Life Cycle Assessment of Switchgrass- and Corn Stover-Derived Ethanol-Fueled Automobiles
Bibliographic record
Abstract
Utilizing domestically produced cellulose-derived ethanol for the light-duty vehicle fleet can potentially improve the environmental performance and sustainability of the transport and energy sectors of the economy. A life cycle assessment model was developed to examine environmental implications of the production and use of ethanol in automobiles in Ontario, Canada. The results were compared to those of low-sulfur reformulated gasoline (RFG) in a functionally equivalent automobile. Two time frames were evaluated, one near-term (2010), which examines converting a dedicated energy crop (switchgrass) and an agricultural residue (corn stover) to ethanol; and one midterm (2020), which assumes technological improvements in the switchgrass-derived ethanol life cycle. Near-term results show that, compared to a RFG automobile, life cycle greenhouse gas (GHG) emissions are 57% lower for an E85-fueled automobile derived from switchgrass and 65% lower for ethanol from corn stover, on a grams of CO2 equivalent per kilometer basis. Corn stover ethanol exhibits slightly lower life cycle GHG emissions, primarily due to sharing emissions with grain production. Through projected improvements in crop and ethanol yields, results for the mid-term scenario show that GHG emissions could be 25-35% lower than those in 2010 and that, even with anticipated improvements in RFG automobiles, E85 automobiles could still achieve up to 70% lower GHG emissions across the life cycle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".