HEFA-to-Jet: Are We Heading in the Right Direction for Sustainable Aviation Fuel Production?
Bibliographic record
Abstract
Hydrotreated ester and fatty acids to jet (HEFA-tJ) is presently the most developed and economically attractive pathway to produce sustainable aviation fuel (SAF). However, an ongoing systematic study of the critical variables of different pathways to SAF has revealed significantly lower GHG reduction potential for the HEFA-tJ pathway compared to competing markets using the same resources for road diesel production. Moderate yield variations between air and road pathways lead to several hundred thousand tons less GHG reduction by project, which is generally not evaluated thoroughly in standard environmental assessments. We also demonstrate that if the HEFA-tJ market has attractive features that biodiesel/renewable diesel does not have, market attractiveness is temporary and will lead to considerable viability risks as HEFA-tJ fuel market integration rises. The negative environmental impact of palm oil production, the primary resource for road production, could also be reduced if methane capture technologies were applied more widely. We emphasize the need for more transparent data and effort in this regard before envisaging rising drastically HEFA-tJ production. Overall, reducing road diesel carbon intensity appears to be less capital-intensive, risky, and several times more efficient in reducing GHG emissions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".