Assessment of social vulnerability to climate change in the eastern coast of India

Ecological Indicators 77 (2017) 105–113 Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind Original Articles An assessment of social vulnerability to climate change among the districts of Arunachal Pradesh, India Sanjit Maiti b,∗ , Sujeet Kumar Jha b , Sanchita Garai b , Arindam Nag c , A.K. Bera a , Vijay Paul a , R.C. Upadhaya d , S.M. Deb a a ICAR-National Research Centre on Yak, Dirang, 790101, Arunachal Pradesh, India Dairy Extension Division, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India c Department of Extension Education, Bihar Agricultural University, Sabour, 813210, Bihar, India d Dairy Cattle Physiology Division, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India b a r t i c l e i n f o Article history: Received 11 April 2015 Received in revised form 3 February 2017 Accepted 6 February 2017 Keywords: Vulnerability to climate change Adaptive capacity Exposure Sensitivity Eastern Himalaya Arunachal Pradesh a b s t r a c t The present study highlighted the state of climate change induced social vulnerability of the districts of Arunachal Pradesh. For the purpose of assessment of one of the most fragile ecosystems of the eastern Himalaya, the ‘Integrated Vulnerability Assessment Approach’ and IPCC’s definition of vulnerability were utilized. The assessment was based on various secondary data, like socio-economic and biophysical indicators, collected from several authenticated sources; and the respective weightage of these indicators was assigned by using ‘Principal Component Analysis’. Vulnerability was calculated as the net effect of exposure and sensitivity on the adaptive capacity. Anjaw district of eastern Arunachal Pradesh was found to be the most vulnerable district, while Tawang district of western Arunachal Pradesh happened to be the least vulnerable. This net effect was found negative in 7 out of 12 districts viz. Anjaw, Upper Siang, West Siang, Lower Dibang Valley, East Siang, East Kameng and Kurung Kurmey. This net negative effect could be construed as an alarming situation. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction ‘Climate change’ is a natural phenomenon; however, the rate at which the change takes place, as of now, is far more than the normal; this is due to anthropogenic activities (Bharali and Khan, 2011). The ‘Fourth assessment report’ of IPCC clearly depicted that the impact of climate change would be more severe in mountain and costal eco-system, especially in developing and least developed countries (IPCC, 2007). The north-eastern states of India are expected to be greatly affected by climate change because of their geo-ecological fragility, strategic location vis-à-vis the eastern Himalayan landscape and international border, their trans-boundary river basins and the inherent socio-economic instabilities. Climate change will affect all natural eco-systems, but the impacts will be more prominent on the already stressed ecosystems of the Eastern Himalayas (ICIMOD, 2010). Cavaliere (2009) and Xu et al. (2009) also explained that high-elevation eco-systems of the Himalayan region are the most vulnerable geographic regions of the world (outside of the polar region) to climate change. ∗ Corresponding author. E-mail addresses: sanjit.ndri@gmail.com, sanjitndri@rediffmail.com (S. Maiti). http://dx.doi.org/10.1016/j.ecolind.2017.02.006 1470-160X/© 2017 Elsevier Ltd. All rights reserved. The ecological systems in Arunachal Pradesh, due to its physiographic condition are more fragile, complex and vulnerable to global climatic change and are found to be easily disturbed. Climate change will not only impact the bio-diversity of Arunachal Pradesh, but also affect the livelihood of local communities, as they happened to be fully dependent on the natural resources and they perceived the changes in climate since last decade (Bharali and Khan, 2011; Maiti et al., 2014). Of late, Arunachal Pradesh experienced extreme climatic events, including two extremely dangerous cloudbursts of unprecedented intensity in the years 2008 and 2010, respectively, which produced devastating flash floods, causing many deaths and enormous loss to the forested and agricultural land. As a part of the Eastern Himalayas, Arunachal Pradesh is also rich in endangered, endemic and threatened floral and faunal species with restricted distribution and narrow habitat ranges (Wikramanayake et al., 2009), which are at particular risk due to climate change (ICIMOD, 2010). However, it is extremely difficult to assess the impact of climate change due to limited data availability, coupled with the uncertainties associated with the climate scenarios. A very little of the impacts of climate change in Arunachal Pradesh is known till now. Therefore, making the future scenario more visionary, an assessment of climate induced vulnerability was 106 S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 Fig. 1. Map of Arunachal Pradesh, India. thought to be the need of the hour, which undertaking the present study. 