A global survey of stakeholder views and experiences for systems needed to effectively and efficiently govern sustainability of bioenergy
Bibliographic record
Abstract
Different governance mechanisms have emerged to ensure biomass and bioenergy sustainability amidst a myriad of related public and private regulations that have existed for decades. We conducted a global survey with 59 questions which examined 192 stakeholders' views and experiences related to (1) the multi‐leveled governance to which they are subjected, (2) the impacts of that governance on bioenergy production and trade, and (3) the most urgent areas for improvement of certification schemes. The survey revealed significant support along the whole supply chain for new legislation which uses market‐based certification schemes to demonstrate compliance (co‐regulation). Some respondents did not see a need for new regulation, and meta‐standards is a promising approach for bridging divergent views, especially if other proof than certification will be an option. Most respondents had so far experienced positive or neutral changes to their bioenergy production or trade after the introduction of new sustainability governance. Legislative requirements and a green business profile were important motivations for getting certified, while lack of market advantages, administrative complexity and costs all were barriers of varying importance. A need to include, e.g., regular standard revision and dealing with conflicting criteria was identified by respondents associated with bioenergy schemes. Respondents associated with forestry schemes saw less need for revisions, but some were interested in supply chain sustainability criteria. Significant differences among schemes suggest it is crucial in the future to examine the tradeoffs between certification costs, schemes' inclusiveness, the quality of their substantive and procedural rules, and the subsequent effectiveness on‐the‐ground. WIREs Energy Environ 2016, 5:89–118. doi: 10.1002/wene.166 This article is categorized under: Bioenergy > Climate and Environment
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".