Abstract

To compensate for the lack of funds for investment in private sector and infrastructure projects, governments may propose public–private partnerships (PPPs) to be able to use share capital and establish the necessary infrastructure of the country. The current study was undertaken to identify and determine the risk factors in PPPs for water supply projects in Iran. After identifying the risk factors using failure mode and effect analysis (FMEA), the risk priority number of each was assessed. This identified the most critical risk factors, which were then categorized into experimental, legal, financial, and technological subcategories. The fuzzy synthetic evaluation (FSE) technique and FMEA method were then blended and the FSE technique was modified for measuring the overall risk level. The computational results show that the levels of risk were ranked as follows (highest to lowest): financial, experimental, technological and legal. The level of risk in the financial subcategory was 6.11, in the experimental was 6.05 and in the technological and legal was 5.94 and 5.83, respectively. The overall risk level in PPPs for Iranian water supply projects considering linguistic variables as the criteria was 5.98, which is high. This level of risk confirms the applicability and suitability of the model.

INTRODUCTION

In recent years, rapid population growth and economic development in many countries have increased the need for new infrastructure (The World Bank 2008), although government funding for infrastructure development appears to be insufficient. Governments have tried to involve private institutions and parties in infrastructure projects as a solution to these shortages. One solution is to promote public–private partnership (PPP) contracts. Although a PPP contract is a good way to overcome the limitations of the government, because of the complexity of the procedure, in many cases it does not meet the objectives of the stakeholders. Long-term PPP contracts, the need for profitability of the projects for the private sector and continuous communication between government organizations and the private sector has sensitized the contract holders to the environmental conditions (political, macroeconomic, state laws, government policies, etc.), project finance and technological features of the project. The stage of preliminary studies and course assignment in these projects is critical, time-consuming and requires deep analysis and intensive study.

PPPs can be applied to various sectors, including the construction of highways, tunnels, airports, stadiums, water supply projects and hospitals. In developing countries such as Iran, increasing the number of PPPs has resulted in economic success and expansion of infrastructure. Several PPPs can be mentioned by name. Heravi & Hajihosseini (2012) and Ghorbani et al. (2014) examined existing risks in PPP projects for Iranian highway construction. Shadpour et al. (2013) studied the effect of PPPs in the health sector (Hasheminejad Kidney Center) and concluded that their implementation improves service quality and efficiency. Azar et al. (2013) worked on discovery and analysis of the main risks involved in PPP projects for power stations. Yazdani-Chamzini (2014) presented a new model of fuzzy risk assessment in tunnel construction projects. They found that the development of infrastructure projects, including those for water supply and distribution facilities as well as collection and treatment of wastewater, are necessary for the prosperity of a country.

Mirghafouri & Kousha (2015) studied assessment of the risks in water transfer pipeline projects (as in Yazd) using the analytical hierarchy process (AHP) and fuzzy failure mode and effect analysis (FMEA). Noorzai et al. (2016) described the implementation of the AHP to select the best financial model for PPP highway projects in Iran. Dadpour & Shakeri (2016) reviewed strategies for improving the PPPs in water projects using the strengths, weaknesses, opportunities, threats (SWOT) method.

LITERATURE REVIEW

Risk factors of PPPs for water supply projects

Bing et al. (2005) studied risks related to PPP contracts in Britain and classified them as being large, medium and small. Kayaga (2008) believed that in most studies and projects, environmental conditions have been neglected or poorly considered, which has caused lack of harmony with local limitations. As a result of these challenges and struggles, most PPPs for water projects in developing countries have not progressed appropriately. Xu et al. (2010) identified 34 risk factors from previously published studies and a two-round questionnaire which contained three new risk factors for China water projects. Ultimately, 17 risk factors were proposed as being the most critical. Among these, government interference, government completion, economic consistency, market environment, construction and manufacturing, and macroeconomic risks were prioritized.

Priya & Jesintha (2011) studied the operation of native and non-native Indian contractors in PPPs for water projects. They observed that both native and non-native contractors performed well in their projects. Wibowo & Mohammad (2010) found 39 risk factors in Malaysia. Some of the most important were poor pricing policies and tariff substantiality, contract violation by government, lack of raw water and the high cost of infrastructure construction. Ezeldin & Badran (2013) reviewed similar studies and experiences, interviewed experts and introduced their own critical risk factors (CRFs). They also asked 25 international experts who were active in the Egyptian business market to complete a questionnaire. The results revealed that more than 26% of risk involved macro-finance and economy, commercial, legal, political, government supervision, government completion, technological and unpredictable factors.

Assessment of environmental factors in developing countries to identify and evaluate PPP contracts appears to be inadequate and limited. Ameyaw & Chan (2013) identified 40 risks for water projects in developing countries and, after focusing on six countries, they listed these affective factors such as lack of strong rules and regulations, financing, non-payment and lack of experience in PPP projects. Ameyaw & Chan (2015) in Ghana proposed 40 risk factors for PPP water supply projects and after deep analysis classified them into three main categories as financial–commercial, socio-political and technical. The financial category was found to have the highest level of risk, followed by the social and political categories. Yin et al. (2015) identified eight main categories as sources of risk in China's water projects: construction and manufacturing, communication, operational, political, environmental, macro-economic, design and financial risks. They claimed that financial risk was the most critical factor in all Chinese water projects. Liu et al. (2016) engaged in similar investigations and proposed 14 critical resources of risk for PPPs for water projects.

The fuzzy synthetic evaluation technique

The fuzzy synthetic evaluation (FSE) technique is a branch of the fuzzy method proposed by Lotfi-Zadeh in 1965 (Ameyaw & Chan 2015). Tah & Carr (2000) used the FSE technique to invert linguistic variables about the effect and severity of risk occurrence to evaluate separate risk levels as well as the overall risk level. Sadiq & Rodriguez (2004) used the FSE technique to invert linguistic variables and identified the types and levels of risk. Dahiya et al. (2007) used the FSE technique to evaluate the acceptable physical–chemical quality of water. Lam et al. (2007) used the FSE technique to model and quantify fuzzy factors to describe the level of risk, the possibility of the occurrence of risk, as well as its severity and effect. Li et al. (2008) used the FSE technique to evaluate the quality of urban solid residual transfer stations. Huang et al. (2008) blended the FSE technique and nonlinear programming (NLP) to evaluate and rank real trade risk factors. Wang et al. (2009) determined 11 criteria for sand quality and used the FSE technique to rate it.

Mi et al. (2011) worked on the shortage of corn in China and recognized the high winds, rainfall and the amount of potassium in the soil. They utilized the FSE technique to assess the overall level of risk in each of these categories. Geng & Xu (2011) used a blending of AHP and the FSE technique to determine the level of quality when teaching network courses. Here, AHP was used to weigh the categories and the FSE technique to evaluate the level of quality of the course. Boussabaine (2013) used the FSE technique to evaluate the complexity of the results of risk level and employed expert opinion in the form of linguistic variables. The level of exposure to risk was also considered.

