Prevalence of heavy fuel oil and black carbon in Arctic shipping, 2015 to 2025.
Bibliographic record
Abstract
Dwindling sea ice is opening new shipping routes through the Arctic, with shipping activity expected to increase with oil and gas development and as ships take advantage of shorter trans-Arctic routes from Asia to Europe and North America. However, with increased shipping comes an increased risk of accidents, oil spills, and air pollution. Potential spills of heavy fuel oil (HFO) and emissions of black carbon (BC) are of particular concern for the Arctic. Heavy fuel oil poses a substantial threat to the Arctic environment because it is extremely difcult to recover once spilled and the combustion of HFO emits BC, a potent air pollutant that accelerates climate change. For these reasons, the Arctic Council (AC) has called HFO “the most significant threat from ships to the Arctic environment” (Arctic Council, 2009). Thus, understanding how much HFO is consumed and carried by ships in the Arctic, and how much BC is emitted by these ships, is critical to assessing the current and future risks of Arctic shipping. This report uses exactEarth satellite Automatic Identification System (AIS) data along with ship characteristic data from IHS Fairplay to estimate HFO use, HFO carriage, the use and carriage of other fuels, BC emissions, and emissions of other air and climate pollutants for the year 2015, with projections to 2020 and 2025. Results are estimated for ships operating in three Arctic regions: (1) the Geographic Arctic (at or above 58.95oN), (2) the International Maritime Organization’s (IMO) Arctic as defined in the Polar Code, and (3) the U.S. Arctic, defined as the portion of the U.S. exclusive economic zone (EEZ) within the IMO Arctic. The risks of HFO and BC in the Arctic are being actively discussed at the AC and the IMO. Because the IMO will likely be the prime decision-making body for international policies that address the environmental risks of Arctic shipping, the Executive Summary focuses primarily on HFO use, HFO carriage, BC emissions, and flag state activity in the IMO Arctic. Heavy fuel oil was the most consumed marine fuel in the Arctic in 2015. In the IMO Arctic, HFO represented nearly 57% of the nearly half million tonnes (t) of fuel consumed by ships, followed by distillate (43%); almost no liquefied natural gas (LNG) was consumed in this area. General cargo vessels consumed the most HFO in the IMO Arctic, using 66,000 t, followed by oil tankers (43,000 t), and cruise ships (25,000 t). Heavy fuel oil also dominated fuel carriage, in tonnes, and fuel transport, in tonnenautical miles (t-nm) in the Arctic in 2015. Although only 42% of ships in the IMO Arctic operated on HFO in 2015, these ships accounted for 76% of fuel carried and 56% of fuel transported in this region. Specifically, bulk carriers, container ships, oil tankers, general cargo vessels, and fishing vessels dominated HFO carriage and transport in the IMO Arctic, together accounting for more than 75% of HFO carried and transported in the IMO Arctic in 2015. Considering the quantity of fuel these vessels carry on board and the distances they travel each year, these ships may pose a higher risk for HFO spills than others. The distribution of HFO use in three Arctic areas is shown in Figure ES-1. The blue outline represents the IMO Arctic boundary. The minimum sea ice extent in 1979 and 2015 are shown as the light blue area and dark black line, respectively. As the figure illustrates, melting sea ice is associated with expanded use and carriage of HFO in the Arctic. Note the 2015 HFO use associated with activity along the northern coast of Russia (part of the Northern Sea Route) and Canada (the Northwest Passage) that would have been ice-locked in 1979.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".