Emissions Effects of Alternative Fuels in Light-Duty and Heavy-Duty Vehicles
Bibliographic record
Abstract
Energy supply and environmental concerns have led to interest in alternative transportation fuels and power-trains. Already, there are significant changes in mainstream gasoline and Diesel formulation to accommodate tighter emissions standards. Some alternative fuels are being promoted as “cleaner” replacements for gasoline and Diesel fuel. There are many research papers which present data on these different alternative fuels, yet it is difficult to compare the fuels with any confidence. The majority of published studies do not use consistent methodology and make many assumptions (which may or may not be reported). Based on an extensive literature review, this study presents emissions results drawn from a smaller number of papers which provide alternative fuel and conventional emissions data in a comparable manner. Both light-duty and heavy-duty vehicles are considered. Reformulated gasoline, compressed natural gas, liquified petroleum gas, methanol-85 and methanol-100 are compared to conventional gasoline and Diesel fuels. The scope of the study includes emissions comparisons on the basis of standard emissions test cycles, low ambient temperature effects, mileage degradation as well as vehicle technology. Additionally, some of the parameters producing variances in emissions values from paper to paper are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".