**4. Discussion**

Based on the results obtained, it can be concluded that the differences between the criteria weights obtained using the classic Saaty's scale and the weights calculated using fuzzy scales, (especially its first variant) are not significant. First of all, despite the application of different scales, the general order of criteria did not change. The global weights of criteria groups influenced the weights of criteria and sub-criteria located at the lower levels of the structure. However, it should be emphasized here that the developed hierarchical structure, together with weights, is only an example aimed at assessing the impact of using different scales (existing in the literature on the subject) on the final set of decision-making factor weights at individual levels.

The FAHP method, just like its classic version, has its advantages and disadvantages. Among the advantages, we should undoubtedly indicate the simplified—from the point of view of the decision-maker—procedure of pairwise comparison, especially in the situation of uncertainty or incomplete information about a given decision-making challenge. On the other hand, it could be considered a certain limitation of FAHP that it operates under a more complicated algorithm when compared to the classical AHP method. The calculation algorithm presented in this paper, based on Buckley's assumptions, is not the only procedure of operation, see [65]. In addition, applying FAHP to more complex decision-making challenges and performing sensitivity analyses without the appropriate computer support significantly increases calculation times.

It should be emphasized that a comparative analysis of the AHP and FAHP methods has already been subject to research, for example during the analysis of the selected decision-making challenges in the food industry [66], resource analysis based on a selected example from the energy industry, [67], supplier selection [68], or a decision-making model that is closest to the subject matter of this paper, i.e., a model in the field of spatial planning [69]. The authors of the aforementioned studies evaluated the application potential of Saaty's method extensions in the context of selected decision-making challenges. In modern times, especially in complex economic conditions where many decisions are accompanied by a level of uncertainty, the MCDM/MCDA methods on fuzzy sets may serve as support tools [65]. According to the authors Kabir Hasin [67], the fuzzy AHP's approach allows for a more accurate description of the decision-making process, allowing one to grasp the vagueness of human thinking. The authors recommend using FAHP if information/evaluations are not certain. On the other hand—in the case of spatial planning and based on their research—the authors stated that: "if the planning aims to identify priority areas for development as a focal point, simpler MCDM methods such as AHP should be sufficient. In this situation, selecting more sophisticated techniques like Fuzzy AHP, which can only be seen as a black box by stakeholders, will not necessarily generate different outcomes" [69] (p. 64).

In summary, both AHP and FAHP have certain limitations. Therefore, the key aspect is correct method selection and adjusting the calculation techniques to the specificity of the decision-making challenge under consideration. In the case of such fields as architecture, urban planning or energy-efficient construction, it is often necessary to take into account the preferences of residents/users of facilities/space. For that reason, the AHP method—together with its extensions—can significantly support the design processes. The algorithm of pairwise comparison, using the traditional Saaty scale, as well as the triangular number scales, can be used when in questionnaire for collecting and assessing

the preferences of decision-makers see e.g., [70]. Examples of decision-making problems that require research the preferences of residents/users include, e.g., delineation of development areas in the city, identifying the preferred urban revitalization scenarios or selection of the best modernization method. Importantly, there are already practical examples of using the MCDM/MCDA methods, for example for the research of residential preferences in Poznan [71].
