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Concession period for PPPs: A win–win model for a fair risk sharing
JPMA-01608; No of Pages 10
Available online at www.sciencedirect.com
ScienceDirect International Journal of Project Management xx (2014) xxx – xxx www.elsevier.com/locate/ijproman
Concession period for PPPs: A win–win model for a fair risk sharing Nunzia Carbonara, Nicola Costantino, Roberta Pellegrino ⁎ (DMMM)Department of Mechanics Mathematics and Management, Polytechnic of Bari, Viale Japigia 182 70126, Bari, Italy Received 22 October 2013; received in revised form 16 January 2014; accepted 21 January 2014
Abstract Public Private Partnership (PPP) is adopted throughout the world for delivering public infrastructure. Despite the worldwide experience has shown that PPP can provide a variety of benefits to the government, to fully gain them several critical aspects related to a PPP project need to be managed, among these the determination of the concession period. This paper provides a methodology to calculate the concession period as the best instant of time that creates a ‘win–win’ solution for both the concessionaire and the government and allows for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects the PPP projects, the Monte Carlo simulation was used. To demonstrate the applicability of the proposed model, a Build–Operate–Transfer (BOT) port project in Italy has been used as case study. © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Concession period; Public Private Partnerships; Risk allocation; Monte Carlo simulation
1. Introduction In the last decades, due to public budget constraints and the severe need for new or upgraded infrastructure, more and more governments have fostered private sector involvement in public investment projects. For this reason, Public Private Partnerships (PPPs) have become a major scheme in delivering public infrastructure (Hodge and Greve, 2007; Kwak et al., 2009). PPPs are “agreements where public sector bodies enter into long-term contractual agreements with private sector entities for the construction or management of public sector infrastructure facilities by the private sector entity, or the provision of services by the private sector entity to the community on behalf of a public sector entity” (Grimsey and Lewis, 2002). The idea of allowing private firms to finance projects of public infrastructure results in the emergence of PPPs (Li and Akintoye, 2003; Tang et al., 2010). The worldwide experience has shown that PPPs can ⁎ Corresponding author at: Viale Japigia 182, 70126 Bari Italy. Tel.: + 39 0805962850. E-mail addresses: [email protected] (N. Carbonara), [email protected] (N. Costantino), [email protected] (R. Pellegrino).
provide a variety of benefits to the government. In particular, PPPs can increase the “value for money” spent for infrastructure services by providing more-efficient, lower-cost, and reliable services; improve the quality and efficiency of infrastructure services, and promote local economic growth and employment opportunities. Furthermore, PPPs allow the public sector to transfer risks related to construction, finance, and operation of projects to the private sector and to keep public sector budget deficiencies down (Kwak et al., 2009). However, to fully gain the above listed benefits several critical aspects related to a PPP project need to be managed. Among these: the assessment of risks associated with the PPP project; the identification of suitable risk allocation and mitigation strategies; the definition of a sound financial plan; the selection of the appropriate concessionaire; and the determination of the concession period. Researchers have proposed several conceptual models, tools and techniques to support the decision processes related to these critical aspects (Garvin and Ford, 2012; Li et al., 2005; Schaufelberger and Wipadapisutand, 2003; Zhang, 2005a, 2005b). This paper focuses on the length of concession period, considered one of the most important issues in the PPP contracts.
0263-7863/$36.00 © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2014.01.007 Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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In the common international practice, the government usually presets the concession period to a fixed length, requests the concessionaire to bid for other project aspects, and guarantees the concessionaire a certain level of internal rate of return (Zhang, 2009). This practice, however, does not generally lead to an efficient selection of concessionaires and it also induces the frequent failure or renegotiation of concession contracts (Gustavo and Rus, 2004). To overcome these problems, there is a need for the government to use a methodology that appropriately calculates the concession period. Recently, some researchers attempted to determine a reasonable concession period. Most of the models proposed in the literature use the net present value (NPV) of the project cash flow as criterion for calculating the concession period. Some of these determine the concession period considering only the maximization of the concessionaire's benefits. For example, Engle et al. (2001) and Vassallo (2006) suggest the least-present-value of revenue (LPVR) method to determine the concession period of toll roads. Other models adopt a win–win approach, which means that the concession period is determined in order to maximize benefits of both the government and the concessionaire (Shen et al., 2002; Zhang and AbouRizk, 2006). Among these, only few models take into account the risk exposure of both partners (Chan et al., 2011; Hanaoka and Palapus, 2012; Shen and Wu, 2005), but none of these allows one for a fair risk sharing between the parties. Finally, most of the developed models determine the concession period as a time interval within which a specific concession period could be agreed upon by the government and the private sector and very few of the previous methods calculate the concession period as the instant of time within which the concession must end. In order to overcome the above discussed limitations, the present paper develops a model for calculating the concession period as the best instant of time that creates a ‘win–win’ solution for both the concessionaire and the government and allows for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects PPP projects, the Monte Carlo simulation is used. To demonstrate the applicability of the proposed model, we apply it to a Build–Operate–Transfer (BOT) port project launched by the Municipality of Bari (Southern Italy) to construct, operate and maintain the “San Cataldo Port of Bari”, consisting in a tourist port, a dock for cruise ships, and six multi-purpose buildings. Section 2 of this paper reviews the existing methodologies for determining concession period. In the third section, we present the win–win concession period model and in the next section we apply it to a Build–Operate–Transfer (BOT) port project in Italy. Conclusions end the paper. 2. Concession period Concession period starts from the signing of the concession agreement between the government and the private sector
indicating the span of time within which the private sector is responsible for the construction phase and operation phase in BOT projects. Concession period is a key decision variable in the arrangement of a PPP contract. The length of concession period is mainly related to the recovery of investment and return required by the concessionaires. The general principle for determining its length is that the concession period should be long enough to allow the concessionaire to recoup investment costs and earn reasonable profits within that period (Smith, 1995). Generally, a longer concession period is more beneficial to the private investor, but a prolonged concession period may induce loss to concerned government. Alternatively, if the concession period is too short, the investor will either reject the contract or be forced to increase the service fees in the operation phase. Consequently, the risk burden due to the short concession period will be shifted to the party who uses and pays for the facilities. Therefore, establishing an appropriate concession period is important for the success of a PPP project (Ng et al., 2007b; Ruizheng and Li, 2010). Each concession has its duration, which may be fixed or variable. The choice depends on various risk factors such as completion time, product price and market demand. Usually, the concession has a fixed period, in which risk factors are managed through tariff design supplemented by other measures. Sometimes, the concession has a variable period, which may be extended if the specified risk factors are worse than expected or shortened if they are better than expected. For example, in order to deal with demand risk, the concession period can be varied according to the market demand. If the market demand is lower than expected, the concession period will be extended to allow the concessionaire to earn a reasonable return, and vice versa. Different models have been developed and proposed in the literature to quantitatively determine the optimal value of the concession period. Most of these models, with the due differences and specificity, use the Net Present Value (NPV) of the project as a basic parameter for calculating the optimal value of the concession period. An exception is the study of Ng et al. (2007a) that considers the internal rate of return (IRR) as criterion for project evaluation and calculates the concession period that is less risky to the concessionaire by fixing the minimum, expected, and maximum IRR acceptable to the concessionaire. Shen et al. (2002, 2007) develop an analytical deterministic method (BOTCcM) for determining the length of the concession period to be granted to the private sector in BOT projects that can protect the interests of both the host government and the private investor. BOTCcM proposes an interval for a concession period negotiation that consists of two critical points: the first point, called the starting point of the interval, aims at protecting the interests of the private investor; the second point, called the endpoint of the interval, aims at protecting the interests of the host government. Wu et al. (2012) improve BOTCcM by considering at project's transfer time its net asset value that is usually significantly greater than zero and represents a revenue for the government. Hanaoka and Palapus (2012) propose a methodology to determine a reasonable concession period that considers
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