Liu et al. (2013) used the FSE technique to evaluate the risk level in scientific drilling projects. They investigated the possibility of occurrence, severity of effect, discovery failure and identifying the worst situation as the four major areas of risk in the project. After assessing each area using AHP and the analytic network process (ANP), they used the FSE technique to measure their overall risk levels. Zhao et al. (2015) studied environmentally friendly buildings in Singapore and identified 28 risk factors. They classified them into 11 groups and evaluated the overall risk level of the project using the FSE technique.

The World Bank report in 2007 emphasized that the rate of yearly reduction of fresh water resources in Iran is 3.6-fold that of global standards and indices. They claimed that agriculture is responsible for 91% of this loss in Iran. When assessing the critical role of water in peoples’ daily lives, it can be said that the number of PPPs related to the Iranian water supply are insufficient. Experts and the World Bank annual report agree that Iran faces a danger of water loss in the near future.

To evaluate risk level, different issues and dimensions of the subject matter must be taken into consideration. One major problem in previous studies is that they did not include the probability of detection of risk factors in the ranking and evaluation of the overall risk level using the FSE technique. This shortcoming creates mismatches between the evaluated data and the real level of risk in PPPs.

The current study was undertaken to analyze the risks of PPPs for Iranian water supply projects by summing the previous studies both from Iran and elsewhere with that of expert opinion to identify risk factors, rate them and represent them in numerical format. Overall risk levels for PPPs for Iranian water supply projects were also assessed. This is the first study of its kind in Iran regarding PPPs. The FSE technique and FMEA method have been blended, the FSE technique was modified and the most important risks to PPPs for Iranian water supply projects were identified. The level of risk in each was then measured quantitatively. The results show that the overall risk of PPPs for Iranian water supply projects is high. The Ameyaw & Chan (2015) method was used and advances to the method have been proposed in the form of the following contributions:

  • This is the first time that such a study on PPPs for Iranian water supply projects has been carried out.

  • Unlike previous studies that did not incorporate the probability of detection of risk factors, the present study has taken this into account and evaluated it using FMEA.

  • The FSE technique and FMEA have been blended for the first time to evaluate the overall risk level of PPPs for Iranian water supply projects.

RESEARCH METHODOLOGY

FMEA

The occurrence of risk can have different causes. As the probability of detection is immensely important to the estimation of risk, the FSE technique was used to evaluate and rank the risk factors. Lipol & Haq (2011) found that risk evaluation can be done by applying a risk priority number (RPN) as measured in Equation (1):  
formula
(1)
where is the probability of occurrence, is the severity of the effect and is the probability of detection.

Identification of risk factors

One important factor contributing to a successful project is to explore the relevant risk factors (Ameyaw & Chan 2015). In the current study, works in different countries were reviewed to identify risk factors, including those by Osei-Kyei & Chan (2015), who reviewed PPPs for water projects from 1990 to 2013, Xu et al. (2010) and Ameyaw & Chan (2015). An initial total of 32 risk factors were identified for Iranian water supply projects. Further study and research by experts led to the addition of seven more factors. A final list of 39 risk factors has been proposed to determine the level of risk in PPPs for Iranian water supply projects.

Questionnaire and participants

Comprehensive questionnaires are required to determine possible risks of management of PPPs (Haarmeyer & Mody 1988; Vives et al. 2006; Zeng et al. 2007; Wibowo & Mohamed 2010; Cheung & Chan 2011; Ameyaw & Chan 2015). To fulfill the aims of this study, 53 participant managers and experts from ten water offices were consulted to investigate and analyze the 39 risk factors (Table 1). Three questionnaires were distributed, each focusing on one risk priority (possibility of occurrence, severity of effect, probability of detection). Participants were asked to rate the questions according to level of risk as: 2 (low), 4 (medium), 6 (high), 8 (very high) and 10 (dangerous, but not alarming).

Table 1

List of organizations and managers

Organizations and managersNumber%
Contract Manager 9.5 
Deputy Finance 11.3 
Department of Enterprise Resource Planning 7.5 
Center for Statistics and Information Technology 11.3 
Office of Budgetary and Economic Analysis 9.5 
Office of Legal Affairs and Land Acquisition 9.5 
Bureau of Education and Human Resources 11.3 
Office of Exploitation 11.3 
Customer Manager 7.5 
Office managers and improving the management quality 11.3 
Total 53 100 
Organizations and managersNumber%
Contract Manager 9.5 
Deputy Finance 11.3 
Department of Enterprise Resource Planning 7.5 
Center for Statistics and Information Technology 11.3 
Office of Budgetary and Economic Analysis 9.5 
Office of Legal Affairs and Land Acquisition 9.5 
Bureau of Education and Human Resources 11.3 
Office of Exploitation 11.3 
Customer Manager 7.5 
Office managers and improving the management quality 11.3 
Total 53 100 

Data analysis and results

The data from the surveys were analyzed in Excel 2016 and XFMEA 8.0 and the risk priority number (RPN) was assessed for each risk factor. The RPNs were then normalized using the formula recommended by Xu et al. (2010). This type of normalization has been used by Xu et al. (2010) and Ameyaw & Chan (2015) in which the normalization number of each risk factor that was equal to or greater than 0.5 was considered to be critical and was taken into consideration for the evaluation of overall risk level. The calculations are provided in Table 2.

