The role of incentive mechanisms in promoting forest restoration
Bibliographic record
Abstract
Forest restoration has been proposed as a scalable nature-based solution to achieve global environmental and socio-economic outcomes and is central to many policy initiatives, such as the Bonn Challenge. Restored forests contain appreciable biodiversity, improve habitat connectivity and sequester carbon. Incentive mechanisms (e.g. payments for ecosystem services and allocation of management rights) have been a focus of forest restoration efforts for decades. Yet, there is still little understanding of their role in promoting restoration success. We conducted a systematic literature review to investigate how incentive mechanisms are used to promote forest restoration, outcomes, and the biophysical and socio-economic factors that influence implementation and program success. We found that socio-economic factors, such as governance, monitoring systems and the experience and beliefs of participants, dominate whether or not an incentive mechanism is successful. We found that approximately half of the studies report both positive ecological and socio-economic outcomes. However, reported adverse outcomes were more commonly socio-economic than ecological. Our results reveal that achieving forest restoration at a sufficient scale to meet international commitments will require stronger assessment and management of socio-economic factors that enable or constrain the success of incentive mechanisms. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".