Impacts of climate change on forest product markets: Implications for North American producers
Bibliographic record
Abstract
This paper examines potential climate change impacts in North American timber markets. The results indicate that climate change could increase productivity in forests in North America, increase productivity in forests globally, and reduce timber prices. North American consumers generally will gain from the potential changes, but producers could lose welfare. If dieback resulting from additional forest fires, increased pest infestation, or storm damage increases appreciably and has market effects, consumers will gain less and producers will lose more than if climate change simply increases the annual flow of timber products by raising forest productivity. Annual producers' surplus losses from climate change in the North American timber sector are estimated to range from $1.4 – $2.1 billion per year on average over the next century, with the higher number resulting from potential large-scale dieback. Within North America, existing studies suggest that producers in northern regions are less susceptible to climate change impacts than producers in southern regions because many climate and ecological models suggest that climates become dryer in the U.S. South. Key words: climate change, impact analysis, timber markets, forest ecosystems
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".