Strategies and Policies for the Bioeconomy and Bio-Based Economy: An Analysis of Official National Approaches
Bibliographic record
Abstract
The onset of formulating strategies and policies regarding the bioeconomy can be, at least partly, attributed to the publication of the policy agenda on the bioeconomy by the Organisation for Economic Cooperation and Development in 2009. The aim of this study is to analyze selected national strategies and policies regarding the development of a bioeconomy and to clarify similarities and differences between them. The article presents a comparative overview of the strategies and policies for developing a bioeconomy in the EU, USA, Canada, Sweden, Finland, Germany and Australia. The documents analyzed are in most cases national strategies or policies. The structures and aims of these documents vary and the analysis is further complicated by the terms “bioeconomy” and “bio-based economy” having as yet no clear definition, a point which is discussed in some depth in this article. In the documents analyzed, strategies and policies on how to promote the bioeconomy are often presented based on the prerequisites of the country in focus; the need for increased research, development and demonstrations in the area is thus particularly stressed. The main emphasis is often to enhance the economy of a nation and provide new employment and business possibilities, whereas the aspects of sustainability and resource availability are addressed only to a limited extent in many of the documents.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".