Arctic shipping emissions inventories and future scenarios
Bibliographic record
Abstract
Abstract. The Arctic is a sensitive region in terms of climate change and a rich natural resource for global economic activity. Arctic shipping is an important contributor to the region's anthropogenic air emissions, including black carbon – a short-lived climate forcing pollutant especially effective in accelerating the melting of ice and snow. These emissions are projected to increase as declining sea ice coverage due to climate change allows for increased shipping activity in the Arctic. To understand the impacts of these increased emissions, scientists and modelers require high-resolution, geospatial emissions inventories that can be used for regional assessment modeling. This paper presents 5 km×5 km Arctic emissions inventories of important greenhouse gases, black carbon and other pollutants under existing and future (2050) scenarios that account for growth of shipping in the region, potential diversion traffic through emerging routes, and possible emissions control measures. Short-lived forcing of ~4.5 gigagrams of black carbon from Arctic shipping may increase climate forcing; a first-order calculation of global warming potential due to 2030 emissions in the high-growth scenario suggests that short-lived forcing of ~4.5 gigagrams of black carbon from Arctic shipping may increase climate forcing due to Arctic ships by at least 17% compared to warming from these vessels' CO2 emissions (~42 000 gigagrams). The paper also presents maximum feasible reduction scenarios for black carbon in particular. These emissions reduction scenarios will enable scientists and policymakers to evaluate the efficacy and benefits of technological controls for black carbon, and other pollutants from ships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".