Cost and carbon-intensity reducing innovation in biofuels for road transportation
Bibliographic record
Abstract
The transportation sector is the leading contributor to greenhouse gas emissions in the United States. Incentives for biofuel production in the form of direct subsidies and tradable performance standards have been substantial. Although research suggests such policies are less cost-effective at reducing emissions than an explicit carbon price, a dynamic assessment that accounts for innovation may differ substantially from static cost estimates. This study evaluates cost and carbon-intensity reducing innovation in the U.S. biofuel industry and estimates the value of social benefits for comparison with the observed level of policy support. Using multi-factor experience curves, this study finds that cost declines have been significant in ethanol production, with an estimated learning rate of 21.8%. However, learning in biodiesel and renewable diesel has been much less pronounced, at 3.23% and 1.33%, respectively. Reductions in carbon intensity are found to be largely related to feedstock choice rather than improvements in production processes. Based on the innovation rates identified in this study, current policy support for biofuels from stacked incentives is found to exceed the social benefits. This misalignment calls for a reassessment of biofuel policies to ensure they are economically and environmentally justified. • Ethanol has maintained a high learning rate of 21.8% • Biodiesel and renewable diesel demonstrate lower learning rates of 3.23% and 1.33% • Carbon intensity innovation primarily results from feedstock choice • Policy support exceeds social benefit of biofuels from emission reduction and innovation
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".