Climatic Change DOI 10.1007/s10584-015-1379-1
Assessment of social vulnerability to climate change in the eastern coast of India Sanjit Maiti & Sujeet Kumar Jha & Sanchita Garai & Arindam Nag & R. Chakravarty & K. S. Kadian & B. S. Chandel & K. K. Datta & R. C. Upadhyay
Received: 6 November 2013 / Accepted: 2 March 2015 # Springer Science+Business Media Dordrecht 2015
Abstract This study highlighted the social vulnerability to climate change of 29 eastern coastal districts across 4 eastern coastal states of India by using the ‘Integrated vulnerability assessment approach’ and IPCC’s definition of vulnerability. The assessment was based on secondary data, like socio-economic and bio-physical indicators, collected from several authenticated sources; and weightage of these indicators were assigned by using Principal Component Analysis. Vulnerability was calculated as the net affect of exposure and sensitivity on the adaptive capacity. Pudukottai district of Tamil Nadu was found to be the most vulnerable district, while East Godavari district of Andhra Pradesh was the least vulnerable. The net effect was found to be negative in 10 districts: South 24-Parganas of West Bengal; Bhadrak of Odisha; Prakasam of Andhra Pradesh; Thiruvallur, Villipuram, Thanjavur, Thoothukkudi, Pudukottai, Ramanathapuram and Cuddalore of Tamil Nadu. This net negative effect may be considered as an indicator of alarming situation. 1 Introduction Climate change is a global phenomenon; however its manifestation and impacts vary locally, so do the adaptive capacities, preferences and strategies (Piya et al. 2012). The area with the richest bio-diversity, in fact, may be the most vulnerable to the climate change. Rural Electronic supplementary material The online version of this article (doi:10.1007/s10584-015-1379-1) contains supplementary material, which is available to authorized users. S. Maiti (*) National Research Centre on Yak, Dirang 790101 Arunachal Pradesh, India e-mail:
[email protected] S. K. Jha : R. Chakravarty : K. S. Kadian : B. S. Chandel : K. K. Datta : R. C. Upadhyay National Dairy Research Institute, Karnal 132001 Haryana, India S. Garai Eastern Regional Station, National Dairy Research Institute, Kalyani 741235 West Bengal, India A. Nag Department of Extension Education, Bihar Agricultural University, Sabour 813210 Bihar, India
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livelihoods and economics based on and dominated by agricultural, pastoral and forests production systems, may be highly sensitive to climate variations. Climate change can and will have both positive and negative impacts on the productivity of these systems, which will, in turn, impact on incomes, costs of production, supplies of food and other commodities, stores of food, livestock and financial savings and food security (Leary et al. 2008). The fifth assessment report of IPCC clearly depicted that the impact of climate change would be more severe in east, southeast and south Asia due to climate change accelerated sea level rise (SLR) (Wong et al. 2014). In addition, tremendous population and development pressure have been building in the coastal areas for the last four decades; as a result, coastal resources as well as inhabitants are more exposed to climatic hazards (Kumar et al. 2010). The present population at risk is estimated to be 270 million people worldwide. This number will increase to 670 and 450 million people by 2100 for the cases without and with coastal protection, respectively (Mimura 2013). Furthermore, global climate change and accelerated sea-level rise exacerbate the already existing high risk of storm surges, severe waves, and tsunamis (Kumar et al. 2010). India has been identified as one amongst 20 most at risk countries to sea level rise (Strauss and Kulp 2014) and observations suggest that the sea level has risen at a rate of 2.5 mm year per year along the Indian coastline since 1950s (Rishi and Mudaliar 2014). A mean SLR of between 15 and 38 cm is projected by the middle of 21st century along India’s coast. Added to this, a 15 % projected increase in intensity of tropical cyclones would significantly enhance the vulnerability of populations living in cyclone prone coastal regions of India (Rishi and Mudaliar 2014). The level of vulnerability of different social groups to climate change is determined by both socio-economic and environmental factors (Deressa et al. 2009). Socio-economic factors found to be most cited in the literature include the level of technological development, infrastructure, institutions, and political setups (Kelly and Adger 2000; McCarthy et al. 2001; Deressa et al. 2009). The environmental attributes mainly includes climatic conditions, quality of soil, and availability of water for irrigation (CIDA 2003; O’Brien et al. 2004; Deressa et al. 2009). These socio-economic and environmental factors are the responsible for the differential levels of the vulnerability to climate change across the different region as well as social group. In climate change research, two distinct notions of vulnerability have been recognised – bio-physical vulnerability and social vulnerability (Nyong et al. 2008). Biophysical vulnerability is concerned with the ultimate impacts of a hazard event, and is often viewed in terms of the amount of damage experienced by a system as a result of an encounter with a hazard. Social vulnerability, on the other hand, is viewed more as a potential state of human societies that can affect the way they experience and respond to natural hazards (Vincent 2004; Adger 1999; Adger and Kelly 1999; Nyong et al. 2008). This type of study or analysis of vulnerability has increasingly been presented with the intention of contributing data on threats or risk to physical, territorial and societal developmental officials; and planning specialists as an ingredient of the decision making process. Therefore, in this paper, an attempt has been made to assess the status of social vulnerability to climate change among the eastern coastal districts of India.
2 Materials and methods 2.1 Study area Indian coastline stretches up to 5700 kms on the mainland and up to 7500 kms while including the two island territories. This vast coastline lies from the Gulf of Kutch in its western most
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corner to the eastern most corner of shoreline near the Sunderbans in West Bengal and distinctly divided into two i.e., eastern and western. Patwardhan et al. (2003) reported that eastern coast is more vulnerable than the western coast with respect to the frequency of occurrence of extreme events like cyclones and depressions. Therefore, the present study was confined within the districts of eastern coast (except district of union territory Puduchhery). A total of 29 districts from North 24-Parganas of West Bengal to Kanyakumari district of Tamil Nadu were considered for the present study (Fig. 1). 2.2 Data sources Secondary data on the district-wise demographic features like population density, decadal growth rate, rural literacy rate, rural population to total population and household data like rural households availing banking service, households not having drinking water sources at their home premises, households having houses in dilapidated condition were taken from the
Fig. 1 Locale of the study (Oval Shape indicate the Eastern Costal District of India)
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official website of Census of India carried out in 2011.1 Climatic indicators were calculated from the high resolution daily gridded temperature and rainfall data for the Indian region during the period of 30 years (1975–2004) developed by the India Metrological Department.2 District wise agricultural productivity (Rs/ha of Net sown area) were taken from the ‘policy paper’ (no 26) entitled ‘Instability and regional variation in Indian agriculture’ published by the National Centre for Agricultural Economics and Policy Research, New Delhi in 2011.3 District wise Human Development Index value was obtained from the human development report of the respective states.4 Data on the district wise land utilisation pattern; agriculture; animal husbandry; fisheries; infrastructure like rural electrification, medical institution; per capita income; and operational land holding were collected from the latest published statistical handbook and official websites of the respective states and districts.5 2.3 Development of ‘social vulnerability Index’ for eastern coastal districts of India There are three major conceptual approaches for analysing vulnerability to climate change: the socio-economic, the bio-physical (impact assessment), and the integrated assessment approaches (Deressa et al. 2008). The integrated assessment approach combines both socioeconomic and bio-physical approaches in order to determine vulnerability. The vulnerability mapping approach (O’Brien et al. 2004; Kumar and Tholkappian 2005) is a good example of this approach, in which both socio-economic and bio-physical factors are systematically combined to determine vulnerability. Thus, this method was followed to analyse the vulnerability of eastern coastal districts to climate change. 2.3.1 Methods for measuring vulnerability to climate change The most common methods used to assess vulnerability to climate change are: econometric and indicator methods. The ‘econometric method’ has its roots in the poverty and development literature. This method uses household-level socio-economic survey data to analyse the level of vulnerability of different social groups (Deressa et al. 2008). The ‘indicator method’ of quantifying vulnerability is based on selecting some indicators from the whole set of potential indicators, and then systematically combining the selected indicators to indicate the levels of vulnerability (Deressa et al. 2008). Adger and Kelly (1999), Kumar and Tholkappian (2005), Patnaik and Narayanan (2005), Deressa et al. (2008), Moreno and Becken (2009), Nyong et al. (2008), Hahn et al. (2009), Nelson et al. (2010), Ravindranath et al. (2011), Tambe et al.(2011), Seidl et al. (2011) and Rama Rao et al. (2013) used index-based approach to analyze social vulnerability to climate change in their respective study area. Thus, an index-based method was applied in the present study to analyse the vulnerability of eastern coastal districts to climate change.
