## 8 Conclusions  
This study examined the use of KPIs in urban planning and policymaking, focusing on a real-life case study involving European cities and countries. The primary objective was to illustrate how KPIs can support policymakers in tracking changes related to urban planning, governance, and sustainability. Key observations from exploring open-source datasets include the following: (i) KPIs are primarily tracked at the country level rather than at the city level; (ii) global trends and growing awareness of sustainability issues have increased interest in tracking KPIs, although many remain underutilized; (iii) governance, social, and cultural dimensions are typically reported at the national level; and (iv) the historical data available for city-specific KPIs are limited.  
This study also employed machine learning techniques to analyze the recorded KPIs, utilizing clustering and visualization methods to provide a clearer understanding of patterns and trends. Through clustering, three key KPIs emerged as the most influential: GDP per capita, the corruption perception index, and contributions to climate-related expenditures. These indicators revealed significant disparities in economic development, access to services, urbanization, and governance effectiveness across European countries. This study's results align with the Agenda 2030 sustainable development goals, showing that data-driven approaches can inform more effective decision-making.  
The time series analysis of KPIs illustrated how the evolution of KPI trends reflects past decisions made by policymakers. This shows that adapting policies can directly affect the future KPI trends, underscoring the importance of continuous monitoring for effective governance. The comparative analysis between city-specific and country-level KPIs highlighted the need for more granular, city-based data, as cities function as distinct governmental units in many cases. Without city-level KPIs, policymakers lack the precise feedback needed to make effective urban planning decisions.  
One of this study's main challenges was the lack of standardized, high-quality datasets for city-level KPIs. Future work will focus on expanding the analysis to include city-specific KPIs, as well as integrating new data sources to improve the completeness and quality of the available information. Additionally, future research will explore the impact of specific urban planning actions on KPIs, using comparative analysis to evaluate the effectiveness of these actions over time.  
Figure 9: Unemployment rate by countries from different clusters (**a**) and countries within the same cluster (**b**).  
**Author Contributions:** Conceptualization, T.F. and L.K.; methodology, A.A.J.; investigation, V.T. and M.A.; data curation, V.T. and M.A.; writing--original draft preparation, V.T. and M.A.; writing--review and editing, A.A.J. and L.K.; supervision, M.A.; project administration, T.F. and L.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the European Commission project UP2030 (HORIZON-MISS-2021-CIT-02-01-101096405).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.