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Risk management is critical to the success of electric vehicle charging infrastructure public-private partnership (EVCI-PPP) projects, as risks are present throughout the whole life cycle of projects. However, in EVCI-PPP projects, risk factors are often interdependent and, consequently, the interrelationships among factors affect the risk management, which is ignored in the existing studies. To identify the risk factors of EVCI-PPP projects and analyze their internal influence relations, this paper develops a risk identification and analysis model of EVCI-PPP projects based on the 2-tuple linguistic representation model and the decision-making trial and evaluation laboratory (DEMATEL) model. First, a risk factor set is established including 22 criteria involved in 5 dimensions of political/legal risk, economic/market risk, social/environment risk, project/technical risk, and managing risk. Next, the 2-tuple model is introduced to integrate the decision makers' evaluation information in a linguistic environment, and the direct relation matrix is calculated. Then, the cause-effect relations and a significant degree of risk factors are interpreted using the extended DEMATEL technique. The results show that economic/market risk is the most significant factor of EVCI-PPP projects, and 22 criteria are classified into 14 cause factors and 8 effect factors. Finally, suggestions are provided for decision-makers to ensure the success of EVCI-PPP projects.

Public‐private partnership (PPP) models, which can mitigate the deflection of charging facilities, have been introduced for electric vehicle charging infrastructure (EVCI) projects. Private‐sector partner selection is a significant step to ensure the success of EVCI‐PPP projects, but it has been rarely studied. This paper therefore identifies selection criteria for private partners in EVCI‐PPP projects and assesses the alternatives using an extended multicriteria operation and compromise solution (VIKOR) method. A partner selection indicator system is established through three development processes, and the factors are categorized into five factor packages: basic ability, management ability, performance of previous performance and credit performance, performance of projects, and sustainable development. These factors include 22 subcriteria. An extended VIKOR‐based method incorporating an intuitionistic fuzzy set is developed to select an optimal private partner. A case study is used to verify the effectiveness and rationalization of the proposed approach. Three potential candidates, P1, P2, P3, are evaluated and ranked according to their evaluation scores, and the results show that P1 is the best partner solution. Relevant charging infrastructure project experience, technical level in charging facilities, and financing plan are the top three criteria for choosing appropriate partners for EVCI‐PPP projects. This study provides a comprehensive evaluation framework for the government to select suitable private‐sector partners to accelerate the sustainable development of charging facilities.

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