1
http://www.censusindia.gov.in/2011census/hlo/HLO_Tables.html http://www.censusindia.gov.in/(S(nzkmz3jbxdtvwi55ihovywjh))/2011census/population_enumeration.aspx 2 http://www.imdpune.gov.in/publication/pub_index.html 3 http://www.ncap.res.in/ncap_policy_papers.html 4 http://planningcommission.gov.in/plans/stateplan/shdr.php?state=b_shdrbody.htm 5 Statistical Hand Book of Tamil Nadu-2012 (http://www.tn.gov.in/deptst/); District Statistical Hand Book of Srikakulam, Visakhapatnam, West Godavari, East Godavari, Krishna, Guntur, Prakasam, S.P.S. Nellore, Vizianagaram, Directorate of Economics and Statistics, Government of Andhra Pradesh, Hyderabad, India; District Statistical Hand Book of Blasore, Bhadrak, Jgatsinghpur, Puri, Kendrapara, Ganjam, Directorate of Economics and Statistics, Government of Odisha, Bhubneswar, India; District Statistical Hand Book of North 24Parganas, South 24-Parganas, Purba Medinipur, Directorate of Economics and Statistics, Government of West Bengal, Kolkata, India (Hard copy of the report were collected)
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2.3.2 Choosing the vulnerability indicators Piya et al. (2012) argued that vulnerability to climate change is multi-dimensional, and is determined by a complex inter-relationship of multiple factors. There are two approaches in the selection of indicators: data-driven and theory-driven (Vincent 2004). But, each approach has its own limitations. Therefore, the best option is to verify the representativeness of the theory-based indicators, with data availability from authentic sources. Both theory and data driven approaches were adopted to select the indicators used in this study. The definition of vulnerability, as given by IPCC (2001), was adopted for this study and it is defined as Bthe degree to which a system is susceptible, or unable to cope with adverse effect of climate change, including climate variability and extremes, vulnerability is a function of the character, magnitude and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity.^ Exposure is the nature and degree to which a system is exposed to significant climatic variations. Sensitivity is the degree to which a system is affected, either adversely or beneficially by climate-related stimuli. Adaptive capacity is the ability of a system to adjust to climate change including climate variability and extremes, to moderate the potential damage from it, to take advantage of its opportunities, or to cope with its consequences. Glimpse of indicators under each componeent of vulnerability i.e., exposure, sensitivity and adaptive capacity is presented in Table 1 and have been discussed as below. Exposure For this study, historical changes in climatic variables, occurrence of extreme climate events and length of coastline were taken as the indicators of exposure. Two climatic parameters i.e., temperature and rainfall were considered for this study. Change in mean temperature, change in mean maximum temperature, and change in mean minimum temperature for the period of 30 years (1975–2004) were considered under ‘temperature’. Numbers of years having less number of rainy days than normal, numbers of years having excess number of rainy days than normal, numbers of years having excess rainfall, numbers of years having moderate metrological drought, numbers of years having metrological severe drought, variation in rainfall, numbers of days having very heavy rainfall, and numbers of days having extremely heavy rainfall during the period of 30 years (1975 to 2004) were also considered in historical changes in climatic parameter ‘rainfall’. Occurrence of two extreme climate events i.e., heat waves and cold waves during the same period was also considered for calculating the degree of exposure of the study area. It was hypothesized a positive relationship of exposure with the rate of change of climate variables, frequency of extreme climate events and length of coastline. Sensitivity Sensitivity could be measured in the best way, by a change in income or livelihood pattern, attributed only to climatic factors. However, it was very difficult to find this type of data. Instead, we made the simple assumption that those areas with higher frequencies of climate extremes were subjected to higher sensitivity due to loss in yield and livelihood of rural masses (Deressa et al. 2008). A total of eight variables were considered for calculating the degree of sensitivity of the sample districts. Eastern coastal districts are predominantly agriculture-based districts, wherein agriculture contributed maximum to per capita income. Majority of the agricultural produces including milk come from the small and marginal farmers of rainfed area. Therefore, production and productivity of agricultural produces including milk may be slowed down due to increasing climatic hazards and finally these may leads to lower income of farm family. People from rural area are very much susceptible to extreme climatic hazard as infrastructure in rural area like road, electricity, telecommunication collapsed due natural calamities like storm surge, flood etc. Rural coastal households used to collect their
Climatic Change Table 1 Indicators of adaptive capacity, exposure and sensitivity with brief description Sl No.
Symbol
List of indicators
Description of the indicators
I. Adaptive capacity IA. Human Capital 1
PD
Population density (per km2)
Number of population per square kilometre of the district
2
DGR
Decadal growth rate
Percentage of population increased in the census 2011 over the census 2001
3
RLR
Rural literacy rate
Percentage of literate rural population to rural population over 6 years of age
Human development index
The human development index is a simple composite measure that gauges the overall status of a region in terms of three basic dimensions - long and healthy life, knowledge and decent standard of living - of human development. According to UNDP methodology, literacy rate, enrolment rate, life expectancy and per capita GNP are the representative indicators for these basic dimensions.
IB. Social capital 4 HDI
IC. Natural capital 5
FC
Area under forest cover
Percentage of area under forest cover to the total geographical area of the district
6
GL
Area under grazing land
Percentage of area under grazing land cover to the total geographical area of the district
7
AFC
8
CI
Area under food crops (000 ha) Cropping intensity
Total area under food crops (both cereal and pulse) in an agricultural year Ratio of gross cropped area to net sown area and multiplied by 100
9
LD
Livestock density (per km2)
Number of livestock per square kilometre of the district
10
NRY
Numbers of years having normal rainfall during 1975 to 2004
If the year wise total rainfall of the district is within the range of 81 to 119 % of normal rainfall. Then, it was considered that particular year has normal rainfall.
