Relative sensitivity value (RSV): A metric for measuring input parameter influence in life cycle assessment modeling

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

Life cycle assessment for the land application of food processing wash‐water and solid residuals

“…Carcinogenic, as this was the most significant environmental impact emissions, was selected for the sensitivity analysis. The results for the one‐at‐a‐time sensitivity analysis are graphically presented as suggested by Bhatt and Abbassi (2022) as shown in Figure 6. It is important to note that the slope of the line indicates the response of the impact category to the magnitude of the input variable (Figure 7).…”