Concession period optimisation in complex projects under uncertainty: a public–private partnership perspective

Abstract

Concession period is decisive in public–private partnership (PPP) projects, particularly for complex projects. A systematic approach incorporating real option analysis (ROA) is proposed to optimise the concession under uncertainty. Options are explored to manage uncertainty in the long concession of PPP projects and an optimisation principle is proposed for the concession optimisation. Sensitivity analysis and case comparison are suggested to test the validity of the optimised concession. Applicability of the approach is tested with a PPP utility tunnel case, in which options to expand and recoup are investigated. Important findings include: (1) interests of participants are balanced through the optimisation, the investment value of the project is increased; (2) profit-sharing mechanism is identified as the most important factor, and a prudent design of it can effectively manage uncertainty; (3) option to recoup can optimise the concession period without harming private developer's interest. This research's novelty lies in (a) exploring the management flexibility and identifying potential options in the concession period of PPP projects; (b) a hybrid approach with the integration of ROA and Sobal-based global sensitivity analysis is proposed to perform the concession optimisation; (c) the profit-risk allocation between participants in PPP projects is balanced under the optimisation principle.

Keywords:

• PPP
• concession period
• optimal design
• complex systems
• real option analysis
• global sensitivity analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

The Ministry of Education Tier 1 Grants, Singapore (No. 04MNP000279C120, No. 04MNP002126C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120) are acknowledged for their financial support of this research.