Exploring the Current Challenges and Opportunities of Life Cycle Sustainability Assessment
Bibliographic record
Abstract
Sustainability decision making is a complex task for policy makers, considering the possible unseen consequences it may entail. With a broader scope covering environmental, economic, and social aspects, Life Cycle Sustainability Assessment (LCSA) is a promising holistic method to deal with that complexity. However, to date, this method is limited to the hotspot analysis of a product, service, or system, and hence only assesses direct impacts and overlooks the indirect ones (or consequences). This critical literature review aims to explore the challenges and the research gaps related to the integration of three methods in LCSA representing three pillars of sustainability: (Environmental) Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA). The challenges and the research gaps that appear when pairing two of these tools with each other are identified and discussed, i.e., the temporal issues, different perspectives, the indirect consequences, etc. Although this study does not aim to remove the shadows in LCSA methods, critical research gaps are identified in order to be addressed in future works. More case studies are also recommended for a deeper understanding of methodological trade-offs that might happen, especially when dealing with the consequential perspective.
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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 | 0.050 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".