An integrated approach for risk‐based life cycle assessment and multi‐criteria decision‐making
Bibliographic record
Abstract
Purpose This paper proposes an integrated methodology for process design to guide decision making under uncertainty by combining life cycle assessment (LCA) with multi‐criteria decision‐making tools. Design/methodology/approach Cleaner and greener technologies for process and product selection and design have gained popularity in recent years. The LCA is a systematic approach that enables selection of cleaner and greener products and processes. Recently, significant progress has been made for the use of LCA for product/process evaluation and selection. However, its use in process design and environmental decision making has not been fully exploited. The proposed methodology GreenPro‐I is a systematic approach to estimate environmental risks/impacts associated with life cycle of products, processes and services. It evaluates environmental burdens by quantifying energy and materials used and waste released into the environment. It identifies and evaluates opportunities, which affect environmental improvements. The assessment includes the extraction/excavation and processing of raw materials, manufacturing, transportation and distribution, use, recycle, and final disposal. Findings GreenPro‐I overcomes many of the problems faced in the conventional approaches and establishes a link between the environmental risks/impacts, cost, and technical feasibility of processes. Originality/value GreenPro‐I provides a comprehensive decision‐making tool for designers, regulatory agencies, business organizations and other stakeholders.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".