Corporate disclosures need a biodiversity outcome focus and regulatory backing to deliver global conservation goals
Bibliographic record
Abstract
Abstract To achieve the goals of the Kunming–Montreal Global Biodiversity Framework (KMGBF), agreed by Parties to the Convention on Biological Diversity, there is an urgent need to address the economic drivers of biodiversity loss. The KMGBF includes a target to encourage businesses and financial institutions to disclose their impacts and dependences on biodiversity. While transparent biodiversity disclosures could help shift business operations away from activities that harm biodiversity, the weak target wording implies voluntary and unstandardized disclosures, which tend to be low quality and ineffective. Moreover, examination of scientific and practical insights strongly indicates that the evolving strategy of disclosures led by businesses may prioritize short‐term business and investment interests while neglecting biodiversity outcomes and the wider systemic risks they pose. We argue that there is a risk of limited if not altogether perverse outcomes from the target, where businesses provide ambiguous disclosures that fail to reduce impacts on biodiversity, yet an increase in volume and frequency of disclosures suggests progress toward the target. Consequently, we advocate for a regulatory approach, supported by scientific engagement in the development of disclosure standards and associated policy indicators, to ensure that the emerging response to the KMGBF target on disclosures avoids perverse outcomes and instead results in positive impacts on biodiversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.031 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".