Cross-Sectoral Climate Vulnerability Assessment of Meghalaya at District Level

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Nongrang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6110405/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This study assesses climate vulnerability at the district level in Meghalaya using a framework aligned with IPCC guidelines. Both biophysical and socio-economic indicators were employed, with normalized values calculated for comparability. Unequal weightage was assigned to the indicators based on their relative importance, as determined through expert consultation and literature review. The calculated vulnerability index ranked districts as highly, moderately, or least vulnerable. Results reveal that West Khasi Hills is the most vulnerable district, primarily due to steep slopes, high poverty rates, and reduced forest cover, while East Jaintia Hills is the least vulnerable. Key drivers of vulnerability, such as high yield variability, deforestation, and steep slopes, were highlighted. These findings underscore the need for targeted adaptation strategies and policy interventions, such as enhancing forest conservation, promoting livelihood stability, and fostering climate-resilient agricultural practices, particularly in highly vulnerable districts. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Vulnerability indicators sensitivity adaptive capacity drivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Meghalaya is one of the highly sensitive State amongst the North-Eastern States in terms of climate change impacts due to its geographic location, socio-economic and ecological fragility. The geographic location of the State in the eastern Himalayan periphery augments the State’s vulnerability towards various climate impacts due its diverse physiographical settings and hilly topography. Further, the underdeveloped economic situation poses a threat to the resilience of the vulnerable community. In addition, the highly dispersed and vulnerable population are poorly equipped to cope/adapt effectively with the adversities of climate change due to their low capabilities, weak institutional mechanisms, lack of adequate resources etc. Climate change has a wide-ranging effect on the environment, and socio-economic and related sectors, including water resources, agriculture, forests, and human health. Many environmental and developmental problems, especially in developing countries, will be profoundly impacted negatively by climate change [23]. It is also increasingly recognized that developing countries such as India are the most vulnerable to climate change impacts due to their fewer and poor resources to adapt: socially, technologically, and financially. A recent study on identifying climate vulnerability hotspots for the State of Meghalaya using high-resolution climate models suggested that the majority of the districts are highly vulnerable to climate change impacts [17]. It is also projected that there will be a rise in temperature by 1.5°C to more than 3.8°C under different projected future climate scenarios. At the same time, rainfall is expected to increase and is likely to become more erratic in nature. Different projected scenarios further indicate that the monsoon precipitation intensity will be more but with a lesser span of rainy days. It is also expected that in future climate scenarios, the number of extreme climate events such as floods, heatwaves, storms, etc., is set to rise. Crucial sectors of the State, such as agriculture, water resources, health, sanitation, and rural development, are likely to be adversely affected by climate change impacts. Climate Vulnerability Assessments (VA) are analytical processes that quantify and qualify the degree to which natural systems and human communities are susceptible to the adverse effects of climate change. Generally, vulnerability is defined as the “propensity or predisposition to be adversely affected” by climate change. The VA framework, widely adopted in the Intergovernmental Panel on Climate Change (IPCC) assessments, has underpinned many vulnerability studies globally. Over the past two decades, the literature has evolved from early biophysical assessments focusing primarily on natural hazards to more integrated approaches that include socioeconomic, cultural, and institutional dimensions. In India, there is a growing body of literature on climate vulnerability assessments that mirrors the global efforts but also addresses the country’s unique challenges. Adger's (2006) foundational paper discusses the concept of vulnerability in the context of environmental change. Adger’s work has been highly influential in shaping subsequent vulnerability assessments by emphasizing the role of adaptive capacity and social factors [1]. O’Brien, K., et al. (2007) critically examine the multiple conceptualizations of vulnerability found in climate change literature. It explains how the choice between “outcome vulnerability” (focusing on impacts) and “contextual vulnerability” (focusing on underlying socio-political conditions) can influence assessment results and policy responses [19]. The USDA Forest Service report (Friggens et al., 2013) focused on the American Southwest and provided a detailed review of VA methodologies. It illustrated that assessments in this region vary widely in their scale (from species-level to landscape-level), and in the selection of vulnerability components. The authors recommended that managers critically evaluate methods when using VA results, particularly emphasizing the importance of matching assessment scales to management needs [11]. De Sherbinin, A., et al. (2019) systematically reviewed 84 studies that map social vulnerability to climate change impacts. It discusses common methodological frameworks, especially using the exposure, sensitivity, and adaptive capacity model and highlights key limitations (e.g., insufficient uncertainty quantification and scale mismatches). Their work revealed that despite diverse geographic coverage, from local to global scales, many studies share common methodological features such as linear index aggregation and the use of the IPCC vulnerability framework. The review also pointed to several limitations, including insufficient incorporation of future climate projections and a lack of uncertainty characterization [6]. Eckstein, D., et al. (2021) primarily focused on climate risk, this index provides a global ranking based on vulnerability factors among other metrics. It is widely cited as a benchmark for comparing national vulnerabilities to climate-induced hazards [8]. IPCC’s AR6 (2022) provides a global perspective on climate impacts and vulnerability, synthesizing the latest evidence on how climate changes are affecting ecosystems, human communities, and economies. It also lays out frameworks for assessing vulnerability that have influenced many subsequent studies [13]. Li, Y., et al. (2023) synthesised reviews on how marine fisheries vulnerability is assessed worldwide. It emphasizes that both biological traits and socioeconomic factors are integrated into vulnerability analyses, and it discusses the need for better cross-scale representation, especially in data‐limited regions [15]. Singh, C., et al. (2017) reviews both peer‐reviewed and grey literature to understand how vulnerability is conceptualized and measured in India, highlighting the predominance of quantitative, indicator‐based methods and noting gaps in urban and temporal analyses. The study systematically reviews 78 peer-reviewed publications and 42 pieces of grey literature to examine how vulnerability is conceptualized and measured in India. It frames the inquiry around four main questions: the conceptualization of vulnerability (what/whom is vulnerable and to what), who conducts the assessments, the methodologies and scales used, and the implications these choices have on outcomes. The study found that methods are largely rooted in disciplinary traditions, with a predominance of quantitative, indicator-based approaches that follow the classic exposure, sensitivity, adaptive capacity framework. Although many studies acknowledge the importance of considering spatial and temporal scales, only a few integrate these aspects rigorously in their methodologies. There is a notable focus on rural areas with less emphasis on urban and peri-urban contexts, despite rapid urbanization in India [21]. Sharma, J., et al. (2018) developed a manual which presents a detailed, step-by-step guide for conducting vulnerability assessments in the Indian Himalayan Region using an indicator-based approach. It covers all stages from scoping and indicator selection to normalization, weighting, aggregation, and mapping of vulnerability indices. The framework is tailored to the unique geographical, socio-economic, and ecological contexts of the Himalayas. It serves as a capacity-building tool for state and district administrators, helping them identify vulnerable communities and prioritize adaptation investments. The VAs being carried out by the State of Meghalaya are based on this very framework. This framework also helps in comparing the assessments done in the Indian Himalayan region and other state to determine the vulnerability ranks and drivers of vulnerabilities [20]. Manglem & Deva (2020) critically reviews the divergent meanings and conceptualizations of vulnerability used in India, comparing outcome vulnerability (focusing on impacts) and contextual vulnerability (considering underlying socio-economic factors). The study discusses how these conceptual choices influence the selection of methods (top-down versus bottom-up approaches) and the resultant vulnerability scores and rankings. The authors argue that the diversity in approaches reflects the multiplicity of social, economic, and ecological drivers in India, which necessitates careful calibration of VAs to inform adaptation planning. Using a combination of trend analysis (via the Modified Mann–Kendall test), risk assessment frameworks (based on IPCC AR6), and multiple linear regression, this study provides a community-level assessment that integrates both observed climate data and household perceptions [16]. CEEW (2021) report applies a composite index–based approach (drawing on the IPCC SREX framework and DST guidelines) to map vulnerability at the district level, showing that over 80% of the population lives in districts highly vulnerable to hydro-meteorological disasters. This report employs a composite index–based vulnerability assessment method using the IPCC’s SREX framework and guidelines from the Indian Department of Science and Technology (DST). It combines spatio-temporal analysis with indicators for exposure, sensitivity, and adaptive capacity to produce a Climate Vulnerability Index (CVI) at the district level. The assessment reveals that more than 80% of India’s population lives in districts highly vulnerable to hydro-meteorological disasters. It highlights regional variations, with the southern zones generally being the most vulnerable, followed by eastern, western, and northern regions [5]. Arora, G., et al. (2023) reviews India’s existing vulnerability and risk assessment frameworks and evaluates their varied scopes and methodologies. It contrasts top-down (often government-led, data-driven) and bottom-up (community-based participatory) approaches and discusses the challenges of integrating equity into adaptation planning. The study finds that despite numerous assessments, few projects have effectively translated vulnerability analysis into actionable adaptation measures. Recommendations are provided for scaling up assessments and mainstreaming vulnerability into policy and practice [3]. Kumar & Mohanasundari (2025) employ a multidimensional approach combining statistical trend analysis using the Modified Mann–Kendall (MMK) test, a risk assessment framework based on the IPCC Sixth Assessment Report (AR6), and multiple linear regression (MLR). The study integrates both quantitative climate data and household perceptions to assess vulnerability at the community level. The analysis shows an increasing trend in rainfall and temperature. Bhil households are found to be at a higher risk than Bhilala households due to differences in exposure and sensitivity. Certain indicators, such as housing quality and food security, are identified as significant contributors to overall vulnerability [14]. International literature on Climate Vulnerability Assessments demonstrates a broad range of methodologies from indicator-based indices and spatial mapping to integrated modeling and participatory approaches. Although a common conceptual framework is emerging, challenges such as data heterogeneity, scale mismatches, and uncertainty characterization remain. The literature highlights the need to standardize methods, invest in data infrastructure, and engage stakeholders to ensure that CVAs effectively inform adaptation and resilience-building efforts worldwide. Indian literature, however, demonstrates a rich diversity of approaches from systematic reviews and composite index mapping to multidimensional assessments at the community level and comprehensive regional guidelines. These studies have provided crucial insights into the spatial, temporal, and socio-economic dimensions of vulnerability in India, highlighting the need for tailored adaptation strategies across different regions and communities. By integrating both top-down data-driven methods and bottom-up participatory approaches, these assessments aim to inform policy decisions and prioritize adaptation investments in a country facing rapid climate change impacts. The necessity of this study is reinforced by Meghalaya's significant reliance on climate-sensitive sectors, such as agriculture and forestry, as well as its vulnerability to extreme weather events, including heavy rainfall, landslides, and floods. Furthermore, disparities in adaptive capacity across districts underscore the need for a detailed assessment to inform targeted policy interventions. By identifying the most vulnerable districts and the key drivers of vulnerability, this study provides a robust scientific foundation for decision-makers to implement effective climate adaptation measures. Additionally, this research contributes to the broader understanding of climate vulnerability in mountainous regions, offering replicable insights for similar geographical contexts. The primary objective of this assessment is to identify, rank, and prioritize the most vulnerable districts in the state. It also aims to determine the drivers of vulnerability at both the state and district levels. The results of the assessment will guide the prioritization of vulnerable districts for adaptation planning and the promotion of awareness initiatives. 