Sentiments of Rural U.S. Communities on Electric Vehicles and Infrastructure: Insights from Twitter Data

Ming (Bryan) Wang, Li Zhao () and Abigail L. Cochran
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Ming (Bryan) Wang: College of Journalism & Mass Communications, University of Nebraska-Lincoln, 330 Andersen Hall, Lincoln, NE 68588-0443, USA
Li Zhao: Department of Civil & Environmental Engineering, University of Nebraska-Lincoln, 262K Prem Paul Research Center at Whittier School, 2200 Vine Street, Lincoln, NE 68583-0851, USA
Abigail L. Cochran: Community and Regional Planning Program, College of Architecture, University of Nebraska–Lincoln, 217 Architecture Hall, Lincoln, NE 68588-0106, USA

Sustainability, 2024, vol. 16, issue 11, 1-17

Abstract: The widespread adoption of electric vehicles (EVs) and the development of charging infrastructure is key to achieving sustainable transportation and reducing greenhouse emissions. This research paper presents a novel exploration of the public sentiments expressed by rural U.S. communities toward EVs and EV infrastructure using Twitter data. To understand the factors influencing public sentiment, three distinct models were developed and applied: Generalized Linear Models, Hierarchical Linear Models, and Geographically Weighted Regression. These models explored the relationships between sentiment and several impact factors, including the topics of the tweets, and the age and sex of tweet senders as well as the number of charging stations and historical accident data in the geographical vicinity of each tweet’s origin. Results indicate that a more positive sentiment on EVs resulted (1) when the tweet discussed EV infrastructure investment and equity, (2) when the tweeter was male, and (3) when more charging stations were present and fewer EV accidents occurred in the county, especially in rural areas. Counties with higher rural percentages generally exhibited more positive sentiments toward EV usage. The paper contributes to the existing literature by shedding light on the sentiments of rural residents toward EVs and the infrastructure.

Keywords: electric vehicles; electric vehicle charging stations; public sentiment; Twitter data; rural areas; modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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