Emerging evidence for the impact of Electric Vehicle sales on childhood asthma: Can ZEV mandates help?
Bibliographic record
Abstract
Growing epidemiological studies indicate a significant fraction of asthma cases can be attributed to traffic-related air pollution (TRAP). Zero emission vehicle (ZEV) mandates — one of the most forward-looking climate policies in the United States — aim to reduce TRAP by mandating automakers to sell a certain fraction of Electric Vehicles (EVs) annually; however, their public health benefits are largely unknown. We conduct the screening step of the health impact assessment (HIA) of real-world EV sales to estimate the impact of ZEV mandates in reducing childhood asthma. Using publicly available US state and national datasets, we isolate the burden of childhood asthma attributable to TRAP from 2013 to 2019 and examine the influence of EV and non-EV vehicle sales and fleets on asthma incidence and prevalence using a generalized linear mixed model. Our analyses indicate that new EV sales have reduced asthma, with one asthma case prevented for every 264 (95% CI: 113-401) new EVs on the road. The rise of new childhood asthma cases from new car sales can be prevented when non-EV sales are replaced with EV sales at an annual market share of 21.4% (7.1-41.6%). Extending our analysis to the entire vehicle fleet, we project that when EVs reach 53.0% (35.5%–76.9%), childhood asthma due to tailpipe emissions can be eradicated completely. Screening results conclude that ZEV mandates implemented over the past decade are already exhibiting measurable public health benefits, suggesting that a broader adoption could significantly reduce the asthma burden, thus we recommend a full HIA for ZEV mandates to fully assess their potential. • Previous studies show that 18%–42% of all childhood asthma cases are due to TRAP. • We study the impact of EV adoption on TRAP-related asthma in the US. • US States with ZEV mandates exhibit much higher EV sales than those without them. • We show new EV sales have reduced childhood asthma incidence and prevalence. • States with ZEV mandates are already experiencing measurable public health benefits.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".