Experimental Evidence on the Impact of Payments and Property Rights on Forest User Decisions
Bibliographic record
Abstract
Clearing forests for swidden agriculture, despite providing food to millions of farmers in the tropics, can be a major driver of deforestation. Payments for ecosystem services schemes can help stop swidden agriculture-induced forest loss by rewarding forest users for maintaining forests. Clear and secure property rights are a key prerequisite for the success of these payment schemes. In this study, we use a novel iterative and dynamic game in Madagascar and Kenya to examine farmer responses to individual and communal rights to forestlands, with and without financial incentives, in the context of swidden agricultural landscapes. We find that farmer pro conservation behaviour, defined by the propensity to keep forests or fallows on their lands, as well as the effects of land tenure and conservation incentive treatments on such behaviour, differ across the two contexts. The average percentages of land left forest/fallow in the game are 65 and 35% in Kenya and Madagascar, respectively. Individual ownership significantly improves decisions to preserve forests or leave land fallow in Madagascar but has no significant effect in Kenya. Also, the effect of the individual tenure treatment varies across education and wealth levels in Madagascar. Subsidy increases farmers' willingness to support conservation interests in both countries, but its effect is four times greater in Kenya. We find no interaction effects of the two treatments in either country. We conclude that the effectiveness of financial incentives for conservation and tenure reform in preserving forestland vary significantly across contexts. We show how interactive games can help develop a more targeted and practical approach to environmental policy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".