Abstract

Top-down vulnerability assessment guided climate adaptation measures are relatively less effective in tackling community level challenges as local concerns are seldom integrated into the process. To adequately reflect local realities there is, therefore, a need to conduct vulnerability studies at a finer scale to incorporate local inputs into the assessment process. In recent times, indicator-based vulnerability assessment is increasingly employed as a viable approach to understand the susceptibility of the local population to climate hazards. The present paper presents the results of local level vulnerability assessment undertaken at three Lakeshore villages (Toubul, Khoijuman, and Kwasiphai) of Loktak Lake, Manipur, India. Two indices, Livelihood Vulnerability Index (LVI) and Climate Vulnerability Index (CVI_IPCC), were utilized to examine, first, the sectors that produce differential livelihood vulnerabilities across the villages, and secondly, the overall susceptibility of the villages with respect to exposure, sensitivity, and adaptive capacity. Primary data for the study was collected from purposively selected 150 households (N=150) from the three villages. Head of the family of each household was administered a structured questionnaire designed to elicit information on seven dimensions of vulnerability. Each dimension consists of variable number of indicators. Since the data on the indicators are measured in different scales they were standardised before composing the indices using equal weighting scheme. Based on the index values, the Toubul village (L VI 0.399, CVI_IPCC -0.070) emerged as the most vulnerable village followed by Khoijuman (LVI 0.377, CVI_IPCC -0.076) and Kwasiphai (LVI 0.377, CVI_IPCC-0.109) according to both the indices. It is also found that in each village complex and interrelated climatic and non-climatic factors operate to impart characteristic vulnerability features to every village, understanding of which may hold significant policy values for future adaptive planning at the micro level.

1. Introduction

Vulnerability to the impacts of climate change is distributed highly unevenly across regions and social groups due to unequal distribution of exposure, sensitivity, and adaptive capacity ¹. This is more so for a climate vulnerable nation like India, where there are immense spatial diversities in environmental conditions, demographic pressure, and economic welfare distribution. A vast swath of the country called the Indian Himalayan Region (IHR), encompassing the northernmost province of Jammu and Kashmir to the north-eastern states, has been identified as an extremely vulnerable region to the effects of climate change ^{1, 2}. Many conditions characteristic to the region such as the fragility of the region’s ecology, demographic dominance of indigenous inhabitants, and over-dependence on climate sensitive sectors for livelihood are attributed to its heightened vulnerability ³. In this region, not only have the recent changes in the climate been more rapid and complex but model-based predictions also suggest a greater rate of temperature changes and a more erratic rainfall regime in the coming decades ^{4, 5, 6}. There is, therefore, an urgent need for the preparation of adaptation strategies at the local scale based on the inputs from community level climate vulnerability assessments ⁷.

Vulnerability assessments of local population conducted through livelihood lens can serve as a first step towards this as it considers local perspective with an enhanced focus on understanding local realities ⁶. Livelihood refers to the ‘means of gaining a living through the use of a combination of resources and activities by people’ ⁸. Adopting livelihood perspectives in vulnerability assessment provides opportunities for aligning broader development goals with local adaptation strategies for reducing climate risk at the community level ⁹. This framework recognises peoples’ ability to use various activities to cope, adapt, improve, diversity, and transform at an individual, household, village, or even at district level for livelihood ¹⁰.

Although there are different approaches to vulnerability assessment, most studies prefer the use of proxy indicators to quantify various aspects of vulnerability through composite indices ¹¹. Here, an attribute (e.g., health, safety, and livelihood) of a vulnerable unit (e.g., locality, community, village, district etc.) is identified for which indicators are collected for the assessment in relation to a hazard (e.g.,flood,climate change etc) ¹². This approach, broadly referred to as Indicator based vulnerability assessments (IBVA), is adopted for the current study by taking livelihood as the valuable attribute of the vulnerable village as a unit. Since sustainability of livelihood is affected by both climate and non-climatic stressors ¹³, this study uses indicators representing multiple vulnerability contributing factors collected at the household level. Therefore, this study measures household level exposure to natural disasters and climate variability, socio-economic conditions of the households that influence their adaptive capacity, and present water, health, and food characteristics affecting sensitivity to climate change impacts ¹⁴.

