EEPSEA-WorldFish Climate Change and Coastal Communities Study Team. Wednesday, November 21, 12

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1 Climate Change Impacts, Vulnerability Assessments, Economic and Policy Analysis of Adaptation Strategies in Selected Coastal Areas in Indonesia and Philippines EEPSEA-WorldFish Climate Change and Coastal Communities Study Team

2 Introduction: Why Focus on Coastal Communities and Climate Change? Coastal and marine ecosystems are vital to most Southeast Asian countries Population living in the coastal areas and are dependent on this resource base are among the poorest in the region Climate change completes a double whammy Continued deforestation and other upstream environmental problems Effects of climate change coming from the sea (sea level rise, etc.)

3 Introduction: Scope of study The study covered selected coastal communities in Jakarta Bay in Indonesia, and Honda Bay and Tayabas Bay in the Philippines. The impacts from three climate hazards affecting coastal communities namely: typhoon/flooding, coastal erosion, and saltwater intrusion were documented. It analyzed planned adaptation options, which communities and local governments can implement Autonomous response of households to protect and insure themselves from these hazards.

4 General Objective The general objective of the study is to understand public (planned) and private (autonomous) adaptation of coastal communities against multiple climate related hazards. This is with the hope of gaining a better understanding of the risks associated with climate change and assess adaptation strategies and policy options to address these risks more efficiently

5 Specific Objectives Validate and assess CC impacts on selected coastal areas in Indonesia, and the Philippines Assess the benefits and costs of planned adaptation strategies to climate change in selected coastal areas Assess the vulnerability, autonomous adaptation, and coping mechanisms of households and communities dependent on coastal ecosystems Recommend viable adaptation options/ strategies/ programs Explore and identify emerging issues in the assessment of vulnerability and economic analysis of adaptation strategies based on the results of the country studies

6 Study Sites and Partners Study Sites Municipality/ Baranggay Research Partners Jakarta Bay, Indonesia Honda Bay, Palawan Sub-District of Cilincing, Kalibaru, Marunda and Rorotan in the District of Cilincing and Sub-District of Kamal Muara and Muara Angke in the District of Penjaringan Barangays Babuyan and Binduyan in Puerto Princesa City Research Center for Marine Fisheries Socio-Economics Ministry of Marine Affairs and Fisheries; Faculty of Earth Science and Technology; Institut Technologi Bandung; Palawan State University; City Government of Puerto Princesa Tayabas, Bay, Batangas Barangays Catmon and Hugom in San Juan, Batangas University of Batangas; Municipality of San Juan

7 Methodology

8 Methodology Community) FGDs) Hazard) Analysis/) Mapping) Vulnerability) Analysis) CEA)of) Identified) Planned) Adaptation) Vulnerable) sectors)or) population/) sectors)at) risk) Sampling) Frame)for) Field)Survey) Field)Survey) Valuation)of) Damages) Autonomous) Adaptation) and)coping) Mechanisms) Vulnerability) Index)(VEP)) - Mix of qualitative and quantitative methods - One methodology builds upon another

9 Three Main Analysis Cost Effectiveness Analysis of Planned Adaptation Strategies Vulnerability to Expected Poverty (VEP) Determinants of Autonomous Adaptation Choices

10 Cost Effectiveness Analysis (CEA) of Planned Adaptation Strategies

11 Community Identified Planned Adaptation Strategies Planned adaptation strategies were identified through participatory methods Strategies varied across study sites because of differences in hazards

12 Batangas Hazard Planned Adaptation Strategies Coastline Erosion Construction of Sea Wall Mangrove Reforestation** Zoning Flooding Typhoon Early Warning, Evacuation** Integrated drainage and flood control + diversification of livelihood

13 Palawan Hazard Storm Surges Planned Adaptation Strategies Breakwater Construction Dike/ levee construction Mangrove reforestation** River bank rehab with vetiver grass** Flooding from overflown rivers Dike construction River dredging Upland reforestation Inland flooding IEC** Relocation

14 Indonesia Indonesian context is unique because it has coastal flooding or rob Strategies that were compared varied across sites Hard to come up with a ranking across study sites in the country. It usually depends on the sites Planting mangroves is most cost effective but in two sites they were not. Because of ownership/ tenure issues. Road elevation also was identified as a planned adaptation strategy Dredging was also cost effective compared to Construction of flood canals

