International Journal of IT, Engineering and Applied Sciences Research (IJIEASR) ISSN: Volume 2, No. 11, November 2013

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1 1 Effect of Climate Change on Out-migration in Uttarakhand: A Statistical Analysis based on Cox-proportional Hazard Model for Kimsar Area and Rajaji National Park N. K. Kamboj, Research Scholar, Department of Statistics, H.N.B.G.U., Srinagar (Garhwal) Uttarakhand, India Prof. O.K. Belwal, Department of Statistics, H.N.B.G.U., Srinagar (Garhwal) Uttarakhand, India ABSTRACT Environmental variables and Demographic variables are strongly related with each other. Human migration patterns have long been a reflection of climatic changes and natural disasters. Landslides, forest fires, cloudbursts and flash-floods are seasonal in nature and these strikes at a certain period of the year with high frequency in Uttarakhand. Earthquake is the most devastating disaster in the mountains and is unpredictable. In this paper the particular interest is whether changes in environmental variables influence out-migration from rural areas. This highlights the importance of assessing the possible migration responses to climate change in the context of exposure to risk and adaptive capacity in the particular area. And another interest is to find the reasons which are responsible for out-migration & climate changes in the study area. A Cox Proportional Hazard model approach is used in the analysis. For this study the data is collected through questionnaires with a sample size of 270 individuals from the study area. Some statistical tools are used with the help of statistical software. Keywords Out-migration, natural disasters, Hazard model, Industrial pollution. Introduction Climate change is a major global environmental and developmental problem and also an issue of great concern to all countries irrespective of their size or level of development. Though all possible consequences of climate change are yet to be understood, it is now established that adverse impacts are likely from increased frequency of extreme weather, floods and droughts, and submergence of coastal areas due to sea level rise and extreme climate variability. Impacts of climate change are also likely to be iniquitous the poor, women, the aged, and the very young especially in underdeveloped or developing area contexts are relatively more vulnerable due to their greater dependence on climate-sensitive sectors like agriculture, fisheries, and forestry for their livelihoods or their limited adaptive capacity. The poor status of infrastructure and essential services in most of the poverty-affected areas also limits their ability to cope with adverse impacts of climate change. The monsoon rains hit North India by June 15, almost two weeks in advance but no one could have imagined what was about to come. Yes it was the Himalayan Tsunami, a spell of deadly cloudburst, flood, glacier burst in Kedarnath, the sacred land of gods, or Dev Bhoomi as we call it, was totally devastated. The disaster can truly be described as a one of epic proportions. What made it worse was the human toll it took in form of pilgrims and tourists that flock to the hill State in the summer months of May/June for Char Dham Yatra before the rains arrive in July. The June flood disaster in Uttarakhand has badly hit the tourism, the largest employment providing sector in the hill state, causing a steep fall of 85 per cent in tourist traffic. Disaster struck on the night of June 16, 2013 when a confluence of two weather systems- one of the north western lies collided with moisture-laden monsoon winds over Uttarakhand and lead to a series of cloud bursts which made rivers like Alaknanda, Mandakani and Bhagirathi burst banks, overflow and destroy anything in their path. The fury of nature has been nothing short of a catastrophe with thousands reportedly killed and an equal number of people missing, while around one lakh people were stranded in Uttarakhand with roads washed away. They were stranded on hilltops in difficult terrain amid rain with no supply of food or water for days. This was all about the climate change, in-fact this is the story of every year in Uttarakhand but not as much as this year. Land sliding, earthquake and clouds bursting are the common in hilly areas of Uttarakhand. The study area is one of those areas where people are suffering from climatic conditions and other natural problems like with reference to the study area there are some wild animal problems also. So these sorts of problems are working as Push Factors in the context of out-migration in Uttarakhand. OBJECTIVES To analyze that are the changes in environmental variables influence out-migration from the study area?

