## 6 Conclusions and Future Research  
Quality of life is a critical factor in assessing smart cities, reflecting how technological advancements and urban innovations improve residents' daily lives. While smart city initiatives aim to enhance urban living through data-driven solutions, their success ultimately depends on the well-being and satisfaction of residents. Evaluating quality of life is crucial to ensure that these initiatives create sustainable and livable environments. City rankings serve as valuable resources, providing insights into smart development, identifying areas for improvement, and guiding urban policy. This study evaluates the quality of life in European cities using the BTOPSIS method, which incorporates a Belief Structure to address uncertainties in survey data. A significant percentage of 'Don't know/No Answer/Refused' responses in several cities observed in the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{1}{c}{\multirow{2}{*}{**1**}} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**99**} & \multirow{2}{*}{**1 or 2**} & \multirow{2}{*}{**3 or 4**} & \multirow{2}{*}{**99**} & \multirow{2}{*}{**(1 or 2) a**} & \multirow{2}{*}{**(3 or 4) a**} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & \\ \hline Zürich & 0.61\% & 2.31\% & 19.80\% & 77.28\% & 0.00\% & 2.92\% & 97.08\% & 0.00\% & 2.92\% & 97.08\% \\ \hline Copenhagen & 1.06\% & 3.19\% & 22.73\% & 72.38\% & 0.64\% & 4.25\% & 95.11\% & 0.64\% & 4.28\% & 95.72\% \\ \hline Groningen & 0.90\% & 3.57\% & 21.39\% & 73.93\% & 0.21\% & 4.47\% & 95.32\% & 0.21\% & 4.48\% & 95.52\% \\ \hline Gd\#sk & 0.97\% & 3.53\% & 34.57\% & 60.22\% & 0.72\% & 4.49\% & 94.79\% & 0.72\% & 4.53\% & 95.47\% \\ \hline Leipzig & 2.09\% & 2.95\% & 31.75\% & 63.21\% & 0.00\% & 5.04\% & 94.96\% & 0.00\% & 5.04\% & 94.96\% \\ \hline Stockholm & 1.83\% & 3.45\% & 19.81\% & 74.92\% & 0.00\% & 5.27\% & 94.73\% & 0.00\% & 5.27\% & 94.73\% \\ \hline \hline \end{tabular}  
* Source: [47]. Notes: 1—Very unsatisfied, 2—Rather unsatisfied, 3—Rather satisfied, 4—Very satisfied, 99—Don’t know/No Answer/Refuses; a Percentages are based on all respondents (excluding ’Don’t know/not answered); numbers are rounded to the unit.  
\end{table} 
Table 10: Comparison of QSL evaluations for the top five ranked cities. survey [47] requires further attention from policy-makers. These responses can highlight gaps in public awareness, dissatisfaction, or lack of information. It is important to understand why many respondents cannot or will not express opinions on key urban aspects like public transport, sports facilities, and educational institutions. Ignoring these uncertainties in multi-criteria analysis could lead to incomplete conclusions. By using BTOPSIS, which integrates uncertain answers, policy-makers can gain a more nuanced understanding of citizen sentiment. This approach ensures that all responses are considered, helping to identify areas where cities can improve communication, public engagement, or services. It also allows for an analysis of the stability of the results when there are no convincing arguments for using any particular utility function to transform responses to survey items. It is performed by including simulation-based sensitivity analysis. Furthermore, simulation results can then be aggregated at various levels depending on the decision-maker's expectations regarding considering uncertainty in these results. Using stochastic dominance analysis allows for capturing nuances in ranking differences arising from various outcomes obtained in successive replications, and constructing a ranking based on identified dominance relationships that take into account incomparability. At the same time, when the decision-making context requires definitive conclusions, a frequency-based interpretation using the classical mean rank can always be proposed.