Table 2

Introducing and ranking risk factors

Risk factorOSDRPNRankNormalizationa
Increasing population and expansion of urban settlement 7.06 7.52 6.98 370.58 1.00 
Inconsistency in weather patterns 4.9 6.42 6.72 211.40 21 0.55 
Continuation of traditional management practices for exploitation in agriculture 6.9 7.36 7.26 368.69 0.99 
Pollution 6.9 8.04 5.44 301.79 0.81 
Problems with water recovery technology 4.72 7.26 7.08 242.61 17 0.64 
Problems with cultural understanding of water protection 4.36 6.44 6.36 178.58 23 0.46 
Weaknesses in water management 6.54 6.72 7.26 319.07 0.86 
Basic studies and experiments 4.52 4.36 4.16 81.98 36 0.19 
Social–public support 4.16 5.8 5.06 122.09 30 0.30 
Political support 4.18 4.32 3.8 68.62 37 0.15 
Macro-economy consistency situation 4.54 4.54 4.56 93.99 34 0.23 
Strong warranty from both of the performing parties 4.72 5.62 4.9 129.98 26 0.33 
Political violence 5.08 5.8 4.14 121.98 29 0.30 
Economic consistency 5.8 5.8 5.26 176.95 24 0.46 
Leadership and handling by officials 6.72 7.8 6.54 342.80 0.92 
Coherent rulings 6.18 6.9 6.54 278.88 11 0.74 
Employment of expert consultants 5.72 6.8 6.26 243.49 16 0.64 
Continuous monitoring 5.36 6.8 6.62 241.29 18 0.64 
Governmental fraud 4.62 6.26 7.9 228.48 20 0.60 
Public resistance against PPP 4.44 5.36 4.08 97.10 32 0.23 
Exclusivity negation 4.26 5.18 4.08 90.03 35 0.21 
Inflation rate 6.08 7.54 7.44 341.07 0.92 
Market demands change 3.9 4.62 5.36 96.58 33 0.23 
Price change 6.08 6.44 7.04 275.65 12 0.73 
Non-payment of bills 6.26 7.72 7.18 346.99 0.93 
High operational cost 5.72 6.44 7.18 264.49 13 0.70 
Fluctuations in absorption rate 5.9 5.36 5.14 162.55 25 0.42 
The management, control and exploitation of water resources 5.36 7.04 7.04 265.65 14 0.71 
Competitive financial offers 2.26 2.72 2.18 13.40 39 0.00 
Qualitative and quantitative security of water resources 4.74 6.44 7.9 241.15 19 0.64 
Advancements and innovations in water technology 4.26 6.08 7.9 204.62 22 0.54 
Equal skill level in both of the performing groups 4.44 5.18 5.54 127.42 27 0.32 
Delivery or assurance of service 3.14 4.54 4.62 65.86 38 0.15 
Effect of environment on the project 4.08 4.26 5.72 99.42 31 0.24 
Pipeline failures during distribution 7.04 7.54 6.44 341.85 0.92 
Lack of PPP experience 4.26 5.36 5.36 122.39 28 0.31 
Poor contract design 5.9 6.8 7.36 295.28 0.79 
Support utility risks 5.72 6.26 7.04 252.08 15 0.67 
Lack of support for infrastructure 5.90 7.54 6.62 294.50 10 0.79 
Risk factorOSDRPNRankNormalizationa
Increasing population and expansion of urban settlement 7.06 7.52 6.98 370.58 1.00 
Inconsistency in weather patterns 4.9 6.42 6.72 211.40 21 0.55 
Continuation of traditional management practices for exploitation in agriculture 6.9 7.36 7.26 368.69 0.99 
Pollution 6.9 8.04 5.44 301.79 0.81 
Problems with water recovery technology 4.72 7.26 7.08 242.61 17 0.64 
Problems with cultural understanding of water protection 4.36 6.44 6.36 178.58 23 0.46 
Weaknesses in water management 6.54 6.72 7.26 319.07 0.86 
Basic studies and experiments 4.52 4.36 4.16 81.98 36 0.19 
Social–public support 4.16 5.8 5.06 122.09 30 0.30 
Political support 4.18 4.32 3.8 68.62 37 0.15 
Macro-economy consistency situation 4.54 4.54 4.56 93.99 34 0.23 
Strong warranty from both of the performing parties 4.72 5.62 4.9 129.98 26 0.33 
Political violence 5.08 5.8 4.14 121.98 29 0.30 
Economic consistency 5.8 5.8 5.26 176.95 24 0.46 
Leadership and handling by officials 6.72 7.8 6.54 342.80 0.92 
Coherent rulings 6.18 6.9 6.54 278.88 11 0.74 
Employment of expert consultants 5.72 6.8 6.26 243.49 16 0.64 
Continuous monitoring 5.36 6.8 6.62 241.29 18 0.64 
Governmental fraud 4.62 6.26 7.9 228.48 20 0.60 
Public resistance against PPP 4.44 5.36 4.08 97.10 32 0.23 
Exclusivity negation 4.26 5.18 4.08 90.03 35 0.21 
Inflation rate 6.08 7.54 7.44 341.07 0.92 
Market demands change 3.9 4.62 5.36 96.58 33 0.23 
Price change 6.08 6.44 7.04 275.65 12 0.73 
Non-payment of bills 6.26 7.72 7.18 346.99 0.93 
High operational cost 5.72 6.44 7.18 264.49 13 0.70 
Fluctuations in absorption rate 5.9 5.36 5.14 162.55 25 0.42 
The management, control and exploitation of water resources 5.36 7.04 7.04 265.65 14 0.71 
Competitive financial offers 2.26 2.72 2.18 13.40 39 0.00 
Qualitative and quantitative security of water resources 4.74 6.44 7.9 241.15 19 0.64 
Advancements and innovations in water technology 4.26 6.08 7.9 204.62 22 0.54 
Equal skill level in both of the performing groups 4.44 5.18 5.54 127.42 27 0.32 
Delivery or assurance of service 3.14 4.54 4.62 65.86 38 0.15 
Effect of environment on the project 4.08 4.26 5.72 99.42 31 0.24 
Pipeline failures during distribution 7.04 7.54 6.44 341.85 0.92 
Lack of PPP experience 4.26 5.36 5.36 122.39 28 0.31 
Poor contract design 5.9 6.8 7.36 295.28 0.79 
Support utility risks 5.72 6.26 7.04 252.08 15 0.67 
Lack of support for infrastructure 5.90 7.54 6.62 294.50 10 0.79 

aNormalized value = (average actual value − average minimum value)/(average maximum value − average minimum value).

After measurement of the and normalization for 22 factors, their normalization numbers were found to be greater than and they were treated consequently as CRFs. These factors were classified under four critical risk subcategories (CRSs): experimental, legal/political, financial and technological (Table 3). The stages of this study are shown in Figure 1.

