Coastal Community Vulnerability Index

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Coastal Community Vulnerability Index

  1. 1. Overview of Vulnerability Physical Changes Stressors New system/ environment Response System Social (Cutter 2003)
  2. 2. Vulnerability in Coastal Systems• Affected by environmental and social systems that bring various hazards• Consisted of vulnerable communities that are at risk from hazard effects• Vulnerability varies according to factors inherent to communities • Exposure to hazards • Limited mitigation capacity (USAID 2007) Knowledge about “factors influencing vulnerability” will support systems for community adaptation and mitigation
  3. 3. In the Philippines, 822 ofthe 1502 municipalities arein coastal zones (55%)• 60% of 87,000,000 population is in the coastal areas (in 2005)• Provides 43% of per capita protein 24 disasters needs in 2010 (CRED 2010 )• Employs 1,000,000 people in the coastal rural areas Potential threats• 5% contribution to GDP (2,500,000 metric tons/year ) • Warmer temperature (i.e. 1998 El Nino)• Economic benefits valued at Study Objective • Stronger typhoons US$3,500,000,000 annually • Overexploitationfactors that affectof Determine the and poor regulation Current Situation coastal communities’ vulnerability resources (brought by population increase and competition)
  4. 4. Case Study•5 coastal villages in Baler, Aurora•Rich in terrestrial and marine•Threatened by natural hazards•Poor social conditions (Manila Observatory 2005)
  5. 5. Conceptual and Methodological Framework Two (2) Composite Index Frameworks were constructed: • coastal community vulnerability index (CCVI); and • IPCC- CCVI (based on IPCC vulnerability elements) Composite Index (UNEP 2002) • a single measure that combines measures of different situations (modified from Buckle et al. 2001) • establishes ranking for comparative analysis useful for vulnerability assessments
  6. 6. The CCVI and IPCC-CCVI Framework Sub-factor Sub-factor Vulnerability 1. CCVI 2. IPCC-CCVI Variables Indicators Factors Frequency and Intensity of Social Hazards Occurrence and Effects of V Social Hazards (2) Frequency and Intensity of 2. Establish scales for and Effects of Occurrence Geographical Factors Exposure u Natural Hazards Natural Hazards (2) measuring variablesDependency on Fish Produced for Food l Level of by the respondent’s for Food (2) Other Food Production Fisheries Food Security Factors n scores Fish Produced for Income Level of Dependency on Economic and Livelihood e Other Income Sources Fisheries for Income (2) Factors r 3. Aggregate scores andAge, Tenure, Occupation and Demographic Factors a Social Information (4) Sensitivity compute for the indices Household Size Indices’ values are computed b Access to Services Importance of Services from based on scored responses of Importance of Services Coastal Ecosystems (2) Environmental Factors i Institutions with Resourcein a social survey individuals Policy and Institutional l Institutions for Natural Management Initiatives Resource Management (2) Factors iParticipation of Communities Adaptive Capacity Capital Goods Factors t y Natural Capital Financial Capital Physical Capital Human Capital Social Capital Availability and Access to Credit 1. Assign indicators and Communication, Work Disruptions Membership and Benefits from Utilization of Land Facilities variablescaused each of Transportation and for by Sickness Social Networks Availability of Livelihood Implements Availability and Access to Liquefiable Assets the seven factors Important Information
  7. 7. Steps in Data Collection and Analysis Field Data CollectionThe Questionnaire Survey: (August to September 2010)-Secondary Major Sections Drafting the Four (4) Data Planning with Pre-testing of village leaders - Sourcing Questionnaire Household Characteristics and Tenure Questionnaire and local -(2months) Use and Access (1month) Resource Survey Survey (2 days) academe - Social and Environmental Trends No hard data Site Manpower - Livelihood and Economic Activities available Selection Limitation- Composed of component variables that are scaled from minimum to maximum values Training of 20- Example: of Validation Assessing the frequency of social Data Analysis Conducting the local Information and Presentation actual 182 hazard, Social discrimination enumerators (2 (GIS, SPSS) (March 2011) Never; 2= Seldom; surveys (4 days) - Scales: 1= days) 3= Occasional; 4= Often; 5= Very often Time Skill - Get the Minimum=1; Maximum=5; Limitation Limitation - Collect all responses to get Average
