Environmental Implications of Municipal Solid Waste-Derived Ethanol
Bibliographic record
Abstract
We model a municipal solid waste (MSW)-to-ethanol facility that employs dilute acid hydrolysis and gravity pressure vessel technology and estimate life cycle energy use and air emissions. We compare our results, assuming the ethanol is utilized as E85 (blended with 15% gasoline) in a light-duty vehicle, with extant life cycle assessments of gasoline, corn-ethanol, and energy crop-cellulosic-ethanol fueled vehicles. We also compare MSW-ethanol production, as a waste management alternative, with landfilling with gas recovery options. We find that the life cycle total energy use per vehicle mile traveled for MSW-ethanol is less than that of corn-ethanol and cellulosic-ethanol; and energy use from petroleum sources for MSW-ethanol is lower than for the other fuels. MSW-ethanol use in vehicles reduces net greenhouse gas (GHG) emissions by 65% compared to gasoline, and by 58% when compared to corn-ethanol. Relative GHG performance with respect to cellulosic ethanol depends on whether MSW classification is included or not. Converting MSW to ethanol will result in net fossil energy savings of 397-1830 MJ/MT MSW compared to net fossil energy consumption of 177-577 MJ/MT MSW for landfilling. However, landfilling with LFG recovery either for flaring or for electricity production results in greater reductions in GHG emissions compared to MSW-to-ethanol conversion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".