Bibliographic record
Abstract
Abstract Interest in recycling of forest products has grown in recent years, one of the goals being to conserve trees or possibly increase their number to compensate for positive externalities generated by the forest and neglected by the market. This paper explores the issue as to whether recycling is an appropriate measure to attain such a goal. We do this by considering the problem of the private owner of an area of land, who, acting as a price taker, decides how to allocate his land over time between forestry and some other use, and at what age to harvest the forest area chosen. Once the forest is cut, he makes a new land allocation decision and replants. He does so indefinitely, in a Faustmann‐like framework. The wood from the harvest is transformed into a final product that is partly recycled into a substitute for the virgin wood, so that past output affects the current price. We show that in such a context, increasing the rate of recycling will result in less area being devoted to forestry. It will also have the effect of increasing the harvest age of the forest, as long as the planting cost is positive. The net effect on the flow of virgin wood being harvested to supply the market will as a result be ambiguous. An important point, however, is that recycling will result in fewer trees in the long run, not more. It would therefore be best to resort to other means if the goal is to conserve the area devoted to forestry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".