Sustainability has recently been acknowledged as a crucial issue in infrastructure projects. Developing a model to evaluate project sustainability according to sustainability indicators plays a major role in promoting the sustainable development of water environment treatment public-private partnership (PPP) projects. Traditional sustainability assessments are mostly based on the triple bottom line (economic, social, and environmental) and lack a more integrated indicator system. To connect the research gap, this paper identifies 27 factors that affect the sustainability of water environment treatment PPP projects from five dimensions: economy, society, resources and environment, engineering, and project management using exploratory factor analysis. The fitting degree between the model and original data is verified by confirmatory factor analysis. The results showed that the fitting was successful. This paper makes two contributions: first, it provides a comprehensive sustainability evaluation indicator system from five aspects, laying a foundation for the evaluation of project sustainability. Second, this study defines a methodology to evaluate and rank factors, identifies the indicators that show the most significant impact on project sustainability in the five dimensions, which provide a reliable reference for the public and private sector to take appropriate measures to improve the sustainability level of water environment treatment public-private partnership projects.
PurposeThe application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.Design/methodology/approachThis paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.FindingsThe feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.Practical implicationsThe research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.Originality/valueA novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.
Major infrastructure projects (MIPs) possess significant strategic positions in the national economy and social development. However, recently, the rent-seeking behavior between supervision units and project contractors has intensified in project construction. This paper aims to study the behavior decision-making of stakeholders in rent-seeking behavior supervision system of MIPs. In the complex and uncertain environment of MIPs, game players have cognitive bias and value perception preference. Therefore, this study introduced prospect theory and constructed the perceived return matrix and evolutionary game model of MIP rent-seeking behavior supervision among project owners, supervision units, and project contractors. From the perspective of risk perception theory, the reasons for the behavioral tendencies of game participants and the conditions for the steady state of strategy selection were explored through system dynamics simulations. The results showed that the stable state of the optimal strategy in the rent-seeking behavior supervision system of MIPs is related to the cognitive bias of the game players and is influenced by the level of regulation cost, the intensity of punishment and the size of accident losses. The contribution of this study lies in providing theoretical basis and decision support for constructing a long-term preventive mechanism for rent-seeking activities in MIPs.
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