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Unlike other project delivery options, a much larger proportion of risks is borne by the private partner in public‐private partnerships, since a large amount of equity is needed to finance the scheme. As a result, it is of paramount importance for the franchisee to analyse the possible project outcomes with due reference to potential risks affecting cash inflow and outflow. The purpose of this paper is to address the shortcomings of deterministic estimations by developing a proposal for a simulation model that aims to unveil the probability distributions of the equity amount and return on equity.

In this paper, a simulation model is developed to establish the probability distributions of these two indicators under the influence of risks. A simple case study is also presented to illustrate the concept and application of this model.

The simulation model can generate the probability distributions related to the net present value of the equity component as well as the rate of return on equity.

The method proposed in this paper should help the private investors analyse the amount of equity to be injected to the project and its corresponding return rate.

By referring to the probability distribution, an equity investor can establish whether they can recover their investment and gain a desired return rate. Based upon the risk attitude of the investor, decision‐makers can then decide whether the scheme should be pursued or not.

The purpose of this paper is to devise a simple but practical model to assist decision makers in evaluating the tariff stability of concession schemes.

To develop such a model necessitates the identification of parameters that could contribute to an increase or decline in investment return. With that a Monte‐Carlo‐based simulation model is devised to determine the probability that the tariff regime remains unchanged even when the identified risks do occur at the operational stage. Sensitivity analysis is performed to identify the most influential factors to investment return and tariff stability.

The results of the scenario indicate that the internal rate of return could be profoundly influenced by the risk factors which reaffirm the needs for a more comprehensive model for tariff stability evaluation.

Through the simulation model, a tariff stability indicator is derived and when integrated with the results of sensitivity analysis this could generate a weighted indicator for alternative tariff regimes for use in decision support systems.

With the aid of simulation techniques, decision makers can predict the impact of a range of possible market conditions and/or levels of demand on the investment return and hence the stability of the tariff regime.

The model could be extended to other types of public‐private partnerships schemes upon suitable adjustment

The present paper aims to introduce a new methodology taking risk behavior of decision maker into account to fine‐tune the value of a risky public‐private‐partnership (PPP) project and the corresponding cost of capital based on the target rate of return set by the project sponsor and the degree of project risks.

The proposed methodology combines the cumulative prospect theory (CPT) to characterize the risk preference of the project sponsor and the Monte Carlo simulation to assess the project riskiness. The methodology requires a pre‐set target rate of return that will define the relative gains and losses for a prospect theory project sponsor. The application was illustrated using a build/operate/transfer toll road project as a case study.

As the project sponsor sets a greater target return, the probability of the project not meeting the target is accordingly greater. Given that losses have greater impact than gains on the decision, other things being equal, a higher target return leads to a higher value correction. It has also been demonstrated that the corresponding project's cost of capital can be up‐ or downadjusted depending on the project's riskiness which may result in a reverse preference to favor a higher risk scenario.

The methodology uses the CPT parameters that need to be further confirmed and validated if applied to value large risky projects like PPP investments.

The proposed methodology offers a different approach to correctly value a risky PPP project by extending the application of the cumulative prospect theory that well explains the irrationality of human decision behavior under risk into a financial decision‐making process. It takes the full benefit of simulation to understand project risks and also assists financial decision‐making.