An economic assessment of using the allowable cut effect for enhanced forest management policies: an Alberta case study
Bibliographic record
Abstract
In Canada, forest policymakers are considering the allowable cut effect (ACE) as a potential mechanism to provide tenure holders with incentives to practice enhanced forest management. To investigate the incentives created by the ACE, this paper estimates returns to ACE investments for a trembling aspen (Populus tremuloides Michx.) - white spruce (Picea glauca (Moench) Voss) mixedwood forest in Alberta. A timber supply model is used to optimize harvesting schedules to maximize net present values over a 200-year planning horizon. A number of scenarios are investigated with variations in intensity of silvicultural investments, beginning age-class distributions, levels of flexibility around the allowable annual cut (AAC), calculations of AACs based on coniferous and mixedwood volumes, and green-up constraints. In our simulations, it was difficult to find positive returns to the ACE. Positive returns only occurred when operating under harvesting constraints with a mature starting forest and AAC calculations that ignored deciduous volumes. In those limited cases where there were positive returns to the ACE, returns were higher for extensive, rather than intensive investments. Combining these results with other potential impediments to the ACE, previously identified in the literature, the probability of tenure holders having incentives to undertake ACE investments is low.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".