2. Objectives

This paper endeavors to assess climate vulnerability of local population inhabiting three Loktak Lakeshore villages of Manipur through two composite indices. The study takes guidance from ref. ¹⁴ pragmatic Livelihood Vulnerability Index (LVI) approach of employing two indices for vulnerability assessment to measure how vulnerable a village is relative to others in terms of multiple dimensions of vulnerability. As detailed in the next section, LVI is a composite index of all major components while IPCC vulnerability approach comprises grouping of major components into exposure, sensitivity and adaptive capacity. This approach is flexible and amenable to adjustment to suit studies in different geographical contexts. An added advantage is the availability of sectoral indices enabling identification of potential dimensions for policy intervention. This research also seeks to demonstrate that complex local factors generate differential vulnerability profiles even at very fine geographical scales among population having almost similar exposure context.

3. Study Area Description

The study area lies within two kilometres from south western part of Loktak Lake shore. Three villages namely, Toubul, Khoijuman and Kwasiphai located at this zone of Loktak Lake are selected for this case study (Figure 1). The climate of the area is sub-tropical monsoon type with average annual rainfall of 1364 mm and annual mean temperature of 20.9°C. The rainfall is not uniformly distributed during the year as summer monsoon months (June, July, August and September) account for the maximum share of the annual rain. July receives maximum rainfall while January is the driest on average. January (13.49°C) and July (25.49°C) are the coldest and warmest months of the year. In terms of the annual range, it is 225.38mm for rain and 12°C in the case of temperature. Observational as well as simulated studies have documented and predicted changes in the regional temperature and rainfall patterns of past and in the future ¹⁵. Wetlands perform variety of crucial socio-economic and ecological functions which are integral to the livelihood sustenance of the lake inhabitants. In fact, the significance of Loktak Lake to the lake inhabitants and to economy, society and culture of Manipur has been such that the lake is called the ‘Lifeline of Manipur. The lake however is under severe stresses emanating mainly from different human activities. These include changes in the hydrological regimes, reduced agriculture area due inundation, loss of fish population and variety and siltation of the lake ¹⁶. Degradation of water quality of the lake with adverse ramifications for the lake ecosystem and subsequently to the livelihood of lakeshore dwellers have been documented ^{17, 18}. As a consequence of the ecological changes induced by the anthropogenic activities, Loktak lake is now listed under Montreux Record which compiles Ramsar wetlands at various stages of degradation. In addition to the local stressors to the lake ecosystem, global climate change has also caused alteration of freshwater wetlands. A changed climate will have consequence on the hydrological regime and ecological functions of the lake compounded by the increase temperature, CO₂ concentrations and flood or drought events ¹⁹ .Hydrological modelling of the Loktak lake under several climate change scenarios of the future shows changes in the water regime as well as ecosystem services ²⁰. Thus, changes introduced both by local activities and global changes have made the Loktak Lake and its lakeshore inhabitants vulnerable to multiple stressors including climate change.

Against this vulnerability background, the three villages were selected as case study sites for the current study as the inhabitants depend on the lake and land resources for their livelihoods engaging mostly in primary activities. These attributes make them ideal representatives of other Loktak lakeshore villages. At the same time, these villages also practice relatively advance agriculture on commercial basis ^{21, 22}. This peculiar combination of livelihood with greater dependence on climate sensitive agriculture sector rather than on lake resources make these villages particularly interesting sites for vulnerability assessment.

4. Methodology

The methodological framework adopted for the study is presented in Figure 2. A composite index called Livelihood Vulnerability Index (LVI) for each of the three-village was prepared to achieve the first objective. Indicators representing seven major components: Socio-Demographic Profile (SDP), Livelihood Strategies (LS), Social Networks (SN), Health (H), Food (F), Water (W), or Natural Disasters and Climate Variability (NDCV) of livelihood vulnerability were used to compose the LVI ⁵. Indicator selection was guided by studying existing literature on the subject and consultation with the community members and other stakeholders to ensure inclusion of local concerns and priorities in the research design of the study.

