Review of life‐cycle greenhouse‐gas emissions assessments of hydroprocessed renewable fuel (<scp>HEFA</scp>) from oilseeds
Bibliographic record
Abstract
Abstract Renewable fuel from the hydroprocessed esters and fatty acids (HEFA) pathway represents a promising short‐term option for reducing fossil fuel use in transportation. However, some life‐cycle assessments (LCAs) have shown that HEFA diesel and jet fuel may have higher life‐cycle greenhouse gas (GHG) emissions than the fossil fuels they replace. Many of these studies examined HEFA fuel derived from oilseed feedstocks. Here, results and methodology from 20 LCAs of HEFA fuel from oilseeds are reviewed in an effort to determine the sources of variability in the reported life‐cycle GHG emissions of HEFA fuels. Although there was a 61–63% reduction in median life cycle GHG emissions of HEFA biojet and renewable diesel compared to conventional petroleum fuels, this review highlights the importance of standardized methodologies for life‐cycle assessment (e.g., CORSIA, RSB) and indicates the need to prevent the conversion of forest land for biofuel production, as well as the potential opportunity for alternative oilseeds such as camelina and carinata as feedstocks to produce HEFA fuels with lower life‐cycle GHG emissions. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".