11
NRD
Numbers of years having normal rainy day during 1975 to 2004
A day is called rainy day according to India Meteorological Department if the rainfall of that is 2.5 mm or more. Frequency of such days were counted year wise during 1975 to 2004 and compare with the normal number of rainy days. If it is found that percentage of rainy days are in the range of 81 to 119, then, it is considered that year has normal numbers of rainy days.
ID. Physical capital 12
VETINS
Number of Veterinary institution per 1000 livestock
Total number all types of veterinary institutions (Hospital, dispensary etc.) per 1000 livestock in the district
13
AICEN
Number of A.I. centre number per 1000 breedable bovine
Total number all types of veterinary institutions (both public, private and public-private partnership) having facility to do AI per 1000 breedable bovine
Climatic Change Table 1 (continued) Sl No.
Symbol
List of indicators
Description of the indicators
14
MED
Number of medical institutions per 1000 population
Total number all types of medical institution (Allopathy, Homeopathy, Unani etc.) per 1000 population in the district
15
RELEC
Rural Electrification (% of village electrified)
Percentage of village had been electrified to total number of village
Total production of egg (million Nos) Total production of fish (MT)
Total production egg in million no in a year
The per capita availability of milk is obtained by dividing the total production of milk of the district of a particular year by estimated population of the same year. The per capita availability of egg is obtained by dividing the total production of egg of the district of a particular year by estimated population of the same year.
IE. Financial capital 16
EGG
17
FISH
18
PCMILK
Per capita availability of milk (gram/day)
19
PCEGG
Per capita availability of egg (nos/annum)
20
PCFISH
Per capita availability of fish (kg/annum)
21
BANK
Percentage of rural household availing banking service
22
LRD
Numbers of years having less number of rainy days than normal during 1975 to 2004
A day is called rainy day according to India Meteorological Department if the rainfall of that is 2.5 mm or more. Frequency of such days were counted year wise during 1975 to 2004 and compare with the normal number of rainy days. If it is found that percentage of rainy days are less than equal 8o, then, it is considered that year has less numbers of rainy days.
23
ERD
Numbers of years having excess number rainy days than normal during 1975 to 2004
A day is called rainy day according to India Meteorological Department if the rainfall of that is 2.5 mm or more. Frequency of such days were counted year wise during 1975 to 2004 and compare with the normal number of rainy days. If it is found that percentage of rainy days are greater than equal to 120, then, it is considered that year has excess numbers of rainy days.
24
ERY
Numbers of years having excess rainfall during 1975 to 2004
25
MMD
Numbers of years having moderate metrological drought during 1975 to 2004
If the year wise total rainfall of the district is greater than equal to 120 % of normal rainfall. Then, it was considered that particular year has excess rainfall. If the year wise total rainfall of the district is between 26 and 50 % of normal rainfall. Then, it was considered that particular year is metrological moderate drought.
Total production fish including marine and fresh water in year
The per capita availability of fish is obtained by dividing the total production of fish of the district of a particular year by estimated population of the same year. Percentage of rural household had an account in any bank
II. Exposure
Climatic Change Table 1 (continued) Sl No.
Symbol
List of indicators
Description of the indicators
26
MSD
Numbers of years having metrological severe drought during 1975 to 2004
If the year wise total rainfall of the district is less than 50 % of normal rainfall. Then, it was considered that particular year is metrological severe drought.
27
RAINV
Variation in rainfall during 1975 to 2004
Coefficient of variation in year wise rainfall during the period of 1975 to 2004.
28
NORR
Numbers of days having very heavy rainfall during 1975 to 2004
A day is called very heavy rainfall day according to India Meteorological Department if the rainfall of that is between 124.5 and 244.5 mm. Frequency of these days were counted during 1975 to 2004.
29
HER
Numbers of days having extremely heavy rainfall during 1975 to 2004
A day is called extremly heavy rainfall day according to India Meteorological Department if the rainfall of that is more than 244.5 mm. Frequency of these days were counted during 1975 to 2004.
30
HEAT
Numbers of heat wave incidences during 1975 to 2004
A heat wave is defined if the maximum temperature at a grid point is 3 °C or more than the normal temperature, consecutively for 3 days or more (Adopted from IMD, Pune).
31
COLD
Numbers of cold wave incidences during 1975 to 2004
Cold wave is defined if the minimum temperature at a grid point is below the normal temperature by 3 °C or more, consecutively for 3 days or more (Adopted from IMD, Pune).
32
MEANT
33
MAXIT
Deviation in daily mean temperature in the year 2004 from the base year 1975 Deviation in daily mean maximum temperature in the year 2004 from the base year 1975
34
MINTE
35
COAST
Change in mean temperature from 1975 to 2004 Change in mean maximum temperature from 1975 to 2004 Change in mean minimum temperature from 1975 to 2004 Length of coastal line (KM)
Deviation in daily mean minimum temperature in the year 2004 from the base year 1975 District wise length of coastline in kilometre
III. Sensitivity 36
PCI
Per-capita income (Rs)
The per capita income is obtained by dividing the estimates of Net State Domestic Product (NSDP) of the district of a particular year by estimated population of the same year.
37
AP
Agricultural productivity (Rs/ha of Net sown area)
The value of output for the major crops including fruits and vegetables was multiplied by ratio of GCAt/GCAc, where GCAt is the reported gross cropped area and GCAc is the sum of area under the major crops including fruits and vegetables to estimate of Value of Crop Output (VCO) for GCAt. This figure was then divided by Net Sown Area to arrive at per hectare productivity (Adopted from NCAP, New Delhi).
38
MILK
Total production of milk ('000MT)
Total production of milk from all sources in a year
Climatic Change Table 1 (continued) Sl No.
