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USING FUZZY ANALYTIC NETWORK PROCESS AND ISM METHODS FOR RISK ASSESSMENT OF PUBLIC-PRIVATE PARTNERSHIP: A CHINA PERSPECTIVE

风险分析(工程) 风险管理 相互依存 公私合营 风险评估 观点 过程(计算) 普通合伙企业 网络分析法 模糊性 业务 模糊逻辑 工程类 精算学 计算机科学 运筹学 财务 计算机安全 政治学 层次分析法 操作系统 艺术 视觉艺术 人工智能 法学
作者
Yan Li,Xinyu Wang
出处
期刊:Journal of Civil Engineering and Management [Taylor & Francis]
卷期号:25 (2): 168-183 被引量:34
标识
DOI:10.3846/jcem.2019.8655
摘要

The public-private partnership (PPP) has been adopted globally to meet intensifying demands for public facilities and services. However, PPP projects contain a variety of risks which may lead to project failure. Many researchers have explored risk factors associated with PPP projects in developing countries. However, these investigations have limited their aim to understanding risk impact without considering the interactions of these factors. Hence, to fill this gap, this study proposes a risk assessment method, addressing vital interrelationships and interdependencies. Two methodologies, fuzzy analytic network process (F-ANP) and interpretive structural modeling (ISM), were applied to avoid vagueness and data inaccuracies. The primary contributions of this paper were considering the relationships among risk factors and risk priority; and offering a risk analysis approach based on linguistic scales and fuzzy numbers to reflect different neutral, optimistic and pessimistic viewpoints from expert respondents’ judgments. Results from this analysis showed that legal and policy risk was the most influential and interdependent risk, and interest rate risk was the most essential risk in Chinese PPP projects. The ISM structure diagram demonstrated that most of 35 identified risk factors had high driving and dependence power. This study proposed a systematic and practical method to identify and assess PPP risk factors, utilizing an integrated approach consisting of F-ANP and ISM, which has not been used for risk assessment in the construction field. This paper provides a new risk assessment tool and a basis for risk management strategies in the construction engineering and management field.
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