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HEFA-to-Jet: Are We Heading in the Right Direction for Sustainable Aviation Fuel Production?

2025· preprint· en· W4411047518 on OpenAlexfundno aff
Mathieu Pominville-Racette, Ralph P. Overend, Inès Esma Achouri, Nicolas Abatzoglou

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Sherbrooke
KeywordsHeading (navigation)AviationJet (fluid)AeronauticsProduction (economics)Aerospace engineeringJet fuelEnvironmental scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.326
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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