Climate Change & Adaptation
5. Policy implications and conclusions
From this investigation we can formulated various recommendation to strengthen the role of vocational education and training to enhance household resilience to disasters. These are applicable both to local government and non-government organization who have direct contributions, and to central government, national and non-government agencies, who support local actors by developing policies, donating and delineating new agenda. Three major policy implications can be drawn from the case study. First, the number of existing vocational institute is not enough and most of them located district level which is far away from costal household level. It seems to be costly to bear of expenditures for the study from long distance by poor family and reduced their interest. The adequacy and effectiveness vocational institutes can be established by GO and NGOs in local level for poorer households, enhancing the opportunity to achieve VET, especially for those residents who early left out from school and unemployed. Second, the dissemination of information about the positive impact of vocational education and training to be reached to the households to enhance their interests. Moreover, institute can presents success story of VET from succeed individuals within their community. Finally, the government and non- government organization can increase the number of VET program on some specialized field such as livestock and crop cultivation, fishing and aquaculture, bee keeping, business and trade, tailoring and boutique with study scholarship which is very urgent for coastal residents in Bangladesh.
6. References
Adger WN (2000) Social and ecological resilience: are they related? Progress in Human Geography 24(3):347-364.
Adger WN (2003) Building resilience to promote sustainability: an agenda for coping with globalisation and promoting justice. International Human Dimensions Programme on Global Environmental Change (IHDP) Newsletter 2, Bonn, Germany.
Adger WN, Hughes TP, C Folke, S R Carpenter, and J Rockström (2005) Social ecological resilience to coastal disasters. Science 309:1036-1039.
Afiouni, F (2013) Human Capital Management: a new name for HRM?. Int. J Learning and Intellectual Capital 10(1):18-34.
Akter S, Mallick, B (2013) The poverty–vulnerability–resilience nexus: Evidence from Bangladesh.
Ecological Economics 96: 114-124.
Alam E, Collins AE (2010) Cyclone disaster vulnerability and response experiences in coastal Bangladesh.
Disasters 34(4):931–54.
Alam G. M (2009) Vocational training and linkage with income. Journal of ILO office in Bangladesh, 1(3).
Alam G. M (2010) A proper plan needed in place for upcoming technical, vocational, education projects.
Journal of University of Malaya 2(3):72-83.
Alan K. M. A, Altman Y et al (2008) Employee Training Needs and Perceived Value of Training in the Pearl River Delta of China: A Human Capital Development Approach. Journal of European Industrial Training 32(1):19-31.
Aldrich DP (2010) The power of people: social capital’s role in recovery from the 1995 Kobe earthquake.
Nat. Hazard, 56:596–611.
Aldrich DP (2012) Building Resilience: Social Capital in Post-disaster Recovery. Chicago, IL: University of Chicago Press.
Ashton D, Green F (1996) Education, Training and the Global Economy. Cheltenham: Edward Elgar.
Asian Development Bank (ADB) (2008) Education and Skills: strategies for accelerated development in Asia and the Pacific (Manila, Department of External Relations).
Bangladesh Meteorological Department-BMD (2013) Named Cyclone over Bay of Bengal during2005 – till date. Dhaka: Ministry of Defense, Government of the People’s Republic of Bangladesh.
Becker GS (1962) ‘Investment in human capital: a theoretical analysis’, Journal of Political Economy 70(5):9–49.
Becker GS (1992) Human Capital: A theoretical and empirical analysis with special reference to education.
Chicago 60637: The University of Chicago Press.
Bontis N (1998) Intellectual capital: an exploratory study that develops measures and models. Management Decision 36:63–76.
Bozbura et al (2007) Prioritization of human capital measurement indicators using fuzzy AHP. Expert System with Applications (32):1100-1112.
Brand FS, Jax K (2007) Focusing the Meaning(s) of Resilience: Resilience as a Descriptive. Ecology and Society 12(1): 23 [online] URL: http://www.ecologyandsociety.org/vol12/iss1/art23/.
Braun B, & Aßheuer, T (2011) Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Natural Hazards, 58(2). http://dx.doi.org/10.1007/s11069-011-97525
BTEB (2006) BTEB annual report (2005-06). Bangladesh technical education board report.
BTEB (2009) Guide book of vocational education system. Bangladesh technical education board report.
Combaz E (2014) Disaster resilience: Topic guide. Birmingham, U´K.