Symbol
List of indicators
Description of the indicators
39
VILLPOP
Rural population to total population (%)
Percentage of district population reside in the rural area to total population of the district
40
RAINFD
Rainfed area (percentage to net sown area)
Percentage of cropped area depended on rainfall to net sown area
41
HOLD
Number of marginal and small holding to total holding (%)
Percentage of marginal (less than 1 ha) and small (1–2 ha) holding to total number of holding of the district
42
DRINKI
Rural households not having drinking water sources in their home premises (%)
Percentage of rural households not having drinking water at their home premises
43
DILAPID
Households having houses in dilapidated condition (%)
Percentage of households having houses in dilapidated condition
drinking water from a long distance and it would be more difficult in increasing climatic hazards. Therefore, they would be forced to take unsafe/contaminated drinking water and subsequently, incidence of water borne disease like diarrhoea etc. will be more frequently with the increasing extreme climatic events. It was also hypothesized that higher the chances of disaster of the houses in dilapidated condition due to impending climate change. Adaptive capacity Adaptive capacity of a household was considered as the summation of five types of livelihood assets viz. physical, human, natural, financial and social. These indicators are not necessarily specific to climate shocks only, but are also relevant in addressing other shocks like food shortages etc. (Piya et al. 2012). Only few of the selected indicators like numbers of years having normal rainfall during 1975 to 2004 and numbers of years having normal rainy day during 1975 to 2004 have a direct role in minimization of risk from climate shocks. Rationale of each indicator in building adaptive capacity has been discussed hereafter. Human asset is represented by population density (per km2), decadal growth rate and rural literacy rate. These indicators are not directly related to climate shocks; however they are still relevant because of development of human capabilities. Formal education empowers the rural masses in enhancing knowledge and awareness of potential impact of climate change and climate resilience agriculture including its adoption. Districts with higher population density and decadal growth rate will have more burdens on the natural resources of the district, thereby reducing the adaptive capacity. Social asset is comprised of a single indicator, i.e., ‘Human Development Index’ of the district, which indicates the social progressiveness of the district. It was hypothesized that higher the ‘Human Development Index’ of the district, higher the capacity to cope up with climate-related stress. There are seven indicators under the natural asset of the district: area under forest cover (% of total geographical area), area under grazing land (% of total geographical area), area under food crops (000 ha), cropping intensity, livestock density (per km2), numbers of years having normal rainfall during 1975 to 2004 and numbers of years having normal rainy days (during 1975 to 2004). Natural assets, by their own nature, are more vulnerable to climate shocks than other types of assets (Piya et al. 2012). Districts possessing higher share of forest cover and grazing
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lands will suffer less from climate disaster. Higher the area under food crops, cropping intensity and livestock density means higher food self-sufficiency and nutritional security, thus higher adaptive capacity. Indian agriculture is primarily dependent on monsoon. Therefore, higher the numbers of years having normal rainfall and normal rainy days indicates good agricultural produce. Subsequently, good agricultural produce enhances the adaptive capacity. Indicators of physical asset include the infrastructural facilities like network of veterinary institutions including artificial insemination centre, medical institutions and electricity in rural area. Though there is no direct relation of these indicators with climate shock, but, possession of better infrastructural facility is expected to enhance the capacity to withstand the risk from any shocks including climate. Finally, the financial asset is represented by the production of egg and fish; per capita availability of milk, egg and fish; and rural households availing banking services. Like other previously discussed indicators, these are not directly related to climate shock. But, better financial strength is also supposed to be the backbone of good adaptive capacity. Fish and egg are the main agricultural commodity in coastal districts. Thus, higher the production of fish and egg means higher financial strength. Per capita availability of milk, egg and fish not only indicate the financial strength, but, also indicate the nutritional security. Households availing banking services indicate about the saving habits of the people of the districts. Saving is one of the most important pillars of the financial strength. It is hypothesized that higher the financial strength of the households, higher the adaptive capacity.
2.3.3 Calculation of the vulnerability index Having chosen the indicators, now these need to be normalized, so as to bring the values of the indicators within the comparable range (Piya et al. 2012; Nelson et al. 2010; Feroze and Chauhan 2010; Gbetibouo and Ringler 2009; Vincent 2004). Normalization was done by subtracting the minimum value from the observed value and dividing by range. Next step was the testing of suitability indicators. Ravindranath et al. (2011) used ‘Principal Component Analysis (PCA)’ to identify the significant indicators and eliminate non-significant indicators. After normalization, three factor analyses (one each for exposure, sensitivity and adaptive capacity) were ran after choosing PCA for extraction and ‘varimax method’ for rotation of the factors in Statistical Software for Social Sciences 20 (SPSS 20). The result of communalities (as shown in Appendix 1) indicated that a high amount of variance for all the indicators could be explained by the factor analysis model. Mohanty et al. (2009) used a thumb rule of communality, indicating that more than 0.6 as a sufficient condition to keep the indicator and/or variable in the factor analysis model. Since all communality values were above 0.6, no indicator was dropped from the factor analysis model and each indicator was considered for next step i.e., assignment of weights to the indicators. The method followed by Feroze and Chauhan (2010) was adopted for this study to assign the weights to the indicators (Appendix 2). The normalized indicators were, then, multiplied with the assigned weights to construct the indices, separately, for each component of vulnerability viz. exposure, sensitivity and adaptive capacity separately. Finally, the vulnerability index for each district was calculated as: V ¼ AC–ðE þ SÞ Where, V is the Vulnerability index, AC is the Adaptive Capacity index, E is the Exposure Index and S is the Sensitivity index
