Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

Mehebub Sahana; Sufia Rehman; Ashish Kumar Paul; Haroon Sajjad

doi:10.1080/24749508.2019.1700670

Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

Sahana, Mehebub; Rehman, Sufia; Paul, Ashish Kumar; Sajjad, Haroon 2021-01-02 00:00:00 GEOLOGY, ECOLOGY, AND LANDSCAPES 2021, VOL. 5, NO. 1, 40–52 INWASCON https://doi.org/10.1080/24749508.2019.1700670 RESEARCH ARTICLE Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India a b c b Mehebub Sahana ,Suﬁa Rehman , Ashish Kumar Paul and Haroon Sajjad Environmental Information System (ENVIS) Centre, Indira Gandhi Conservation Monitoring Centre-Geographic Information System (IGCMC), WWF-India, New Delhi, India; Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India; Department of Geography & Environment Management, Vidyasagar University, Midnapur, West Bengal, India ABSTRACT ARTICLE HISTORY Received 18 August 2019 Indian Sundarban Biosphere Reserve (SBR), a fragile ecosystem, is susceptible to frequent Accepted 28 November 2019 cyclones, ﬂoods, and storm surge. The study impeccably analyzed the socio-economic vulner- ability in SBR using pragmatic approach. Average storm surge height, slope amount, ﬂood KEYWORDS inundation, drainage proximity, and drainage density were used for assessing exposure while Vulnerability; exposure; sensitivity and adaptation were examined from the data derived through a comprehensive sensitivity; adaptation; ﬁeld survey of 570 households in SBR. The revelation of the study manifested very high pragmatic approach; SBR vulnerability in Basanti, Gosaba, Kultali, Namkhana, and Patharpratima blocks and high vulner- ability in Kakdwip, Sagar, and Hingalganj blocks of SBR. Constant exposure to cyclones and storm surges, frivolous infrastructural setup, impoverish social structure, and lamentation of losses are major barriers to overall socio-economic upliftment of communities. Consolidated infrastructural setup, proper early warning system, disaster monitoring centres, better trans- port connectivity within remote islands, better livelihood opportunities, education, and aware- ness may help in improving the socio-economic conditions of the communities. Pragmatic approach assisted in the cogent understanding of climate change impacts and indicated adaptive and mitigation measures to improve coastal society in SBR. Thus, the approach has proven to be eﬀective for analyzing the impact of climate change-induced hazards on socio- economic condition on the communities in coastal areas. 1. Introduction the extreme disaster events inclusively have risen around 151% during the last 20 years (UNISDR, IPCC (2007) has remarkably stated that “scientiﬁc evi- 2018). Developing nations are the most vulnerable to dence for warming of the climate system is unequivocal.” the destructions and damages caused by abrupt disas- Rising concerns over climate change impacts have ter events. Studies demonstrated that impoverish led to the realization of intrinsic variability in climatic groups are more vulnerable to disasters and suﬀer regimes on the planet Earth. Researchers across the most from their consequences (Twigg, 2004; globe are concerned about understanding of climate UNISDR, 2018; Wisner, Gaillard, & Kelman, 2012). change and exploring its relative impacts on the eco- Vulnerability can be assessed through analyzing the system (Moss et al., 2010). The temperature has relationship between physical and social systems using increased up to 1°C, and the last ﬁve years recorded a range of techniques. Selection of suitable site-speciﬁc the highest temperature globally (UNDP: United indicators is required to address multifaceted issues for Nations Development Programme, 2019). If the tem- vulnerability assessment (Hahn, Riederer, & Foster, perature continues to rise at the current rate, it is 2009). Various scholars have utilized appropriate meth- plausible that it may reach 1.5°C globally between ods to assess vulnerability, e.g., gap method (Sullivan, 2030 and 2052. The mean global temperature is also Meigh, & Fediw, 2002), human development index anticipated to reach around 4°C by the end of the (Bray, Jemal, Grey, Ferlay, & Forman, 2012), composite century (IPCC, 2018). Climate-induced disasters vulnerability index (Rygel, O’sullivan, & Yarnal, 2006), have also become evident in many parts of the world. sustainable livelihood security index (Sajjad & Nasreen, Nearly 1.3 million deaths and 1.4 billion injuries were 2016), and fuzzy logic (Ahmed et al., 2018). Index- reported due to geophysical and climatic disasters based vulnerability analysis helps in explicit vulnerabil- (storms, ﬂoods, tsunamis, heatwaves, drought, earth- ity assessment by integrating various indicators mani- quake, etc.) during 1998–2017. These vicious disasters festing diﬀerent vulnerability scenarios. Scholars have have caused displacement and rendered homeless of widely used these indices as eﬀective policy tools many people in disaster-aﬀected nations. Losses from CONTACT Haroon Sajjad haroon.geog@gmail.com Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India Supplemental data for this article can be accessed here. © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 41 (Kelkar, Balachandra, & Gurtoo, 2011;Malakar & complexity of varied problems. These indices are more Mishra, 2017). Vulnerability assessment in a hazard- critical for climate change assessment (Sathyan, Funk, aﬀected region depends on its social and economic Aenis, Winker, & Breuer, 2018). Various antecedent conditions (Malakar & Mishra, 2017). Such an assess- approaches were adopted to predict the future conse- ment is very essential for determining the degree of quences of global climate change. One such approach suﬀering of the relied population and economic struc- popularly known as pragmatism claimedwideacknowl- ture due to disasters. It can be accomplished using both edgment in various disciplines. Pragmatistic thinking the data sources, i.e.,, primary (Challinor, Simelton, was evolved in the United States during the late nine- Fraser, Hemming, & Collins, 2010) and secondary teenth and the advent of the twentieth centuries. This (Sahana & Sajjad, 2019). Vulnerability assessment and approach consisted of method and seeking truth behind adaptation to climate change have inculcated cogitation the theory (Hammersley,2018). A practical approach to among the scientiﬁc community (Tian & Lemos, 2018). climate mitigation and adaptation is essential for eﬃca- Successively, various case studies were carried out to cious hazard preparedness and indelible management. examine the vulnerability to natural hazards (Bohle, Thus, the pragmatic approach calls for the practical Downing, & Watts, 1994;Dumenu&Obeng, 2016; implementation of actions to identify the best practices Fischer, Shah, Tubiello, & Van Velhuizen, 2005; for solving the problem (Miettinen, Samra-Fredericks, Owusu & Nursey-Bray, 2019). Abid, Schilling, & Yanow, 2009). Scheﬀran, and Zulﬁqar (2016) examined the farm- Indian Sundarban Biosphere Reserve (SBR) sets an level vulnerability to changing climate and extreme example of a dynamic ecosystem having the largest weather events using vulnerability components and mangrove stretch among the important biodiversity farmers’ aﬃrmation. Climate change impacts on the reserves in the world. Impacts of cyclones, ﬂoods, and socio-ecological system are complex and dynamic due storm surge are being evident since its existence to inherent heterogeneity and uncertainty and require (Mukhopadhyay, 2009). Climate change has also con- precise assessment. Pandey and Bardsley (2015)exam- tributed to increasing the frequency of extreme weather ined socio-ecological vulnerability to climate change events (Raha, Das, Banerjee, & Mitra, 2012). The fre- and unscrupulous resource distribution in the quent onset of climate change-induced disasters has Himalayan region. Vulnerability assessment aims to always been detrimental to the local community. distinguish the implications of changing climate and Thus, a social and economic vulnerability assessment adaptation strategies in a given system. is imperative for understanding the smack of disasters Scientiﬁc engineering and technical measures were and the formulation of eﬃcient management strategies. prominent for vulnerability assessment during the However, a scientiﬁc assertion on this problem is scant 1970s (Brooks, 2003), while social science-oriented (Slettebak, 2013). In this backcloth, this paper intends a approaches were carried out for vulnerability assess- cogent examination of socio-economic vulnerability ment during the 1980s. Vulnerability was partially ana- using pragmatic approach in the Reserve. The main lyzed using the earlier approach which was later focus has been given to the increasing vulnerability of replaced by a human-oriented approach involving the local community to evident disaster events and environmental, social, economic, institutional, and eco- changing climate scenarios. Sea-level rise, cyclonic nomic parameters (Blaikie, Cannon, Davis, & Wisner, depressions, ﬂoods, and rising temperature have signif- 2005; Ciurean, Schröter, & Glade, 2013). Vulnerability icantly contributed to induce