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 approach to examine how the position of forest industry in respective value chains determines the production value. Consequently, an assessment is provided as to the degree 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% percent market share in selected global markets implies a potential increase in revenues of 18-75 billion euros per annum s in the four selected countries by 2030. This corresponds to 10–43% of the production value of forest industries in these countries in 2016. This range compares to a projected decline of graphic paper industry revenue of 5.5 billion euros by 2030. The respective impact on wood use are manifold, as many of the new products utilize byproducts as feedstock. The impact on primary wood use, almost entirely attributable to the construction and to some extent to textile markets, would be in the range of 15-133 million m3, corresponding to 2-21 % of the current industrial roundwood use in these four 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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".