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Record W4414148798 · doi:10.1016/j.erss.2025.104297

Why do consumers reject zero-emissions vehicles? Applying a comprehensive framework of resistance to Canadian car buyers

2025· article· en· W4414148798 on OpenAlexafffundabout
Zoe Long, Jonn Axsen, Viviane H. Gauer, Taco Niet

Bibliographic record

VenueEnergy Research & Social Science · 2025
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaMitacs
KeywordsStatus quoPerceptionResistance (ecology)Opposition (politics)Conceptual frameworkAssociation (psychology)

Abstract

fetched live from OpenAlex

Most of the consumer research on zero-emissions vehicles (ZEVs) has focused on what motivates adoption. This study explores the concept of “ZEV resistance”: why consumers might not want to adopt ZEVs in the near- to long-term. Such resistance challenges governments seeking 100% ZEV sales. We develop a comprehensive conceptual framework that assesses functional, environmental, social influence, and status quo aspects of resistance, implemented via 43 questions in a survey of Canadian new vehicle buyers ( n = 2555). Our analyses identify eight subcategories of ZEV perceptions. Regression and latent class choice analyses find that at least one aspect of all four categories is associated with ZEV resistance. First, functional aspects of resistance are primarily driven by perceived lack of benefits from ZEV usage (rather than purchase concerns). The second association is negative perceptions of pro-societal aspects, which include environmental impacts and moral norms towards ZEVs. Third is the perception that ZEVs do not convey social approval (whereas perceptions of ZEVs' commonality and inspirational qualities are less associated with resistance). The last association is satisfaction with the status quo of conventional vehicles (rather than opposition to change more generally). ZEV resistance is complex and linked to many varied dimensions of consumer perceptions, and if left unaddressed could be challenging for a smooth transition to 100% ZEVs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.347
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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