Equitable charging infrastructure for electric vehicles: access and experience
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
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.
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 | 0.001 |
| 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 it