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Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran

Allahmoradi, E ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.3390/en15124269
  3. Publisher: MDPI , 2022
  4. Abstract:
  5. Given the various advantages of electric vehicles compared to conventional gasoline vehicles in terms of energy efficiency and environmental pollution (among others), this paper studies the factors affecting customers’ willingness to purchase electric vehicles. An integrated discrete choice and agent-based approach is applied to model the customers’ choice for the valuation of electric vehicles based on the internal reference price. The agent-based model evaluates customers’ preferences for a number of personal and vehicle attributes, according to which vehicle they chose. Data from 376 respondents are collected to estimate a random-parameter logit model where customers are asked to reveal their preferences about five attributes of electric vehicles, including travel range, top speed, charge cost, government incentives, and price. The role of social networks of customers and their threshold purchase price is also examined in the agent-based model. The scenario simulation results indicate that the allocation of government incentives for electric vehicles, decreasing electric vehicle/non-electric vehicle price gap, expanding electric vehicle travel range, increasing gasoline prices, and enhancing electric vehicle top speed stimulate electric vehicle market shares, respectively. © 2022 by the authors. Licensee MDPI, Basel, Switzerland
  6. Keywords:
  7. Discrete choice model ; Market research ; Transport policy ; Commerce ; Competition ; Computational methods ; Cost benefit analysis ; Electric vehicles ; Energy efficiency ; Gasoline ; Sales ; Simulation platform ; Agent-based model ; Case-studies ; Conventional gasoline ; Discrete choice models ; Government incentive ; Market researches ; Policy instruments ; Travel ranges ; Willingness to pay ; Autonomous agents
  8. Source: Energies ; Volume 15, Issue 12 , 2022 ; 19961073 (ISSN)
  9. URL: https://www.mdpi.com/1996-1073/15/12/4269