Gaseous fuel supply chain configuration selection: A life cycle thinking-based decision support framework
Bibliographic record
Abstract
This paper analyzes the impact of stakeholder priorities and causal relationships between decision criteria when determining the most suitable renewable natural gas (RNG) and hydrogen gaseous fuels production path. The criteria indicators were defined to reflect the life cycle of environmental desirability and economic feasibility . The study introduces a framework-based approach using fuzzy multi-objective optimization by ratio analysis (FMOORA) with an integrated causality and importance weighting system. The core novelty of this study is the novel criterion weighting system that simultaneously considers the impacts of the internal and external causal relationships and the criteria importance arising from stakeholder priorities to derive a more realistic weighting scheme. Weighting schemes using fuzzy cognitive maps and the best-worst method were provided to consider the decision-maker’s priorities in highlighting each causality or importance weighting concept. Accordingly, the highest importance values of 0.152 and 0.267 were obtained for levelized cost of energy with fuzzy cognitive maps and the best-worst method, respectively. The profitability index scored the least importance values in both methods (fuzzy cognitive map – 0.071 and best-worst method – 0.038). Accordingly, RNG from livestock with pressure swing adsorption was ranked first under all weighting schemes. Hydrogen from steam methane reforming achieved the best spot under hydrogen production routes, with ranks varying from 8 to 12 when both RNG and hydrogen production scenarios are considered. The findings indicate that the proposed framework is useful for conducting preliminary feasibility assessments of gaseous fuel investment strategies. Additionally, the integrated weighting system can be considered in conjunction with other multi-criteria decision-making techniques to make more reliable decisions in different problems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".