Projected US timber and primary forest product market impacts of climate change mitigation through timber set-asides
Bibliographic record
Abstract
Whereas climate change mitigation involving payments to forest landowners for accumulating carbon on their land may increase carbon stored in forests, it will also affect timber supply and prices. This study estimated the effect on US timber and primary forest product markets of hypothetical timber set-aside scenarios where US forest landowners would be paid to forego timber harvests for 100 years to increase carbon storage on US timberland. The scenarios featured payments to landowners of $0 (business-as-usual (BAU)), $10, and $15 per each additional metric ton (t) of carbon dioxide equivalent (CO 2 e) sequestered on the set-aside timberlands, with maximum annual expenditures of $3 billion. For the set-aside scenarios, reduction in timberland available for harvest resulted in increased timber prices and changes in US domestic production, consumption, net export, and timber market welfare. Economic analyses indicated that the scenario with more area set aside and the largest carbon mitigation benefit (lower carbon price, $10/t CO 2 e) would result in the largest decrease in market welfare, suggesting that climate change mitigation policies and programs would need to consider such impacts when evaluating the costs and benefits of climate change mitigation strategies in the forest sector.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".