Air Quality Impacts of Climate Mitigation: UK Policy and Passenger Vehicle Choice
Bibliographic record
Abstract
In 2001-2002 the UK began taxing vehicles according to CO2 emission rates. Since then, there has been a significant increase in consumer choice of small cars and diesel engines. We estimate CO2 reductions and air quality impacts resulting from UK consumers switching from petrol to diesel cars from 2001 to 2020. Annual reductions of 0.4 megatons (Mt) of CO2 and 1 million barrels of oil are estimated from switching to diesels. However, diesels emit higher levels of particulate matter estimated to result in 90 deaths annually (range 20-300). We estimate 570, 460, and 0 additional deaths per Mt of CO2 abated, for Euro III, Euro IV, and post-Euro IV emission class vehicles, respectively. CO2 policies are suspected to have contributed substantially to diesel growth, but the magnitude of impact has yet to be quantified rigorously. To the extent that CO2 policies contribute to diesel growth, coordinating CO2 controls with tightening of emission standards would save lives. This research shows that climate policy, while reducing fuel use and CO2, does not always ensure ancillary health benefits. Lessons from the UK can help inform policies designed elsewhere which strive to balance near-term ambient air quality and health with long-term climate mitigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".