Risky business: Protecting nature, protecting wealth?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it