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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".