• Low-lying areas of the Ganga-Brahmaputra-Meghna (GBM) Delta with high population density is experiencing intense climate change impacts like sea level rise, coastal erosion, salinization, frequent cyclones and floods, etc., which stresses this resource dependent community. • This situation is further complicated by the multi-dimensional variables of poverty and lack of social well-being such as poor access to education, child mortality, food and energy security, malnutrition, lack of safe drinking water and sanitation facilities. • Communities, with varying adaptive capacity to respond to climatic impacts, are likely to experience different levels of vulnerability. • This study aims to assess social vulnerability of coastal communities of GBM Delta in the context of the underlying social conditions of coastal people, ignoring the political boundary between India and Bangladesh. • Social Vulnerability being one of the thrust area of DECCMA, this poster could be useful, significant and interesting for all relevant ongoing research activities in the sphere of Climate Change Adaptation. Social Vulnerability to Climate Change - An Assessment of Ganga-Brahmaputra-Meghna Delta Shouvik Das Doctoral Fellow, DECCMA-India, School of Oceanographic Studies, Jadavpur University, Kolkata, India Email ID: [email protected] Concept of Social Vulnerability • Social scientists tend to view vulnerability as representing the set of socio- economic factors that determine people’s ability to cope with stress or change (Allen, 2003), climate scientists often view vulnerability in terms of the likelihood of occurrence and impacts of weather and climate related events (Nicholls et al., 1999). • Social Vulnerability is viewed as an inherent property of a system arising from its internal characteristics (Adger, 1999; Adger and Kelly, 1999) and determined by factors such as poverty and inequality, marginalisation, food entitlements, access to insurance, and housing quality (Blaikie et al., 1994; Adger and Kelly, 1999; Cross, 2001). • Biophysical vulnerability is a function of the frequency and severity (or probability of occurrence) of a given type of hazard, while social or inherent vulnerability is not. Pearson's r Avg.HH_Size Disability Sex Ratio Agri_Dependan cy Poverty Kutcha House Avg.HH_Size - -0.143 0.067 -0.332 -0.208 0.243 Disability -0.143 - 0.221 0.178 0.005 -0.572 Sex Ratio 0.067 0.221 - -0.031 0.049 -0.019 Agri_Dependancy -0.332 0.178 -0.031 - 0.485 0.101 Poverty -0.208 0.005 0.049 0.485 - 0.349 Kutcha House 0.243 -0.572 -0.019 0.101 0.349 - Introduction Variables Description Relation References Household Size Average size of households Positive Adger 1999 Dependency Ratio Ratio of the population under 15 and over 65 years of age to the population between 15 and 65 years of age (economically active) Positive Dwyer et al. 2004 Kuhlicke et al., 2011, Reid et al. 2009, Cutter et al. 2003 Disabled Persons Percentage of disabled Persons to total population Positive Sex Ratio (Male-Female Ratio) Ratio of the number of females per 100 males Negative Parameters of Sensitivity and Adaptive capacity, WP 2 & 3, DECCMA & Population Monograph, Bangladesh Work Participation Rate Percentage of total workers (main and marginal)(IND)/employed(BD) to total population Negative Agricultural Dependency Percentage of cultivators and agricultural labours (dependent on agriculture) to total population Positive Poverty (Headcount Ratio) Percentage of households who belong to BPL category (IND) and below the upper poverty line (BD) Positive Cutter et al. 2003, Vincent 20004, Kutcha House (Temporary Structure) Percentage of