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The Minimum revenue guarantee and toll revenue cap optimization for PPP highways: Pareto optimal state approach

Author(s):


Medium: journal article
Language(s): English
Published in: The Baltic Journal of Road and Bridge Engineering, , n. 4, v. 10
Page(s): 365-371
DOI: 10.3846/bjrbe.2015.46
Abstract:

In the Public-Private Partnership highway projects the Minimum Revenue Guarantee and Toll Revenue Cap policies are effective measures for risk and benefit sharing between the government and the private sector. However, if the Minimum Revenue Guarantee and Toll Revenue Cap values are unreasonable, it may lead one part of the investors to take too much risk and financial burden. This paper mainly establishes six objectives from the return and risk perspectives of the government, the concessionaire and the overall situation respectively. Because the traditional Discount Cash Flow method does not consider the risk factors, this paper proposes to use Monte Carlo simulation and scatter search algorithm to calculate the optimal values of the Minimum Revenue Guarantee and Toll Revenue Cap under different objectives. Compared with the statistics of the Net Present Value under different cases, it was summarized that when the objective is minimizing the variance of the total Net Present Value, the investors will realize the Pareto optimal state between the return and risk. In addition, it was found that the government is more sensitive to the Minimum Revenue Guarantee and Toll Revenue Cap marginal values according to the sensitive analysis. Therefore, the model has an effect on improving the fairness of the risk sharing measures, reducing the financial burden of the investors especially the government, and increasing the investment attraction of the private sectors.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/bjrbe.2015.46.
  • About this
    data sheet
  • Reference-ID
    10084642
  • Published on:
    12/11/2018
  • Last updated on:
    12/11/2018
 
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