Optimization in Life Cycle Sustainability Assessment (LCSA): A Systematic Literature Review
Bibliographic record
Abstract
This secondary scientific research follows the modality of review research through the systematic literature review (SLR) technique and aims to answer the following question: What methods in the literature use optimization techniques to develop Life Cycle Sustainability Assessment (LCSA)? The objective is to identify and map information, relate their applications, and provide a critical analysis. The research was conducted in a global context, on April 2, 2022, with no time horizon limit, in English, using Harzing's Publish or Perish tool and search sources: Scopus®, Web of Science™, and Google Scholar. After the automatic exclusion of duplicate titles, the selection of articles was carried out in five further steps: first filtering, second filtering, third filtering, fourth filtering, and finally the snowball system. Regarding the years of selected publications, a slow but continuous upward trend (growth) can be seen. A diversity of journals can be observed and that most methods integrating optimization and LCSA focus on each product/system or application. Each optimization model proposal has its specific characteristics when combined with LCSA. Overall, there is a greater frequency of articles that propose to evaluate by comparing alternative products/supply chains/production processes, followed by those that use optimization for product development in selecting the best design from a set of alternatives. This research confirms that Operational Research (OR) operates in different segments and can use different techniques to obtain optimal solutions and decision support. The optimization techniques found in this or other literature reviews should be evaluated against an established analytical model by applying them to the same case study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".