Potential of different governance mechanisms for achieving Global Biodiversity Framework goals
Bibliographic record
Abstract
Abstract The Kunming-Montreal Global Biodiversity Framework includes a target of 30% of land protected by 2030 and refers to other effective area based conservation measures (OECMs) as complementary to PAs, but robust evaluations of the effectiveness of governance mechanisms that could act as OECMs in preventing forest loss and carbon emissions remain sparse. Here we assessed the impact of PAs and two potential OECMS: Indigenous Lands (ILs), and Non-Timber Forest products Concessions (NTCs) on forest loss and its associated carbon emissions in the Peruvian Amazon from 2000 to 2021. We also assessed two governance mechanisms with a commercial extractive use, Logging (LCs) and Mining Concessions (MCs). We used a robust before–after control intervention study design, with statistical matching, to account for the non-random spatial distribution of deforestation pressure and the governance mechanisms analysed. PAs were the most effective, having avoided 88% of the expected forest loss, followed by NTCs (64%) and ILs (44%). LCs also reduced expected forest loss by 29%, while MCs increased expected forest loss by 24%, showing that extractive governance mechanisms can have marked differences in their impact to forest cover. Our study provides evidence of long-term positive impacts of potential OECMs and other mechanisms at preventing forest loss and reducing carbon emission. This information is key to more effectively achieve targets from the Kunming-Montreal Global Biodiversity Framework and the UN Framework Convention on Climate Change.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".