the effect of risks on uncertain concession items in project evaluation. In particular, by using Monte Carlo simulation and bargaining game theory the methodology generates a concession period interval within which a specific concession period could be agreed upon by the government and the private sector. Any point within the interval could be considered as the optimal concession period that would be advantageous to both BOT players. Ye and Tiong (2003) evaluate the mean net present value (NPV), variance and NPV-at-risk of different concession period structures through Monte Carlo simulation, so that both the government and the concessionaire can understand their risk exposure and rewards. Zhang (2009) proposes a win–win concession period determination methodology, in which BOT projects are addressed as a principal-agent maximization problem. In this research both deterministic and simulation-based methods are provided to determine the concession period, with detailed step-by-step procedures. Engel et al. (2001) suggest the least-present-value of revenue (LPVR) method to determine the concession period of toll roads so that the franchise length is adjusted endogenously to demand realization. Other researchers use the fuzzy approach to determine the concession period appropriately. Mostafa et al. (2010) adopt a Fuzzy-Delphi technique to calculate the length of concession period considering uncertainties. In particular, they determine the values of different uncertain factors affecting a BOT project by considering the opinions of a group of experts (Delphi), and then calculate the NPV value by taking into account the resulted aggregated values of uncertain input parameters, finally determine the concession period using the fuzzy approach. The proposed methodology offers a fuzzy number for the length of concession period. Ng et al. (2007b) develop a fuzzy multiobjective decision model to evaluate and establish the most satisfactory concession item options for BOT projects. Table 1 lists the existing models developed to determine the concession period, classified with respect to three key aspects. The first aspect concerns the computation of the concession period. Respect to this, the discussed models can be classified in two main categories: 1) models that allow the calculation of the instant of time within which the concession must end;
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2) models that calculate the concession period as a time interval within which the concession contract must end. The second aspect involves the uncertainties and risk factors affecting PPP projects. By reviewing the literature, we found models which do not take into account risks and uncertainties that affect over a long period many of the input variables; and models in which uncertainties are accounted for. In the first case a deterministic approach is adopted to calculate the concession period, while in the second case stochastic models have been developed, in which each variable affected by uncertainty is modeled by a statistical distribution and the concession period is determined by using simulation methods. Finally, the third aspect concerns the perspective adopted in calculating the concession period, in terms of interest to safeguard and party to satisfy. In this respect, some models, focusing only on the private party perspective, require that the concession should be long enough to allow the concessionaire to obtain a reasonable profit. Recently, in contrast with this approach, in order to safeguard the multiple interests of the public sector and the profit-making interest of the private sector, some researchers proposed a win–win approach to determine the concession period that takes into account both the government and investor interest perspective. Each of these studies implements the win–win approach in different ways. For example, according to Zhang (2009) the win–win approach is implemented by posing the following two constraints: 1) the concession should be long enough to allow the concessionaire to obtain a reasonable IRR and 2) the concessionaire acts in the interest of the government, for example, the concessionaire may be required to continuously improve efficiency, cost effectiveness and service quality; sustain a stable and public-affordable price regime; and transfer excessive profits to the government. Following the win–win approach, Hanaoka and Palapus (2012) employ the bargaining game theory to find a reasonable concession period. Thus, the specific concession period is considered as a bargaining process wherein (i) the government and the private sector act as the players in reaching a BOT agreement, (ii) the benefit to be generated within the concession period interval within which the end of the concession period could occur, is the conflict of interest, and (iii) the negotiation does not end until an agreement of both BOT players is reached.
Table 1 Models for concession period calculation. Model
Concession period calculation Uncertainty/risk factors Satisfied party Instant of time Time interval
Private Public Both parties
LPVR
X
X
Fuzzy-Delphi Fuzzy multi-objective Fuzzy Petri Net–genetic algorithm Simulation model X MC-PDM BOTCcM CPM-MCS MCS-bargaining game theory BOTCcM-R
X X X X X X X X
X X X X
X X X
Source
X X X X X X X X X
Engle et al., 2001; Gomez-Lobo and Hinojosa, 2000; Vassallo, 2006. Mostafa et al., 2010. Ng et al., 2007b Shen and Wang, 2010 Ng et al., 2007a Zhang and AbouRizk, 2006. Shen et al., 2002; Wu et al., 2012. Zhang, 2009 Hanaoka and Palapus, 2012 Chan et al., 2011; Shen and Wu, 2005
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Finally according to Wu et al. (2012), creating a win–win situation means both assuring that the private investor not only recoups the investment but also earns a profit during the franchise operation period, and protecting the government in recouping the project depreciation costs incurred during the post-transfer operation period in a way that the cumulative cash flow during its post-transfer operation period are greater than the net asset value at its transfer. Table 1 shows that there is not a model that addresses all the three key aspects. Furthermore the methodologies adopting a win– win approach do not deal with the concept of a fair risk sharing between the two parties. Therefore, none of the previous models has the capability to support the government decision-making in the choice of a concession time that satisfies the interests of both parties by taking into account unforeseen risks and uncertainties and that allows a fair risk sharing between parties. To overcome this limitation, we propose a win–win concession period determination methodology, which calculates the concession period as the instant of time within which the concession must end, takes into consideration risks and uncertainties, and safeguards the interests of both parties involved in the concession contract, the public sector and the private sector, by fairly allocating the risk between the two parties.
In the following, we propose an innovative win–win concession period model that addresses the following issues: 1. Taking into consideration the win–win principle, in the sense that the estimated value of the concession period should be able to protect the interests of the private investor and the government simultaneously and to assure that the interests of the two parties are satisfied in a balanced way; 2. Calculating the instant of time within which the concession must end; and 3. Considering the effect of uncertainty. To address these issues, we develop a methodology that calculates the optimal instant of time in which the concession should end (Tc) as result of the following equations: ð1Þ
Where: - NPVC is the net present value of the of the project's cumulative net cash flow n X C Ft ð 1 þ r Þt t¼0
- IC is the total investor's capital investment IC ¼
tX constr t¼0
C Ft ð1 þ rÞt
Eq. (1) expresses the principle that the net present value of the project's cumulative net cash flow should be no less than the private investor's expected minimum return on investment. NPVC ≤ IC Rmax
ð2Þ
Where: - Rmax is the maximum return rate allowed by the government to investors. Eq. (2) expresses the condition that the net present value of the project's cumulative net cash flow should be no higher than a cap rate of return fixed by the government in order to avoid lucrative conditions. NPVG ð FÞ≥ 0
ð3Þ
Where:
3. The win–win model
NPVC ≥ IC Rmin
- Rmin is the investor's expected minimum return rate from his capital investment - r is the discount rate - tconstr. is the construction period.
- NPVG (F) is the net present value of the project's cumulative net cash flow during the post-transfer operation; - F is the end of the life time of the project. Eq. (3) expresses the principle that the net present value of the project's cumulative net cash flow during the post-transfer operation and calculated at the end of the life time of the project (F) should be no less than zero. By solving Eq. (1) we obtain the minimum value of the concession period (tmin), i.e. the instant of time before which the project should not be transferred because it does not allow the private investor to receive the expected minimum return on investment. By solving Eq. (2) the maximum value of the concession period (tmax) is calculated. This is the instant of time after which the granting of the project should not continue, in order to avoid an excessive profit to the private sector. Eq. (3) allows the calculation of tc, that is the instant of time before which the investor should transfer the project to the government, in order to guarantee its interests. The time interval within which fix the concession period is defined by tmin, tmax and tc (see Fig. 1). Three possible situations can occur: 1. tc b tmin: in this situation it is impossible to determine a concession period that jointly satisfies the interests of the private investor and the host government; 2. tc ≥ tmax: in this situation the concession period of the project can assume any value within that interval [tmin–tmax]; 3. tmin ≤ tc b tmax: in this case the interval for a concessionperiod negotiation is fixed between the two points tmin and tc,
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
therefore the concession period can assume any value within that interval [tmin − tc]. Once the interval for a concession period negotiation has been derived through the previous procedures, namely the time interval that jointly satisfies the interests of the private investor and the host government, it is possible to determine the instant of time within which the concession must end (Tc). In order to satisfy the win–win condition, Tc is calculated as the instant of time that minimizing the difference between NPVC and NPVG, by solving the objective function given by the following equation: Tc э′MinðNPVC −NPVG Þ
ð4Þ
Eq. (4) expresses the condition that both parties, the private investor and the host government, are equally satisfied, that is, it assures that the interests of the two parties are satisfied in a balanced way. Concession period Tc is a function of a set of variables affected by uncertainty. To take into consideration the effect of uncertainty, we adopt the Monte Carlo simulation. In particular, first we develop a stochastic model by assigning to each random variable a statistical distribution, which requires the selection of a suitable theoretical distribution function and the estimation of its parameters. Then, by running the model at the end of simulation we derive the statistical distribution of the concession period (Tc). Once the distribution of Tc is known we determine the value of Tc that allows the difference between the risks of loss borne by the two parties to be minimized. These risks are measured as follow: - risk borne by the concessionaire = Prob (NPVC b Ic ∗ Rmin); and - risk borne by the government = Prob (NPVG(F) b 0).
