The sensitivity analysis of a study defines the extent to which the variation of an input parameter or a choice leads to influence of the study result. In brief, an assessment model is sensitive toward a parameter if a minor change in this parameter will result in a large change in the model result, while a model is insensitive concerning a parameter if any change in that parameter will have no (or negligible) effect on the model result. Sensitivity may be analyzed for both continuous and discrete input parameters, and it can also be evaluated for options heading to discrete sets of input values (Hauschild, M.Z., Rosenbaum, R.K., and Olsen, 2018; ILCD, 2010). According to Wei et al. (2015), sensitivity analysis is a substantial tool for evaluates the robustness of results and their sensitivity to uncertainty factors in LCA. It highlights the most important set of model parameters to determine whether data quality requires to be improved and to enhance interpretation of results.

On the other hand, a sensitivity check aims at identifying the crucial processes and most important elementary flows as those elements that contribute highly to the global impacts from the product system. A sensitivity check allows in an explanatory manner to determine and document the influence of the altered parameter on the final result. The results of the sensitivity analyses are: i) the adjusted parameter does not modify or insignificantly affects the results; ii) further detailed sensitivity analyses are needed; iii) the results are barely valid within margins, which requires to be considered within the conclusions (Klopffer, W. and Grahl, B. 2014).

Additional information related to how to perform a proper sensitivity analysis can be found in more 4.2.3.