Economic Concepts, Methods, and Tools for Risk Analysis in Forestry under Climate Change
Bibliographic record
Abstract
Climate change will affect the expected values and distributions of key variables that influence forest management decisions. Risk analysis will likely play a more prominent role in forestry decision making. There are, however, different types of risk problems and different types of models and approaches to choose from. Three possible models that could have application in a climate change risk context are: (1) the Markowitz Portfolio Frontier Model; (2) Expected Value-variance/Chance Constraint Hybrid Model; (3) Discrete Stochastic Programming. These models are applicable in different contexts and answer different questions. For example, the Markowitz model looks for the asset mix that minimizes portfolio variance subject to a minimum expected return. The expected value-variance/chance constraint model accounts for risk preferences and uncertainty in both objective function and constraints variables. The objective function is to maximize certainty equivalent. The discrete stochastic programming model allows for learning to occur and for the decision maker to modify his/her decisions as new information becomes available over a planning horizon.Request access from your librarian to read this chapter's full text.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".