15 Vulnerability to Expected Poverty (VEP)

16 Vulnerability to Expected Poverty: The Concept Vulnerability is defined as the probability that a household will fall below a minimum consumption threshold level (or probability that household will move to poverty in the future) The analysis is based from the assumption that climate extremes, climatic shocks or hazards, will affect the probability that households consumption will fall below a given minimum level --- vulnerable (Deressa, Hassan & Ringler, 2009)

17 Vulnerability to Expected Poverty: The Procedure Estimate Consumption=f(Demographic variables,shocks, other variables) Use FGLS Calculate Prob(Estimated Consumption< USD1.25) Parametric, i.e. use normal distribution 0 VEP 1, i.e. a probability Interpretation: If VEP=0.36, means HH has a 36% probability of falling below USD 1.25 consumption per capita per day Convention: (Not Vulnerable); (Moderately Vulnerable); (Highly Vulnerable).

18 Results: Mean VEP Levels Mean vulnerability estimate for all households was highest for Palawan at 0.51, while for Batangas at 0.38, and Indonesia at 0.37 Mean VEP, however, for those who are considered highly vulnerable is highest for Indonesia at 0.92 Mean VEP estimates for the highly vulnerable group in Batangas and Palawan are 0.88 and 0.87, respectively

19 Results: VEP Distribution

20 Characteristics of Vulnerable Groups Mostly fishermen and farmers (Batangas (55%); Palawan (73%)) For Indonesia (49%) are service-oriented workers, trade and related workers, plant and machine operators, laborers and unskilled workers Mostly have no information about climate change (Batangas (47%); Indonesia (70%)) But in Palawan 48% have a little bit of knowledge Level of preparedness varies Indonesia - not prepared (63%) Palawan and Batangas - somewhat prepared (39% and 43%, respectively)

21 Analyzing Adaptation Choice

22 Multivariate Probit Regression Most common use of discrete dependent model Estimate decision to adapt or not for every hazard separately Separate probit regressions: Pr(adapt to flooding); Pr(adapt to CSR); Pr(adapt to SI) We instead jointly estimate decision to adapt or not for three different hazards (flooding, coastal soil erosion (CSE), and saltwater intrusion(si)) Leads to use Joint or Multivariate Probit Regression Pr(adapt to flooding, adapt to CSE, adapt to SI)

23 Why Joint Estimation FGDs as well as literature points households face multiple and often simultaneous threats from these hazards Preferences for risks will be common for decision Analogy to consumption of goods Technical: We hypothesize that error terms across each discrete decision are correlated If this is true, then separate estimations will lead to: Large standard errors and wrong inference from adaptation models

24 Adaptation No. of HH Adapting to Flooding Batangas Palawan Indonesia Did not Adapt 180 (60%) 275 (92%) 181 (60%) Adapted 120 (40%) 25 (8%) 122 (40%) No. of HH Adapting to Coastal Erosion Batangas Palawan Indonesia Did not Adapt 258 (86%) 286 (95%) 275 (91%) Adapted 42 (14%) 14 (5%) 28 (9%) No. of HH Adapting to Saltwater Intrusion Batangas Palawan Indonesia Did not Adapt 69 (23%) 80 (27%) Adapted 231 (77%) 220 (73%) 303 (100%)

25 Results Reject the hypothesis that error terms across adaptation decisions are not correlated Means that joint estimation is appropriate

26 Results Flooding CSE SI HH Size (+) Number of Female (-) Dummy variable for Palawan (-) Dummy variable for Vietnam (+) Presence of Mangrove (-) Distance form Creeks/ Rivers (-) % Fishing income in total income (+) Value of damages (+) # of people whom you can surely borrow from (+) HH Size (+) Dummy variable for Palawan (-) Dummy variable for Vietnam (+) Distance from Creeks/ Rivers (-) Type of roofing materials (-) Income from Gifts, Reliefs, Support, etc. (+) % Fishing income in total income (-) # of people whom you can surely borrow from (+) Age of HH Head (+) Years of Schooling (+) Number of Female (+) Dummy variable for Vietnam (+) Dummy variable for Indonesia (+) Own pigs (+) % Fishing income in total income (-) Presence of riverbank rehabilitation (+)

27 Results: Some lessons from the regressions Geographical location matters The nature of the hazard matters and is somehow related to a gender dimension in adaptation Government projects may crowd out autonomous strategies Increasing trust in a community also leads to increased autonomous adaptation In terms of methodology, there is gain in modeling adaptation options as joint decisions in the case of coastal communities