vulnerability in the region assessment varies with the people, place, and type of (Ghosh, Schmidt, Fickert, & Nüsser, 2015;Mahadevia disaster implications (Dintwa, Letamo, & Ghimire & Vikas, 2012). Climate change has also threa- Navaneetham, 2019). Vulnerability assessment in large tened the health of mangroves (Manna & areas requires a more holistic and interdisciplinary Raychaudhuri, 2018). Thus, socio-economic vulnerabil- approach (Ciurean et al., 2013). Most of the studies on ity assessment has important consideration in the social vulnerability assessment have followed a semi- Indian SBR. We used a bootstrap methodology for quantitative approach based on spatial, socio-economic, identifying and selecting site-speciﬁc indicators for con- demographic, and ﬁeld-derived indicators (Fekete, structing a vulnerability index. The vulnerability analy- 2019). The index-based disaster resilience assessment sis through this framework can be useful for assessing is an integral part of the management and planning of relative regional vulnerability and identifying priority natural hazards. Indices are helpful in ascertaining the regions for lessening the impact of climate change- changes driven by hazards and priority areas of concern induced vulnerability. which may be based on inductive, deductive, qualita- tive, and quantitative approaches (Ogie & Pradhan, 2. Study area 2019). Composite indices are informative, analytical, and collaborative. They facilitate eﬀective decision- With immense heterogeneity in biodiversity, SBR is situ- making, help in planning, and assist in raising the con- ated at the vertex of Bay of Bengal between 21°40ʹ to 22° cerns for policy incentives by recognizing the 40ʹ north latitudes and 88°03ʹ to 89°07ʹ east longitudes 42 M. SAHANA ET AL. (Mitra, Banerjee, Sengupta, & Gangopadhyay, 2009). Of economic activities in SBR (Mahadevia Ghimire & 2 2 the total area of the Reserve (9630 km ), 4264 km is Vikas, 2012). under mangrove forest, 2195 km is under wetlands cover, and 5,366 km is under built up. SBR consists of 3. Database and methodology 19 blocks (administrative divisions of the district) spreading over north and south Twenty-Four Parganas The composite socio-economic index was constructed districts of West Bengal (Hazra, Ghosh, DasGupta, & as a function of exposure, sensitivity, and adaptation. Sen, 2002). This magniﬁcent deltaic ecosystem sustains Average storm surge height, slope amount, ﬂood inun- a large variety of mangrove species (Excoecaria dation, drainage proximity, and drainage density were agallochaLinn., Porteresia coarctata Roxb., Phoenix palu- used for assessing exposure. Flood inundation layer dosa Roxb., etc.) and beasts (Royal Bengal Tiger, various was prepared using a shuttle radar topographic mis- reptiles, spotted dear, marine turtles, and Gangetic dol- sion digital elevation model (SRTM DEM-1 arc sec- phins). Having 102 islands (48 inhabited and 54 being ond) through spatial modeling. Proximity to the uninhabited), SBR enjoys a tropical wet climate with a drainage layer was prepared by digitizing the rivers short dry spell between November and April. The from topographical sheet (1:50,000) and Google earth Reserve experiences high relative humidity and tempera- and using buﬀer analysis in ArcGIS. Euclidean dis- ture throughout the year with heavy rainfall during tance function in ArcGIS was used to prepare the monsoon season. The minimum temperature ranges drainage density layer. The slope was calculated from between 2° and 4°C, while the maximum reaches to 43° SRTM DEM data using spatial analysis tool in ArcGIS, C in March. Mean annual precipitation ranges between while data of average storm surge height were 150 and 200 cm. Tropical cyclones, storm surges, and obtained from Indian Meteorological Department ﬂoods are the common phenomena during monsoon (IMD) and converted into raster format using the (Figure 1). Hooghly, Ganga, Muriganga,and Ichamati IDW function in ArcGIS. The data regarding indica- are the signiﬁcant rivers in the study area. SBR owes tors of sensitivity and adaptation were collected at the recent geological origin (6000–7000 years before present) household level using multi-stage cluster sampling as an outcome of long depositional work of River Ganga method. We ﬁrst selected villages from the study and Bay of Bengal (Banerjee, Senthilkumar, Purvaja, & area. Each block was divided into two strata (nearer Ramesh, 2012;Gopal&Chauhan, 2006). This fragile to waterbody and one situated in the mainland). From delta is a home of 4.37 million people and provides each of these strata, one village was selected randomly. immense resources for their sustenance. Agriculture is From each block (the study area is divided into 19 the mainstay of the economy, while prawn cultivation, blocks), two villages were selected randomly. In this ﬁshing, and honey collection are the other important way, 38 villages were selected from the study area. In Figure 1. (a) Location of West Bengal in India, (b) location of SBR in West Bengal state, and (c) location of community development blocks in SBR. GEOLOGY, ECOLOGY, AND LANDSCAPES 43 the second stage, the selection of households was made Component vulnerability score was then calculated by on the basis of a stratiﬁed random sampling technique. averaging the weighted score of all sub-components In this stage, the strata considered were occupation of for each domain category: the community (cultivators, ﬁshermen, daily wage ðWISÞ k¼1 k labourers, businessmen, and government servants). COV ¼P i n ðaverage weightÞ k¼1 k From each occupation class, three households were selected. In this way, 15 households were selected where COV is the component score of the vulnerabil- from each village. Thus, a total 570 households were ity index of each block; WIS is the weighted index selected for in-depth study. A questionnaire was score of each sub-component. designed to collect the relevant information related Composite socio-economic vulnerability with its to socio-economic vulnerability. The questionnaire three components of adaptation capacity, sensitivity, contained questions on household proﬁle, demo- and exposure following IPCC pragmatic approach was graphic, social, economic, health, food and water, calculated: and physical and infrastructural indicators. Suﬃcient DO care was taken to make the questionnaire communic- COV ¼ adaptation capacity able to the respondents. One of the authors belonged to the study area and worked as a major source of help in asking questions. IPCC pragmatic approach was DO COV ¼ sensitivity used to construct a composite socio-economic vulner- ability index (SeVI) using six major socio-economic components and their sub-indicators (Figure 2, COV ¼ DO exposure m m¼1 Appendix 1). The sub-components were standardized between 0 where j, l, and m denote the number of components and 1. Each sub-component has an equal contribution under exposure, sensitivity, and adaptive capacity to the SeVI index, and a balanced weighted approach (Table 4), and “i” denotes the blocks. Finally, the was used for calculating the SeVI (Hahn et al., 2009): block-level composite socio-economic vulnerability (SeVI) was obtained: Weighted index score ðWISÞ DM þ DM þ DM adaptation capacity sensitivity exposure ¼ðcomponent index scoreÞðaverage index scoreÞ SeVI ¼ Figure 2. Methodological framework of the study. 44 M. SAHANA ET AL. livestock death, and percentage of households suﬀered 3.1 Rationale for the selection of indicators from home damage were chosen for assessing eco- Vulnerability demands assessment and policy mea- nomic conditions of surveyed households in SBR. sure, and thus, scholars have used various indicators Distance to health centre, percentage of households to quantify the degree of vulnerability (Abuodha & aﬀected from vector-borne diseases, percentage of Woodroﬀe, 2010; Hahn et al., 2009; Luers, Lobell, households aﬀected with waterborne diseases, percen- Sklar, Addams, & Matson, 2003; Orencio, 2014; Sam, tage of households with death caused due to natural Kumar, Kächele, & Müller, 2017; Szlafsztein & Sterr, hazards, and percentage of households attending 2007). In the present study, we have examined socio- health awareness camp and frequent check-up in the economic vulnerability in SBR using its major compo- near hospital were chosen as sub-components for nents and their site-speciﬁc sub-indicators. Major assessing health vulnerability. components, namely, demographic, social, economic, Socio-economic vulnerability assessment incapaci- food and water, and infrastructural components were tates the analysis of the direction of socio-economic used to examine the degree of sensitivity and adapta- implications caused by climate change-induced disas- tion. Exposure was analyzed using average storm surge ters. Income, type of housing structures, health status, height, slope amount, ﬂood inundation, drainage level of education, family structure, disabled popula- proximity, and drainage density indicators. Slope tion, and occupation have been identiﬁed as the essen- amount is an important factor in highlighting the tial components for vulnerability assessment (Demel, coastal susceptibility to ﬂoods and storm