Constructing a comprehensive disaster resilience index: The case of Italy

PLoS One. 2019 Sep 16;14(9):e0221585. doi: 10.1371/journal.pone.0221585. eCollection 2019.

Abstract

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 non-liner 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Attitude
  • Climate Change
  • Disasters / prevention & control*
  • Humans
  • Italy
  • Models, Theoretical*
  • Resilience, Psychological*
  • Risk Reduction Behavior

Grant support

This publication is part of the project CLARA "Climate forecast enabled knowledge services" that has received funding from the European Union's Horizon 2020 research and innovation programme under the Grant Agreement No 730482.