2. Materials and methods 2.1. Study area Arunachal Pradesh, the largest state in the north-eastern region of India, is located between 26◦ 28′ –29◦ 30′ N lat. and 91◦ 30′ –97◦ 30′ E long., and covers a geographical area of 83,743 sq. Km, with a population density of 17/sq. km (2011 Census). The state forms a major part of the Eastern Himalayas, and is predominantly hilly and mountainous (Bharali and Khan, 2011). The state is also recognized as one among the 200 globally important eco-regions (Olson and Dinerstein, 1998). There are 16 districts in the state of Arunachal Pradesh (Fig. 1). Among these sixteen districts, 13 districts (except Tirap, Changlam, and Lohit) are in alpine and sub-temperate alpine agro-climatic region. Hence, these thirteen districts namely Tawang, West Kameng, East Kameng, Kurung Kurmey, Papumpare, Lower Subansiri, Upper Subansiri, East Siang, West Siang, Upper Siang, Dibang Valley, Lower Dibang Valley and Anjaw were covered to prepare district level vulnerability profile. Dibang valley district was dropped during data collection due to non-availability of climatic data at India Metrological Department, Pune, India. Therefore, 12 districts of Arunachal Pradesh were finally selected for the present study. 2.2. Data sources The present study was based on the secondary data and district level data, which were obtained from the diverse sources. Data on the district-wise demographic features like population density, decadal growth rate, rural literacy rate, rural population to total population as well as household data like rural households availing banking service, households not having drinking water sources at their home premises, households having houses in dilapidated conditions were taken from the official website of Census of India, as per 2011 census. Climatic indicators were calculated from the high resolution daily gridded temperature and rainfall data for the Indian region during the period of 25 years (1975–1999), as developed by the India Metrological Department, Pune, India. District-wise agricultural productivity (Rs/ha of Net sown area) were taken from the ‘Policy Paper no’ 26 (entitled as ‘Instability and regional variation in Indian Agriculture’), as published by the National Centre for Agricultural Economics and Policy Research, India in the year 2011. District-wise ‘Human Development Index’ value was obtained from the human development report of the Govt. of Arunachal Pradesh. Data on the district-wise land utilisation pattern; agriculture; animal husbandry; fisheries; infrastructure like rural electrification, medical institution; per capita income; and operational holding were collected from the latest published statistical handbook and official websites of Arunachal Pradesh and respective districts. 2.3. Development of social vulnerability index for the districts of Arunachal Pradesh, 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 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 fol- S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 107 2.3.2. Choosing the vulnerability indicators Piya et al. (2012) argued that vulnerability to climate change is multidimensional and is determined by a complex interrelationship 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 may be: to verify the representativeness of the theorybased indicators with data availability from authentic sources. Judicial combination of both theory and data-driven approaches was adopted during selection of the indicators used in this study. The concept of vulnerability as given by the IPCC (2001) was adopted for this study. The IPCC defines vulnerability to climate change as “the 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. Indicators under each sub-sectors of vulnerability i.e. exposure, sensitivity and adaptive capacity have been presented in Table 1, and discussed as below: ‘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-resilient agriculture, including its adoption. Districts with higher population density and decadal growth rate will have more burdens on the natural resource of the concerned district, thereby reducing the adaptive capacity. Implication of higher population density is common to any type of shocks, including climate. ‘Social asset’ is comprised of a single indicator, i.e. ‘Human Development Index’ (HDI) of the district, wherein HDI indicates the ‘socially progressiveness’ of the district. It is hypothesized that higher the HDI of the district, higher would be the capacity to cope up with climate-related stress. There are seven indicators under the ‘natural asset’ of the district, viz: area under forest cover (% to total geographical area), area under grazing land (% total geographical area), area under food crops (000 ha), cropping intensity, livestock density (per km2 ), numbers of years having normal rainfall (during 1975–1999) and numbers of years having normal rainy day during 1975–1999. 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 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 mainly dependent on monsoon. In Aruanachal Pradesh, only 19 and 15 per cent of the net and gross cultivated area, respectively, is irrigated (quoted from http://arunachalpradesh.gov.in/ nnap.htm. on June 03, 2016). Hence, rainfall is the major source of water for the crops in Arunachal Pradesh. Therefore, more numbers of years having normal rainfall and normal rainy days mean good agricultural produces. Subsequently, good agricultural produces enhance the adaptive capacity of the people. Indicators of ‘physical asset’ is mainly comprise of the infrastructural facilities, like network of veterinary institutions including artificial insemination centres, medical institutions and electricity in the rural area. Though there is no direct relation of these indicators with climate shock, but, possession of better infrastructural facility will enhance the capacity to withstand the risk from any shocks including climate. Finally, ‘financial asset’ is represented a single indicator i.e. rural households availing banking services. Like other previously discussed indicators, this indicator is not directly related to climate shock. But, better financial strength could also be the backbone, while developing good adaptive capacity. Households availing banking services indicate the saving habits of the habitat of the districts. Saving is one of the most important pillars of the financial strength. It is hypothesized that higher the households availing banking services, higher the adaptive capacity. 