Catalyzing healthier air: the impact of escalating fossil fuel prices on air quality and public health and the need for transition to clean fuels
Bibliographic record
Abstract
In this study, we reviewed previous information regarding the effect of fossil fuel pricing intervention on the consumption of fossil fuels, air quality, and population health. In a rapid review, we searched and reviewed reports about the effect of fossil fuel pricing interventions on each or some of the following outcomes: fossil fuel consumption, concentrations of air pollutants, and mortalities or morbidities attributable to exposure to air pollutants. As part of our investigation, we also present the findings of an unpublished, original study that specifically estimated the effects of an elevated gasoline price in Iran's ten most populous cities. Pricing interventions were effective in reducing the consumption of fossil fuels, both in developing and developed countries. Price elasticity for transport gasoline was reported to be -0.227 and -0.715 for short- and long-term, respectively. Reductions in concentrations of air pollutants, especially NOx and particulate matter, were reported in several studies. This review study demonstrates the effects of escalating fossil fuel prices on reducing consumption, air pollution, and mortalities and morbidities attributable to air pollution. Considering the external costs of fossil fuels through environmental, climate, traffic congestions and accidents, and population health, the real costs of fossil fuels are much higher than their retail price. Pro-rich policies of subsidizing fossil fuels should be replaced by alternative policies that support clean public transport, which is suitable for population health, environment, and equity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".