From social vulnerability to resilience: measuring progress toward disaster risk reduction - Source
- Autore: Munich Re Foundation
- Anno: 2013
- Casa Editrice: Munich Re Foundation; United Nations University Institute for Environment and Human Security (UNU-EHS)
- Sito: http://www.munichre-foundation.org/dms/MRS/Documents/UNU-EHS/2012-UNU-EHS/Source2013_Cutter_Corendea.pdf
- E-Mail: email@example.com
Author(s): Cutter, Susan; Corendea, Cosmin (eds.)
Number of pages: 136 p.
This Source edition as a product of the seventh Summer Academy comprises seven scientific papers from participants originating from different countries and working in various disciplines debating issues associated with social vulnerability and resilience. The seven papers address various aspects of integrating social, environmental and infrastructure elements in understanding vulnerability and resilience. They represent new and innovative approaches to vulnerability and resilience metrics, with an eye towards informing policy.
1) Atzl and Keller offer in their paper a conceptual framework on infrastructure vulnerability utilizing a systems perspective.
2) Hummell provides an overview of the availability of research and data on hazard exposure and vulnerability in Brazil.
3) Carrera et al. examined the integration of social vulnerability and flood risk exposure in the Po River Basin as a methodological proof of concept for compliance with EU Flood Risk Management Directive 2007/60/EC.
4) Ignacio and Henry illustrated the intersection of social and biophysical vulnerability to riverine flash flooding in the Philippines.
5) Hagenlocher used four climate-related variables (seasonal rainfall, temperature patterns, drought occurrences and major flood events) in the Sahel region to identify and delineate hotspots of cumulative climate change impacts.
6) Borderon also took an innovative approach to exposure assessment examining the problem of urban malaria in Dakar
7) A methodological contribution on social vulnerability index construction was provided by Siagian et al. who used a model based clustering method with minimum message length (MML) criterion.