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The variables used as input to the model are categorized into: 1) deterministic/certain and 2) uncertain variables. Deterministic variables are defined in this paper as variables of which values could be considered as stable over time. Uncertain variables, on the other hand, are those inputs which may be subject to change and high level of uncertainty over time since their future values are difficult to predict. The deterministic variables are: - The construction period and project life time: the first is the period from the project start-up date until the target date of completion. While in reality construction period may change due to some events that may occur during construction preventing the project to be delivered on time, it is assumed equal to 3 years. The project life time is assumed equal to 60 years. - The investment cost and the residual value of the project: the first includes expenditure items needed until the completion of the project, equal to € 28.096.177. The residual value of the project at the transfer time is € 5.6 million, equal to 20% of the investment cost of the project. - The maximum return rate allowed by the government to the investor (Rmax), equal to 15% and the minimum rate of return for the investor (Rmin), equal to 8%. - The discount rate r, that in the case of Monte Carlo simulation is generally a risk free rate (Brealey and Myers, 2000), since the risk is already included in the project cash flows that depend on the randomly-chosen values of the input parameters. The risk-free discount rate can be taken as the interest rate on Government bonds. In particular, in this case r is fixed at 5%, taken the average interest rate on long term Italian Government Bonds as a proxy. - Expenses during operation, including items such as the state concession fee, regular annual maintenance costs, overhead operating costs, and insurance. - Revenues from real estate activities (rental fees for commercial and services areas).
4. Case study The uncertain variables are: We applied the developed model to the case of the BOT tourist port project launched by the Municipality of Bari, located on the South-East coast of Italy. The port includes: a cruise terminal, floating docks with a capacity of 447 boats, accessible by boats up to 30 m, a parking for 577 vehicles, office, service, and multifunctional buildings (covering an area of about 8000 m2), a bunkering area, and 104 storage spaces. In the operation phase the revenues mainly consist of port dues, land rent, mooring dues, and parking space rent. The port dues cover the use of the nautical port infrastructure (such as pilotage and docking fees) and are paid by cruise ships. The mooring dues consist of: long-term leases, annual leases, and transit fees. The operation costs mainly consist of management expenses, operations and maintenance costs, insurance, and State Government concession fee. The estimation of the operating revenues and costs has been based on historical data, data collected on similar projects, and experts' opinions.
- Revenues associated on the operation period and determined by the services management. These come from port dues, mooring dues, and parking space rent. The port dues cover the use of the nautical port infrastructure (such as pilotage and docking fees) and are paid by cruise ships. The mooring dues consist of: long-term leases, annual leases, and transit fees. These are determined by the unitary fee to be charged to the users of the specific services and the number of users expected for those services. In particular, as for the mooring services, the users are the vessel owners, for parking service the users are both the vessel owners and port visitors. As for the nautical port infrastructure, the users are the cruise ships. - Costs associated on the operation period and due to the services management. These are assumed equal to the 12% of the operating revenues, thus their distribution over time follows the distribution of the revenues related to the corresponding service.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
Table 2 Statistical distribution of input random variables. Input random variables
Table 3 Statistics of tmin, tmax and tc.
Probability distribution function
Parameters
Number of seasonal rented moorings
Binomial distribution
Number of full year rented moorings
Binomial distribution
Number of transit rented moorings
Binomial distribution
n = 45 p = 80% n = 224 p = 90% High season n = 43 p = 95% Middle season n = 43 p = 80% Low season n = 43 p = 60% μQ = 1.5% σ = 10% n = 162 p1 = 5% a p2 = 10% p3 = 20% p4 = 30% p5 = 50% p6 = 60% p7 = 70% p8 = 90% n = 415 p1 = 20% p2 = 30% p3 = 50% p4 = 60% p5 = 70% p6 = 90%
Traffic volume of cruise ships Number of occupied parking spaces for port visitors
Number of occupied parking spaces for vessel owners
Geometric Brownian motion Binomial distribution
Binomial distribution
a
On the basis of the historical data, we have fixed different values of p, each corresponding at different monthly occupancy rates.
The assumptions used for modeling the statistical distributions of the uncertain variables are summarized in Table 2. In particular, the table shows for each input random variables the corresponding probability distribution function and its defining parameters, defined on the basis of the historical empirical data. Notice that, the discrete input variables, namely the number of rented moorings and the number of occupied parking spaces, and the related revenues, have been modeled with the binomial distribution that is a discrete probability distribution, where the random variable is the number of “successes” (i.e. rented moorings or occupied parking spaces) in n independent trials (i.e. available moorings or parking spaces) with a probability of success constant and depending on the seasonal occupancy rates. The conditions of applicability of the binomial probability distribution are satisfied since there is a fixed number of n trials (i.e. available moorings or parking spaces); the outcome of a given trial is either a “success” (rented/occupied) or “failure” (not rented/not occupied); the probability of success (p) remains constant from trial to trial (seasonal occupancy rates); and the trials are independent. Instead, we have assumed that the traffic volume of cruise ships, and the related revenues, will vary stochastically in time following a geometric Brownian motion (GBM), as is standard
tmin tmax tc
Mean
Std dev
Minimum
Maximum
39.05 54.72 43.39
1.88 13.87 18.05
0.00 0.00 0.00
55.00 6000 5400
in the literature (Garvin and Cheah, 2004; Iyer and Sagheer, 2011; Pichayapan et al., 2003). The stochastic evolution of traffic volume (Q) that follows a GBM can be modeled in yearly periods as a function of the value in previous period according to the following equation: μ 2 pffiffiffiffi Q σ Qtþ1 ¼ Qt e − 2 Δtþσε Δt Where: μQ is the expected traffic growth rate; σ is the annual volatility of the variable; ε ~ N(0,1) is the standard Wiener process. This model implies that the traffic can never be negative and its evolution can be completely specified considering only its initial value Q0, a yearly growth rate and the volatility of the process, which we assume to be constant during the concession period. After establishing the input data modeling, the Monte Carlo simulation approach has been used for calculation of NPV. In particular, in each computer iteration, the random values of the stochastic input variables are generated on the basis of their statistical distributions as established in input data modeling. Each simulation consists of 1000 computer runs. The outcome of the simulation model, taking into account the project variables above, is the distribution of the project's cumulative net cash flow over the life-time of the project F. By solving Eqs. (1)–(3) we calculate the probability distributions of tmin, tmax and tc (see Table 3). The instant of time within which the concession must end (Tc) is contained in the interval [Tcmin–Tcmax], where: - Tcmin = lower bound concession interval = tmin and - Tcmax = upper bound concession interval = min[tc;tmax]. Figs. 2 and 3 show the probability distribution of Tcmin and Tcmax. Tc is then determined by solving the objective function Min(NPVC–NPVG). Fig. 4 plots the statistical distribution of Tc derived after 1000 computer iterations. The simulation returns a value of 0 in all situations/scenarios where the conditions of the model are not satisfied, i.e. it is impossible to determine a concession period that jointly satisfied the interest of both parties. Tc range within the interval [45–51] years with a probability of 80% (the 20th percentile is 45 years), with a mean 40.55 years, mode 48.00, and median equal to 47 years.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
Fig. 1. Cash flow profile and time interval for the concession period determination.
