Framework to determine factors affecting the willingness to pay for electric vehicle conversion (EVC)

Name: Kritsakon Kiewsongsakool

keyword: CO2 emission

; Contingent valuation method

; Electric vehicle conversion

; Electric vehicle

; Willingness to pay

; Binary logistic regression

; Multiple regression

Abstract: The transport sector is one of the main contributors to carbon dioxide emission in Thailand. This is because of the continuous increase in the utilization of gasoline. Government industrial agencies have acknowledged the importance of reducing carbon dioxide emissions. Consequently, the automotive industry has been developing an advanced technology that can release less carbon dioxide ; one such technology is the development of an electric vehicle (EV) that uses electric energy instead of gasoline. However, electric vehicles have not received much interest from consumers in Thailand, as they are usually more expensive than regular gasoline cars when compared to cars of the same size. Recently, the Electricity Generating Authority of Thailand (EGAT) and the National Science and Technology Development Agency (NSTDA) have jointly collaborated to develop electric vehicle conversion (EVC). It is a car modification that converts an existing non-EV vehicle to be an EV. The conversion cost is about 200,000 Thai Baht (excluding battery). The existing functions in an old car such as the air-conditioner, radio, and electric systems are maintained in the conversion process. However, EVC is still in the development phase, and a plan to commercialize EVC has not been developed. The objective of this research is focused on determining significant factors affecting the willingness to pay (WTP) for EVC. A questionnaire survey was developed and distributed randomly to 406 people at Bangkok International Motor Show 2019 event in Thailand. The descriptive results show that approximately 69% of respondents are willing to convert a regular gasoline vehicle to an EV, the average price that they are willingness to pay for conversion is about 160,931 Baht. The results of binary logistic regression analysis and multiple regression indicated that, the factors that the factors that affect a customer's willingness to pay are monthly income, education, household size, number of owned cars, maintenance cost, age of the owned car, driving distance per day, environmental awareness, and environmental behavior. Some recommendations are discussed

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Suthathip Suanmali

Role: advisor

Created: 2019

Modified: 2022-10-28

Issued: 2022-10-28

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2019.1449

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Engineering and Technology

Grantor: Thammasat University

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