An EVSE Equity Analysis Framework Considering Differential Job Access
Bibliographic record
Abstract
Abstract The transition to electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions has been a large part of climate policy. EV adoption relies on the charging stations that provide EVs with energy. The placement of public charging stations impacts their utility, and unadvised planning could risk furthering the transportation infrastructure divide between rural and urban areas. This study provides an unconventional perspective on the problem. It is argued that the provision of public charging stations, or electric vehicle supply equipment (EVSE), should be considered in the context of the differential ability to substitute private vehicle travel by other modes or land use reforms. We first examine the travel patterns of urban and rural areas, sequentially excluding non-private vehicle then shopping journeys replaceable by online purchases, to establish the varied need for charging stations and measure the equity of the existing public charging station sites. The use of job access as a measure of rurality reveals both the longer daily mileage traveled by people in rural areas and the influence of job accessibility as a proxy for amenity access. An equity framework is formulated based on the classic Lorenz curve, with an extension to jointly consider job and EVSE access via a Cobb-Douglas-style production function. We characterize the state of equity in this production function for the United States as of 2024.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".