2.3.2.1. Adaptive capacity. Adaptive capacity of a household is taken to be an emergent property among 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). Although only few of the selected indicators like: Numbers of years having normal rainfall (during 1975–1999) and Numbers of years having normal rainy day (during 1975–1999) have a direct role in minimization of risk from climate shocks, yet all of these indicators enhance the capabilities to combat climate shocks, risk pooling, risk distribution or as buffer during extreme climatic events. Rationale of each indicator in building adaptive capacity has been discussed hereafter, accordingly. 2.3.2.2. Exposure. For this study, historical changes in climatic variables and occurrences of extreme climate events 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, change in mean minimum temperature for the period of twenty-five years (1975–1999) 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 lowed to analyse the vulnerability to climate change of the alpine districts of Arunachal Pradesh. 2.3.1. Methods for measuring vulnerability to climate change Most common methods used to assess vulnerability to climate change are: the econometric, and indicator methods. The econometric method has its roots in the poverty and development literature. This method use household-level socio-economic survey data, in order 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), Haan et al. (2001), Nelson et al. (2010), Ravindranath et al. (2011), Tambe et al. (2011), Seidl et al. (2011) and Maiti et al. (2015) used index-based approach to analyse social vulnerability to climate change in their respective study area. Thus, index-based method was applied to analyse the vulnerability to climate change of the alpine districts of Arunachal Pradesh. Methodology for the development district wise vulnerability index used by Maiti et al. (2015) was used for index development in this present study, which has been thoroughly described as below: 108 S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 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 2 PD DGR Population density (per km2 ) Decadal growth rate 3 RLR Rural literacy rate Number of population per square kilometre of the district Percentage of population increased in the census 2011 over the census 2001 Percentage of literate rural population to rural population over 6 years of age IB. Social capital 4 HDI 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. IC. Natural Capital 5 FC Area under forest cover 6 GL Area under grazing land 7 AFC Area under food crops (000 ha) 8 CI Cropping intensity 9 10 LD NRY Livestock density (per km2 ) Numbers of years having normal rainfall during 1975–1999 11 NRD Numbers of years having normal rainy day during 1975–1999 Percentage of area under forest cover to the total geographical area of the district Percentage of area under grazing land cover to the total geographical area of the district 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 Number of livestock per square kilometre of the district If the year wise total rainfall of the district is with in the range of 81–119 percent of normal rainfall. Then, it was considered that particular year has normal rainfall. 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 1975–1999 and compare with the normal number of rainy days. If it is found that percentage of rainy days are in the range of 81–119, then, it is considered that year has normal numbers of rainy days. ID. Physical Capital 12 VETINS 13 MED 14 RELEC Number of Veterinary institution per 1000 livestock Number of medical institutions per 1000 population Rural Electrification (% of village electrified) Total number all types of veterinary institutions (Hospital, dispensary etc) per 1000 livestock in the district Total number all types of medical institution (Allopathy, Homeopathy, Unani etc) per 1000 population in the district Percentage of village had been electrified to total number of village IE. Financial Capital 15 BANK Percentage of rural household availing banking service Percentage of rural household had an account in any bank II. Exposure 16 LRD Numbers of years having less number of rainy days than normal during 1975–1999 17 ERD Numbers of years having excess number rainy days than normal during 1975–1999 18 ERY Numbers of years having excess rainfall during 1975–1999 19 MMD Numbers of years having moderate metrological drought during 1975–1999 20 MSD Numbers of years having metrological severe drought during 1975–1999 21 RAINV Variation in rainfall during 1975–1999 22 NORR Numbers of days having very heavy rainfall during 1975–1999 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 1975–1999 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. 