surges. The Udayanga, Gajanayake, Hapuarachchi, & Perera, 2019). lower slope is more aﬀected by seawater penetration Percentage of the sampled households with own agri- and inundation (Kumar, Mahendra, Nayak, cultural production, percentage of households which Radhakrishnan, & Sahu, 2010). The coastal areas of have lost their agricultural land, percentage of house- SBR are adversely aﬀected by ﬂood inundation and holds fetching water from >1 km distance, percentage of storm surge; thus, these indicators were chosen to households using pond water as drinking water, per- analyze the vulnerability of the coastal blocks. Higher centage of households using boiled water for drinking, stream density results in a higher probability of spread and percentage of households having water facilities and inundation (Golladay & Battle, 2002). Mean within premises sub-components were chosen to assess stream density, stream frequency, and morphology of the food and water vulnerability. Percentage of house- the basin determine the ﬂuvial dynamics such as ﬂood holds without electricity facility, percentage of house- occurrence, frequency, and size of the aﬀected region holds without toilet facility, percentage of households (Bhattacharjee, 2016). The demographic vulnerability with no accessibility to paved road, percentage of was assessed by using six important sub-components, households having cemented house, distance to school, namely, dependency ratio in the sampled households and percentage of households located along the river/ (HHs), percentage of illiterate household heads, per- road side sub-components were used for assessing the centage of female-headed households, percentage of physical and infrastructural vulnerability. household heads with higher education, percentage of households with basic housing structure, and percen- tage of old and child population. Age, gender, class, 4. Results and economic status assume greater signiﬁcance in Most of the coastal blocks of SBR have experienced analyzing disaster risk reduction (Ayeb-Karlsson et high and very high vulnerability. High degree of expo- al., 2019). sure and sensitivity while low level of adaptation has The social characteristics such as level of education, substantially contributed to very high to high vulner- income, and disabled population are signiﬁcant indi- ability of these blocks. Demographic, social, economic, cators for the risk mitigation process (Papathoma- food and water, and infrastructure components con- Köhle, Cristofari, Wenk, & Fuchs, 2019). Six sub- tributed to varying degrees of vulnerability in these components, namely, percentage of illiterate house- blocks. Coastal blocks were found more exposed to holds, percentage of landless households, percentage coastal disasters and sensitive to the damages driven of scheduled castes/scheduled tribes (SC/ST) house- by them. Low level of adaptation attributed to very holds, percentage of households with family members high and high vulnerability in the coastal blocks of migrated outside, percentage of households with pri- SBR. Most of the sampled households of coastal mary source of income, and ratio of non-agricultural islands were badly aﬀected by asset loss and home income to total income were chosen for assessing damages. Soil erosion and coastal inundation are social vulnerability in SBR. Percentage of households major causes of vector-borne and waterborne diseases depending on natural sources for income, percentage among the sampled households. The composite socio- of unemployed households, percentage of households economic vulnerability revealed that low health status below poverty line, percentage of households with and less access to food and water, low economic asset and land losses, percentage of households with GEOLOGY, ECOLOGY, AND LANDSCAPES 45 condition, and low provision of infrastructure are economic, health, food and water, and physical and infra- causes for high and very highsocio-economic vulner- structural factors increased the sensitivity of these blocks. ability of the sampled households in SBR. The compo- The high dependency ratio, unemployment, high propor- nents of the SeVI are presented in Figure 3. tion of child and women, unavailability of electricity, sanitation facilities, high vector and waterborne diseases, and unavailability of paved roads were identiﬁed signiﬁ- 4.1 Relative performance of vulnerability cant sub-indicators for raising the degree of sensitivity of components these blocks. Hingalganj, Sagar, and Kakdwip blocks experienced high sensitivity due to low social, demo- Very high exposure was found in Patharpratima, graphic, and economic structure. Most of the households Namkhana, Kultali, and Gosaba blocks. All the factors in these blocks were illiterate, largely aﬀected by disaster of exposure were found inducing very high exposure in damages, and located along the rivers. Moderate sensitiv- these blocks. Basanti, Kakdwip, Sagar, Sandeshkhali II, ity prevailed in Sandeshkhali II, Hasnabad, and and Hasnabad blocks experienced high exposure. High Sandeshkhali II blocks. Demographic, health, and food drainage density, drainage proximity, and high storm and water components contributed moderate sensitivity surge height were identiﬁed the main factors for in these blocks (Figure 5 and Table 2). Minakhan, increasing the exposure in these blocks. Only two Mathurapur I, Mathurapur II, Jaynagar II, Haroa, blocks, namely, Sandeshkhali I and Minakhan blocks Canning II, Jaynagar I, and Canning I blocks were experi- were found under moderate exposure. Low exposure enced low sensitivity. These blocks have a good socio- was found in Mathurapur I, Mathurapur II, Haroa, economic status than the rest of the blocks in SBR. Jaynagar I, Jaynagar II, Canning I, and Canning II In the case of adaptation, Jaynagar II, Haroa, blocks (Figure 4 and Table 1). These blocks being Canning II, Jaynagar I, Canning I, and Mathurapur located away from the coast are less aﬀected by ﬂood II blocks registered very high adaptation. The high inundation and storm surge. adaptation was identiﬁed in Minakhan, Mathurapur Sensitivity analysis revealed that Patharpratima, I, Kakdwip, and Sandeshkhali I blocks. These blocks Namkhana, Kultali, Gosaba, and Basanti blocks were have performed well in higher education, have a good very highly sensitive in SBR. Demographic, social, Figure 3. Components of socio-economic vulnerability index: (a) demographic, (b) economic, (c) health, (d) social, (e) food and water, and (f) physical and infrastructure. 46 M. SAHANA ET AL. Figure 4. Degree of household exposure in SBR. Figure 5. Degree of household sensitivity in SBR. health condition, have a high percentage of cemented Patharpratima, Namkhana, Kultali, Gosaba, and Basanti houses, and have sources of water within their pre- blocks. These blocks registered a high degree of exposure mises. Only two blocks, namely, Sagar and Basanti and sensitivity. The frequent onset of disasters, lack of experienced moderate adaptation, while Hasnabad, early warning system, and unscrupulous health facilities Sandeshkhali II, Namkhana, Gosaba, Patharpratima, are also responsible for increasing the vulnerability of Hingalganj, and Kultali blocks were found under low these blocks. Thus, adaptation must be increased to lessen adaptation (Table 3 and Figure 6). These blocks regis- the impact of vulnerability in these blocks. High vulner- tered low level of higher education, lack of amenities ability prevailed in Sagar, Kakdwip, and Hingalganj and facilities, and prevalence of muddy structures. blocks of SBR. High degree of exposure and sensitivity Eﬀective adaptation measures are required to increase while low adaptation contributed high vulnerability in the level of adaptation in these blocks. these blocks. Minakhan, Sandeshkhali I, Sandeshkhali II, and Hasnabad blocks were found under moderate vul- nerability, while Jaynagar II, Haroa, Mathurapur I, 4.2 Composite socio-economic vulnerability (SeVI) Mathurapur II, Canning II, Jaynagar I, and Canning I blocks found under low vulnerability (Table 4 and The vulnerability scores are presented in Figure 7. Figure 8). These blocks are situated inland areas and Analysis of composite socio-economic vulnerability have ahighdegreeofadaptationthanthe coastalblocks. (SeVI) revealed a very high vulnerability in Table 1. Index values of exposure indicators in SBR. Block Flood inundation Proximity to the drainage Drainage density Slope amount Average storm surge height Exposure Canning I 0.21 0.23 0.26 0.21 0.24 0.23 Jaynagar I 0.27 0.26 0.27 0.24 0.31 0.27 Canning II 0.27 0.25 0.24 0.25 0.28 0.25 Mathurapur II 0.31 0.31 0.35 0.29 0.29 0.31 Mathurapur I 0.36 0.33 0.32 0.41 0.39 0.36 Haroa 0.32 0.31 0.33 0.29 0.35 0.32 Jaynagar II 0.31 0.29 0.32 0.29 0.34 0.31 Hasnabad 0.59 0.59 0.54 0.62 0.63 0.59 Sandeshkhali I 0.54 0.56 0.58 0.55 0.56 0.55 Sandeshkhali II 0.68 0.68 0.67 0.71 0.68 0.68 Minakhan 0.52 0.56 0.55 0.51 0.51 0.53 Hingalganj 0.81 0.79 0.75 0.76 0.78 0.78 Kakdwip 0.69 0.71 0.71 0.67 0.67 0.69 Sagar 0.7 0.69 0.67 0.66 0.68 0.68 Basanti 0.76 0.76 0.77 0.74 0.73 0.75 Gosaba 0.79 0.82 0.83 0.78 0.83 0.81 Kultali 0.87 0.89 0.88 0.87 0.81 0.86 Namkhana 0.85 0.91 0.95 0.98 0.96 0.93 Patharpratima 