Developing a Sustainable and Circular Bio-Based Economy in EU: By Partnering Across Sectors, Upscaling and Using New Knowledge Faster, and For the Benefit of Climate, Environment & Biodiversity, and People & Business
Bibliographic record
Abstract
This paper gives an overview of development of the EU-bioeconomy, 2014-2020. The Vision of the new Circular Bio-based Economy, CBE is presented: Unlocking the full potential of all types of sustainably sourced biomass, crop residues, industrial side-streams, and wastes by transforming it into value-added products. The resulting product portfolio consists of a wide spectrum of value-added products, addressing societal and consumer needs. Food and feed, bio-based chemicals, materials, health-promoting products; and bio-based fuels. The pillars of CBE are described, including biotechnology, microbial production, enzyme technology, green chemistry, integrated physical/chemical processing, policies, conducive framework conditions and public private partnerships. Drivers of CBE are analyzed: Biomass supply, biorefineries, value chain clusters, rural development, farmers, foresters and mariners; urgent need for climate change mitigation and adaptation, and stopping biodiversity loss. Improved framework conditions can be drivers but also obstacles if not updated to the era of circularity. Key figures, across the entire BBI-JU project portfolio (2014-2020) are provided, including expansion into biomass feedstocks, terrestrial and aquatic, and an impressive broadening of bio-based product portfolio, including higher-value, health-promoting products for man, animal, plants and soil. Parallel to this, diversification of industrial segments and types of funding instruments developed, reflecting industrial needs and academic research involvement. Impact assessment is highlighted. A number of specific recommendations are given; e.g., including international win/win CBE-collaborations, as e.g., expanding African EU collaboration into CBE. In contrast to fossil resources biological resources are found worldwide. In its outset, circular bio-based economy, can be implemented all over, in a just manner, not the least stimulating rural development.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".