Table 3

Critical risk factors

CRFsURPNOverall rankRank within category
Experimental subcategory U1       
Increasing population and expansion of urban settlement U11 370.58 
Inconsistency in weather patterns U12 211.40 21 
Continuation of traditional management practices for exploitation in agriculture U13 368.69 
Pollution U14 301.79 
The management, control and exploitation of water resources U15 242.61 17 
Weaknesses in water management U16 319.07 19 
Qualitative and quantitative security of water resources U17 241.15 
Legal subcategory U2    
Leadership and handling by officials U21 342.80 
Coherent rulings U22 278.88 11 
Employment of expert consultants U23 243.49 16 
Continuous monitoring U24 241.29 18 
Governmental fraud U25 228.48 20 
Financial subcategory U3    
Inflation rate U31 341.07 
Price change U32 275.65 12 
High operational cost U33 264.49 13 
Non-payment of bills U34 346.99 
Technological subcategory U4    
Advancements and innovations in water technology U41 204.62 22 
Pipeline failures during distribution U42 341.85 
Poor contract design U43 295.28 
Support utility risks U44 252.08 15 
Lack of support for infrastructure U45 294.50 10 
Problems with water recovery technology U46 242.61 17 
CRFsURPNOverall rankRank within category
Experimental subcategory U1       
Increasing population and expansion of urban settlement U11 370.58 
Inconsistency in weather patterns U12 211.40 21 
Continuation of traditional management practices for exploitation in agriculture U13 368.69 
Pollution U14 301.79 
The management, control and exploitation of water resources U15 242.61 17 
Weaknesses in water management U16 319.07 19 
Qualitative and quantitative security of water resources U17 241.15 
Legal subcategory U2    
Leadership and handling by officials U21 342.80 
Coherent rulings U22 278.88 11 
Employment of expert consultants U23 243.49 16 
Continuous monitoring U24 241.29 18 
Governmental fraud U25 228.48 20 
Financial subcategory U3    
Inflation rate U31 341.07 
Price change U32 275.65 12 
High operational cost U33 264.49 13 
Non-payment of bills U34 346.99 
Technological subcategory U4    
Advancements and innovations in water technology U41 204.62 22 
Pipeline failures during distribution U42 341.85 
Poor contract design U43 295.28 
Support utility risks U44 252.08 15 
Lack of support for infrastructure U45 294.50 10 
Problems with water recovery technology U46 242.61 17 
Figure 1

The stages of this study.

Figure 1

The stages of this study.

The FSE technique

The FSE technique is a multi-dimensional method for decision-making about risk factors in water projects (Xu et al. 2010). Here, it includes three sections as follows:

  • i.

    A collection of 22 CRFs that were identified in the increasing population and expansion of urban settlement and the management, control and exploitation of water resources. It also detected inconsistency in weather patterns, qualitative and quantitative security of water resources, continuation of traditional management practices for exploitation in agriculture, pollution, weaknesses in water management, leadership and handling by officials, coherent rulings, employment of expert consultants, continuous monitoring, governmental fraud, inflation rate, price changes, high operational cost, non-payment of bills, advancements and innovations in water technology, pipeline failure during distribution, poor contract design, support utility risks, lack of support for infrastructure and problems with water recovery technology.

  • ii.

    A set of ratings were employed for the three dimensions of the RPN (possibility of occurrence, severity of effect, probability of detection): e1 = low, e2 = mid, e3 = high, e4 = very high, e5 = dangerous, but not alarming.

  • iii.

    For each subcategory, the evaluation matrix was employed in which is the percentage of reaction to each rate (Figure 2).

Figure 2

The FSE technique structure.

Figure 2

The FSE technique structure.

Here, includes input information such as the percentage of reaction to each rate and the weight of each risk factor, is the converter of inputs to outputs and is the intended output (the calculation of which is discussed below).

Calculation of membership function for CRF and CRS

Ameyaw & Chan (2015) stated that fuzzy mathematics could be used to produce membership functions (MF). As discussed, a 3D comparative framework exists with five levels for the evaluation of the probability of occurrence, severity of effect and probability of detection. It is possible to calculate the MF as:  
formula
(2)
where n is the rank of the risk factor in each subcategory. denotes the reaction to each dimension of risk factors. For each risk factor, the MF can be expressed as:  
formula
(3)
can be calculated for as:  
formula
 
formula
 
formula

The MF of each risk factor is shown in Table 4.