0.86 0.92 0.96 0.97 0.95 0.93 Source: Authors’ own calculations based on satellite data. GEOLOGY, ECOLOGY, AND LANDSCAPES 47 Table 2. Index values of sensitivity indicators in SBR. Block Demographic Social Economic Health Food & water Physical & Infrastructural Sensitivity Index Canning I 0.21 0.25 0.18 0.19 0.22 0.20 0.21 Jaynagar I 0.20 0.21 0.23 0.24 0.24 0.21 0.22 Canning II 0.23 0.22 0.22 0.26 0.24 0.24 0.23 Mathurapur II 0.24 0.30 0.32 0.36 0.29 0.28 0.30 Mathurapur I 0.34 0.27 0.31 0.31 0.31 0.33 0.31 Haroa 0.28 0.25 0.30 0.34 0.32 0.24 0.29 Jaynagar II 0.29 0.32 0.27 0.28 0.32 0.27 0.29 Hasnabad 0.43 0.40 0.43 0.39 0.41 0.42 0.41 Sandeshkhali-I 0.42 0.43 0.38 0.40 0.37 0.38 0.40 Sandeshkhali-II 0.42 0.41 0.38 0.41 0.44 0.43 0.41 Minakhan 0.37 0.38 0.39 0.38 0.42 0.37 0.39 Hingalganj 0.75 0.69 0.71 0.75 0.72 0.72 0.72 Kakdwip 0.71 0.67 0.67 0.68 0.64 0.69 0.68 Sagar 0.73 0.71 0.69 0.71 0.71 0.72 0.71 Basanti 0.81 0.79 0.75 0.78 0.78 0.81 0.79 Gosaba 0.81 0.82 0.76 0.78 0.82 0.77 0.79 Kultali 0.83 0.79 0.80 0.84 0.84 0.79 0.82 Namkhana 0.88 0.85 0.87 0.88 0.84 0.86 0.86 Patharpratima 0.90 0.91 0.95 0.92 0.89 0.90 0.91 Source: Authors’ own calculations based on ﬁeld survey (2018) Table 3. Index values of adaptation indicators in SBR. Block Demographic Social Economic Health Food and water Physical and infrastructural Adaptive capacity Canning I 0.63 0.64 0.65 0.7 0.7 0.71 0.67 Jaynagar I 0.69 0.66 0.69 0.72 0.71 0.69 0.69 Canning II 0.72 0.75 0.79 0.65 0.68 0.69 0.71 Mathurapur II 0.62 0.65 0.68 0.69 0.66 0.62 0.65 Mathurapur I 0.63 0.61 0.62 0.63 0.62 0.61 0.62 Haroa 0.63 0.72 0.75 0.78 0.72 0.71 0.71 Jaynagar II 0.71 0.74 0.69 0.73 0.75 0.76 0.73 Hasnabad 0.38 0.4 0.39 0.4 0.42 0.41 0.4 Sandeshkhali I 0.54 0.52 0.48 0.52 0.55 0.58 0.53 Sandeshkhali II 0.39 0.38 0.39 0.49 0.36 0.34 0.39 Minakhan 0.64 0.65 0.64 0.62 0.61 0.64 0.63 Hingalganj 0.32 0.34 0.3 0.3 0.3 0.29 0.31 Kakdwip 0.53 0.54 0.53 0.52 0.53 0.54 0.53 Sagar 0.52 0.53 0.5 0.5 0.51 0.52 0.51 Basanti 0.42 0.43 0.45 0.42 0.4 0.39 0.41 Gosaba 0.33 0.32 0.34 0.35 0.36 0.34 0.34 Kultali 0.26 0.29 0.29 0.29 0.31 0.33 0.29 Namkhana 0.35 0.33 0.34 0.34 0.34 0.34 0.34 Patharpratima 0.29 0.28 0.33 0.32 0.32 0.32 0.31 Source: Authors’ own calculations based on ﬁeld survey (2018). Table 4. SeVI and its component indices in SBR. 5. Discussion and policy implication Composite The overall analysis of socio-economic vulnerability to socio-economic Exposure Sensitivity Adaptation vulnerability climate change revealed that the majority of the Blocks indices indices indices index sampled households in SBR was highly aﬀected by Canning I 0.23 0.21 0.67 0.37 ﬂood and cyclone hazards. Decrease in ﬁsh production Jaynagar I 0.27 0.22 0.69 0.39 Canning II 0.25 0.23 0.71 0.40 and salinization of agricultural land are the major Mathurapur II 0.31 0.30 0.65 0.42 threats to coastal communities. Water- and vector- Mathurapur I 0.36 0.31 0.62 0.43 Haroa 0.32 0.29 0.71 0.44 borne diseases and inadequacy of medical facilities Jaynagar II 0.31 0.29 0.73 0.44 are also aﬀecting the health of the people. Many agri- Hasnabad 0.59 0.41 0.4 0.47 Sandeshkhali I 0.55 0.40 0.53 0.49 cultural lands in coastal blocks were severely aﬀected Sandeshkhali II 0.68 0.41 0.39 0.49 by salinity intrusion and thus rendered without culti- Minakhan 0.53 0.39 0.63 0.52 vations aftermath of Aila cyclone in 2009. Moreover, Hingalganj 0.78 0.72 0.31 0.60 Kakdwip 0.69 0.68 0.53 0.63 due to huge losses of mangrove forests, there are Sagar 0.68 0.71 0.51 0.63 several restrictions from the government for the col- Basanti 0.75 0.79 0.41 0.65 Gosaba 0.81 0.79 0.34 0.65 lection of forest products. Consequently, large-scale Kultali 0.86 0.82 0.29 0.66 migration has occurred from these coastal blocks. Namkhana 0.93 0.86 0.34 0.71 Patharpratima 0.93 0.91 0.31 0.72 This has aﬀected the socio-economic conditions of Source: Authors’ own calculations based on satellite data and ﬁeld survey SBR to a larger extent. (2018). 48 M. SAHANA ET AL. increasing the knowledge about disaster implication at the households’ level and assist in location-speciﬁc vulnerability assessment (Ahsan & Warner, 2014; Kontogianni, Damigos, Kyrtzoglou, Tourkolias, & Skourtos, 2019; Sorg et al., 2018). SeVI analysis revealed that Patharpratima, Namkhana, Kultali, Gosaba, Basanti, Sagar, Kakdwip, Hingalganj, Minakhan, Sandeshkhali II, Sandeshkhali I, and Hasnabad blocks require immediate attention to mini- mize the level of vulnerability (Table 3). These blocks have registered a high degree of exposure and sensi- tivity with negligible adaptation. These priority blocks are located along the coast and have been experiencing huge devastation due to disasters. Thus, eﬀorts could be made to increase the adaptive capacity in these blocks as improving the early warning system, disaster preparedness, infrastructural development, provision of basic health facilities, and improvement of infra- structure to reduce the socio-economic vulnerability. Jaynagar II, Haroa, Mathurapur I, Mathurapur II, Figure 6. Degree of household adaptation in SBR. Canning II, Jaynagar I, and Canning I blocks also require attention to minimize the degree of exposure SeVI has been identiﬁed as an eﬀective tool for while eﬀorts should be made to minimize the sensitiv- analyzing vulnerability at the household level. It ity in Canning II, Jaynagar I, and Canning I blocks of enhances disaster management intervention by SBR (Table 5). Figure 7. IPCC-dimension-wise vulnerability scores for the blocks of SBR. GEOLOGY, ECOLOGY, AND LANDSCAPES 49 6. Conclusion This article has explored the extent of household vulnerability in the tenuous ecosystem of the Indian SBR using pragmatic approach. A total of 6 socio-economic components and 36 sub-compo- nents were used to develop the composite SeVI. Spatial analysis of composite socio-economic vul- nerability revealed very high vulnerability in Basanti, Gosaba, Kultali, Namkhana, and Patharpratima blocks. These blocks are highly exposed to extreme weather events and socio-eco- nomically sensitive. Kakdwip, Sagar, and Hingalganj blocks experienced high socio-economic vulnerability due to deprived social structure, fri- volous infrastructure, low economic condition, and meagre healthcare facilities. These blocks also suf- fered from severe water, food, and health problems. Many of the sampled households were found using pond or river water as drinking water in these blocks. Mathurapur I, Mathurapur II, Haroa, Figure 8. Composite socio-economic vulnerability index (SeVI) of the sampled households in various blocks of SBR. Minakhan, Sandeshkhali I, Sandeshkhali II, and Hasnabad blocks came under moderate socio-eco- nomic vulnerability. Mathurapur I, Mathurapur II, Table 5. Priority blocks for vulnerability reduction in SBR. and Jaynagar II require immediate attention and Exposure Sensitivity Adaptation Blocks indices indices indices eﬃcacious policy measures for socio-economic Canning I √√ – development in SBR. Jaynagar I √√ – Strengthening the social system with provision of Canning II √√ – Mathurapur II √ –– economic opportunities is essential for lessening Mathurapur I √ –– socio-economic vulnerability. Education and aware- Haroa √ –– ness among local communities may ameliorate the Jaynagar II √ –– Hasnabad √√ √ understanding of the magnitude and implications of Sandeshkhali I √√ √ severe weather events. Consolidated infrastructural Sandeshkhali II √√ √ Minakhan √√ √ setup, proper early warning system, disaster monitor- Hingalganj √√ √ ing centres, and better transport connectivity within Kakdwip √√ √ Sagar √√ √ remote islands may enhance the mobility and provide Basanti √√ √ long-term sustainability in the development of SBR. Gosaba √√ √ Kultali √√ √ Improvement in healthcare facilities, indurate Namkhana √√ √ embankment along the rivers and villages located Patharpratima √√ √ along coastal areas, capturing better livelihood oppor- Source: Authors’ own calculation based on SeVI analysis. tunities as tourism, minimizing population pressure on resources, mangrove conservation, and disaster risk management are other measures which if adopted Formulation of holistic vulnerability framework, may uplift the physical and social structure of the inclusion of coastal communities in suggesting adaptative Reserve. Moreover, enhancing the traditional activities measures, and eﬀective coastal zone management plan may have a credible positive impact than advancing will be helpful in lessening the impact of vulnerability in the new ones. Further, vulnerability studies may SBR. However, scientiﬁc uncertainty leads to decision include analysis of the magnitude of disaster damages, paralysis and biasness in policy for local-level adaptation. and more inherent indicators for vulnerability assess- Cost-eﬀective solutions, increasing knowledge among ment must be incorporated. Use of comprehensive coastal communities, enhancing resistance capacity, facil- index may also increase the feasibility of climate itating disaster response, and enhancing coordination change-induced vulnerability assessment. 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Publisher: Taylor & Francis
Copyright: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).