households live in kutcha (BD)/temporary structure of houses (IND) Positive Cutter et al. 2003 Rural Population Percentage of rural population, defined as the difference between the total population and urban population Positive Vincent 2004 Methodological Framework Social Vulnerability Index 0 0.2 0.4 0.6 0.8 1 Avg. HH Size Disability Sex Ratio Agricultural Dependency Poverty Kutcha House Manpura Maheshkhali Ukhia Hatiya Kultali Basanti Char Fasson Teknaf Ramgati Banshkhali 0 0.2 0.4 0.6 0.8 Avg. HH Size Disability Sex Ratio Agricultural Dependency Poverty Kutcha House Habra-II Habra-I Sonarpur Barasat-I Gaighata Rajarhat Thakurpukur- Maheshtala Barrackpur-I Barrackpur-II Kotwali Poverty Map The study has been carried out at the sub-district level in the 19 coastal districts of Bangladesh (Coastal Zone of Bangladesh) and 2 large districts of India using secondary data from Census of India and Bangladesh Bureau of Statistics with a simple equal weightage methodology. The study area within the GBM delta Selection of Variables based on literature and available data Normalization for all the variables Is = (Ia – Imin)/ (Imax – Imin) (Positive) Is = (Imax – Ia)/ (Imax – Imin) (Negative) Correlation Matrix for reducing the number of variables (coefficient greater than +/- 0.6) Set_1: Social 1. Avg. HH Size 2. Disability 3. Male-Female Ratio Set_2: Economic 1. Poverty 2. Agricultural Dependency 3. Kutcha House Average Index for each major component/set AI = ∑Isi/N Social Vulnerability Index (SoVI) ∑ (Wi. AIi)/∑Wi Methodological Steps Set_1 Set_2 Results and Discussion Conclusion The world’s climate is changing, and the social vulnerabilities associated with these changes are increasing. Thus assessing social vulnerability will help to understand how communities are exposed to natural catastrophes. It is significant to know who are mostly affected in the community and their ability to withstand and recover from damage. People in this coastal area are more dependent on natural resources such as agriculture, water and mangrove forest and they have limited set of livelihood options. Researchers should identify alternative livelihood options for coastal communities. There is a urgent need to reduce the gender gap or inequality in terms of education, social security and other aspects of life. Strong coordination and implementation of policies for the local people are still missing. This study tries to give a representation of the social vulnerability of the coastal regions; however it also suggests further studies on the adaptation options and coping mechanism to reduce the social vulnerability. Social Vulnerability Map with Sub-District level Relative Ranking 10 Most Vulnerable Sub-Districts 10 Less Vulnerable Sub-Districts This work was carried out under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA), with financial support from the UK Government’s Department for International Development (DFiD) and the International Development Research Centre (IDRC), Canada. The views expressed in this work are those of the creators and do no necessarily represent those of DFiD and IDRC or its Board of Governors. Variable Mean SD Avg.HH_Size 0.408 0.207 Disability 0.420 0.225 Sex Ratio 0.681 0.123 Agri_Dependancy 0.438 0.181 Poverty 0.522 0.229 Kutcha House 0.608 0.335 Correlation Matrix 0 0.2 0.4 0.6 0.8 1 Kotwali Barrackpur-II Barrackpur-I Thakurpukur-Maheshtala Rajarhat Gaighata Barasat-I Sonarpur Habra-I Habra-II Budge-Budge-I Bagdah Deganga Barasat-II Amdanga Barguna Sadar Betagi Bamna Hathazari Phultala Bhangar-II Baduria Bishnupur-II Basirhat-II Bongaon Baruipur Raozan Haroa Budge-Budge-II Amtali Bishnupur-I