Economics of mixed-species forestry with ecosystem services
Bibliographic record
Abstract
The Faustmann–Hartman setup is widely established for specifying the economics of forest values besides timber, but it is criticized as restrictive for capturing diversity values. We show that extending the model to cover diversity attributes, i.e., mixed species and internal heterogeneity within species, is not enough to overcome these restrictions. Additionally, it is necessary to extend forest harvesting regimes to cover thinning, continuous cover forestry, and the management of commercially useless trees. Restrictions in the Faustmann–Hartman setup are first shown analytically with optimized thinning but without tree size structures. The empirical significance of these findings is shown by a model that includes four tree species, tree size structures, an extended set of forest management activities, a detailed description of harvesting costs, and a measure for stand diversity as a key factor behind ecosystem services. We show how an optimal harvesting regime, net revenues, wood output, and stand diversity depend on model flexibility, economic parameters, and the valuation of ecosystem services. In a setup allowing flexible management regimes, the costs of reaching a specified level of ecosystem services are negligible compared with those of the Faustmann–Hartman specification.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".