Forest certification costs and global forest product markets and trade: a general equilibrium analysis
Bibliographic record
Abstract
The impacts of forest certification costs on the output, price, and trade of forest products were assessed via computable general equilibrium modeling under various scenarios representing tropical, temperate, and global forest certification. Despite causing more severe and extensive impacts, global certification seems more acceptable than regional certification to major timber-producing countries. The regions that would suffer the most from global certification would not be major timber-producing regions, but major net importers of forest products like East Asia. With 5%25% increases in forestry production costs resulting from certification, the world's forestry output would decline by 0.3%5.1%, while the world price would rise by 1.6%34.6%; impacts on global lumber and pulp and paper markets would be much more moderate. In general, forest certification would have larger impacts on trade and price than on output. While causing trade diversion and substitutions between tropical and temperate forest products and affecting regional forest product markets, forest certification would not substantially induce substitutions between wood and nonwood products at the global aggregate level. Because of the possible leakages (deforestation elsewhere) associated with regional certification and the land-use shifts resulting from sectoral production shifts at the regional level, forest certification may not necessarily curb tropical deforestation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".