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Khalid, Mahmood and Rukh Impact of Climate Changes on Economic and Agricultural Value Added Share in GDP Ali Awais Khalid, Faisal Mahmood and Gul Rukh Lahore Business School, The University of Lahore, Lahore, Pakistan [email protected] ABSTRACT The purpose of this study is to investigate the effect of climate changes on agriculture sector and overall economies. Our main focus is on negative effects of climate changes on GDP and agricultural value added share in GDP. For the empirical analysis, we collected data of countries adversely affected by climate changes from World Bank development indicators over the period 1990-2014. Our dataset is panel data, so we applied OLS and fixed effects regression techniques. The statistical results of the fixed effects estimation method support the view that climate changes negatively affect the GDP of these countries. However, we did not find support for the effect of climate change on Agriculture value added share in the GDP. The findings of this study will help the society by directing them to focus more on climate changes as these changes are affecting most of the countries. There is a need to respond to these changes for survival of the developed and developing countries. This is one of the few studies which have investigated the effect of climate changes on Agriculture value added share in GDP by considering the most affected countries. Keywords: Carbon Emissions, Agricultural Value Added GDP Share, GDP. INTRODUCTION Global climate temperature has been increasing in recent years because of greenhouse gases emissions. International climate policy has been focusing on reduction of climate rise. This is an important element for the developed countries in the current climate conditions. It has been recognized that developed countries need to reduce their emissions to 80-90% of 1990 levels till 2050 (Ruamsuke, Dhakal, & Marpaung, 2015). Depending on the cumulative emissions accumulated in the atmosphere, the long-term global temperature stabilization level can be translated into a range of emissions levels by year with different chances of reaching the 2_Centigrade target in the next century. Why climate change has been the subject of research in recent years? The reason is that climate change has a profound effect on economic and social wellbeing of a country. Economic growth and future welfare is also affected by the climate change. The horizontal linkages of sectors also prove to be crucial in determining the effects of climate change (Ciscar et al., 2011; Tol, 2009). For example, due to the effect of Asian Management Research Journal 1(1) © 2016 SAMR 35 Khalid, Mahmood and Rukh climate change on agriculture, the irrigation needs may rise and water supply may decrease. One of the dynamic effects of climatic change and economic growth is capital accumulation. The main dynamic effect is via capital accumulation. If the savings rate is assumed constant, the amount of investment in an economy will reduce in case the climate change has a negative influence on the GDP of the country. These negative consequences of climate change for economies will lower the availability of investments and capital stock. Consequently, per capita GDP consumption of agricultural products will also decrease. The shortage of capital for agricultural and industrial needs due to lower GDP may also have adverse impact on development in technology and development of human capabilities through education and training programs. Second, savings behaviour should change due the significant climate changes as the glaciers are melting down and heat waves and storm are affecting these economies as glaciers are melting down,. Hence, to overcome financial problems arising due to future natural calamities, we need to accumulate the capital for maintaining the future GDP. This increased saving will be beneficial in future to compensate the losses due to climate changes. However, these changes in the climate have adverse impact on the capital productivity as lower returns are offered for investment. The effect of capital accumulation and savings are studied based on neo-classical growth theory. Climate change affects agriculture and food production in complex ways. Due to climate changes, conditions for agricultural production are changing. As a result, lower output affects the economic growth and the income of the producers and consumers. Since, the effect of climate changes depends on the nature of proxy we will use for climate changes, therefore, the consensus has not been developed so far. The existing studies, such as Creutzig et al. (2015), suggest that any change in rainfall and temperature affects the agricultural output and crops yields. Climate change will also affect the ability of individuals to use food by altering the conditions for food safety and changing the disease pressure from water, and food-borne diseases. The frequent heat waves, storms and floods will negatively affect the food production. However, an increase in temperature will increase the cultivatable land area that subsequently will increase the food production. Schmidhuber and Tubiello (2007) suggested that availability and access to food items is necessary for wellbeing of world population. This effect is significant in most of the developing countries in which peasants and small landowners produce the food on their land. Ito and Kurosaki (2009) explained that the change in weather reduces the food yield in developing countries. The small farmers are vulnerable to climate changes, as they do not have necessary capital Asian Management Research Journal 1(1) © 2016 SAMR 36 Khalid, Mahmood and Rukh stock to recover losses due to floods, storms and other natural calamities. The climate change can also affect the agriculture of any country. Mostly, the agriculture sector of developing countries contributes to a larger part of