Multicriteria assessment of technology pathways to produce renewable and sustainable biofuels: case study in eastern Canada
Bibliographic record
Abstract
Abstract One of the key pathways that can contribute to net‐zero goals is to enhance the use of renewable and sustainable biofuels. However, uncertainties remain owing to critical challenges to selecting the right sustainable biofuels technology most suitable for the longer term. These uncertainties can be partly addressed using a multidisciplinary perspective for scenarios and multicriteria decision‐making analysis. Multicriteria decision support tools (DSTs) are a powerful means to conduct the comparison of technology pathways at different technology readiness levels (TRLs). For this case study, a multidimensional DST was used to compare two technology pathways: (1) gasification combined with Fischer–Tropsch; and (2) hydrothermal liquefaction (HTL) to produce biofuels. The metrics available in the DST were examined, and five metrics related to evaluating profitability, robustness and capital efficiency as well as socio‐economic performance were selected. HTL was found to have a higher yield for renewable gasoline and diesel, largely due to higher economic performance and reduced environmental footprint as compared with the integrated gasification Fischer–Tropsch process. However, the TRL of 8–9 of biomass gasification was higher than that of HTL, which was still at 5–6, reflecting that the gasification process is essentially proven at a pre‐commercial scale. Based on sensitivity and scenario analyses, the importance of government support for capital cost and of biofuel price production incentives was identified as critical. From this comparison, HTL emerges as an attractive process for the future because of its outperformance from technical and environmental perspectives, but further demonstration efforts are still needed at the commercial scale. © 2023 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".