Nitasha Sharma

Texas A&M University, College Station, Texas, United States

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Publications (5)7.77 Total impact

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    ABSTRACT: 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. Overseas Development Assistance, in the form of aid, will help rural communities to counter these impacts; several development agencies already require that the adaptation to climate change risks be included as project activities in the aid programme. However, it is often difficult to accurately target development aid in developing countries due to uneven and cluster-like development of areas. To help counter this problem, we developed a poverty index intended to help prioritize development aid towards communities at risk, in order of need. The district-wise poverty index was created for seven states of northeast India, a region with highly uneven development, and has been developed from data available from the North-East Data Bank (DoNER). The indicators were selected to adequately represent the poverty of the people as well as to act as a prioritizing mechanism in a data scarce region. The inclusion of a Gini coefficient of land distribution is new to poverty indexes, and helps to capture the pattern of highly unequal land distribution in northeast India, which in turn affects the distribution of income. Although primarily developed for northeast India, the index can be used in other developing countries with imbalances in regional development. If the biophysical factors affecting vulnerability are known, this index can be used in a weighted combination with vulnerability.
    Climate and Development 01/2013; 5(1). · 1.21 Impact Factor
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    ABSTRACT: Climate change vulnerability profiles are developed at the district level for agriculture, water and forest sectors for the North East region of India for the current and projected future climates. An index-based approach was used where a set of indicators that represent key sectors of vulnerability (agriculture, forest, water) is selected using the statistical technique principal component analysis. The impacts of climate change on key sectors as represented by the changes in the indicators were derived from impact assessment models. These impacted indicators were utilized for the calculation of the future vulnerability to climate change. Results indicate that majority of the districts in North East India are subject to climate induced vulnerability currently and in the near future. This is a first of its kind study that exhibits ranking of districts of North East India on the basis of the vulnerability index values. The objective of such ranking is to assist in: (i) identifying and prioritizing the most vulnerable sectors and districts; (ii) identifying adaptation interventions, and (iii) mainstreaming adaptation in development programmes.
    Current science 08/2011; Vol.101(No.3):384-394. · 0.91 Impact Factor
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    ABSTRACT: We examine the potential for adaptation to climate change in Indian forests, and derive the macroeconomic implications of forest impacts and adaptation in India. The study is conducted by integrating results from the dynamic global vegetation model IBIS and the computable general equilibrium model GRACE-IN, which estimates macroeconomic implications for six zones of India. By comparing a reference scenario without climate change with a climate impact scenario based on the IPCC A2-scenario, we find major variations in the pattern of change across zones. Biomass stock increases in all zones but the Central zone. The increase in biomass growth is smaller, and declines in one more zone, South zone, despite higher stock. In the four zones with increases in biomass growth, harvest increases by only approximately 1/3 of the change in biomass growth. This is due to two market effects of increased biomass growth. One is that an increase in biomass growth encourages more harvest given other things being equal. The other is that more harvest leads to higher supply of timber, which lowers market prices. As a result, also the rent on forested land decreases. The lower prices and rent discourage more harvest even though they may induce higher demand, which increases the pressure on harvest. In a less perfect world than the model describes these two effects may contribute to an increase in the risk of deforestation because of higher biomass growth. Furthermore, higher harvest demands more labor and capital input in the forestry sector. Given total supply of labor and capital, this increases the cost of production in all the other sectors, although very little indeed. Forestry dependent communities with declining biomass growth may, however, experience local unemployment as a result. KeywordsForests–Impacts–India–Climate change–Integrated modeling–Macroeconomics
    Mitigation and Adaptation Strategies for Global Change 01/2011; 16(2):229-245. · 1.86 Impact Factor
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    ABSTRACT: Due to large scale afforestation programs and forest conservation legislations, India’s total forest area seems to have stabilized or even increased. In spite of such efforts, forest fragmentation and degradation continues, with forests being subject to increased pressure due to anthropogenic factors. Such fragmentation and degradation is leading to the forest cover to change from very dense to moderately dense and open forest and 253km2 of very dense forest has been converted to moderately dense forest, open forest, scrub and non-forest (during 2005–2007). Similarly, there has been a degradation of 4,120km2 of moderately dense forest to open forest, scrub and non-forest resulting in a net loss of 936km2 of moderately dense forest. Additionally, 4,335km2 of open forest have degraded to scrub and non-forest. Coupled with pressure due to anthropogenic factors, climate change is likely to be an added stress on forests. Forest sector programs and policies are major factors that determine the status of forests and potentially resilience to projected impacts of climate change. An attempt is made to review the forest policies and programs and their implications for the status of forests and for vulnerability of forests to projected climate change. The study concludes that forest conservation and development policies and programs need to be oriented to incorporate climate change impacts, vulnerability and adaptation. KeywordsForest policies–Pressures on land–Forest status–Climate change impacts–Vulnerability reduction–India
    Mitigation and Adaptation Strategies for Global Change 01/2011; 16(2):177-197. · 1.86 Impact Factor
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    ABSTRACT: Certain parts of the State of Nagaland situated in the northeastern region of India have been experiencing rainfall deficit over the past few years leading to severe drought-like conditions, which is likely to be aggravated under a climate change scenario. The state has already incurred considerable losses in the agricultural sector. Regional vulnerability assessments need to be carried out in order to help policy makers and planners formulate and implement effective drought management strategies. The present study uses an ‘index-based approach’ to quantify the climate variability-induced vulnerability of farmers in five villages of Dimapur district, Nagaland. Indicators, which are reflective of the exposure, sensitivity and adaptive capacity of the farmers to drought, were quantified on the basis of primary data generated through household surveys and participatory rural appraisal supplemented by secondary data in order to calculate a composite vulnerability index. The composite vulnerability index of village New Showba was found to be the least, while Zutovi, the highest. The overall results reveal that biophysical characteristics contribute the most to overall vulnerability. Some potential adaptation strategies were also identified based on observations and discussions with the villagers.
    Regional Environmental Change 13(1). · 1.95 Impact Factor