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 1975–1999 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. If the year wise total rainfall of the district is greater than equal to 120 percent of normal rainfall. Then, it was considered that particular year has excess rainfall. If the year wise total rainfall of the district is between 26–50 percent of normal rainfall. Then, it was considered that particular year is metrological moderate drought. If the year wise total rainfall of the district is less than 50 percent of normal rainfall. Then, it was considered that particular year is metrological severe drought. Coefficient of variation in year wise rainfall during the period of 1975–1999. A day is called very heavy rainfall day according to India Meteorological Department if the rainfall of that is between 124.5 mm to 244.5 mm. Frequency of these days were counted during 1975–1999. S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 109 Table 1 (Continued) Sl No. Symbol List of indicators Description of the indicators 23 HER Numbers of days having extremely heavy rainfall during 1975–1999 24 HEAT Numbers of heat wave incidences during 1975–1999 25 COLD Numbers of cold wave incidences during 1975–1999 26 MEANT 27 MAXIT 28 MINTE Change in mean temperature from 1975 to 1999 Change in mean maximum temperature from 1975 to 1999 Change in mean minimum temperature from 1975 to 1999 A day is called extremely 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 1975–1999. 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). 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). Deviation in daily mean temperature in the year 1999 from the base year 1975 Deviation in daily mean maximum temperature in the year 1999 from the base year 1975 Deviation in daily mean minimum temperature in the year 1999 from the base year 1975 III. Sensitivity 29 PCI Per-capita income (Rs) 30 AP Agricultural productivity (Rs/ha of Net sown area) 31 BPL Household below poverty line (%) 32 VILLPOP Rural population to total population (%) 33 RAINFD Rainfed area (percentage to net sown area) 34 HOLD 35 DRINKI 36 DILAPID Number of marginal and small holding to total holding (%) Rural households not having drinking water sources in their home premises (%) Households having houses in dilapidated condition (%) rainfall, numbers of days having extremely heavy rainfall during the period of twenty-five years (1975–1999) 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 that higher rate of change of climate variables, higher the frequency of extreme climate events and higher will be the exposure of the districts to climate change and variability. 2.3.2.3. Sensitivity. Sensitivity could best be measured by a change in income or livelihood, albeit attributed exclusively to climatic factors. However, it was not possible to find this type of data. Instead, we were obliged to make 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; Handmer et al., 2012). This principle was applied in this study. A total of seven variables were considered for calculating the degree of sensitivity of the studied districts. Districts of Arunachal Pradesh are predominantly agriculture-based districts, as agriculture contributes as the major share of per capita income. Majority of the agricultural produces including milk comes from the small and marginal farmers of rainfed area. Bharali and Khan (2011) reported that extreme precipitation events (heavy rainstorm, cloudburst) may have their own impacts on the fragile geomorphology of the Arunachal Pradesh and two extremely intense cloudbursts of unprecedented intensity in Arunachal Pradesh in 2008 and 2010 produced devastating flash floods causing many deaths and enormous loss to the forested and 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. 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). Percentage of households in below poverty line to total households Percentage of district population reside in the rural area to total population of the district Percentage of cropped area depended on rainfall to net sown area Percentage of marginal (less than 1 ha) and small (1–2 ha) holding to total number of holding of the district Percentage of rural households not having drinking water at their home premises Percentage of households having houses in dilapidated condition agricultural land. Therefore, past climatic hazards will increase the sensitivity of the districts to such events. It was hypothesized that higher rate of change of climate variables and higher the frequency of extreme climate events, higher will be the sensitivity vis-a-vis getting access to purified drinking water and higher the chances of disaster of the houses in dilapidated conditions. 2.3.3. Calculation of the vulnerability index Having chosen the indicators, now these needed 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 Chouhan, 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 is the testing of suitability indicators. Ravindranath et al. (2011) used ‘Principal Component Analysis (PCA)’ to identify the significant indicators while eliminating non-significant indicators. After normalization, three factor analyses (one each for exposure, sensitivity and adaptive capacity) were ran, choosing ‘Principal Component Analysis’ 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 Annexure 1 in Supplementary material) indicates that a high amount of variance for all the indicators could be explained by the factor analysis model. Mohanty et al. (2009) use a thumb rule of communality value being 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 indicators was 110 S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 Table 2 Index values of components adaptive capacity across the districts of Arunachal Pradesh. Sl No Districts Human Asset Social Asset Natural Asset Physical Asset Financial Asset 1 2 3 4 5 6 7 8 9 10 11 12 Tawang West Kameng East Kameng Papum Pare Lower Subansiri Kurung Kurmey Upper Subansiri West Siang East Siang Upper Siang Lower Dibang Valley Anjaw 4.317 4.208 4.396 10.982 8.441 6.228 4.906 3.166 6.307 1.130 3.807 1.320 3.454 3.812 0.000 3.776 1.127 1.127 1.360 3.508 5.333 2.899 5.315 2.792 14.710 10.812 11.931 11.701 15.809 14.013 10.376 13.093 15.846 12.963 13.741 13.579 7.972 6.458 2.742 6.090 5.391 0.670 1.646 5.523 4.216 4.200 4.212 5.546 5.728 3.145 0.744 4.639 4.495 0.000 1.809 3.213 3.948 3.171 2.346 2.552 dropped from the factor analysis model and was considered for next step i.e. assignment of weights to the indicators. The method followed by Feroze and Chouhan (2010) was adopted for this study, to assign the weights to the indicators (Annexure 2 in Supplementary material). 