Measuring disaster resilience is a key component of successful disaster risk management
and climate change adaptation. Quantitative, indicator-based assessments are typically
applied to evaluate resilience by combining various indicators of performance into a single
composite index. Building upon extensive research on social vulnerability and coping/adaptive
capacity, we first develop an original, comprehensive disaster resilience index (CDRI) at
municipal level across Italy, to support the implementation of the Sendai Framework for
Disaster Risk Reduction 2015–2030. As next, we perform extensive sensitivity and robustness
analysis to assess how various methodological choices, especially the normalisation
and aggregation methods applied, influence the ensuing rankings. The results show patterns
of social vulnerability and resilience with sizeable variability across the northern and
southern regions. We propose several statistical methods to allow decision makers to
explore the territorial, social and economic disparities, and choose aggregation methods
best suitable for the various policy purposes. These methods are based on linear and nonliner
normalization approaches combining the OWA and LSP aggregators. Robust resilience
rankings are determined by relative dominance across multiple methods. The dominance
measures can be used as a decision-making benchmark for climate change
adaptation and disaster risk management strategies and plans