in the secondary market, and the recovery of economic development in the post-COVID-19 era. This study fills the research gap in PPP-ABS. It proves the rationality and feasibility of PPP-ABS and is expected to provide a reference for investors’ decision-making and promote the sustainable and healthy development of PPP-ABS.

Suggested Citation

  • Lijun Zhang & Junwen Feng & Bo Feng, 2023. "Evaluation of PPP-ABS investment environment based on combined weighting of level difference maximization and TOPSIS method," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-25, December.
  • Handle: RePEc:plo:pone00:0295856
    DOI: 10.1371/journal.pone.0295856
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