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 is calculated as: V = AC– (E + S) (1) Where, V is the Vulnerability index, AC is the Adaptive Capacity index, E is the Exposure Index and S is the Sensitivity index. The overall vulnerability index facilitated inter-district comparison. Higher value of vulnerability index indicated lower vulnerability. However, the negative value of the index indicated that the net effect of adaptive capacity, exposure and sensitivity was negative. It may be considered as more vulnerable and may be considered as an alarming situation. This index does not give the absolute measurement of vulnerability; rather the index value highlighted a comparative judgement of studied districts. 3. Results Table 2 represents the index values for adaptive capacity and its components across the alpine districts of Arunachal Pradesh. It was found that Papum Pare district scored the highest, in term of overall adaptive capacity, followed by Tawang district, East Siang district, Lower Subansiri district, Lower Dibang valley district, West Siang district, and West Kameng district. Whereas, East Kameng district, Upper Subansiri district, Kurung Kurmey district, Upper Siang district and Anjaw district had comparatively lower adaptive capacity to combat with the negative impact of climate change. Papum Pare district is an urban-based district of Arunachal Pradesh. Therefore, index score across each asset category were comparatively higher in Papum Pare district than the other districts. In human asset category, Papum Pare district and Lower Subansiri district scored considerably high. Whereas, districts like Upper Siang and Anjaw had comparatively lower score in human asset. In case of social asset, districts like East Siang and Lower Dibang valley scored considerably higher than the others studied districts. District East Kameng scored zero in social asset category. This did not imply that ‘Human Development Index’ of the district was zero. This happened due to consideration of normalized value of only one indicator i.e. ‘Human Development Index’ for this asset category. As far as natural asset was concerned, East Siang district and Lower Subansiri district scored higher. Districts like Tawang and West Kameng made a higher score in physical asset category than the other studied districts. In case of financial asset category, Tawang, Lower Subansiri, and Papum Pare districts scored considerably high. District Kurung Kurmey scored zero in financial asset category. This did not imply that ‘percent of household availing banking service’ of the district was zero. This happened due to consideration of normalized value of only one indicator for this asset category. Overall exposure index composed of 13 indicators. All these 13 indicators were related to exposure of different climatic variables including extreme climatic events like heat & cold waves and meteorological drought. During the analysis, two indicators i.e. ‘numbers of years having less number of rainy days than normal (during 1975–1999)’ and ‘variation in rainfall (during 1975–1999)’ were found to be insignificant; and accordingly, these two indicators were dropped for calculation of degree of exposure among the districts of Arunachal Pradesh. Index score of the remaining 11 indicators as well as overall exposure have been presented in Table 3. There are some ‘zero values’ also in the above-mentioned table. This did not imply that indicators were worthless for the respective districts. It happened due to consideration of normalized value of those particular indicators. From the same table, it was also found that some districts possessed equal value for exposure. This happened due to consideration of one coordinate for those districts. For example, 27◦ 50′ N × 95◦ 50′ E coordinate used to extract the daily climatic data of Lower Dibang Valley and Anjaw district. Therefore, exposure score of these two districts were equal. West Siang district had the highest exposure, but, Tawang had comparative lower exposure than the other studied districts. A critical observation on the same table clearly stated that districts of eastern Arunachal Pradesh were comparatively highly exposed. Sensitivity index of the alpine districts of Arunachal Pradesh were considered as the summation of the index score of 6 indicators. Index score of each indicator and overall sensitivity have been presented in Table 4. Like exposure, there were some ‘zero values’ in the above-mentioned table. Again, this did not imply that indicators were worthless for the respective districts. It happened due to consideration of normalized value of those particular indicators. Among all the studied districts of alpine Arunachal Pradesh, Anjaw district was found to be the highest sensitive, whereas, West Siang district of was the least sensitive. Indices for adaptive capacity, exposure and sensitivity were separately calculated, as described in the methodology section, and discussed in the above paragraphs. Overall vulnerability index was calculated by subtraction of sum of exposure and