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Constructing a comprehensive disaster resilience index: The case of Italy
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 ex...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746365/ https://www.ncbi.nlm.nih.gov/pubmed/31525230 http://dx.doi.org/10.1371/journal.pone.0221585 |
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author | Marzi, Sepehr Mysiak, Jaroslav Essenfelder, Arthur H. Amadio, Mattia Giove, Silvio Fekete, Alexander |
author_facet | Marzi, Sepehr Mysiak, Jaroslav Essenfelder, Arthur H. Amadio, Mattia Giove, Silvio Fekete, Alexander |
author_sort | Marzi, Sepehr |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6746365 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67463652019-09-27 Constructing a comprehensive disaster resilience index: The case of Italy Marzi, Sepehr Mysiak, Jaroslav Essenfelder, Arthur H. Amadio, Mattia Giove, Silvio Fekete, Alexander PLoS One Research Article 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. Public Library of Science 2019-09-16 /pmc/articles/PMC6746365/ /pubmed/31525230 http://dx.doi.org/10.1371/journal.pone.0221585 Text en © 2019 Marzi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Marzi, Sepehr Mysiak, Jaroslav Essenfelder, Arthur H. Amadio, Mattia Giove, Silvio Fekete, Alexander Constructing a comprehensive disaster resilience index: The case of Italy |
title | Constructing a comprehensive disaster resilience index: The case of Italy |
title_full | Constructing a comprehensive disaster resilience index: The case of Italy |
title_fullStr | Constructing a comprehensive disaster resilience index: The case of Italy |
title_full_unstemmed | Constructing a comprehensive disaster resilience index: The case of Italy |
title_short | Constructing a comprehensive disaster resilience index: The case of Italy |
title_sort | constructing a comprehensive disaster resilience index: the case of italy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746365/ https://www.ncbi.nlm.nih.gov/pubmed/31525230 http://dx.doi.org/10.1371/journal.pone.0221585 |
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