2. Objectives The objective of this study is to identify vulnerable areas, systems, and communities across the state while fostering stakeholder demand for adaptation actions. This comprehensive assessment will evaluate vulnerability levels and analyze its underlying drivers, providing insights into regions exposed to climatic stressors and identifying significant vulnerable systems. By focusing on areas where adaptive measures are both necessary and feasible, the study will guide adaptation planning and policy development. Covering all 11 districts of Meghalaya, the assessment will produce district-level vulnerability maps and deliver strategic guidance for effective cross-sectoral adaptation planning. Material and Methods/Methodology The State’s vulnerability and risk assessment is based on the framework provided in IPCC Fifth Assessment Report (AR5). The IPCC framework considers risk as a function of hazard, exposure, and vulnerability. The framework assumes risk of climate-related impacts results from the interaction of climate-related hazards (including hazardous events and trends) with the vulnerability and exposure of human and natural systems. Changes in both the climate system (left) and socioeconomic processes including adaptation and mitigation (right) are drivers of hazards, exposure, and vulnerability [10] as illustrated in Figure 1. Notably, the IPCC Fourth Assessment Report (AR4), released in 2007, categorized "exposure" as one of the three elements of vulnerability, alongside sensitivity and adaptive capacity. However, in the AR5 framework, "exposure" is no longer treated as a component of vulnerability. Instead, vulnerability is considered an inherent characteristic of a system that reflects its current internal state. The AR5 framework emphasizes that hazard, exposure, and vulnerability interact to produce risk within the broader climatic, physical, and socio-political environments. The Vulnerability Assessment framework and methodology was developed by premier institutes of India during a pan-Himalayan study on climate vulnerability for the Indian Himalayan Region. The detailed methodology is outlined in the 2018-19 manual "Climate Vulnerability and Risk Assessment: Framework, Methods and Guidelines for the Himalayan Regions" [20]. The manual provides different methodological approaches/steps for assessing the risk depending upon the objective, scope, and level. The methodological steps adopted in this assessment are illustrated below in Figure 2. The process begins with scoping and defining objectives to identify and rank vulnerable districts, aiming to aid adaptation planning and raise awareness among policymakers and rural communities. An integrated approach was chosen, combining biophysical and socio-economic indicators for a comprehensive analysis. Tier 1 methods were used, leveraging secondary data for a rapid assessment. The study was conducted at the district level, focusing on the short-term future (2030s). Indicators were selected based on their relevance to sensitivity and adaptive capacity, quantified, and normalized according to their relationship with vulnerability. Expert consultation determined the weights assigned to each indicator, with the total weight summing to 100. These weighted, normalized indicators were then aggregated to create a composite vulnerability index for each district. The results were presented through spatial maps, charts, and tables, with districts ranked as low, medium, or high vulnerability. Key drivers of vulnerability were identified and visually represented to support adaptation planning. 3. Indicator selected, rationale for selection, and source of data A Tier 1 vulnerability assessment in Meghalaya, using eight carefully chosen indicators, highlights interconnected sensitivities and adaptive capacities influenced by the state’s unique geography and socio-economic conditions. Meghalaya's hilly terrain, with elevations ranging from 60 m to 1,950 m, contributes to challenges like yield variability in agriculture, which supports 81% of the population. This is further compounded by environmental risks, as steep slopes and extreme rainfall heighten vulnerabilities, affecting food grain adaptability and productivity. Forests, covering 76.33% of the state's area, play a dual role by mitigating climate impacts and being vulnerable to changing climatic conditions. Socio-economic factors add to this complexity, as the female literacy rate of 72.9% varies significantly across districts, with higher literacy enabling better adaptation to climate risks, while lower literacy correlates with reduced resilience. Similarly, the infant mortality rate (IMR) of 29 per 1,000 live births reflects healthcare disparities, which are tied to economic conditions highlighted by the Multidimensional Poverty Index (MPI) of 27.79%, showing constraints in access to education, healthcare, and basic amenities. Adaptive capacity is further assessed through the average man-days under NREGS, where low enrolment points to limited livelihood stability, increasing economic disparities and reducing resilience. The details of the indicators selected, their rationale, and the source of data used in this assessment are laid down in the table below. (Table 1). Table 1. List of Indicators, indicator type, functional relationship with vulnerability, rationale for selection, and data source Indicators Indicator Type Functional relationship with Vulnerability Rationale for selection Data Source % area with Slope gradient >30% Bio-physical Sensitivity (+) Higher slope proportion contributes to higher soil erosion and sedimentation, inaccessibility and cause more damage during disasters and extreme climate events. Atlas for Natural Resource Management in Meghalaya, MBDA (2020) % of area under forest (%) Bio-physical Adaptive Capacity (-) Forests provide safeguard to ecological processes, biophysical stability and alternative livelihood options. Thus, reduction in forest area leads to lower adaptive capacity. Atlas for Natural Resource Management in Meghalaya, MBDA (2020) Yield Variability Bio-physical Sensitivity (+) Higher variability in food grain production signifies an upset production leading to farmers’ stress and food insecurity. State Level Crop Statistics Report on Kharif Crops 2022-23, Directorate of Economic & Statistics, Government of Meghalaya Population Density (PD) Socio-economic Sensitivity (+) Denser population reflects higher pressure on resources Statistical Abstract, Directorate of Economic & Statistics, Government of Meghalaya, 2023 Female Literacy Rate Socio-economic Adaptive Capacity (-) Higher the no. of female literates better is their preparedness & response to calamities, improved decision-making ability and enhanced income opportunities. Statistical Abstract, Directorate of Economic & Statistics, Government of Meghalaya, 2023 Infant Mortality Rate (IMR) Socio-economic Sensitivity (+) IMR is an indicator of the overall state of the public health, access to improved water, sanitation & medical infrastructure. Higher IMR indicates poor health conditions in the region. Department of Health and Family Welfare, Government of Meghalaya, 2024 Multidimensional Poverty Index Socio-economic Sensitivity (+) A high (MPI) indicates high vulnerability because it shows people face multiple deprivations in health, education, and living standards. The MPI measures these areas beyond just income, revealing how combined problems make people more vulnerable and in need of support. NITI Aayog, A Progress Review, 2023 Average man days under NREGS Institutional Adaptive Capacity (-) Low enrolment depicts lower AC of the community and will increase economic disparity Ministry of Rural Development, 2023-24 3.1. Normalisation of indicators This section presents the measurement units of indicators that are expressed in terms of percentage and relative values for all the districts in the State (Table 2). Normalized values are unit-free and all lie between 0 and 1 which implies that 0 is the least vulnerability and 1 is the highest vulnerability and can be used for ranking and comparison. Normalisation is based on the indicators’ functional relationship with vulnerability, whether positive or negative, and the corresponding formulae were used [20]. Case I: The indicator has a positive relationship with vulnerability Case II: The indicator has a negative relationship with vulnerability Where NV is Normalised value and I.V. is Indicator value Table 2. Sub-indicator values and normalised scores for the indicator Districts % Area with Slope gradient >30º % Area under Forest Yield Variability Population Density Female Literacy Rate Infant Mortality Rate (IMR) Multidimensional Poverty Index % Average man-days under NREGS AV NV AV NV AV NV AV NV AV NV AV NV AV NV AV NV EGH 7.66 0.31 72.52 0.45 5.93 1 101 0.17 37142 0.96 81 0.04 14.96 0.16 237.98 0.17 EJH 9.1 0.41 84.66 0.09 0.02 0 60 0 27786 1 54 0 43.79 0.8 237.97 0.25 EKH 12.55 0.65 53.69 1 0.43 0.07 301 1 290760 0 743 1 24.1 0.36 237.99 0.08 NGH 4.77 0.11 70.62 0.51 2.3 0.39 148 0.37 52492 0.91 70 0.02 13.26 0.12 237.94 0.5 RB 10.54 0.51 59.62 0.83 0.05 0.01 106 0.19 74882 0.82 94 0.06 31.67 0.53 237.98 0.17 SGH 8.76 0.39 87.91 0 3.32 0.56 75 0.06 37059 0.96 57 0 9.77 0.04 237.88 1 SWGH 3.26 0 59.00 0.84 1.5 0.25 205 0.6 46197 0.93 97 0.06 18.27 0.23 237.97 0.25 SWKH 5.89 0.18 67.92 0.58 0.36 0.06 71 0.05 27894 1 80 0.04 40.98 0.74 238.00 0 WGH 12.03 0.61 62.73 0.74 1.91 0.32 166 0.44 119067 0.65 313 0.38 8.00 0 237.90 0.83 WJH 7.9 0.32 70.02 0.52 0.02 0 153 0.39 72370 0.83 185 0.19 52.08 0.99 237.97 0.25 WKH 17.54 1 65.93 0.64 0.02 0 74 0.06 85477 0.78 157 0.15 52.48 1 237.90 0.83 * Here, AV = actual value and NV = normalized value 3.2. Weights Assigned The weights are assigned to each of the eight indicators according to their importance in determining the vulnerability (Table 3). This process involved conducting a literature review and consulting with stakeholders and experts. For Meghalaya, yield variability of major foodgrains (rice, wheat and maize) was assigned the highest weightage (WI = 30) as it was felt that it is the only indicator representing agricultural sector which is the single major contributor to the States GDP (22%) on which majority of the State’s population depends on besides, directly being a climate sensitive parameter. The second highest weightage was assigned to percentage of area with slope greater than 30 degrees (WI=20). The topography of the State and the hilly terrain adds to the sensitivity of the area to climate change impacts. The spatial distribution of the steep slopes coincides with areas receiving heavy rainfall, making the region vulnerable to soil erosion, landslides, sedimentation in low lands, causing damage to agricultural lands and infrastructures. Indicator with third highest weightage assigned was the percentage of area under forest (WI = 18). Although the State currently has a good percentage of area under forest, the inherent concerns continue to exist in the form of shifting agriculture, logging, mining and other human activities which have been responsible for fragmentation, destruction and degradation of the forests in the State. Further, the future projections based on a scientific study suggests that 70% of the forested grids would become extremely vulnerable in the long term under RCP 8.5 scenario [17]. The indicator of households living below poverty line was