The Law and Policy of Biofuels
Bibliographic record
Abstract
At the most recent meeting of G7 countries, the leaders of some of the most powerful economies in the world agreed to "decarbonize" the global economy by 2100. Following in the footsteps of nascent initiatives at the national, regional and international levels, this was an additional signal that the search will be ongoing over the coming decades to find an alternative to fossil fuels as a source of energy. There are many contenders, but there is no certainty as to which will emerge as the new dominant energy source. The biofuel sector was early "out of the gate" in the race for a renewable substitute to fossils fuels, but the growth of this sector, as has been the case for competing alternatives, has raised a number of concerns and questions. The IUCN Academy of Environmental Law, a network of close to 200 law schools around the world that pursue teaching and research in environmental law, took note, early on in its existence, of the simultaneous as well as accelerating development of policies and laws in various countries on the topic. Given the dearth of law and governance analysis of biofuels policies, the Academy, with the generous financial support of the Law Foundation of Ontario and the University of Ottawa, organized a number of workshops over the years that have allowed it to identify experts in the area and to facilitate exchanges between them. This book is a direct result of these efforts as experts from around the world take an in-depth look at the challenges and opportunities emerging as the biofuels sector expands. The chapters consider the role of policies and laws, as support for, and control over, this sector. As analysis of policies and laws cuts across many disciplines, the editors invited contributions by experts from many different fields. The contributions are wide-ranging, in terms of themes and geography.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".