Global Sensitivity Analysis of Background Life Cycle Inventories

In recent years many Life Cycle Assessment (LCA)studies have been conducted to quantify the environmentalperformance of products and services. Some of these studiespropagated numerical uncertainties in underlying data to LCAresults, and several applied Global Sensitivity Analysis (GSA) tosome parts of the LCA model to determine its main uncertaintydrivers. However, only a few studies have tackled the GSA ofcomplete LCA models due to the high computational cost of suchanalysis and the lack of appropriate methods for very high-dimensional models. This study proposes a new GSA protocolsuitable for large LCA problems that, unlike existing approaches,does not make assumptions on model linearity and complexity andincludes extensive validation of GSA results. We illustrate thebenefits of our protocol by comparing it with an existing method in terms offiltering of noninfluential and ranking of influentialuncertainty drivers and include an application example of Swiss household food consumption. We note that our protocol obtainsmore accurate GSA results, which leads to better understanding of LCA models, and less data collection efforts to achieve morerobust estimation of environmental impacts. Implementations supporting this work are available as free and open source Pythonpackages.