assigned the fourth highest weightage (WI = 17). Meghalaya has around 12% of its population and over 2 lakh households living under the BPL. The impacts of climate change are worst felt by the economically marginalized section of the society; thus, it was necessary to give higher weightage to this indicator. The lowest weightage was attributed to female literacy rate (WI =0.5) and population density (WI = 1.0). Female literacy rate was the only indicator which captures the gender perspective and has a negative relationship with adaptive capacity. In Meghalaya, the female literacy rate is at 72.89% which is above the national average (65.46%). Meghalaya has a population density of only 132 persons per sq.km; thus, this demographic indicator was decided not to be an important contributor to the State’s vulnerability since it is less than one-third of the national average. Table 3. Weights assigned to indicators and sub-indicators and the weights to be multiplied with normalized scores Indicators Weights (WI) Weights to be multiplied with normalized scores (WI*Wi) % area with > 30% Slope 20 0.20 % area under Forest 18 0.18 MGNREGA 10 0.10 Infant Mortality Rate 3.5 0.04 Female Literacy Rate 0.5 0.01 Population Density 1 0.01 BPL 17 0.17 Yield Variability 30 0.30 Total 100 1.00 4. Result and discussion 4.1. Vulnerability profile, ranking, and category of districts Vulnerability index (VI) indicates the level of vulnerability of the area based on the index value to have a comparative ranking. The higher the value of VI of the particular area, the higher will be the vulnerability. For the State of Meghalaya, comparative ranking is being carried out for eleven districts based on the eight indicators. The result of the ranking for the State as mentioned in Table 4, depicted that West Khasi Hills is the most vulnerable district with the highest vulnerability index value (0.58) and ranks 1 among the 10 districts of Meghalaya followed by East Garo Hills (0.49) at rank 2, district of West Garo Hills (0.46) at rank 3 and East Khasi Hills (0.45) at rank 4 whereas the district of East Jaintia Hills with a VI value of 0.27 was found to be relatively the least vulnerable district. Further, based on the vulnerability index value, the districts have been categorized into 3 classes namely High (0.47-0.58), Medium (0.38-0.46), and Low (0.27-0.37). Both the districts of West Khasi Hills and East Garo Hills fall under the “High” category. The districts of West Garo Hills and East Khasi Hills fall under the “Medium” category followed by Ri Bhoi, West Jaintia Hills, South Garo Hills, North Garo Hills, South West Garo Hills, South West Khasi Hills and East Jaintia Hills fall under the “Low” category (Table 4). In Figure 3, the map illustrates the vulnerability ranking of various districts in Meghalaya, India, based on a specific vulnerability assessment, with each district color-coded to indicate its relative vulnerability level. The scale ranges from 1 (highest vulnerability) to 11 (lowest vulnerability), where darker colors represent higher vulnerability and lighter colors indicate lower vulnerability. Similarly, Figure 4 depicts the vulnerability categories of the districts, using three color codes: high vulnerability (0.47 - 0.58), shaded in dark red for areas most at risk; medium vulnerability (0.37 - 0.47), shown in light brown for moderate risk; and low vulnerability (0.27 - 0.37), depicted in light yellow for regions with lower risk. Districts with higher vulnerability (darker shades) are influenced by factors such as steep slopes, reduced forest cover, high food grain yield variability, and poverty, making them more susceptible to climate and socio-economic challenges. In contrast, districts with lower vulnerability (lighter shades) tend to have better adaptive capacities due to improved literacy, better healthcare access, and economic stability. Table 4. Vulnerability index values, corresponding ranks, and categories of districts in the state of Meghalaya Districts Vulnerability Index Ranking of Districts based on VI Vulnerability Category West Khasi Hills 0.58 1 High East Garo Hills 0.49 2 West Garo Hills 0.46 3 Medium East Khasi Hills 0.45 4 Ri Bhoi 0.37 5 Low West Jaintia Hills 0.37 6 South Garo Hills 0.36 7 North Garo Hills 0.31 8 South West Garo Hills 0.30 9 South West Khasi Hills 0.29 10 East Jaintia Hills 0.27 11 4.2. Drivers of Vulnerability 4.2.1. Overall Vulnerability Drivers of vulnerability were identified by the contribution of each indicator to vulnerability. Based on the percentage contribution of each indicator across all districts to the aggregate vulnerability index, it was found that a lack of area under forest contributes the most to overall vulnerability, accounting for 26.38% followed by steepness of the slope, contributing 21.21%, and a high rate of Multidimensional Poverty Index at 19.98%. Additional factors include high food grain of yield variability (18.75%), a low average number of man-days under the National Rural Employment Guarantee Scheme (NREGS) at 10.23%, a high infant mortality rate (1.6%), a high female literacy rate (1.04%), and high population density (0.78%), as illustrated in Figure 5. 5.2.2. Major Drivers of Vulnerability at District level During the assessment of the integrated vulnerability, it was found that West Khasi Hills (0.58) was the most vulnerable district, followed by East Garo Hills (0.49), and West Garo Hills (0.46) and all three districts are categorized by three major drivers as mentioned in Table 5. A closer analysis reflects that the three districts (WKH, EGH, and WGH) ranking highest in the integrated vulnerability assessment share two major common drivers, i.e., steepness of slope and a lack of area under forest. Table 5. District Vulnerability Index and their major drivers District (VI) Major Drivers West Khasi Hills (0.58) · Steepness of slope (0.20) · High rate of Multidimensional Poverty Index (0.17) · Lack of area under forest (0.12) East Garo Hills (0.49) · High food grain of Yield Variability (0.30) · Lack of area under forest (0.08) · Steepness of slope (0.06) West Garo Hills (0.46) · Lack of area under forest (0.13) · Steepness of slope (0.12) · High food grain of Yield Variability (0.10) Table 6 shows the contribution of each indicator to sectoral vulnerability on a district-by-district basis. The drivers with a contribution of more than 0.06 index value have been highlighted. These represent the specific areas or gaps in each district that require attention through strategic development plans and adaptation actions. Figure 6 is a stacked bar diagram illustrating the contribution of each indicator to the vulnerability of each district. This visual representation highlights the key indicators for planning and policy-making. Table 6. District-wise contribution of each indicator to the sectoral vulnerability Drivers Integrated Vulnerability Index EGH EJH EKH NGH RB SGH SWGH SWKH WGH WJH WKH % Area with Slope >30% (+) 0.06 0.08 0.13 0.02 0.10 0.08 0.00 0.04 0.12 0.06 0.20 % Area under Forest 0.08 0.02 0.18 0.09 0.15 0.00 0.15 0.11 0.13 0.09 0.12 Yield Variability (+) 0.30 0.00 0.02 0.12 0.00 0.17 0.08 0.02 0.10 0.00 0.00 PD (+) 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 Female Literacy Rate (-) 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 IMR (+) 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 Multidimensional Poverty Index % (+) 0.03 0.14 0.06 0.02 0.09 0.01 0.04 0.13 0.00 0.17 0.17 Average man-days under NREGS (-) 0.02 0.03 0.01 0.05 0.02 0.10 0.03 0.00 0.08 0.03 0.08 District Total Vulnerability Index 0.49 0.27 0.45 0.31 0.37 0.36 0.30 0.29 0.46 0.37 0.58 Climate change vulnerability assessments are critical tools for identifying regions and communities at risk from climate-related hazards. These assessments support decision-making for adaptation planning and policy development. To provide a comparative analysis of vulnerability assessments conducted in Meghalaya (India) and other regions globally, a comparative statement highlighting methodologies, scales, indicators, and focus areas is presented. Meghalaya’s District Level Vulnerability Assessment offers hyper-local insights by assessing vulnerability at the district level. Uses 15 indicators covering exposure, sensitivity, and adaptive capacity. Directly supports the State Action Plan on Climate Change (SAPCC) with tailored recommendations. Bangladesh Climate Change Strategy and Action Plan (BCCSAP, 2009) focuses on both national and local vulnerabilities, especially in coastal areas and flood-prone regions. Emphasizes adaptation strategies for agriculture and coastal management. It integrates climate risk with socio-economic development priorities to guide national adaptation efforts [12]. Nepal Vulnerability and Risk Assessment (2010–2019): Employs participatory approaches and the CRiSTAL tool to map district-level risks. Combines local surveys with community-based data to address mountain-specific vulnerabilities. Provides a baseline for iterative improvements in adaptive capacity over time [24]. Bhutan Climate Vulnerability Baseline Assessment (2019): focuses on sub-district (Gewog) analysis, emphasizing the link between climate impacts and local livelihoods. Assesses vulnerabilities related to agriculture, glacier melt, and biodiversity. Uses a livelihood-based approach tailored to Bhutan’s unique geographical context [22]. Philippines Local Climate Change Action Plan (LCCAP) developed at the village (barangay) level, integrating detailed GIS analysis with local climate projections. Bridges climate adaptation strategies directly with local development and planning processes. Emphasizes asset exposure to inform targeted, community-level adaptation measures [4]. Vietnam GIZ Climate Change and Vulnerability Mapping (2020) utilizes the GIZ Sourcebook methodology for comprehensive mapping of climate vulnerabilities. Focuses on coastal, deltaic, and agricultural vulnerabilities specific to Vietnam’s geography. Provides spatial data that inform targeted adaptation strategies in vulnerable provinces [7]. Africa Adaptation Initiative (AAI) Assessments (2018) covers multiple countries across Africa using multi-criteria analysis and household surveys. Targets drought-prone and food-insecure regions with tailored adaptation financing strategies. Facilitates regional policy dialogue by highlighting cross-country vulnerabilities and priorities [2]. United States Climate Resilience Toolkit (2021) provides interactive web tools and comprehensive social vulnerability indices for community planning. Aggregates climate projections, case studies, and decision-support data for regional resilience strategies. Designed to assist local governments and communities in proactive adaptation planning [18]. European Union – EU Climate-ADAPT (2021) integrates sector-specific climate models with detailed socio-economic data at multiple administrative scale. Supports urban planning and public health strategies through a robust, data-driven platform. Aligns national adaptation efforts with EU-wide climate policies and funding mechanisms [9]. Global Climate Risk Index (2021) Ranks countries based on the historical impacts of extreme weather events and climate-related disasters. Focuses on empirical data to highlight immediate climate risks and disaster vulnerabilities. Emphasizes the human and economic impacts of climate events to inform global resilience efforts [8]. Meghalaya's district-level climate vulnerability assessment aligns with global best practices in methodology and focus, yet stands out for its distinctive characteristics. The current assessment stands out due to its District-level granularity, offering hyper-local insights, unlike global or national assessments, which focus on broader administrative units. The assessment emphasizes localized issues like infrastructure and service delivery, while others often prioritize large-scale risks such as coastal flooding or national-level agricultural impacts. While the assessment uses a straightforward CVI, larger assessments leverage advanced statistical and geospatial modeling techniques. The assessment is designed for direct incorporation into the SAPCC, paralleling global best practices of linking vulnerability findings to actionable policies. The Meghalaya Assessment identifies central & western Meghalaya districts as key vulnerable areas, while the global and national assessments identify coastal (Bangladesh, Vietnam), arid (Africa), and urban (EU, US) regions as vulnerable. The dominant risk arising from limited infrastructure, education, and healthcare in Meghalaya and disaster risks (coastal zones), drought (Africa), and agriculture, as per global assessments. All assessments feature policy recommendations with the Meghalaya assessment targeting improvements in governance, infrastructure, and global assessments targeting disaster preparedness, adaptation strategies, urban resilience. Similarly, the assessments provide a focus on climate impacts on agriculture, water resources, and public health. Conclusion This study provides valuable insights into the climate vulnerability of