Real option valuation of timber harvesting contracts
Bibliographic record
Abstract
Abstract A primary challenge for forestry businesses is valuing timber harvesting contracts. This article presents an empirical application of determining the optimal harvest volumes under stochastic prices to determine the economic value of timber harvesting contracts and manage business risk using real options theory. We illustrate a dynamic optimization solution procedure and the choice between a single long‐term versus two short‐term timber harvesting contracts from a risk management perspective. A case study of timber harvesting contracts sold in British Columbia is used to demonstrate specification of the many details and adaptations that are required in such valuation problems. Our article offers some interesting results. The highest timber harvesting contract values occur with optimal harvest quantities rather than an equal annual allowable cut (AAC). The difference in values can be substantial (two to three‐fold). Consequently, forestry businesses can benefit by timing their harvest to nonequal quantities in later years of a timber harvesting contract. Depending on the quality of timber, the maximum stumpage value for the second short‐term timber harvesting contract ranges from $12 to $17.8 per cubic meter, resulting in lower profit margins for equal AAC. In contrast, with optimal harvesting, the maximum stumpage value ranges from $2 to $8.5 per cubic meter and higher profit margins. These bounds for stumpage price and profit margins would be useful for forest businesses to better manage business risks when bidding for timber harvesting contracts.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".