Methodological framework to find links between life cycle sustainability assessment categories and the UN Sustainable Development Goals based on literature
Bibliographic record
Abstract
Abstract Life cycle sustainability assessment (LCSA) can be used as a tool to understand how products and operating systems can meet the United Nations’ Sustainable Development Goals (SDGs). However, existing linkages between SDGs and LCSA are limited and an analysis of coverage in literature is needed. In this paper, we propose a generic methodological framework establishing connections between LCSA categories at micro‐level and SDGs at macro‐level based on derivation from the literature. The qualitative heuristic research method developed builds on keyword literature search, bibliometric analysis, mapping, and narrative literature review for connection rationales. By using qualitative assessment levels, an assessment of linkages between LCSA categories and SDGs reveal that “technology development,” “public commitment to sustainability issues,” “access to material resources,” and “education provided in the local community” have the highest number of reported relationships with SDGs. Twenty‐two LCSA categories were found with no direct/indirect connection with any SDG; reasons include absence of life cycle thinking perspective in SDGs and lack of sustainability‐based discussion for workers, consumers, and value chain actors' stakeholder groups. Despite these gaps, the results provide new insights for industries looking to measure the contribution of their product systems along their life cycle in the context of SDGs supporting them to some extent, to select LCSA categories with either highest number of identified relationships to SDGs or that contribute to prioritized list of SDGs. The approach provides a starting point to improve transparency and consistency of reporting of sustainability performance of product systems by connecting LCSA to the global agenda for sustainable development.
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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 | 0.072 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.076 | 0.048 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".