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Front. Eng    2018, Vol. 5 Issue (1) : 64-77    https://doi.org/10.15302/J-FEM-2018067
RESEARCH ARTICLE
A fuzzy model for assessing the risk exposure of procuring infrastructure mega-projects through public-private partnership: The case of Hong Kong-Zhuhai-Macao Bridge
Albert P.C. CHAN1, Robert OSEI-KYEI1(), Yi HU2, Yun LE2
1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
2. Department of Construction Management and Real Estate, School of Economics and Management, Tongji University, Shanghai 200092, China
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Abstract

Considering the rapid urbanization growth rate particularly in developing countries, the number of infrastructure mega-projects over the past years has risen tremendously. Essentially, because infrastructure mega-projects require huge investment funds, better management skills, well qualified and experienced international expertise and technology innovation, they are mostly preferred to be procured using the PPP method compare to the use of the traditional bid-build system. In this regard, this paper aims to develop a fuzzy evaluation model for assessing the suitability of procuring infrastructure mega-projects through PPP by considering their risk exposure. The main body of Hong Kong-Zhuhai-Macao Bridge (HZMB) is used as a case project to demonstrate the practicality of the risk evaluation model. The risk evaluation model consists of four critical risk groupings, these include, construction and land risks, commercial risks, operational risks and political risks. Using the risk evaluation equation, a risk index of 4.53 out of 5.00 is computed for the selected project if it is procured through the PPP scheme. This outcome shows that the case project is not suitable for the PPP approach because its risk exposure is very high. The model developed will enable PPP practitioners to predict the likely risk exposure of procuring infrastructure mega-projects through the PPP scheme.
Keywords infrastructure mega-projects      public-private partnership      Hong Kong-Zhuhai-Macao Bridge      Hong Kong      fuzzy     
Corresponding Author(s): Robert OSEI-KYEI   
Just Accepted Date: 15 January 2018   Online First Date: 31 January 2018    Issue Date: 21 March 2018
 Cite this article:   
Albert P.C. CHAN,Robert OSEI-KYEI,Yi HU, et al. A fuzzy model for assessing the risk exposure of procuring infrastructure mega-projects through public-private partnership: The case of Hong Kong-Zhuhai-Macao Bridge[J]. Front. Eng, 2018, 5(1): 64-77.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2018067
https://academic.hep.com.cn/fem/EN/Y2018/V5/I1/64
Risk Factors Cheung and Chan (2011b) Ke et al. (2011) Li (2003) Chan et al. (2014) Roumboutsos and Anagnostopoulos (2008) Ameyaw and Chan (2013) Chou et al. (2012) Wibowo and Mohammed (2010) Thomas et al. (2003) Ibrahim et al. (2006) Shen et al. (2006) Ozdoganm and Birgonul (2000)
Corruption x x x x x x
Exchange rate fluctuation x x x x x x
Political/ public opposition x x x
Interest rate fluctuation x x x x x x x
Poor public decision making x x x x x x x
High financing cost x x x x x x x
Construction changes x x
Change in technology x x
Operational cost overruns x x x x x x x x x x
Change in market demand x x x x x x
Tariff change x x x
Construction cost overruns x x x x x
Tax regulations change x x
Changes in shareholdings of the Project Company x x
Delay in land acquisition x x x x x
