The Attractiveness of Electric Vehicles for Heterogeneous Consumers: A Range-Cost-Emission Comparative Analysis
Bibliographic record
Abstract
This study offers a complete comparative method that examines the long- and short-term range–cost–emission attractiveness of different electric vehicle (EV) types for heterogeneous consumers. Despite the centrality of range satisfaction in EV adoption decisions, existing studies rely heavily on subjective user perceptions. This study bridges this gap by proposing the first data-driven framework to quantify range satisfaction, comparing EVs and conventional vehicles (CVs) through real-world mobility patterns and subsidy scenarios. The distribution of daily vehicle miles traveled was used to cluster mobility patterns and driving demands. In addition, EV subsidy rollback scenarios were modeled to quantify their impact on EV adoption in both long- and short-term ownership situations. The findings show that the attractiveness of EVs for different user types for long- and short-term adoption varies. EVs may not provide the same level of range satisfaction that CVs do, especially for users with high travel demands. Although driving costs can be reduced with EVs, not all users will benefit from them. Before acquiring an EV, users must examine their usage patterns and driving demands, as well as prepare for long- or short-term ownership. Changes in subsidies can affect not only the attractiveness of EVs but also the adoption duration. Finally, acknowledging that the environmental attractiveness of EVs is tied to the electricity production profile, it also varies based on vehicle utilization pattern.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".