  8. 8. Process for Computing Indices of Variables, Sub-factors and Factors of Vulnerability • Sample Computation: Social Standardization of 82 discrimination in SabangComponent variable component indices Variable Scales n 5= Very Often Index 4= Often 3= Occasional 47 1.32 1 5 0.08 Computation of the 23 sub- 2=Seldom factor variable indices 1= Never Variables Frequency of All Types of Social Hazards Human environmental Computation of the 21 sub- destruction 0.42 factor indices Social conflict 0.34 0.25 0.25 Sub- Social discrimination 0.08 factors Social security 0.16 Computation of the 7 major Sub-factors of Geographical factors indices Factors Frequency of Natural Hazards 0.54 Factors Intensity of Natural Hazards 0.90 0.58 Frequency of Social Hazards 0.25 Intensity of Social Hazards 0.62
  9. 9. Process for Computing for Vulnerability using CCVI • Sample Computation: CCVISub- Sabang Coastal Community Vulnerability Major Factors Index (CCVI) is computed based on Major Factors Σ factors Σ the weighted average of all the factors Geographical Factors Geographical 4 0.58 4 2.32 Factors (GF) Environmental 2 1.08 Environmental 0.54 Factors 2 Factors (EF) Food Security Factors Food Security 0.74 2 1.48 2 Factors (FF) Economic and Livelihood Economic and 2 Factors 0.62 2 1.24 11.21 Livelihood 21 0.53 Factors (ELF) Policy and and Institutional Policy Institutional 0.60 2 1.2 2 Factors Factors (PIF) Demographic Demographic Factors Factors (DF) 0.51 4 2.04 4 Capital Good Factors (CGF)Good Factors Capital 0.37 5 1.85 5
  10. 10. Factors and CCVI of Five (5) Coastal Communities Major Factors Buhangin Pingit Reserva Sabang Zabali Geographic Factors (GF) 0.52 0.39 0.47 0.58 0.24 Environmental Factors (EF) 0.50 0.54 0.58 0.54 0.50 Food Factors (FF) 0.57 0.70 0.61 0.74 0.80 Economic and Livelihood Factors (ELF) 0.56 0.65 0.50 0.62 0.70 Policy and Institutional Factors (PIF) 0.72 0.62 0.66 0.60 0.52 Demographic Factors (DF) 0.51 0.50 0.50 0.51 0.46 Capital Good Factors (CGF) 0.38 0.39 0.37 0.37 0.41 CCVI 0.51 0.50 0.50 0.53 0.47High 0.8 High CCVI 0.7 0.55 Factor Contribution 0.6 Buhangin 0.50 Vulnerability Pingit 0.5 0.45 Reserva 0.4 Sabang 0.40 Zabali 0.3 Average 0.35Low Buhangin Pingit Reserva Sabang Zabali 0.2 Low GF EF FF ELF PIF DF CGF
  11. 11. Correlation of Indices of Major Factors with CCVI 0.70 0.60 Major Factors Y= 0.43+0.16x 0.50 Y=0.03+0.95x 0.40 Y=0.93-1.11x 0.30 0.20 0.45 0.47 0.49 0.51 0.53 0.55 CCVI R R2 Geographical Factors 0.96 0.93 Environmental Factors 0.3 0.09 Food Security Factors -0.36 0.13 Economic and Livelihood Factors -0.42 0.18 Policy and Institutional Factors 0.52 0.27 Demographic Factors 0.91 0.83 Capital Good Factors -0.85 0.73
  12. 12. Normalized Maps of Factors and CCVI Geographical Demographic Environmental Food SecurityLegend BalerMunicipalMaprnnccviValue Max High : 1 Economic and Livelihood Policy and Institutional Capital Good CCVI Mapping Software: ArcGIS 9.3.1 Tool: Spatial Analyst Geo-reference coordinate system: WGS 1984 Low : 0Min Map source: GADM Version 0.8 from http://biogeo.berkeley.edu/gadm/
  13. 13. Conclusion• There were little difference in resulting CCVI among the five (5) coastal communities (Sabang, with the highest CCVI, is the most vulnerable )• Food, policy and economic factors have high values that deem to influence vulnerability of coastal communities the most• Variation of indices at factor level assume areas of vulnerability for a coastal community, the factor contributions vary accordingly on the values at the other index levels• When there is no hard data source, the method may be effective but only for rapid appraisal and its strength depends on quality of surveyed data within a specific time• Focus of future study: • improve identifying suitable and objective variables and indicators • create a hybrid method for indexing vulnerability that combines social survey data with hard information sources • analysis of relevant of indicators by statistical tools (i.e. principal component analysis, factor analysis, regression analysis) • modeling using multi criteria decision analysis of factors (AHP, ANP, game theory)• Communicate results to local government to encourage robust data collection and information management system (i.e. fish catch monitoring, satellite data)

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