Welfare Implications of the Allowable Cut Effect in the Context of Sustained Yield and Sustainable Development Forestry
Bibliographic record
Abstract
Welfare implications of the Allowable Cut Effect (ACE) have been largely ignored in the literature since the early 1980s. This paper re-assesses the welfare implications of the ACE in the context of sustained yield and sustainable development forestry. With respect to sustained yield forestry, the resolution of the ACE issue was incomplete. Concerns regarding the subsidization of silvicultural investments with existing timber values, and the inability of the ACE to consider values, were not reconciled with the acceptance of the ACE, which occurred upon realization that the ACE reduces the shadow price of sustained yield constraints. This paper attempts to reconcile these two phases in the literature. Furthermore, problems associated with the ACE were essentially assumed away with the acceptance of sustained yield, rather than considering ACE concerns as legitimate problems associated with sustained yield policies. The absence of a resolution to these issues could impede a transition from sustained yield forestry, focussed on timber volumes, to sustainable development forestry, that focuses on sustaining forest resource values. Although such a paradigm shift could potentially alleviate some of the concerns associated with the ACE, similar problems arise that are endemic to the use of sustainability constraints.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".