Economics of strip harvesting in drained boreal peatland forests
Bibliographic record
Abstract
This study examines the economics of strip harvesting on drained boreal peatlands as an alternative method to traditional even-aged forest management that causes high negative externalities due to nutrient loads to receiving water courses. Strip harvesting avoids large clear-cuts, eliminates the need for ditch network maintenance and facilitates maintaining the water table at an environmentally beneficial level, thus reducing negative eutrophication externalities. In the rotation framework, a forest manager maximizes the present value of net harvest revenue subject to a constraint on the water table level. Starting with an initial even-aged stand, the harvesting regime consists of a transition period and steady-state period. In the transition period, the initial stand area is allocated between parallel strips to be harvested in a row with simultaneous determination of rotation ages in the strips. In the steady state, only the rotation ages are chosen. In the considered drained Scots pine-dominated peatland site located in southern Finland, even-aged management provides higher private net revenue than strip harvesting. From society’s viewpoint, strip harvesting significantly reduces nutrient load damage compared to even-aged management. The water table level constraint plays an important role in the design of harvesting and the resulting social net benefits. JEL classification: Q23, Q24, Q25.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".