Developing Composite Indicators to Evaluate the Feasibility of Green Walls in Urban Environments

Urban areas across the world are increasingly confronted with complex environmental challenges, including climate change, air pollution, reduced biodiver- sity, and the intensification of urban heat island effects. Within the broader framework of nature-based solutions, green walls represent a promising strategy to enhance urban resilience, improve microclimatic conditions, promote ecological sustain- ability, and contribute to healthier and more liveable cities. However, planning and implementing green wall systems requires robust, multidimensional assessment tools capable of integrating heterogeneous information—technical, ecological, economic, social, and regulatory. This study proposes an integrated methodological framework for evaluating the feasibility of green walls through the construction of a Composite Indicator (CI). Building on the statistical theory of CI development, our work deals with a structured approach to indicator selection, data transformation, weighting, and aggregation. The methodology is applied to a case study involving buildings on the University of Palermo campus, with technical feasibility indicators repre- senting structural, environmental, regulatory, and spatial characteristics. Different transformation methods (ranking, min–max normalisation, z-score standardisation) and weighting schemes (equal weights, expert-based weights, and aggregation rules) are compared to assess their influence on the resulting CI. The method used follows the Analytical Hierarchy Process (AHP) that is a decision-making framework that simplifies complex choices by organising them into a hierarchy of goals, criteria, and alternatives. A sensitivity analysis is conducted using Kendall’s tau, Spearman’s rho, and Pearson’s correlation coefficients to evaluate the robustness of building rank- ings under different methodological scenarios. This paper offers both methodological insights and practical implications for urban sustainability planning. The results show that while rankings are generally highly correlated, the CI is sensitive to method- ological choices, emphasising the need for transparency, justification of assumptions, and robustness checks. The proposed framework is adaptable to different contexts and provides urban planners and policymakers with a scientifically informed tool for decision-making regarding vertical greening interventions.