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Record W1531720346

Real options in harvesting decision on publicly owned forest lands

2002· preprint· en· W1531720346 on OpenAlexaboutno aff
Margaret Insley, Kimberly Rollins

Bibliographic record

VenueRePEc: Research Papers in Economics · 2002
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHedgeMean reversionValue (mathematics)EconomicsFlexibility (engineering)Natural resource economicsVolatility (finance)LoggingForest managementBusinessAgricultural economicsAgroforestryEconometricsEnvironmental scienceForestryMathematicsStatisticsGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.306
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2002
Admission routes1
Has abstractyes

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