Table 4

Membership function for all CRFs

SubcategoryMF of any risk factor for occurrence(O) (level 3)MF of each subcategory for occurrence (level 2)MF of any risk factor for severity of effect(S) (level 3)MF of each subcategory for severity of effect (S) (level 2)MF of any risk factor probability of detection(D) (level 3)MF of each subcategory for probability of detection (D) (level 2)
Experimental subcategory  (0.09,0.24,0.28,0.27,0.08)  (0.04,0.15,0.23,0.34,0.24)  (0.22,0.28,0.20,0.18,0.12) 
Increasing population and expansion of urban settlement (0.00,0.27,0.32,0.32,0.09)  (0.04,0.18,0.27,0.32,0.19)  (0.13,0.19,0.22,0.28,0.18)  
Inconsistency in weather patterns (0.14,0.36,0.36,0.14,0.00)  (0.00,0.19,0.27,0.32,0.22)  (0.23,0.4,0.19,0.09,0.09)  
Continuation of traditional management practices for exploitation in agriculture (0.09,0.23,0.32,0.27,0.09)  (0.14,0.18,0.18,0.23,0.27)  (0.27,0.18,0.23,0.23,0.09)  
Pollution (0.09,0.09,0.22,0.37,0.23)  (0.00,0.00,0.23,0.50,0.27)  (0.09,0.23,0.32,0.27,0.09)  
The management, control and exploitation of water resources (0.14,0.26,0.23,0.23,0.14)  (0.04,0.14,0.14,0.36,0.32)  (0.23,0.27,0.14,0.18,0.18)  
Weaknesses in water management (0.09,0.18,0.23,0.32,0.18)  (0.04,0.23,0.23,0.27,0.23)  (0.18,0.36,0.18,0.14,0.14)  
Qualitative and quantitative security of water resources (0.19,0.36,0.27,0.14,0.04)  (0.00,0.14,0.32,0.36,0.18)  (0.37,0.32,0.18,0.09,0.04)  
Legal subcategory  (0.11,0.29,0.31,0.19,0.10)  (0.06,0.13,0.24,0.22,0.35)  (0.32,0.26,0.17,0.15,0.10) 
Leadership and handling by officials (0.05,0.18,0.27,0.32,0.18)  (0.00,0.00,0.27,0.50,0.23)  (0.23,0.18,0.18,0.27,0.14)  
Coherent rulings (0.09,0.36,0.32,0.14,0.09)  (0.04,0.18,0.23,0.32,0.23)  (0.18,0.32,0.23,0.13,0.14)  
Employment of expert consultants (0.13,0.23,0.32,0.23,0.09)  (0.08,0.14,0.23,0.32,0.23)  (0.36,0.23,0.23,0.14,0.04)  
Continuous monitoring (0.09,0.36,0.37,0.14,0.04)  (0.04,0.18,0.27,0.33,0.18)  (0.36,0.23,0.18,0.14,0.09)  
Governmental fraud (0.23,0.36,0.27,0.09,0.05)  (0.17,0.17,0.17,0.27,0.22)  (0.46,0.32,0.04,0.09,0.09)  
Financial subcategory  (0.14,0.23,0.26,0.24,0.13)  (0.08,0.11,0.21,0.37,0.23)  (0.21,0.26,0.24,0.18,0.10) 
Inflation rate (0.09,0.27,0.27,0.23,0.14)  (0.00,0.14,0.23,0.36,0.27)  (0.14,0.27,0.32,0.18,0.09)  
Price change (0.14,0.18,0.27,0.27,0.14)  (0.17,0.14,0.14,0.32,0.23)  (0.13,0.23,0.27,0.23,0.14)  
High operational cost (0.13,0.32,0.23,0.18,0.14)  (0.18,0.09,0.18,0.37,0.18)  (0.27,0.23,0.18,0.23,0.09)  
Non-payment of bills (0.19,0.18,0.27,0.27,0.09)  (0.00,0.09,0.27,0.41,0.23)  (0.32,0.32,0.18,0.09,0.09)  
Technological subcategory  (0.13,0.27,0.28,0.21,0.10)  (0.07,0.15,0.23,0.33,0.21)  (0.23,0.27,0.23,0.18,0.09) 
Advancements and innovations in water technology (0.37,0.32,0.18,0.09,0.04)  (0.14,0.18,0.27,0.27,0.14)  (0.41,0.28,0.18,0.09,0.04)  
Pipeline failures during distribution (0.04,0.23,0.23,0.27,0.23)  (0.00,0.09,0.27,0.41,0.23)  (0.14,0.22,0.14,0.27,0.23)  
Poor contract design (0.14,0.23,0.27,0.27,0.09)  (0.14,0.14,0.09,0.36,0.27)  (0.18,0.27,0.23,0.23,0.09)  
Support utility risks (0.09,0.32,0.32,0.18,0.09)  (0.04,0.27,0.32,0.23,0.14)  (0.23,0.27,0.27,0.14,0.09)  
Lack of support for infrastructure (0.09,0.32,0.37,0.18,0.04)  (0.04,0.14,0.14,0.36,0.32)  (0.27,0.32,0.23,0.14,0.04)  
Problems with water recovery technology (0.13,0.23,0.32,0.23,0.09)  (0.09,0.13,0.32, 0.33,0.13)  (0.13,0.23,0.32,0.23,0.09)  
SubcategoryMF of any risk factor for occurrence(O) (level 3)MF of each subcategory for occurrence (level 2)MF of any risk factor for severity of effect(S) (level 3)MF of each subcategory for severity of effect (S) (level 2)MF of any risk factor probability of detection(D) (level 3)MF of each subcategory for probability of detection (D) (level 2)
Experimental subcategory  (0.09,0.24,0.28,0.27,0.08)  (0.04,0.15,0.23,0.34,0.24)  (0.22,0.28,0.20,0.18,0.12) 
Increasing population and expansion of urban settlement (0.00,0.27,0.32,0.32,0.09)  (0.04,0.18,0.27,0.32,0.19)  (0.13,0.19,0.22,0.28,0.18)  
Inconsistency in weather patterns (0.14,0.36,0.36,0.14,0.00)  (0.00,0.19,0.27,0.32,0.22)  (0.23,0.4,0.19,0.09,0.09)  
Continuation of traditional management practices for exploitation in agriculture (0.09,0.23,0.32,0.27,0.09)  (0.14,0.18,0.18,0.23,0.27)  (0.27,0.18,0.23,0.23,0.09)  
Pollution (0.09,0.09,0.22,0.37,0.23)  (0.00,0.00,0.23,0.50,0.27)  (0.09,0.23,0.32,0.27,0.09)  
The management, control and exploitation of water resources (0.14,0.26,0.23,0.23,0.14)  (0.04,0.14,0.14,0.36,0.32)  (0.23,0.27,0.14,0.18,0.18)  
Weaknesses in water management (0.09,0.18,0.23,0.32,0.18)  (0.04,0.23,0.23,0.27,0.23)  (0.18,0.36,0.18,0.14,0.14)  
Qualitative and quantitative security of water resources (0.19,0.36,0.27,0.14,0.04)  (0.00,0.14,0.32,0.36,0.18)  (0.37,0.32,0.18,0.09,0.04)  
Legal subcategory  (0.11,0.29,0.31,0.19,0.10)  (0.06,0.13,0.24,0.22,0.35)  (0.32,0.26,0.17,0.15,0.10) 
Leadership and handling by officials (0.05,0.18,0.27,0.32,0.18)  (0.00,0.00,0.27,0.50,0.23)  (0.23,0.18,0.18,0.27,0.14)  
Coherent rulings (0.09,0.36,0.32,0.14,0.09)  (0.04,0.18,0.23,0.32,0.23)  (0.18,0.32,0.23,0.13,0.14)  
Employment of expert consultants (0.13,0.23,0.32,0.23,0.09)  (0.08,0.14,0.23,0.32,0.23)  (0.36,0.23,0.23,0.14,0.04)  
Continuous monitoring (0.09,0.36,0.37,0.14,0.04)  (0.04,0.18,0.27,0.33,0.18)  (0.36,0.23,0.18,0.14,0.09)  
Governmental fraud (0.23,0.36,0.27,0.09,0.05)  (0.17,0.17,0.17,0.27,0.22)  (0.46,0.32,0.04,0.09,0.09)  
Financial subcategory  (0.14,0.23,0.26,0.24,0.13)  (0.08,0.11,0.21,0.37,0.23)  (0.21,0.26,0.24,0.18,0.10) 
Inflation rate (0.09,0.27,0.27,0.23,0.14)  (0.00,0.14,0.23,0.36,0.27)  (0.14,0.27,0.32,0.18,0.09)  
Price change (0.14,0.18,0.27,0.27,0.14)  (0.17,0.14,0.14,0.32,0.23)  (0.13,0.23,0.27,0.23,0.14)  
High operational cost (0.13,0.32,0.23,0.18,0.14)  (0.18,0.09,0.18,0.37,0.18)  (0.27,0.23,0.18,0.23,0.09)  
Non-payment of bills (0.19,0.18,0.27,0.27,0.09)  (0.00,0.09,0.27,0.41,0.23)  (0.32,0.32,0.18,0.09,0.09)  
Technological subcategory  (0.13,0.27,0.28,0.21,0.10)  (0.07,0.15,0.23,0.33,0.21)  (0.23,0.27,0.23,0.18,0.09) 
Advancements and innovations in water technology (0.37,0.32,0.18,0.09,0.04)  (0.14,0.18,0.27,0.27,0.14)  (0.41,0.28,0.18,0.09,0.04)  
Pipeline failures during distribution (0.04,0.23,0.23,0.27,0.23)  (0.00,0.09,0.27,0.41,0.23)  (0.14,0.22,0.14,0.27,0.23)  
Poor contract design (0.14,0.23,0.27,0.27,0.09)  (0.14,0.14,0.09,0.36,0.27)  (0.18,0.27,0.23,0.23,0.09)  
Support utility risks (0.09,0.32,0.32,0.18,0.09)  (0.04,0.27,0.32,0.23,0.14)  (0.23,0.27,0.27,0.14,0.09)  
Lack of support for infrastructure (0.09,0.32,0.37,0.18,0.04)  (0.04,0.14,0.14,0.36,0.32)  (0.27,0.32,0.23,0.14,0.04)  
Problems with water recovery technology (0.13,0.23,0.32,0.23,0.09)  (0.09,0.13,0.32, 0.33,0.13)  (0.13,0.23,0.32,0.23,0.09)  

