Smart cities are emerging as transformative solutions to contemporary urban challenges with big data being collected. A study by Juan et al. (2023) examines how cities can leverage data analytics to explore the collected data and guide decision making create more sustainable futures. In the context of the UP2030 project, the study establishes a comprehensive framework that employs key performance indicators (KPIs) and advanced data analytics to monitor and evaluate urban sustainability initiatives.
An illustrative study is presented in the below figures, in which an analysis of KPIs across major European capitals (London, Milan, Madrid, and Paris) reveals notable patterns in urban environmental management. Such plots visualize the differences resulted from urban environmental management policies. Matching the KPIs with adapted policies facilitates the base for improving KPIs. For example, Madrid distinguishes itself as a leader in environmental sustainability, demonstrating excellence across multiple ecological indicators. Nevertheless, despite its environmental achievements, the city continues to face significant challenges in critical areas, particularly food security and housing affordability triggering the impact of adapted policies.
Radar plots for the comparison between selected cities’ KPIs (see source . https://doi.org/10.3390/en16207195)
Similar analysis is found in Soriano et al. (2023), in which the authors found striking performance similarities between Barcelona and Madrid, with Valencia excelling in several KPIs as shown in the radar plots below. However, broader European comparisons reveal mixed results: while Spanish cities exceed European averages in greenhouse gas and PM2.5 emissions, they demonstrate exemplary performance in PM10 control and public transportation accessibility, affordability, and utilization.
Radar plots (see source: https://doi.org/10.3390/logistics7040075)
The analysis is extended to countries in Ammouriova et al. (2024), who identify a critical data gap: while comprehensive data exists at national levels, city-specific KPI measurements remain insufficient for evidence-based urban planning. Through machine learning analysis using k-means clustering, their research classified European countries based on three primary factors: GDP per capita, corruption perception index, and climate-related expenditure. Time series analysis is used to reveal interesting patterns in electricity consumption and unemployment rates as is demonstrated in the below figure. Germany maintained consistently higher electricity usage compared to countries like Greece, Belgium, and Spain, reflecting differences in industrial scale and electrification. In terms of unemployment, countries like Spain and Greece showed higher historical rates that gradually decreased over time, while others like Belgium demonstrated more stable employment markets.
Time series visualization (see source: https://doi.org/10.3390/app14209501)
What's the takeaway? Data is the king in support decision making in planning greener cities. These detailed scorecards give city planners and policymakers the insights they need to make better decisions. Sure, there are hurdles - getting good data isn't always easy. But these tools offer a solid roadmap for creating cities that work better for both people and the planet. As urban areas face growing environmental challenges, these data-driven strategies aren't just nice to have - they're essential for building cities that can thrive in the future.
Sources
Ammouriova, M., Tsertsvadze, V., Juan, A. A., Fernandez, T., & Kapetas, L. (2024). On the Use of Machine Learning and Key Performance Indicators for Urban Planning and Design. Applied Sciences, 14(20), 9501. https://doi.org/10.3390/app14209501
Juan, A. A., Ammouriova, M., Tsertsvadze, V., Osorio, C., Fuster, N., & Ahsini, Y. (2023). Promoting Energy Efficiency and Emissions Reduction in Urban Areas with Key Performance Indicators and Data Analytics. Energies, 16(20), 7195. https://doi.org/10.3390/en16207195
Soriano-Gonzalez, R., Perez-Bernabeu, E., Ahsini, Y., Carracedo, P., Camacho, A., & Juan, A. A. (2023). Analyzing Key Performance Indicators for Mobility Logistics in Smart and Sustainable Cities: A Case Study Centered on Barcelona. Logistics, 7(4), 75. https://doi.org/10.3390/logistics7040075
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them. This project has received funding from the Horizon Innovation Actions under the grant agreement n° 101096405.