GIS-Based Analysis of Rooftop Suitability for Green Roofs and Agroecology: Advancing Climate Resilience and Urban Ecosystem Services in Padua, Italy

ABSTRACT Urbanization and climate change effects are rapidly increasing, posing a significant threat to urban sustainability, resilience, and environmental justice. As cities look for new and creative ways to address these challenges, green infrastructure (GI) is becoming increasingly important, especially with the help of rooftop applications, such as green roofs, rooftop agroecology, and solar energy systems. This study developed a GIS-based spatial decision support system to identify and prioritize the potential suitability of rooftops for GI deployment in Padua, Italy by integrating high-resolution geospatial data, socio-environmental factors, and a multi-criterion decision-making analysis to ensure a balanced approach to consider technical feasibility, environmental sustainability, and social justice. The research is structured around three primary objectives: (1) Evaluating the rooftop potential in terms of slope, aspect, and area by utilizing Light Detection and Ranging (LiDAR)-derived Digital Surface (DSM) and Digital Terrain Model (DTM); (2) determining the suitability by incorporating socio-environmental factors such as the Composite Vulnerability Index (CVI) and green density distribution to address disparities in vegetation cover and urban heat island effects; and (3) prioritizing rooftops with the help of the Multi-Criteria Decision Analysis (MCDA) framework for developing a prioritization system for intervention opportunities at the building and urban scales. The analysis revealed that 82.16% of the total rooftop area (10.09 km²) in Padua have the potential for GI applications, and that flat and low slope rooftops are most suitable for intensive green roofs and solar energy systems, respectively. To ensure the reliability of the rooftop classification, a sample-based accuracy assessment was performed, and the overall classification accuracy was 92.0% with a Kappa coefficient of 0.89, indicating high agreement with the reference data. This validation enhanced the methodological rigor and applicability of the spatial analysis presented in this study. The prioritization results identified some important high-priority intervention zones, including Arcella, Fiera, and Sant’Osvaldo, where low vegetation cover combined with high socio-environmental vulnerability and a high level of urban heat stress underscores the urgent need for rooftop GI interventions. In contrast, areas such as the Zona Industriale, while physically suitable, were given lower overall priority scores owing to lower socio-environmental vulnerability, highlighting the role of equity-driven prioritization in ensuring that GI investments support climate resilience in the most affected communities. This study offers a replicable and scalable model for GI identification and prioritization process that connects spatial data analysis to real-world planning and policy applications. The findings reinforce the transformative potential of rooftop GI in mitigating urban heat islands, enhancing biodiversity, improving air quality, supporting local food security, and promoting social equity. Future studies should include cost analysis, dynamic modelling, and stakeholder involvement to enhance this framework and assist cities in creating sustainable and climate-adapted urban infrastructures. Keywords: GIS, green infrastructure, rooftop agroecosystems, multi-criteria decision-making, climate justice, urban sustainability, SDGs 2030.