Developing a risk assessment model for PPP projects in China — A fuzzy synthetic evaluation approach

Department of Construction Management, Tsinghua University, Beijing, China
Automation in Construction (Impact Factor: 1.81). 11/2010; 19(7):929-943. DOI: 10.1016/j.autcon.2010.06.006


Earlier research works on PPP showed that an objective, reliable, and practical risk assessment model for PPP projects is essential to the successful implementation of PPP projects. However, actual empirical research studies in this research area are rather limited. This paper reports the second stage of a funded research study, which aims to develop a fuzzy synthetic evaluation model for assessing the risk level of a particular critical risk group (CRG) and the overall risk level associated with PPP projects in China. At the first research stage, thirty-four risk factors were identified through a comprehensive literature review and 3 new risk factors were proposed during a two-round Delphi questionnaire survey. The most critical 17 risk factors were selected through the calculation of normalized values. The correlation of these 17 critical risk factors (CRFs) was further analyzed via factor analysis and 6 CRGs were formulated, namely: (1) Macroeconomic Risk; (2) Construction and Operation Risk; (3) Government Maturity Risk; (4) Market Environment Risk; (5) Economic Viability Risk; and (6) Government Intervention. On the basis of the research works conducted at the first research stage, the weightings for each of the 17 critical risk factors (CRFs) and 6 CRGs were determined through the two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the 17 CRFs and 6 CRGs. The empirical research findings showed that the overall risk level of PPP highway projects is between “moderate risk” and “high risk”. Hence it could be construed that investment in PPP highway projects in China may be considered as risky. In fact, the Delphi survey respondents perceived that “Government Intervention” is the most CRG; with “Government Maturity Risk” being the second; “Economic Viability Risk” the third; “Market Environment Risk” the fourth; “Construction and Operation Risk” the fifth; and “Macroeconomic Risk” the last. These findings revealed that government intervention and corruption may be the major hurdles to the success of PPP highway projects in China. These may be caused by inadequate law and supervision system and poor public decision-making process. Although the fuzzy synthetic evaluation model was primarily developed for PPP projects in general, the research method could be replicated in a specific type of PPP project, such as water treatment projects and hospital projects, to produce similar models for inter-type comparisons. By doing so, it provides an opportunity for practitioners to assess the risk level of different types of PPP projects based on objective evidence rather than subjective judgment. The most CRG for different types of PPP projects could be identified and both precautionary and remedial actions could be taken as soon as possible. Such an extension would provide a deeper understanding of managing different types of PPP projects.

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Available from: Shou Qing Wang, Feb 08, 2014
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    • "Therefore, the data are appropriate to be further analyzed. Normally, an evaluation model is developed from a hierarchical framework (Xu et al. 2010). Therefore, to hierarchize the framework of measurement items of corruption, factor analysis was conducted utilizing SPSS 17.0. "
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    • "Moreover, the validity of the indicators was improved by applying PPP data from Australia (Jin, 2010), Tehran (Heravi and Hajihosseini, 2012), and India (Singh and Kalidindi, 2006), which have extensive PPP experiences. Table 3 lists a summary form of indicators adapted from Bing et al. (2005a), Hwang et al. (2013), Chou et al. (2012), Ke et al. (2010), Chan et al. (2011), Jin (2010), Xu et al. (2010) "
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    • "To select the critical hindrances, the normalized values of the mean scores were calculated. This method was adopted by Xu et al. (2010), who identified the factors with normalized values equal to or greater than 0.50 as critical factors. This principle was also adopted in this study. "
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