Adoption of Electric Vehicles in Car Sharing Market
Bibliographic record
Abstract
In this study, we examine whether it is optimal to use electric vehicles (EVs) in the car sharing market and investigate the environmental impact of pulling the EVs from the market. We develop a model consisting of a profit‐maximizing car sharing company (CSC) and a population of utility‐maximizing customers. The CSC sets the number of EVs, the number of fuel vehicles (FVs), and the rental price jointly to maximize its profit. Customers decide whether to use EVs, FVs, or public transportation to complete their trips considering the rental price. We show that it is optimal to use EVs only if the charging speed, the number of charging stations, and the range of EVs are high enough. Among these three conditions, the recharging speed is the most important and the number of charging stations is more important than the range of EVs. We also find that including EVs in the car sharing market may lead to a higher total emission when ignoring customers’ other transportation choices (due to a lower rental price that results in a higher usage rate). Moreover, we consider the problem with the objective of maximizing the social welfare and find that when considering the environmental impact, governments should tax the CSC to induce a higher rental price and when ignoring this impact, they should subsidize the CSC to reduce the rental price. We demonstrate our results with the case study of Car2go. These results are in line with that the slow recharging speed may have been one of the contributing factors to that Car2go replaced EVs with FVs in San Diego.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".