A Life Cycle Sustainability Assessment-Based Decision-Analysis Framework
Bibliographic record
Abstract
One of the main challenges in using life cycle sustainability assessment (LCSA) is the difficulty of integrating the interrelationships between the three dimensions (environmental, economic and social dimensions) of LCSA results in decision-making toward proposing sustainability improvements for existing product systems. This paper is addressing this challenge by presenting an LCSA-based decision-analysis framework, which is a systematic and structured framework that appraises the pool of potential actions determined based on LCSA results and evaluates their trade-offs to propose potential sustainability solutions. The framework is composed of two parts: (a) LCSA application; (b) decision-analysis approach. The decision analysis part of the framework is built based on some features from previous decision-making approaches and considering the characteristics of LCSA results. The decision-analysis part of the framework, which is the main focus of this study, is divided into five phases to propose and select some recommendations to improve the sustainability performance of product systems. The framework developed is illustrated using results from a previous LCSA case study. The framework can handle the complexity in understanding the interrelationships between the three dimensions of LCSA results, through a structured way of dividing the process into manageable steps. Further work is still needed to apply this framework to a real case study.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".