Meghalaya by employing a comprehensive district-level assessment, integrating biophysical and socio-economic indicators to highlight the multifaceted challenges faced by the state. Meghalaya's unique geography, with its steep slopes, undulating terrain, and reliance on climate-sensitive sectors like agriculture and forestry, amplifies its susceptibility to climate variability. Indicators such as reduced forest cover, food grain yield variability, high rates of poverty (MPI), and healthcare disparities exemplified by infant mortality rates (IMR) underscore the interconnected nature of vulnerabilities across districts. The study's findings reveal a strong need for targeted interventions to enhance resilience in high-risk districts such as West Khasi Hills and East Garo Hills, which are particularly impacted by factors like steep slopes and deforestation. The broader implications of this study emphasize the importance of integrating vulnerability assessments into climate adaptation planning and policy-making. Policymakers should prioritize district-specific strategies to strengthen adaptive capacities, such as improving healthcare and education infrastructure, promoting climate-resilient agricultural practices, and enhancing community participation in programs like NREGS. Investing in forest conservation and sustainable land-use practices is also critical to mitigate environmental risks. In practice, developing disaster-resilient infrastructure, ensuring equitable access to social safety nets, and conducting capacity-building programs for women and marginalized communities can significantly improve adaptive responses to climate impacts. For future research, this study suggests a need for hyper-localized vulnerability assessments with granular data to identify localized risks and resource gaps. Exploring long-term climatic impacts on biodiversity and forest ecosystems, as well as understanding the socio-economic effects of climate variability on vulnerable populations, can offer deeper insights. These efforts will not only strengthen Meghalaya's resilience to climate challenges but also provide a replicable framework for other regions with similar geographic and socio-economic contexts. By bridging gaps between vulnerability assessment, policy-making, and on-the-ground actions, this study sets a foundation for sustainable and inclusive adaptation strategies that are integrated into the Meghalaya State Action Plan on Climate Change. Declarations Acknowledgment We would like to express our sincere gratitude to those who contributed significantly to this research study. Firstly, we are immensely grateful to the Climate Change Program, Department of Science & Technology, Government of India, for their unwavering support throughout this project. Special thanks to IIT-Mandi, IIT-Guwahati, and CSTEP, whose expertise and guidance were invaluable in shaping this research. We also extend our heartfelt appreciation to the Chair and Members of the Meghalaya State Council on Climate Change and Sustainable Development for their insightful feedback and for facilitating access to essential resources and data. We sincerely thank the CEO and ED MBDA for ensuring resources were provided whenever needed. Lastly, we are grateful for the financial support from the National Mission for Sustaining the Himalayan Ecosystem (DST/CCP/NMSHE/SCCC-IHR/Meghalaya/224/2023 (G)), which enabled us to conduct this research. This work would not have been possible without the collaborative efforts and encouragement from all the mentioned individuals and institutions. Thank you all for your tremendous support and contribution. Author Contributions ML and VL contributed to writing and refining the paper. AN and NW handled data collection and analysis. EW drafted the manuscript. JB and AC contributed to writing, reviewing, and approving the final version, and in funding acquisition. Funding statement The paper has been developed with the funding support from the Department of Science & Technology, Government of India under the National Mission for Sustaining the Himalayan Ecosystem which was granted under the sanction order number DST/CCP/NMSHE/SCCC-IHR/Meghalaya/224/2023 (G). Data availability statement Data is provided within the manuscript. Competing Interests Statement The authors declare that they have no competing interests. References Adger, W.N. Vulnerability. Global Environmental Change , 16(3) , 268–281 (2006). African Development Bank. Africa Adaptation Initiative: Vulnerability Assessment and Climate Resilience Building (2018). Arora, G., et al. (2023). From Risk to Resilience: Climate Vulnerability Assessments in India. ORF Occasional Paper No. 408 , (2023). Climate Change Commission, Philippines. Local Climate Change Action Plan (LCCAP) Guidelines and Best Practices. Manila, Philippines (2020). Council on Energy, Environment and Water (CEEW). Mapping India’s Climate Vulnerability: A District Level Assessment (c. 2021). De Sherbinin, A., et al. Climate vulnerability mapping: A systematic review and future prospects. WIREs Climate Change (2019). Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). Climate Change and Vulnerability Mapping in Vietnam (2020). Eckstein, D., Künzel, V. & Schäfer L. Global Climate Risk Index. Germanwatch (2021). European Environment Agency. EU Climate-ADAPT: Climate Change Adaptation in Europe (2021). Field, C.B., et al. White, editors. Summary for policymakers. In: Climate Change: 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, United Kingdom and New York, NY, USA, pp. 1-32 (2014). Friggens, M., Bagne, K., Finch, D., Falk, D., Triepke, J., & Lynch, A. Review and recommendations for climate change vulnerability assessment approaches with examples from the Southwest. Gen. Tech. Rep. RMRS-GTR-309. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station (2013). Government of Bangladesh & United Nations Development Programme (UNDP). Bangladesh Climate Change Strategy and Action Plan (BCCSAP). Dhaka, Bangladesh (2009). IPCC. Climate Change: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press (2022). Kumar, A., & Mohanasundari, T. Assessing climate change risk and vulnerability among Bhil and Bhilala tribal communities in Madhya Pradesh, India: A multidimensional approach . Scientific Reports, 15, Article 7096 (2025). Li, Y, Sun, M, Kleisner, K.M., Mills, K.E., & Chen, Y. A global synthesis of climate vulnerability assessments on marine fisheries: Methods, scales, and knowledge co-production. Global Change Biology (2023). Manglem, A., & Deva, N. B. Climate Change Vulnerability Assessment: An Indian Perspective. Disaster Advances , 13(1), 1–8, (2020). Mishra, V., et al. Identification of climate vulnerability hot-spots in Meghalaya using high-resolution climate projections prepared by IIT Gandhinagar & IIM Gandhinagar, India, (2017). National Oceanic and Atmospheric Administration (NOAA). U.S. Climate Resilience Toolkit (2021). O’Brien, K., Eriksen, S., Nygaard, L.P., & Schjolden, A. Why different interpretations of vulnerability matter in climate change discourses. Climate Policy , 7(1) , 73–88 (2007). Sharma J, et al. Vulnerability and Risk Assessment: Framework, Methods and Guideline, Indian Institute of Science. http://himalayageoportal.in/wp-content/ uploads/Knowledge Resources/Vulnerability- Manual_ IISC_IHCAP.pdf, (2018). Singh, C., Deshpande, T., & Basu, R. How do we assess vulnerability to climate change in India? A systematic review of literature. Regional Environmental Change , 17(2) (2017). UNDP Bhutan. Bhutan Vulnerability Baseline Assessment, Gross National Happiness Commission Secretariat Thimphu, Bhutan (2016). UNFCC. “Climate Change: Impacts, Vulnerability and Adaptation in Developing Countries”. Bonn: UNFCCC. (a2007). United Nations Development Programme (UNDP) Nepal. Vulnerability and Risk Assessment Report for Nepal. Kathmandu: UNDP Nepal (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 13 Jul, 2025 Reviews received at journal 13 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 28 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 20 Apr, 2025 Submission checks completed at journal 20 Apr, 2025 First submitted to journal 15 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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This map was generated using ArcGIS Version 10.8. (https://www.esri.com)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6110405/v1/b4d449f9ae94f58add1ea296.jpg"},{"id":81116674,"identity":"8616f088-2fcc-4165-9fe8-a73ef415a49d","added_by":"auto","created_at":"2025-04-22 11:52:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64452,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing Integrated Vulnerability Category of Meghalaya at District level. This map was generated using ArcGIS Version 10.8. (\u003ca href=\"https://www.esri.com/\"\u003ehttps://www.esri.com\u003c/a\u003e)\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6110405/v1/a9bec2d86d0f05d3a268fbb8.jpg"},{"id":81117466,"identity":"7890ad79-2f58-4f24-bef9-d484555a04e1","added_by":"auto","created_at":"2025-04-22 12:00:04","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52099,"visible":true,"origin":"","legend":"\u003cp\u003eBar diagram showing the contribution (in percentage) made by each of the indicators in the sectoral vulnerability assessment for the State of Meghalaya\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6110405/v1/2c7c83418f4e2f52b59246c2.jpg"},{"id":81116225,"identity":"03f75dff-0f32-4b80-887c-7afe33e7c069","added_by":"auto","created_at":"2025-04-22 11:44:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64419,"visible":true,"origin":"","legend":"\u003cp\u003eThe Stacked bar diagram shows the contribution of all indicators to the total Vulnerability Index at district level\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6110405/v1/a5d1de5138a6b9b84a14b577.jpg"},{"id":97178275,"identity":"b8c7f1ce-90a8-4271-b36a-802919c259e3","added_by":"auto","created_at":"2025-12-01 16:06:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1399098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6110405/v1/b527b5d5-9f0d-4db8-8fb7-46ce119682f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross-Sectoral Climate Vulnerability Assessment of Meghalaya at District Level","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eMeghalaya is one of the highly sensitive State amongst the North-Eastern States in terms of climate change impacts due to its geographic location, socio-economic and ecological fragility. The geographic location of the State in the eastern Himalayan periphery augments the State\u0026rsquo;s vulnerability towards various climate impacts due its diverse physiographical settings and hilly topography. Further, the underdeveloped economic situation poses a threat to the resilience of the vulnerable community. In addition, the highly dispersed and vulnerable population are poorly equipped to cope/adapt effectively with the adversities of climate change due to their low capabilities, weak institutional mechanisms, lack of adequate resources etc. Climate change has a wide-ranging effect on the environment, and socio-economic and related sectors, including water resources, agriculture, forests, and human health. Many environmental and developmental problems, especially in developing countries, will be profoundly impacted negatively by climate change [23]. It is also increasingly recognized that developing countries such as India are the most vulnerable to climate change impacts due to their fewer and poor resources to adapt: socially, technologically, and financially. A recent study on identifying climate vulnerability hotspots for the State of Meghalaya using high-resolution climate models suggested that the majority of the districts are highly vulnerable to climate change impacts [17]. It is also projected that there will be a rise in temperature by 1.5\u0026deg;C to more than 3.8\u0026deg;C under different projected future climate scenarios. At the same time, rainfall is expected to increase and is likely to become more erratic in nature. Different projected scenarios further indicate that the monsoon precipitation intensity will be more but with a lesser span of rainy days. It is also expected that in future climate scenarios, the number of extreme climate events such as floods, heatwaves, storms, etc., is set to rise. Crucial sectors of the State, such as agriculture, water resources, health, sanitation, and rural development, are likely to be adversely affected by climate change impacts.\u003c/p\u003e\n\u003cp\u003eClimate Vulnerability Assessments (VA) are analytical processes that quantify and qualify the degree to which natural systems and human communities are susceptible to the adverse effects of climate change. Generally, vulnerability is defined as the \u0026ldquo;propensity or predisposition to be adversely affected\u0026rdquo; by climate change. The VA framework, widely adopted in the Intergovernmental Panel on Climate Change (IPCC) assessments, has underpinned many vulnerability studies globally. Over the past two decades, the literature has evolved from early biophysical assessments focusing primarily on natural hazards to more integrated approaches that include socioeconomic, cultural, and institutional dimensions. In India, there is a growing body of literature on climate vulnerability assessments that mirrors the global efforts but also addresses the country\u0026rsquo;s unique challenges.