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Enregistrement W6945514685 · doi:10.25607/obp-1733

Prevalence of heavy fuel oil and black carbon in Arctic shipping, 2015 to 2025.

2017· report· en· W6945514685 sur OpenAlexaboutno aff

Notice bibliographique

RevueIOC of UNESCO (Intergovernmental Oceanographic Commission) · 2017
Typereport
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésArcticThe arcticPollutantClimate changeSea iceAir pollutionArctic ice packFuel oilFossil fuel

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesMéta-épidémiologie (sens strict)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,170
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,307
Écart entre enseignants0,279 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations15
Publié2017
Routes d'admission1
Résumé présentoui

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