2 2 To find the reasons which are responsible for outmigration & climate changes in the study area. To fit a Cox-proportional Hazard model on problem variables. STUDY AREA & METHODOLOGY The study area falls in Yamkeshwar Developmental Block of the Pauri Garhwal District of the Uttarakhand State, India. The Kimsar Area comprises 28 small villages that are located in close proximity of the Chilla Range of Rajaji National Park (Figure 1 about here). The area consists of highly undulating mountainous landscape are now isolated from other revenue lands by a 16 Km stretch of protected forest of RNP. The remote and in accessible villages where regular visit cannot be feasible mainly due to location on highly steeped, rough terrain, wild animals and wild life mafia only 20 villages are selected for extensive survey and monitoring, to know their views about the changing climatic conditions and their outmigration from the Kimsar area. After selection of villages, 270 individuals were investigated at random with the help of questionnaire. For the analysis and interpretation of data statistical software are used. To show the relationship of climatic conditions and out-migration Cox Proportional Hazard model is used. The study shows a positive relationship between climatic changes and high risk of out-migration. DATA ANALYSIS AND INTERPRETATION The study is based on primary data collected from the field, to analyze the problem there are three types of variables are used first variable is Migration-time time based variable which is responsible for the migration period and it is also used to defined the type of migration weather is it temporary migration or a permanent migration, second variable Status is based on the respondents view in a binary form which reflects the status of the variable Migration-time. The third variable is the explanatory variable Climate this variable is a consolidated form of other variables like crop production, changes in pattern of rain fall, clouds bursting, Land sliding, forest fire and other variables. The values of climate variable are 1 if all the climatic variables have been changed with respect to past time in problem creating manner for the people and 0 otherwise (Table 1 about here). The formula for the Cox Proportional Hazard model is pp h(tt, XX) = h 0 (tt) exp ( ii=1 ββ ii XX ii ) where XX = (XX 1, XX 2, XX pp ) are the explanatory variables. This model is product of two quantities, h 0 (tt) is called the baseline pp hazard and exp ( ii=1 ββ ii XX ii ) is exponential form. The baseline hazard does not depend on X but only on t time. For this study the null hypothesis H 0 : There is no outmigration due to climate changes in the study area. Where the alternative hypothesis, H 1 : There is out-migration due to climate changes in the study area. The model equation for the problem variables are given by: h(mmmmmmmmmmmmmmmmmm_tttttttt, CCCCCCCCCCCCCC) = h 0 (MMMMMMMMMMMMMMMMMM_tttttttt)e β.climate Measure of model fit: smaller the value Log Likelihood function better the fit, Initially (-2 Log likelihood =) in next block it is which is the lesser one (Table 2 about here). Measure of hazard or risk to unit change in the variable, everything else staying constant. The values of β more than 1 shows the grater hazard or risk ratio here β = and exp(β) = which shows the very high risk with reference to climate change(table 3 about here). The p-value is 0.00 which is significant so the H 0 is rejected and alternative hypothesis takes place there is high risk of out-migration due to climate change in the study area. CONCLUSIONS & DISCUSSION Thus the analysis shows that there are out-migration patterns in study area due to climate changes. According to survey conducted in the study area it is also influenced by the industrial pollution of nearly located Industrial area of Haridwar District, so there are some factors for climate changes industrial pollution and in the past time there was more greenery in comparison current time, forest fire, global warming. The crop production is very less in comparison to the past time, there exists the changes in pattern of rain fall; cloud bursting and lands sliding are the very common in the rainy season. The villages in the study area are going to be empty or with less human population due to some problems like social, economical, educational and wild animal problems of RNP. Every human being wants to be happy in any aspect of life. So the people of the Kimsar area have to migrate for the betterment of their future. Kimsar area is a very beautiful area with rich bio-diversity and natural beauty of Rajaji National Park. There are the possibilities to make this area well developed and out resources can provide the better livelihood for the villagers. The people can be included in the RNP activities by some changes in the park rules. So they will be able to understand the park values. For the individual point of view migration is good for him/her but as a global issue it is creating an unbalance in future surviving. This study was the representative study of the Himalayan region for out-migration in India. All the hilly areas are suffering from out-migration with reference to some special problems. Thus these areas require massive schemes and attention to prevent the disasters and other problems not only for human beings, for the all living and nonliving creature of God.

3 3 Table 1: Variable Definitions S.N. Basic Variables Description New derived Values variable 1. Migration-Time Migration period in months - Scale value 2. Status Defining migration (people are - 1 = Yes migrating or not) 3. Crop-production Crop production order, respondents view 4. Rain-fall Changes in the pattern of rain fall 5. Cloud bursting & Land Respondents are suffering or sliding not 6. Wild animals of RNP Respondents are suffering or not Table 2: Omnibus Tests of Model Coefficients -2 Log Likelihood Climate (derived variable from all the variables S.N. 3 to 6, the values of climate variable is based on respondents view in suffering manner then it holds the value 1 and 0 otherwise) 0= Otherwise 1 = Yes 0= Otherwise Overall (score) Change From Previous Step Change From Previous Block Chi-square df Sig. Chi-square df Sig. Chi-square df Sig Initial Log Likelihood function: -2 Log likelihood: Table 3: Variables in the Equation Climate B SE Wald df Sig. Exp(B) Figure1:StudyArea

4 4 Image1: India; Image: 2 Uttarakhand; Image: 3 Pauri District

5 5 REFERENCES [1] Uttarakhand State Action Plan for Climate Change, Government of Uttarakhand, Report: [2] Kumar, K. & Agarwal, S.C., An Econometric Analysis of Causes of Migration in Assam. Demography India, Vol. 32, No [3] India Unlocking Opportunities for Forest- Dependent People in India, Main Report: Volume I February 6, [4] J. Fox, Applied Regression Analysis, Linear Models, and Related Methods, Sage Publications, [5]