Coupling greenhouse gas credits with biofuel production cost in determining conversion plant size
Bibliographic record
Abstract
Biofuel plant size is one of the key variables in biofuel supply chain analysis as it plays a pivotal role in controlling the efficacy of both feedstock supply and feedstock-to-biofuel conversion. The unit production cost and greenhouse gas (GHG) balance of biofuels vary with plant size. We develop an analytical framework for integrating biofuel production costs and GHG balance derived from life-cycle analysis into supply chain optimization, followed by its application to ethanol production using forest biomass in the southern United States. We derive formulas for determining the optimal biofuel plant size and the corresponding feedstock supply radius based on the minimization of biofuel production costs less GHG benefits. Our results indicate that though biofuel plant size and feedstock supply radius should be augmented by considering GHG benefits, the GHG price will have a more significant impact on net biofuel production costs than on conversion plant size or feedstock supply radius. With a rise in the GHG price the net biofuel production cost tends to increase while the directions of change in plant size and feedstock supply radius are uncertain, depending upon the costs and GHG emissions of biomass transport and feedstock-to-fuel conversion. Combining GHG offset values with biofuel production costs enables us to more holistically examine the biofuel supply chain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".