Electric vehicles: How do preferences differ for additional versus replacement household vehicles?
Bibliographic record
Abstract
The electrification of vehicles is often a significant component of global efforts to decarbonize transportation. To promote EV adoption, there is a need to understand the factors influencing it and how these factors vary across the population. While many have studied this topic, few (if any) have distinguished the factors influencing EV adoption based on whether the EV is replacing an internal combustion engine vehicle (ICEV) or adding to a household’s fleet. Using a nationwide survey, this study examines how preferences towards hybrid, battery electric, and plug-in hybrid vehicles vary based on transaction type (i.e., to replace vs. acquire a car). Model results highlight differences in the effect and magnitude of factors influencing the propensity to choose EVs between households replacing an ICEV and expanding their vehicle fleet. However, regardless of transaction type, homeowners, younger adults, and individuals with higher levels of educational attainment are more likely to adopt EVs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".