Calculating penalties for reforestation failures: an Alberta case study
Bibliographic record
Abstract
Provincial governments across Canada rely on regeneration requirements and penalties to promote reforestation following harvesting. However, little has been written on how to determine optimal levels of penalties for noncompliance such that tenure holders have incentives to further social reforestation objectives. This paper shows how reforestation penalties may be calculated in the case of Alberta. The calculation of the penalty is shown to be dependent on (i) changes in the values of future annual allowable cuts caused by failure to promptly regenerate, (ii) the portion of stumpage values collected with stumpage fees, (iii) nontimber values influenced by reforestation, (iv) differences in private and social discount rates, (v) costs of detecting noncompliance, and (vi) the probability of detecting infractions. In the case of Alberta, (v) and (vi) are minor considerations, as detection costs are low and probability of detection is high. However, values of (i) through (iv) have large potential impacts on the optimal penalties. For example, if (i) annual allowable cuts drop by 99 m3 for a 3-year reforestation delay (vs. an acceptable 2-year delay) on a 783-ha forest, (ii) stumpage fees are $10 below stumpage value, (iii) nontimber values are zero, and (iv) the private discount is 9%, while the social rate is 6%, then the optimal penalty is $49.17CAN·ha1. However, if we change (ii) and (iv) such that stumpage fees are $30CAN below stumpage value and the private discount is 9%, while the social rate is 3%, then the optimal penalty is $168.58CAN·ha1. With zero nontimber values, zero monitoring costs, no divergence between public and private interest rates at 9%, and a probability of detection of 1.00, the current penalty of $30CAN·ha1·year1 would approximate the optimal amount if stumpage fees were $20CAN·m3. The variability in values of (i) through (iv) across forests and over time suggests that problems will arise in establishing a constant penalty for all provincial forests and that penalties should be revised as values change over time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".