Cost-effectiveness of Natura 2000 forest contracts for biodiversity conservation
Bibliographic record
Abstract
Natura 2000 contracts in the European Union aim to maintain or restore natural habitats to a favorable conservation status. This article aims to analyze the cost-effectiveness of Natura 2000 forest contracts at the individual level of intervention areas in France. The level of long-term biodiversity was assessed using ex ante and ex post levels of conservation status, evaluated on a 100-point scale. Data collection was conducted on the contract areas using a combination of plotless and line intersection sampling methods. Cost-effectiveness was analyzed by modeling a cost function of conservation measures that is estimated simultaneously with the ex ante and ex post biodiversity equations. We performed an empirical illustration based on a small number of observations, and, therefore, the results deserve to be confirmed using larger databases. The conservation measures implemented in the contracts studied had a significant effect on maintaining and restoring biodiversity. Nevertheless, we found pronounced diseconomies of scale and low cost-effectiveness. This suggests the possibility of less ecologically ambitious contracts with lower average costs. Our results also recommend new targeting and prioritizing rules to implement more cost-effective conservation measures (e.g., veteran trees) in a less costly context (e.g., targeting main tree species in larger intervention areas). These recommendations could make Natura 2000 contracts significantly more cost-effective.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".