Inflation rate fluctuation x x x x x x
Project approvals and permits delays x x x x
Project operation changes x x x
Political interference x x x x x
Inexperienced private partner x x
High maintenance cost x x
Force majeure x x x
Design deficiency x x x x
Conflict between partners x
Poor quality of workmanship x x
Unavailability of labor and material x x
Lack of commitment from project parties x
Third party liabilities x
Environmental risk x
Delay in project completion x x x x x x
Legislation changes x x x x x
Absence of competition x x
Tab.1  Summary of literature on risk factors in PPP (Osei-Kyei and Chan, 2017)
Risk factors Probability Severity Risk impact Rank Normalization
Delay in land acquisition 4.12 4.31 4.21 1 1.00
Operational cost overruns 4.23 4.08 4.15 2 0.97
Construction cost overruns 4.08 3.88 3.98 3 0.88
Delay in project completion 3.50 3.58 3.54 4 0.65
Political interference 3.42 3.46 3.44 5 0.60
Unavailability of labor and material 3.46 3.42 3.44 6 0.60
Change in market demand 3.35 3.50 3.42 7 0.59
High financing cost 3.31 3.31 3.31 8 0.53
Construction changes 3.23 3.35 3.29 9 0.52
Design deficiency 3.12 3.35 3.23 10 0.49
Project approvals and permits delays 3.12 3.23 3.17 11 0.46
Political/ public opposition 2.96 3.35 3.15 12 0.45
High maintenance cost 3.19 3.12 3.15 13 0.45
Environmental risk 3.08 3.19 3.13 14 0.44
Project operation changes 3.00 3.12 3.06 15 0.40
Conflict between partners 2.96 3.12 3.04 16 0.39
Lack of commitment from project parties 2.81 3.19 2.99 17 0.36
Poor public decision making 2.92 3.04 2.98 18 0.36
Poor quality of workmanship 2.69 3.19 2.93 19 0.33
Interest rate fluctuation 2.88 2.92 2.90 20 0.32
Legislation changes 2.46 3.19 2.80 21 0.27
Tax regulations change 2.50 2.88 2.69 22 0.21
Tariff change 2.42 2.96 2.68 23 0.20
Exchange rate fluctuation 2.58 2.77 2.67 24 0.20
Inflation rate fluctuation 2.15 3.04 2.56 25 0.14
Force majeure 2.27 2.88 2.56 26 0.14
Change in technology 2.38 2.73 2.55 27 0.14
Inexperienced private partner 2.23 2.73 2.47 28 0.09
Third party liabilities 2.27 2.65 2.45 29 0.08
Absence of competition 2.19 2.58 2.38 30 0.05
Corruption 2.04 2.62 2.31 31 0.01
Changes in shareholdings of the project company 2.31 2.27 2.29 32 0.00
Tab.2  Critical risk factors in PPP projects in Hong Kong
No. Risk factors Risk probability Weightings
for each CRF		 Total MS for each CRG Weightings for each CRG Risk severity Weightings for each CRF Total MS for each CRG Weightings for each CRG
CRF1 Delay in land acquisition 4.12 0.22 4.31 0.23
CRF3 Construction cost overruns 4.08 0.22 3.88 0.21
CRF4 Delay in project completion 3.50 0.19 3.58 0.19
CRF6 Unavailability of labor and materials 3.46 0.19 3.42 0.18
CRF9 Construction changes 3.23 0.18 3.35 0.18
CRG1 Construction and land risks 18.39 0.56 18.54 0.56
CRF8 High financing cost 3.31 0.50 3.31 0.49
CRF7 Change in market demand 3.35 0.50 3.50 0.51
CRG2 Commercial risks 6.66 0.20 6.81 0.21
CRF2 Operational cost overruns 4.23 1.00 4.08 1.00
CRG3 Operational risks 4.23 0.13 4.08 0.12
CRF5 Political interference 3.42 1.00 3.46 1.00
CRG4 Political risks 3.42 0.10 3.46 0.11
Total CSFG 32.70 32.89
Tab.3  Weightings of CRFs and CRGs for PPP projects in Hong Kong
No. CRFs and CRGs Weightings for CRFs Membership functions for level 2
(CRFs)		 Membership functions for Level 1
(CRGs)
CRG1 Construction and land risks
CRF1 Delay in land acquisition 0.22 0.00 0.08 0.08 0.50 0.35 0.01 0.07 0.32 0.43 0.18
CRF3 Construction cost overruns 0.22 0.00 0.04 0.15 0.50 0.31
