Development of a Financing Optimization Framework Based on Risk Simulation in BOT Projects: A Case Study of the Waste-to-Energy Project

D. Yaghubi, M. Doroodian, M. A. Adibi and Reza Lotfi

Complexity, 2023, vol. 2023, 1-23

Abstract: Infrastructure projects that are mostly characterized by high uncertainty usually face various risks at all stages as timing risks, cost risks, and disruption in the executive processes (by the reason of unpredictable obstacles in financing risks, technology production, and so on). Owing to the complex nature of infrastructure projects, the build-operate-transfer (BOT) contract is usually concluded between the private and public sectors. Considering that the public sector transfers all or part of its financial risk to the private sector (contractors), in this type of contract, the distribution of risks is different from that of traditional contracts. Besides, project implementation methods and the lack of risk management might lead to the failure of the project. As the implementation of such projects, along with the risks of the projects that require a large amount of investment, it would be necessary to develop a proper financing schedule with consideration of the effect of repayment of various loans in the project to ensure the feasibility of the project. So, in this project, considering the effects of risks in a waste-to-energy infrastructure project, an optimal project financing framework is developed. In the current research, using Monte Carlo simulation, the impact of risks on the project is investigated during the construction period and the operation period. The results have shown that consideration of the impact of the risks on projects might have a significant effect on the increase of time and cost; nevertheless, the cost of optimal financing might reduce the project profit by 23%. The results indicate that choosing the appropriate financing solution guarantees the project’s final profit. Besides, it can help project managers to make the best financing decisions based on realistic situations.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8129256

DOI: 10.1155/2023/8129256

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