Figure 4
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The Intergovernmental Panel on Climate Change (2007) reports that the number of extreme precipitation and temperature events in India are projected to increase in the short term. The negative effects of this on rural populations in India may include crop and livestock loss, livelihood risk, health and sanitation disruptions and shelter risk. Overse...
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Context 1
... poverty values calculated from the steps enumerated in Section 5 is mapped spatially over the states of North-East India. In addition, the distribution of poverty profiles of North-East India is represented in Table 2. From the table 2 and Figure 2, it can be observed that in the North East region of India, 34% of districts exhibit high to very high level of poverty. Around 22% of the districts exhibit moderate levels of poverty, while 43% of districts are distributed in the low to very low levels in the poverty index scale. The state with the highest levels of poverty are Meghalaya (57% of districts are in - high to very high poverty levels), followed by Sikkim (50% of districts are in high to very high levels of poverty). The State with the most number of districts having moderate levels of poverty is Manipur, (33% of districts). Manipur with 55% of the districts and Mizoram with 51% of the districts, have the least poverty. The poverty of the largest state North East, Assam, is spatially represented in Figure 3. The districts of Assam are evenly distributed on either ends of the poverty scale. Out of a total of 23 districts, 43% are low to very low poverty level and 39% are in the high to very high level of poverty. The main differentiating factor of these districts across the scale is their per capita income. The districts exhibiting the highest level of poverty are Dhubri and Kokrajhar which located at the western-most region of Assam and Nagaon, which is located in the middle of Assam. In the case of the State of Manipur (Figure 4), majority of districts (55%) exhibit low to very low level of poverty. The town of Imphal, the seat of Manipur Government and the surrounding districts exhibit low levels of poverty. Chandel is the poorest district in Manipur, with high levels of rural population and very high disparity of income. This district also shares a border with ...
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... This study contributes to the literature in several important ways. Presently, there are a few studies (e.g., Blaikie & Muldavin, 2014;Darlong & Barik, 2020;Nair et al., 2013;NITI Aayog, 2018a) that investigate the impact of NERCORMP on socio-economic aspects and natural resource management. However, a comprehensive assessment of the impact of NERCORMP is imperative, given a lack of discussion on broader challenges in the region associated with income uncertainties, social exclusions, market linkages and low capacity. ...
... The NERCORMP is one of the major externally funded projects in India, targeting sustainable livelihoods in north-eastern region (Bhandari, 2009;Blaikie & Muldavin, 2014;IFAD, 2018;Kumar et al., 2019;Nair et al., 2013;NITI Aayog, 2018a;Thapa & Hessel, 2017). It is a joint initiative in which North-eastern Council (NEC), Ministry of Development of North-eastern Region, represents the GoI; and IFAD functions as the specialised agency of the United Nations. ...
This article investigates the impact of North‐eastern Region Community Resource Management Project for Upland Areas (NERCORMP) and draws inferences on mechanism of change, context of an intervention and implementation design. Utilising data from three waves of surveys, we find that while there is evidence of significant changes in financial inclusion, productivity and social security, there are some issues in replication and sustenance aspects. Since the impact of intervention is observed across various domains such as socio‐economic situation and natural resource management, thus, we believe that this paper presents an important case study in understanding the intervention efficacy, relevance and sustenance.
... 26 These districts are also considered to be economically vulnerable and are poorer than other districts of Assam. 27 Some of these districts are also very prone to floods and disasters every year and this may have contributed to the lower socio-economic status of the residents which in turn contributed to the prevalence of nutritional issues. Some of these districts have also been facing an influx of Illegal immigrants in large numbers for decades. ...
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Methods: We utilized the data from the report of the National Family Health Survey, NFHS-4, (2015–16) and
NFHS-5 (2019–21). Trend and spatial analysis were used to identify the persistent and hotspot clusters/areas of
stunting, wasting, and underweight.
