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Private Partner Selection and Bankability Assessment of PPP in Infrastructure Projects

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Private Partner Selection and Bankability Assessment of PPP in Infrastructure Projects

El Fathali, Hassan Ibrahim (2015) Private Partner Selection and Bankability Assessment of PPP in Infrastructure Projects. PhD thesis, Concordia University.

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Abstract

The use of Public Private Partnerships (PPPs) for infrastructure projects has garnered much international attention over the past few decades. The inclusion of private investors and operators has expanded and improved the quality of public services. When entering a PPP, the most important decision for governments is the selection of the private partner. The selection process should identify and pre-qualify those prospective partners that have the best potential for the successful development and delivery of the proposed PPP project. A successful partnership should ensure that both partners, i.e. the public sector department and the private corporation, have an effective business relationship. While private partner selection is a critical factor, essential for the successful completion of PPP projects, there is a general lack of decision making tools available to assist governments in the selection process. This research aims to assist governments with such decisions by (1) identifying and studying the criteria for selecting the most appropriate private partners for PPP projects; (2) developing a model to select the best private partner; and (3) developing model(s) and a framework that can assess a project’s risk profile from the financing agencies’ perspective.
This research proposes two integrated models. The first model is developed to select the best private partners for PPP infrastructure projects. The selection process is modeled using a fuzzy analytic network process (FANP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to adequately handle the process’s imprecision, vagueness and uncertainty. This model takes into account the possible dependencies among selection criteria as well as between alternatives and selection criteria, providing a more realistic solution than deterministic models which ignore such interdependencies. The second model is developed to improve the credit evaluation procedure for evaluating private partners for PPP projects, wherein their bankability is assessed before accepting their request to borrow funds. Publicly-available financial information is utilized to drive a clear understanding and sound interpretation of a project’s free cash flow and its forecasted future free cash flow. In this model, a set of criteria are defined from a survey conducted with credit experts, and then the TOPSIS method is used to calculate the weights of the criteria. Four criteria are used to assess private partners' financial ability based on the detailed free cash flows in multiple scenarios. The developed framework is applied to six PPP projects in Africa where the private partners of these projects were analyzed, evaluated, and prioritized based on their bankability. The model’s result provides creditors with two benefits; it ranks the private partners according to their overall suitability based on the projects’ characteristics and creditors' requirements, and it calculates the maximum amount a creditor would be willing to pay as a loan to each of the partners.
The developed framework is expected to contribute to the body of knowledge in four main aspects. First, it provides a structured tool for governments and decision makers to use to evaluate potential private partner's ability to achieve their strategic objectives, as well as identifying the partners’ strengths and weaknesses. Second, the decision-making tool accounts for influential factors other than the already widely-considered technical and financial aspects, such as safety, environmental, political and managerial concerns. Third, the bankability assessment model combines risk and credit analysis, which enables creditors to rank projects according to their overall suitability based on a projects’ characteristics and the creditors' requirements. Finally, the developed framework provides credit analysts with a tool to quantify the risks affecting projects, and to calculate the maximum amount a creditor would be willing to pay as a loan to each of the projects.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:El Fathali, Hassan Ibrahim
Institution:Concordia University
Degree Name:Ph. D.
Program:Civil Engineering
Date:20 October 2015
Thesis Supervisor(s):Moselhi, Osama and Zayed, Tarek
ID Code:980650
Deposited By: HASSAN IBRAHIM EL FATHALI
Deposited On:16 Jun 2016 15:23
Last Modified:18 Jan 2018 17:51
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