## 4 Discussion  
The analysis presented in this study focuses on economic aspects related to the construction of renovation scenarios, namely, LCC analysis and sensitivity by the OAT and MC variation methods. However, the authors wish to highlight that the composition of the renovation measures should at all times also account for the indoor climate criteria as well as the comfort and safety of the occupants. The approach used in this paper provides findings that shed light on the importance of different parameters in an LCC calculation, their interaction, and rank of importance. The findings are, however, limited to the scope of the analysis. In this paper, the analysis considers the perspective of a multi-family building owner, disregarding income. For such cases, the income in the LCC calculations can originate from rent, rent increases, selling electricity to the grid, savings of purchased energy from the grid, etc. If one or more of these profits are integrated into an LCC analysis, the results for the influence and relative share of importance for each parameter could change significantly. The applied combination of local (OAT) and global (MC) methods seemed appropriate, as the OAT approaches are good estimators of sensitivity for linear or nearly linear mathematical models. The linearity of the investigated baseline model (and its variations) was confirmed by the very high R2 values of the multivariate linear regression. It should be noted that the linearity of this (these) model(s) is (are) expected as boundary conditions, and the model inputs are investigated separately. Models where the variation of boundary conditions and inputs are variated simultaneously may prove to be non-linear. The MC and quasi-random sampling approach is selected due to the ability of covering the solution space comprehensively. This, however, is true when the sample number is sufficient to represent the combinations of variated parameters. A \"sufficient\" number of samples is typically determined based on the number of variated parameters and investigated variations. The MC simulations at hand are limited to five variation parameters and 5000 samples for each investigated energy system. While this is a relatively low number of samples for a MC simulation, even the coverage of the solution space is assured by the use of Sobol quasi-random sample selection [16]. Moreover, given that the calculation models proved to be linear, a relatively small number of samples may even be sufficient to provide coverage of the solution space. To determine whether the 5000 samples are an adequate number of simulations for the investigated models, the DH baselines were simulated with a different number of samples. The spread in the results is compared in Figure 10 by a histogram depicting the standardized frequency to the sample number. The figure shows that despite the different number of samples, the spread of the results is nearly equal in all cases. Considering the linearity of the model, this result was anticipated. A slight difference is evident for the cases with 100 and 500 input samples, where the distribution differs slightly, but the results' range is still similar to all the other cases. A growing body of the literature has explored the sensitivity of financial assumptions in LCC calculations, specifically DR and energy price developments [5; 20; 21; 22; 24]. Despite the variety in the applied methods for assessing the sensitivity and scope (the entities considered in the calculation) of the referenced papers, there is a consensus that financial assumptions significantly impact the LCC output. This corresponds well with the findings of this paper, considering the comparability between the OAT-based sensitivity indexes for the boundary conditions and those of the most influencing energy efficiency measures. Even though the interactions of the financial and building-related inputs and their effects on the output are not investigated in this paper, there is a need for further exploration on the matter.