Bibliographic record
Abstract
Abstract Electric vehicle charging infrastructure is currently heavily subsidized in the United States at the local, state, and federal levels. However, the future success and growth of charging infrastructure to meet future EV demand will likely require chargers to become a sustainable business independent of government intervention. In this study, we examine the business case of electric vehicle chargers, focusing specifically on DC fast chargers. Our analysis employs empirical datasets, with rate plans down to the charging plug level and utilization data representing several major charging networks with over 5 million individual charging events across 1,300 DC fast chargers in California. We find that for charging rates based on energy [$/kWh] or a combination of energy and time [$/kWh and $/hr], customers pay an average of about $0.124/mi and $0.129/mi respectively. Rates based solely on time (dominated by the Tesla Supercharger network) is substantially cheaper at $0.084/mi. However, when coupling these findings with utilization data and comparing it to costs associated with charger deployment, we find that the revenues are nowhere near being able to payback the capital and operating costs of the cheapest DC fast chargers observed in the literature in a three-year period—even when doubling the average number of events and amount of energy dispensed to charge vehicles. Despite these challenges, we also conduct a spatial analysis of local businesses and services co-located with EV chargers and identify this as a possible alternative revenue source for chargers in the future.
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 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 | 0.001 |
| 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 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".