Climate and health implications of future aerosol emission scenarios
Bibliographic record
Abstract
Anthropogenic aerosols have a net cooling effect on climate and also cause adverse health effects by degrading air quality. In this global-scale sensitivity study, we used a combination of the aerosol-climate model ECHAM-HAMMOZ and the University of Victoria Earth System Climate Model to assess the climate and health effects of aerosols emissions from three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5) and two new (LOW and HIGH) aerosol emission scenarios derived from RCP4.5, but that span a wider spectrum of possible future aerosol emissions. All simulations had CO 2 emissions and greenhouse gas forcings from RCP4.5. Aerosol forcing declined similarly in the standard RCP aerosol emission scenarios: the aerosol effective radiative forcing (ERF) decreased from −1.3 W m −2 in 2005 to between −0.1 W m −2 and −0.4 W m −2 in 2100. The differences in ERF were substantially larger between LOW (−0.02 W m −2 in 2100) and HIGH (−0.8 W m −2 ) scenarios. The global mean temperature difference between the simulations with standard RCP aerosol emissions was less than 0.18 °C, whereas the difference between LOW and HIGH reached 0.86 °C in 2061. In LOW, the rate of warming peaked at 0.48 °C per decade in the 2030s, whereas in HIGH it was the lowest of all simulations and never exceeded 0.23 °C per decade. Using present-day population density and baseline mortality rates for all scenarios, PM 2.5 -induced premature mortality was 2 371 800 deaths per year in 2010 and 525 700 in 2100 with RCP4.5 aerosol emissions; in HIGH, the premature mortality reached its maximum value of 2 780 800 deaths per year in 2030, whereas in LOW the premature mortality at 2030 was below 299 900 deaths per year. Our results show potential trade-offs in aerosol mitigation with respect to climate change and public health as ambitious reduction of aerosol emissions considerably increased warming while decreasing mortality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".