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Enregistrement W4411652037 · doi:10.21248/gups.90898

Analyzing the global impacts of food and feed production, trade and consumption on terrestrial and marine ecosystems

2025· dissertation· en· W4411652037 sur OpenAlexaboutno aff
Giorgio Bidoglio

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Trade and Competitiveness
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésConsumption (sociology)Production (economics)EcosystemMarine ecosystemTerrestrial ecosystemEnvironmental scienceNatural resource economicsBusinessEcologyEconomicsBiology

Résumé

récupéré en direct d'OpenAlex

Global food supply chains play a crucial role in the functioning of both social and ecological systems, providing essential resources like food and feed products. However, the supply of food to humanity is accompanied by the alteration of planetary boundaries such as biosphere integrity, biogeochemical cycles and climate stability, potentially leading to irreversible tipping points and threatening the ability of future generations to meet their needs. Furthermore, the globalization of trade has increased the spatial disconnect between producers and consumers, meaning that local impacts of agricultural production are driven by consumption in geographically distant places. Feeding the world sustainably in a context of population growth and climate change requires then transformative changes of current production and consumption patterns, as advocated by numerous international initiatives, such as the UN Sustainable Development Goals, the Convention on Biological Diversity and the EU European Green Deal. This thesis analyzes and quantifies how global terrestrial and marine ecosystems are affected by production, trade and consumption of agricultural products in a telecoupled world. Building on multiple approaches from different disciplines, it addresses specific cases of human-nature metabolism at different spatial scales by exploring: A. The role of trade and consumption of agricultural products in driving global biodiversity loss, B. How spatial patterns of agricultural expansion vs. intensification drive environmental impacts, C. What environmental indicators best describes different aspects of biodiversity loss, and D. How production intensities, i.e. impact per unit product, vary between food commodities. The work on these themes has resulted in three research papers. The thesis is structured around six interrelated chapters delineating the context inspiring my research, discussing the methodological orientation of my work, summarizing major findings and how they contribute knowledge to the general themes at the heart of my dissertation, and reflecting on implications for policy and practice. In the first paper, I investigated how the transfer of 151 crops through global trade networks spreads the responsibility of oxygen depletion impacts on local marine ecosystems in the country where production takes place to geographically distant consumers. I used a spatially explicit Life Cycle Assessment (LCA)-based model and global data on synthetic fertilizers, manure and nitrogen fixation to estimate production intensities (i.e. oxygen depletion impact per kcal of produced crop) and extent of oxygen depletion in 66 Large Marine Ecosystems (LMEs). Linking this information with crop trade data allowed me to disaggregate the estimated impacts across traded and non-traded agricultural products. Results show large differences between impacts driven by production for domestic consumption and production for export, depending on the type of crop, country and affected LMEs. I found that production of cereals and oil crops accounts for the bulk of oxygen depletion impacts, with export-driven production accounting for 15.9% of total global impact. However, for some large exporting countries like Canada, Argentina or Malaysia, this share often makes up to three-quarters of their production impacts, while for some importing countries located in eutrophication sensitive LMEs like Japan, South Korea, Finland or Italy, importing of crops can reduce pressure on already highly affected coastal ecosystems. The second paper adds breadth to the current debate on the environmental costs of animal-based protein supply and is relevant for an improved understanding of the contribution of livestock production to the ongoing biodiversity and climate crises. I made use of three indicators that have not yet been systematically assessed for livestock products: deforestation, biodiversity loss and marine eutrophication. I used global biophysical data on the production, trade and consumption of primary crops and grazed biomass utilized as livestock feed, as well as livestock production data, to determine the environmental impact intensities of livestock feed per ton of livestock protein produced. Results locate the largest deforestation and biodiversity impact intensities in the tropics in Central and South America, Southeast Asia and Central Western Africa, while the highest marine eutrophication intensities can be found in countries in Northern Europe and in South and in East Asia. The third paper highlights the importance of using spatially explicit biodiversity indicators to better address the multidimensionality of the biodiversity concept. I report on the development of two complementary and contrasting spatial scale indicators to estimate the impending terrestrial vertebrate species loss (across four taxa: reptiles, amphibians, birds and mammals) at the landscape (grid) and global level.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,158
Score d'incertitude au seuil0,590

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,017
Tête enseignante GPT0,240
Écart entre enseignants0,223 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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