Results: Each of the northeastern states reported 22% or more stunted children in 2019–21, and at least 9% and
12% of children were wasted and underweight respectively. The hotspot clusters for stunting were found in the
district of Assam and Meghalaya in both NFHS-4, 2015–16, and NFHS-5, 2019–21. Jantai Hills, East Khasi Hills,
West Khasi Hills, and Ribhoi in Meghalaya, and Dhubri, Bongaigaon, and Barpeta in the district of Assam were
found persistent. And the hotspot cluster of underweight found persistent in both NFHS-4(2015–16) and NFHS-5
(2019–21) are Dhubri, Goalpara, Kokrajhar, Bongaigaon, Karimganj, and Darrang of Assam, and North Tripura of
Tripura.
Conclusion: Numerous districts have seen a rise in the prevalence of stunting, wasting, and underweight, in the
northeastern states of India. This finding suggests that district-focused programs should be strengthened and
sustained, and the outcomes of the study could help to target intervention programs and schemes aimed at
eliminating child malnutrition at the district level, particularly in hotspot clusters.
... As a consequence of inefficient tea-growing practices, excessive pesticide use, low soil fertility, and more erosion could all be caused by the use of too many pesticides, among other things (Van der Wal, 2008). Small farmers have less ability to adapt to changes in the climate than larger producers (Nair et al., 2013), who are under more stress from climate change. ...
India's economy is primarily reliant on tea, with Assam being the world's largest producer in both quantity and quality. Tea production is at risk from a number of social and environmental factors, but millions of people rely on it to make money. There is a paucity of information about tea production and the lives that go along with it. In addition, there has been a lack of multi-stakeholder assessment of the tea landscape's sensitivity to climate change. As a result, this research aims to fill in some of the gaps in our understanding of Assam's tea environment by using a sustainable living approach. Researchers did a thorough literature review (SLR). Tranfield covers the essentials of SLR (2003). The findings shed light on the lives of individuals in Assam communities who depend on the tea industry in all of the state's main tea-growing districts. It looks like there are a lot of farming techniques in place to maximise output, improve livelihoods, and manage the landscape effectively in the face of climate change. People who grow tea need to adapt their landscapes to changing climate conditions and get help from professional tea advisory groups to keep their businesses going for a long time, according to the findings of this study.
... Reckien et al. 2017), which are, considering the demographic shift of industrialized societies and their age structures in cities, very important for urban areas (Curriero et al. 2002). Poverty and low income as well as poor health have been shown to increase sensitivity in many cities and countries (Luber and McGeehin 2008;McGeehin and Mirabelli 2001;Nair et al. 2013), while factors such as the availability of personal air conditioning during heat waves (Luber and McGeehin 2008) and the degree of integration into the community or neighborhood (Klinenberg 2002(Klinenberg , 2013) are shown to lower excess mortality during heat. Factors that contribute to social vulnerability might differ depending on the stressor, such as heat waves, floods, or storm surges, but the list of Cutter et al. (2003) covers a wide range of factors from census data that have found to be important in many studies and for many stressors (Tapsell et al. 2010). ...
Mapping social vulnerability is a prominent way to identify regions in which the lack of capacity to cope with the impacts of weather extremes is nested in the social setting, aiding climate change adaptation for vulnerable residents, neighborhoods, or localities. Calculating social vulnerability usually involves the construction of a composite index, for which several construction methods have been suggested. However, thorough investigation of results across methods or applied weighting of vulnerability factors is largely missing. This study investigates the outcome of the variable addition—both with and without weighting of single vulnerability factors—and the variable reduction approach/model on social vulnerability indices calculated for New York City. Weighting is based on scientific assessment reports on climate change impacts in New York City. Additionally, the study calculates the outcome on social vulnerability when using either area-based (person/km²) or population-based (%) input data. The study reveals remarkable differences between indices particularly when using different methods but also when using different metrics as input data. The variable addition model has deductive advantages, whereas the variable reduction model is useful when the strength of factors of social vulnerability is unknown. The use of area-based data seems preferable to population-based data when differences are taken as a measure of credibility and quality. Results are important for all forms of vulnerability mapping using index construction techniques.
Electronic supplementary material
The online version of this article (10.1007/s10113-017-1273-7) contains supplementary material, which is available to authorized users.
