Monetary Incentives to Reduce Open-Field Rice-Straw Burning in the Plains of Nepal
Bibliographic record
Abstract
In southern Nepal, rice straw burning in open fields is common practice. This is problematic because biomass burning contributes to smoke, black carbon and greenhouse gases. While some studies have examined the reasons for burning, few have tried to identify incentives that might stop farmers from burning. In this study, we use a uniform price unitsupply reverse auction, followed by an actual payment, in order to measure the amount of incentive required to stop smallholder farmers from burning rice straw. 317 farmers from 18 villages in Rupandehi and Kapilvastu districts participated in the reverse auction and signed an agreement to avoid burning for a payment. About 86 percent of the farmers fully complied with the agreement for a median payment of NPR 5, 610 per ha (USD 78/ha). We also assessed the factors affecting the bid amount and compliance with the agreement. The supply of ecosystem services by the farmers through avoided burning is unit elastic, indicating that there is a linear relationship between monetary incentives and avoided burning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".