Risky business: Protecting nature, protecting wealth?
Bibliographic record
Abstract
Abstract Finance is a precondition for many of the activities that harm ecosystems, but how to address this underlying driver of biodiversity loss remains a topic of debate. This paper reviews the Task Force on Nature‐Related Financial Disclosures (TNFD), a corporate‐led effort that aims to identify how changes to biodiversity may create financial risks for companies and investors. This approach is also promoted as a strategy for managing the impact of business on biodiversity, with the assumption that risk disclosure will more effectively price biodiversity‐harming activities. We assess the potential of the TNFD toward this end, and invite conservation scientists, practitioners, and policymakers to engage critically with its theory of change. We find that the relationship between disclosing biodiversity risk and redirecting finance away from environmental degradation is tenuous and unproven, making this mechanism insufficient for addressing the impact of the financial sector on nature. We question the embrace of another industry‐led mechanism that implies that a lack of information is the greatest barrier to stopping biodiversity loss. Further, there are risks that this financial sector approach to biodiversity will reinforce the highly unequal concentration of power and wealth, which is itself inimical to transformative change, as called for by the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".