Barisal Sadar Dighalia Jessore Sadar Basirhat-I Swarupnagar Boalkhali Sitakunda Feni Sadar Diamond Harbour-II Rupsa Abhaynagar Falta Bhangar-I Patiya Diamond Harbour-I Daganbhuiyan Debhata Chatkhil Fakirhat Bagerhat Sadar Minakhan Mograhat-I Chhagalnaiya Jhalokati Sadar Chandanaish Dumuria Mograhat-II Gopalganj Sadar Begumganj Ramganj Senbagh Satkhira Sadar Rangunia Babuganj Jhikargachha Chandpur Sadar Pirojpur Sadar Hasnabad Satkania Tungipara Lohagara Sharsha Nesarabad Patharghata Namkhana Gaurnadi Manirampur Mongla Rajapur Banari Para Batiaghata Wazirpur Jaynagar-I Shariatpur Sadar Canning-I Companiganj Mollahat Mirsharai Kakdwip Sagar Parshuram Cox's Bazar Sadar Narail Sadar Kalaroa Keshabpur Faridganj Lakshmipur Sadar Kanthalia Roypur Chaugachha Bagherpara Agailjhara Damudya Bhandaria Lohagara Fatikchhari Anowara Kachua Muksudpur Kashiani Mandirbazar Nalchity Kalia Muladi Mirzaganj Noakhali Sadar Sonagazi Bakerganj Patuakhali Sadar Tala Kotalipara Paikgachha Sandwip Zanjira Kaliganj Sandeshkhali-I Terokhada Kulpi Hingalganj Mathurapur-II Bhedarganj Mathurapur-I Hizla Canning-II Rampal Hajiganj Assasuni Bauphal Matlab Kala Para Bhola Sadar Patharpratima Shahrasti Dacope Morrelganj Nazirpur Gosairhat Chitalmari Gosaba Kawkhali Mathbaria Chakoria Shyamnagar Kachua Daulat Khan Dashmina Mehendiganj Tazumuddin Lalmohan Burhanuddin Sandeshkhali-II Galachipa Kutubdia Koyra Haim Char Jaynagar-II Ramu Banshkhali Ramgati Teknaf Char Fasson Basanti Kultali Hatiya Ukhia Maheshkhali Manpura 1. Adger, W. N. (1999) Social Vulnerability to Climate Change and Extremes in Coastal Vietnam, World Development Vol. 27, No. 2, pp. 249-269. 2. Brooks, N. (2003). Vulnerability, risk and adaptation: A conceptual framework, Tyndall Centre Working Paper 38. 3. Cutter, S. L., Boruff, B. J. and Shirley, W. (2003). Social Vulnerability to Environmental Hazards, Social Science Quarterly 84(2), pp. 242-261. 4. Kuhlicke, C., Scolobig, A., Tapsell, S., Steinführer, A. and De Marchi, B. (2011). Contextualizing social vulnerability: findings from case studies across Europe. Natural Hazards 58(2), pp. 789-810. 5. Population Monograph of Bangladesh (2015), Population Density and Vulnerability: A Challenge for Sustainable Development of Bangladesh, Bangladesh Bureau of Statistics (BBS), Government of the People’s Republic of Bangladesh. 6. Population and Housing Census (2011), Census of India, Government of India 7. Population and Housing Census (2011), Bangladesh Bureau of Statistics (BBS), Government of the People’s Republic of Bangladesh 8. Vincent, K. (2004) Creating an index of social vulnerability to climate change for Africa, Tyndall Centre Working Paper 56. References Sex Ratio Kutcha House Poverty is one of the important indicators of vulnerability assessment. The poverty map indicates that several sub-districts like Basanti, Mehendiganj, Hizla, Haim Char, Sandeshkhali II, Gosairhat, Sandeshkhali I,Muladi, Kachua etc., belong to higher poverty ratio i.e. more than 45%. The continuous degradation of natural resources and unsustainable pattern of economic activity creates the worst economic situation and poverty in this study area. Lower the Sex Ratio, Higher the Social Vulnerability. Sub-Districts with Low Sex Ratio: Ramganj, Faridganj, Sandwip, Chatkil, Matlab, Shahrasti, Daganbhu, Muladi Vulnerable Sub-Districts: Manpura,Dashima, Kotalipara, Tazumuddin, Galachipa, Kala Para, Hatiya, Ramgati, Bhedarganj, Haim Char, Rampal etc. Very High High Moderate Low Very Low Indian Bengal Delta Bangladesh Delta Vulnerable Districts: 1. Cox’s Bazar 2. South 24 Parganas 3. Bhola 4. Satkhira 5. Patuakhali 6. Lakshmipur GBM Delta