sensitivity from the adaptive capacity. Index scores of the studied districts have been presented in Fig. 2 and Table 5. Districts with higher negative score of vulnerability index were more vulnerable; however positive score 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, Anjaw district was the most vulnerable district, while Tawang district was the least vulnerable. Anjaw district had the lower adaptive capacity, coupled with second highest exposure and highest sensitivity. As a result of which, 111 S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 Table 3 Index values of exposure and its indicators across the district of Arunachal Pradesh. Sl No. 1 2 3 4 5 6 7 8 9 10 11 12 a Alpine Districts Tawang West Kameng East Kameng Papum Pare Lower Subansiri Kurung Kurmey Upper Subansiri West Siang East Siang Upper Siang Lower Dibang Valley Anjaw Indicators of Exposurea Exposure ERD ERY MMD MSD NORR HER HEAT COLD MEANT MAXIT MINTE 0.694 2.081 2.081 1.387 1.387 1.387 1.387 1.387 1.387 1.387 0.000 0.000 2.702 1.351 1.351 1.351 0.000 0.000 0.000 2.702 5.403 2.702 5.403 5.403 0.000 0.000 0.000 6.512 0.000 0.000 0.000 4.341 6.512 4.341 6.512 6.512 0.000 0.000 0.000 0.000 0.000 0.000 0.000 7.438 7.438 7.438 7.438 7.438 1.167 0.629 0.629 0.000 0.180 0.180 0.180 7.273 6.644 7.273 6.914 6.914 0.418 0.000 0.000 0.000 0.000 0.000 0.000 5.856 5.019 0.000 5.856 5.856 0.000 4.288 4.288 5.003 2.144 2.144 2.144 7.862 6.433 7.862 6.433 6.433 0.000 3.817 3.817 3.817 0.000 0.000 0.000 7.634 7.634 7.634 7.634 7.634 0.200 2.134 2.134 2.185 2.180 2.180 2.180 1.921 0.000 1.921 0.112 0.112 0.000 3.454 3.454 4.497 4.573 4.573 4.573 7.432 5.404 7.432 5.576 5.576 3.233 5.382 5.382 4.959 4.923 4.923 4.923 2.888 0.000 2.888 0.128 0.128 8.414 23.135 23.135 29.711 15.387 15.387 15.387 56.734 51.874 50.878 52.006 52.006 for full name of the indicators of exposure, please see the Table 1. Table 4 Index values of sensitivity and its indicators across the district of Arunachal Pradesh. Indicators of sensitivity* Sl. No Alpine District 1 2 3 4 5 6 7 8 9 10 11 12 Tawang West Kameng East Kameng Papum Pare Lower Subansiri Kurung Kurmey Upper Subansiri West Siang East Siang Upper Siang Lower Dibang Valley Anjaw * Sensitivity PCI BPL AP VILL DRINKI DILAPID 0.451 1.596 0.000 0.467 2.013 2.013 0.435 0.280 0.053 0.270 1.673 2.759 0.000 0.289 0.505 0.936 0.453 1.490 0.331 0.629 0.502 0.977 0.346 2.479 0.990 0.385 0.346 1.354 0.242 0.242 0.285 0.000 0.557 0.395 0.321 1.645 2.350 1.821 1.703 0.000 2.124 2.817 2.076 1.760 1.456 1.958 1.821 2.708 0.505 0.201 0.997 0.362 0.150 1.127 0.896 0.191 0.551 0.000 0.507 0.493 0.603 0.421 0.405 0.644 0.162 2.399 0.350 0.112 0.384 0.000 0.560 1.070 4.898 4.713 3.957 3.763 5.144 10.088 4.373 2.972 3.504 3.600 5.229 11.154 for full name of the indicators of sensitivity, please see the Table 1. Index score of vulnerability index 30 20 10 0 -10 -20 -30 -40 Districts of Arunachal Pradesh Fig. 2. Vulnerability to climate change across the districts of Arunachal Pradesh. Table 5 Vulnerability index with its components across the districts of Arunachal Pradesh. Sl No. Districts Adaptive capacity Exposure Sensitivity Vulnerability 1 2 3 4 5 6 7 8 9 10 11 12 Tawang West Kameng East Kameng Papum Pare Lower Subansiri Kurung Kurmey Upper Subansiri West Siang East Siang Upper Siang Lower Dibang Valley Anjaw 36.181 28.435 19.814 37.188 35.264 22.040 20.097 28.503 35.649 24.362 29.421 25.788 8.414 23.135 23.135 29.711 15.387 15.387 15.387 56.734 51.874 50.878 52.006 52.006 4.898 4.713 3.957 3.763 5.144 10.088 4.373 2.972 3.504 3.600 5.229 11.154 22.869 0.587 −7.278 3.714 14.732 −3.436 0.337 −31.204 −19.729 −30.116 −27.814 −37.371 112 S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 Anjaw district was the most vulnerable. Papum Pare district, on the other hand, in spite of having the highest adaptive capacity was ranked the third least vulnerable due to its comparable moderately higher exposure. Though Lower Subansiri district and Tawang district were having lower adaptive capacity than Papumapre district, yet, both the districts were least and second least vulnerable district as both the districts had lower exposure. Though West Sinag District had lowest sensitivity, yet, due its highest exposure, district West Siang was the second most vulnerable district. Comparatively, districts of Eastern Arunachal Pradesh were more vulnerable than the other districts, as they had higher exposure. It was also observed that newly created districts were more vulnerable. Ravindranath et al. (2011) assessed the current Agricultural Vulnerability Index (AVI) of districts of North-eastern region and they reported that West Siang was the most vulnerable district to current climate vulnerability and they also reported that higher agricultural vulnerability was observed in the northern parts i.e. alpine district of Arunachal Pradesh. 