Diversification of the forest industries: role of new wood-based products
Bibliographic record
Abstract
This study identifies new wood-based products with considerable potential and attractive markets, including textiles, liquid biofuels, platform chemicals, plastics, and packaging. We apply a mixed-methods review to examine how the position of the forest industry in a given value chain determines the respective production value. An assessment is provided as to the degree to which these emerging wood-based products could compensate for the foreseen decline of graphic paper markets in four major forest industry countries: USA, Canada, Sweden, and Finland. A 1%–2% market share in selected global markets implies a potential increase in revenues of 18–75 billion euros per annum in the four selected countries by 2030. This corresponds to 10%–43% of the production value of forest industries in 2016 and compares with a projected decline of graphic paper industry revenue of 5.5 billion euros by 2030. The respective impacts on wood use are manifold, as many of the new products utilize by-products as feedstock. The increase in primary wood use, which is almost entirely attributed to construction and to some extent textiles markets, would be in the range of 15–133 million m 3 , corresponding to 2%–21% of the current industrial roundwood use in the selected countries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".