Crawford R (1991) In the Era of Human Capital. New York: Harpercollins.
Cuaresma JC (2010) Natural Disaster and Human Capital Accumulation. THE WORLD BANK ECONOMIC REVIEW 24(2):280-302.
Cutter SL, Ash KD, Emrich CT (2014) The geographies of community disaster resilience. Global Environmental Change 29:65-77.
Cutter SL, Barnes L, Berry M, et al (2008a) A place-based model for understanding community resilience to natural disasters. Glob. Environ. Change 18:598–606.
Cutter SL, Burton C G, Emrich C T, (2010) Disaster resilience indicators for benchmarking baseline conditions. J. Homel. Secur. Emerg. Manag. 7, Article 51:1–22.
Dasgupta S, et al (2014) Cyclones in a changing climate: the case of Bangladesh. Clim. Dev (2): 96–110.
Dau MQ (2013) Factor endowment, human capital, and inequalities in developing countries. Journal of Economic Studies 98-106.
European Training Foundation (2007b) Financing Vocational Education and Training: priorities and mechanisms in South Eastern Europe (Luxembourg, EU Publications Office).
Fahr R (2005) Loafing or learning?—the demand for informal education. European Economic Review 49(1):
75-98.
Harpana I, Draghicia A (2014) Debate on the multilevel model of the human capital measurement. Procedia Social and Behavioral Sciences 124:170–177.
Hossain MN (2015) Analysis of human vulnerability to cyclone and storm surges based on influencing physical and socioeconomic factors: Evidence from coastal Bangladesh. International Journal of Disaster Risk Reduction 13:66-75.
Hudner D, K J. (2014). DO FINANCIAL SERVICES BUILD? Mercy Corps. Portland, Oregon 97204:
MERCY CORPS.
Labour Organisation).
Islam R, Walkerden G (2014) How bonding and bridging networks contribute to disaster resilience and recovery on the Bangladeshi coast. International Journal of Disaster Risk Reduction 281-291.
Kwon, Dae-Bong (2009) Human capital and its measurement. The 3rd OECD World Forum on "Statistics, Knowledge and Policy" Charting Progress, Building Visions, Improving Life Busan, Korea: Korea University: 05-35
Lepak DP, Snell SA (1999) The human resource architecture: Toward a theory of human capital allocation and development. Academy of Management Review 24:31-48.
Lucas R (1988) On the Mechanics of Economic Development. Journal of Monetary Economics, 22(1):3-42.
Mayunga JS (2007) Understanding and Applying the Concept of Community Disaster Resilience: A Capital- based Approach. Summer Academy for Social Vulnerability and Resilience Building 1–16.
Naughton L (2013) Geographical narratives of social capital: Telling different stories about the socio- economy with context, space, place, power and agency. Progress in Human Gepgraphy 1-19.
Newaz et al (2013) Vocational education and training in Bangladesh: Why it is. International Journal of Research Studies in Education 2:29-40.
NICOLA GENNAIOLI, R L D. (2012). HUMAN CAPITAL AND REGIONAL DVELOPMENT. The Quarterly Journal of Economics, 105-164.
Norris FH, Stevens SP, Pfefferbaum B, Wyche KF, Pfefferbaum RL (2008) Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. Am. J. Community Psychol 41: 127–
150.
OECD (2001) The Well-being of Nations. The Role of Human and Social Capital (Paris, OECD).
Paton D, Johnston D (2006) Disaster Resilience: An Integrated Approach. Charles C. Thomas, Springfield, IL
Paul BK (1998) Coping with the 1996 tornado in Tangail, Bangladesh: an analysis of field data. The Professional Geographer 50(3): 287–301.
Paul SK, Routray JK (2011) Household response to cyclone and induced surge in coastal Bangladesh: coping strategies and explanatory variables .Nat.Hazards 57(2):477–99.
Pelling M (2003) The Vulnerability of Cities: Social Resilience and Natural Disaster. Earthscan, London.
Philip Young P Hong, S P (2007). Human capital as structural vulnerability of US poverty. Equal Opportunities International 26:18-43.
Putnam RD (1995) Bowling alone: America’s declining social capital. J. Democr 6(1):65–78.
Ritchie LA, Gill DA (2007) Social capital theory as an integrating framework for technological disaster research. Sociol. Spectr. 27:1–26.
