ZIP-Code–Level Drivers of EV Adoption in Washington: Socioeconomics, Urbanization, and Charger Proximity (2020–2025)
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
Electric vehicles (EVs) offer lower noise and zero tailpipe emissions compared to traditional internal combustion engine (ICE) vehicles. This study quantifies EV adoption across Washington State using ZIP-code–level data from 2020–2025. Multivariable linear regressions relate the monthly change in EV share to socioeconomic factors, urbanization, family structure, and the roll-out of public charging infrastructure. Results indicate that adoption growth is positively associated with higher per-capita income, higher educational attainment, greater urbanization, and larger average family size. Proximity to infrastructure matters: the number of newly built local stations (0–20 miles) is positively associated with adoption growth, whereas a higher number of newly built distant stations (≥30 miles) correlates negatively, consistent with rurality and limited access. By providing a comprehensive ZIP-code–level analysis, the study addresses aggregation bias common in county-level work and reveals localized patterns that broader geographies can obscure. The findings offer policy-relevant evidence for charger siting and for targeting incentives.
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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 | 0.000 |
| 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