Developing a Fuzzy Risk Allocation Model for PPP Projects in China
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 8
Abstract
Equitable allocation of risks between the government and the private sector in concession agreement is essential to the success of public-private partnership (PPP) projects. The decision-making process, based on the established risk allocation principles expressed in linguistic terms, requires qualitative judgment and experiential knowledge of construction experts. However, it is subjective, partial, and implicit in actual application. This paper aims to develop a fuzzy synthetic evaluation model for determining an equitable risk allocation between the government and the private sector. By doing so, it assists the PPP project practitioners to transform the risk allocation principles in linguistic terms into a more usable and systematic quantitative-based analysis using fuzzy set. Twenty-three principles and influencing factors for risk allocation were identified through a comprehensive literature review. Nine critical risk allocation criteria (RACs) that evaluate the risk carrying capability of project participants were further identified, validated, and compiled based on the experts’ knowledge via face-to-face interviews. On the other hand, the weighting for each critical risk allocation criterion was determined through a two-round Delphi questionnaire survey. A set of knowledge-based fuzzy inference rules was then established to set up the membership function for the nine RACs. Based on the research findings, a fuzzy synthetic evaluation model was finally established to determine an equitable risk allocation between the government and the private sector.
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Acknowledgments
The work described in this paper was part of the first writer's Ph.D. research and partly supported by a joint grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UNSPECIFIEDN_PolyU 514/07) and the National Science Foundation Council Research Grant of China (Project No. UNSPECIFIED70731160634).
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Received: Sep 7, 2009
Accepted: Jan 19, 2010
Published online: Jan 22, 2010
Published in print: Aug 2010
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