Innovative forest products in the circular bioeconomy
Bibliographic record
Abstract
Background: The forest-based industry has been moving towards the manufacture of bio-based products in response to the increasing concern by consumers and governments regarding the use of non-renewable materials and the generation of residues. Various innovative technologies geared towards reducing the environmental footprint of products and processes are currently being developed and applied in the forest-based industry. This study presents some innovative wood-based products that are about to enter the market or that are already being commercialized but have the potential to expand in market size. Methods: We collected data from interviews and a survey with organisations working with product development and manufacturing, and from the literature. Results: Many innovative products that are already produced at an industrial scale, such as cross-laminated timber, wood-based composites, and lyocell, can still increase their market share in the coming years. Some of the up-and-coming products with high potential to substitute fossil-based materials and will likely enter the market in the near future are wood foam, lignin-based adhesives, glycols, bioplastics, and textile fibres. Our study indicates that, although biomass demand is expected to increase, stakeholders do not consider future supply a limiting factor. Conclusions: The ease of market introduction of innovative products relies heavily on the products’ ability to take advantage of existing value chains. Overall, many of the reviewed products have the advantage of being ‘drop-in’. This is because products that require adjustments to production lines are less likely to get into the market without strong external drivers that push for bio-based alternatives. According to stakeholders, the economic viability and the market expansion of these products could be encouraged to a certain extent by EU policies, and certain barriers could be alleviated by reducing bureaucracy, increasing the support for pilot-scale to full-scale production, and increasing subsidies for bio-based alternatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".