\u003c/p\u003e\n\u003cp\u003eAdger\u0026apos;s (2006) foundational paper discusses the concept of vulnerability in the context of environmental change. Adger\u0026rsquo;s work has been highly influential in shaping subsequent vulnerability assessments by emphasizing the role of adaptive capacity and social factors [1]. O\u0026rsquo;Brien, K., et al. (2007) critically examine the multiple conceptualizations of vulnerability found in climate change literature. It explains how the choice between \u0026ldquo;outcome vulnerability\u0026rdquo; (focusing on impacts) and \u0026ldquo;contextual vulnerability\u0026rdquo; (focusing on underlying socio-political conditions) can influence assessment results and policy responses [19]. The USDA Forest Service report (Friggens et al., 2013) focused on the American Southwest and provided a detailed review of VA methodologies. It illustrated that assessments in this region vary widely in their scale (from species-level to landscape-level), and in the selection of vulnerability components. The authors recommended that managers critically evaluate methods when using VA results, particularly emphasizing the importance of matching assessment scales to management needs [11].\u003c/p\u003e\n\u003cp\u003eDe Sherbinin, A., et al. (2019) systematically reviewed 84 studies that map social vulnerability to climate change impacts. It discusses common methodological frameworks, especially using the exposure, sensitivity, and adaptive capacity model and highlights key limitations (e.g., insufficient uncertainty quantification and scale mismatches). Their work revealed that despite diverse geographic coverage, from local to global scales, many studies share common methodological features such as linear index aggregation and the use of the IPCC vulnerability framework. The review also pointed to several limitations, including insufficient incorporation of future climate projections and a lack of uncertainty characterization [6]. Eckstein, D., et al. (2021) primarily focused on climate risk, this index provides a global ranking based on vulnerability factors among other metrics. It is widely cited as a benchmark for comparing national vulnerabilities to climate-induced hazards [8]. IPCC\u0026rsquo;s AR6 (2022) provides a global perspective on climate impacts and vulnerability, synthesizing the latest evidence on how climate changes are affecting ecosystems, human communities, and economies. It also lays out frameworks for assessing vulnerability that have influenced many subsequent studies [13].\u003c/p\u003e\n\u003cp\u003eLi, Y., et al. (2023) synthesised reviews on how marine fisheries vulnerability is assessed worldwide. It emphasizes that both biological traits and socioeconomic factors are integrated into vulnerability analyses, and it discusses the need for better cross-scale representation, especially in data‐limited regions [15]. Singh, C., et al. (2017) reviews both peer‐reviewed and grey literature to understand how vulnerability is conceptualized and measured in India, highlighting the predominance of quantitative, indicator‐based methods and noting gaps in urban and temporal analyses. The study systematically reviews 78 peer-reviewed publications and 42 pieces of grey literature to examine how vulnerability is conceptualized and measured in India. It frames the inquiry around four main questions: the conceptualization of vulnerability (what/whom is vulnerable and to what), who conducts the assessments, the methodologies and scales used, and the implications these choices have on outcomes. The study found that methods are largely rooted in disciplinary traditions, with a predominance of quantitative, indicator-based approaches that follow the classic exposure, sensitivity, adaptive capacity framework. Although many studies acknowledge the importance of considering spatial and temporal scales, only a few integrate these aspects rigorously in their methodologies. There is a notable focus on rural areas with less emphasis on urban and peri-urban contexts, despite rapid urbanization in India [21].\u003c/p\u003e\n\u003cp\u003eSharma, J., et al. (2018) developed a manual which presents a detailed, step-by-step guide for conducting vulnerability assessments in the Indian Himalayan Region using an indicator-based approach. It covers all stages from scoping and indicator selection to normalization, weighting, aggregation, and mapping of vulnerability indices. The framework is tailored to the unique geographical, socio-economic, and ecological contexts of the Himalayas. It serves as a capacity-building tool for state and district administrators, helping them identify vulnerable communities and prioritize adaptation investments. The VAs being carried out by the State of Meghalaya are based on this very framework. This framework also helps in comparing the assessments done in the Indian Himalayan region and other state to determine the vulnerability ranks and drivers of vulnerabilities [20]. Manglem \u0026amp; Deva (2020) critically reviews the divergent meanings and conceptualizations of vulnerability used in India, comparing outcome vulnerability (focusing on impacts) and contextual vulnerability (considering underlying socio-economic factors). The study discusses how these conceptual choices influence the selection of methods (top-down versus bottom-up approaches) and the resultant vulnerability scores and rankings. The authors argue that the diversity in approaches reflects the multiplicity of social, economic, and ecological drivers in India, which necessitates careful calibration of VAs to inform adaptation planning. Using a combination of trend analysis (via the Modified Mann\u0026ndash;Kendall test), risk assessment frameworks (based on IPCC AR6), and multiple linear regression, this study provides a community-level assessment that integrates both observed climate data and household perceptions [16].\u003c/p\u003e\n\u003cp\u003eCEEW (2021) report applies a composite index\u0026ndash;based approach (drawing on the IPCC SREX framework and DST guidelines) to map vulnerability at the district level, showing that over 80% of the population lives in districts highly vulnerable to hydro-meteorological disasters. This report employs a composite index\u0026ndash;based vulnerability assessment method using the IPCC\u0026rsquo;s SREX framework and guidelines from the Indian Department of Science and Technology (DST). It combines spatio-temporal analysis with indicators for exposure, sensitivity, and adaptive capacity to produce a Climate Vulnerability Index (CVI) at the district level. The assessment reveals that more than 80% of India\u0026rsquo;s population lives in districts highly vulnerable to hydro-meteorological disasters. It highlights regional variations, with the southern zones generally being the most vulnerable, followed by eastern, western, and northern regions [5]. Arora, G., et al. (2023) reviews India\u0026rsquo;s existing vulnerability and risk assessment frameworks and evaluates their varied scopes and methodologies. It contrasts top-down (often government-led, data-driven) and bottom-up (community-based participatory) approaches and discusses the challenges of integrating equity into adaptation planning. The study finds that despite numerous assessments, few projects have effectively translated vulnerability analysis into actionable adaptation measures. Recommendations are provided for scaling up assessments and mainstreaming vulnerability into policy and practice [3].\u003c/p\u003e\n\u003cp\u003eKumar \u0026amp; Mohanasundari (2025) employ a multidimensional approach combining statistical trend analysis using the Modified Mann\u0026ndash;Kendall (MMK) test, a risk assessment framework based on the IPCC Sixth Assessment Report (AR6), and multiple linear regression (MLR). The study integrates both quantitative climate data and household perceptions to assess vulnerability at the community level. The analysis shows an increasing trend in rainfall and temperature. Bhil households are found to be at a higher risk than Bhilala households due to differences in exposure and sensitivity. Certain indicators, such as housing quality and food security, are identified as significant contributors to overall vulnerability [14].\u003c/p\u003e\n\u003cp\u003eInternational literature on Climate Vulnerability Assessments demonstrates a broad range of methodologies from indicator-based indices and spatial mapping to integrated modeling and participatory approaches. Although a common conceptual framework is emerging, challenges such as data heterogeneity, scale mismatches, and uncertainty characterization remain. The literature highlights the need to standardize methods, invest in data infrastructure, and engage stakeholders to ensure that CVAs effectively inform adaptation and resilience-building efforts worldwide.\u003c/p\u003e\n\u003cp\u003eIndian literature, however, demonstrates a rich diversity of approaches from systematic reviews and composite index mapping to multidimensional assessments at the community level and comprehensive regional guidelines. These studies have provided crucial insights into the spatial, temporal, and socio-economic dimensions of vulnerability in India, highlighting the need for tailored adaptation strategies across different regions and communities. By integrating both top-down data-driven methods and bottom-up participatory approaches, these assessments aim to inform policy decisions and prioritize adaptation investments in a country facing rapid climate change impacts.\u003c/p\u003e\n\u003cp\u003eThe necessity of this study is reinforced by Meghalaya\u0026apos;s significant reliance on climate-sensitive sectors, such as agriculture and forestry, as well as its vulnerability to extreme weather events, including heavy rainfall, landslides, and floods. Furthermore, disparities in adaptive capacity across districts underscore the need for a detailed assessment to inform targeted policy interventions. By identifying the most vulnerable districts and the key drivers of vulnerability, this study provides a robust scientific foundation for decision-makers to implement effective climate adaptation measures. Additionally, this research contributes to the broader understanding of climate vulnerability in mountainous regions, offering replicable insights for similar geographical contexts. The primary objective of this assessment is to identify, rank, and prioritize the most vulnerable districts in the state. It also aims to determine the drivers of vulnerability at both the state and district levels. The results of the assessment will guide the prioritization of vulnerable districts for adaptation planning and the promotion of awareness initiatives.\u003c/p\u003e"},{"header":"2.\tObjectives","content":"\u003cp\u003eThe \u0026nbsp;objective of this study is to identify vulnerable areas, systems, and communities across the state while fostering stakeholder demand for adaptation actions. This comprehensive assessment will evaluate vulnerability levels and analyze its underlying drivers, providing insights into regions exposed to climatic stressors and identifying significant vulnerable systems. By focusing on areas where adaptive measures are both necessary and feasible, the study will guide adaptation planning and policy development. Covering all 11 districts of Meghalaya, the assessment will produce district-level vulnerability maps and deliver strategic guidance for effective cross-sectoral adaptation planning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and Methods/Methodology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe State\u0026rsquo;s vulnerability and risk assessment is based on the framework provided in IPCC Fifth Assessment Report (AR5). The IPCC framework considers risk as a function of hazard, exposure, and vulnerability. The framework assumes risk of climate-related impacts results from the interaction of climate-related hazards (including hazardous events and trends) with the vulnerability and exposure of human and natural systems. Changes in both the climate system (left) and socioeconomic processes including adaptation and mitigation (right) are drivers of hazards, exposure, and vulnerability [10] as illustrated in Figure 1. Notably, the IPCC Fourth Assessment Report (AR4), released in 2007, categorized \u0026quot;exposure\u0026quot; as one of the three elements of vulnerability, alongside sensitivity and adaptive capacity. However, in the AR5 framework, \u0026quot;exposure\u0026quot; is no longer treated as a component of vulnerability. Instead, vulnerability is considered an inherent characteristic of a system that reflects its current internal state. The AR5 framework emphasizes that hazard, exposure, and vulnerability interact to produce risk within the broader climatic, physical, and socio-political environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Vulnerability Assessment framework and methodology was developed by premier institutes of India during a pan-Himalayan study on climate vulnerability for the Indian Himalayan Region. The detailed methodology is outlined in the 2018-19 manual \u0026quot;Climate Vulnerability and Risk Assessment: Framework, Methods and Guidelines for the Himalayan Regions\u0026quot; [20]. The manual provides different methodological approaches/steps for assessing the risk depending upon the objective, scope, and level. The methodological steps adopted in this assessment are illustrated below in Figure 2.