Designing ecologically effective and economically efficient conservation compensation funds: lessons from theory and practice
Bibliographic record
Abstract
The global community has committed to a substantial increase in the scale of investment in nature via Target 19 of the Kunming-Montreal agreement. An important but understudied mechanism for attracting private investment into biodiversity outcomes is conservation compensation funds – funds that aggregate payments to compensate for negative impacts on biodiversity to contribute to strategic objectives. We described the principles of effective compensation funds based on economic and ecological theory, and assembled by far the largest database of operational compensation funds to date (32 funds across 17 countries) through a mixed methods review. We explored the variety of practice in real-world implementation, and how empirical practice compares to theory, highlighting key gaps. In doing so, we provided a guide to the design of ecologically effective compensation funds, a hitherto understudied but potentially substantial source of investment for biodiversity outcomes.
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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 | 0.080 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".