Primary data was collected through a household survey, using questionnaires whose contents were based on previous literature and consultations with key informants from each village ²³, in which the head of each family was interviewed. Fifty households from each village (n=50) were purposively selected for the survey . This process was repeated for all the villages (N=150 for three villages). The use of primary data negates the reliance on predictions from climate models, which are still very spatially coarse and not detailed enough to be relevant at local level policy making ²⁴. LVI approach enables identification of small variations in sectoral contributors to local vulnerabilities (e.g., related to health, food so forth), which may form valuable inputs in designing local policies of resource-dependent communities ^{14, 25}.

An equal weighting scheme was used in devising the LVI, in which each component is considered to contribute equally to the overall vulnerability of a village ²⁶. This weighting scheme is simple in approach, easy to interpret, and useful in vulnerability assessment since many unknown contributions and connections among components produce the final vulnerability ²⁷.

Since indicators are measured in different scales (ratios, percentages, etc.) there is a need for normalisation to enable comparison and to put into a single index. United Nations Development Programme (UNDP) equation that calculates Human Development Index (HDI) is used for normalisation ²⁸:

where, is the sub-component or indicator value for a village.

and is the minimum and maximum value of indicator across all three villages.

The final scores of the main components are calculated by averaging the obtained normalised value of the indicators using:

where, the value of a major component of the seven major components of livelihood vulnerability. is indicator, belonging to major component for village, n the number of sub-components in the major component. For example, the average scores for indicators – “Dependency ratio”, “% Household heads did not attend school and % Households with members needing dependent care” give the index score for the major component, “Socio-demographic Profile”.

After obtaining major component values the LVI score of a village is calculated by combining the weighted averages of all the major components. The weight of the major components is equal to the number of indicators it is composed:

here is the Livelihood Vulnerability Index of a village v and the weight of a major component i.e., the number of its indicators. The LVI value is scaled from 0 (least vulnerable) to 0.5 (most vulnerable) ⁵.

assesses vulnerability of villages under the IPCC vulnerability framework using the same major components, indicators, and their scores employed in calculating LVI. The seven major components of the LVI are, however, categorised into exposure, sensitivity, and adaptive capacity contributing factors (CFs) of (Figure 3) ^{14, 29}. After allotting the seven components into the three contributing factors (CFs), the climate vulnerability index (CVI_IPCC) is composed using equal weighted scheme for assigning weights ³⁰. Each major component is regarded to contribute equally to the CF values. The indices of three CFs -exposure, sensitivity, and adaptive capacity -are calculated as:

Where, W₁ is the weight for the major component, equal to the number of indicators.

Where, and is the weight for the major component, equal to the number of indicators.

Since adaptive capacity has an opposite functional relationship with vulnerability, normalisation for nine indicators of adaptive capacity in socio-demographic profile, livelihood strategies, and social networks components were performed with the formula:

Where, is the indicator value for a village v, and is the minimum and maximum value of indicator across all three villages.

The adaptive capacity index is then computed as:

Where, and are the weights for the major components, equal to the number of indicators.

IPPC defined climate vulnerability (CVI_IPCCV ) of a village is, thus, found by

Here, is the exposure score of a village v, is the calculated adaptive capacity value for village and is the obtained sensitivity score of a village . The value of was scaled from -1(most vulnerable) to +1 (most vulnerability) and should be interpreted as an approximation of the relative vulnerability of compared villages ³¹.

5. Results and Discussion

The normalised values of the indicators and indices of major components and final LVIs across the three villages are given in Table 1. LVI scores of all the study areas are above 0.370 in the 0 to 0.5 scale (LVI_Toubul 0.399, LVI_Khoijuman 0.377, and LVI_Kwasiphai 0.371) vulnerability scale, which indicate a relatively high overall livelihood vulnerability. Figure 4 shows the LVI scores on a linear scale for comparative evaluation of livelihood vulnerability of the three villages. Here, Toubul village is the most vulnerable village followed by Khoijuman and Kwasiphai being the least vulnerable among the three villages. Inter village variation in LVI scores is produced by the unequal occurrence of seven major component values among the villages. Variation in seven major components across the three villages inter-village LVI differences which is apparent in the Figure 5.

To better appreciate the spider diagram and the factors that shape vulnerability at each of these villages, major components scores are examined in relation to the indicators that form them.