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The overall vulnerability index facilitated inter-district comparison. Higher value of vulnerability index indicated lower vulnerability. However negative value of the index indicated the net effect of adaptive capacity, exposure and sensitivity was found to be negative. It may be considered as more vulnerable, and hence considered as an alarming situation. This index does not give the absolute measurement of vulnerability; instead, the index value highlighted a comparative judgement of sample districts.
3 Results and discussion The index values of adaptive capacity and its component for each district are presented in Table 2. It was found that West Godavari district of Andhra Pradesh scored highest in overall Table 2 Index values of adaptive capacity and its components across the eastern coastal districts of India Sl No.
State
1
West Bengal
Eastern coastal districts
Human asset
Social asset
Natural asset
Physical asset
Financial asset
Adaptive capacity
North 24 Parganas
11.21
5.18
19.69
4.88
2.61
43.57
2
South 24 Parganas
7.71
3.97
17.22
3.74
1.91
34.56
3
Purba Medinipur
9.83
4.37
18.78
1.44
3.33
37.76
Balasore
7.25
3.15
15.14
4.78
2.53
32.85
Bhadrak
7.64
4.90
11.88
2.67
1.24
28.32
4
Odisha
5 6
Jagatsinghpur
7.60
3.11
13.95
10.02
4.71
39.38
7
Puri
7.56
5.12
14.27
9.82
2.68
39.44
8 9
Kendrapara Ganjam
7.51 4.26
4.49 2.99
15.02 16.93
4.33 6.87
3.28 2.01
34.63 33.05 34.52
10
Srikakulam
1.94
1.02
15.76
11.15
4.64
11
Andhra Pradesh
Visakhapatnam
1.77
3.03
14.44
9.24
4.65
33.12
12
West Godavari
3.99
4.11
16.50
10.10
12.68
47.39
13
East Godavari
3.37
3.69
19.21
10.09
7.21
43.57
14
Krishna
4.11
4.43
15.32
10.48
10.55
44.90
15
Guntur
2.94
3.95
14.98
9.16
13.31
44.35
16 17
Prakasam S.P.S. Nellore
2.07 2.96
2.61 3.27
11.42 12.34
9.57 10.42
9.36 6.06
35.02 35.05
18
0.51
0.00
13.98
11.61
5.99
32.08
Thiruvallur
10.51
5.06
9.12
5.97
2.06
32.71
20
Villupuram
5.48
3.71
9.48
5.55
2.90
27.13
21
Thanjavur
6.36
4.57
6.74
6.49
3.10
27.27
22
Thoothukkudi
6.32
6.04
7.26
4.48
6.28
30.38
23
Tirunelveli
6.65
5.14
10.87
5.04
7.04
34.73
24 25
Pudukkottai Ramanathapuram
5.16 5.78
4.33 4.55
6.66 4.93
4.73 6.25
4.74 3.91
25.63 25.42
19
Vizianagaram Tamil Nadu
26
Kancheepuram
11.04
6.22
8.77
5.29
2.14
33.46
27
Kanniyakumari
9.90
6.20
10.08
10.71
5.01
41.90
28
Nagapattinam
6.92
5.06
12.37
5.97
4.40
34.71
29
Cuddalore
6.47
4.86
5.97
7.75
3.44
28.48
Italics Lowest value; Bold Highest value; Underline Highest value in last column
Climatic Change
adaptive capacity followed by North 24-Parganas district of West Bengal, East Godavari, Krishna, Guntur districts of Andhra Pradesh, and Kanyakumari district of Tamil Nadu. Whereas, Ramanathapuram, Pudukottai, Villipuram and Thanjavur districts of Tamil Nadu had comparatively lower adaptive capacity to combat with the negative impact of climate change. North 24-Parganas district scored highest in human asset category whereas Vizianagaram districts scored lowest in this asset category. In case of social asset, districts of Tamil Nadu like Kancheepuram, Kanyakumari and Thoothukkudi scored considerably higher than the other sample districts; whereas, districts of Andhra Pradesh scored comparative lower in social asset category. In fact, the district Vizianagaram scored ‘zero’ in social asset category. This did not imply that ‘Human Development Index’ of the district was zero. This happened because of consideration of normalized value of only one indicator i.e., ‘Human Development Index’ for the asset category. As far as natural asset was concerned, districts of West Bengal, Andhra Pradesh and Odisha scored higher than the districts of Tamil Nadu. Districts of Andhra Pradesh made higher score in physical asset category than the other sample districts. In case of financial asset category, districts of Andhra Pradesh scored considerably high. Whereas, districts of Odisha scored comparatively lower in financial asset category. Districts of Andhra Pradesh had comparatively lower score in human and social asset category, but, had higher score in other three asset categories viz. natural, physical and financial asset. Therefore, districts of Andhra Pradesh scored comparatively higher in overall adaptive capacity than the other sample districts. The BOverall Exposure Index^ comprised of 14 indicators. Out of these 14 indicators, 13 were related to exposure of different climatic variables including extreme climatic events like heat & cold waves and meteorological drought. Index score of each and every indicator as well as overall exposure have been presented in Table 3. There are some ‘zero values’ in this table, however this did not imply that indicators were worthless for the respective districts. It happened because of consideration of normalized value of those particular indicators. From the same table, it was found that Prakasam district of Andhra Pradesh had the highest exposure, but, Srikakulam district of same state had comparative lower exposure than the other sample districts. A critical observation on the same table depicted that districts of Tamil Nadu were comparatively highly exposed on meteorological severe drought, higher variation in rainfall including less & excess number of rainy days. As a result, districts of Tamil Nadu scored higher in overall exposure index than other sample districts. Districts of Andhra Pradesh were having higher incidences of heat wave during the period of 1975–2004. Whereas, incidence of cold wave were mainly observed in the districts of Odisha during the above said period. Degree of change in minimum temperature from 1975 to 2004 was more prominent in the districts of West Bengal. But, the trend in change of mean and maximum temperature from 1975 to 2004 were almost similar across all the sample districts. Sensitivity index of the sample districts were considered as the summation of the index score of 8 indicators. Index score of each indicator and overall sensitivity have been presented in Table 4. Like exposure, there are some ‘zero values’ in the above-mentioned table, which did not imply that indicators were worthless for the respective districts. It happened because of consideration of normalized value of those particular indicators. Among all the sample districts, Purba Medinipur district of West Bengal was found to be the most sensitive, whereas, Nagapattinam district of Tamil Nadu was the least sensitive. It was also found that districts of West Bengal were comparatively more sensitive, whereas, districts of Tamil Nadu were comparatively lower sensitive to climate change. As far as indicators of sensitivity index were concerned, it was found that per capita income of the people of the districts of Andhra Pradesh were more sensitive than the other sample districts. In case of ‘agricultural productivity’, districts of West Bengal were more sensitive than the other districts. It was also found that
