Off the charts? Reasons to be skeptical of the growth in biodiversity finance
Bibliographic record
Abstract
Recent estimates point to dramatic increases in private capital flowing to biodiversity. Examining main sources of this increase — equity investments and debt — this review asks how biodiversity finance is being calculated, and whether private capital flowing to biodiversity action is growing as much as reported. Furthermore, by examining the literature on the standards and metrics, we ask whether these increases are likely to facilitate biodiverse outcomes. Ultimately, some growth can be ascribed to conceptual innovations in measuring biodiversity-related finance. In several cases, the dollar value represented in nominally biodiversity-related transactions does not reflect actual amounts spent on biodiversity. This review points to a risk of overestimating private financing of biodiversity targets, which may generate overconfidence in this approach. Consequently, this review argues that optimism for private capital solutions should be tempered and accompanied by an upscaling of policy alternatives and regulations that address the financial drivers of biodiversity loss.
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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 | 0.030 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".