Social Vulnerability and Community Resilience Measurement and Tools

Transcription

1 Social Vulnerability and Community Resilience Measurement and Tools Susan L. Cutter Natural Disaster Resilience: An Urgent Need and Opportunity for Chile CNID and CREDEN August 25, 2016

2 Vulnerability and Resilience Science What circumstances place people and localities at risk? What enhances or reduces the ability to prepare for, respond to, recover from, successfully adapt to or anticipate environmental threats? How does vulnerability and resilience vary geographically and socially? Vulnerability and Resilience Goal: Provide scientific basis for disaster and hazard reduction policies through the development of methods and metrics for analyzing societal vulnerability and resilience to environmental hazards and extreme events

3 Measuring Social Vulnerability Examples of broad concepts: Special Needs populations difficult to identify (infirm, transient) let alone measure; invariably left out of recovery efforts; often invisible in communities Age (elderly and children) affect mobility out of harm s way; need special care; more susceptible to harm Socioeconomic status (rich; poor) ability to absorb losses and recover (insurance, social safety nets), but more material goods to lose Race and ethnicity (non-white; non-anglo) impose language and cultural barriers; affect access to post-disaster recovery funding; tend to occupy high hazard zones Gender (women) gender-specific employment, lower wages, caregiving role Family structure female-headed households, people per household Identification of population characteristics that influence the social burdens of risk How those factors affect the distribution of risks and losses Heinz Center, Human Links to Coastal Disasters. Washington D.C.: The H. John Heinz III Center for Science, Economics and the Environment.

4 Social Vulnerability Index (SoVI) Identification of population characteristics that influence social burdens of risk (e.g. special needs, age, socioeconomic status, gender, housing type and tenure) Social Vulnerability Index (SoVI ) 2010 Comparative metric using 30 variables Measured from block to county levels Identify multi-dimensional drivers 7 variables explain 72% variance in data (2010) Amenable to cross cultural comparisons: Norway, Brazil, Indonesia, Lisbon, China See Cutter et al Social Vulnerability to Environmental Hazards, Social Science Quarterly 84 (1):

5 Factors: 1. Poverty 2. Urban/rural development 3. Migration 4. Special needs population 5. Race (Indian) and poor infrastructure 6. Lack of public employment 7. Tourism-base economy 8. Racial diversity 9. Population density 10. Extractive industries Factors explain 67% of variance Hummell, B. M. de L., S. L. Cutter, and C. T. Emrich, Social vulnerability to natural hazards in Brazil. Intl. J. Disaster Risk Science 7:

6 SoVI in Operation: South Carolina October 2015 Flooding SoVI coupled with FEMA verified loss counts tells the story of where resources are needed to support recovery.

7 Increasing Interest in Resilience

8 The Resilience Concept Many definitions of resilience: Bouncing back, bouncing forward Inherent Resilience: Ability to prepare and plan for, absorb, recover from or more successfully adapt to actual or potential adverse events (US NRC 2012) Process Outcome Adaptive Event Resilience can be an outcome (static), a process (dynamic) or both Can have inherent or pre-existing resilience, also adaptive resilience Applied to multiple scales and units of analysis (individual, group, sectors, systems

9 What is Community Resilience? Natural Systems Built Environment and Engineered Systems Human Systems Resilience: Ability to prepare and plan for, absorb, recover from or more successfully adapt to actual or potential adverse events Community: a broad range of scales of community organization (neighborhood to city, county, state, region, nation) with a set of interrelated systems (NRC 2012). Resilience is NOT the flip-side (or opposite) of vulnerability

10 Measuring Resilience: A messy landscape of options (indices, tools, scorecards) Domain Top Down Bottom Up 1. Method top down or bottom up, qualitative or quantitative Method 2. Spatial scale from local to global; unit of analysis ranges from individual to whole community Characteristics Indices Scorecards Tools Domain Capacities 3. Focus describing specific assets for resilience, determining broader baselines for places Method 4. Domain examination of characteristics or attributes of places that foster resilience, or capacities (evaluation of performance or quality; abilities) to undertake resilience Source: S.L. Cutter, The landscape of disaster resilience indicators in the United States, Natural Hazards, 80:

11 Creating a Baseline for Community Resilience The Need: How can you measure progress if you don t have a starting point? How do you know if programs have been effective or targets reached? The Challenges: Need for simplicity Ability to replicate over time Evidentiary-based Meaningful from local to national scales

12 Baseline Resilience Index for Communities (BRIC) Components Social age, educational level, special needs Economic homeowners, employed, type of economic activity Infrastructure shelters, road miles, medical facilities Institutional capacity previous experience, municipal expenditures for emergency services Community capacity social capital, innovation potential, attachment to place Environmental wetlands, energy use, perviousness, water stress Inherent Vulnerability Inherent Resilience Natural Systems

13 Science of Resilience Indicators: Putting it all Together Data Use existing national data from Census, other government agencies Need periodic updates (every 3-5 years) Enumeration unit varies (census tract to county to state) depending on purpose Procedures Scale values from 0 to 1 where 0 reduces resilience; 1 increases resilience (called linear min-max scaling where X-min/max-min) Scores theoretically range from 0 to 49 (all variables); or from 0 to 6 (using sub-indices) Create means of each sub-index (to reduce impact of different number of variables within each subindex) Sum sub-index means for overall score

14 Scaling Example Normalization = = = =

15 Category Weighting Schemes Weighting 10% 20% Social Unequal 18% 20% 14% 16% Economic Community competence Institutional Housing/Infrastruct ure Social 49 Total Variables Social=10 variables Economic=8 variables Community=7 variables Institutional=10 variables Housing/Infrastructure=9 variables Environmental=5 variables Equal Economic Community competence Institutional Housing/Infrastr ucture Environmental

16 Disaster resilience index (BRIC) for the contiguous United States, 2010 Cutter, S. L., K. D. Ash, and C. T. Emrich, The geographies of disaster resilience. Global Environmental Change 29:

17 Community Disaster Resilience Composite of six broad categories influencing community disaster resilience A. Social B. Economic C. Community Capital D. Institutional E. Housing/Infrastructure F. Environmental Not the resilience of each of category but how these characteristics contribute to overall community resilience

18 Why Resilience Measurement? A resilience measurement tool can help by Assessing/prioritizing needs and goals Establishing baselines for monitoring progress and recognizing success Understanding costs (investments) and benefits (results) Evaluating the effects of different policies/approaches BUT A single, one-size metric for all facets of resilience may not work at the bottom-up community scale. Lots of tools out there, few are used (too complex, too simple, not known..) Communities have potential to develop or adapt simple measurement systems/tools to gauge their own baselines. Measurement tools are helpful in identifying disaster risk, taking steps toward reducing it, assessing how they are doing, and getting stakeholders to work together. Measurement tools cannot create a resilient community, but they can help show the path towards becoming safer, stronger, and more vibrant in the face of unanticipated events.

19 Muchas Gracias! REFERENCES Cutter et al Social vulnerability to environmental hazards, Social Science Quarterly 84 (1): Cutter, S. L., K. D. Ash, and C. T. Emrich, The geographies of disaster resilience, Global Environmental Change 29: Cutter, S.L., The landscape of disaster resilience indicators in the United States, Natural Hazards, 80: Heinz Center, Human Links to Coastal Disasters. Washington D.C.: The H. John Heinz III Center for Science, Economics and the Environment. Hummell, B. M. de L., S. L. Cutter, and C. T. Emrich, Social vulnerability to natural hazards in Brazil, Intl. J. Disaster Risk Science 7: National Research Council, Disaster Resilience: A National Imperative. Washington DC: National Academies Press.