Fig. 2. Probability distribution of the lower bound concession interval (Tcmin).
Fig. 3. Probability distribution of the upper bound concession interval (Tcmax). Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Fig. 4. Probability distribution of the concession period (Tc).
In order to define the optimal value of Tc we have conducted a risk analysis. For each values of Tc within the interval [45–51], we have calculated the probability that the net present value of the project's cumulative net cash flow is below the private investor's expected minimum return on investment (private risk) and the probability that the net present value of the project's cumulative net cash flow during the post-transfer operation calculated at the end of the life time of the project is less than zero (public risk). Fig. 5 depicts the risk borne by the two parties. Notice that at Tc = 49 years the difference between the risks of loss borne by the two parties is minimized, so as having the best risk allocation between the two parties. Fig. 6 (a and b) shows the probability distributions of NPVC and NPVG calculated for the optimal value of the concession period (Tc = 49 years) and indicates the value of risk borne by the two parties.
To get a feel for the likely impact of the interest rate r on movement of the model outcome, sensitivity analysis was carried out, yielding the results shown in Fig. 7. The sensitivity analysis indicates that the increase of Tc is more than proportional to the increase of r. In this case we found that for r = 6% it is impossible to determine a concession period that jointly satisfies the interests of the private investor and the host government, since the instant of time before which the investor should transfer the project to the government, in order to guarantee its interests (tc) is lower than the instant of time before which the project should not be transferred because it does not allow the private investor to receive the expected minimum return on investment (tmin). The results of the sensitivity analysis therefore show that the interest rate affects on the choice of the concession period, so much that there could not be an instant of time that jointly satisfies the interests of the private investor and the host
100,00% 90,00% 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00%
45
46
47
48
Private risk (Prob(NPVc-Ic*Rmin)<0)
49
50
51
Public risk (Prob(NPVg<0)
Fig. 5. Risk borne by the concessionaire and the government for each value of Tc. Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
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Fig. 6. Probability distributions of NPVC and NPVG for Tc = 49 years.
government within the life time of the project. The outcome of the sensitivity analysis can be also useful for supporting the choice of the rate during the negotiation. 5. Conclusion A key factor of a PPP project is the agreement on the length of concession period. The concession period is one of the most important decision variables for arranging a successful PPP contract because its value decides when ownership of a project will be transferred from the investor to the government, thereby 55 50
TC
45 40 35 30 25 2%
3%
4%
5%
r Fig. 7. Sensitivity analysis results.
6%
demarcating the authority, responsibility, and benefits between the private party and the government. This paper provides a new model for calculating the concession period as the best instant of time that creates a ‘win–win’ solution for both project promoter and the host government and allows one for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects the PPP projects, the Monte Carlo simulation technique was used, which requires establishing the statistical distributions of the random input variables. A case study on a BOT port project launched by the municipality of Bari located in Southern Italy were used to check the applicability of the proposed model for determining the reasonable length of the concession period that safeguards the multiple interests of the public sector and the profit-making interest of the private sector and allows for a fair risk sharing between the public and private sectors, by minimizing the difference between the risks of loss borne by the two parties. The application shows that the developed model can be a valid tool for supporting the public authority in the decision-making process about the length of the concession period.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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The application of the model requires, as usual in this kind of problems, some assumptions on input parameters. However, we have done a sensitivity analysis on one of the most critical parameter of the model, namely the discount rate, and proven that even if the concession period is affected by the selected value of the interest rate the model keeps its usefulness for supporting the negotiation process in choosing an appropriate discount rate, thus avoiding costly renegotiation. It can be observed that the validity and usefulness of the model has been proven through a single case study. However, it is important to observe that the structure of the model and its equations do not change from case to case, thus assuring the generalizability of the model. The main limitation indeed relies on the input data modeling, which is a critical step inherent in the simulation-based approach, affecting the outcome. In fact it may not be easy for decision-makers to accurately predict the uncertain parameters and their probability distributions. Further research will be devoted to apply the proposed methodology to other PPP projects which may have different cash flow structure to strength its effectiveness and prove its validity beyond the specific case analyzed by the present study. Future works may also focus on further refinement of the simulation model by gathering more information on key variables to accurately assign their probability distribution.
References Brealey, R., Myers, S., 2000. Principles of corporate finance, 6th edition. Irwin McGraw-Hill, Boston. Chan, A.P.C., Yeung, J.F.Y., Yu, C.C.P., Wang, S.Q., Ke, Y., 2011. Empirical study of risk assessment and allocation of public–private partnership projects in China. J. Manag. Eng. 27 (3), 136–148. Engel, E.M.R.A., Fischer, R.D., Galetovic, A., 2001. Least-present-value of revenue auctions and highway franchising. J. Polit. Econ. 109 (5), 993–1020. Garvin, M.J., Cheah, C.Y.J., 2004. Valuation techniques for infrastructure investment decisions. Constr. Manag. Econ. 22 (4), 373–383. Garvin, M.J., Ford, D.N., 2012. Real options in infrastructure projects: theory, practice and prospects. Eng. Proj. Organ. J. 2 (1–2), 97–108. Gomez-Lobo, A., Hinojosa, S., 2000. Broad roads in a thin country: infrastructure concessions in Chile. Research Paper 2279The World Bank, Washington DC. Grimsey, D., Lewis, M.K., 2002. Evaluating the risks of public–private partnerships for infrastructure projects. Int. J. Proj. Manag. 20 (2), 107–118. Gustavo, N., Rus, G., 2004. Flexible-term contracts for road franchising. Transp. Res. A Policy Pract. 38 (3), 163–179. Hanaoka, S., Palapus, H.P., 2012. Reasonable concession period for build–operate– transfer road projects in the Philippines. Int. J. Proj. Manag. 30, 938–949. Hodge, G.A., Greve, C., 2007. Public–private partnerships: an international performance review. Public Adm. Rev. 67 (3), 545–558. Iyer, K.C., Sagheer, M., 2011. A real options based traffic risk mitigation model for build–operate–transfer highway projects in India. Constr. Manag. Econ. 29 (8), 771–779.
