The uncertainty of seven energy-related inputs with respect to the LCC calculation is investigated in [23] for two scenarios, using the one-at-a-time (OAT) method. The focus of [24] is limited to financial assumptions, yet it applies an optimization-based scheme to investigate many scenarios for a single-family house in Norway, including energy-saving measures, renewable energy sources, and systems. Another example of optimization-based schemes employing solution space exploration is presented in [25]. Such multi-objective optimizations are robust, as the considered parameters' sensitivity is integrated into the method itself. However, a drawback of such methods is that these can be very time and expertise-demanding and implicated by practicalities. There are also studies focusing on how a specific aspect of a renovation affects cost-optimal solutions. For instance, [8] investigates occupant behavior, window-opening activities, and their influence on cost-optimality. Nonetheless, the interest rate, energy prices, and their expected developments seem to be in focus when it comes to sensitivity in the LCC. Moreover, sensitivity analysis seems to be a secondary objective in research related to cost-optimal solutions, where a common main objective is the selection of an optimal package. For instance, the authors of [6] propose several combinations of EEM to obtain a cost-efficient retrofitting solution for school buildings, while performing a sensitivity analysis that disregards investment costs and focuses only on the interest rates and the increase in electricity prices. Another interesting study [18] presents a sensitivity study by indicating how different input parameters influence the resulting cost-optimal renovation of a multifamily building. The proposed sensitivity accounts for a wide spectrum of input parameters, such as the thermal properties of the building envelope, district heating prices, operation costs, the price increase in district heating, the cost of EEM, ventilation heat recovery, and the discount rate. The applied method varies only one parameter at a time, making it unable to detect the combined effect of input parameters on the output, but obtaining multiple parallel sensitivity analyses [18]. Another comprehensive study proposes a holistic process for determining a cost-effective renovation that could support building energy certification and increase renovation rates [26]. The proposed procedure is composed of three steps, where the final step incorporates the LCC and the renovation plan. The weight of each considered parameter is analyzed to identify the most impactful ones on the energy demand decrease. The method further accounts for the cost and lifetime of each EEM, but in a simplified manner, where the cost of each intervention is split into conservative, medium, and ambitious. The sensitivity analysis in this case disregards the financial parameters, energy prices, the lifespan, variable, and maintenance costs. To the authors' best knowledge at the time of writing, only study [27] performs an uncertainty characterization on 18 LCC input parameters. The study applies a probabilistic methodology employing Sobol sampling combined with the Monte Carlo (MC) approach. The convergence criteria are used to assess the sensitivity in a calculation concerning a single-family house in Italy. The case applies special economic consequences (penalties) for a reduced usable floor area due to an internal insulation with vacuum panels. As mentioned above, while most of the referenced studies seek and select a cost-optimal package, the first objective of this study is to define the input parameters of a holistic LCC calculation and quantify their individual and combined influence on the output. This is accomplished using local (OAT) and global (MC) methods for the variation of the inputs as described in [14], and further discussed in Sections 2.2 and 2.3, respectively.