A Model of EV Adoption and Rank-Based Contributing Factors

Details

• Creators:
  Robbennolt, Dale ; Hardman, Scott ; Firestone, Jeremy ; Bhat, Chandra R.
• Corporate Creators:
  University of Texas at Austin. Cockrell School of Engineering
• Corporate Contributors:
  National Center for Understanding Future Travel Behavior and Demand (TBD) ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Climate Change Data Analysis Demographics Electric Vehicles EV Adoption Latent Perceptions Joint Mixed Model Literature Reviews Rank-Ordered Model Revealed EV Behavior Revealed Preference Revealed Preferences Surveys

  More +
• Publication/ Report Number:
  69A3552344815 ; 69A3552348320
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: 2024-2025
• Corporate Publisher:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Abstract:
  Electric vehicles (EVs) have broad potential to mitigate climate change and can provide significant benefits for individual owners, but adoption rates have, thus far, remained relatively low. While there is interest in understanding adoption patterns through a study of actual individual-level adoption behaviors, the nascent stage of the EV market has made such investigations difficult. In particular, many existing EV adoption studies are confined to an examination stated adoption intentions rather than actual revealed adoption behaviors. Accordingly, we examine the ways that demographics, lifestyle preferences, and perceptions of EV characteristics impact revealed EV adoption behaviors. In addition to the binary EV adoption decision, using information elicited from a survey that asked current EV owners to rank the importance of a set of factors that influenced their adoption decision, we investigate the motivations for EV ownership. Importantly, by modeling EV adoption and the motivations in a joint binary-ranked choice framework, we account for sample selection effects, enabling us to generalize these motivations to the population at large (including current non-EV owners), and allowing us to identify policy measures that can encourage EV adoption among those who have yet to adopt. This approach, a first in the EV literature as well as (to our knowledge) in the broader econometric literature (where an outcome is rank ordered with a binary selection mechanism), provides important insights for the implementation of EV incentive policies, the deployment of EVs, and the development of EV charging infrastructure.
• Format:
  PDF
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:b0ccd038b38a8c867290fee53cb21416a925a8d20bb5e044af5e936c23f05cc7c76e72098aaaf1a534c02337e75f5713015babeb671ce63e977f552264fa515b
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/91953/dot_91953_DS1.pdf
• File Type:
  [PDF - 1.31 MB ]