Bio-based circular economy in European national and regional strategies
Bibliographic record
Abstract
In circular economy (Ce), the value of products and materials is maintained for as long as possible. What has previously been considered waste is now a resource that can be reused and reintroduced to the production cycle. Therefore, waste management of both technical and bio-based waste streams plays a central role in the transition towards Ce. In bioeconomy, the materials are to a certain extent circular by nature. however, biomaterials may also be used in a rather linear way. according to the european Commission, the transition towards Ce needs to be supported on local, regional and national levels. Thus, to enhance sustainability and get the full potential out of bioeconomy, the Ce principles should be applied to reach bio-based Ce. This paper presents the results of a qualitative assessment that was carried out in Finland, Spain, Slovakia, greece, romania and France. Selected national and regional strategies were identified, compared and analyzed from the perspective of Ce and bio-based Ce. at the time of the study, the added value of Ce was recognized in most of the national and regional level strategies studied, through objectives concerning e.g. waste management or bioenergy. Bio-based Ce was hardly ever included as a term but circularity aspects were referred to for example through biowaste management. Waste management appears to be the main driver in the transition towards Ce. This is evident also in the case presented from Slovakia. yet, in order for Ce to become an integral part of national and regional policies, a more comprehensive understanding of the Ce mechanisms should be achieved. Supported actions on both small and large-scale are needed. The research is partly an outcome of the ongoing Interreg europe project BIOregIO, where the bio-based circular economy is boosted through a transfer of expertise about best practices, aiming at changing regional policies to support bio-based Ce.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".