Systematic assessment of triticale‐based biorefinery strategies: environmental evaluation using life cycle assessment
Bibliographic record
Abstract
Abstract Triticale (X Triticosecale Wittmack) is a non‐food energy crop with potential as a biorefinery feedstock. In addition to technical, economical, and commercial risks, it is of critical importance that environmental issues be considered in the decision‐making process regarding the development of the triticale‐based biorefinery. In this study, life cycle assessment (LCA) has been used for this purpose. To facilitate overall decision‐making including economic and other metrics, the number of environmental indicators should be minimized, and yet at the same time, these indicators should be representative, comprehensive, and easy to interpret. To identify such a set of environmental indicators from LCA results, a multi‐criteria decision‐making (MCDM) panel study was carried out and an external set of normalization factors used to assist in this decision‐making context. The influence of eight technology choices on the environmental impacts resulting from the production of ethanol, polylactic acid (PLA), and thermoplastic starch (TPS) blend was assessed. Moreover, the environmental benefits of improved triticale yield and its ability to grow on marginal land were assessed. Although the complete spectrum of environmental impact categories was evaluated, the MCDM panel selected four criteria to be brought forward to an overall decision‐making panel. The greenhouse gas (GHG) emissions metric was judged as the most important, followed by non‐renewable resource depletion, cropland occupation, and human health. Moreover, it was shown that certain technology choices such as ultra‐filtration and cogeneration significantly influence the environmental impacts of the triticale biorefinery. © 2018 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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".