Estimating the future economic effects of biodiversity loss and strategies to mitigate it: evidence from soil quality and pollination, and half-earth protection scenarios
Notice bibliographique
Résumé
The global natural system faces significant pressure from societal and economic demands, threatening air, water, soil, and biodiversity. The financial sector increasingly recognises that its stability depends on climate, nature, and biodiversity, and their interconnected relationship. Governments seek to measure and reduce impacts on essential resources and integrate nature into financial decisions. Knowledge gap:• The macroeconomic effects of biodiversity loss at the sector and country level, considering its direct effects and indirect effects through trade and reallocation of production between sectors and countries.• Monetised costs and benefits of the measures that can abate the loss of biodiversity. In the first year of the project, BiROFin• developed global scenarios regarding the effects of biodiversity-loss-induced changes in pollination, and soil quality on crop productivity by 2050, and how those effects change under the presence of climate-change-induced extreme climate events;• estimated the macroeconomic impacts of these changes using the MAGNET general equilibrium model, which incorporates international trade, supply chain linkages, consumer market developments, and input substitution, enabling the project to estimate the varying impact of biodiversity loss on various sectors in different countries;• estimated the monetary costs and benefits of six nature-based measures, which can abate biodiversity, soil quality, and pollination loss, and at the same time increase crop productivity, in Brazil, France, Germany, Italy, the Netherlands, Spain, the United Kingdom, and the United States;• identified macroeconomic outcomes of an existing conservation policy that protects half of the Earth from biodiversity loss and thereby soil quality and pollination loss.From exposure to ecosystem services loss to estimating the effect of risks and opportunities to abate them 5 The document includes the following results from the first year of the BiROFin for specialists and practitioners in the financial sector, government, and other private sector organisations focusing on environment and nature topics:• Risks of human-induced biodiversity loss on crop productivity by 2050 due to declining soil quality and loss of insect pollinators under climate-change-induced extreme climate events.• Global macroeconomic repercussions of soil quality and pollination loss due to biodiversity loss, affecting economies and domestic and international markets through trade and supply chains by 2050.• Cost and benefit implications for implementing nature-based measures to abate soil quality and pollination losses caused by biodiversity decline by 2050 in Brazil, France, Germany, Italy, the Netherlands, Spain, the United Kingdom, and the United States.• Macroeconomic risks of implementing a conservation policy that protects half of the Earth from socio-economic activity to abate biodiversity loss, thereby soil quality and pollination loss by 2050.• For a shorter summary of the results presented in the document, please refer to our Executive Summary intended for policymakers. To understand the methodology and assumptions behind our scenarios, macroeconomic estimations and cost-benefit analyses, please visit the following appendices on our BiROFin website
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».