Alternative jet fuel scenario analysis report
Bibliographic record
Abstract
This analysis presents a “bottom up” projection of the potential production of alternative aviation (jet) fuels in North America (United States, Canada, and Mexico) and the European Union in the next decade. The analysis is based on available plans from individual companies and considers existing and emerging fuel production technologies. The analysis also forecasts how alternative fuels might contribute to greenhouse gas (GHG) goals. Based on a review of fuel production companies’ stated plans to produce jet fuel, the study incorporated company-specific data into seven scenarios varying alternative jet fuel production and expansion assumptions. This study supports the use of advanced alternative fuels as one important component of achieving emissions and environmental targets, although other additional measures and/or new technologies may also be required. The analysis suggests that the Federal Aviation Administration (FAA) goal of 1 billion gallons of alternative jet fuel use by U.S. aviation in 2018 is achievable. A combination of the most optimistic demand forecasts and the “product switch” production scenarios leads to North American aviation greenhouse gas emissions leveling off or decreasing between years by 2020. For the limited scenarios considered, additional measures would be needed to return to 2005 emissions levels in North America in 2020. In the European analysis, leveling of GHG emissions by 2020 only occurs in cases where ethanol and/or biodiesel producers switch to producing some jet fuel. As this “bottom up” projection could not account for all potential alternative fuel producers (either because public data were not available or because these companies were unknown to the authors), the results presented should be viewed as one possible range of future production levels that could occur in North America and Europe. It does not consider the amount of alternative fuels that could be produced from all potentially available feedstocks (i.e., technical potential) which would be much greater. Further, production outside of North America and Europe was not included in the analysis so actual demand for alternative jet fuels in North America and Europe could be met with alternative fuels produced outside the region. Finally, the development of new technologies, new market conditions, new participants, and improved processes for known technologies could all lead to production levels higher than shown in this analysis. In fact the technical potential of biofuels production greatly exceeds projected demand. Likewise, policies and economic conditions could lead to lower, or nonexistent production levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".