Kwak, Y.H., Chih, Y., Ibbs, C.W., 2009. Towards a comprehensive understanding of public private partnerships for infrastructure development. Calif. Manage. Rev. 51 (2), 51–78. Li, B., Akintoye, A., 2003. An overview of public–private partnership. In: Akintoye, A., Beck, M., Hardcastle, C. (Eds.), Public–Private Partnerships: Managing Risks and Opportunities. Blackwell Science Ltd., UK. Li, B., Akintoye, A., Edwards, P.J., Hardcastle, C., 2005. The allocation of risk in PPP/PFI construction projects in the UK. Int. J. Proj. Manag. 23 (1), 25–35. Mostafa, K., Farnad, N., Majid, A., 2010. Using Fuzzy-Delphi technique to determine the concession period in BOT projects. Proceedings of the 2nd IEEE International Conference on Information and Financial Engineering, 17–19 September. ISBN: 978-1-4244-6927-7, pp. 442–446. Ng, S.T., Xie, J.Z., Skitmore, M., Cheung, Y.K., 2007a. A fuzzy simulation model for evaluating the concession items of public private partnership schemes. J. Autom. Constr. 17, 22–29. Ng, S.T., Xie, J.Z., Cheung, Y.K., Jefferies, M., 2007b. A simulation model for optimizing the concession period of public–private partnerships schemes. Int. J. Proj. Manag. 25 (8), 791–798. Pichayapan, P., Kishi, K., Hino, S., Satoh, K., 2003. Real Option Analysis (ROA) in evaluation of expressway projects under uncertainties. J. East. Asia Soc. Transp. Stud. 5, 3015–3030. Ruizheng, F., Li, W., 2010. The optimal concession period in the built– operate–transfer project. Proceedings of the International Conference on Management of e-Commerce and e-Government, Washington, DC, USA, pp. 223–227. http://dx.doi.org/10.1109/ICMeCG.2010.53. Schaufelberger, J.E., Wipadapisutand, I., 2003. Alternate financing strategies for build–operate–transfer projects. J. Constr. Eng. Manag. 129 (2), 205–213. Shen, J., Wang, S., 2010. The decision-making model of TOT concession period based on Fuzzy Stochastic Petri Net. Proceedings of the Management and Service Science International Conference, Wuhan. http://dx.doi.org/ 10.1109/ICMSS.2010.5576927. Shen, L.Y., Wu, Y.Z., 2005. Risk concession model for build/operate/transfer contract projects. J. Constr. Eng. Manag. 131 (2), 211–220. Shen, L.Y., Li, H., Li, Q.M., 2002. Alternative concession model for build operate transfer contract projects. J. Constr. Eng. Manag. 128 (4), 326–330. Shen, L.Y., Bao, H.J., Wu, Y.Z., Lu, W.S., 2007. Using bargaining-game theory for negotiating concession period for BOT-type contract. J. Constr. Eng. Manag. 133 (5), 385–392. Smith, N.I., 1995. Engineering project management. Blackwell Science, Boston. Tang, L., Shen, Q., Cheng, E.W.L., 2010. A review of studies on Public– Private Partnership projects in the construction industry. Int. J. Proj. Manag. 28, 683–694. Vassallo, J.M., 2006. Traffic risk mitigation in highway concession projects. J. Transp. Econ. Policy 40 (3), 359–381. Wu, M., Chau, K.W., Shen, Q., Shen, L.Y., 2012. Net asset value-based concession duration model for BOT contracts. J. Constr. Eng. Manag. 138, 304–308. Ye, S., Tiong, R.L.K., 2003. The effect of concession period design on completion risk management of BOT projects. Constr. Manag. Econ. 21, 471–482. Zhang, X.Q., 2005a. Financial viability analysis and capital structure optimization in privatized public infrastructure projects. J. Constr. Eng. Manag. 131 (6), 656–668. Zhang, X.Q., 2005b. Criteria for selecting the private-sector partner in public– private partnerships. J. Constr. Eng. Manag. 131 (6), 631–644. Zhang, X.Q., 2009. Win–win concession period determination methodology. J. Constr. Eng. Manag. 135 (6), 550–558. Zhang, X.Q., AbouRizk, S.M., 2006. Determining a reasonable concession period for private sector provision of public works and services. Can. J. Civ. Eng. 33 (5), 622–631.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Concession period for PPPs: A win–win model for a fair risk sharing Nunzia Carbonara, Nicola Costantino, Roberta Pellegrino ⁎ (DMMM)Department of Mechanics Mathematics and Management, Polytechnic of Bari, Viale Japigia 182 70126, Bari, Italy Received 22 October 2013; received in revised form 16 January 2014; accepted 21 January 2014
Abstract Public Private Partnership (PPP) is adopted throughout the world for delivering public infrastructure. Despite the worldwide experience has shown that PPP can provide a variety of benefits to the government, to fully gain them several critical aspects related to a PPP project need to be managed, among these the determination of the concession period. This paper provides a methodology to calculate the concession period as the best instant of time that creates a ‘win–win’ solution for both the concessionaire and the government and allows for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects the PPP projects, the Monte Carlo simulation was used. To demonstrate the applicability of the proposed model, a Build–Operate–Transfer (BOT) port project in Italy has been used as case study. © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Concession period; Public Private Partnerships; Risk allocation; Monte Carlo simulation
1. Introduction In the last decades, due to public budget constraints and the severe need for new or upgraded infrastructure, more and more governments have fostered private sector involvement in public investment projects. For this reason, Public Private Partnerships (PPPs) have become a major scheme in delivering public infrastructure (Hodge and Greve, 2007; Kwak et al., 2009). PPPs are “agreements where public sector bodies enter into long-term contractual agreements with private sector entities for the construction or management of public sector infrastructure facilities by the private sector entity, or the provision of services by the private sector entity to the community on behalf of a public sector entity” (Grimsey and Lewis, 2002). The idea of allowing private firms to finance projects of public infrastructure results in the emergence of PPPs (Li and Akintoye, 2003; Tang et al., 2010). The worldwide experience has shown that PPPs can ⁎ Corresponding author at: Viale Japigia 182, 70126 Bari Italy. Tel.: + 39 0805962850. E-mail addresses: [email protected] (N. Carbonara), [email protected] (N. Costantino), [email protected] (R. Pellegrino).
provide a variety of benefits to the government. In particular, PPPs can increase the “value for money” spent for infrastructure services by providing more-efficient, lower-cost, and reliable services; improve the quality and efficiency of infrastructure services, and promote local economic growth and employment opportunities. Furthermore, PPPs allow the public sector to transfer risks related to construction, finance, and operation of projects to the private sector and to keep public sector budget deficiencies down (Kwak et al., 2009). However, to fully gain the above listed benefits several critical aspects related to a PPP project need to be managed. Among these: the assessment of risks associated with the PPP project; the identification of suitable risk allocation and mitigation strategies; the definition of a sound financial plan; the selection of the appropriate concessionaire; and the determination of the concession period. Researchers have proposed several conceptual models, tools and techniques to support the decision processes related to these critical aspects (Garvin and Ford, 2012; Li et al., 2005; Schaufelberger and Wipadapisutand, 2003; Zhang, 2005a, 2005b). This paper focuses on the length of concession period, considered one of the most important issues in the PPP contracts.
