Policies for establishing hybrid poplar plantations on private and public lands in western Canada for bioethanol feedstock: a forest-level financial analysis
Bibliographic record
Abstract
A forest-level model is developed that estimates how policies towards hybrid poplar plantations on private and public land impact harvest levels and values for producing biofuel feedstock. We simulate three policy changes: (i) permitting an increase in harvest levels on public land as a result of establishing hybrid poplar plantations on private land; (ii) permitting the establishment of hybrid poplar plantations on public land; and (iii) including forest carbon emission offsets in the net benefits realized by the forest operator. We are interested in whether the increase in harvest created by the policies might be enough to supply a biorefinery, and how the value of the operation changes. Our results suggest that jointly managing public and private lands under sustained yield can increase harvest by between 7% and 93%, and increase the value of the operation by between 39% and 263%. Results also suggest that hybrid poplar plantations could enable a leaseholder of one million hectares of public forestland to initiate an allowable cut effect and thereby increase harvest enough to supply a new biorefinery, in addition to its existing pulp mill. Carbon offsets further increase the value of the forest, although harvest begins to decline at high carbon prices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".