Socio-demographic Profile (SDP)

Three indicators measure the socio-demographic profile of each village covering aspects whose higher values indicate higher vulnerability to climate change impacts. Starting with the dependency ratio, Toubul and Khoijuman village scores are tied with an index value of 0.23 which is higher than Kwasiphai (0.21). A higher dependency ratio implies the share of the economically dependent population to the economically productive section is relatively more for Toubul and Khoijuman than Kwasiphai, putting greater economic burden on the overall population. Having a higher proportion of dependent population puts heavy economic as well as social burdens on the households enhancing their vulnerability to external stress including climate change. On the other hand, the education of the head of a family is a critical factor among natural-resource dependent societies in determining livelihood choices that households make during adverse times. For these parameters, Toubul (0.20 &0.30) and Khoijuman (0.02 &0.62) index values are higher than Kwasiphai (0.40 &0.22) respectively. Overall SDP vulnerability is highest in Khoijuman (SDP_Khoijuman0.290) than Kwasiphai (SDP_Kwasiphai0.276) and Toubul (SDP_Toubul0.243).

Livelihood strategies (LS)

Communities employ numerous livelihood strategies to make a living which have bearing on their vulnerability state. For instance, greater dependence on climate sensitive sectors like agriculture for livelihood is believe to increase vulnerabilities of the communities to past and future climate variabilities. The index values of livelihood strategies among the study sites vary from 0.322 to 0.497, suggesting the existence of different levels of vulnerability to climate. Khoijuman and Toubul villages, for instance, have higher livelihood strategy vulnerability than Kwasiphai (LS _Khoijuman 0.497; LS _Toubul 0.435; LS _Kwasiphai 0.322). But on a whole, all households in the three villages derive their income mainly from agriculture or similar climate-sensitive livelihoods. As 54% and 47% of households in Khoijuman and Toubul earn their main income from agriculture whereas for Kwasiphai the percentage is 42%. Although this suggests high livelihood vulnerability but there are also indications that households have diversified their livelihood options. The survey found many households with members engaging in non-agricultural activities like selling lower-order goods, adopting artisanship, employed in formal sectors e.g. government jobs among others Figure 6 (a). Livelihood diversification makes households more resilient as it offers them greater opportunities to buffer shocks as there are more livelihood bases to rely upon in the event of one sector getting hit by climate stress.

Social Networks (SN)

The formal and informal relations between individuals, communities, and organizations that involve goods exchanges, help both in cash and kind, knowledge and information sharing and networking is called social networks and influence vulnerability. A society with robust social networks enables its members to face changes that make them less vulnerable ³².

Since the measuremment of social networks is not straightforward, in this study, three proxy indicators are used to quantify the relations. The overall normalised values of social networks for the three villages show some difference (SN_Khoijuman 0.335 SN_Toubul 0.380; SN_Kwasiphai 0.416). Khoijuman has the lowest SN normalised score followed by Toubul and Kwaisiphai. Observed evidence suggests strong informal relations among the households but formal relations between community members and government institutions are very tenuous.

This is borne out by 90% of surveyed households in three villages have not approached the government in the last 12 months for seeking any kind of help, for Toubul the figure is 98% of the households. Considering the critical role played by government agencies in rural development the lack of meaningful relations between community and government institutions is a significant vulnerability augmenting factor. Capacity building measures of the institutions, therefore, should be taken up at the earliest to inspire confidence among the public so that they come for help from the institutions during the time of stress.

Health(H)

The state of health within a community exercises a strong influence on its vulnerability to both climatic and non-climatic stresses. Prevalence of chronic illness within a family provides a good indication of health vulnerability at the household level. This is because the need for constant care of the patient and the ensuing recurrent medical expenditures divert crucial human and economic resources from the family which otherwise could be used for coping stress. The incidence of chronic illness was reported in 22% of the households in Toubul compared to 35.63 % and 36 % of households in Khoijuman and Kwasiphai villages. Disease burdens on the village were measured by the percentage of family member/s missing work due to illness in the two weeks preceding the survey which again is a vulnerability enhancing condition. Toubul, 7.54%, Kwasiphai 42%, and Khoijuman (19.54%) households had one or member missing work due to illness. Accessibility of health infrastructures to the community members is another important component of health. No significant difference was observed in this respect across the villages. The Health index, which is a combined measure of the three indicators however showed discernible variations as Kwasiphai (H_Kwasipahi: 0.400) has highest value followed by Khoijuman (H_Khoijuman 0.303) and Toubul (H_Toubul: 0.215).