0263-7863/$36.00 © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2014.01.007 Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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In the common international practice, the government usually presets the concession period to a fixed length, requests the concessionaire to bid for other project aspects, and guarantees the concessionaire a certain level of internal rate of return (Zhang, 2009). This practice, however, does not generally lead to an efficient selection of concessionaires and it also induces the frequent failure or renegotiation of concession contracts (Gustavo and Rus, 2004). To overcome these problems, there is a need for the government to use a methodology that appropriately calculates the concession period. Recently, some researchers attempted to determine a reasonable concession period. Most of the models proposed in the literature use the net present value (NPV) of the project cash flow as criterion for calculating the concession period. Some of these determine the concession period considering only the maximization of the concessionaire's benefits. For example, Engle et al. (2001) and Vassallo (2006) suggest the least-present-value of revenue (LPVR) method to determine the concession period of toll roads. Other models adopt a win–win approach, which means that the concession period is determined in order to maximize benefits of both the government and the concessionaire (Shen et al., 2002; Zhang and AbouRizk, 2006). Among these, only few models take into account the risk exposure of both partners (Chan et al., 2011; Hanaoka and Palapus, 2012; Shen and Wu, 2005), but none of these allows one for a fair risk sharing between the parties. Finally, most of the developed models determine the concession period as a time interval within which a specific concession period could be agreed upon by the government and the private sector and very few of the previous methods calculate the concession period as the instant of time within which the concession must end. In order to overcome the above discussed limitations, the present paper develops a model for calculating the concession period as the best instant of time that creates a ‘win–win’ solution for both the concessionaire and the government and allows for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects PPP projects, the Monte Carlo simulation is used. To demonstrate the applicability of the proposed model, we apply it to a Build–Operate–Transfer (BOT) port project launched by the Municipality of Bari (Southern Italy) to construct, operate and maintain the “San Cataldo Port of Bari”, consisting in a tourist port, a dock for cruise ships, and six multi-purpose buildings. Section 2 of this paper reviews the existing methodologies for determining concession period. In the third section, we present the win–win concession period model and in the next section we apply it to a Build–Operate–Transfer (BOT) port project in Italy. Conclusions end the paper. 2. Concession period Concession period starts from the signing of the concession agreement between the government and the private sector
indicating the span of time within which the private sector is responsible for the construction phase and operation phase in BOT projects. Concession period is a key decision variable in the arrangement of a PPP contract. The length of concession period is mainly related to the recovery of investment and return required by the concessionaires. The general principle for determining its length is that the concession period should be long enough to allow the concessionaire to recoup investment costs and earn reasonable profits within that period (Smith, 1995). Generally, a longer concession period is more beneficial to the private investor, but a prolonged concession period may induce loss to concerned government. Alternatively, if the concession period is too short, the investor will either reject the contract or be forced to increase the service fees in the operation phase. Consequently, the risk burden due to the short concession period will be shifted to the party who uses and pays for the facilities. Therefore, establishing an appropriate concession period is important for the success of a PPP project (Ng et al., 2007b; Ruizheng and Li, 2010). Each concession has its duration, which may be fixed or variable. The choice depends on various risk factors such as completion time, product price and market demand. Usually, the concession has a fixed period, in which risk factors are managed through tariff design supplemented by other measures. Sometimes, the concession has a variable period, which may be extended if the specified risk factors are worse than expected or shortened if they are better than expected. For example, in order to deal with demand risk, the concession period can be varied according to the market demand. If the market demand is lower than expected, the concession period will be extended to allow the concessionaire to earn a reasonable return, and vice versa. Different models have been developed and proposed in the literature to quantitatively determine the optimal value of the concession period. Most of these models, with the due differences and specificity, use the Net Present Value (NPV) of the project as a basic parameter for calculating the optimal value of the concession period. An exception is the study of Ng et al. (2007a) that considers the internal rate of return (IRR) as criterion for project evaluation and calculates the concession period that is less risky to the concessionaire by fixing the minimum, expected, and maximum IRR acceptable to the concessionaire. Shen et al. (2002, 2007) develop an analytical deterministic method (BOTCcM) for determining the length of the concession period to be granted to the private sector in BOT projects that can protect the interests of both the host government and the private investor. BOTCcM proposes an interval for a concession period negotiation that consists of two critical points: the first point, called the starting point of the interval, aims at protecting the interests of the private investor; the second point, called the endpoint of the interval, aims at protecting the interests of the host government. Wu et al. (2012) improve BOTCcM by considering at project's transfer time its net asset value that is usually significantly greater than zero and represents a revenue for the government. Hanaoka and Palapus (2012) propose a methodology to determine a reasonable concession period that considers
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
the effect of risks on uncertain concession items in project evaluation. In particular, by using Monte Carlo simulation and bargaining game theory the methodology generates a concession period interval within which a specific concession period could be agreed upon by the government and the private sector. Any point within the interval could be considered as the optimal concession period that would be advantageous to both BOT players. Ye and Tiong (2003) evaluate the mean net present value (NPV), variance and NPV-at-risk of different concession period structures through Monte Carlo simulation, so that both the government and the concessionaire can understand their risk exposure and rewards. Zhang (2009) proposes a win–win concession period determination methodology, in which BOT projects are addressed as a principal-agent maximization problem. In this research both deterministic and simulation-based methods are provided to determine the concession period, with detailed step-by-step procedures. Engel et al. (2001) suggest the least-present-value of revenue (LPVR) method to determine the concession period of toll roads so that the franchise length is adjusted endogenously to demand realization. Other researchers use the fuzzy approach to determine the concession period appropriately. Mostafa et al. (2010) adopt a Fuzzy-Delphi technique to calculate the length of concession period considering uncertainties. In particular, they determine the values of different uncertain factors affecting a BOT project by considering the opinions of a group of experts (Delphi), and then calculate the NPV value by taking into account the resulted aggregated values of uncertain input parameters, finally determine the concession period using the fuzzy approach. The proposed methodology offers a fuzzy number for the length of concession period. Ng et al. (2007b) develop a fuzzy multiobjective decision model to evaluate and establish the most satisfactory concession item options for BOT projects. Table 1 lists the existing models developed to determine the concession period, classified with respect to three key aspects. The first aspect concerns the computation of the concession period. Respect to this, the discussed models can be classified in two main categories: 1) models that allow the calculation of the instant of time within which the concession must end;
3
2) models that calculate the concession period as a time interval within which the concession contract must end. The second aspect involves the uncertainties and risk factors affecting PPP projects. By reviewing the literature, we found models which do not take into account risks and uncertainties that affect over a long period many of the input variables; and models in which uncertainties are accounted for. In the first case a deterministic approach is adopted to calculate the concession period, while in the second case stochastic models have been developed, in which each variable affected by uncertainty is modeled by a statistical distribution and the concession period is determined by using simulation methods. Finally, the third aspect concerns the perspective adopted in calculating the concession period, in terms of interest to safeguard and party to satisfy. In this respect, some models, focusing only on the private party perspective, require that the concession should be long enough to allow the concessionaire to obtain a reasonable profit. Recently, in contrast with this approach, in order to safeguard the multiple interests of the public sector and the profit-making interest of the private sector, some researchers proposed a win–win approach to determine the concession period that takes into account both the government and investor interest perspective. Each of these studies implements the win–win approach in different ways. For example, according to Zhang (2009) the win–win approach is implemented by posing the following two constraints: 1) the concession should be long enough to allow the concessionaire to obtain a reasonable IRR and 2) the concessionaire acts in the interest of the government, for example, the concessionaire may be required to continuously improve efficiency, cost effectiveness and service quality; sustain a stable and public-affordable price regime; and transfer excessive profits to the government. Following the win–win approach, Hanaoka and Palapus (2012) employ the bargaining game theory to find a reasonable concession period. Thus, the specific concession period is considered as a bargaining process wherein (i) the government and the private sector act as the players in reaching a BOT agreement, (ii) the benefit to be generated within the concession period interval within which the end of the concession period could occur, is the conflict of interest, and (iii) the negotiation does not end until an agreement of both BOT players is reached.
Table 1 Models for concession period calculation. Model
Concession period calculation Uncertainty/risk factors Satisfied party Instant of time Time interval
Private Public Both parties
LPVR
X
X
Fuzzy-Delphi Fuzzy multi-objective Fuzzy Petri Net–genetic algorithm Simulation model X MC-PDM BOTCcM CPM-MCS MCS-bargaining game theory BOTCcM-R
X X X X X X X X
X X X X
X X X
Source
X X X X X X X X X
Engle et al., 2001; Gomez-Lobo and Hinojosa, 2000; Vassallo, 2006. Mostafa et al., 2010. Ng et al., 2007b Shen and Wang, 2010 Ng et al., 2007a Zhang and AbouRizk, 2006. Shen et al., 2002; Wu et al., 2012. Zhang, 2009 Hanaoka and Palapus, 2012 Chan et al., 2011; Shen and Wu, 2005
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Finally according to Wu et al. (2012), creating a win–win situation means both assuring that the private investor not only recoups the investment but also earns a profit during the franchise operation period, and protecting the government in recouping the project depreciation costs incurred during the post-transfer operation period in a way that the cumulative cash flow during its post-transfer operation period are greater than the net asset value at its transfer. Table 1 shows that there is not a model that addresses all the three key aspects. Furthermore the methodologies adopting a win– win approach do not deal with the concept of a fair risk sharing between the two parties. Therefore, none of the previous models has the capability to support the government decision-making in the choice of a concession time that satisfies the interests of both parties by taking into account unforeseen risks and uncertainties and that allows a fair risk sharing between parties. To overcome this limitation, we propose a win–win concession period determination methodology, which calculates the concession period as the instant of time within which the concession must end, takes into consideration risks and uncertainties, and safeguards the interests of both parties involved in the concession contract, the public sector and the private sector, by fairly allocating the risk between the two parties.