Romer PM. (1990) Human Capital and Growth: Theory and Evidence. Carnegie-Rochester Conference Series on Public Policy 32:251–286
Rose A (2007) Economic resilience to natural and man-made disasters: multidisciplinary origins and contextual dimensions. Environ. Hazards 7(4):383–398.
SAKDAPOLRAK MK (2013) WHAT IS SOCIAL RESILIENCE? LESSION LEARNED AND WAYS FORWARD. ERDKUNDE 67: 5-9.
Sheffrin MS (2003) Economics: Principles in Action. New Jersey: Pearson Prentice Hall
Sherrieb K, Louis CA, Pfefferbaum RL, Pfefferbaum JDB, Diab E, Norris FH (2012) Assessing community resilience on the US coast using school principals as key informants. Int. J. Disaster Risk Reduction 2:6–15.
Sutter D, Simmons KM (2010) Tornado fatalities and mobile homes in the United States. Nat. Hazards 53:
125–137
THULSTRUP AW (2015) Livelihood Resilience and Adaptive Capacity: Tracing Changes. World Development 74:352–362.
Tierney K, Bruneau M (2007) Conceptualizing and Measuring Resilience: A Key to Disaster Loss Reduction. TR News (May-June):14–17.
Tobin GA, Whiteford LM (2012) Provisioning capacity: a critical component of vulnerability and resilience under chronic volcanic eruptions. In: Pfeifer, K., Pfeifer, N. (Eds.), Forces of Nature and Cultural Responses.
Springer : 136–166
Tudor SL (2013) Formal - Non-formal – Informal In Education. Procedia - Social and Behavioral Sciences 76: 821 – 826.
Tzu-Shian Han et al (2008) Developing human capital indicators: a three way approach. Int. J. Learning and Intellectual Capital 5:387-403.
WALLENBORN M (2010) Vocational Education and Training and Human. European Journal of Education 45: 181-198.
Walters P (2015) The problem of community resilience in two flooded cities: Dhaka. Habitat International, Habitat International 50:51-56.
Zhou H, Wang J, Wan J, Jia H (2010) Resilience to natural hazards: a geographic perspective. Nat. Hazards 53:21–41.
Title: Preliminary Assessment Of Socio-Economic Vulnerability In The Coastal Region: A Case Study Of Barguna District
Momtaz Jahan, Rubaiya Kabir, Noor-E-Ashmaul Husna, Anisul Haque and Md. Munsur Rahman
1. Introduction
Coastal zones are the interfaces of land and ocean balancing geosphere, atmosphere and biosphere; major biological activity centers favorable for easy living and about 3 billion people are living in the coastal zones. The coastal area of Bangladesh is very unique and diversified with variety of resources and activities. Coastline is 710 km along the north and north-east part of the Bay of Bengal. The coast can be broadly divided into three regions:
the deltaic eastern region (Pacific type), the deltaic central region, and the stable deltaic western region (Atlantic type). The zone constitutes 32 percent of the area and 28 percent of the population of Bangladesh (Islam, 2004).
Coastal area is a hub of diversified activities and functions that create or comes to the people as resources. It has biodiversity, marine ecology, fishery, estuary and lots of other things that are called resources. However, some problems often create disasters and make the lives hard and the whole coastal ecosystem is being disturbed. Coastal area is vulnerable to several natural disasters like Cyclone, storm surge, river bank erosion, salinity intrusion etc. In case of climate change scenario, it is already predicted that the situation will be worsen. The severity of climate change impacts or any natural or manmade disaster depends not only on the nature of climate hazards and the resilience of natural ecosystems, but also on factors such as the degree of socio-economic development, social inequalities, human adaptive capacities, health status and health services, demographic characteristics, economic livelihood alternatives etc. Therefore, socio- economic information is an integral part of assessing impacts and vulnerability to disaster or climate change, as well as in adaptation planning. Socio-economic information can highlight the differential exposure to climate threats of coastal regions and communities with different socio-economic endowments. It is also a crucial ingredient for any assessment of vulnerabilities and adaptive capacities of different economic sectors and communities
of the coastal region and for understanding how they will be affected by threat of disaster or climate change. For identifying the intense of vulnerability, the demand for socio- economic information is wide-ranging and includes economic and demographic data, such as GDP and population distribution, analyses of land use and land-use changes, etc. Socio- economic information is assessed for vulnerability assessments mainly through the construction of indicators. These indicators together represent a simple assessment that describes the resultant socio-economic vulnerability for particular region of coastal area.
