Decision Support System for Selecting Sustainable Alternatives to Conventional Jet Fuel: Impact of Emissions, Production Costs and Carbon Pricing
Bibliographic record
Abstract
The United States Environmental Protection Agency (EPA) in June 2015, took a step toward regulating carbon emissions from airlines, following an assessment that airlines contribute to climate change. On July 25, 2016, the final endangerment finding (Note 1) under section 231(a) (2) (A) of the Clean Air Act for aviation emissions was issued by the EPA. The European Union had issued a similar finding previously and had proposed implementing an emission trading scheme in which the airlines would be required to participate in a cap and trade scheme for emissions from jet fuel. Traditional jet fuel is derived from petroleum, whose price is volatile and depends on geopolitical stability. Fuel burn is a significant cost for airlines and affects their profitability and value. Fuel burn is also a significant source of greenhouse gas emissions. An investigation of alternatives to jet fuel and switching from conventional jet fuel based on varying emission profiles, production costs and varying carbon prices is therefore timely. We use a simple decision support system to examine the link between the life-cycle greenhouse gas emissions of a range of fuels, economic costs of production and varying carbon prices. This analysis should be of interest to regulators, traders, risk managers and executives in the airline industry as well as practitioners of sustainability management.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".