Adding rewards to regulation: The impacts of watershed conservation on land cover and household wellbeing in Moyobamba, Peru
Bibliographic record
Abstract
We estimate the effects of Peru's oldest watershed payments for environmental services (PES) initiative in Moyobamba (Andes-Amazon transition zone) and disentangle the complex intervention into its two main forest conservation treatments. First, a state-managed protected area (PA) was established, allowing sustainable use but drastically limiting de facto land use and land rights of households in the upper watershed through command-and-control interventions. Second, a subset of those environmentally regulated households also received incentives: PES-like voluntary contracts with conditional in-kind rewards, combined with access to participation in sustainable income-generating activities of the integrated conservation and development project (ICDP) type. To evaluate impacts, we perform matching procedures and adjustment regressions to obtain the average treatment effect on the treated (ATT) of each intervention. We investigate impacts on plot-level forest cover and household welfare for the period 2010-2016. We find that both treatments-command-and-control restrictions and the incentive package-modestly but significantly mitigated primary forest loss. Incentive-induced conservation gains came at elevated per-hectare implementation costs. We also find positive effects on incentive-treated households' incomes and assets; however, their self-perceived wellbeing counterintuitively declined. We hypothesise that locally frustrated beneficiary expectations vis-a-vis the ambitiously designed PES-cum-ICDP intervention help explain this surprising finding. We finalise with some recommendations for watershed incentives and policy mix design in Moyobamba and beyond.
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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".