Spatial and temporal variance in public perception of electric vehicles: A comparative analysis of adoption pioneers and laggards using twitter data

Xiaodong Qian and Konstantina Gkritza

Transport Policy, 2024, vol. 149, issue C, 150-162

Abstract: Promotional strategies for electric vehicle (EV) adoption need to be tailored to the public's perception of these vehicles. However, there is a notable gap in understanding how public perception of EVs differs between adoption pioneers and those slower to adopt (“adoption laggards”) in terms of the percentage of registered EVs. This understanding is vital for the development of effective promotional strategies, especially for adoption laggards. To address this research gap, this study compares various public perception of EVs among adoption pioneers and laggards in U.S. states by analyzing historical Twitter discussions using Natural Language Processing (NLP) techniques, including topic and sentiment analysis. The findings reveal distinct patterns in online EV discussions between the two groups, with adoption laggards being concerned more about affordability and gas prices, and adoption pioneers consistently discussing various topics covering EV and battery manufacturing, charging infrastructure, government EV initiatives, and the EV market. Adoption pioneers exhibit stable, positive attitudes, while adoption laggards display frequent attitude fluctuations, including more negative ones recently. These differences emphasize the importance of EV affordability for adoption laggards and indicate a possible widening gap in EV adoption due to ongoing debates among adoption laggards surrounding the value of transportation electrification. By addressing adoption laggards' concerns and promoting the benefits of EVs, policymakers and stakeholders can enhance perceptions toward EVs, bridging the gap between adoption pioneers and laggards, and promoting widespread adoption of cleaner transportation options.

Keywords: Electric vehicle; Public perception; Twitter data; Spatial-temporal analysis; Natural language processing (NLP) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tranpol.2024.02.011

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