(i) model structure, (ii) system non-deterministic behaviors, (iii) numerical error, and (iv) model inputs and parameters. Among these sources, the uncertainty of model structure can be considered by empirically comparing or aggregating the predictions of multiple viable models; the uncertainty due to a system's non-deterministic behaviors can be characterized by incorporating stochastic elements into a deterministic model; and the numerical error in calculating model results is usually much smaller than other sources of uncertainty. Most essential to QSD is the uncertainty from model inputs and parameters (i.e., QSD inputs), which can be aleatory, epistemic, or both. Aleatory uncertainty (also called variability or irreducible uncertainty) arises from randomness or variations due to "hidden" factors that are not included in the model. Epistemic uncertainty (i.e., true uncertainty, reducible uncertainty), on the other hand, derives from a lack of knowledge of the "true" values. Uncertainty associated with decision variables are inherently aleatory as their values are subject to the choice of the designer or the operator, and probability distributions can be used to reflect the designer's or operator's preference within the ranges of feasible decisions. In contrast, uncertainties associated with technological parameters and contextual parameters can be caused by randomness (aleatory), lack of knowledge (epistemic), or both. For example, a location's average temperature on a certain date in the future (a contextual parameter) is random to some extent (i.e., aleatory), and at the same time, practitioners may have imperfect knowledge of its "true" range of variability based on historic data (i.e., epistemic). For LCA, uncertainty is also distinguished based on whether their sources are in the foreground (e.g., the quantity of stainless steel that comprises an impeller) or background (e.g., the LCI of the stainless steel) systems.