4. Discussion and policy implications The present study clearly depicts that the combined effect of exposure and sensitivity suppressed over the adaptive capacity of 7 districts among those 12 studied districts viz. Anjaw, Lower Dibang Valley, Upper Siang, East Siang, West Siang, Kurung Kurmey and East Kameng. This may be considered as an alarming situation. A critical discussion on the possible reasons of this alarming situation is as follows: First, several researchers (Adger, 2006; Leiserowitz and Akerlof, 2010; Macchi et al., 2008) argued that indigenous people are among the most vulnerable to climate change. In Arunachal Pradesh, there are 26 major tribes and more than 100 sub-tribes. They constitute nearly 68 percent of the state population. This may be the reason of higher level of vulnerability in the state of Arunachal Pradesh. Second, Convention on Biological Diversity held in 2010 concluded that the areas richest in bio-diversity are the most vulnerable to climate change. Leary et al. (2008) also claimed that rural livelihoods and economics are based on and dominated by agricultural, pastoral and forest production system that are highly sensitive to climate variations. The state of Arunachal Pradesh (83,743 km2 ) occupies a major portion of the Indian Eastern Himalayas and has 82% forest cover. It is located in the Eastern Himalaya–a global biodiversity hotspot and is also among the 200 globally important ecoregions. The state have a massive 94 percent rural population with having monthly per-capita income 12–13 USD and economy is based on the agriculture, jhum (shifting terrace) cultivation and pastoral livestock production system. Therefore, people of the Arunachal Pradesh are highly sensitive. Sometimes, severity of sensitivity increase due to several other factors during extreme climatic events. For example, nearly 60 percent of household do not have access drinking water facility within their premises, more than 90 percent of the house are Kutchha (not have metal roof) etc and these households used to face difficulty in extreme climatic events particularly in extremely heavy rainfall and cloudburst. Bharali and Khan (2011) also reported that extremely heavy rainfall and cloudburst are common phenomena in Arunachal Pradesh in recent past. Third, Arunachal Pradesh is comparatively a new state of India; and many districts have been created recently by dividing the bigger districts (for example, Anjaw district in 2004; Lower Dibang Valley-2001; East Siang-1999). These new districts have infrastructural deficiency. From the present study, it was also found that these newly created districts happened to be more vulnerable, due to their lower adaptive capacity. Therefore, policy makers may take initiatives for proper infrastructure development in these districts to mitigate the impact of climate change while strengthening the adaptive capacity. Fourth, the state Arunachal Pradesh is having a large (nearly 1900 km) international border and nine amongst twelve studied districts are sharing their border with international borders. The three non-border districts (Papum Pare, Lower Subansiri and East Siang) had the higher adaptive capacity than the others. Hence, different strategies may be adopted for border area and non-border area to improve the adaptive capacity. Fifth, in the present study, adaptive capacity was considered as the summation of all the asset categories. Infrastructural facilities in Arunachal Pradesh are not evenly distributed and it is the fact that concentration is higher in urban area. Papum Pare district is an urban-based district of Arunachal Pradesh and having all type of infrastructural facilities. For example, there are degree colleges in Papumpare district, whereas, only one single college in the three districts namely Tawang, West Kamneg and East Kameng. In case of literacy rate, Papumpare district (79 percent) is far ahead of these three districts (average 62 percent). This was the main reason for Papum Pare district to score the highest in adaptive capacity. Hence, policy makers can directly intervene to establish critical infrastructural facilities like educational institutes, hospital facilities, veterinary institutions etc for the easy and quick improvements in adaptive capacity. 5. Conclusion The present study analysed social vulnerability of the districts of Arunachal Pradesh, India to the climate change, by creating vulnerability indices and comparing the indices across the districts of Arunachal Pradesh. The vulnerability analysis followed the IPCC (2001) definition of vulnerability, which explains it is a function of adaptive capacity, sensitivity and exposure. Integrated vulnerability assessment approach was adopted by combining socio-economic indicators, like demographic features, etc and biophysical indicators, like production & productivity of crop etc were adopted for the present study. The method of ‘Principal Component Analysis’ (PCA) was employed to give weightage 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 7 districts viz. Anjaw, Lower Dibang Valley, Upper Siang, East Siang, West Siang, Kurung Kurmey and East Kameng. These districts were considered as more vulnerable than the other studied districts. It was also found that Anjaw district was the most vulnerable district while Tawang district was the least vulnerable. From the present study, it was also found that higher vulnerable districts having the characteristics of lower adaptive capacity due to imbalance distribution of infrastructural facilities. Therefore, proper infrastructural facilities must be developed in these districts to mitigate the impact of climate change while strengthening the adaptive capacity. Acknowledgement Authors of this paper express their sincere gratitude to: 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, India for providing climatic data. We are also grateful to Director, National Dairy Research Institute, Karnal, Haryana, India for guidance, support and encouragement. S. Maiti et al. / Ecological Indicators 77 (2017) 105–113 Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2017. 