Proximate determinants of particulate matter (PM2.5) emission, mortality and life expectancy in Europe, Central Asia, Australia, Canada and the US
Bibliographic record
Abstract
The growing concern with environmental related impacts on mortality and morbidity means that the conceptual framework of environment-health-economic policy nexus is salient in the global debate on air pollution. With time series data spanning 2000–2016, this study explored the proximate determinants of ambient air pollution, mortality, and life expectancy in North America, Europe & Central Asia, and East Asia & Pacific regions. The study applied historical data on urban population, total pollution, energy consumption, GDP per capita, life expectancy, mortality rate and industrial PM2.5 emissions to develop six parsimonious models using the generalized least squares (GLS) random-effects model estimation with first-order autoregressive [AR(1)] disturbance across 54 countries. An increase in income level by 1% declined mortality rate by 0.01% and increased longevity by ~0.02% (95% Confidence Interval [CI]) in the long-run. An increase in industrial PM2.5 emissions per capita by 1% decreased life expectancy by 0.004% and mortality rate by 0.02% (95% CI). Intensification of energy consumption and its related services by 1% were found to increase industrial PM2.5 emissions by 0.42–0.45% (95% CI). An inversed-U shaped curve between PM2.5 emissions per capita and income levels was found at a turning point of US$ 48,061. The validity of an environmental Kuznets curve hypothesis between ambient air pollution and urbanization was confirmed, while a rapid increase in population had a significant positive impact on ambient air pollution. Ambient air pollution contributes significantly in reducing life expectancy and increasing mortality. However, sustained economic development, along with energy efficiency, and sustainable urban settlement planning and management are potential options for reducing ambient air pollution while improving quality of life and environmental sustainability.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".