Impacts of co‐location, co‐production, and process energy source on life cycle energy use and greenhouse gas emissions of lignocellulosic ethanol
Bibliographic record
Abstract
Abstract The performance of lignocellulosic ethanol in reducing greenhouse gas (GHG) emissions and fossil energy use when substituting for gasoline depends on production technologies and system decisions, many of which have not been considered in life cycle studies. We investigate ethanol production from short rotation forestry feedstock via an uncatalyzed steam explosion pre‐treatment and enzymatic hydrolysis process developed by Mascoma Canada, Inc., and examine a set of production system decisions (co‐location, co‐production, and process energy options) in terms of their influence on life cycle emissions and energy consumption. All production options are found to reduce emissions and petroleum use relative to gasoline on a well‐to‐wheel (WTW) basis; GHG reductions vary by production scenario. Land‐use‐change effects are not included due to a lack of applicable data on short rotation forestry feedstock. Ethanol production with wood pellet co‐product, displacing coal in electricity generation, performs best amongst co‐products in terms of GHG mitigation (−109% relative to gasoline, WTW basis). Maximizing pellet output, although requiring import of predominately fossil‐based process energy, improves overall GHG‐mitigation performance (−130% relative to gasoline, WTW). Similarly, lower ethanol yields result in greater GHG reductions because of increased co‐product output. Co‐locating ethanol production with facilities exporting excess steam and biomass‐based electricity (e.g. pulp mills) achieves the greatest GHG mitigation (−174% relative to gasoline, WTW) by maximizing pellet output and utilizing low‐GHG process energy. By exploiting co‐location opportunities and strategically selecting co‐products, lignocellulosic ethanol can provide large emission reductions, particularly if based upon sustainably grown, high yield, low input feedstocks. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".