\u003c/p\u003e\n\u003cp\u003eThe process begins with scoping and defining objectives to identify and rank vulnerable districts, aiming to aid adaptation planning and raise awareness among policymakers and rural communities. An integrated approach was chosen, combining biophysical and socio-economic indicators for a comprehensive analysis. Tier 1 methods were used, leveraging secondary data for a rapid assessment. The study was conducted at the district level, focusing on the short-term future (2030s). Indicators were selected based on their relevance to sensitivity and adaptive capacity, quantified, and normalized according to their relationship with vulnerability. Expert consultation determined the weights assigned to each indicator, with the total weight summing to 100. These weighted, normalized indicators were then aggregated to create a composite vulnerability index for each district. The results were presented through spatial maps, charts, and tables, with districts ranked as low, medium, or high vulnerability. Key drivers of vulnerability were identified and visually represented to support adaptation planning.\u003c/p\u003e"},{"header":"3.\tIndicator selected, rationale for selection, and source of data","content":"\u003cp\u003eA Tier 1 vulnerability assessment in Meghalaya, using eight carefully chosen indicators, highlights interconnected sensitivities and adaptive capacities influenced by the state\u0026rsquo;s unique geography and socio-economic conditions. Meghalaya\u0026apos;s hilly terrain, with elevations ranging from 60 m to 1,950 m, contributes to challenges like yield variability in agriculture, which supports 81% of the population. This is further compounded by environmental risks, as steep slopes and extreme rainfall heighten vulnerabilities, affecting food grain adaptability and productivity. Forests, covering 76.33% of the state\u0026apos;s area, play a dual role by mitigating climate impacts and being vulnerable to changing climatic conditions. Socio-economic factors add to this complexity, as the female literacy rate of 72.9% varies significantly across districts, with higher literacy enabling better adaptation to climate risks, while lower literacy correlates with reduced resilience. Similarly, the infant mortality rate (IMR) of 29 per 1,000 live births reflects healthcare disparities, which are tied to economic conditions highlighted by the Multidimensional Poverty Index (MPI) of 27.79%, showing constraints in access to education, healthcare, and basic amenities. Adaptive capacity is further assessed through the average man-days under NREGS, where low enrolment points to limited livelihood stability, increasing economic disparities and reducing resilience. The details of the indicators selected, their rationale, and the source of data used in this assessment are laid down in the table below. (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e List of Indicators, indicator type, functional relationship with vulnerability, rationale for selection, and data source\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional relationship with Vulnerability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRationale for selection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e% area with Slope gradient \u0026gt;30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBio-physical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHigher slope proportion contributes to higher soil erosion and sedimentation, inaccessibility and cause more damage during disasters and extreme climate events.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAtlas for Natural Resource Management in Meghalaya, MBDA (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e% of area under forest (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBio-physical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAdaptive Capacity (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eForests provide safeguard to ecological processes, biophysical stability and alternative livelihood options. Thus, reduction in forest area leads to lower adaptive capacity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAtlas for Natural Resource Management in Meghalaya, MBDA (2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eYield Variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBio-physical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHigher variability in food grain production signifies an upset production leading to farmers\u0026rsquo; stress and food insecurity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eState Level Crop Statistics Report on Kharif Crops 2022-23, Directorate of Economic \u0026amp; Statistics, Government of Meghalaya\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003ePopulation Density (PD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSocio-economic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eDenser population reflects higher pressure on resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eStatistical Abstract, Directorate of Economic \u0026amp; Statistics, Government of Meghalaya, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eFemale Literacy Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSocio-economic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAdaptive Capacity (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eHigher the no. of female literates better is their preparedness \u0026amp; response to calamities, improved decision-making ability and enhanced income opportunities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eStatistical Abstract, Directorate of Economic \u0026amp; Statistics, Government of Meghalaya, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eInfant Mortality Rate (IMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSocio-economic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eIMR is an indicator of the overall state of the public health, access to improved water, sanitation \u0026amp; medical infrastructure. Higher IMR indicates poor health conditions in the region.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDepartment of Health and Family Welfare, Government of Meghalaya, 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMultidimensional Poverty Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSocio-economic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSensitivity (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eA high (MPI) indicates high vulnerability because it shows people face multiple deprivations in health, education, and living standards. The MPI measures these areas beyond just income, revealing how combined problems make people more vulnerable and in need of support.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNITI Aayog, A Progress Review, 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eAverage man days under NREGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAdaptive Capacity (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eLow enrolment depicts lower AC of the community and will increase economic disparity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMinistry of Rural Development, 2023-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cem\u003e3.1. \u0026nbsp;Normalisation of indicators\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis section presents the measurement units of indicators that are expressed in terms of percentage and relative values for all the districts in the State (Table 2). \u0026nbsp;Normalized values are unit-free and all lie between 0 and 1 which implies that 0 is the least vulnerability and 1 is the highest vulnerability and can be used for ranking and comparison. Normalisation is based on the indicators\u0026rsquo; functional relationship with vulnerability, whether positive or negative, and the corresponding formulae were used [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCase I: The indicator has a positive relationship with vulnerability\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"533\" height=\"46\"\u003e\u003c/p\u003e\n\u003cp\u003eCase II: The indicator has a negative relationship with vulnerability\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" width=\"516\" height=\"48\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere NV is Normalised value and I.V. is Indicator value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSub-indicator values and normalised scores for the indicator\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"119%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistricts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area with Slope gradient \u0026gt;30\u0026ordm;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area under Forest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYield Variability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Literacy Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfant Mortality Rate (IMR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultidimensional Poverty Index %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage man-days under NREGS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eEGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e72.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e37142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e14.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eEJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e84.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e27786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e43.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eEKH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e12.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e53.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n 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\u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e4.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e70.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n 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\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e10.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e59.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e74882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e31.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eSGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e87.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e37059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eSWGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e59.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e46197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e18.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eSWKH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e67.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e27894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e40.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e238.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eWGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e12.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e62.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e119067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eWJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e70.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e72370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e52.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eWKH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e17.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e65.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e85477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e52.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e237.