CRF4 Delay in project completion 0.19 0.00 0.08 0.46 0.35 0.12
CRF6 Unavailability of labor and materials 0.19 0.04 0.04 0.39 0.50 0.04
CRF9 Construction changes 0.18 0.00 0.12 0.58 0.27 0.04
CRG2 Commercial risks
CRF8 High financing cost 0.50 0.00 0.12 0.50 0.35 0.04 0.00 0.08 0.56 0.33 0.04
CRF7 Change in market demand 0.50 0.00 0.04 0.62 0.31 0.04
CRG3 Operational risks
CRF2 Operational cost overruns 1.00 0.00 0.04 0.08 0.50 0.39 0.00 0.04 0.08 0.50 0.39
CRG4 Political risks
CRF5 Political interference 1.00 0.00 0.12 0.39 0.46 0.04 0.00 0.12 0.39 0.46 0.04
Tab.4  Membership functions for risk probability of all CRFs and CRGs for PPP projects in Hong Kong
No. CRFs and CRGs Weightings for CRFs Membership functions for level 2
(CRFs)		 Membership functions for Level 1
(CRGs)
CRG1 Construction and land risks
CRF1 Delay in land acquisition 0.23 0.00 0.00 0.08 0.54 0.39 0.00 0.07 0.30 0.43 0.19
CRF3 Construction cost overruns 0.21 0.00 0.04 0.35 0.31 0.31
CRF4 Delay in project completion 0.19 0.00 0.08 0.35 0.50 0.08
CRF6 Unavailability of labor and materials 0.18 0.00 0.12 0.46 0.31 0.12
CRF9 Construction changes 0.18 0.00 0.15 0.35 0.50 0.00
CRG2 Commercial risks
CRF8 High financing cost 0.49 0.00 0.08 0.54 0.39 0.00 0.02 0.08 0.42 0.45 0.04
CRF7 Change in market demand 0.51 0.04 0.08 0.31 0.50 0.08
CRG3 Operational risks
CRF2 Operational cost overruns 1.00 0.00 0.04 0.19 0.42 0.35 0.00 0.04 0.19 0.42 0.35
CRG4 Political risks
CRF5 Political interference 1.00 0.00 0.12 0.39 0.42 0.08 0.00 0.12 0.39 0.42 0.08
Tab.5  Membership functions for risk severity of all CRFs and CRGs for PPP projects in Hong Kong
No. CRGs Probability Severity
1 2 3 4 5 Index 1 2 3 4 5 Index
CRG1 Construction and land risks 0.01 0.07 0.32 0.43 0.18 3.73 0.00 0.07 0.30 0.43 0.19 3.71
CRG2 Commercial risks 0.00 0.08 0.56 0.33 0.04 3.36 0.02 0.08 0.42 0.45 0.04 3.44
CRG3 Operational risks 0.00 0.04 0.08 0.50 0.39 4.27 0.00 0.04 0.19 0.42 0.35 4.08
CRG4 Political risks 0.00 0.12 0.39 0.46 0.04 3.45 0.00 0.12 0.39 0.42 0.08 3.49
Tab.6  Probability and severity indices of all CRGs for PPP projects in Hong Kong
No. CRGs for PPP projects Probability Severity Risk evaluation index Coefficient*
CRG1 Construction and land risks 3.73 3.71 3.72 0.25
CRG2 Commercial risks 3.36 3.44 3.40 0.23
CRG3 Operational risks 4.27 4.08 4.17 0.28
CRG4 Political risks 3.45 3.49 3.47 0.24
Tab.7  Risk evaluation index of each CRG for PPP projects in Hong Kong
ID Position Institution Sector Years of PPP experience
EXP1 Lecturer Local university Academic 11– 15 years
EXP2 Civil engineer Local construction /Consultancy firm Private/Public 11 -15 years
EXP3 Civil engineer Local construction firm Private 11–15 years
EXP4 Assistant professor Local university Academic 6 – 10 years
EXP5 Associate professor Local university Academic 16– 20 years
Tab.8  Background of experts
No. Risk factors Probability Severity Risk impact score Risk impact of CRG Total risk impact score
CRF1 Delay in land acquisition 4.20 4.00 4.10
CRF3 Construction cost overruns 4.80 4.40 4.60
CRF4 Delay in project completion 4.25 4.80 4.52
CRF6 Unavailability of labor and materials 4.00 4.40 4.20
CRF9 Construction changes 4.20 4.50 4.35
CRG1 Construction and land risks 4.35 21.77
CRF8 High financing cost 4.54 4.00 4.26
CRF7 Change in market demand 4.20 4.00 4.10
CRG2 Commercial risks 4.18 8.36
CRF2 Operational cost overruns 4.40 4.90 4.64
CRG3 Operational risks 4.64 4.64
CRF5 Political interference 4.87 5.00 4.93
CRG4 Political risks 4.93 4.93
