Sustainability of forest bioenergy feedstock supply chains: Local, national and international policy perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In 2009, the European Union ( EU ) Renewable Energy Directive ( RED ) mandated that 20% of the EU ’s final energy consumption consist of renewable sources by 2020, and included sustainability criteria for liquid biofuels. Discussions around extending criteria to solid and gaseous biomass, including wood pellets, have been ongoing. Continued investment in forest bioenergy feedstock production is partly dependent on the stability of global market demand and the economic viability of feedstock production and trade. For trans‐boundary governance mechanisms such as the RED to be efficient, a proper assessment of the specific forest and land policy contexts of wood pellet exporters that the mechanism will affect, such as Canada, the USA , and Russia, is crucial. This paper builds on sustainability criteria for biodiversity protection and assurance of sustainable forest management ( SFM ) for woody biomass that are currently under discussion for inclusion in the RED and compares them with national and local regulations of those three countries. This illustrates potential challenges in the establishment of sustainability criteria related to: differences in land definitions, delineation and reporting systems; lack of a uniform definitional paradigm for SFM ; and difficulties in establishing efficient monitoring/auditing systems. Regulators wanting to implement supra‐national sustainability schemes such as the EU RED need to be aware of challenges that such schemes carry and make efforts to reduce or eliminate pitfalls. There is also a need to assess the aggregated effects of these various tools, and a need for communication, collaboration and outreach among stakeholders. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it