Food(F)

Food security is of paramount importance for coping with climate change challenges. Absolute availability of and accessibility to food is essential for food security. Deriving food from own farms is more favourable for reduced vulnerability than the converse. In Kwasiphai village 36% of households do not get food from family’s own farm, followed by Khoijuman (24.13%) and Toubul 20% of households. Marketing food produces provide valuable income to augment the livelihood security of the households. There is wide variability in the percentage of households with market access for food produce among the villages as it is only 16% in Kwasiphai, 19%, and 22.64% for Khoijuman and Toubul respectively. It is worth noting here that the relationship between greater market penetrations in agriculture and reduced vulnerability is more nuanced and complex particularly for natural resource-dependent communities. For instance, while selling commercial crops provides access to valuable income on the one hand but it also simultaneously reduces the area cultivated under food grains. This means that the families are more exposed to the market volatility, in a year with below average market price for the produce, there will be less income at the disposal of the family to buy food grains for consumption. The three villages three has no clear difference in food vulnerability as indicated by the food index (F_Toubul: 0.296; F_Kwasiphai: 0.313; F_Khoijuman: 0.326).

Water(W)

The water sector is another crucial element of livelihood vulnerability that can be affected by climate change and variability .In an ideal condition, there should be proper access to water of adequate quantity and quality for domestic and agriculture use in an agrarian society. Access to tap water for domestic consumption is a basic need for reduced incidence of water-borne and overall health. But this measure varies considerably among the villages. Toubul, for example, had no households with access to tap water at home at the time when the survey was conducted, 76% of households depend on natural sources and the rest purchase water consumption. On account of this Toubul has the highest water vulnerability index, W_Toubul : 0.776, while Khoijuman (W _Khoijuman: 0.446), and Kwasiphai (W_kwasiphai: 0.420) have fewer differences. Our survey of the villages also discovered that commercial farms had no irrigation facilities and hence were constantly exposed to the mercy of the rainfall variations. Practicing rain-fed agriculture, despite locating within few kilometers of Loktak lake, whose water is maintained at a constant level for hydroelectric generation, has so far prevented further intensification of agriculture in the area.

Natural Disaster and Climate Variability (NDCV)

Combination of instrument-based climate data and villagers’ experience of past climatic changes and hazards are used for understanding NDVC profiles of the villages. Since the climate data is the same for the villages, it is more meaningful to examine the differences in the perception of climate and hazard experienced across the villages. The human perceptions of changing pattern of climate are shaped by observations of their local environment and is often reliable source in the context of the local decision making. More often it is the perceived knowledge of the changes in the local environment that guides critical livelihood decisions in such socio-ecological settings. High percentages of the population in the surveyed villages have experienced changes in rainfall patterns. It was 71% in Toubul, and 61% and 56% in Kwasiphai and Khoijuman. Many households also have experienced increased temperature, the highest number is for Kwasiphai (69%) followed by Khoijuman (65%) and Toubul (58%). Floods and droughts are less common across these villages as their average frequency of occurrence are less than 3 over the three years before the survey. Moreover, very few households reported experiencing adverse effects both in terms of life and property. In Khoijuman and Kwasiphai, for instance, only 10% of the households suffered material damages due to flood and drought, the percentage is even lower for Khoijuman with 4% of households Figure 6 (b). Overall, Toubul and Kwasiphai are relatively more vulnerable to natural hazards and climate variability with higher overall scores (NDCV_Toubul: 0.415; NDCV_Kwasiphai: 0.412) compared to Khoijuman (0.390).

The seven major components of livelihood vulnerability discussed so far are not mutually exclusive in determining the degree of vulnerabilities of the villages. Rural livelihood is interlinked with various other factors ³³, and the results of the analysis also reaffirm the notion of mutual interdependency among the components of livelihood vulnerability. For instance, changes in rainfall pattern which is an indicator of the NDCV component can influence water availability thereby increasing the vulnerability of the water component.

