Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption - Pasura Aungkulanon; Walailak Atthirawong; Pongchanun Luangpaiboon (My University, 2023)
In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.
Keywords: Capital cost; Electric Vehicles and Infrastructure