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Dynamic material flow analysis enables the forecasting of secondary raw material potential for waste volumes in future periods, by assessing past, present, and future stocks and flows of materials in the anthroposphere. Analyses of waste streams of buildings stocks are uncertain with respect to data and model structure. Wood construction in Viennese buildings serve as a case study to compare different modeling approaches for determining end-of-life (EoL) wood and corresponding contaminant flows (lead, chlorine, and polycyclic aromatic hydrocarbons). A delayed input and a leaching stock modeling approach are used to determine wood stocks and flows from 1950 until 2100. Cross-checking with independent estimates and sensitivity analyses are used to evaluate the results' plausibility. In the situation of the given data in the present case study, the delay approach is a better choice for historical observations of EoL wood and for analyses at a substance level. It has some major drawbacks for future predictions at the goods level, though, as the durability of a large number of historical buildings with considerably higher wood content is not reflected in the model. The wood content parameter differs strongly for the building periods and has therefore the highest influence on the results. Based on this knowledge, general recommendations can be derived for analyses on waste flows of buildings at a goods and substance level. Keywords:building stock modeling contaminants dynamic material flow analysis (MFA) end-of-life wood industrial ecology uncertainty analysis Supporting information is linked to this article on the JIE website Conflict of interest statement: The authors have no conflict to declare.

Summary Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.