Designing Voluntary Subsidies for Forest Owners under Imperfect Information
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we study voluntary subsidies offered to forest owners to increase rotation periods. We assume that a forest owner takes private amenity values into account when making decisions, but these values are lower than the social amenity values; therefore, an amenity value externality arises. Furthermore, the regulator has imperfect information regarding the timber profit of the forest owner. We show that voluntary subsidies must reflect the difference between (a) private and social amenity values and (b) timber profit among the possible types of the forest owner.In this way, we solve the amenity value externality and the problem of imperfect information about timber profit in a second-best optimal way. We have also investigated what happens if the regulator excludes private amenity values when fixing voluntary subsidies and we show that two sources of efficiency losses arise: (a) non-optimal rotation periods and (b) non-truthful revelation of private information.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it