Cost and Life Cycle Emissions of Ethanol Produced with an Oxyfuel Boiler and Carbon Capture and Storage
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Decarbonization of transportation fuels represents one of the most vexing challenges for climate change mitigation. Biofuels derived from corn starch have offered modest life cycle greenhouse gas (GHG) emissions reductions over fossil fuels. Here we show that capture and storage of CO 2 emissions from corn ethanol fermentation achieves ∼58% reduction in the GHG intensity (CI) of ethanol at a levelized cost of 52 $/tCO 2 e abated. The integration of an oxyfuel boiler enables further CO 2 capture at modest cost. This system yields a 75% reduction in CI to 15 gCO 2 e/MJ at a minimum ethanol selling price (MESP) of $2.24/gallon ($0.59/L), a $0.31/gallon ($0.08/L) increase relative to the baseline no intervention case. The levelized cost of carbon abatement is 84 $/tCO 2 e. Sensitivity analysis reveals that carbon-neutral or even carbon-negative ethanol can be achieved when oxyfuel carbon capture is stacked with low-CI alternatives to grid power and fossil natural gas. Conservatively, fermentation and oxyfuel CCS can reduce the CI of conventional ethanol by a net 44–50 gCO 2 /MJ. Full implementation of interventions explored in the sensitivity analysis would reduce CI by net 79–85 gCO 2 /MJ. Integrated oxyfuel and fermentation CCS is shown to be cost-effective under existing U.S. policy, offering near-term abatement opportunities.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".