Program Outcomes of Payments for Watershed Services in Brazilian Atlantic Forest: How to Evaluate to Improve Decision-Making and the Socio-Environmental Benefits
Bibliographic record
Abstract
In 2014, the Paraíba do Sul River Basin Integration Committee (CEIVAP) established its Pilot Program of Payments for Ecosystem Services (PES), focusing on water resources. The projects from this program share the same goal: to disseminate the use of PES as a tool for land management in watersheds. Contemplating 11 municipalities, including 84 landowners, conserving 718.63 ha and restoring 188.58 ha, this program was concluded in April 2020. Reviewing its historical and contextualizing features, we have observed that the outcomes from this program extend beyond these numbers. Here, we propose an evaluation methodology comparing the efficiency, performance, and impact of the PES projects. Based on new indicators that are easy to measure, we have identified key elements that have asymmetrically affected the projects. The complexity of the project scope and the execution of high-cost, and risky interventions on rural properties, have resulted in expensive projects with little tangible outputs. Our results support the upgrade of public policy for investment in ecosystem services by CEIVAP in the Paraíba do Sul watershed. In addition, our results can be more successful by improving the decision-making processes for similar projects in other watersheds.
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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 | 0.027 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".