Adoption of Electric Vehicles in Car Sharing Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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 it