Equitable charging infrastructure for electric vehicles: access and experience
Bibliographic record
Abstract
Abstract The shift toward electric vehicles (EVs) as a decarbonisation strategy in transportation raises important energy justice concerns, particularly regarding fair access to charging infrastructure. This perspective synthesizes evidence on how access to, and experience of, charging infrastructure may differ across socio-economic groups across North America. We present a framework for assessing charging infrastructure equity that includes: (i) accessibility—proximity, reliability, visibility, affordability; and (ii) user experiences—safety, payment ease, and co-located other services. The framework helps characterize the varied impacts across socio-demographic groups, including on low-income and marginalized communities. We explore how the direct and indirect effects of accessibility and user experience might influence the distribution and design of EV charging stations. Considerations of socio-economic diversity in the deployment of charging infrastructure are critical to ensure equitable benefits from electric mobility. We conclude that targeted actions from manufacturers, charging operators, and governments are needed to alleviate the disparities in access and experiences with public EV charging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".