One- or two-step processes: Which have a lower GHG emissions intensity for production of synthetic aviation fuel via indirect CO2 electrolysis?
Bibliographic record
Abstract
The development of sustainable aviation fuel (SAF) could pave the way towards addressing the dual challenges faced by the aviation sector: meeting rising demand for air transport and achieving net-zero targets. In this study, the well-to-pump (WtP) and well-to-wake (WtW) greenhouse gas (GHG) emissions intensity (EI) of aviation fuel production via four CO 2 -indirect pathways (intermediate products are required) is estimated and the WtW GHG EI compared to conventional fossil-based and bio-ethanol pathways. We aim to determine whether a one- or two-step electrochemical conversion is more likely to result in lower GHG intensity aviation fuel, under what conditions pathways incorporating these electrochemical processes have a lower GHG EI than conventional crude oil-based and biomass-based jet fuels, and whether these CO₂-derived sustainable aviation fuel (CO₂-SAF) pathways can approach “carbon neutrality.” The key findings from this work are: (1) processes using ethylene as an intermediate tend to have a lower GHG EI, although there is not a meaningful difference between one- and two-step pathways; (2) all pathways could achieve a lower GHG EI than fossil and biomass based routes if the location is carefully selected to minimize the GHG EI of electricity supply and if the CO₂ source is strategically chosen; and (3) while these pathways have the potential to approach zero GHG emissions, emissions from fuel manufacturing will be challenging to eliminate entirely. Notably, the GHG EI of CO₂-based SAF is far more sensitive to background system parameters, such as the carbon intensity of electricity and CO₂ supply, than to technical parameters. Therefore, we suggest that background factors may play a greater role in determining GHG EI than technical 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".