In the following, we propose an innovative win–win concession period model that addresses the following issues: 1. Taking into consideration the win–win principle, in the sense that the estimated value of the concession period should be able to protect the interests of the private investor and the government simultaneously and to assure that the interests of the two parties are satisfied in a balanced way; 2. Calculating the instant of time within which the concession must end; and 3. Considering the effect of uncertainty. To address these issues, we develop a methodology that calculates the optimal instant of time in which the concession should end (Tc) as result of the following equations: ð1Þ
Where: - NPVC is the net present value of the of the project's cumulative net cash flow n X C Ft ð 1 þ r Þt t¼0
- IC is the total investor's capital investment IC ¼
tX constr t¼0
C Ft ð1 þ rÞt
Eq. (1) expresses the principle that the net present value of the project's cumulative net cash flow should be no less than the private investor's expected minimum return on investment. NPVC ≤ IC Rmax
ð2Þ
Where: - Rmax is the maximum return rate allowed by the government to investors. Eq. (2) expresses the condition that the net present value of the project's cumulative net cash flow should be no higher than a cap rate of return fixed by the government in order to avoid lucrative conditions. NPVG ð FÞ≥ 0
ð3Þ
Where:
3. The win–win model
NPVC ≥ IC Rmin
- Rmin is the investor's expected minimum return rate from his capital investment - r is the discount rate - tconstr. is the construction period.
- NPVG (F) is the net present value of the project's cumulative net cash flow during the post-transfer operation; - F is the end of the life time of the project. Eq. (3) expresses the principle that the net present value of the project's cumulative net cash flow during the post-transfer operation and calculated at the end of the life time of the project (F) should be no less than zero. By solving Eq. (1) we obtain the minimum value of the concession period (tmin), i.e. the instant of time before which the project should not be transferred because it does not allow the private investor to receive the expected minimum return on investment. By solving Eq. (2) the maximum value of the concession period (tmax) is calculated. This is the instant of time after which the granting of the project should not continue, in order to avoid an excessive profit to the private sector. Eq. (3) allows the calculation of tc, that is the instant of time before which the investor should transfer the project to the government, in order to guarantee its interests. The time interval within which fix the concession period is defined by tmin, tmax and tc (see Fig. 1). Three possible situations can occur: 1. tc b tmin: in this situation it is impossible to determine a concession period that jointly satisfies the interests of the private investor and the host government; 2. tc ≥ tmax: in this situation the concession period of the project can assume any value within that interval [tmin–tmax]; 3. tmin ≤ tc b tmax: in this case the interval for a concessionperiod negotiation is fixed between the two points tmin and tc,
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
therefore the concession period can assume any value within that interval [tmin − tc]. Once the interval for a concession period negotiation has been derived through the previous procedures, namely the time interval that jointly satisfies the interests of the private investor and the host government, it is possible to determine the instant of time within which the concession must end (Tc). In order to satisfy the win–win condition, Tc is calculated as the instant of time that minimizing the difference between NPVC and NPVG, by solving the objective function given by the following equation: Tc э′MinðNPVC −NPVG Þ
ð4Þ
Eq. (4) expresses the condition that both parties, the private investor and the host government, are equally satisfied, that is, it assures that the interests of the two parties are satisfied in a balanced way. Concession period Tc is a function of a set of variables affected by uncertainty. To take into consideration the effect of uncertainty, we adopt the Monte Carlo simulation. In particular, first we develop a stochastic model by assigning to each random variable a statistical distribution, which requires the selection of a suitable theoretical distribution function and the estimation of its parameters. Then, by running the model at the end of simulation we derive the statistical distribution of the concession period (Tc). Once the distribution of Tc is known we determine the value of Tc that allows the difference between the risks of loss borne by the two parties to be minimized. These risks are measured as follow: - risk borne by the concessionaire = Prob (NPVC b Ic ∗ Rmin); and - risk borne by the government = Prob (NPVG(F) b 0).
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The variables used as input to the model are categorized into: 1) deterministic/certain and 2) uncertain variables. Deterministic variables are defined in this paper as variables of which values could be considered as stable over time. Uncertain variables, on the other hand, are those inputs which may be subject to change and high level of uncertainty over time since their future values are difficult to predict. The deterministic variables are: - The construction period and project life time: the first is the period from the project start-up date until the target date of completion. While in reality construction period may change due to some events that may occur during construction preventing the project to be delivered on time, it is assumed equal to 3 years. The project life time is assumed equal to 60 years. - The investment cost and the residual value of the project: the first includes expenditure items needed until the completion of the project, equal to € 28.096.177. The residual value of the project at the transfer time is € 5.6 million, equal to 20% of the investment cost of the project. - The maximum return rate allowed by the government to the investor (Rmax), equal to 15% and the minimum rate of return for the investor (Rmin), equal to 8%. - The discount rate r, that in the case of Monte Carlo simulation is generally a risk free rate (Brealey and Myers, 2000), since the risk is already included in the project cash flows that depend on the randomly-chosen values of the input parameters. The risk-free discount rate can be taken as the interest rate on Government bonds. In particular, in this case r is fixed at 5%, taken the average interest rate on long term Italian Government Bonds as a proxy. - Expenses during operation, including items such as the state concession fee, regular annual maintenance costs, overhead operating costs, and insurance. - Revenues from real estate activities (rental fees for commercial and services areas).
4. Case study The uncertain variables are: We applied the developed model to the case of the BOT tourist port project launched by the Municipality of Bari, located on the South-East coast of Italy. The port includes: a cruise terminal, floating docks with a capacity of 447 boats, accessible by boats up to 30 m, a parking for 577 vehicles, office, service, and multifunctional buildings (covering an area of about 8000 m2), a bunkering area, and 104 storage spaces. In the operation phase the revenues mainly consist of port dues, land rent, mooring dues, and parking space rent. The port dues cover the use of the nautical port infrastructure (such as pilotage and docking fees) and are paid by cruise ships. The mooring dues consist of: long-term leases, annual leases, and transit fees. The operation costs mainly consist of management expenses, operations and maintenance costs, insurance, and State Government concession fee. The estimation of the operating revenues and costs has been based on historical data, data collected on similar projects, and experts' opinions.
- Revenues associated on the operation period and determined by the services management. These come from port dues, mooring dues, and parking space rent. The port dues cover the use of the nautical port infrastructure (such as pilotage and docking fees) and are paid by cruise ships. The mooring dues consist of: long-term leases, annual leases, and transit fees. These are determined by the unitary fee to be charged to the users of the specific services and the number of users expected for those services. In particular, as for the mooring services, the users are the vessel owners, for parking service the users are both the vessel owners and port visitors. As for the nautical port infrastructure, the users are the cruise ships. - Costs associated on the operation period and due to the services management. These are assumed equal to the 12% of the operating revenues, thus their distribution over time follows the distribution of the revenues related to the corresponding service.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Table 2 Statistical distribution of input random variables. Input random variables
Table 3 Statistics of tmin, tmax and tc.
Probability distribution function
Parameters
Number of seasonal rented moorings
Binomial distribution
Number of full year rented moorings
Binomial distribution
Number of transit rented moorings
Binomial distribution
n = 45 p = 80% n = 224 p = 90% High season n = 43 p = 95% Middle season n = 43 p = 80% Low season n = 43 p = 60% μQ = 1.5% σ = 10% n = 162 p1 = 5% a p2 = 10% p3 = 20% p4 = 30% p5 = 50% p6 = 60% p7 = 70% p8 = 90% n = 415 p1 = 20% p2 = 30% p3 = 50% p4 = 60% p5 = 70% p6 = 90%
Traffic volume of cruise ships Number of occupied parking spaces for port visitors
Number of occupied parking spaces for vessel owners
Geometric Brownian motion Binomial distribution
Binomial distribution
a
On the basis of the historical data, we have fixed different values of p, each corresponding at different monthly occupancy rates.
