Beyond SO<sub>x</sub> reductions from shipping: assessing the impact of NO<sub>x</sub> and carbonaceous-particle controls on human health and climate
Bibliographic record
Abstract
Abstract Historically, cargo ships have been powered by low-grade fossil fuels, which emit particles and particle-precursor vapors that impact human health and climate. We used a global chemical-transport model with online aerosol microphysics (GEOS-Chem-TOMAS) to estimate the aerosol health and climate impacts of four emission-control policies: (1) 85% reduction in sulfur oxide (SO x ) emissions (Sulf); (2) 85% reduction in SO x and black carbon (BC) emissions (Sulf-BC); (3) 85% reduction in SO x , BC, and organic aerosol (OA) emissions (Sulf-BC-OA); and (4) 85% reduction in SO x , BC, OA, and nitrogen oxide (NO x ) emissions (Sulf-BC-OA-NO x ). The SO x reductions reflect the 0.5% fuel-sulfur cap implemented by the International Maritime Organization (IMO) on 1 January 2020. The other reductions represent realistic estimates of future emission-control policies. We estimate that these policies could reduce fine particulate matter (PM 2.5 )-attributable mortalities by 13 300 (Sulf) to 38 600 (Sulf-BC-OA-NO x ) mortalities per year. These changes represent 0.3% and 0.8%, respectively, of annual PM 2.5 -attributable mortalities from anthropogenic sources. Comparing simulations, we estimate that adding the NO x cap has the greatest health benefit. In contrast to the health benefits, all scenarios lead to a simulated climate warming tendency. The combined aerosol direct radiative effect and cloud-albedo indirect effects (AIE) are between 27 mW m −2 (Sulf) and 41 mW m −2 (Sulf-BC-OA-NO x ). These changes are about 2.1% (Sulf) to 3.2% (Sulf-BC-OA-NO x ) of the total anthropogenic aerosol radiative forcing. The emission control policies examined here yield larger relative changes in the aerosol radiative forcing (2.1%–3.2%) than in health effects (0.3%–0.8%), because most shipping emissions are distant from populated regions. Valuation of the impacts suggests that these emissions reductions could produce much larger marginal health benefits ($129–$374 billion annually) than the marginal climate costs ($12–$17 billion annually).
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".