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DOI: 10.1080/24749508.2019.1700670
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Abstract

GEOLOGY, ECOLOGY, AND LANDSCAPES 2021, VOL. 5, NO. 1, 40–52 INWASCON https://doi.org/10.1080/24749508.2019.1700670 RESEARCH ARTICLE Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India a b c b Mehebub Sahana ,Suﬁa Rehman , Ashish Kumar Paul and Haroon Sajjad Environmental Information System (ENVIS) Centre, Indira Gandhi Conservation Monitoring Centre-Geographic Information System (IGCMC), WWF-India, New Delhi, India; Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India; Department of Geography & Environment Management, Vidyasagar University, Midnapur, West Bengal, India ABSTRACT ARTICLE HISTORY Received 18 August 2019 Indian Sundarban Biosphere Reserve (SBR), a fragile ecosystem, is susceptible to frequent Accepted 28 November 2019 cyclones, ﬂoods, and storm surge. The study impeccably analyzed the socio-economic vulner- ability in SBR using pragmatic approach. Average storm surge height, slope amount, ﬂood KEYWORDS inundation, drainage proximity, and drainage density were used for assessing exposure while Vulnerability; exposure; sensitivity and adaptation were examined from the data derived through a comprehensive sensitivity; adaptation; ﬁeld survey of 570 households in SBR. The revelation of the study manifested very high pragmatic approach; SBR vulnerability in Basanti, Gosaba, Kultali, Namkhana, and Patharpratima blocks and high vulner- ability in Kakdwip, Sagar, and Hingalganj blocks of SBR. Constant exposure to cyclones and storm surges, frivolous infrastructural setup, impoverish social structure, and lamentation of losses are major barriers to overall socio-economic upliftment of communities. Consolidated infrastructural setup, proper early warning system, disaster monitoring centres, better trans- port connectivity within remote islands, better livelihood opportunities, education, and aware- ness may help in improving the socio-economic conditions of the communities. Pragmatic approach assisted in the cogent understanding of climate change impacts and indicated adaptive and mitigation measures to improve coastal society in SBR. Thus, the approach has proven to be eﬀective for analyzing the impact of climate change-induced hazards on socio- economic condition on the communities in coastal areas. 1. Introduction the extreme disaster events inclusively have risen around 151% during the last 20 years (UNISDR, IPCC (2007) has remarkably stated that “scientiﬁc evi- 2018). Developing nations are the most vulnerable to dence for warming of the climate system is unequivocal.” the destructions and damages caused by abrupt disas- Rising concerns over climate change impacts have ter events. Studies demonstrated that impoverish led to the realization of intrinsic variability in climatic groups are more vulnerable to disasters and suﬀer regimes on the planet Earth. Researchers across the most from their consequences (Twigg, 2004; globe are concerned about understanding of climate UNISDR, 2018; Wisner, Gaillard, & Kelman, 2012). change and exploring its relative impacts on the eco- Vulnerability can be assessed through analyzing the system (Moss et al., 2010). The temperature has relationship between physical and social systems using increased up to 1°C, and the last ﬁve years recorded a range of techniques. Selection of suitable site-speciﬁc the highest temperature globally (UNDP: United indicators is required to address multifaceted issues for Nations Development Programme, 2019). If the tem- vulnerability assessment (Hahn, Riederer, & Foster, perature continues to rise at the current rate, it is 2009). Various scholars have utilized appropriate meth- plausible that it may reach 1.5°C globally between ods to assess vulnerability, e.g., gap method (Sullivan, 2030 and 2052. The mean global temperature is also Meigh, & Fediw, 2002), human development index anticipated to reach around 4°C by the end of the (Bray, Jemal, Grey, Ferlay, & Forman, 2012), composite century (IPCC, 2018). Climate-induced disasters vulnerability index (Rygel, O’sullivan, & Yarnal, 2006), have also become evident in many parts of the world. sustainable livelihood security index (Sajjad & Nasreen, Nearly 1.3 million deaths and 1.4 billion injuries were 2016), and fuzzy logic (Ahmed et al., 2018). Index- reported due to geophysical and climatic disasters based vulnerability analysis helps in explicit vulnerabil- (storms, ﬂoods, tsunamis, heatwaves, drought, earth- ity assessment by integrating various indicators mani- quake, etc.) during 1998–2017. These vicious disasters festing diﬀerent vulnerability scenarios. Scholars have have caused displacement and rendered homeless of widely used these indices as eﬀective policy tools many people in disaster-aﬀected nations. Losses from CONTACT Haroon Sajjad haroon.geog@gmail.com Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India Supplemental data for this article can be accessed here. © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 41 (Kelkar, Balachandra, & Gurtoo, 2011;Malakar & complexity of varied problems. These indices are more Mishra, 2017). Vulnerability assessment in a hazard- critical for climate change assessment (Sathyan, Funk, aﬀected region depends on its social and economic Aenis, Winker, & Breuer, 2018). Various antecedent conditions (Malakar & Mishra, 2017). Such an assess- approaches were adopted to predict the future conse- ment is very essential for determining the degree of quences of global climate change. One such approach suﬀering of the relied population and economic struc- popularly known as pragmatism claimedwideacknowl- ture due to disasters. It can be accomplished using both edgment in various disciplines. Pragmatistic thinking the data sources, i.e.,, primary (Challinor, Simelton, was evolved in the United States during the late nine- Fraser, Hemming, & Collins, 2010) and secondary teenth and the advent of the twentieth centuries. This (Sahana & Sajjad, 2019). Vulnerability assessment and approach consisted of method and seeking truth behind adaptation to climate change have inculcated cogitation the theory (Hammersley,2018). A practical approach to among the scientiﬁc community (Tian & Lemos, 2018). climate mitigation and adaptation is essential for eﬃca- Successively, various case studies were carried out to cious hazard preparedness and indelible management. examine the vulnerability to natural hazards (Bohle, Thus, the pragmatic approach calls for the practical Downing, & Watts, 1994;Dumenu&Obeng, 2016; implementation of actions to identify the best practices Fischer, Shah, Tubiello, & Van Velhuizen, 2005; for solving the problem (Miettinen, Samra-Fredericks, Owusu & Nursey-Bray, 2019). Abid, Schilling, & Yanow, 2009). Scheﬀran, and Zulﬁqar (2016) examined the farm- Indian Sundarban Biosphere Reserve (SBR) sets an level vulnerability to changing climate and extreme example of a dynamic ecosystem having the largest weather events using vulnerability components and mangrove stretch among the important biodiversity farmers’ aﬃrmation. Climate change impacts on the reserves in the world. Impacts of cyclones, ﬂoods, and socio-ecological system are complex and dynamic due storm surge are being evident since its existence to inherent heterogeneity and uncertainty and require (Mukhopadhyay, 2009). Climate change has also con- precise assessment. Pandey and Bardsley (2015)exam- tributed to increasing the frequency of extreme weather ined socio-ecological vulnerability to climate change events (Raha, Das, Banerjee, & Mitra, 2012). The fre- and unscrupulous resource distribution in the quent onset of climate change-induced disasters has Himalayan region. Vulnerability assessment aims to always been detrimental to the local community. distinguish the implications of changing climate and Thus, a social and economic vulnerability assessment adaptation strategies in a given system. is imperative for understanding the smack of disasters Scientiﬁc engineering and technical measures were and the formulation of eﬃcient management strategies. prominent for vulnerability assessment during the However, a scientiﬁc assertion on this problem is scant 1970s (Brooks, 2003), while social science-oriented (Slettebak, 2013). In this backcloth, this paper intends a approaches were carried out for vulnerability assess- cogent examination of socio-economic vulnerability ment during the 1980s. Vulnerability was partially ana- using pragmatic approach in the Reserve. The main lyzed using the earlier approach which was later focus has been given to the increasing vulnerability of replaced by a human-oriented approach involving the local community to evident disaster events and environmental, social, economic, institutional, and eco- changing climate scenarios. Sea-level rise, cyclonic nomic parameters (Blaikie, Cannon, Davis, & Wisner, depressions, ﬂoods, and rising temperature have signif- 2005; Ciurean, Schröter, & Glade, 2013). Vulnerability icantly contributed to induce vulnerability in the region assessment varies with the people, place, and type of (Ghosh, Schmidt, Fickert, & Nüsser, 2015;Mahadevia disaster implications (Dintwa, Letamo, & Ghimire & Vikas, 2012). Climate change has also threa- Navaneetham, 2019). Vulnerability assessment in large tened the health of mangroves (Manna & areas requires a more holistic and interdisciplinary Raychaudhuri, 2018). Thus, socio-economic vulnerabil- approach (Ciurean et al., 2013). Most of the studies on ity assessment has important consideration in the social vulnerability assessment have followed a semi- Indian SBR. We used a bootstrap methodology for quantitative approach based on spatial, socio-economic, identifying and selecting site-speciﬁc indicators for con- demographic, and ﬁeld-derived indicators (Fekete, structing a vulnerability index. The vulnerability analy- 2019). The index-based disaster resilience assessment sis through this framework can be useful for assessing is an integral part of the management and planning of relative regional vulnerability and identifying priority natural hazards. Indices are helpful in ascertaining the regions for lessening the impact of climate change- changes driven by hazards and priority areas of concern induced vulnerability. which may be based on inductive, deductive, qualita- tive, and quantitative approaches (Ogie & Pradhan, 2. Study area 2019). Composite indices are informative, analytical, and collaborative. They facilitate eﬀective decision- With immense heterogeneity in biodiversity, SBR is situ- making, help in planning, and assist in raising the con- ated at the vertex of Bay of Bengal between 21°40ʹ to 22° cerns for policy incentives by recognizing the 40ʹ north latitudes and 88°03ʹ to 89°07ʹ east longitudes 42 M. SAHANA ET AL. (Mitra, Banerjee, Sengupta, & Gangopadhyay, 2009). Of economic activities in SBR (Mahadevia Ghimire & 