Assessment of Technologies to Meet a Low Carbon Fuel Standard
Bibliographic record
Abstract
California's low carbon fuel standard (LCFS) was designed to incentivize a diverse array of available strategies for reducing transportation greenhouse gas (GHG) emissions. It provides strong incentives for fuels with lower GHG emissions, while explicitly requiring a 10% reduction in California's transportation fuel GHG intensity by 2020. This paper investigates the potential for cost-effective GHG reductions from electrification and expanded use of biofuels. The analysis indicates that fuel providers could meetthe standard using a portfolio approach that employs both biofuels and electricity, which would reduce the risks and uncertainties associated with the progress of cellulosic and battery technologies, feedstock prices, land availability, and the sustainability of the various compliance approaches. Our analysis is based on the details of California's development of an LCFS; however, this research approach could be generalizable to a national U.S. standard and to similar programs in Europe and Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".