Science and technical priorities for private sector action to address biodiversity loss
Bibliographic record
Abstract
Target 15 of the Kunming-Montreal Global Biodiversity Framework recognizes the importance of the private sector monitoring, assessing and disclosing biodiversity-related risks, dependencies and impacts. Many businesses and financial institutions are progressing with science-based assessments, targets and disclosures and integrating into strategy, risk management and capital allocation decisions. Developments will continue in response to investor expectations, emerging corporate sustainability reporting regulations in Europe, China and elsewhere and evolving global sustainability reporting standards. Voluntary action is also being encouraged by the disclosure recommendations of the Taskforce on Nature-related Financial Disclosures and the target-setting methods of the Science Based Targets Network. Based on experience supporting the private sector in practice, we identify four critical science and technical advances needed to enable business action at scale and to redirect finance globally to halt and reverse biodiversity loss. First, consensus on indicators and metrics for measuring changes in the state of nature and provision of ecosystem services. Second, access to global, regularly updated, location-specific and consistent nature data. Third, standardized and consistent accounting systems that structure data, support risk management and create accountability at corporate, ecosystem and national levels. Fourth, integrated risk assessment approaches to help corporates, financial institutions, central banks and supervisors to assess nature-related risks.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".