2 2 the total area of the Reserve (9630 km ), 4264 km is Vikas, 2012). under mangrove forest, 2195 km is under wetlands cover, and 5,366 km is under built up. SBR consists of 3. Database and methodology 19 blocks (administrative divisions of the district) spreading over north and south Twenty-Four Parganas The composite socio-economic index was constructed districts of West Bengal (Hazra, Ghosh, DasGupta, & as a function of exposure, sensitivity, and adaptation. Sen, 2002). This magniﬁcent deltaic ecosystem sustains Average storm surge height, slope amount, ﬂood inun- a large variety of mangrove species (Excoecaria dation, drainage proximity, and drainage density were agallochaLinn., Porteresia coarctata Roxb., Phoenix palu- used for assessing exposure. Flood inundation layer dosa Roxb., etc.) and beasts (Royal Bengal Tiger, various was prepared using a shuttle radar topographic mis- reptiles, spotted dear, marine turtles, and Gangetic dol- sion digital elevation model (SRTM DEM-1 arc sec- phins). Having 102 islands (48 inhabited and 54 being ond) through spatial modeling. Proximity to the uninhabited), SBR enjoys a tropical wet climate with a drainage layer was prepared by digitizing the rivers short dry spell between November and April. The from topographical sheet (1:50,000) and Google earth Reserve experiences high relative humidity and tempera- and using buﬀer analysis in ArcGIS. Euclidean dis- ture throughout the year with heavy rainfall during tance function in ArcGIS was used to prepare the monsoon season. The minimum temperature ranges drainage density layer. The slope was calculated from between 2° and 4°C, while the maximum reaches to 43° SRTM DEM data using spatial analysis tool in ArcGIS, C in March. Mean annual precipitation ranges between while data of average storm surge height were 150 and 200 cm. Tropical cyclones, storm surges, and obtained from Indian Meteorological Department ﬂoods are the common phenomena during monsoon (IMD) and converted into raster format using the (Figure 1). Hooghly, Ganga, Muriganga,and Ichamati IDW function in ArcGIS. The data regarding indica- are the signiﬁcant rivers in the study area. SBR owes tors of sensitivity and adaptation were collected at the recent geological origin (6000–7000 years before present) household level using multi-stage cluster sampling as an outcome of long depositional work of River Ganga method. We ﬁrst selected villages from the study and Bay of Bengal (Banerjee, Senthilkumar, Purvaja, & area. Each block was divided into two strata (nearer Ramesh, 2012;Gopal&Chauhan, 2006). This fragile to waterbody and one situated in the mainland). From delta is a home of 4.37 million people and provides each of these strata, one village was selected randomly. immense resources for their sustenance. Agriculture is From each block (the study area is divided into 19 the mainstay of the economy, while prawn cultivation, blocks), two villages were selected randomly. In this ﬁshing, and honey collection are the other important way, 38 villages were selected from the study area. In Figure 1. (a) Location of West Bengal in India, (b) location of SBR in West Bengal state, and (c) location of community development blocks in SBR. GEOLOGY, ECOLOGY, AND LANDSCAPES 43 the second stage, the selection of households was made Component vulnerability score was then calculated by on the basis of a stratiﬁed random sampling technique. averaging the weighted score of all sub-components In this stage, the strata considered were occupation of for each domain category: the community (cultivators, ﬁshermen, daily wage ðWISÞ k¼1 k labourers, businessmen, and government servants). COV ¼P i n ðaverage weightÞ k¼1 k From each occupation class, three households were selected. In this way, 15 households were selected where COV is the component score of the vulnerabil- from each village. Thus, a total 570 households were ity index of each block; WIS is the weighted index selected for in-depth study. A questionnaire was score of each sub-component. designed to collect the relevant information related Composite socio-economic vulnerability with its to socio-economic vulnerability. The questionnaire three components of adaptation capacity, sensitivity, contained questions on household proﬁle, demo- and exposure following IPCC pragmatic approach was graphic, social, economic, health, food and water, calculated: and physical and infrastructural indicators. Suﬃcient DO care was taken to make the questionnaire communic- COV ¼ adaptation capacity able to the respondents. One of the authors belonged to the study area and worked as a major source of help in asking questions. IPCC pragmatic approach was DO COV ¼ sensitivity used to construct a composite socio-economic vulner- ability index (SeVI) using six major socio-economic components and their sub-indicators (Figure 2, COV ¼ DO exposure m m¼1 Appendix 1). The sub-components were standardized between 0 where j, l, and m denote the number of components and 1. Each sub-component has an equal contribution under exposure, sensitivity, and adaptive capacity to the SeVI index, and a balanced weighted approach (Table 4), and “i” denotes the blocks. Finally, the was used for calculating the SeVI (Hahn et al., 2009): block-level composite socio-economic vulnerability (SeVI) was obtained: Weighted index score ðWISÞ DM þ DM þ DM adaptation capacity sensitivity exposure ¼ðcomponent index scoreÞðaverage index scoreÞ SeVI ¼ Figure 2. Methodological framework of the study. 44 M. SAHANA ET AL. livestock death, and percentage of households suﬀered 3.1 Rationale for the selection of indicators from home damage were chosen for assessing eco- Vulnerability demands assessment and policy mea- nomic conditions of surveyed households in SBR. sure, and thus, scholars have used various indicators Distance to health centre, percentage of households to quantify the degree of vulnerability (Abuodha & aﬀected from vector-borne diseases, percentage of Woodroﬀe, 2010; Hahn et al., 2009; Luers, Lobell, households aﬀected with waterborne diseases, percen- Sklar, Addams, & Matson, 2003; Orencio, 2014; Sam, tage of households with death caused due to natural Kumar, Kächele, & Müller, 2017; Szlafsztein & Sterr, hazards, and percentage of households attending 2007). In the present study, we have examined socio- health awareness camp and frequent check-up in the economic vulnerability in SBR using its major compo- near hospital were chosen as sub-components for nents and their site-speciﬁc sub-indicators. Major assessing health vulnerability. components, namely, demographic, social, economic, Socio-economic vulnerability assessment incapaci- food and water, and infrastructural components were tates the analysis of the direction of socio-economic used to examine the degree of sensitivity and adapta- implications caused by climate change-induced disas- tion. Exposure was analyzed using average storm surge ters. Income, type of housing structures, health status, height, slope amount, ﬂood inundation, drainage level of education, family structure, disabled popula- proximity, and drainage density indicators. Slope tion, and occupation have been identiﬁed as the essen- amount is an important factor in highlighting the tial components for vulnerability assessment (Demel, coastal susceptibility to ﬂoods and storm surges. The Udayanga, Gajanayake, Hapuarachchi, & Perera, 2019). lower slope is more aﬀected by seawater penetration Percentage of the sampled households with own agri- and inundation (Kumar, Mahendra, Nayak, cultural production, percentage of households which Radhakrishnan, & Sahu, 2010). The coastal areas of have lost their agricultural land, percentage of house- SBR are adversely aﬀected by ﬂood inundation and holds fetching water from >1 km distance, percentage of storm surge; thus, these indicators were chosen to households using pond water as drinking water, per- analyze the vulnerability of the coastal blocks. Higher centage of households using boiled water for drinking, stream density results in a higher probability of spread and percentage of households having water facilities and inundation (Golladay & Battle, 2002). Mean within premises sub-components were chosen to assess stream density, stream frequency, and morphology of the food and water vulnerability. Percentage of house- the basin determine the ﬂuvial dynamics such as ﬂood holds without electricity facility, percentage of house- occurrence, frequency, and size of the aﬀected region holds without toilet facility, percentage of households (Bhattacharjee, 2016). The demographic vulnerability with no accessibility to paved road, percentage of was assessed by using six important sub-components, households having cemented house, distance to school, namely, dependency ratio in the sampled households and percentage of households located along the river/ (HHs), percentage of illiterate household heads, per- road side sub-components were used for assessing the centage of female-headed households, percentage of physical and infrastructural vulnerability. household heads with higher education, percentage of households with basic housing structure, and percen- tage of old and child population. Age, gender, class, 4. Results and economic status assume greater signiﬁcance in Most of the coastal blocks of SBR have experienced analyzing disaster risk reduction (Ayeb-Karlsson et high and very high vulnerability. High degree of expo- al., 2019). sure and sensitivity while low level of adaptation has The social characteristics such as level of education, substantially contributed to very high to high vulner- income, and disabled population are signiﬁcant indi- ability of these blocks. Demographic, social, economic, cators for the risk mitigation process (Papathoma- food and water, and infrastructure components con- Köhle, Cristofari, Wenk, & Fuchs, 2019). Six sub- tributed to varying degrees of vulnerability in these components, namely, percentage of illiterate house- blocks. Coastal blocks were found more exposed to holds, percentage of landless households, percentage coastal disasters and sensitive to the damages driven of scheduled castes/scheduled tribes (SC/ST) house- by them. Low level of adaptation attributed to very holds, percentage of households with family members high and high vulnerability in the coastal blocks of migrated outside, percentage of households with pri- SBR. Most of the sampled households of coastal mary source of income, and ratio of non-agricultural islands were badly aﬀected by asset loss and home income to total income were chosen for assessing damages. Soil erosion and coastal inundation are social vulnerability in SBR. Percentage of households major causes of vector-borne and waterborne diseases depending on natural sources for income, percentage among the sampled households. The composite socio- of unemployed households, percentage of households economic vulnerability revealed that low health status