their GDP. Hence, it can be anticipated that developing countries will suffer more in the changing environment. The production of agricultural products is affected by higher temperatures, heat waves, changing weather patterns and high carbon dioxide levels in the atmosphere. However, the effect of these factors is positive in some countries and negative in others. The production and the quality of cultivated crops and their use of water are influenced directly by local climate variables and atmospheric Carbon Dioxide (CO2). Agriculture is particularly susceptible to climate change. Agriculture is the main user of land and water that plays a dominant economic role in many countries. However, in the existing literature, no study investigated the role of climate changes on GDP in the countries those are mostly affected by these climate change events (floods, storms and heat waves). Hence, this study contributes in the existing literature by empirically finding the answer to the following research question that how climate changes impact the GDP and the Agricultural value added share in the GDP? Our findings support our hypothesis that climate changes affect the GDP of these countries but we did not find convincing evidence regarding the effect of climate changes on Agriculture value added share in the GDP. This may be due to small share of the Agriculture sector towards GDP. Hence, these findings guide the policy makers to work for mitigation of these climate changes by signing different environmental protection agreement with developed countries for restriction of carbon emissions as the climate changes in future will significantly reduce the GDP of developing countries. LITERATURE REVIEW The existing literature shows the different effects of climate changes on health, agricultural and economic problems associated with these changes. Climate changes lead towards economic losses to the most vulnerable countries. These economic losses include the loss of GDP and the consumption due to reduced agricultural productivity or damages to infrastructure of these countries i.e. roads, bridges and other means of transportation of products from agricultural farms and markets. Mostly, existing studies discussed the role of climate change in determination of GDP and consumption of products. Second, strand of literature discussed the consumption and its impact on temperature and greenhouse gases, for example, increased consumption of electricity and Air conditioners increase the emission of these gases. Following studies explained different aspects of this relationship. Asian Management Research Journal 1(1) © 2016 SAMR 37 Khalid, Mahmood and Rukh Pindyck (2007) tested the relationship of change in temperature on GDP growth rate by expecting the long term impact of global warming on future GDP and consumption. However, other authors criticize future uncertainties associated with environment, as we cannot predict the future weather patterns. Tol (2013) was interested in determining the effect of climate change in last two centuries. Tol found that an increase in temperature has reduced the water resources and adversely affected the health. The author further reported the negative effect of these changes for poor countries until 1980s as the effect of climate is positive in 20th century but it turns negative in the 21st century that is associated with shortage of water as sea level rise, food shortage and biodiversity. Lo and Chow (2015) investigated the role of climate change on the wealth of the countries by empirical examination of 33 countries. They reported that 'the importance of climate change’ is positively associated with per capita GDP but negatively associated with the risk perception. The perception of these threats regarding weather changes leads towards provision of resources to decrease the future risk associated with these events. Ciscar et al. (2011) discussed the physical and economic effects of climate change in the Europe. They stressed the need for regional studies for effective policymaking. The author checked the impact of climate change on floods, agriculture, tourism and coastal areas and human health. They expect the rise in temperature in Europe that may reduce the 50% welfare growth of Europe because of losses, but the effect of these changes on agriculture is positive. The existing literature has also examined the effect of climate change on different factors including health (McMichael, Woodruff, & Hales, 2006; Patz, Campbell-Lendrum, Holloway, & Foley, 2005), agriculture and water resources (Darwin, Tsigas, Lewandrowski, & Raneses, 1995; M. Parry, 1992; M. L. Parry & Ruttan, 1991; Piao et al., 2010) and economic factors (Ciscar et al., 2011; Eboli, Parrado, & Roson, 2010; Kane, Reilly, & Tobey, 1992). Unfortunately, the consensus has not been developed so far. Hence, we will contribute to this stream of literature by examining the role of climate change in GDP and Agriculture value added empirically by choose the most vulnerable countries for these climate changes as this sample is not taken in the existing literature. RESEARCH DESIGN AND METHODOLOGY Population and Data Collection We have collected the data from two sources i.e. World Bank Development Indicators and Food and Agriculture Organization. Food and Agriculture organization provides the data about production and consumption of crops, environment, input prices and macro-economic variables affecting the agriculture sector. Specifically, we have collected the data of GDP and share of agriculture in GDP and control variables from the Food and Agriculture organization database. Asian Management Research Journal 1(1) © 2016 SAMR 38 Khalid, Mahmood and Rukh On the other hand, World Bank Development indicators database provides information about the macro economic variables, investments in financial markets and financial development in developed and developing countries of the world. We