Tab.9  Average risk impact score for each CRG
1 Akintoye A, Hardcastle C, Beck M, Chinyio E, Asenova D (2003). Achieving best value in private finance initiative project procurement. Construction Management and Economics, 21(5): 461–470
https://doi.org/10.1080/0144619032000087285
2 Ameyaw E E, Chan A P C (2013). Identifying public-private partnership (PPP) risks in managing water supply projects in Ghana. Journal of Facilities Management, 11(2): 152–182
https://doi.org/10.1108/14725961311314651
3 Ameyaw E E, Chan A P C (2015). Evaluation and ranking of risk factors in public-private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach. Expert Systems with Applications, 42(12): 5102–5116
https://doi.org/10.1016/j.eswa.2015.02.041
4 Chan A P, Lam P T, Chan D W, Cheung E, Ke Y (2010). Potential obstacles to successful implementation of public-private partnerships in Beijing and the Hong Kong Special Administrative Region. Journal of Management Engineering, 26(1): 30–40
https://doi.org/10.1061/(ASCE)0742-597X(2010)26:1(30)
5 Chan A P, Lam P T, Wen Y, Ameyaw E E, Wang S, Ke Y (2014). Cross-sectional analysis of critical risk factors for PPP water projects in China. Journal of Infrastructure Systems, 21(1): 04014031
https://doi.org/10.1061/(ASCE)IS.1943-555X.0000214
6 Chan A P C, Cheung E, (2013). Public Private Partnerships in International Construction: Learning from Case Studies. Hoboken: Routledge
7 Chan A P C, Lam P T, Chan D W, Cheung E, Ke Y (2009). Drivers for adopting public private partnerships. Journal of Construction Engineering and Management, 135(11): 1115–1124
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000088
8 Chan A P C, Yeung J F, Yu C C P, Wang S Q, Ke Y (2011). Empirical study of risk assessment and allocation of public-private partnership projects in China. Journal of Management Engineering, 27(3): 136–148
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000049
9 Cheung E, Chan A P C (2011a). Evaluation model for assessing the suitability of public-private partnership projects. Journal of Management Engineering, 27(2): 80–89
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000044
10 Cheung E, Chan A P C (2011b). Risk factors of public-private partnership projects in China: Comparison between the water, power, and transportation Sectors. Journal of Urban Planning and Development, 137(4): 409–415
https://doi.org/10.1061/(ASCE)UP.1943-5444.0000086
11 Cheung E, Chan A P C (2009). Is BOT the best financing model to procure infrastructure projects? A case study of the Hong Kong-Zhuhai-Macao Bridge. Journal of Property Investment & Finance, 27(3): 290–302
https://doi.org/10.1108/14635780910951984
12 Cheung E, Chan A P C, Kajewski S (2009). Enhancing value for money in public private partnership projects. Journal of Financial Management of Property and Construction, 14(1): 7–20
https://doi.org/10.1108/13664380910942617
13 Cheung E, Chan A P C, Kajewski S (2012). Factors contributing to successful public private partnership projects. Journal of Facilities Management, 10(1): 45–58
https://doi.org/10.1108/14725961211200397
14 Chou J S, Tserng H P, Lin C, Yeh C P (2012). Critical factors and risk allocation for PPP policy: Comparison between HSR and general infrastructure projects. Transport Policy, 22: 36–48
https://doi.org/10.1016/j.tranpol.2012.05.009
15 Flyvbjerg B, Bruzelius N, Rothengatter W (2003). Megaprojects and risk: An anatomy of ambition. Cambridge: Cambridge University Press