Prior going to the main study it is essential to have a view of what this socio-economic vulnerability is and what does it means.
Environmental disasters are the combined result of stress and exposure on one hand, and the fragility and vulnerability of the human society on the other hand (Weichselgartner, 2001; Turner et al., 2003; Adger et al., 2004; UN/ISDR, 2004; Downing and Patwardhan, 2004; Kasperson and Kasperson, 2005; Birkmann, 2006b). As the amount of losses from disasters increases at a striking pace, to understand and to define socio-economic
vulnerability becomes highly important, especially concerning present degrading situation.
The common understanding of social vulnerability and economic vulnerability and the ability to measure it become keys to addressing disasters through managing the consequences and setting targets (Kasperson and Kasperson, 2005; Birkmann, 2006a).
According to the study of Cutter, Boruff and Shirley (2003) "There is a general consensus within the social science community about some of the major factors that influence social vulnerability. These include lack of access to resources (including information, knowledge, and technology); limited access to political power and representation; social capital, including social networks and connections; beliefs and customs; building stock and age; frail and physically limited individuals; and type and density of infrastructure and lifelines (Cutter et al, 2001). Disagreements arise in the selection of specific variables to represent these broader concepts." These factors were considered when developing and selecting the indices.
This research attempts to construct a picture of socioeconomic context of vulnerability by focusing on indicators that measure both the state of development of the study area as well as its capacity to progress further. It also indicates current potential socio-economic vulnerability of the study area. This also attempts to suggest strategies of intervention and mitigation strategies in the context of current socio-economic condition.
2. Study Area
About 710 km long coast of Bangladesh comprising the complex delta of the Ganges Brahmaputra-Meghna river system has immense resources for development. Bangladesh has an area of about 144,000 square kilometres and a population of more than 140 million, of which 23% of the total population lives in the coastal region. It is situated in the north- eastern part of the South Asian subcontinent and has a vast sea area to the south in the Bay of Bengal (a northern, extended arm of Indian Ocean, covering about 510,000 square kilometres).
Fig. 1: Study Area
of Patuakhali district which was upgraded to district in 1984. Barguna district is bounded on the north by Barisal, Jhalokati and Patuakhali districts, on the east by Patuakhali district, on the south by the Bay of Bengal and on the west by Pirojpur district and a part of Sundarbans under Bagerhat district. It lies between 21º48′ and 22º29′ north latitudes and between 89º52′ and 90º22′ east longitudes. The total area of the district is 1,831.31 sq.km.
(707.07 sq.miles) of which 399.74 sq.km. is riverine and 97.18 sq.km. is under forest. The maximum and minimum temperature of Barguna district varies from 33.3°C to 12.1°C.
The annual average rainfall of the district is recorded as 2506 mm.
3. Methodology
Vulnerability is the combination of exposure, sensitivity and adaptive capacity. Exposure and sensitivity are also termed as potential impact for any hazard event. Adaptive capacity is the ability of people, organizations and systems, using available skills and resources, to face and manage adverse conditions, emergencies or disasters. Vulnerability is the degree to which a system, subsystem, or system component is likely to experience harm due to exposure and sensitivity to a hazard, either a perturbation of stress/stressor. In calculative terms, Potential Impact –Adaptive Capacity = Vulnerability.
For analysis of Socio-Economic vulnerability, based on literature review and socio- economic conditions of the study area six social indicators and six economic indicators were selected. The social indicators are (a) Population density (b) Literacy rate, (c) Male/Female ratio, (d) Social dependence, (e) Water Supply and (f) Cyclone shelter. The economic indicators are (a) Poverty rate, (b) GDP, (c) Type of household, (d) Proportion of crop land, (e) Cropping intensity and (f) Road density. Related data for the selected indicators were collected from the Census Data 2011. (BBS, 2011)
For vulnerability assessment, as social indicator for potential impact, where population density is higher, vulnerability is also higher and same perception consider for male/female ratio, social dependency( no. of women, children and elderly) and water supply condition. For adaptive capacity, where literacy rate is higher, vulnerability lowers and where there is large number of cyclone shelter established, it also lowers vulnerability.
As economic indicator for potential impact, where poverty rate is higher, vulnerability is also higher and same perception consider for GDP, type of Kucha household and proportion of crop land. For adaptive capacity, where cropping intensity and road density are higher, vulnerability lowers.
