Risk management of public-private partnership charging infrastructure projects in China based on a three-dimension framework

Zhao

Chai

et al. 2019

WEVJ

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.

Section: Risk Analysis Of Evci-ppp Projectsmentioning

confidence: 99%

“…Insufficient goods supply * * * 3 13 Market demand change * * * * * 5 14 Financial risk * * * * * * 6 15 Payment risk * * * * * * * 7 16 Inflation risk * * * * * 5 17…”

Section: No Risk Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk Identification and Analysis for PPP Projects of Electric Vehicle Charging Infrastructure Based on 2-Tuple and the DEMATEL Model

Zhao

Chai

et al. 2019

WEVJ

“…explored risk factors and developed an evaluation model to assess the risk levels of different EVCI‐PPP projects. Based on this model, proposed a risk management framework. Taken together, these studies show that the PPP model has attracted the attention of scholars, as a valuable model to stimulate private participations, and the research stream gradually shifted from a macroperspective to practical applications.…”

Section: Introductionmentioning

confidence: 99%

Private‐sector partner selection for public‐private partnership projects of electric vehicle charging infrastructure

Energy Science & Engineering

Zhao

Kan³

2019

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.

“…The fuzzy set theory, as proposed by Zadeh [22], has emerged as a powerful way to represent such uncertain phenomena. By combining the fuzzy set theory and traditional evaluation methods, some fuzzy evaluation methods have been developed by scholars; these include fuzzy synthetic evaluation (FSE) [23] and fuzzy multi-criteria decision-making (MCDM) approaches, such as fuzzy Order Preference by Similarity to Ideal Solution (TOPSIS) [24], fuzzy VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) [25], fuzzy PROMETHEE [26] and fuzzy TODIM [27]. The FSE method is a comprehensive evaluation approach based on fuzzy mathematics.…”

mentioning

confidence: 99%

Sustainability Performance Evaluation of Hybrid Energy System Using an Improved Fuzzy Synthetic Evaluation Approach

Xin

Kan³

2019

Sustainability

The hybrid energy system (HES) has attracted more and more attention since it can not only achieve multi-energy supply but realize cascade utilization of energy resources. However, the performances of the HES in relation to economic, environmental, social, and technological aspects are rarely studied. Therefore, this paper tries to fill this research gap to evaluate the sustainability performance of an HES. First, an evaluation criteria system is established based on a literature review. After that, the group analytic hierarchy process (GAHP) technique is used to obtain the importance weights of these criteria. Later, the sustainability performance of the HES is calculated through an improved fuzzy synthetic evaluation (FSE) approach based on a cloud model. The applicability of this approach is demonstrated by a real case study in Zhejiang province, China. Finally, the sensitivity analysis results reveal that the overall consequence is that the performance of an HES is robust when the criteria weight is floating within a certain range (−30–30%), and the comparative analysis with the traditional FSE also reveals that the proposed approach is superior.