Results of life cycle assessment (LCA) studies are affected by uncertainties from various sources. These uncertainties decrease the reliability of LCA results. While uncertainty cannot be avoided, the impact of uncertainty can be quantified using uncertainty analysis. Still, despite recommendations to consider uncertainties, most LCA studies neglect uncertainty analysis.This thesis aims to facilitate uncertainty analysis in LCA through practical applications and enhanced methods. In a first step, basic uncertainty analysis includes parameter variations and scenario analysis. Basic uncertainty analysis is feasible even for low data availability and can easily be integrated in every LCA study. More sophisticated quantitative uncertainty analysis can employ analytical uncertainty analysis based on Taylor series expansion. However, the current first-order Taylor series expansion gives only a linear approximation of the uncertainty and limits the validity of the analytical approach to small uncertainties. To overcome this limitation, analytical uncertainty analysis is extended towards a second-order approximation. The developed second-order approximation provides higher accuracy at slightly increased computational cost as long as the LCA problem is small-scale.To assess uncertainty in large-scale LCA, uncertainty information of the background system can be gathered from LCA databases. In the widely used ecoinvent LCA database, the deterministic default values are represented by the median values of the log-normally distributed uncertain data. However, LCA results from studies with and without uncertainty analysis differ if the input uncertainties are represented by the median values. Using mean values for any uncertain data instead of median values is proposed to ensure consistency and comparability across LCA results with and without uncertainty analysis.The presented methods for uncertainty analysis in LCA are applied to a case study concerned with a novel hydrogen production process by methane pyrolysis. The case study shows that the developed methods for uncertainty analysis can be applied easily and increase trust into the results of the case study. Therefore, the presented methods meet the goal of this thesis and will hopefully enhance reliability of LCA results.
