Timber production versus old-growth preservation with endogenous prices and forest age-classes
Bibliographic record
Abstract
This study combines timber production and environmental values, applying a dynamic forest-level economic model with any number of forest age-classes. The model includes endogenous timber price or nonlinear harvesting costs and various possibilities to specify the dependence of environmental values (related e.g. to species persistence) on the forest age-class structure. The nonlinearities in the net benefits from timber production have the consequence that fluctuations in optimal timber harvesting may totally vanish or at least become smaller than in forest scheduling models without ad hoc even flow constraints. If environmental values are specified to depend on the fraction of forest land preserved as old growth, the optimal long run allocation between timber production and old growth is represented by an equilibrium continuum. Thus the optimal long run allocation depends on the initial age-class distribution. The continuum and the dependence of initial age-class distribution vanish when the rate of discount approaches zero. If the environmental values of age-classes increase smoothly with age, the long run equilibrium may simultaneously include multiple rotation periods. The model determines the optimality of producing timber and environmental values separately at different parts of the forest or at the same piece of forest land. Numerical computation suggests that the optimal solution always converges toward some optimal long run stationary age-class distribution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".