Weight calculation for CRF and CRS

The weight of each of the 22 risk factors can be calculated (Xu et al. 2010; Ameyaw & Chan 2015) as:  
formula
(4)
where is the weight of CRS/CRF, is the mean CRS/CRF and is the total mean of CRF/CRS. The calculated weights for all risk factors and subcategories are shown in Table 5.
Table 5

The weight of all CRFs and each subcatagory

 Possibility of occurrence (O)
Severity of effect (S)
Probability of detection (D)
SubcategoryOWeighting of CRFTotal mean of CRSWeighting of CRSSWeighting of CRFTotal mean of CRSWeighting of CRSDWeighting of CRFTotal mean of CRSWeighting of CRS
Experimental subcategory   42.4 0.329   49.54 0.322   48.64 0.316 
Increasing population and expansion of urban settlement 7.06 0.167   7.52 0.152   6.98 0.144   
Inconsistency in weather patterns 4.90 0.116   6.42 0.130   6.72 0.138   
Continuation of traditional management practices for exploitation in agriculture 6.90 0.163   7.36 0.149   7.26 0.149   
Pollution 6.90 0.163   8.04 0.162   5.44 0.112   
The management, control and exploitation of water resources 5.36 0.126   7.04 0.142   7.04 0.145   
Weaknesses in water management 6.54 0.154   6.72 0.136   7.26 0.149   
Qualitative and quantitative security of water resources 4.74 0.112   6.44 0.130   7.90 0.162   
Legal subcategory   28.60 0.222   34.56 0.225   33.86 0.220 
Leadership and handling by officials 6.72 0.235   7.80 0.226   6.54 0.193   
Coherent rulings 6.18 0.216   6.90 0.200   6.54 0.193   
Employment of expert consultants 5.72 0.200   6.80 0.197   6.26 0.185   
Continuous monitoring 5.36 0.187   6.80 0.197   6.62 0.196   
Governmental fraud 4.62 0.162   6.26 0.181   7.90 0.233   
Financial subcategory   24.14 0.188   28.14 0.183   28.84 0.188 
Inflation rate 6.08 0.252   7.54 0.268   7.44 0.258   
Price change 6.08 0.252   6.44 0.229   7.04 0.244   
High operational cost 5.72 0.237   6.44 0.229   7.18 0.249   
Non-payment of bills 6.26 0.259   7.72 0.274   7.18 0.249   
Technological subcategory   33.54 0.261   41.48 0.270   42.44 0.276 
Advancements and innovations in water technology 4.26 0.127   6.08 0.147   7.90 0.186   
Pipeline failures during distribution 7.04 0.210   7.54 0.182   6.44 0.152   
Poor contract design 5.90 0.176   6.80 0.164   7.36 0.173   
Support utility risks 5.72 0.171   6.26 0.151   7.04 0.166   
Lack of support for infrastructure 5.90 0.176   7.54 0.182   6.62 0.156   
Problems with water recovery technology 4.72 0.141   7.26 0.175   7.08 0.167   
 Possibility of occurrence (O)
Severity of effect (S)
Probability of detection (D)
SubcategoryOWeighting of CRFTotal mean of CRSWeighting of CRSSWeighting of CRFTotal mean of CRSWeighting of CRSDWeighting of CRFTotal mean of CRSWeighting of CRS
Experimental subcategory   42.4 0.329   49.54 0.322   48.64 0.316 
Increasing population and expansion of urban settlement 7.06 0.167   7.52 0.152   6.98 0.144   
Inconsistency in weather patterns 4.90 0.116   6.42 0.130   6.72 0.138   
Continuation of traditional management practices for exploitation in agriculture 6.90 0.163   7.36 0.149   7.26 0.149   
Pollution 6.90 0.163   8.04 0.162   5.44 0.112   
The management, control and exploitation of water resources 5.36 0.126   7.04 0.142   7.04 0.145   
Weaknesses in water management 6.54 0.154   6.72 0.136   7.26 0.149   
Qualitative and quantitative security of water resources 4.74 0.112   6.44 0.130   7.90 0.162   
Legal subcategory   28.60 0.222   34.56 0.225   33.86 0.220 
Leadership and handling by officials 6.72 0.235   7.80 0.226   6.54 0.193   
Coherent rulings 6.18 0.216   6.90 0.200   6.54 0.193   
Employment of expert consultants 5.72 0.200   6.80 0.197   6.26 0.185   
Continuous monitoring 5.36 0.187   6.80 0.197   6.62 0.196   
Governmental fraud 4.62 0.162   6.26 0.181   7.90 0.233   
Financial subcategory   24.14 0.188   28.14 0.183   28.84 0.188 
Inflation rate 6.08 0.252   7.54 0.268   7.44 0.258   
Price change 6.08 0.252   6.44 0.229   7.04 0.244   
High operational cost 5.72 0.237   6.44 0.229   7.18 0.249   
Non-payment of bills 6.26 0.259   7.72 0.274   7.18 0.249   
Technological subcategory   33.54 0.261   41.48 0.270   42.44 0.276 
Advancements and innovations in water technology 4.26 0.127   6.08 0.147   7.90 0.186   
Pipeline failures during distribution 7.04 0.210   7.54 0.182   6.44 0.152   
Poor contract design 5.90 0.176   6.80 0.164   7.36 0.173   
Support utility risks 5.72 0.171   6.26 0.151   7.04 0.166   
Lack of support for infrastructure 5.90 0.176   7.54 0.182   6.62 0.156   
Problems with water recovery technology 4.72 0.141   7.26 0.175   7.08 0.167   

