Should climate change make us think more about the economics of forest management?
Bibliographic record
Abstract
Forest management agencies have budget constraints and continually face difficult questions regarding how much to invest in silviculture and when to harvest forests. Economic thought suggests these decisions should be guided by the pursuit of economic efficiency and tools like net present value (NPV) analysis. In forestry this would make use of the so-called Faustmann model and generally result in shorter rotation ages than the Maximum Sustained Yield (MSY) criterion, which is often used as a policy objective in forest management. The two approaches have caused tension and controversy between foresters and economists. Climate change is adding yet another uncertainty dimension to the forest management challenge. Global climate models suggest massive changes in climate this coming century that will surely affect forests. Here we use climate change as a backdrop to compare the MSY and Faustmann results for black spruce (Picea mariana) and white pine (Pinus strobus) in Ontario. Climate change is adding new risks to silvicultural investments. Our intent is not to “resolve” the management problem but highlight some issues and differences between the two approaches. We suggest that climate change could, or should, cause a resurgence of the debate over pursuit of intertemporal efficiency in forest management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".