Towards an efficient implementation of the EU biodiversity strategy in forests – An analysis of alternative voluntary conservation mechanisms and selection criteria
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework and the European Union's Biodiversity Strategy aim to halt biodiversity loss by 2030. Both include ambitious goals to increase the area of protected land and sea to 30 % with 10 % devoted to strict protection. The required land areas are large and challenge current instruments tailored to meet much less ambitious conservation goals. Forest conservation traditionally relies on voluntary flat-rate or cost compensation policies inviting predominantly conservation-minded landowners to conserve their forests. More efficient instruments are needed to meet the ambitious goals of forest biodiversity conservation. We examine how alternative auction mechanisms perform relative to the current instruments under different selection criteria in promoting strict conservation targets. We demonstrate that the studied mechanisms differ in their ability to invite sites to the conservation program. The auction incentivises higher participation from landowners who do not have strong conservation motives and decreases information rents from landowners with strong conservation motives. When selection criteria favour high-quality sites, like old-growth stands, the auction mechanisms outperform the cost compensation policy by providing the largest area of conserved land and the highest ecological values. Thus, auctions offer a promising option for implementing forest biodiversity conservation in accordance with the EU Biodiversity Strategy. • Ambitious biodiversity conservation targets for forests require new policy instruments. • Conservation auctions analysed against a cost compensation policy, data from a Finnish PES scheme. • Auctions outperform the widely applied cost compensation policy in most studied cases. • Auctions invite sites of higher quality and sites from landowners with low conservation motives. • Auctions can cost-effectively implement the conservation goals of the EU biodiversity strategy.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".