02.006. References Adger, W.N., Kelly, P.M., 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. Change 4 (3–4), 253–266 (R). Adger, W.N., 2006. Vulnerability. Glob. Environ. Change 16, 268–281. Bharali, S., Khan, M.L., 2011. Climate change and its impact on biodiversity; some management options for mitigation in Arunachal Pradesh. Curr. Sci. 101 (7), 855–860. Cavaliere, C., 2009. The effect of climate change on medicinal and aromatic plants. Herbal Gram 81, 44–57. Deressa, T.t., Hassan, R.M., Ringler, C., 2008. Measuring Ethiopian Farmers’ Vulnerability to Climate Change Across Regional States. International Food Policy Research Institute, Washington, D.C, IFPRI Discussion Paper 00806. Feroze, S.M., Chouhan, A.K., 2010. Performance of dairy self help groups in India: principal component analysis approach. Indian J. Agric. Econ. 65 (2), 308–318. Gbetibouo, G.A., Ringler, C., 2009. Mapping South African Farming Sector Vulnerability to Climate Change and Variability. A Subnational Assessment. International Food Policy Research Institute (IFPRI), Washington, D.C, IFPRI Discussion Paper 00885. Haan, N., Farmer, G., Wheeler, R., 2001. Chronic Vulnerability to Food Insecurity in Kenya-2001. A World Food Programme Pilot Study for Improving Vulnerability Analysis. http://www.wfp.org/operations/vam/country experience/kenya vam/index.asp. Handmer, J., Honda, Y., Kundzewicz, Z.W., Arnell, N., Benito, G., Hatfield, J., Mohamed, I.F., Peduzzi, P., Wu, S., Sherstyukov, B., Takahashi, K., Yan, Z., 2012. Changes in impacts of climate extremes: human systems and ecosystems. In: Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.-K., Allen, S.K., Tignor, M., Midgley, P.M. (Eds.), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 231–290, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). ICIMOD (International Centre for Integrated Mountain Development), 2010. Climate Change Impact and Vulnerability in the Eastern Himalayas, Synthesis Report, 2010. IPCC (Intergovernmental Panel on Climate Change), 2001. Climate Change 2001: Impacts, Adaptation, and Vulnerability. In: McCarthy, J., Canziani, O., Leary, N., Dokken, D., White, K. (Eds.). Cambridge University Press, Cambridge. IPCC (Intergovernmental Panel on Climate Change), 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. In: Parry, M., Canziani, O., Palutikof, J., Van der Linden, P., Hanson, C. (Eds.). Cambridge University Press, Cambridge, United Kingdom. Kumar, K.S., Tholkappian, S., 2005. Relative vulnerability of indian coastal districts to sea-level rise and climate extremes. Int. Rev. Environ. Strateg. 6 (1), 3–22. Leary, N., Adejuwon, J., Bailey, W., Barros, V., Batima, P., Caffera, R.M., Cinvanno, S., Conde, C., Comarmond, A.D., Sberbinin, A.D., Downing, T., Eakin, H., Nyong, A., Opondo, M., Osman-Elassha, B., Payet, R., Pulbin, F., Pulbin, J., Ratnisri, J., Sanjak, E., Maltitz, M.W., 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, UK, pp. 3–30. Leiserowitz, A., Akerlof, K., 2010. Race, Ethnicity and Public Responses to Climate Change. Yale University and George Mason University, New Haven, CT, Yale Project on Climate Change http://environment.yale.edu/uploads/ RaceEthnicity2010.pdf. 113 Macchi, M., Oviedo, G., Gotheil, S., Cross, K., Boedhihartono, A., Wolfangel, C., Howell, M., 2008. Indigenous and Traditional Peoples and Climate Change: Issues Paper. IUCN https://cmsdata.iucn.org/downloads/indigenous peoples climate change.pdf. Maiti, S., Jha, S.K., Garai, S., Nag, A., Chakravarty, R., Kadian, K.S., Meena, B.S., 2014. Determinants to climate change adaptation among the Brokpa pastoral nomads of Western Arunachal Pradesh. Indian Res. J. Ext. Educ. 14 (2), 36–40. Maiti, S., Jha, S.K., Garai, S., Nag, A., Chakravarty, R., Kadian, K.S., Chandel, B., Datta, S., Upadhaya, K.K., 2015. Assessment of social vulnerability to climate change in the eastern coast of India. Clim. Change, http://dx.doi.org/10.1007/s10584015-1379-1. Mohanty, A.K., Kumar, G.A.K., Singh, B.B., Meera, S.N., 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, S.M., 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, UK, 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. Change 14, 303–313. Olson, D.M., Dinerstein, E., 1998. The global 200: a representation approach to conserving the Earth’s most biologically valuable eco-regions. Conserv. Biol. 12, 502–515. Patnaik, U., Narayanan, K., 2005. Vulnerability and Climate Change: an analysis of the Eastern coastal districts of India. In: International Workshop on Human Security and Climate Change at Holmen Fjord Hotel, Asker, near Oslo, 21–23 June 2005, 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) (Retrieved Online at https:// mpra.ub.uni-muenchen.de/22062/ MPRA Paper No. 22062, posted 14. April 2010 01:06 UTC). Piya, L., Maharjan, K.L., Joshi, N.P., 2012. Vulnerability of rural households to climate change and extremes: analysis of Chepang households in the mid-hills of Nepal. In: Selected Paper Prepared for Presentation at the International Association of Agricultural Economics (IAAE) Triennial Conference, Foz do Iguacu, Brazil, 18–24 August, 2012. Ravindranath, N.H., Rao, S., Sharma, N., Nair, M., Gopalakrishnan, R., Rao, A.S., 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. Seidl, R., Rammer, W., Lexer, M.J., 2011. Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps. Can. J. For. Res. 41, 694–706. Tambe, S., Arrawatia, M.L., Bhutia, N.T., 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. 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, UK. Wikramanayake, E., et al., 2009. Terrestrial Ecoregions of the Indo Pacific: A Conservation Assessment. Island Press, Washington, D.C, pp. 2009. Xu, J., Grumbine, R.E., Shrestha, A., Eriksson, M., Yang, X., Wang, Y., Wilkes, A., 2009. The melting Himalayas: cascading effects of climate change on water, biodiversity and livelihoods. Conserv. Biol. 23, 520–530.