Benefits Beyond Borders: Assessing Landowner Willingness-to-Accept Incentives for Conservation Outside Protected Areas
Bibliographic record
Abstract
Unplanned land-use change surrounding protected areas (PAs) can lead to degradation and fragmentation of wildlife habitats, thereby placing tremendous pressure on PAs especially in tropical countries. Incentivizing the expansion of habitats beyond PAs will not only benefit wildlife but also has the potential to create livelihood opportunities for marginalized communities living adjacent to PAs. Our study explored landowners’ willingness to participate in an incentive-based, wildlife-friendly land-use program using a discrete choice modeling approach. We surveyed 699 landowners living in 287 villages within a five-kilometer buffer around Nagarahole and Bandipur National Parks in India. We found that landowners preferred wildlife-friendly land-use over their ongoing farming practices. Landowners preferred short-term programs, requiring enrolling smaller parcels of land for wildlife-friendly land-use, and offering higher payment amounts. Landowners with larger landholdings, a longer history of living next to the PA, and growing fewer commercial crops were more likely to prefer enrolling large parcels of land. Landowners who grew more commercial crops were likely to prefer long term programs. We also estimated the average monetary incentive to be INR 64,000 (US$ 914) per acre per year. Wildlife-friendly land use, in developing economies like India with shrinking wildlife habitats and expanding infrastructural developments, could supplement rural incomes and potentially expand habitat for wildlife, thereby being a promising conservation strategy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".