below poverty line, percentage of households with and less access to food and water, low economic asset and land losses, percentage of households with GEOLOGY, ECOLOGY, AND LANDSCAPES 45 condition, and low provision of infrastructure are economic, health, food and water, and physical and infra- causes for high and very highsocio-economic vulner- structural factors increased the sensitivity of these blocks. ability of the sampled households in SBR. The compo- The high dependency ratio, unemployment, high propor- nents of the SeVI are presented in Figure 3. tion of child and women, unavailability of electricity, sanitation facilities, high vector and waterborne diseases, and unavailability of paved roads were identiﬁed signiﬁ- 4.1 Relative performance of vulnerability cant sub-indicators for raising the degree of sensitivity of components these blocks. Hingalganj, Sagar, and Kakdwip blocks experienced high sensitivity due to low social, demo- Very high exposure was found in Patharpratima, graphic, and economic structure. Most of the households Namkhana, Kultali, and Gosaba blocks. All the factors in these blocks were illiterate, largely aﬀected by disaster of exposure were found inducing very high exposure in damages, and located along the rivers. Moderate sensitiv- these blocks. Basanti, Kakdwip, Sagar, Sandeshkhali II, ity prevailed in Sandeshkhali II, Hasnabad, and and Hasnabad blocks experienced high exposure. High Sandeshkhali II blocks. Demographic, health, and food drainage density, drainage proximity, and high storm and water components contributed moderate sensitivity surge height were identiﬁed the main factors for in these blocks (Figure 5 and Table 2). Minakhan, increasing the exposure in these blocks. Only two Mathurapur I, Mathurapur II, Jaynagar II, Haroa, blocks, namely, Sandeshkhali I and Minakhan blocks Canning II, Jaynagar I, and Canning I blocks were experi- were found under moderate exposure. Low exposure enced low sensitivity. These blocks have a good socio- was found in Mathurapur I, Mathurapur II, Haroa, economic status than the rest of the blocks in SBR. Jaynagar I, Jaynagar II, Canning I, and Canning II In the case of adaptation, Jaynagar II, Haroa, blocks (Figure 4 and Table 1). These blocks being Canning II, Jaynagar I, Canning I, and Mathurapur located away from the coast are less aﬀected by ﬂood II blocks registered very high adaptation. The high inundation and storm surge. adaptation was identiﬁed in Minakhan, Mathurapur Sensitivity analysis revealed that Patharpratima, I, Kakdwip, and Sandeshkhali I blocks. These blocks Namkhana, Kultali, Gosaba, and Basanti blocks were have performed well in higher education, have a good very highly sensitive in SBR. Demographic, social, Figure 3. Components of socio-economic vulnerability index: (a) demographic, (b) economic, (c) health, (d) social, (e) food and water, and (f) physical and infrastructure. 46 M. SAHANA ET AL. Figure 4. Degree of household exposure in SBR. Figure 5. Degree of household sensitivity in SBR. health condition, have a high percentage of cemented Patharpratima, Namkhana, Kultali, Gosaba, and Basanti houses, and have sources of water within their pre- blocks. These blocks registered a high degree of exposure mises. Only two blocks, namely, Sagar and Basanti and sensitivity. The frequent onset of disasters, lack of experienced moderate adaptation, while Hasnabad, early warning system, and unscrupulous health facilities Sandeshkhali II, Namkhana, Gosaba, Patharpratima, are also responsible for increasing the vulnerability of Hingalganj, and Kultali blocks were found under low these blocks. Thus, adaptation must be increased to lessen adaptation (Table 3 and Figure 6). These blocks regis- the impact of vulnerability in these blocks. High vulner- tered low level of higher education, lack of amenities ability prevailed in Sagar, Kakdwip, and Hingalganj and facilities, and prevalence of muddy structures. blocks of SBR. High degree of exposure and sensitivity Eﬀective adaptation measures are required to increase while low adaptation contributed high vulnerability in the level of adaptation in these blocks. these blocks. Minakhan, Sandeshkhali I, Sandeshkhali II, and Hasnabad blocks were found under moderate vul- nerability, while Jaynagar II, Haroa, Mathurapur I, 4.2 Composite socio-economic vulnerability (SeVI) Mathurapur II, Canning II, Jaynagar I, and Canning I blocks found under low vulnerability (Table 4 and The vulnerability scores are presented in Figure 7. Figure 8). These blocks are situated inland areas and Analysis of composite socio-economic vulnerability have ahighdegreeofadaptationthanthe coastalblocks. (SeVI) revealed a very high vulnerability in Table 1. Index values of exposure indicators in SBR. Block Flood inundation Proximity to the drainage Drainage density Slope amount Average storm surge height Exposure Canning I 0.21 0.23 0.26 0.21 0.24 0.23 Jaynagar I 0.27 0.26 0.27 0.24 0.31 0.27 Canning II 0.27 0.25 0.24 0.25 0.28 0.25 Mathurapur II 0.31 0.31 0.35 0.29 0.29 0.31 Mathurapur I 0.36 0.33 0.32 0.41 0.39 0.36 Haroa 0.32 0.31 0.33 0.29 0.35 0.32 Jaynagar II 0.31 0.29 0.32 0.29 0.34 0.31 Hasnabad 0.59 0.59 0.54 0.62 0.63 0.59 Sandeshkhali I 0.54 0.56 0.58 0.55 0.56 0.55 Sandeshkhali II 0.68 0.68 0.67 0.71 0.68 0.68 Minakhan 0.52 0.56 0.55 0.51 0.51 0.53 Hingalganj 0.81 0.79 0.75 0.76 0.78 0.78 Kakdwip 0.69 0.71 0.71 0.67 0.67 0.69 Sagar 0.7 0.69 0.67 0.66 0.68 0.68 Basanti 0.76 0.76 0.77 0.74 0.73 0.75 Gosaba 0.79 0.82 0.83 0.78 0.83 0.81 Kultali 0.87 0.89 0.88 0.87 0.81 0.86 Namkhana 0.85 0.91 0.95 0.98 0.96 0.93 Patharpratima 0.86 0.92 0.96 0.97 0.95 0.93 Source: Authors’ own calculations based on satellite data. GEOLOGY, ECOLOGY, AND LANDSCAPES 47 Table 2. Index values of sensitivity indicators in SBR. Block Demographic Social Economic Health Food & water Physical & Infrastructural Sensitivity Index Canning I 0.21 0.25 0.18 0.19 0.22 0.20 0.21 Jaynagar I 0.20 0.21 0.23 0.24 0.24 0.21 0.22 Canning II 0.23 0.22 0.22 0.26 0.24 0.24 0.23 Mathurapur II 0.24 0.30 0.32 0.36 0.29 0.28 0.30 Mathurapur I 0.34 0.27 0.31 0.31 0.31 0.33 0.31 Haroa 0.28 0.25 0.30 0.34 0.32 0.24 0.29 Jaynagar II 0.29 0.32 0.27 0.28 0.32 0.27 0.29 Hasnabad 0.43 0.40 0.43 0.39 0.41 0.42 0.41 Sandeshkhali-I 0.42 0.43 0.38 0.40 0.37 0.38 0.40 Sandeshkhali-II 0.42 0.41 0.38 0.41 0.44 0.43 0.41 Minakhan 0.37 0.38 0.39 0.38 0.42 0.37 0.39 Hingalganj 0.75 0.69 0.71 0.75 0.72 0.72 0.72 Kakdwip 0.71 0.67 0.67 0.68 0.64 0.69 0.68 Sagar 0.73 0.71 0.69 0.71 0.71 0.72 0.71 Basanti 0.81 0.79 0.75 0.78 0.78 0.81 0.79 Gosaba 0.81 0.82 0.76 0.78 0.82 0.77 0.79 Kultali 0.83 0.79 0.80 0.84 0.84 0.79 0.82 Namkhana 0.88 0.85 0.87 0.88 0.84 0.86 0.86 Patharpratima 0.90 0.91 0.95 0.92 0.89 0.90 0.91 Source: Authors’ own calculations based on ﬁeld survey (2018) Table 3. Index values of adaptation indicators in SBR. Block Demographic Social Economic Health Food and water Physical and infrastructural Adaptive capacity Canning I 0.63 0.64 0.65 0.7 0.7 0.71 0.67 Jaynagar I 0.69 0.66 0.69 0.72 0.71 0.69 0.69 Canning II 0.72 0.75 0.79 0.65 0.68 0.69 0.71 Mathurapur II 0.62 0.65 0.68 0.69 0.66 0.62 0.65 Mathurapur I 0.63 0.61 0.62 0.63 0.62 0.61 0.62 Haroa 0.63 0.72 0.75 0.78 0.72 0.71 0.71 Jaynagar II 0.71 0.74 0.69 0.73 0.75 0.76 0.73 Hasnabad 0.38 0.4 0.39 0.4 0.42 0.41 0.4 Sandeshkhali I 0.54 0.52 0.48 0.52 0.55 0.58 0.53 Sandeshkhali II 0.39 0.38 0.39 0.49 0.36 0.34 0.39 Minakhan 0.64 0.65 0.64 0.62 0.61 0.64 0.63 Hingalganj 0.32 0.34 0.3 0.3 0.3 0.29 0.31 Kakdwip 0.53 0.54 0.53 0.52 0.53 0.54 0.53 Sagar 0.52 0.53 0.5 0.5 0.51 0.52 0.51 Basanti 0.42 0.43 0.45 0.42 0.4 0.39 0.41 Gosaba 0.33 0.32 0.34 0.35 0.36 0.34 0.34 Kultali 0.26 0.29 0.29 0.29 0.31 0.33 0.29 Namkhana 0.35 0.33 0.34 0.34 0.34 0.34 0.34 Patharpratima 0.29 0.28 0.33 0.32 0.32 0.32 0.31 Source: Authors’ own calculations based on ﬁeld survey (2018). Table 4. SeVI and its component indices in SBR. 5. Discussion and policy implication Composite The overall analysis of socio-economic vulnerability to socio-economic Exposure Sensitivity Adaptation vulnerability climate change revealed that the majority of the Blocks indices indices indices index sampled households in SBR was highly aﬀected by Canning I 0.23 0.21 0.67 0.37 ﬂood and cyclone hazards. Decrease in ﬁsh production Jaynagar I 0.27 0.22 0.69 0.39 Canning II 0.25 0.23 0.71 0.40 and salinization of agricultural land are the major Mathurapur II 0.31 0.30 0.65 0.42 threats to coastal communities. Water- and vector- Mathurapur I 0.36 0.31 0.62 0.43 Haroa 0.32 0.29 0.71 0.44 borne diseases and inadequacy of medical facilities Jaynagar II 0.31 0.29 0.73 0.44 are also aﬀecting the health of the people. Many agri- Hasnabad 0.59 0.41 0.4 0.47 Sandeshkhali I 0.55 0.40 0.53 0.49 cultural lands in coastal blocks were severely aﬀected Sandeshkhali II 0.68 0.41 0.39 0.49 by salinity intrusion and thus rendered without culti- Minakhan 0.53 0.39 0.63 0.52 vations aftermath of Aila cyclone in 2009. Moreover, Hingalganj 0.78 0.72 0.31 0.60 Kakdwip 0.69 0.68 0.53 0.63 due to huge losses of mangrove forests, there are Sagar 0.68 0.71 0.51 0.63 several restrictions from the government for the col- Basanti 0.75 0.79 0.41 0.65 Gosaba 0.81 0.79 0.34 0.65 lection of forest products. Consequently, large-scale Kultali 0.86 0.82 0.29 0.66 migration has occurred from these coastal blocks. Namkhana 0.93 0.86 0.34 0.71 Patharpratima 0.93 0.91 0.31 0.72 This has aﬀected the socio-economic conditions of Source: Authors’ own calculations based on satellite data and ﬁeld survey SBR to a larger extent. (2018). 