Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Lead to Undervalued Benefits of Miscanthus-based Sustainable Aviation Fuel
Bibliographic record
Abstract
Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) are intended to reduce the greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite the emergence of field-to-fuel GHG monitoring technologies that could verify such benefits, programmatic constraints in CI accounting procedures may limit fuel producers’ ability to capitalize on these opportunities. To elucidate the implications of this challenge, this work examines a miscanthus-to-sustainable aviation fuel (SAF) pathway (i) to demonstrate how program provisions drive estimates of biofuel CIs and (ii) to explore potential CI and financial benefits of spatially explicit life cycle assessment (LCA). In comparing policy-based vs. spatially explicit CI scores (estimated via DayCent and BioSTEAM) for SAF production from miscanthus via alcohol-to-jet (ATJ), programmatic CI accounting requirements underestimated GHG benefits in 60-99% of simulated scenarios. These underestimates result in policy-induced SAF price differentials of -1.19 [(-)3.46 to (-)0.23], -0.07 [(-)1.06 to (+)0.37], and -0.48 [(-)2.46 to (+)0.16] $·L-1 for the RFS, CFR, and LCFS, respectively. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".