Biofuel policies used by IEA Bioenergy Task 39 countries: the transition to using the carbon intensity (CI) of biofuels to set targets
Bibliographic record
Abstract
Abstract The International Energy Agency (IEA) Bioenergy Task 39 countries share a collective ambition to decarbonize their transport sectors, with biofuels and ‘enabling’ policies playing a key role in achieving this goal. ‘Market‐pull’ and ‘technology‐push’ policies have been used successfully, with countries' targets often set using volumetric or energy content values. However, more recently, policies that address carbon emissions have increased in importance. For example, California's Low Carbon Fuel Standard has been pioneered at the state level and the USA's Inflation Reduction Act/ Renewable Fuels Standard and Europe's Renewable Energy Directives/RefuelEU policies have highlighted the importance of reducing the carbon intensity of biofuels. Although various policies will continue to be important if the transport sector is to decarbonize, technology‐agnostic policies that reduce the carbon intensity of fuels will become increasingly important. The work of IEA Bioenergy Task 39 has provided an international compare‐and‐contrast forum to assess biofuel policy development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".