To facilitate a fair bioeconomy transition, stronger regional‐level linkages are needed
Bibliographic record
Abstract
Abstract The great hopes in Brussels that a circular bioeconomy will help bridge the growing divide between urban and rural areas and allow the hinterlands to prosper from ‘green growth’ are addressed in this article, which reflects on insights from three Nordic case studies of brown, green and blue biomass use at different levels of technology readiness. A closer examination of the forward, backward, fiscal and final demand linkages at regional level from increased biomass utilization, from eastern Finland and northern Sweden to Jutland and North Atlantic islands, suggests that linkages are and will remain relatively weak, predominantly dashing the expectations. As suppliers and exporters of natural resources, disadvantaged regions may all too easily get locked into a ‘staples trap’, where the value creation evaporates owing in part to the steep start‐up costs and the associated boom‐and‐bust cycles, which place them in a weak position vis‐à‐vis the resource manufacturers and consumers. To make the prospects of development, employment and prosperity in the hinterlands materialize, measures are needed to strengthen the regional‐level economic linkages. Regional‐level revolving funds based on benefit‐sharing instruments related to natural resources can be used to bolster economic development, as reflected in such schemes present in both China and Canada. We call for further research into whether and how such approaches can be replicated successfully by channeling revenues from biomass cultivation to regional‐scale revolving funds, with mandates to strengthen long‐term economic linkages and prosperity within the hinterlands. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".