The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety
Open accessAbstract
Electric vehicle (EV) owners enjoy many positive aspects when driving their cars, including low running costs and zero tailpipe gas emissions, which makes EVs a clean technology provided that they are sourced through renewable sources, e.g., biomass, solar power, or wind energy. However, their driving behaviour is often negatively affected by the so-called range anxiety phenomenon, i.e., a concern that an EV might not have enough driving range to reach the desired destination due to its limited battery size. The perception of range anxiety may also affect potential buyers in their decisions on whether to purchase an internal combustion engine vehicle as opposed to an EV. This paper investigates some factors that influence range anxiety through a comparative analysis of two target groups: (i) existing EV owners, and (ii) non-EV owners (i.e., potential EV owners). The specially crafted survey was used to collect range anxiety data from more than 200 participants. In particular, participants provided their perceptions on (i) the potential relationship between existing gas station infrastructure and the desired EV charging station infrastructure, and (ii) the potential relationship between range anxiety and two influencing variables, namely the current state of charge and remaining range. Concerning the existing gas station infrastructure, evidence suggests that both target groups think that the distances between gas stations could be increased. Moreover, our analysis shows that the desired distances between charging stations correspond to the distances between the existing gas stations, which indicates that both EV owners and non-EV owners have a common view on the optimal gas station and charging station topology. Furthermore, we find that the type of settlement (urban vs rural) influences preferred distances, where both target groups living in cities desire shorter distances, and that non-EV owners, as opposed to EV owners, are more prone to be affected by the state of charge and remaining range. Quantitatively, we are able to define a measure for range anxiety, which is connected with the preferred distance between two neighbouring charging stations. Throughout our analyses, we find that the mean preferred distance between two neighbouring charging stations is 7 km, but this value significantly differs based on the settlement type of a (potential) EV owner.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical power analysis for the behavioral sciences
(1990) · Computers, Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,269
A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
Nakagawa, S., & Schielzeth, H. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00261.x · Cited by 10,356
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
The Rhetoric of Reaction: Perversity, Futility, Jeopardy
HIRSCHMAN, A. O. (1991) · Harvard University Press eBooks · doi:10.2307/j.ctvjnrs9q · Cited by 1,478
Statistical Power Analysis for the Behavioral Sciences
Wassertheil, S., & Cohen, J. (1970) · Biometrics · doi:10.2307/2529115 · Cited by 1,383
Willingness to pay for electric vehicles and their attributes
Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011) · Resource and Energy Economics · doi:10.1016/j.reseneeco.2011.02.002 · Cited by 1,117
Web Survey Design and Administration
COUPER, M. P., TRAUGOTT, M. W., & LAMIAS, M. J. (2001) · Public Opinion Quarterly · doi:10.1086/322199 · Cited by 776
Incentives for promoting Battery Electric Vehicle (BEV) adoption in Norway
Bjerkan, K. Y., Nørbech, T. E., & Nordtømme, M. E. (2016) · Transportation Research Part D: Transport and Environment · doi:10.1016/j.trd.2015.12.002 · Cited by 654
Who will buy electric vehicles? Identifying early adopters in Germany
Plötz, P., Schneider, U., Globisch, J., & Dütschke, E. (2014) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2014.06.006 · Cited by 517
Cited by
Mastoi, M. S., Zhuang, S., Munir, H. M., Haris, M., Hassan, M., Usman, M., Bukhari, S. S. H., & Ro, J.-S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.011 · Cited by 523
The Role of Pickup Truck Electrification in the Decarbonization of Light-Duty Vehicles
Woody, M., Vaishnav, P., Keoleian, G. A., De Kleine, R., Kim, H. C., Anderson, J. E., & Wallington, T. J. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac5142 · Cited by 46
Metadata
- Authors
- Pevec, D., Babic, J., Carvalho, A., Ghiassi-Farrokhfal, Y., Ketter, W., & Podobnik, V.
- Venue
- Journal of Cleaner Production
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 276
- Pages
- 122779
- ISSN
- 0959-6526 (ISSN-L)
- OpenAlex
- W3037478778
- Cited by
- 283
- References
- 32
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. https://app.cochat.ai/papers/10.1016/j.jclepro.2020.122779
- The paper
- Pevec, D., Babic, J., Carvalho, A., Ghiassi-Farrokhfal, Y., Ketter, W., & Podobnik, V. (2020). A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2020.122779
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.