The assumptions used for modeling the statistical distributions of the uncertain variables are summarized in Table 2. In particular, the table shows for each input random variables the corresponding probability distribution function and its defining parameters, defined on the basis of the historical empirical data. Notice that, the discrete input variables, namely the number of rented moorings and the number of occupied parking spaces, and the related revenues, have been modeled with the binomial distribution that is a discrete probability distribution, where the random variable is the number of “successes” (i.e. rented moorings or occupied parking spaces) in n independent trials (i.e. available moorings or parking spaces) with a probability of success constant and depending on the seasonal occupancy rates. The conditions of applicability of the binomial probability distribution are satisfied since there is a fixed number of n trials (i.e. available moorings or parking spaces); the outcome of a given trial is either a “success” (rented/occupied) or “failure” (not rented/not occupied); the probability of success (p) remains constant from trial to trial (seasonal occupancy rates); and the trials are independent. Instead, we have assumed that the traffic volume of cruise ships, and the related revenues, will vary stochastically in time following a geometric Brownian motion (GBM), as is standard
tmin tmax tc
Mean
Std dev
Minimum
Maximum
39.05 54.72 43.39
1.88 13.87 18.05
0.00 0.00 0.00
55.00 6000 5400
in the literature (Garvin and Cheah, 2004; Iyer and Sagheer, 2011; Pichayapan et al., 2003). The stochastic evolution of traffic volume (Q) that follows a GBM can be modeled in yearly periods as a function of the value in previous period according to the following equation: μ 2 pffiffiffiffi Q σ Qtþ1 ¼ Qt e − 2 Δtþσε Δt Where: μQ is the expected traffic growth rate; σ is the annual volatility of the variable; ε ~ N(0,1) is the standard Wiener process. This model implies that the traffic can never be negative and its evolution can be completely specified considering only its initial value Q0, a yearly growth rate and the volatility of the process, which we assume to be constant during the concession period. After establishing the input data modeling, the Monte Carlo simulation approach has been used for calculation of NPV. In particular, in each computer iteration, the random values of the stochastic input variables are generated on the basis of their statistical distributions as established in input data modeling. Each simulation consists of 1000 computer runs. The outcome of the simulation model, taking into account the project variables above, is the distribution of the project's cumulative net cash flow over the life-time of the project F. By solving Eqs. (1)–(3) we calculate the probability distributions of tmin, tmax and tc (see Table 3). The instant of time within which the concession must end (Tc) is contained in the interval [Tcmin–Tcmax], where: - Tcmin = lower bound concession interval = tmin and - Tcmax = upper bound concession interval = min[tc;tmax]. Figs. 2 and 3 show the probability distribution of Tcmin and Tcmax. Tc is then determined by solving the objective function Min(NPVC–NPVG). Fig. 4 plots the statistical distribution of Tc derived after 1000 computer iterations. The simulation returns a value of 0 in all situations/scenarios where the conditions of the model are not satisfied, i.e. it is impossible to determine a concession period that jointly satisfied the interest of both parties. Tc range within the interval [45–51] years with a probability of 80% (the 20th percentile is 45 years), with a mean 40.55 years, mode 48.00, and median equal to 47 years.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
Fig. 1. Cash flow profile and time interval for the concession period determination.
Fig. 2. Probability distribution of the lower bound concession interval (Tcmin).
Fig. 3. Probability distribution of the upper bound concession interval (Tcmax). Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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Fig. 4. Probability distribution of the concession period (Tc).
In order to define the optimal value of Tc we have conducted a risk analysis. For each values of Tc within the interval [45–51], we have calculated the probability that the net present value of the project's cumulative net cash flow is below the private investor's expected minimum return on investment (private risk) and the probability that the net present value of the project's cumulative net cash flow during the post-transfer operation calculated at the end of the life time of the project is less than zero (public risk). Fig. 5 depicts the risk borne by the two parties. Notice that at Tc = 49 years the difference between the risks of loss borne by the two parties is minimized, so as having the best risk allocation between the two parties. Fig. 6 (a and b) shows the probability distributions of NPVC and NPVG calculated for the optimal value of the concession period (Tc = 49 years) and indicates the value of risk borne by the two parties.
To get a feel for the likely impact of the interest rate r on movement of the model outcome, sensitivity analysis was carried out, yielding the results shown in Fig. 7. The sensitivity analysis indicates that the increase of Tc is more than proportional to the increase of r. In this case we found that for r = 6% it is impossible to determine a concession period that jointly satisfies the interests of the private investor and the host government, since the instant of time before which the investor should transfer the project to the government, in order to guarantee its interests (tc) is lower than the instant of time before which the project should not be transferred because it does not allow the private investor to receive the expected minimum return on investment (tmin). The results of the sensitivity analysis therefore show that the interest rate affects on the choice of the concession period, so much that there could not be an instant of time that jointly satisfies the interests of the private investor and the host
100,00% 90,00% 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00%
45
46
47
48
Private risk (Prob(NPVc-Ic*Rmin)<0)
49
50
51
Public risk (Prob(NPVg<0)
Fig. 5. Risk borne by the concessionaire and the government for each value of Tc. Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
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Fig. 6. Probability distributions of NPVC and NPVG for Tc = 49 years.
government within the life time of the project. The outcome of the sensitivity analysis can be also useful for supporting the choice of the rate during the negotiation. 5. Conclusion A key factor of a PPP project is the agreement on the length of concession period. The concession period is one of the most important decision variables for arranging a successful PPP contract because its value decides when ownership of a project will be transferred from the investor to the government, thereby 55 50
TC
45 40 35 30 25 2%
3%
4%
5%
r Fig. 7. Sensitivity analysis results.
6%
demarcating the authority, responsibility, and benefits between the private party and the government. This paper provides a new model for calculating the concession period as the best instant of time that creates a ‘win–win’ solution for both project promoter and the host government and allows one for a fair risk sharing between the two parties. In other words, the concession period is able to satisfy the private and the government by guaranteeing for both parties a minimum profit, and, at the same time, to fairly allocate risks between parties. In order to take into account the uncertainty that affects the PPP projects, the Monte Carlo simulation technique was used, which requires establishing the statistical distributions of the random input variables. A case study on a BOT port project launched by the municipality of Bari located in Southern Italy were used to check the applicability of the proposed model for determining the reasonable length of the concession period that safeguards the multiple interests of the public sector and the profit-making interest of the private sector and allows for a fair risk sharing between the public and private sectors, by minimizing the difference between the risks of loss borne by the two parties. The application shows that the developed model can be a valid tool for supporting the public authority in the decision-making process about the length of the concession period.
Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007
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N. Carbonara et al. / International Journal of Project Management xx (2014) xxx–xxx
The application of the model requires, as usual in this kind of problems, some assumptions on input parameters. However, we have done a sensitivity analysis on one of the most critical parameter of the model, namely the discount rate, and proven that even if the concession period is affected by the selected value of the interest rate the model keeps its usefulness for supporting the negotiation process in choosing an appropriate discount rate, thus avoiding costly renegotiation. It can be observed that the validity and usefulness of the model has been proven through a single case study. However, it is important to observe that the structure of the model and its equations do not change from case to case, thus assuring the generalizability of the model. The main limitation indeed relies on the input data modeling, which is a critical step inherent in the simulation-based approach, affecting the outcome. In fact it may not be easy for decision-makers to accurately predict the uncertain parameters and their probability distributions. Further research will be devoted to apply the proposed methodology to other PPP projects which may have different cash flow structure to strength its effectiveness and prove its validity beyond the specific case analyzed by the present study. Future works may also focus on further refinement of the simulation model by gathering more information on key variables to accurately assign their probability distribution.
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Please cite this article as: N. Carbonara, et al., 2014. Concession period for PPPs: A win–win model for a fair risk sharing, Int. J. Proj. Manag. http://dx.doi.org/10.1016/ j.ijproman.2014.01.007