Forest taxation in multiple-stand forestry with amenity preferences
Bibliographic record
Abstract
This paper investigates the impacts of forest taxes within an age-class forest model where the landowner derives utility from both consumption and the amenity values of standing forest. The model generalizes the existing models used to analyze forestry taxes. It is shown how the age-class model enables a more detailed analysis of the substitution and income effects of various taxes. These effects are shown to be linked to the properties of the utility function and to the distribution of the landowner’s assets between forests and nonforest assets. The results indicate that for most utility functions, income effect is unlikely to dominate the substitution effect of taxes on forest owner timber harvesting decisions. Numerical examples are used to demonstrate how the tax impacts can differ in the short run and long run. The results imply that higher consumption levels combined with profit or sales taxation applied in forestry will effectively reinforce each other in increasing optimal rotations. On the other hand, a lump-sum tax may be used to counteract the impact of increasing consumption levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".