• 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: geo.shk@gmail.com

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.

Correlation

Pearson's r Avg.HH_Size Disability Sex RatioAgri_Dependan

cyPoverty Kutcha House

Avg.HH_Size - -0.143 0.067 -0.332 -0.208 0.243Disability -0.143 - 0.221 0.178 0.005 -0.572Sex Ratio 0.067 0.221 - -0.031 0.049 -0.019

Agri_Dependancy -0.332 0.178 -0.031 - 0.485 0.101Poverty -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

0.8Avg. HH Size

Disability

Sex Ratio

Agricultural

Dependency

Poverty

Kutcha House

Habra-II

Habra-I

Sonarpur

Barasat-I

Gaighata

Rajarhat

Thakurpukur-

MaheshtalaBarrackpur-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 Minimum Median Maximum

Avg.HH_Size 0.408 0.207 0.00 0.368 1.00

Disability 0.420 0.225 0.00 0.360 1.00

Sex Ratio 0.681 0.123 0.00 0.696 1.00

Agri_Dependancy 0.438 0.181 0.00 0.441 1.00

Poverty 0.522 0.229 0.00 0.547 1.00

Kutcha House 0.608 0.335 0.00 0.744 1.00

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