A Life-Cycle Comparison of Alternative Automobile Fuels
Bibliographic record
Abstract
We examine the life cycles of gasoline, diesel, compressed natural gas (CNG), and ethanol (C2H5OH)-fueled internal combustion engine (ICE) automobiles. Port and direct injection and spark and compression ignition engines are examined. We investigate diesel fuel from both petroleum and biosources as well as C2H5OH from corn, herbaceous bio-mass, and woody biomass. The baseline vehicle is a gasoline-fueled 1998 Ford Taurus. We optimize the other fuel/powertrain combinations for each specific fuel as a part of making the vehicles comparable to the baseline in terms of range, emissions level, and vehicle lifetime. Life-cycle calculations are done using the economic input-output life-cycle analysis (EIO-LCA) software; fuel cycles and vehicle end-of-life stages are based on published model results. We find that recent advances in gasoline vehicles, the low petroleum price, and the extensive gasoline infrastructure make it difficult for any alternative fuel to become commercially viable. The most attractive alternative fuel is compressed natural gas because it is less expensive than gasoline, has lower regulated pollutant and toxics emissions, produces less greenhouse gas (GHG) emissions, and is available in North America in large quantities. However, the bulk and weight of gas storage cylinders required for the vehicle to attain a range comparable to that of gasoline vehicles necessitates a redesign of the engine and chassis. Additional natural gas transportation and distribution infrastructure is required for large-scale use of natural gas for transportation. Diesel engines are extremely attractive in terms of energy efficiency, but expert judgment is divided on whether these engines will be able to meet strict emissions standards, even with reformulated fuel. The attractiveness of direct injection engines depends on their being able to meet strict emissions standards without losing their greater efficiency. Biofuels offer lower GHG emissions, are sustainable, and reduce the demand for imported fuels. Fuels from food sources, such as biodiesel from soybeans and C2H5OH from corn, can be attractive only if the co-products are in high demand and if the fuel production does not diminish the food supply. C2H5OH from herbaceous or woody biomass could replace the gasoline burned in the light-duty fleet while supplying electricity as a co-product. While it costs more than gasoline, bioethanol would be attractive if the price of gasoline doubled, if significant reductions in GHG emissions were required, or if fuel economy regulations for gasoline vehicles were tightened.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".