Constructing a comprehensive disaster resilience index: The case of Italy

English version

Autoři: Sepehr Marzi ^aff001; Jaroslav Mysiak ^aff001; Arthur H. Essenfelder ^aff001; Mattia Amadio ^aff001; Silvio Giove ^aff002; Alexander Fekete ^aff003
Působiště autorů: Centro Euro-Mediterraneo sui Cambiamenti Climatici and Università Ca' Foscari Venezia, via della Libertà, Venice Marghera, Italy ^aff001; Department of Economics, Università Ca' Foscari Venezia, Cannaregio 873 –Fondamenta San Giobbe, Venice, Italy ^aff002; Institute of Rescue Engineering and Civil Protection, TH Köln (University of Applied Sciences), Betzdorfer Straße 2, Cologne, Germany ^aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0221585

Souhrn

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.

Klíčová slova:

Computer and information sciences – Network analysis – Network resilience – Earth sciences – Atmospheric science – Climatology – Climate change – Geography – Human geography – Human mobility – People and places – Population groupings – Ethnicities – European people – Italian people – Geographical locations – Europe – European Union – Italy – Social sciences – Economics – Human capital – Economics of training and education – Labor economics – Engineering and technology – Management engineering – Risk management

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