Relative sensitivity value (RSV): A metric for measuring input parameter influence in life cycle assessment modeling
Bibliographic record
Abstract
Life cycle assessment (LCA) is a commonly used tool to quantify life cycle environmental footprints of products. Uncertainty in LCA modeling, particularly from uncertainty in production practices (represented through input parameter arguments), can lead to incorrect conclusions and hamper decision-making. Characterization of uncertainty through stochastic means and sensitivity analysis is utilized in a small fraction of LCA case studies, and the majority of studies default to scenario analysis due to its lower barrier to implementation and its results are easier to interpret. In this article, we introduce a sensitivity metric, relative sensitivity value (RSV), which allows LCA practitioners to gauge the relative influence of production practices on life cycle impacts in multiple phases and impact categories. Relative sensitivity value bridges the gap between scenario analysis and global sensitivity analysis, and it allows an LCA practitioner to provide an easy-to-interpret metric for quantifying the degree to which incremental changes in production practices influences the life cycle environmental footprint. We present the methodology used to calculate RSV and provide programming code, which can be readily used by an LCA practitioner to calculate RSV for their LCA model. We demonstrate the usage of RSV through a livestock husbandry LCA case study, in which we show how RSV results may be presented and interpreted, and how conclusions regarding production practices may be drawn. Integr Environ Assess Manag 2023;19:547-555. © 2022 SETAC.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".