... Poor tea production practices can cause adverse environmental impacts through land clearance for cultivating tea crops, high energy demands for crop processing, excessive pesticide application to counteract pest attacks, reduced soil fertility (which increases agrochemical use for soil nutrient fertilisation) and increased occurrence of erosion (Van der Wal, 2008). All producers are under added pressure from increasing climate change susceptibility, but smallholders have a lower capacity to adapt to climate threats (Nair et al., 2013). ...
p>Tea plays a pivotal role in India's national economy, with the state of Assam the world's largest regional producer of black tea. However, various socio-environmental factors are threatening tea production and the livelihoods of millions of people reliant upon the industry. Little documentation exists which provides comprehensive spatial insights into tea production practices and associated livelihoods. Additionally, vulnerability from climate change to those livelihoods dependent upon the tea landscape has received limited multi-level stakeholder qualification. Consequently, this paper goes towards addressing this knowledge deficit through adopting a sustainable livelihoods approach for investigating the tea landscape of Assam. Mixed social survey methods were used to document responses from multiple stakeholder groups representing managers of commercial plantations, smallholders and tea workers. Results provide a comprehensive insight into the livelihoods of those reliant upon tea production activities across the four major tea growing regions of Assam. Outputs indicate that varying production practices are in place to optimise yield, increase livelihood resilience and manage the landscape effectively under a changing climate, with tea workers possessing varying diversity in assets to support their livelihoods. Outcomes from this research highlight the need for adaptable and climate-smart landscape activities, with active support from expert tea advisory agencies, to promote long-term socio-environmental sustainable cultivation of tea under changing climatic conditions.</p
... Methodology adopted by Nair et al. [29] was used for generating poverty map, which was brought in GIS domain (Fig. 2). The population density map was generated with the help of map. 1 Poverty index and population density index map data corresponding to fire incidences was utilized (Fig. 3). ...
... Based upon the literature of Nair et al. [29] on poverty, it has categorized poverty index into five classes of very low (0-1), low (1-2), medium (2-3), high (3)(4) and very high (4)(5). ...
Monitoring and management of forest fire is imperative in India where 50% of forest cover is prone to the fire. The study aims for applying the geospatial technology towards forest fire characterization and evaluation of relationship with meteorological thematic layers. Spatial analysis of forest fires in the state of Arunachal Pradesh was carried out based upon the decadal (2008–2016) forest fire count datasets, which was assessed for spatial variability over the known Himalayan biodiversity hotspot in diverse geographical and socio-economic gradients. Result suggested that Kameng districts had maximum fire incidences (25.2%) whereas it has 15.2% of state forest, established the districts as ‘forest fire hotspot’ in the state. Maximum number of incidences (88%) occurred in areas of low elevation (< 1500 m). There was high correlation with socio-economy where 42.3% forest fire points falls in high poverty index areas and 73% of fire incidences in the areas having population density 6–50. All districts showed high fire incidences, therefore an urgent intervention is greatly required by the policy makers towards conservation and management of forest fire prevention and control by adopting focused intervention, strategic allocation of limited resources in potent areas in order to safeguard Himalayan region of highest biodiversity.
... • End-point indicators (cover conditions and processes that only operate well if the causes leading up to them are also operating well) [46] • General indicators and indices should be downscaled to site-specific indices to render them functional [47] • Accurate [44] • The utility of national-level indicators in lower-resolution studies [48] • Holistic perspective: include review of the whole system as well as its parts; scope: define the space of study large enough to include not only local but also long distance impacts on people and ecosystems [42] Coverage ...
