The Contributions of Biomass Supply for Bioenergy in the Post-COVID-19 Recovery
Bibliographic record
Abstract
This research investigates how biomass supply chains (BSChs) for bioenergy within the broader bioeconomy could contribute to the post-COVID-19 recovery in three dimensions: boosting economic growth, creating jobs, and building more resilient and cleaner energy systems in four future scenarios, in the short term (by 2023) and long term (by 2030). A SWOT analysis on BSChs was used for generating a questionnaire for foresight by a two-round Delphi study. To interpret the results properly, a short survey and literature review is executed to record BSChs behavior during the pandemic. In total, 23 (55% response rate) and 28 (46% response rate) biomass experts from three continents participated in the Delphi and the short survey, respectively. The strongest impact from investment in BSChs would be on economic growth, followed by a contribution to the resilient and cleaner energy systems and job creation. The effects would be more visible in the long- than in the short-term period. Investments with the most impact on recovery are those that improve biomass material efficiency and circularity. Refurbishment of current policies to enhance the supply of biomass as a renewable resource to the future economy is a must.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".