Clean Technologies and Environmental Policy

, Volume 19, Issue 7, pp 1927–1939 | Cite as

Mass deployment of sustainable transportation: evaluation of factors that influence electric vehicle adoption

Original Paper
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Abstract

Mass penetration of electric vehicles into the market will have a number of impacts and benefits, including the ability to substantially reduce greenhouse gas emissions from the transportation sector. Therefore, it is expected that in coming years this technology will progressively penetrate the market. This research presents an analysis of factors that influence electric vehicle adoption by modeling the conditions under which an individual, particularly one with an engineering or technical background, is more or less likely to adopt an electric vehicle. This model is developed by considering demographic determinants as well as behavioral and attitudinal measures that affect individual adoption of the technology. The methodology involves applying logistic regression to provide a good fit and predict the response given explanatory variables. Analyzing these outcomes generates empirical findings that better inform electric vehicle technology and policy development. This study takes into account preferences of potential customers and analyzes how individuals with engineering and technology background differ in electric vehicle adoption considerations compared to the general population. Therefore, this research provides both engineers and policy makers with critical information for developing future electric vehicle technology. The model results show that several factors including willingness to pay for new appealing technology, distance driven, perceptions of electric vehicles as good for the environment, perception of EV speed are statistically significant in influencing willingness to purchase an electric vehicle.

Keywords

Alternative fuel vehicles Electric vehicles Consumer attitudes Electric vehicle adoption 

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial EngineeringUniversity of Minnesota DuluthDuluthUSA
  2. 2.Department of Engineering Management and Systems EngineeringMissouri University of Science and TechnologyRollaUSA
  3. 3.Department of Mathematics and StatisticsMissouri University of Science and TechnologyRollaUSA

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