Ethanol or Bioelectricity? Life Cycle Assessment of Lignocellulosic Bioenergy Use in Light-Duty Vehicles
Bibliographic record
Abstract
Our study evaluates life cycle energy use and GHG emissions of lignocellulosic ethanol and bioelectricity use in U.S. light-duty vehicles. The well-to-pump, pump-to-wheel, and vehicle cycle stages are modeled. All ethanol (E85) and bioelectricity pathways have similar life cycle fossil energy use (~ 100 MJ/100 vehicle kilometers traveled (VKT)) and net GHG emissions (~5 kg CO2eq./100 VKT), considerably lower (65-85%) than those of reference gasoline and U.S. grid-electricity pathways. E85 use in a hybrid vehicle and bioelectricity use in a fully electric vehicle also have similar life cycle biomass and total energy use (~ 350 and ~450 MJ/100 VKT, respectively); differences in well-to-pump and pump-to-wheel efficiencies can largely offset each other. Our energy use and net GHG emissions results contrast with findings in literature, which report better performance on these metrics for bioelectricity compared to ethanol. The primary source of differences in the studies is related to our development of pathways with comparable vehicle characteristics. Ethanol or vehicle electrification can reduce petroleum use, while bioelectricity may displace nonpetroleum energy sources. Regional characteristics may create conditions under which either ethanol or bioelectricity may be the superior option; however, neither has a clear advantage in terms of GHG emissions or energy use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".