Amenities and Risk in Forest Management∗
Bibliographic record
Abstract
The objective of the paper is to analyze the risk management behavior of a non-industrial private forest owner under uncertainty about timber production. Two types of hedging strategies with harvesting decisions are studied: a financial practice versus a physical one. We develop a two-period model of hedging and harvesting decisions when the forest owner values the amenity services of forest. We study the properties of optimal current and future harvesting and hedg-ing decisions. We show that, except when both hedging instruments are perfect substitutes, the forest owner chooses a single tool, her/his choice depending on the rate of return of the hedging instrument. We also prove that the greater the marginal utility of amenity services, the smaller the harvesting amount. We provide a comparative statics analysis on current and future harvesting and on the hedging strategies. We are interested in the impact of an increase in initial stocks (wealth and timber), timber prices (periods 1 and 2), opportunity costs of the hedging instruments (rate of return for savings and cost of the regeneration process for physical practice) and expected risk. We show, for example, that an increase in expected risk has a negative impact on period 1 harvesting and the use of hedging tools for both strategies, while the impact on period 2 harvesting is positive for savings and null for physical practice. ∗The authors would like to thank Hippolyte d’Albis, participants at the 12 th Joint Seminar of EALE and Geneva Association at Lecce, and at the annual conference of the SCSE at Québec.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".