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Enregistrement W4313417741 · doi:10.1111/jiec.13301

Industrial ecology for the oceans

2022· article· en· W4313417741 sur OpenAlexaff
Ian Vázquez‐Rowe, Robert Parker, Helen A. Hamilton, Huan Liu

Notice bibliographique

RevueJournal of Industrial Ecology · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueMicroplastics and Plastic Pollution
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésFishingPopulationFossil fuelNatural resource economicsEcologyGeographyEnvironmental resource managementEnvironmental scienceBiology

Résumé

récupéré en direct d'OpenAlex

Industrial ecology for the oceansHuman interaction with ocean resources has historically been challenging due to the difficulties that arise when a terrestrial species aims at becoming successful in a marine environment.Shipwrecks, for instance, have doomed coastal communities for centuries, and even today fishing is one of the deadliest sectors in the labor force.Similarly, human-induced marine environmental catastrophes, such as oil spills for instance (Trevors & Saier, 2010), have commonly been laborious to clean up due to the inherent difficulty of humans performing beyond terrestrial ecosystems.Continued human population and economic growth since the beginning of the Industrial Revolution have exacerbated the need of human societies for mineral ores, fossil fuels, and other sources of energy, water, and food.This has led to the occupation of vast areas of terrestrial land, to the extent that humans now have a noticeable footprint in all the world's terrestrial biomes.In the world's oceans this same pattern has occurred at a slower pace throughout the decades, with fishing activities becoming more efficient with the arrival of steam vessels in the 1880s, diesel in the 20th century (Engelhard, 2008), and the incorporation of sophisticated detection systems turning ancestral coastal fishing activities into highly industrialized systems that land millions of metric tons of fish and other marine species annually (Fornshell & Tesei, 2013).Similarly, oil rigs spread quickly in the world's ocean to provide additional fossil fuel supplies for thirsty growing economies (Nyman, 2015), marine fright soared with the process of globalization (Mersin et al., 2019) with thousands of cargo vessels swarming the seas and, more recently, seabed mining has appeared in the public and private agenda as an alternative and lucrative sector to maintain the supply of metal ores in the technosphere (Levin et al., 2020).This increased pressure of human activities on the ocean and its resources has translated into a series of environmental impacts that have affected marine conservation (Knowlton, 2021) and degraded vast areas of the ocean.However, it must be noted that not all environmental impacts affecting the ocean are located in the ocean itself, but rather are created by terrestrial activities.In this sense, nutrient loading linked to wastewater treatment plants, agriculture, and cattle ranching are responsible for vast dead zones generated in multiple coastal zones across the globe (Diaz & Rosenberg, 2008), and it is also mainly terrestrial activities that are responsible for the accumulation of plastic waste in the world's oceans (Beaumont et al., 2019).Interestingly, many of these environmental impacts have only been analyzed in detail in recent years.For instance, marine plastic accumulation due to anthropogenic activities and its impacts on ecosystems and human health have only become a relevant field of research in the past decade after the Call for Action "Our Ocean, Our Future" of the Ocean Conference, organized by the United Nations in New York on June 5-9, 2017 (Sonnemann & Valdivia, 2017).In this context, although the focus of oceans-based research has traditionally been narrowly focused, researchers are recognizing the value of a wider, systems-based perspective with the aim of linking industrial uses with the environmental and resource impacts they engender.We argue that the field of industrial ecology is well-suited to fill that gap, as it is interdisciplinary in nature, rapidly growing, and has systems analysis at its core.The current special issue of the Journal of Industrial Ecology, entitled "Industrial Ecology for the Oceans," explores all of the above-mentioned issues with the ultimate objective of catalyzing and compiling novel research regarding the use of industrial ecology in the world's oceans.A total of 24 articles were accepted for publication in the current special issue.These can be divided into five main topics: (i) fishing and aquaculture; (ii) shipping; (iii) ocean acidification; (iv) marine plastics; (v) nutrient flows; and (vi) seabed mining, and are described below. FISHING AND AQUACULTUREFishing and aquaculture constitute the biggest section of papers that are published in this special issue.Out of the 12 studies, 8 of them focus on applying a variety of life cycle methods to different case studies linked to the marine environment, 3 are linked to modeling fishing gear, and one final study analyzes the implications of including a circular economy perspective in the aquaculture sector.In terms of life cycle assessment (LCA) studies, Ziegler et al. (2022) analyzed the greenhouse gas (GHG) emissions of a wide range of seafood products in Norway.Their results indicate that the fuel use intensity of most products has increased over the past decade in terms of fisheries, and

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,212

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,005
Communication savante0,0080,004
Science ouverte0,0010,005
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0630,014

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 Opus0,038
Tête enseignante GPT0,234
Écart entre enseignants0,196 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
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

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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