48 M. SAHANA ET AL. increasing the knowledge about disaster implication at the households’ level and assist in location-speciﬁc vulnerability assessment (Ahsan & Warner, 2014; Kontogianni, Damigos, Kyrtzoglou, Tourkolias, & Skourtos, 2019; Sorg et al., 2018). SeVI analysis revealed that Patharpratima, Namkhana, Kultali, Gosaba, Basanti, Sagar, Kakdwip, Hingalganj, Minakhan, Sandeshkhali II, Sandeshkhali I, and Hasnabad blocks require immediate attention to mini- mize the level of vulnerability (Table 3). These blocks have registered a high degree of exposure and sensi- tivity with negligible adaptation. These priority blocks are located along the coast and have been experiencing huge devastation due to disasters. Thus, eﬀorts could be made to increase the adaptive capacity in these blocks as improving the early warning system, disaster preparedness, infrastructural development, provision of basic health facilities, and improvement of infra- structure to reduce the socio-economic vulnerability. Jaynagar II, Haroa, Mathurapur I, Mathurapur II, Figure 6. Degree of household adaptation in SBR. Canning II, Jaynagar I, and Canning I blocks also require attention to minimize the degree of exposure SeVI has been identiﬁed as an eﬀective tool for while eﬀorts should be made to minimize the sensitiv- analyzing vulnerability at the household level. It ity in Canning II, Jaynagar I, and Canning I blocks of enhances disaster management intervention by SBR (Table 5). Figure 7. IPCC-dimension-wise vulnerability scores for the blocks of SBR. GEOLOGY, ECOLOGY, AND LANDSCAPES 49 6. Conclusion This article has explored the extent of household vulnerability in the tenuous ecosystem of the Indian SBR using pragmatic approach. A total of 6 socio-economic components and 36 sub-compo- nents were used to develop the composite SeVI. Spatial analysis of composite socio-economic vul- nerability revealed very high vulnerability in Basanti, Gosaba, Kultali, Namkhana, and Patharpratima blocks. These blocks are highly exposed to extreme weather events and socio-eco- nomically sensitive. Kakdwip, Sagar, and Hingalganj blocks experienced high socio-economic vulnerability due to deprived social structure, fri- volous infrastructure, low economic condition, and meagre healthcare facilities. These blocks also suf- fered from severe water, food, and health problems. Many of the sampled households were found using pond or river water as drinking water in these blocks. Mathurapur I, Mathurapur II, Haroa, Figure 8. Composite socio-economic vulnerability index (SeVI) of the sampled households in various blocks of SBR. Minakhan, Sandeshkhali I, Sandeshkhali II, and Hasnabad blocks came under moderate socio-eco- nomic vulnerability. Mathurapur I, Mathurapur II, Table 5. Priority blocks for vulnerability reduction in SBR. and Jaynagar II require immediate attention and Exposure Sensitivity Adaptation Blocks indices indices indices eﬃcacious policy measures for socio-economic Canning I √√ – development in SBR. Jaynagar I √√ – Strengthening the social system with provision of Canning II √√ – Mathurapur II √ –– economic opportunities is essential for lessening Mathurapur I √ –– socio-economic vulnerability. Education and aware- Haroa √ –– ness among local communities may ameliorate the Jaynagar II √ –– Hasnabad √√ √ understanding of the magnitude and implications of Sandeshkhali I √√ √ severe weather events. Consolidated infrastructural Sandeshkhali II √√ √ Minakhan √√ √ setup, proper early warning system, disaster monitor- Hingalganj √√ √ ing centres, and better transport connectivity within Kakdwip √√ √ Sagar √√ √ remote islands may enhance the mobility and provide Basanti √√ √ long-term sustainability in the development of SBR. Gosaba √√ √ Kultali √√ √ Improvement in healthcare facilities, indurate Namkhana √√ √ embankment along the rivers and villages located Patharpratima √√ √ along coastal areas, capturing better livelihood oppor- Source: Authors’ own calculation based on SeVI analysis. tunities as tourism, minimizing population pressure on resources, mangrove conservation, and disaster risk management are other measures which if adopted Formulation of holistic vulnerability framework, may uplift the physical and social structure of the inclusion of coastal communities in suggesting adaptative Reserve. Moreover, enhancing the traditional activities measures, and eﬀective coastal zone management plan may have a credible positive impact than advancing will be helpful in lessening the impact of vulnerability in the new ones. Further, vulnerability studies may SBR. However, scientiﬁc uncertainty leads to decision include analysis of the magnitude of disaster damages, paralysis and biasness in policy for local-level adaptation. and more inherent indicators for vulnerability assess- Cost-eﬀective solutions, increasing knowledge among ment must be incorporated. Use of comprehensive coastal communities, enhancing resistance capacity, facil- index may also increase the feasibility of climate itating disaster response, and enhancing coordination change-induced vulnerability assessment. Monitoring among stakeholders, communities, and regional policy- the spatial pattern of physical changes in landscape makers may help to overcome the policy issues, increase and adoption of sustainable approach (ecological, in adaptation, and lessening the vulnerability in coastal social, and economic components) may be eﬀective areas. Management of natural resources and increasing in planning and managing the critical natural adaptation among farmers will also help to reduce the resources in vulnerable hotspots. vulnerability in SBR. 50 M. SAHANA ET AL. Bohle, H. G., Downing, T. E., & Watts, M. J. (1994). Climate Highlights change and social vulnerability: Toward a sociology and Pragmatic approach was utilized for analyzing socio- geography of food insecurity. Global Environmental economic vulnerability in SBR Change, 4(1), 37–48. Components and sub-components of exposure, sensitiv- Bray, F., Jemal, A., Grey, N., Ferlay, J., & Forman, D. (2012). ity, and adaptation were analyzed Global cancer transitions according to the Human Coastal blocks were highly and very highly vulnerable to Development Index (2008–2030): A population-based disasters study. The Lancet Oncology, 13(8), 790–801. Remoteness, frivolous infrastructural setup, and land Brooks, N. (2003). Vulnerability, risk and adaptation: A losses restricted development conceptual framework. Tyndall Centre for Climate Monitoring centres, early warning system, and awareness Change Research Working Paper, 38(38), 1–16. mechanism are suggested Challinor, A. J., Simelton, E. S., Fraser, E. D. G., Hemming, D., & Collins, M. (2010). Increased crop failure due to climate change: Assessing adaptation options using mod- Acknowledgments els and socio-economic data for wheat in China. Environmental Research Letters, 5(3), 034012, (8pp). The authors are incredibly thankful to the anonymous Ciurean, R. L., Schroter, D., & Glade, T. (2013). 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Jan 2, 2021

Keywords: Vulnerability; exposure; sensitivity; adaptation; pragmatic approach; SBR

References

Climate change vulnerability, adaptation and risk perceptions at farm level in Punjab, Pakistan
Assessing vulnerability to sea-level rise using a coastal sensitivity index: A case study from southeast Australia
Morphometric parameters-based prioritization of sub-watersheds using fuzzy analytical hierarchy process: A case study of lower Barpani Watershed, India
The socioeconomic vulnerability index: A pragmatic approach for assessing climate change led risks—A case study in the south-western coastal Bangladesh
I will not go, I cannot go: Cultural and social limitations of disaster preparedness in Asia, Africa, and Oceania
Sedimentation and trace metal distribution in selected locations of Sundarbans mangroves and Hooghly estuary, northeast coast of India
Links among drainage density, frequency and flooding in Nanoi Rivers Basin, Assam
Climate change and social vulnerability: Toward a sociology and geography of food insecurity
Global cancer transitions according to the Human Development Index (2008–2030): A population-based study
Vulnerability, risk and adaptation: A conceptual framework
Increased crop failure due to climate change: Assessing adaptation options using models and socio-economic data for wheat in China
Developing a socioeconomic vulnerability index using two agricultural communities exposed to climate change: A case study in Wanathawilluwa in Puttlam District of Sri Lanka
Measuring social vulnerability to natural hazards at the district level in Botswana
Climate change and rural communities in Ghana: Social vulnerability, impacts, adaptations and policy implications
Social vulnerability (re-) assessment in context to natural hazards: Review of the usefulness of the spatial indicator approach and investigations of validation demands
Socio-economic and climate change impacts on agriculture: An integrated assessment, 1990–2080
The Indian Sundarban mangrove forests: History, utilization, conservation strategies and local perception
Effects of flooding and drought on water quality in gulf coastal plain streams in Georgia
Biodiversity and its conservation in the Sundarban mangrove ecosystem
The livelihood vulnerability index: A pragmatic approach to assessing risks from climate variability and change—A case study in Mozambique
Sea level and associated changes in the Sundarbans
Fourth assessment report
Assessing Indian cities for vulnerability to climate change
Development of a composite climate change vulnerability index for small craft harbours
Coastal vulnerability assessment for Orissa State, east coast of India
A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico
Climate change—Impact on the Sundarbans, a case study
Assessing socio-economic vulnerability to climate change: A city-level index-based approach
Mapping distribution of Sundarban mangroves using sentinel-2 data and new spectral metric for detecting their health condition
Re-turn to practice: An introductory essay
Pulse of climate change in Indian Sundarbans: A myth or reality?
The next generation of scenarios for climate change research and assessment
Natural hazards and social vulnerability of place: The strength-based approach applied to Wollongong, Australia
Developing and applying composite indicators for assessing and characterizing vulnerability and resilience of coastal communities to environmental and social change
Socio-economic and institutional drivers of vulnerability to climate change in urban slums: The case of Accra, Ghana
Social-ecological vulnerability to climate change in the Nepali Himalaya
The importance of indicator weights for vulnerability indices and implications for decision making in disaster management
Climate change impacts on Indian Sunderbans: A time series analysis (1924–2008)
A method for constructing a social vulnerability index: An application to hurricane storm surges in a developed country
Vulnerability to storm surge flood using remote sensing and GIS techniques: A study on Sundarban Biosphere Reserve, India
Assessing farm-level agricultural sustainability using site-specific indicators and sustainable livelihood security index: Evidence from Vaishali district, India
Quantifying household vulnerability triggered by drought: Evidence from rural India
Sensitivity analysis of a climate vulnerability index—A case study from Indian watershed development programmes
Climate change, natural disasters, and post-disaster unrest in India
Capturing the multifaceted phenomena of socioeconomic vulnerability
Derivation and testing of the water poverty index phase 1
A GIS-based vulnerability assessment of coastal natural hazards, state of Pará, Brazil
Household livelihood differentiation and vulnerability to climate hazards in rural China
NDC global outlook report 2019
Reduction. Economic losses, poverty & disasters: 1998–2017

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Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

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Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

Assessing socio-economic vulnerability to climate change-induced disasters: evidence from Sundarban Biosphere Reserve, India

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