Fuzzy matrix

To determine the level of risk in the CRS of the project, the MF should be calculated for all CRFs in all three dimensions as:  
formula
(5)
Using measured weights , the fuzzy matrix for each CRS can be evaluated as:  
formula
(6)
For example, the evaluation of the fuzzy matrix for the severity of effect in the experimental subcategory is:  
formula
In MF, each degree is denoted as (Equation (7)) and is controlled by the weighted mean method. This method determines the effect of each risk factor, classifies them and stabilizes the highest measured weight (i.e. 1) after normalization of the risk factors (Hsiao 1998; Ameyaw & Chan 2015). The weighted mean method is widely used in multi-dimensional fuzzy decision-making conditions (Xu et al. 2010).  
formula
(7)
After the evaluation of the fuzzy matrix, for the evaluation of the overall risk level in each dimension, the fuzzy matrix is defined as:  
formula
(8)
where , , , and are the experimental, legal and political, financial, and technological subcategories, respectively. The weight of each subcategory in Table 5 must be used for the relationships described for the calculation of the overall risk level in each subcategory. The weights are calculated as and is the fuzzy matrix and is defined as:  
formula
(9)
This matrix should be calculated for the three dimensions of the possibility of occurrence, severity of effect and probability of detection. These should be multiplied by the linguistic variables in order to measure the overall risk level as:  
formula
(10)
where is assessed for each dimension and is the numerical value of the linguistic variables. Ameyaw & Chan (2015) and Xu et al. (2010) use the following to blend the levels and calculate the overall risk level as:  
formula
(11)
In the current study, this method has been modified by blending the FSE technique and FMEA as a new method of overall risk level calculation as:  
formula
(12)
where , and represent possibility of occurrence, severity of effect and probability of detection, respectively. Considering the blending of these methods and Table 6, the overall risk level in each subcategory can be calculated as:  
formula
This method also is correct for the other subcategories.  
formula
 
formula
 
formula
Table 6

MF for each subcategory

 Weighting of CRSMF for each subcategory (level 2)MF for overall risk level (level 1)
Occurrence 
 Experimental subcategory 0.329 (0.09,0.24,0.28,0.27,0.08)  
 Legal subcategory 0.222 (0.11,0.29,0.31,0.19,0.10)  
 Financial subcategory 0.187 (0.14,0.23,0.26,0.24,0.13) (0.11,0.26,0.28,0.23,0.10) 
 Technological subcategory 0.26 (0.13,0.27,0.28,0.21,0.10)  
Severity of effect 
 Experimental subcategory 0.336 (0.04,0.15,0.23,0.34,0.24)  
 Legal subcategory 0.189 (0.06,0.13,0.24,0.22,0.35)  
 Financial subcategory 0.191 (0.08,0.11,0.21,0.37,0.23) (0.06,0.14,0.23,0.32,0.25) 
 Technological subcategory 0.282 (0.07,0.15,0.23,0.33,0.21)  
Probability of detection 
 Experimental subcategory 0.316 (0.22,0.28,0.20,0.18,0.12)  
 Legal subcategory 0.22 (0.32,0.26,0.17,0.15,0.10)  
 Financial subcategory 0.187 (0.21,0.26,0.24,0.18,0.10) (0.24,0.27,0.20,0.17,0.10) 
 Technological subcategory 0.276 (0.23,0.27,0.23,0.18,0.09)  
 Weighting of CRSMF for each subcategory (level 2)MF for overall risk level (level 1)
Occurrence 
 Experimental subcategory 0.329 (0.09,0.24,0.28,0.27,0.08)  
 Legal subcategory 0.222 (0.11,0.29,0.31,0.19,0.10)  
 Financial subcategory 0.187 (0.14,0.23,0.26,0.24,0.13) (0.11,0.26,0.28,0.23,0.10) 
 Technological subcategory 0.26 (0.13,0.27,0.28,0.21,0.10)  
Severity of effect 
 Experimental subcategory 0.336 (0.04,0.15,0.23,0.34,0.24)  
 Legal subcategory 0.189 (0.06,0.13,0.24,0.22,0.35)  
 Financial subcategory 0.191 (0.08,0.11,0.21,0.37,0.23) (0.06,0.14,0.23,0.32,0.25) 
 Technological subcategory 0.282 (0.07,0.15,0.23,0.33,0.21)  
Probability of detection 
 Experimental subcategory 0.316 (0.22,0.28,0.20,0.18,0.12)  
 Legal subcategory 0.22 (0.32,0.26,0.17,0.15,0.10)  
 Financial subcategory 0.187 (0.21,0.26,0.24,0.18,0.10) (0.24,0.27,0.20,0.17,0.10) 
 Technological subcategory 0.276 (0.23,0.27,0.23,0.18,0.09)  

Calculation of overall risk level of PPPs for Iranian water supply projects

As shown in the previous section, calculation of the occurrence dimension is:  
formula
The calculation of the severity of effect and probability of detection is:  
formula
 
formula
Using the modified the FSE technique, the overall risk level can be calculated as:  
formula

RESULTS AND DISCUSSION

Table 7 shows that the overall level of risk for PPPs for Iranian water supply projects is 5.98, which is high when compared with the linguistic variables. Distributors and officials should try to decrease this significant level of risk to increase investment in PPPs for Iranian water supply projects. The possibility of occurrence of these risks is 7.10 and their severity is 5.97, which are destructive. These disadvantages should be minimized by considering the experiences of previous PPPs. Although the probability of detection of these risks is 5.21, greater skill can result in earlier risk detection.

Table 7

Overall risk level

 IndexLinguistic variablesRank
Probability of occurrence in each subcategory 
 Experimental subcategory U1 5.76 High 
 Legal subcategory U2 5.74 High 
 Financial subcategory U3 5.96 High 
 Technological subcategory U4 5.77 High 
Severity of effect in each subcategory 
 Experimental subcategory U1 7.20 Very high 
 Legal subcategory U2 7.07 Very high 
 Financial subcategory U3 7.10 Very high 
 Technological subcategory U4 6.90 High 
Probability of detection in each subcategory 
 Experimental subcategory U1 5.37 Average 
 Legal subcategory U2 4.89 Average 
 Financial subcategory U3 5.39 Average 
 Technological subcategory U4 5.28 Average 
Overall risk level 
 Experimental subcategory U1 6.05 High 
 Legal subcategory U2 5.83 High 
 Financial subcategory U3 6.11 High 
 Technological subcategory U4 5.94 High 
 Overall risk level of PPP project water in Iran 5.98 High  
 IndexLinguistic variablesRank
Probability of occurrence in each subcategory 
 Experimental subcategory U1 5.76 High 
 Legal subcategory U2 5.74 High 
 Financial subcategory U3 5.96 High 
 Technological subcategory U4 5.77 High 
Severity of effect in each subcategory 
 Experimental subcategory U1 7.20 Very high 
 Legal subcategory U2 7.07 Very high 
 Financial subcategory U3 7.10 Very high 
 Technological subcategory U4 6.90 High 
Probability of detection in each subcategory 
 Experimental subcategory U1 5.37 Average 
 Legal subcategory U2 4.89 Average 
 Financial subcategory U3 5.39 Average 
 Technological subcategory U4 5.28 Average 
Overall risk level 
 Experimental subcategory U1 6.05 High 
 Legal subcategory U2 5.83 High 
 Financial subcategory U3 6.11 High 
 Technological subcategory U4 5.94 High 
 Overall risk level of PPP project water in Iran 5.98 High  

CRS 1: Experimental subcategory U1

This subcategory contains seven risk factors which are mostly experimental and relate to management affairs. These factors carry a high total risk level of 6.05. Calculations indicate that this subcategory ranks second in the CRFs in this study. The possibility of occurrence risk level for this subcategory is 5.76. It also exhibits a high value of severity of effect of 7.20 and a medium value for probability of detection of 5.37. The increase in population and urban settlement is the most important risk factor in this subcategory.

