Real options in harvesting decision on publicly owned forest lands
Bibliographic record
Abstract
This paper extends the literature on optimal tree harvesting assuming stochastic prices. With volatile prices, the value of a stand of trees is increased when harvesting dates are flexible, depending on wood volume and product prices of the day. Flexibility adds value because a forest owner can delay harvesting when prices are depressed, or can harvest earlier than planned if there is a uptick in prices. The stand owner thus has a natural hedge against price volatility. Regulatory policy in some jurisdictions has reduced the flexibility of firms harvesting on public lands by imposing allowable cut restrictions. This paper develops a two factor real options model of the harvesting decision over infinite rotations with mean reverting stochastic prices. The model is used to examine a proposed investment in intensive forest management in Ontario's boreal forests. The value of a representative stand in the Romeo Malette forest is estimated assuming complete harvesting flexibility. This value is then compared to the value when regulations dictate a window of time during which harvesting must occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".