16 He Q, Luo L, Hu Y, Chan A P (2015). Measuring the complexity of mega construction projects in China—A fuzzy analytic network process analysis. International Journal of Project Management, 33(3): 549–563
https://doi.org/10.1016/j.ijproman.2014.07.009
17 Hsu T H, Yang T S (1997). The application of fuzzy synthetic decision to the human resource management. Fu Jen Management Reviews, 4(2): 85–100
18 Hu Y, Chan A P, Le Y, Jin R Z (2015). From construction megaproject management to complex project management: Bibliographic analysis. Journal of Management Engineering, 31(4): 04014052
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000254
19 Hu Y, Chan A P, Le Y, Xu Y, Shan M (2016). Developing a program organization performance index for delivering construction megaprojects in China: Fuzzy synthetic evaluation analysis. Journal of Management Engineering, 32(4): 05016007
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000432
20 Ibrahim A D, Price A D F, Dainty A R J (2006). The analysis and allocation of risks in public-private partnerships in infrastructure projects in Nigeria. Journal of Financial Management of Property and Construction, 11(3): 149–164
https://doi.org/10.1108/13664380680001086
21 Javed A A (2013). A model of output specifications for public-private partnership projects. Dissertation for the Doctoral Degree. Hong Kong: The Hong Kong Polytechnic University
22 Ke Y, Wang S, Chan A P, Cheung E (2009). Research trend of public-private partnership in construction journals. Journal of Construction Engineering and Management, 135(10): 1076–1086
https://doi.org/10.1061/(ASCE)0733-9364(2009)135:10(1076)
23 Ke Y, Wang S, Chan A P, Cheung E (2011). Understanding the risks in China’s PPP projects: Ranking of their probability and consequence. Engineering, Construction, and Architectural Management, 18(5): 481–496
https://doi.org/10.1108/09699981111165176
24 Ke Y, Wang S, Chan A P, Lam P T (2010). Preferred risk allocation in China’s public-private partnership (PPP) projects. International Journal of Project Management, 28(5): 482–492
https://doi.org/10.1016/j.ijproman.2009.08.007
25 Li B (2003). Risk Management of Construction Public Private Partnership Projects. Dissertation for the Doctoral Degree. Glasgow: Glasgow Caledonian University
26 Li B, Akintoye A, Edwards P J, Hardcastle C (2005). The allocation of risk in PPP/PFI construction projects in the UK. International Journal of Project Management, 23(1): 25–35
https://doi.org/10.1016/j.ijproman.2004.04.006
27 Li T H, Ng S T, Skitmore M (2013). Evaluating stakeholder satisfaction during public participation in major infrastructure and construction projects: A fuzzy approach. Automation in Construction, 29: 123–135
https://doi.org/10.1016/j.autcon.2012.09.007
28 Liu J, Li Q, Wang Y (2013). Risk analysis in ultra-deep scientific drilling project—A fuzzy synthetic evaluation approach. International Journal of Project Management, 31(3): 449–458
https://doi.org/10.1016/j.ijproman.2012.09.015
29 Miller R, Lessard D (2000). The Strategic Management of Large Engineering Projects: Shaping Institutions, Risks and Governance. Boston: MIT Press
30 Osei-Kyei R, Chan A P C (2016). Developing transport infrastructure in Sub-Saharan Africa through public-private partnerships: Policy practice and implications. Transport Reviews, 36(2): 170–186
https://doi.org/10.1080/01441647.2015.1077288
31 Osei-Kyei R, Chan A P C (2017). Risk assessment in public-private partnership infrastructure projects. Construction Innovation, 17(2): 204–223