South 24 Parganas
Purba Medinipur
Balasore
3
4
Tirunelveli Pudukkottai
23 24
19
Thoothukkudi
Thiruvallur
18
22
Vizianagaram
17
Thanjavur
S.P.S. Nellore
15 16
21
Guntur Prakasam
14
Villupuram
Krishna
13
20
West Godavari
East Godavari
12
Tamil Nadu
Srikakulam
Visakhapatnam
0.39
Ganjam
9
10
11
1.01 0.44
Puri Kendrapara
7 8
0.33
Jagatsinghpur
0.32 0.28
1.06
0.29
0.26
0.18
0.19
1.10
0.28 0.68
0.72
1.05
0.13
0.88
1.30
0.44
Bhadrak
0.52
0.39
6
Andhra Pradesh
0.00
0.63
5
Odisha
West Bengal North 24 Parganas
1
3.07 3.83
4.60
3.83
2.30
2.30
0.77
3.07
3.07 3.83
1.53
0.00
3.83
0.00
0.00
0.00
0.77 1.53
1.53
1.53
0.77
1.53
0.00
1.53
3.09 3.70
4.32
3.70
3.70
3.70
2.47
2.47
3.70 3.70
3.70
1.85
2.47
1.85
1.85
1.85
0.62 1.23
1.23
3.09
2.47
1.23
1.85
0.00
0.60 0.47 3.61 1.88
3.01 1.41
3.61 1.88
1.81 1.41
2.41 1.41
0.60 1.88
2.41 2.35
1.81 1.88 2.41 3.29
1.81 1.88
0.60 2.35
1.81 1.88
1.20 1.41
0.60 0.47
1.20 2.35
1.81 0.94 0.60 0.94
1.20 1.41
2.41 0.94
0.00 0.00
1.20 0.47
0.60 1.41
1.20 0.94
3.71 3.71
3.71
3.71
5.57
5.57
0.00
0.00
0.00 0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00 1.86
1.86
0.00
0.00
0.00
0.00
0.00
1.70 3.45
4.72
3.58
4.94
3.54
1.27
1.92
2.23 2.49
1.40
1.25
1.31
1.57
0.61
2.03
1.60 1.01
1.60
0.74
0.00
0.00
2.34
0.42
0.12 0.24
0.24
0.24
0.85
0.49
0.85
1.34
0.24 0.49
0.73
1.22
0.61
1.34
0.61
1.10
1.59 1.10
1.22
0.98
1.71
2.08
4.03
1.83
0.00 0.00
0.00
0.00
1.05
1.05
1.05
0.00
0.00 1.05
2.09
0.00
0.00
1.05
0.00
2.09
2.09 2.09
0.00
1.05
0.00
1.05
3.14
3.14
0.00 0.00
0.00
0.00
0.25
1.43
1.60
2.78
3.54 3.29
4.13
3.79
4.63
2.70
1.26
1.26
1.18 1.10
1.68
4.97
3.37
1.77
1.35
1.85
0.00 0.52
0.13
0.52
0.77
1.55
1.81
2.06
1.68 1.55
1.29
1.42
0.90
1.42
2.97
4.90
3.87 3.61
4.38
4.90
4.90
3.48
3.22
3.09
1.05 1.15
1.27
1.15
1.00
1.11
1.11
1.21
1.38 1.57
1.31
0.87
1.15
1.00
0.41
0.48
1.11 1.31
1.12
0.00
1.40
1.60
1.47
1.47
2.90 1.88
2.64
1.88
2.14
1.98
1.68
2.64
3.81 4.12
3.31
1.58
2.64
1.17
1.12
1.22
2.59 3.26
2.70
0.46
3.00
2.54
1.22
0.46
0.88 1.49
1.49
1.49
1.05
1.32
1.43
1.36
1.37 1.72
1.36
0.91
1.20
1.36
0.00
0.12
1.12 1.39
1.12
0.45
1.70
2.32
2.47
2.73
COAST LRD ERD ERY MMD MSD RAINV NORR HER HEAT COLD MEANT MAXIT MINTEM
Eastern coastal districts Indicators of exposure
2
Sl No. States
Table 3 Index values of exposure and its indicators across the eastern coastal districts of India
17.90 25.76
28.61
25.90
27.11
28.04
16.69
24.71
24.99 30.18
25.26
16.89
22.57
16.96
11.20
19.00
20.28 21.47
21.49
21.83
19.83
19.67
23.74
18.67
Exposure
Climatic Change
Kancheepuram
Kanniyakumari
Nagapattinam
Cuddalore
27
28
29
1.54
0.37
1.22
0.46
0.57
3.07
2.30
3.07
3.83
1.53
3.09
3.70
3.09
3.09
4.32
3.01 1.88
1.81 1.41
1.20 1.41
1.20 0.47
2.41 0.94
3.71
5.57
3.71
3.71
3.71
3.50
4.90
2.78
1.86
2.97
0.00
0.85
0.98
0.12
0.12
0.00
1.05
0.00
0.00
0.00
0.00
0.25
0.00
0.00
0.25
0.77
0.39
0.00
1.03
0.26
0.99
1.00
1.11
1.05
1.13
2.14
2.09
2.90
0.00
0.31
1.05
1.30
0.88
2.09
1.61
27.94
21.57
18.81
23.39
21.42
Exposure
Italics Lowest value; Bold Highest value; Underline Highest value in last column LRD Numbers of years having less number of rainy days than normal during 1975 to 2004, ERD Numbers of years having excess number rainy days than normal during 1975 to 2004, ERY Numbers of years having excess rainfall during 1975 to 2004, MMD Numbers of years having moderate metrological drought during 1975 to 2004, MSD Numbers of years having metrological severe drought during 1975 to 2004, RAINV Variation in rainfall during 1975 to 2004, NORR Numbers of days having very heavy rainfall during 1975 to 2004, HER Numbers of days having extremely heavy rainfall during 1975 to 2004, HEAT Numbers of heat wave incidences during 1975 to 2004, COLD Numbers of cold wave incidences during 1975 to 2004, MEANT Change in mean temperature from 1975 to 2004, MAXIT Change in mean maximum temperature from 1975 to 2004, MINTE Change in mean minimum temperature from 1975 to 2004, COAST Length of coastal line (KM)
Ramanathapuram
26
COAST LRD ERD ERY MMD MSD RAINV NORR HER HEAT COLD MEANT MAXIT MINTEM
Eastern coastal districts Indicators of exposure
25
Sl No. States
Table 3 (continued)
Climatic Change
Thoothukkudi
Tirunelveli Pudukkottai
22
23 24
Thiruvallur
19
Thanjavur
Vizianagaram
18
21
S.P.S. Nellore
17
Villupuram
Guntur Prakasam
15 16
20
Krishna
14
Tamil Nadu
East Godavari
13
2.81
Visakhapatnam
West Godavari
11
10
12
0.88
Ganjam
Srikakulam
9
Andhra Pradesh
Puri Kendrapara
7 8
0.97 0.45
1.34
0.59
0.15
1.24
1.01
1.75
1.59 1.95
2.32
1.92
1.61
0.40
0.07 0.00
0.80
Jagatsinghpur
6
0.07
Bhadrak
0.20
1.08
0.33
0.56
PCI
1.54 1.02
0.97
1.33
1.27
1.59
0.88
1.14
1.87 0.75
1.40
2.15
2.26
0.88
0.75
1.02
1.76 1.19
1.73
0.96
0.75
2.68
1.83
2.69
AP
1.39 0.40
0.61
0.44
0.62
0.48
0.76
0.94
2.07 2.09
1.85
1.49
1.54
1.12
0.74
0.16
0.15 0.07
0.14
0.00
0.18
0.46
0.33
0.92
MILK
Indicators of sensitivity
5
Purba Medinipur
Balasore
Odisha
3
4
North 24 Parganas
South 24 Parganas
West Bengal
1
Eastern coastal districts
2
States
Sl No.
1.34 2.57
1.32
1.93
2.76
0.70
2.50
2.17
1.98 2.56
1.69
2.32
2.52
1.42
2.70
2.47
2.72 3.12
2.94
2.85
2.91
2.88
2.31
1.01
VILLPOP
Table 4 Index values of sensitivity and its indicators across the eastern coastal districts of India
0.71 0.67
1.69
0.27
0.56
0.41
1.23
0.59
0.87 1.44
0.75
0.71
0.25
1.60
0.94
0.59
0.00 0.32
0.98
0.55
1.14
0.73
1.55
0.91
RAINFD
2.34 2.71
1.09
2.53
2.56
2.73
1.73
0.98
1.34 0.00
1.27
1.85
1.48
1.44
2.08
1.03
1.95 0.93
1.32
1.28
1.52
2.65
2.79
2.93
HOLD
0.05 0.89
0.44
0.00
0.23
0.02
1.03
0.81
1.45 1.84
1.54
1.02
0.73
2.02
1.72
2.31
2.33 2.50
1.73
1.91
1.85
2.60
2.82
1.94
DRINKI
0.00 0.50
0.06
0.48
0.11
0.10
0.35
0.49
0.45 0.50
0.42
0.62
0.62
0.18
0.39
1.53
1.90 1.30
1.13
1.56
2.05
3.33
2.66
2.14
DILAPID
8.36 9.20
7.53
7.56
8.26
7.28
9.49
8.87
11.63 11.14
11.25
12.08
11.01
11.46
10.18
9.50
10.89 9.42
10.78
9.18
10.60
16.42
14.63
13.11
Sensitivity
Climatic Change
Eastern coastal districts
Cuddalore
29
0.75
0.63
1.74
0.52 1.09
PCI
1.83
0.34
2.41
0.00 1.22
AP
1.97
2.44
0.00
2.06 0.77
VILLPOP
0.63
0.32
1.44
1.39 0.21
RAINFD
2.57
2.51
2.90
2.05 2.72
HOLD
0.17
0.19
0.26
1.34 0.08
DRINKI
0.16
0.35
0.33
0.26 0.00
DILAPID
8.48
7.06
9.26
7.72 6.57
Sensitivity
PCI Per-capita income (Rs), AP Agricultural productivity (Rs/ha of Net sown area), MILK Total production of milk ('000MT), VILLPOP Rural population to total population (%), RAINFD Rainfed area (percentage to net sown area), HOLD Number of marginal and small holding to total holding (%), DRINKI Rural households not having drinking water sources in their home premises (%), DILAPID Households having houses in dilapidated condition (%)
0.40
0.28
0.18
0.10 0.47
MILK
Indicators of sensitivity
Italics Lowest value; Bold Highest value; Underline Highest value in last column
Kanniyakumari
Nagapattinam
27
28
Ramanathapuram Kancheepuram
States
25 26
Sl No.