The Figure 7 reveals that the overall state of provisions of health, water, food, and including social network components are affected by the NDVC, SDP, LS, and SN. It is interesting to note that social networks not only influence other components but also gets influenced by other components viz. SDP of the village for instance. Socio-demographic profile of a village is important in terms of affecting the vulnerability levels of food, social networks and health vulnerability. A village having higher dependency ratio, higher population requiring dependent care and less educated household heads enhance vulnerability. It is evident from the Figure 7 that lack of education among the household heads have partly reduced interaction with the government, affected food choices by predominantly growing commercial crops hence reducing self-reliance. Low livelihood diversification and overwhelming dominance of agriculture as primary livelihood perhaps represents another influence of SDP on livelihood choices. These results suggest adoption of holistic approach to reducing vulnerability and enhancing adaptive capacity of the villagers by offering range of livelihood strategies for a diversified income base.

By arranging the LVI major components into exposure, sensitivity, and adaptive capacity contributing factors to vulnerability, the climate vulnerability index (CVI_IPCC) is obtained under the IPCC framework. Although IPCC has encapsulated vulnerability under the risk framework in recent assessments, the definition of vulnerability as a function of the three contributing factors is still employed by many scholars for its conceptual simplicity. In the index, the exposure and sensitivity determine the potential impacts while actual impacts are a result of a combination of both along with adaptive capacity. The Figure 8 summarises the values of the three contributing factors and the overall climate vulnerability of the three villages. Toubul (CVI_IPCC -0.070) is the most vulnerable village and Kwasiphai (CVI_IPCC -0.076) the least vulnerable whereas Khoijuman (CVI_IPCC – 0.109) has intermediate vulnerability relative to the other villages. Variation in three contributing factors across the three villages is examined below to better comprehend the detailed dynamics of vulnerability generating processes.

Exposure Index

Exposure refers to the system’s encounter with nature and degree of climate variations and other hazards. Measuring exposure helps understand the hazard environment within which the society is situated. Major component NDVC used in LVI calculation with 7 indicators represents exposure in this study. Exposure value of Toubul (0.415) is the highest followed by Kwasiphai (0.412) and Khoijuman (0.390). Exposure indices for the three villages do not exhibit large differences suggesting a similar hazard context. However, in the broader definition of vulnerability, impacts from a given hazard even for similarly exposed communities can be quite different. This is because other characteristics of the communities like socio-cultural, technological or even political contexts strongly influence the actual impacts.

Adaptive capacity index

Adaptive capacity is an integral component of vulnerability and it denotes the pre-existing conditions that enable adaptation to change. Its underlying determinants include financial, social, and human capitals along with the willingness and capability to translate the resources at hand to sound adaptive actions ³⁴. Therefore, ten indicators encompassing three major components of LVI viz. SDP, LS, and SNs are used for obtaining adaptive capacity index. The village with the highest adaptive capacity is Kwasiphai (0.703) and declines to 0.580 for Toubul and 0.604 for Khoijuman. The local level differences in adaptive capacity are a significant factor in producing differential vulnerabilities at the micro level. The unequal socio-demographic, livelihood strategies and social network characteristics mean that the ability of the villages to respond to climate change is also different. The village with the highest adaptive capacity can not only respond better to changes but also in general take more efficient adaption actions. Thus, adaptive capacity has a modulating effect on the vulnerability ³⁵ as shown in the Figure 8.

Sensitivity Index

Sensitivity refers to the responding condition of a system under study to climate related hazards and the index value of sensitivity expresses its degree. It is influenced by the conditions that exist at the community and household levels. The role of sensitivity is to multiply the existing susceptible conditions within society. Together with exposure, sensitivity determines the potential impact of climate events hence an important component of understanding vulnerability. Sensitivity in this study concerns the responding level of households in the three villages located along the Loktak lakeshore. Nine indicators across three major components - Food, Health, and Water - of LVI are used to construct sensitivity index.