https://doi.org/10.1108/CI-08-2016-0043
32 Osei-Kyei R, Dansoh A, Ofori-Kuragu J K (2014). Reasons for adopting Public-Private Partnership (PPP) for construction projects in Ghana. International Journal of Construction Management, 14(4): 227–238
https://doi.org/10.1080/15623599.2014.967925
33 Ozdoganm I D, Birgonul M T (2000). A decision support framework for project sponsors in the planning stage of build-operate-transfer (BOT) projects. Construction Management and Economics, 18(3): 343–353
https://doi.org/10.1080/014461900370708
34 Remington K, Pollack J (2008). Tools for Complex Projects. Aldershot: Routledge
35 Roumboutsos A, Anagnostopoulos K P (2008). Public-private partnership projects in Greece: Risk ranking and preferred risk allocation. Construction Management and Economics, 26(7): 751–763
https://doi.org/10.1080/01446190802140086
36 Sadiq R, Rodriguez M J (2004). Fuzzy synthetic evaluation of disinfection by-products—a risk-based indexing system. Journal of Environmental Management, 73(1): 1–13
https://doi.org/10.1016/j.jenvman.2004.04.014 pmid: 15327842
37 Salman A F, Skibniewski M J, Basha I (2007). BOT viability model for large-scale infrastructure projects. Journal of Construction Engineering and Management, 133(1): 50–63
https://doi.org/10.1061/(ASCE)0733-9364(2007)133:1(50)
38 Shen L Y, Platten A, Deng X P (2006). Role of public private partnerships to manage risks in public sector projects in Hong Kong. International Journal of Project Management, 24(7): 587–594
https://doi.org/10.1016/j.ijproman.2006.07.006
39 Szolnoki G, Hoffmann D (2013). Online, face-to-face and telephone surveys—Comparing different sampling methods in wine consumer research. Wine Economics and Policy, 2(2): 57–66
https://doi.org/10.1016/j.wep.2013.10.001
40 Tam C M (1999). Build-operate-transfer model for infrastructure developments in Asia: Reasons for successes and failures. International Journal of Project Management, 17(6): 377–382
https://doi.org/10.1016/S0263-7863(98)00061-1
41 Thomas A V, Kalidindi S N, Ananthanarayanan K (2003). Risk perception analysis of BOT road project participants in India. Construction Management and Economics, 21(4): 393–407
https://doi.org/10.1080/0144619032000064127
42 Toor R S, Ogunlana S O (2010). Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects. International Journal of Project Management, 28(3): 228–236
https://doi.org/10.1016/j.ijproman.2009.05.005
43 Wibowo A, Mohamed S (2010). Risk criticality and allocation in privatised water supply projects in Indonesia. International Journal of Project Management, 28(5): 504–513
https://doi.org/10.1016/j.ijproman.2009.08.003
44 World Bank (2015). Private Participation in Infrastructure (PPI)., 2015–5
45 Xu Y, Yeung J F, Chan A P C, Chan D W, Wang S Q, Ke Y (2010). Developing a risk assessment model for PPP projects in China—A fuzzy synthetic evaluation approach. Automation in Construction, 19(7): 929–943
https://doi.org/10.1016/j.autcon.2010.06.006
46 Yescombe E R (2011). Public-Private Partnerships Principles of Policy and Finance. Oxford: Elsevier
47 Yeung J F, Chan A P, Chan D W (2009). Developing a performance index for relationship-based construction projects in Australia: Delphi study. Journal of Management Engineering, 25(2): 59–68
https://doi.org/10.1061/(ASCE)0742-597X(2009)25:2(59)
48 Zadeh L A (1965). Fuzzy sets. Information and Control, 8(3): 338– 353
https://doi.org/10.1016/S0019-9958(65)90241-X
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