CRS 2: Legal subcategory U2

This subcategory includes five risk factors which have a legal/political essence. Appropriate leadership and handling by officials is the most CRF. The overall risk level of this subcategory is 5.83, which is high. It has a high possibility of occurrence of risk of 5.74, a very high severity of effect of 7.07 and a medium probability of detection of 4.89. This subcategory ranks fourth among the four subcategories for overall risk level.

CRS 3: Financial subcategory U3

In this subcategory, the overall risk level is 6.11, which is high. Because of the importance of financial issues in all projects, as the level of risk decreases in this subcategory, the rate of investment in projects and infrastructure development will increase remarkably. Non-payment of bills is the most CRF and this subcategory has been identified as the most critical subcategory of the four. The possibility of occurrence, severity of effect, and probability of detection are 5.96, 7.10, and 5.39, respectively. The first two dimensions are notably high in this subcategory, especially the severity of effect.

CRS 4: Technological subcategory U4

There are six risk factors in this subcategory, which recorded a risk level of 5.94, which is high. Here, pipeline failure during distribution is the most CRF. The possibility of occurrence, severity of effect and probability of detection are 5.78, 6.90, and 5.28, respectively. The first two factors are high, but the last falls into the medium level. This subcategory ranks third out of four. The low-risk level in this subcategory can accelerate projects technically.

Comparison analysis

The proposed model differs from Ameyaw & Chan (2015) in that evaluation of the risk level has taken the probability of detection into account. As shown in Equation (11), the probability of occurrence and severity of effect dimensions were used in Ameyaw & Chan (2015). After calculation of the level of risk for each subcategory, the results are presented in Table 8. As shown, and in contrast to Ameyaw & Chan (2015) which shows a lower level of risk, the proposed method provides more accurate and real levels of risk for each subcategory as well as the overall risk level. It also provides more tangible information about different dimensions.

Table 8

Comparing the results obtained by the proposed model with the model of Ameyaw & Chan (2015) 

Total risk levelIndex in proposed modelIndex in Ameyaw & Chan (2015) modelRank in proposed modelRank in Ameyaw & Chan (2015) model 
Experimental subcategory U1 6.05 6.43  
Legal subcategory U2 5.83 6.36  
Financial subcategory U3 6.11 6.50  
Technological subcategory U4 5.94 6.31  
Total risk level of PPP project water in Iran 5.98 6.41      
Spearman's rank correlation coefficient      0.80 
Total risk levelIndex in proposed modelIndex in Ameyaw & Chan (2015) modelRank in proposed modelRank in Ameyaw & Chan (2015) model 
Experimental subcategory U1 6.05 6.43  
Legal subcategory U2 5.83 6.36  
Financial subcategory U3 6.11 6.50  
Technological subcategory U4 5.94 6.31  
Total risk level of PPP project water in Iran 5.98 6.41      
Spearman's rank correlation coefficient      0.80 
To evaluate the validity and applicability of the results of the proposed model, Spearman's rank correlation coefficient was compared with Ameyaw & Chan (2015) in which x and y are the calculated ranks (Table 8). Spearman's rank correlation coefficient is for the population parameter and for a sample statistic. It is appropriate when one or both variables are skewed or ordinal and is robust for extreme values. For correlation of variables x and y, the formula is:  
formula
(13)
where is the difference in ranks for x and y (Mukaka 2012). Equation (13) shows that Spearman's rank correlation coefficient is 0.8 at the 95% confidence level, which confirms the high validity of the proposed model.

CONCLUSIONS

Governmental budget limitations mean that governmental organizations must cooperate with the private sector in infrastructure projects in the construction of airports, highways, hospitals, water supply projects, and others in order to compensate for financial difficulties. The current study was undertaken to investigate PPP in Iranian water supply projects. Experts and the annual reports of the World Bank warn that Iran is facing a water crisis; thus, risks in Iranian water supply projects were extracted from earlier studies and national conditions and were analyzed carefully.

A literature review helped to identify 32 factors related to risk in PPPs and conditions unique to Iran suggested a further seven factors. The opinions of 53 experts about these risks were evaluated in three dimensions for the possibility of occurrence, severity of effect and probability of detection. FMEA was then used to measure the RPN of each risk factor. After the normalization and assessment of expert findings, 22 of these risks were classified as CRFs. These were categorized into experimental, legal, financial and technological subcategories.

The FSE technique was used to calculate the level of risk for each subcategory and the overall risk level of PPPs for Iranian water supply projects was determined. Calculation of the risk level for each subcategory and the overall subcategory helps politicians identify critical risks and find solutions to overcome the difficulties of water supply projects. It also helps private sector stakeholders to evaluate the level of risk of such projects and gain beneficial information necessary for partnerships. The FSE technique and FMEA method were blended and the FSE technique was modified to allow measurement of the probability of occurrence, severity of effect and probability of detection. After calculation, rankings of each subcategory in each dimension are as follows:

  • Probability of occurrence: financial, technological, experimental, and legal subcategories were ranked first to fourth, respectively.

  • Severity of effect: experimental, financial, legal, and technological subcategories were ranked first to fourth, respectively.

  • Probability of detection: financial, experimental, technological, and legal subcategories were ranked first to fourth, respectively.

For the overall risk level of the three dimensions, the subcategories were ranked from first to fourth as: financial, experimental, technological, and legal. The linguistic variables indicated that the overall risk level was too high for PPPs for Iranian water supply projects; this must not be disregarded during the development of infrastructure projects. Iran is a developing country. Identification of the risk factors and determination of their risk levels will speed up the performance of the projects and ameliorate national shortcomings.

Considering the limited amount of research on PPPs for Iranian water supply projects and the significance of these types of studies, it is suggested that more research should be undertaken in this domain. This will help identify further risk factors and determine the level of risk for each and could improve project efficiency and prosperity. The tangible results of related research are the identification and determination of risk levels of PPPs for express railway projects, gas transfer projects, road construction projects and airport construction projects and to determine the technological and physical levels of hospitals. In addition, the subcategory and overall risk levels can be measured using the proposed methods.

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