... • Be indicative, significant [36], Method of computation (values used to represent the indicator) [38] • Have a fixed set of tested indicators [43] • Limit bias or subjectivity of data collator [33] • Be scientifically valid [31], Validity, accuracy, data comparability, analytically and statistically sound [17], Deliver risk information in a scientifically acceptable way [49] • Use systematical risk assessment [52] • Verify: Evaluate validity and plausible outcome, compare with findings of relevant studies, comparable past event, case study, explaining relationships, significant statistical relationships [45] • Have empirical proof of validity and/or refinement of the methodology are needed; the selection of indicators must be guided by correlations with proxy measures and not by a theoretical framework or expert opinion; compared with independent measurements of same variable; many weaknesses of social vulnerability indices can be eliminated with a sound index design that adheres to basic statistical rules and that incorporates findings from disciplines such as environmental economics; the performed analyses underscore the need for conducting uncertainty and sensitivity analyses; by doing so, initially subjective design choices can be corrected, modified, and ultimately justified, which in turn increases the reliability of the index [ • Be direct [35] • Have a narrow range [51], Have an appropriate scope, only measure important key-elements instead of trying to indicate all aspects [17] • Use indicators that have already been recognized by researchers as important contributors to social vulnerability [33] • Be dynamic: Multiple pressures, processes affecting factors of vulnerability [45] • Avoid conditions such as the presence of multi-collinearity and eliminate redundant variables from a statistical and index design standpoint; the incorporation of a multitude of indicators though-an attempt to increase the dimensionality of an index-makes an index highly prone to uncertainties; with a large number of indicators, the complexity of the aggregation structure often increases as well; indices suffer from redundant indicators and in most cases the latter is rarely suited to balance indicator representation [2] • Realize that interrelationships between indicators might be an ignored factor in the analysis [48] Transformation ...
Despite a long developmental history of water-related disaster risk indicators, there is still no consensus or reliable system for selecting objective data, no methodological system for choosing and verifying the relevancy of water-related disaster risk indicators, and no linking results back to root causes or addressing possible impacts on policies or actors to instigate change.
Global policy documents such as the Sendai Framework for Disaster Risk Reduction (DRR) 2015–2013 [1] emphasize the urgent need for indicators capable of measuring risk reduction. However, developing and determining risk indicators faces many issues. Most disaster risk indices published do not yet include a basic overview of what data was used and how it was collected, let alone provide a systematic explanation of why each indicator was included, and why others were not. This consequently complicates linking the findings to their potential policy impacts. It also complicates the providing of clear-cut recommendations for improving resilience, which is a common intent of disaster risk indices.
This study, which focuses on water-related hazards, aims to provide disaster managers with a set of criteria for evaluating existing datasets used in disaster risk indices, index construction methods, and the links back to policy impacts. So far, there has been no comprehensive overview of indicator requirements or scoring systems. Previous studies concerning indicator evaluating metrics [2] have fewer metrics and have not yet addressed the different tiers of requirements, namely objective indicator data quality, methodological/epistemological aspects of index composition, and, most importantly, policy and actors of change (impact requirements). Further testing of these metrics in local studies can lead to the greatly needed scientific justification for indicator selection and can enhance index robustness.
The results aid in developing an evaluation system to address issues of data availability and the comparability of commonly used indicator sources, such as the World Bank. Once indicators can be scientifically linked to impacts through policy devices, national governments or other actors can become more likely to claim ownership of the data management of indicators. Future studies should expand this evaluation system to other natural hazards and focus on investigating the links between indicators and DRR in order to further validate indicator selection robustly.
This project report provides a detailed description of the drivers of deforestation in Meghalaya. This report is available at
https://www.indiawaterportal.org/articles/identification-drivers-deforestation-meghalaya#:~:text=An%20extensive%20literature%20review%20reveals,irrespective%20of%20rank%2F%20impact)
Fire is among major threats to the world's forests, leading to tremendous biodiversity losses. Forest fire in India has greatly increased in the last few decades; the state of Arunachal Pradesh, a recognized Himalayan biodiversity hotspot, is extremely prone to this disaster. The objective of this study was to develop GIS integrated mapping of direct and indirect factors, leading to a predictive model to identify settlements/villages for the strategic allocation of resources towards damage mitigation and control. Initial hotspots were generated by integrating factors of socio-economy, geography and climate, using differential weightage. The intersection of these with fire data, led to identification of final hotspots within the predictive model. The model was improved by linking it to a mobile App and the WebGIS portal. Of the 5258 settlements/villages, a total of 560 were found to be at high fire risk. Percentage correlation increased from 63 to 74, after data revision through the App. A focused intervention on predicted villages was undertaken, resulting in a decrease of 31% of fire incidence in comparison of last five years (2015-20) data. Such advanced information about fire disaster with optimal use of limited resources was greatly helpful, and helped protect the rich Himalayan biodiversity.
