Environmental and socio-economic consequences of forest carbon payments in Bolivia: Results of the OSIRIS-Bolivia model
Bibliographic record
Abstract
Bolivia has significant potential to abate climate change by reducing deforestation. This opportunity presents economic and environmental tradeoffs. While these tradeoffs have been hotly debated, they have as yet been the subject of little quantitative analysis. We introduce the OSIRIS-Bolivia model to provide a quantitative basis for decision-making. OSIRIS-Bolivia is an Excel-based tool for analyzing the potential effects of incentive payments to reduce emissions from deforestation (REDD) in Bolivia. It is based on a spatial econometric model of deforestation in Bolivia during the period 2001-2005, and uses information on forest cover, deforestation rates, geographical conditions, and drivers of deforestation, including agricultural opportunity costs, for more than 120,000 pixels covering the whole country. OSIRIS-Bolivia is based on a partial equilibrium model in which reductions in deforestation in one region reduce the supply of agricultural products to the domestic market, which in turn causes an increase in the price of agricultural products, making conversion of land to agriculture more attractive and thus stimulating an increase in deforestation in other regions (leakage). The model can help answer questions such as: Where in Bolivia are carbon incentive payments most likely to result in reduced deforestation? Who are most likely to benefit from REDD? How much money will it take to reduce deforestation by a given amount? To what extent might transaction costs or preferences for agricultural income undermine the goals of the REDD program?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".