Table 4 (continued)
Climatic Change
Climatic Change
15
10
Fig. 2 Social vulnerability to climate change index of the eastern coastal coastal district of India
Cuddalore
Nagapanam
Kanniyakumari
Kancheepuram
Tirunelveli
Pudukkoai
Thanjavur
Thoothukkudi
Thiruvallur
Ramanathapuram
Eastern coastal districts of India
Villupuram
S.P.S. Nellore
Vizianagaram
Guntur
Prakasam
Krishna
East Godavari
West Godavari
Visakhapatnam
Ganjam
Srikakulam
Puri
Kendrapara
Bhadrak
Jagatsinghpur
-10
Balasore
-5
Purba Medinipur
0
South 24 Parganas
5
North 24 Parganas
Index score of social vulnerability index
‘total production of milk’ was most affected in the districts of Andhra Pradesh. The same table also revealed that village population and rainfed farming would be affected across all the sample districts in almost similar trend. Small and marginal holders of the districts of West Bengal and Tamil Nadu were greater contributors to the overall sensitivity index than those from other sample districts. Households of the districts of West Bengal and Odisha were more sensitive to climate change, on account of having higher percentage of houses in dilapidated conditions coupled with non-availability of drinking water inside their home premises. The BOverall Vulnerability Index^ was calculated by subtraction of sum of exposure and sensitivity from the adaptive capacity. Index scores of the sample districts have been presented in Fig. 2 (also see Appendix 3). Districts with more negative vulnerability score were found to be more vulnerable. However, positive scores of vulnerability index did not mean that districts were not vulnerable at all; it just meant that these districts were comparatively less vulnerable. According to the same figure, Pudukottai district of Tamil Nadu was the most vulnerable district, while East Godavari district of Andhra Pradesh was found to be least vulnerable. Pudukottai district had the second least adaptive capacity, coupled with comparatively higher exposure and moderately higher sensitivity; as a result of which, it was found to be the most vulnerable. West Godavari district on the other hand, despite of having the highest adaptive capacity ranked the third least vulnerable due to its comparable higher exposure. Though East Godavari district and Kanyakumari district were having lower adaptive capacity than West Godavari district, yet, both districts were comparatively less vulnerable districts due to lower exposure. Comparing between the two most vulnerable districts i.e., Pudukottai and Cuddalore, both were found to be similar in terms of adaptive capacity, exposure and sensitivity. It was also found that combined effect of exposure and sensitivity was greater than the adaptive capacity of 10 districts viz. South 24-Parganas of West Bengal; Bhadrak of Odisha; Prakasam of Andhra Pradesh; Thiruvallur, Villipuram, Thanjavur, Thoothukkudi, Pudukottai, Ramanathapuram and Cuddalore of Tamil Nadu. Comparatively, the districts of Tamil Nadu were more vulnerable than the other districts due to their higher exposure as districts of Tamil Nadu faced comparatively higher number of meteorological severe drought, higher variation in rainfall including less and excess number of rainy days. On the other hand districts of Andhra Pradesh were relatively lower vulnerable. Rama Rao et al. (2013) calculated districtwise vulnerability profile of Indian agriculture and they showed that coastal districts of Tamil Nadu were comparetively more vulnerable, whereas, coastal districts of Andhra Pradesh were least vulnerable. Intra state comparison from this study highlighted that Kanyakumari
Climatic Change
district was found to be the least vulnerable district and Pudukkottai was the most vulnerable among the coastal districts of Tamil Nadu. Udayakumar (2014) also reported that Kanyakumari district was the least vulnerable district of coastal Tamil Nadu. In Andhra Pradesh, Prakasam district was found to be the most vulnerable and East Godavari was the least vulnerable district. Rama Rao et al. (2013) also concluded that Prakasam district was most vulnerable district of coastal Andhra Pradesh. They also remarked that districts KrishnaGodavari delta were the least vulnerable districts. Intra districts comparison of coastal Odisha was in the line of Kumar and Tholkappian (2005) who reported that Bhadrak and Balasore were the most vulnerable districts of coastal Odisha. Kumar et al. (2010) also analysed coastal vulnerability of Odisha and reported that parts of the coastal area of Bhadrak and Balasore district were highly vulnerable. The present study also got support from Kumar and Tholkappian (2005) and Rama Rao et al. (2013) who reported that the South 24 Parganas district was found to be most vulnerable among the coastal districts of West Bengal.
4 Summary and conclusion The present study analysed social vulnerability of eastern coastal districts of India to climate change by calculating vulnerability indices and comparing across the region. All the 29 coastal districts of four states across east coast of India (Odisha, Tamil Nadu, Andhra Pradesh and West Bengal) were considered for this study. The vulnerability analysis is based on the IPCC (2001) definition of vulnerability, which explained vulnerability happened to be a function of adaptive capacity, sensitivity and exposure. Integrated vulnerability assessment approaches were adopted, by combining socio-economic indicators (like demographic features), biophysical indicators (like production and productivity of crop, etc.) were adopted for the present study. The method of PCA was employed for assigning weights to the different indicators of vulnerability. Vulnerability was calculated as the net affect of exposure and sensitivity on the adaptive capacity. This net effect was found to be negative in 10 districts viz. South 24Parganas of West Bengal; Bhadrak of Odisha; Prakasam of Andhra Pradesh; Thiruvallur, Villipuram, Thanjavur, Thoothukkudi, Pudukottai, Ramanathapuram and Cuddalore of Tamil Nadu. These districts are comparatively more vulnerable than the other sample districts, and their present status may be considered as an alarming situation. It was also found that Pudukottai district of Tamil Nadu found to be most vulnerable district, while East Godavari district of Andhra Pradesh was the least vulnerable. There is a strong relationship between extreme climatic events (like meteorological drought, cold wave, heat wave, extremely heavy rainfall etc.) and vulnerability. Therefore, location-specific contingency planning must be prepared to cope up with these extreme climatic events in the near future. The results presented in above demonstrate an integrated approach of vulnerability assessment that can be used to assess the climate change impacts on social change. The present study developed a new approach for assessment of climate induced social vulnerability and submitted to a growing body of literature in social science. Being a new approach, it is better to recognise both weakness and strength. Foremost weakness of the approach is not to consider the change in adaptive capacity and sensitivity over time. Another limitation of the approach is not giving the absolute measurement of vulnerability; rather the index value highlights a comparative ranking of studied districts. The strongest strength of the developed approach is that it provides a means for evaluating the relative distribution of vulnerability to multiple stressors at district level. To capture potential changes over time, the indicators of adaptive capacity might be calculated using alternative scenarios in future researches.
Climatic Change Acknowledgments Authors acknowledge the contributions of National Initiative on Climate Resilient Agriculture (NICRA) at NDRI, Karnal, India for timely help and cooperation during the research work; and ADG (MR), National Climate Centre, IMD, Pune for providing climatic data. We also extend our gratitude to the Director, ICAR-National Dairy Research Institute, Karnal, Haryana, India for guidance, support and encouragement.
