ABSTRACT
Life cycle assessments (LCA) inherently encompass a multitude of factors that can be confounded by significant degrees of variation and uncertainty. Powerful analytical methods are necessary to process such complexities and to enable well-informed sustainable decisions. In this chapter, Simulation Decomposition (SimDec) is used to comprehensively analyze the driving factors behind the greenhouse gas (GHG) emissions resulting from the entire life cycle of protective face masks. An LCA of the conventional single-use medical mask (widely worn during the COVID-19 pandemic) is compared to that of a reusable mask, currently prototyping. SimDec is employed concurrently to identify the main factors affecting the LCA results, while many uncertainty sources and possible variations are present, including how often the masks are changed and the reusable ones are disinfected. The LCA shows that reusable masks generate fewer GHG emissions than single-use ones due to less raw material consumption and less waste disposal. Conversely, if reusable masks are not used appropriately over a longer period of time, the result can be higher GHG emissions than for their single-use counterparts. The innovative incorporation of SimDec into the analysis results in deeper insights into the driving forces underlying the uncertainty, the discovery of nested conditional effects, an opportunity to analyze the impact of intermediary outputs, and explicit guidance for the GHG modelling process.
