The importance of timber prices and other factors for harvest increase among non-industrial private forest owners
Bibliographic record
Abstract
Increased harvest is high on the forestry and climate policy agenda in several countries. By carrying out a national-wide survey of forest owners, we explored to what extent private non-industrial forest owners in Norway are willing to increase harvest due to elevated hypothetical prices. The results indicate that owners who have not harvested timber for sale in the last 15 years do not respond to large price shifts. Instead, ownership objectives and knowledge of a key policy instrument predict willingness to enter the timber market among these owners. The willingness among owners who have sold timber the last 15 years depends on these factors, in addition to price, forest area, income, and gender. Female owners were significantly less willing than male owners to increase harvest. Once the decision to harvest was taken, the stated timber supply volume per area unit decreases with productive forest area among both active and inactive owners. With regard to sources of information, owners who have not harvested timber the last 15 years use the information sources to a lesser extent than other owners do. Forest policies and extension services should acknowledge that for stimulating forest owners outside the timber market to supply wood, factors other than price are important and that alternative information pathways should be explored for reaching these owners.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".