Economic Indicators for Life Cycle Sustainability Assessment: Going beyond Life Cycle Costing
Bibliographic record
Abstract
Life Cycle Costing (LCC) is universally accepted as the method of choice for economic assessment in Life Cycle Sustainability Assessment (LCSA) but the singular focus on costs is ineffective in representing the multiple facets of economic sustainability. This review intends to identify other economic indicators to potentially complement the use of LCC in LCSA. Papers for the review were identified in the Web of Science Core Collection database for the years 2010–2021. The shortlisted indicators were analyzed using 18 criteria. The 21 indicators analyzed performed well with respect to the review criteria for indicator methodology and use but most are unsuitable for direct integration into the LCC/LCSA framework due to the inability to aggregate across life cycles and a lack of correspondingly granular data. The indicators were grouped into six economic impact categories—profitability, productivity, innovation, stability, customers, and autonomy—each of which represents a significant aspect of economic sustainability. On this basis, a conceptual framework is proposed that could maintain the utility of LCC while integrating additional indicators to enable more holistic economic assessments in LCSA. Considering additional economic indicators in LCSA ensures that the positive aspects of LCC are preserved while also improving economic assessment in LCSA.
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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 | 0.030 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".