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Here, AV = actual value and NV = normalized value\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2. \u0026nbsp;Weights Assigned\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe weights are assigned to each of the eight indicators according to their importance in determining the vulnerability (Table 3). \u0026nbsp;This process involved conducting a literature review and consulting with stakeholders and experts. For Meghalaya, yield variability of major foodgrains (rice, wheat and maize) was assigned the highest weightage (WI = 30) as it was felt that it is the only indicator representing agricultural sector which is the single major contributor to the States GDP (22%) on which majority of the State\u0026rsquo;s population depends on besides, directly being a climate sensitive parameter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second highest weightage was assigned to percentage of area with slope greater than 30 degrees (WI=20). The topography of the State and the hilly terrain adds to the sensitivity of the area to climate change impacts. The spatial distribution of the steep slopes coincides with areas receiving heavy rainfall, making the region vulnerable to soil erosion, landslides, sedimentation in low lands, causing damage to agricultural lands and infrastructures. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndicator with third highest weightage assigned was the percentage of area under forest (WI = 18). Although the State currently has a good percentage of area under forest, the inherent concerns continue to exist in the form of shifting agriculture, logging, mining and other human activities which have been responsible for fragmentation, destruction and degradation of the forests in the State. Further, the future projections based on a scientific study suggests that 70% of the forested grids would become extremely vulnerable in the long term under RCP 8.5 scenario [17].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The indicator of households living below poverty line was assigned the fourth highest weightage (WI = 17). Meghalaya has around 12% of its population and over 2 lakh households living under the BPL. \u0026nbsp;The impacts of climate change are worst felt by the economically marginalized section of the society; thus, it was necessary to give higher weightage to this indicator.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe lowest weightage was attributed to female literacy rate (WI =0.5) and population density (WI = 1.0). Female literacy rate was the only indicator which captures the gender perspective and has a negative relationship with adaptive capacity. In Meghalaya, the female literacy rate is at 72.89% which is above the national average (65.46%). Meghalaya has a population density of only 132 persons per sq.km; thus, this demographic indicator was decided not to be an important contributor to the State\u0026rsquo;s vulnerability since it is less than one-third of the national average.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eWeights assigned to indicators and sub-indicators and the weights to be multiplied with normalized scores\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeights (WI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeights to be multiplied with normalized scores (WI*Wi)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e% area with \u0026gt; 30% Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e% area under Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eMGNREGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eInfant Mortality Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eFemale Literacy Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003ePopulation Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eBPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eYield Variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.\tResult and discussion ","content":"\u003ch2\u003e\u003cem\u003e4.1. Vulnerability profile, ranking, and category of districts\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eVulnerability index (VI) indicates the level of vulnerability of the area based on the index value to have a comparative ranking. The higher the value of VI of the particular area, the higher will be the vulnerability. For the State of Meghalaya, comparative ranking is being carried out for eleven districts based on the eight indicators. The result of the ranking for the State as mentioned in Table 4, depicted that West Khasi Hills is the most vulnerable district with the highest vulnerability index value (0.58) and ranks 1 among the 10 districts of Meghalaya followed by East Garo Hills (0.49) at rank 2, district of West Garo Hills (0.46) at rank 3 and East Khasi Hills (0.45) at rank 4 whereas the district of East Jaintia Hills with a VI value of 0.27 was found to be relatively the least vulnerable district.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, based on the vulnerability index value, the districts have been categorized into 3 classes namely High (0.47-0.58), Medium (0.38-0.46), and Low (0.27-0.37). Both the districts of West Khasi Hills and East Garo Hills fall under the \u0026ldquo;High\u0026rdquo; category. The districts of West Garo Hills and East Khasi Hills fall under the \u0026ldquo;Medium\u0026rdquo; category followed by Ri Bhoi, West Jaintia Hills, South Garo Hills, North Garo Hills, South West Garo Hills, South West Khasi Hills and East Jaintia Hills fall under the \u0026ldquo;Low\u0026rdquo; category (Table 4). In Figure 3, the map illustrates the vulnerability ranking of various districts in Meghalaya, India, based on a specific vulnerability assessment, with each district color-coded to indicate its relative vulnerability level. The scale ranges from 1 (highest vulnerability) to 11 (lowest vulnerability), where darker colors represent higher vulnerability and lighter colors indicate lower vulnerability. Similarly, Figure 4 depicts the vulnerability categories of the districts, using three color codes: high vulnerability (0.47 - 0.58), shaded in dark red for areas most at risk; medium vulnerability (0.37 - 0.47), shown in light brown for moderate risk; and low vulnerability (0.27 - 0.37), depicted in light yellow for regions with lower risk. Districts with higher vulnerability (darker shades) are influenced by factors such as steep slopes, reduced forest cover, high food grain yield variability, and poverty, making them more susceptible to climate and socio-economic challenges. In contrast, districts with lower vulnerability (lighter shades) tend to have better adaptive capacities due to improved literacy, better healthcare access, and economic stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eVulnerability index values, corresponding ranks, and categories of districts in the state of Meghalaya\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistricts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVulnerability Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRanking of Districts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ebased on VI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVulnerability Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWest Khasi Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eEast Garo Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWest Garo Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eEast Khasi Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eRi Bhoi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 126px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eWest Jaintia Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSouth Garo Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNorth Garo Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSouth West Garo Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSouth West Khasi Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eEast Jaintia Hills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cem\u003e4.2. Drivers of Vulnerability\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e4.2.1. Overall Vulnerability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDrivers of vulnerability were identified by the contribution of each indicator to vulnerability. Based on the percentage contribution of each indicator across all districts to the aggregate vulnerability index, it was found that a lack of area under forest contributes the most to overall vulnerability, accounting for 26.38% followed by steepness of the slope, contributing 21.21%, and a high rate of Multidimensional Poverty Index at 19.98%. Additional factors include high food grain of yield variability (18.75%), a low average number of man-days under the National Rural Employment Guarantee Scheme (NREGS) at 10.23%, a high infant mortality rate (1.6%), a high female literacy rate (1.04%), and high population density (0.78%), as illustrated in Figure 5.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.2.2. \u0026nbsp; \u0026nbsp; \u0026nbsp;Major Drivers of Vulnerability at District level\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDuring the assessment of the integrated vulnerability, it was found that West Khasi Hills (0.58) was the most vulnerable district, followed by East Garo Hills (0.49), and West Garo Hills (0.46) and all three districts are categorized by three major drivers as mentioned in Table 5. A closer analysis reflects that the three districts (WKH, EGH, and WGH) ranking highest in the integrated vulnerability assessment share two major common drivers, i.e., steepness of slope and a lack of area under forest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eDistrict Vulnerability Index and their major drivers\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict (VI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor Drivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eWest Khasi Hills (0.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026middot; Steepness of slope (0.20)\u003c/p\u003e\n \u003cp\u003e\u0026middot; High rate of Multidimensional Poverty Index (0.17)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Lack of area under forest (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eEast Garo Hills (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026middot; High food grain of Yield Variability (0.30)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Lack of area under forest (0.08)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Steepness of slope (0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eWest Garo Hills (0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026middot; Lack of area under forest (0.13)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Steepness of slope (0.12)\u003c/p\u003e\n \u003cp\u003e\u0026middot; High food grain of Yield Variability (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 shows the contribution of each indicator to sectoral vulnerability on a district-by-district basis.\u0026nbsp;The drivers with a contribution of more than 0.06 index value have been highlighted. These represent the specific areas or gaps in each district that require attention through strategic development plans and adaptation actions.\u0026nbsp;Figure 6 is a stacked bar diagram illustrating the contribution of each indicator to the vulnerability of each district. This visual representation highlights the key indicators for planning and policy-making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e District-wise contribution of each indicator to the sectoral vulnerability\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"11\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntegrated Vulnerability Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEJH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEKH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWKH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWJH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWKH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% Area with Slope \u0026gt;30% (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% Area under Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYield Variability (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale Literacy Rate (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIMR (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultidimensional Poverty Index % (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage man-days under NREGS (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistrict Total Vulnerability Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eClimate change vulnerability assessments are critical tools for identifying regions and communities at risk from climate-related hazards. These assessments support decision-making for adaptation planning and policy development. To provide a comparative analysis of vulnerability assessments conducted in Meghalaya (India) and other regions globally, a comparative statement highlighting methodologies, scales, indicators, and focus areas is presented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeghalaya\u0026rsquo;s District Level Vulnerability Assessment offers hyper-local insights by assessing vulnerability at the district level. Uses 15 indicators covering exposure, sensitivity, and adaptive capacity. Directly supports the State Action Plan on Climate Change (SAPCC) with tailored recommendations.