Changing the deforestation impacts of Eco-/REDD payments: Evolution (2000-2005) in Costa Rica's PSA program
Bibliographic record
Abstract
Costa Rica’s PSA (Pagos por Servicios Ambientales) environmental services payments, started in 1997, were the pioneers. Broadly cited, they have led to numerous suggestions that others emulate the PSA approach. Yet the PSA program has itself evolved over time. Following earlier work (Sanchez et al. 2007 and Pfaff et al. 2008 on PSA 1997-2000), we can evaluate here whether a change in implementation changes impacts on deforestation. Examining the PSA forest-protection contracts during 2000 and 2005, we find that less than 5 in 1000 (about 0.4%) parcels enrolled in the program would have been deforested annually without payments. To first order, this matches the 1997-2000 findings of low deforestation impact and may be explained by.low agricultural returns relative to those in ecotourism as well as by other conservation policies including the forestry law of 1996. However, there are differences in results which are instructive. First, the overall impact is in fact slightly higher than in 1997-2000; despite net reforestation, more deforestation took place and thus the PSA had a bit more land-use change to prevent. More important for upcoming policies such as global carbon payments, the shifts in PSA implementation eliminated the bias of the PSA payments towards lands that are relatively unprofitable and thus unlikely to be cleared even without payments. Thus we can see that even within the same country for the same basic policy idea, the details of implementation do matter. In this and other settings significant potential gains can be realized by increased targeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".