Competitive assessment of biofuel strategies for a case study in eastern Canada: techno‐economic and multi‐criteria decision‐making analysis
Bibliographic record
Abstract
Abstract Decarbonizing the energy sector through biomass use entails risks and uncertainties linked to implementing early stage technologies. To address this issue, this work presents a panel‐based multicriteria decision‐making (MCDM) framework for a corn‐ethanol plant seeking to reduce its carbon footprint while investing in new bioenergy products. Six technology pathways were considered including gasification technologies, pyrolysis, hydrothermal liquefaction, and alcohol to jet, with products such as renewable natural gas (RNG), biochar, green diesel, green marine, and biojet. The alternatives were assessed based on a set of six criteria classified into three categories: economic performance, technology and commercial risks, and competitive advantages. The MCDM framework was based on the multiattribute utility theory (MAUT) for which an in‐person panel of decision‐making stakeholders set the criteria weights. Internal rate of return (IRR) was considered the most important criterion, followed by technology readiness level (TRL) and total installed capital cost (TICC). Surprisingly, the commercial readiness level was allocated a low weight. Finally, the potential for a fossil‐free site (PFFS) criterion was perceived by the panel members more as a desirable attribute rather than a requirement. Among the alternatives considered, the flexible production of synthetic natural gas (FlexSNG) gasification‐methanation process was found to be preferred. The panel‐based MCDM framework proved to be a practical and effective tool for assessing early‐stage biofuel strategies, encouraging alignment with technological innovation, sustainable development, and the company's values and future ambitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".