Opportunities to encourage mobilization of sustainable bioenergy supply chains
Bibliographic record
Abstract
Significant opportunities exist to reduce greenhouse gas emissions, increase domestic energy security, boost rural economies, and improve local environmental conditions through the deployment of sustainable bioenergy and bio‐based product supply chains. There is currently a wide selection of possible feedstocks, a variety of conversion routes, and a number of different end products that can be produced at a range of scales. However, economic slowdown, low oil prices, lack of global political will, and lingering questions regarding land use change and the sustainability of bioenergy production systems provide a challenging global context to speed the pace of investment. The opinions expressed in this paper are derived from our collaboration within IEA Bioenergy to determine opportunities as well as barriers that need to be overcome to realize opportunities on a wider scale. This comprehensive and novel collaborative effort confirmed that feedstocks produced using logistically efficient production systems can be mobilized to make significant contributions to achieving global targets for bioenergy. At the same time, significant barriers to large‐scale implementation exist in many regions. The mobilization potential identified in the study will depend on both increases in supply chain efficiencies and profits and strong policy support to increase stakeholder and investor confidence. WIREs Energy Environ 2017, 6:e237. doi: 10.1002/wene.237 This article is categorized under: Bioenergy > Economics and Policy Energy Policy and Planning > Climate and Environment
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".