Toubul village has the highest sensitivity to climate variability and changes with a score of 0.429 compared to Kwasiphai’s 0.377 while Khoijuman with a value of 0.357 is the least climate sensitive village. The higher sensitivity of Toubul is primarily due to absolute lack of access to pipe borne water as a result large percentage of households use water from natural sources for consumption. Moreover, Toubul also has a relatively higher percentage of households that market their crops. Food and water sectors are therefore the main contributors to Toubul’s higher climate sensitivity. Kwasiphai has intermediate sensitivity among the three villages because of performing poorly in two indicators of Health component and one indicator of Food sector. The cause for Khoijuman village’s low sensitivity to climate events is its consistent moderate scores on all indicators of sensitivity.

6. Practical Implications

Since vulnerability is determine by exposure, sensitivity and adaptive capacity, a closer study of the performance of major components and subcomponents indicators explains the weaknesses and strengths of a village implicitly. The strengths and weaknesses that contribute accentuate or attenuate vulnerability to climate variability and hazards in each village is given at Table 2. This information holds crucial policy value as it can aid in prioritise sectors and dimensions of vulnerability where urgent resources can be allocated. For instance, the sensitivity of households in Kwasiphai is because of the high disease load, but adaptation actions such as greater livelihood diversification, better demographic balance, and more access for their farm produce to the market have decreased its overall climate vulnerability.

Having a high adaptive capacity for Kwasiphai means the human agency of this village is robust because of the better translation of existing human, social, financial, natural, or physical capitals into successful adaptation results ¹⁶. Despite having a strong health sector and tightly knit social networks, the precarious water situation makes Toubul the most sensitive village to climate change impacts and it also has the highest exposure to NDVC having experienced higher numbers of floods. These adverse conditions are responsible for Toubul being the most vulnerable village to climate change effects. Finally, sensitivity and exposure scores for Khoijuman are the lowest as a greater number of households have access to piped water and fewer people perceiving rainfall pattern change. Any policy formulated to reduce vulnerability and enhance coping capacity of the villages has to consider the interlinkages among the factors. This would translate into adoption of inter-sectoral intervention approach that touches upon the various interrelated vulnerability-causing factors for each village. Our analysis has thus revealed variability of vulnerability even at fine scales due to the local interplay of exposure, sensitivity, and adaptive capacity in each village.

7. Limitations of the Study

The limitations of the present study can be group into three broad categories. First, measurement of a theoretical concept like vulnerability through assessment process is a difficult exercise because of which its results often get misinterpreted ³⁶. Secondly, the indicator approach used in the study can introduce subjectivity in indicators selection, and also equal weighting scheme is only one of the many schemes available ^{30, 37}. For instance, if long term comprehensive statistical data on the different components of vulnerability was available for the area, the dimensionality of the data could have been reduced allowing the use of unequal weighting scheme in composing the indices ²⁷. More importantly, associating vulnerability to a particular place as it is done here limits the application of the approach in different settings making it difficult to validate the results ³⁸. Thirdly, indicators employed in the study are not the final set of indicators to be used in other population or even for the same population in different time periods. As the social, economic, technological and institutional contexts evolved for the population, the vulnerability profile may also evolve needing selection of a new set of indicators to reflect the new realities ¹⁴. Therefore, the findings presented here should only serve as a baseline data on vulnerability for the villages under study.

8. Conclusion

The two complementary indices-Livelihood Vulnerability Index (LVI) and CVI_IPCC were composed using 26 indicators derived through a site-specific consultative process to assess climate vulnerability of the livelihood of three Loktak lakeshore villages. LVI allowed the comparison of differential climate vulnerabilities among the villages caused by variation in local factors. While CVI_IPCC examined inter-village differences in the degree of contribution from exposure, sensitivity, and adaptive capacity as vulnerability contributing factors. Each index was able to provide an in-depth description of numerous local factors producing village level vulnerabilities. It was observed that there were small variations in LVI and CVI_IPCC values across the three villages. Both indices agreed that the livelihood of Toubul village is the most vulnerable to climate impacts, with Kwasiphai emerging as the least vulnerable village and Khoijuman has intermediate vulnerability. The data on major components and indicators exhibited considerable variations among the villages revealing existing village specific vulnerabilities and coping strategies. To successfully cope with the challenges of climate change through the preparation of local adaptation strategies, there is, therefore, a compelling need for assessing vulnerabilities at the local level. In this regard, the LVI framework should be utilized to assess local vulnerabilities across the state of Manipur to help local adaptation planning.

References