Each indicator value was normalized using equation (1) and scored in the scale of 0 to 100.
Score
=
Actual
−Wor st Best (1)
−Worst
Individual indicator map was prepared using ArcGIS. The individual indicator maps are then combined using weighted sum tool of ArcGIS to prepared social and economic vulnerability maps. All the indicators were given equal preference as they are equally important to determine the vulnerability condition of the study area. Then the combined Socio-Economic Vulnerability map was prepared.
Tab 1: Weights for socio-economic vulnerability
Information type Weight
Social Vulnerability 0.4
Economic Vulnerability 0.6
Based on expert opinion economic vulnerability was given higher preferences over social vulnerability as economic vulnerability is dominant in determining the resiliency. A comparative map was also prepared to show the comparison of the social and economic vulnerability of the study area.
4. Results and Discussion The individual social indicator maps are shown in the figure 2.
(a) (b)
(c) (d)
(e) (f)
Figure 2: Social Indicators (a) Population density, (b) Literacy rate, (c) Male/Female ratio, (d) Social dependence, (e) Water Supply and (f) Cyclone shelter Maps
The individual economic indicator maps are shown in the figure 3.
(a) (b)
(c) (d)
(e) (f)
Figure 3: Economic Indicators (a) Poverty rate, (b) GDP, (c) Type of household, (d) Proportion of crop land, (e) Cropping intensity and (f) Road density Maps
Based on the actual value of each indicator, scoring was done in a scale of 0 to 100 which is shown in Table 2. From the analysis, in Barguna district, Bamna upazila got all the five social indicators maximum score but social dependence zero where Amtali upazila is the most vulnerable because of high vulnerable score in social dependence and water supply and Patharghata is the least vulnerable because of its higher adaptive capacity.
Table 2: Social Vulnerability Calculation
Social Indicators
Potential Impact Adaptive Capacity
Upazila Population Male/Female Social Water Literacy Cyclone Vulnerability
Density ratio Dependence Supply Rate Shelter
Amtali 0 25 98.958 97.921 0 20.678 201.201
Bamna 100 100 0 100 100 12.786 187.214
BargunaSadar 48.418 75 9.895 92.723 69.880 0 156.158
Betagi 78.345 0 100 93.971 87.952 100 84.364
Patharghata 11.436 75 9.993 0 92.771 53.765 0.000
The Social Vulnerability map of the study area is shown in figure 4.
Figure 4: Social Vulnerability Map
In terms of economic vulnerability, Barguna Sadar is the most vulnerable in Barguna district. Higher poverty rate indicates the people are more vulnerable to any disaster while road density is considered as the coping capacity because it increases the mobility of people during any hazardous event. The economic vulnerability calculation is shown in table 3 following the economic vulnerability map in the figure 5.
Table 3: Economic Vulnerability Calculation Economic Indicators
Potential Impact Adaptive Capacity
Vulnerability Upazila Poverty GDP Type of Prop. Of Cropping Road
Rate household crop land Intensity Density
Amtali 7 100 0 100 0 13.442 193.979
Bamna 0 0 100 27 100 17.066 9.582
Barguna
1 95 91 93 17.649 11.144 251.632
Sadar
Betagi 100 20 65 0 47.856 100 36.389
Patharghata 95 44 84 40 16.041 0 246.938
Figure 5: Economic Vulnerability Map
The calculation for Socio-Economic Vulnerability is shown in Table 4.
Table 4: Socio-Economic Vulnerability Calculation
Upazila Social Economic Socio-Economic
Name Vulnerability Vulnerability Vulnerability
Amtali 201.201 193.979 196.868
Bamna 187.214 9.582 80.635
BargunaSadar 156.158 251.632 213.442
Betagi 84.364 36.389 55.579
Patharghata 0 246.938 148.163
The final Socio-Economic Vulnerability map is shown in figure 6.
Figure 6: Socio-Economic Vulnerability Map
With the above assessment, it can be said that Amtali Upazila and Barguna Sadar Upazila are vulnerable in socio-economic analysis respect. This result shows the higher vulnerability of exposed coastal areas as exposed coasts are mainly susceptible to cyclones and storm surge and subjected to severe damages frequently which generally impact on socio-economic conditions. This result also indicates the economic vulnerability higher than social vulnerability of Barguna district.