\u003c/p\u003e\n\u003cp\u003eBangladesh Climate Change Strategy and Action Plan (BCCSAP, 2009) focuses on both national and local vulnerabilities, especially in coastal areas and flood-prone regions. Emphasizes adaptation strategies for agriculture and coastal management. It integrates climate risk with socio-economic development priorities to guide national adaptation efforts [12]. Nepal Vulnerability and Risk Assessment (2010\u0026ndash;2019): Employs participatory approaches and the CRiSTAL tool to map district-level risks. Combines local surveys with community-based data to address mountain-specific vulnerabilities. Provides a baseline for iterative improvements in adaptive capacity over time [24]. Bhutan Climate Vulnerability Baseline Assessment (2019): focuses on sub-district (Gewog) analysis, emphasizing the link between climate impacts and local livelihoods. Assesses vulnerabilities related to agriculture, glacier melt, and biodiversity. Uses a livelihood-based approach tailored to Bhutan\u0026rsquo;s unique geographical context [22]. Philippines Local Climate Change Action Plan (LCCAP) developed at the village (barangay) level, integrating detailed GIS analysis with local climate projections. Bridges climate adaptation strategies directly with local development and planning processes. Emphasizes asset exposure to inform targeted, community-level adaptation measures [4]. Vietnam GIZ Climate Change and Vulnerability Mapping (2020) utilizes the GIZ Sourcebook methodology for comprehensive mapping of climate vulnerabilities. Focuses on coastal, deltaic, and agricultural vulnerabilities specific to Vietnam\u0026rsquo;s geography. Provides spatial data that inform targeted adaptation strategies in vulnerable provinces [7].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfrica Adaptation Initiative (AAI) Assessments (2018) covers multiple countries across Africa using multi-criteria analysis and household surveys. Targets drought-prone and food-insecure regions with tailored adaptation financing strategies. Facilitates regional policy dialogue by highlighting cross-country vulnerabilities and priorities [2]. United States Climate Resilience Toolkit (2021) provides interactive web tools and comprehensive social vulnerability indices for community planning. Aggregates climate projections, case studies, and decision-support data for regional resilience strategies. Designed to assist local governments and communities in proactive adaptation planning [18]. European Union \u0026ndash; EU Climate-ADAPT (2021) integrates sector-specific climate models with detailed socio-economic data at multiple administrative scale. Supports urban planning and public health strategies through a robust, data-driven platform. Aligns national adaptation efforts with EU-wide climate policies and funding mechanisms [9]. Global Climate Risk Index (2021) Ranks countries based on the historical impacts of extreme weather events and climate-related disasters. Focuses on empirical data to highlight immediate climate risks and disaster vulnerabilities. Emphasizes the human and economic impacts of climate events to inform global resilience efforts [8].\u003c/p\u003e\n\u003cp\u003eMeghalaya\u0026apos;s district-level climate vulnerability assessment aligns with global best practices in methodology and focus, yet stands out for its distinctive characteristics. The current assessment stands out due to its District-level granularity, offering hyper-local insights, unlike global or national assessments, which focus on broader administrative units. The assessment emphasizes localized issues like infrastructure and service delivery, while others often prioritize large-scale risks such as coastal flooding or national-level agricultural impacts. While the assessment uses a straightforward CVI, larger assessments leverage advanced statistical and geospatial modeling techniques. The assessment is designed for direct incorporation into the SAPCC, paralleling global best practices of linking vulnerability findings to actionable policies. The Meghalaya Assessment identifies central \u0026amp; western Meghalaya districts as key vulnerable areas, while the global and national assessments identify coastal (Bangladesh, Vietnam), arid (Africa), and urban (EU, US) regions as vulnerable. The dominant risk arising from limited infrastructure, education, and healthcare in Meghalaya and disaster risks (coastal zones), drought (Africa), and agriculture, as per global assessments.\u003c/p\u003e\n\u003cp\u003eAll assessments feature policy recommendations with the Meghalaya assessment targeting improvements in governance, infrastructure, and global assessments targeting disaster preparedness, adaptation strategies, urban resilience. \u0026nbsp;Similarly, the assessments provide a focus on climate impacts on agriculture, water resources, and public health.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable insights into the climate vulnerability of Meghalaya by employing a comprehensive district-level assessment, integrating biophysical and socio-economic indicators to highlight the multifaceted challenges faced by the state. Meghalaya\u0026apos;s unique geography, with its steep slopes, undulating terrain, and reliance on climate-sensitive sectors like agriculture and forestry, amplifies its susceptibility to climate variability. Indicators such as reduced forest cover, food grain yield variability, high rates of poverty (MPI), and healthcare disparities exemplified by infant mortality rates (IMR) underscore the interconnected nature of vulnerabilities across districts. The study\u0026apos;s findings reveal a strong need for targeted interventions to enhance resilience in high-risk districts such as West Khasi Hills and East Garo Hills, which are particularly impacted by factors like steep slopes and deforestation.\u003c/p\u003e\n\u003cp\u003eThe broader implications of this study emphasize the importance of integrating vulnerability assessments into climate adaptation planning and policy-making. Policymakers should prioritize district-specific strategies to strengthen adaptive capacities, such as improving healthcare and education infrastructure, promoting climate-resilient agricultural practices, and enhancing community participation in programs like NREGS. Investing in forest conservation and sustainable land-use practices is also critical to mitigate environmental risks. In practice, developing disaster-resilient infrastructure, ensuring equitable access to social safety nets, and conducting capacity-building programs for women and marginalized communities can significantly improve adaptive responses to climate impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor future research, this study suggests a need for hyper-localized vulnerability assessments with granular data to identify localized risks and resource gaps. Exploring long-term climatic impacts on biodiversity and forest ecosystems, as well as understanding the socio-economic effects of climate variability on vulnerable populations, can offer deeper insights. These efforts will not only strengthen Meghalaya\u0026apos;s resilience to climate challenges but also provide a replicable framework for other regions with similar geographic and socio-economic contexts. By bridging gaps between vulnerability assessment, policy-making, and on-the-ground actions, this study sets a foundation for sustainable and inclusive adaptation strategies that are integrated into the Meghalaya State Action Plan on Climate Change.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to those who contributed significantly to this research study. Firstly, we are immensely grateful to the Climate Change Program, Department of Science \u0026amp; Technology, Government of India, for their unwavering support throughout this project. Special thanks to IIT-Mandi, IIT-Guwahati, and CSTEP, whose expertise and guidance were invaluable in shaping this research. We also extend our heartfelt appreciation to the Chair and Members of the Meghalaya State Council on Climate Change and Sustainable Development for their insightful feedback and for facilitating access to essential resources and data. We sincerely thank the CEO and ED MBDA for ensuring resources were provided whenever needed. Lastly, we are grateful for the financial support from the National Mission for Sustaining the Himalayan Ecosystem (DST/CCP/NMSHE/SCCC-IHR/Meghalaya/224/2023 (G)), which enabled us to conduct this research. This work would not have been possible without the collaborative efforts and encouragement from all the mentioned individuals and institutions. Thank you all for your tremendous support and contribution.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eML and VL contributed to writing and refining the paper. AN and NW handled data collection and analysis. EW drafted the manuscript. JB and AC contributed to writing, reviewing, and approving the final version, and in funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paper has been developed with the funding support from the Department of Science \u0026amp; Technology, Government of India under the National Mission for Sustaining the Himalayan Ecosystem which was granted under the sanction order number DST/CCP/NMSHE/SCCC-IHR/Meghalaya/224/2023 (G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdger, W.N. 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A systematic review of literature. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, \u003cstrong\u003e17(2)\u003c/strong\u003e (2017). \u003c/li\u003e\n\u003cli\u003eUNDP Bhutan. Bhutan Vulnerability Baseline Assessment, Gross National Happiness Commission Secretariat Thimphu, Bhutan (2016). \u003c/li\u003e\n\u003cli\u003eUNFCC. \u0026ldquo;Climate Change: Impacts, Vulnerability and Adaptation in Developing Countries\u0026rdquo;. Bonn: UNFCCC. (a2007).\u003c/li\u003e\n\u003cli\u003eUnited Nations Development Programme (UNDP) Nepal. Vulnerability and Risk Assessment Report for Nepal. Kathmandu: UNDP Nepal (2015). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vulnerability, indicators, sensitivity, adaptive capacity, drivers","lastPublishedDoi":"10.21203/rs.3.rs-6110405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6110405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study assesses climate vulnerability at the district level in Meghalaya using a framework aligned with IPCC guidelines. Both biophysical and socio-economic indicators were employed, with normalized values calculated for comparability. Unequal weightage was assigned to the indicators based on their relative importance, as determined through expert consultation and literature review. The calculated vulnerability index ranked districts as highly, moderately, or least vulnerable. Results reveal that West Khasi Hills is the most vulnerable district, primarily due to steep slopes, high poverty rates, and reduced forest cover, while East Jaintia Hills is the least vulnerable. Key drivers of vulnerability, such as high yield variability, deforestation, and steep slopes, were highlighted. These findings underscore the need for targeted adaptation strategies and policy interventions, such as enhancing forest conservation, promoting livelihood stability, and fostering climate-resilient agricultural practices, particularly in highly vulnerable districts.","manuscriptTitle":"Cross-Sectoral Climate Vulnerability Assessment of Meghalaya at District Level","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 11:43:59","doi":"10.21203/rs.3.rs-6110405/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-14T11:47:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-13T13:01:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-13T04:18:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130238334023211865354685589998865682570","date":"2025-07-12T16:23:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206417484724828862512117350600380158179","date":"2025-05-28T13:55:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T08:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230192467298311755453923107091627452524","date":"2025-05-16T07:50:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64191227078897381013481284993017320555","date":"2025-04-24T07:00:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-20T13:36:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-20T04:07:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-16T03:47:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99de2794-4393-47e7-bb1f-f23f3b657452","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47422252,"name":"Earth and environmental sciences/Climate sciences"},{"id":47422253,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47422254,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-12-01T16:00:12+00:00","versionOfRecord":{"articleIdentity":"rs-6110405","link":"https://doi.org/10.1038/s41598-025-26651-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-27 15:57:11","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-04-22 11:43:59","video":"","vorDoi":"10.1038/s41598-025-26651-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26651-4","workflowStages":[]},"version":"v1","identity":"rs-6110405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6110405","identity":"rs-6110405","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}