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Record W7110648188

Estimating the future economic effects of biodiversity loss and strategies to mitigate it: evidence from soil quality and pollination, and half-earth protection scenarios

2025· other· en· W7110648188 on OpenAlexaff

Bibliographic record

VenueSocio-Environmental Systems Modeling · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsBiodiversityQuality (philosophy)Soil qualityBiodiversity conservationEconomic impact analysisEcosystem
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.019
GPT teacher head0.248
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractno

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