U.S. Proposed Tariff on the Wood Sector within North America
Bibliographic record
Abstract
This study evaluates the implications of the United States (U.S.) proposed tariffs on North American wood markets. Two scenarios were modeled using a ‘wood sector’-tailored FOrest Trade Equilibrium Model (FOTEM), which utilizes a general equilibrium model framework. The first scenario considered the U.S. imposing tariffs without retaliation from Canada and Mexico, and the second scenario modeled retaliatory tariffs from Canada and Mexico. Results indicate that U.S. GDP remains largely unaffected. Still, the U.S. wood sector suffers output losses under retaliation, particularly in hardwood lumber and paper-related industries (printing and publishing), which depend heavily on North American trade flows. Both Canada and Mexico experience contraction in their GDP and would be better off without retaliation. Canada emerges as the most affected partner, with severe disruptions in engineered panels and softwood products. Mexico’s Fiberboard, Furniture, Oriented Strand Board, and Pellets sectors show increased output due to trade diversion, though domestic resource and production constraints may limit these gains. The study highlights that protectionist measures can shield select industries in the short term but risk disrupting integrated supply chains and weakening overall competitiveness. The study concludes that coordinated trade policies are essential to sustain domestic production while reducing destabilization across North American wood markets.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".