References Adger WN (1999) Social vulnerability to climate change and extremes in Coastal Vietnam. World Dev 27(2): 249–269 Adger WN, Kelly PM (1999) Social vulnerability to climate change and the architecture of entitlements. (IPCC Special Issue on ‘Adaptation to Climate Change and Variability’). Mitig Adapt Strateg Glob Chang 4(3–4): 253–266 (R) Canadian International Development Agency (CIDA) (2003) Methodology used in the CIDA study: An example of India. AIACC Asia-Pacific Region Open Meeting. http://www.aiaccproject.org/meetings/Bangkok_03/ Workshop%20presetations/C1_S_Bhadwal.ppt. Accessed 20 Aug 2011 Deressa TT, Hassan RM, Ringler C (2008) Measuring Ethiopian farmers’ vulnerability to climate change across regional states. IFPRI Discussion Paper 00806. International Food Policy Research Institute, Washington Deressa TT, Hassan RM, Ringler C (2009) Assessing household vulnerability to climate change. The case of farmers in the Nile basin of Ethiopia. IFPRI discussion paper 00935. International Food Policy Research Institute, Washington Feroze SM, Chauhan AK (2010) Performance of dairy self help groups in India: principal component analysis approach. Indian J Agric Econ 65(2):308–320 Gbetibouo GA, Ringler C (2009) Mapping south African farming sector vulnerability to climate change and variability. A subnational assessment. IFPRI discussion paper 00885. International Food Policy Research Institute (IFPRI), Washington Hahn MB, Riederer AM, Foster SO (2009) The livelihood vulnerability index: a pragmatic approach to assessing risks from climate variability and change—a case study in Mozambique. Glob Environ Chang Vol 19(1):74–88 Intergovernmental Panel on Climate Change (IPCC) (2001) In: McCarthy J, Canziani O, Leary N, Dokken D, White K (eds) Climate change 2001: impacts, adaptation, and vulnerability. Cambridge University Press, Cambridge Kelly PM, Adger WN (2000) Theory and practice in assessing vulnerability to climate change and facilitation adaptation. Clim Chang 47(4):925–1352 Kumar KS, Tholkappian S (2005) Relative vulnerability of Indian coastal districts to sea-level rise and climate extremes. Int Rev Environ Strateg 6(1):3–22 Kumar TS, Mahendra RS, Nayak S, Radhakrishnan K, Sahu KC (2010) Coastal vulnerability assessment for Orissa state, east coast on India. J Coast Res 26(3):523–534 Leary N, Adejuwon J, Bailey W, Barros V, Batima P, Caffera RM, Cinvanno S, Conde C, Comarmond AD, Sberbinin AD, Downing T, Eakin H, Nyong A, Opondo M, Osman-Elassha B, Payet R, Pulbin F, Pulbin J, Ratnisri J, Sanjak E, Maltitz MW, Yin Y, Ziervogel G (2008) For whom the bell tolls: Vulnerability in a changing climate. In: Leary N, Conde C, Kulkarni J, Nyong A, Pulbin J (eds) Climate change and vulnerability. Earthscan, London, pp 3–30 McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (2001) Climate change 2001: impacts, adaptation and vulnerability. Cambridge University Press, Cambridge Mimura N (2013) Sea-level rise caused by climate change and its implications for society, Proceedings of the Japan Academy. Ser B Phys Biol Sci 89(7):281–301 Mohanty AK, Kumar GAK, Singh BB, Meera SN (2009) Developing multidimensional scale for effective measurement of rural leadership. Indian Res J Ext Educ 9(2):57–63 Moreno A, Becken S (2009) A climate change vulnerability assessment methodology for coastal tourism. J Sustain Tour 17(4):473–488 Nelson R, Kokic P, Crimp S, Meinke H, Howden SM (2010) The vulnerability of Australian rural communities to climate variability and change: part I - conceptualizing and measuring vulnerability. Environ Sci Policy 13:8–17 Nyong A, Dabi D, Adepetu A, Berthe A, Ibemegbulem V (2008) Vulnerability in the sahelian zone of northern Nigeria: a household-level assessment. In: Leary N, Conde C, Kulkarni J, Nyong A, Pulbin J (eds) Climate change and vulnerability. Earthscan, London, pp 218–238 O’Brien K, Leichenko R, Kelkar U, Venema H, Aandahl G, Tompkins H, Javed A, Bhadwal S, Barg S, Nygaard L, West J (2004) Mapping vulnerability to multiple stressors: climate change and globalization in India. Glob Environ Chang 14:303–313
Climatic Change Patnaik U, Narayanan K (2005) Vulnerability and Climate Change: An analysis of the eastern coastal districts of India. Published in International workshop on BHuman security and climate change^ at Holmen Fjord Hotel, Asker, near Oslo, 21–23 June 2005 organised by Centre for the Study of Civil War, International Peace Research Institute, Oslo (PRIO), Centre for International Environmental and Climate Research at the University of Oslo (CICERO) and the Global Environmental Change and Human Security Program (GECHS) Patwardhan A, Narayanan K, Parthasarathy D, Sharma U (2003) Assessment of Vulnerability and Adaptive Capacity in Coastal Zones. In: Shukla (ed) Climate change and India vulnerability assessment and adaptation. Universities Press, Hyderabad Piya L, Maharjan KL, Joshi NP (2012) Vulnerability of rural households to climate change and extremes: Analysis of Chepang households in the mid-hills of Nepal. Selected paper prepared for presentation at the International Association of Agricultural Economics (IAAE) Triennial Conference, Foz do Iguacu, Brazil Rama Rao CA, Raju BMK, Subba Rao AVM, Rao KV, Rao VUM, Kausalya R, Venkateswarlu B, Sikka AK (2013) Atlas on vulnerability of Indian agriculture to climate change. Central Research Institute for Dryland Agriculture, Hyderabad, p 116 Ravindranath NH, Rao S, Sharma N, Nair M, Gopalakrishnan R, Rao AS, Malaviya S, Tiwari R, Sagadevan A, Munsi M, Krishna N, Bala G (2011) Climate change vulnerability profiles for North east India. Curr Sci 101(3):384–394 Rishi P, Mudaliar R (2014) Climate stress, behavioral adaptation and subjective well being in coastal cities of India. Am J Appl Psychol 2(1):13–21 Seidl R, Rammer W, Lexer MJ (2011) Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps. Can J For Res 41:694–706 Strauss B, Kulp S (2014) New analysis shows global exposure to sea level rise, Climate Central, Retrieved from http://www.climatecentral.org/news/new-analysis-global-exposure-to-sea-level-rise-flooding-18066 on Dec 16 2014 Tambe S, Arrawatia ML, Bhutia NT, Swaroop B (2011) Rapid, cost-effective and high resolution assessment of climate-realted vulnerability of rural Communities of Sikkim Himalaya, India. Curr Sci 101(2):165–173 Udayakumar P (2014) Effects of sea level change on vulnerable east coast of India. Res J Mar Sci 2(1):1–5 Vincent K (2004) Creating an index of social vulnerability to climate change for Africa. Technical Report 56. Tyndall Center for Climate Change Research, University of East Anglia, Norwich Wong PP, Losada IJ, Gattuso JP, Hinkel J, Khattabi A, McInnes KL, Saito Y, Sallenger A (2014) Coastal systems and low-lying areas. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel BE, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 361–409