used this database for extraction of the carbon dioxide emissions in countries included in our sample.1 From this database, we have filtered the countries mostly affected by the natural disasters because of climate change. The choice of countries is based on Global Climate Risk Index Report 2015 by Kreft, Eckstein, Junghans, Kerestan, and Hagen (2014).2 According to this report, the selected countries are mostly affected by changes in weather and suffered huge losses because of climate changes and natural disasters (i.e. heat waves, floods and storms). The time span of the study is 1990-2013 as in this time natural climate is affected by carbon dioxide emissions from industries, transportation etc. Our final dataset includes 239 observations but this data is unbalanced panel data as missing values are also present in our data as data of some countries and variables is not available from the secondary data sources used in this study. Research Methodology We have used pooled OLS regression, random and fixed effects regressions to test the relationship of climate change on GDP and Agriculture value added as share of GDP. First, we applied Pooled OLS regression for checking the impact of climate changes on GDP and Agriculture value added as share of GDP with some control variables. However, for panel data, use of OLS regression ignores the panel data structure and does not provide consistent results. Therefore, we have also applied fixed and random effects methods first for calculation of Hausman test and then after selection of fixed or random effects method we applied fixed effects for explanation of results. Before going further, we will explain the estimation methodologies. We have unbalanced panel data of different countries from 1990 to 2013. This time period is enough to observe different changes in the climate as the carbon emission is rapidly increased with the industrialization in last two decades. So this dataset includes those times when these emissions are not higher and the current period of high carbon emissions. Panel data includes the repeating observations of countries in different times. This data structure is more useful than time series or cross sectional data as it considers both changes in cross sectional units (countries) of the study and the time in years. So panel data tests explain changes due to both cross sectional units and time. Further, panel dataset provides more consistent and efficient estimates than OLS. Therefore, we used fixed or random tests to control country specific factors. We have also used OLS regression. We have unbalanced panel data; therefore, our focus is on fixed or random effects. We used Hausman test to check if random effect or a fixed effect is more appropriate for the after observing Hausman test p1 2 Appendix Table 7 shows the list of countries included in our sample. https://germanwatch.org/en/download/10333.pdf Asian Management Research Journal 1(1) © 2016 SAMR 39 Khalid, Mahmood and Rukh value. Hausman test is also used to test whether random effects provide the more appropriate estimates or the fixed effects model provides consistent outcomes. This is determined by observing Hausman p-value. If the p-value is less than 0.05 then fixed effects is more appropriate otherwise random effects is applied. These fixed and random effects have different characteristics such as, random effects model is in which individual firm specific effects are random and have no correlation with explanatory variables used in the study. On the other hand, fixed effects method consider individual firm specific effect with explanatory variables. Fixed effects model controls the individual effects by assuming that the individual firms might affect the results and this will exclude the time invariant variables. We have also applied fixed effects estimation method in this study. The fixed data is more appropriate for small sample size as our sample size is small and fixed effect is also suggested by Hausman test. Research Hypotheses The hypothesis, no doubt, plays very significant role for designing and comparing different research techniques empirically as well as theoretically. For this particular study, hypothesis developed and tested empirically to discuss the relationship between climate changes and GDP and Agriculture share in GDP. Our main consideration is on climate changes and we will explain how these changes impact the Agricultural output and GDP of the countries in the sample. Carbon dioxide emission is used as indicator of climate changes and more emissions of these greenhouse gases disturb the natural climate that have devastating effects on the GDP and these countries suffer losses due to floods, storms and heat waves (Kreft et al., 2014). Climate changes and global warming are associated with different factors such as the increase in temperature due to the higher emission of greenhouse gases is a threat for developed and developing countries. These climate changes are associated with the rise in temperature and decrease in rainfall in different countries. Moreover, these changes in climate are hurting the agriculture sector as floods, heat waves and storms occurring at regular intervals are destroying the agricultural crops, cereals and fruits and have adverse impact on livestock. These countries are suffering huge losses due to floods and storms because of these changes. The temperature of the world is increased from the last decade that changes the environmental eco systems while farmers in these countries are using the traditional cultivation methods. Due to those traditional methods they are vulnerable to these losses as advanced farming methods are not applied to increase the output. As a result, the agriculture output is reduced which means Agriculture share in the GDP is reducing as crops are destroyed by these natural calamities which later reduce the GDP of these countries. Asian Management Research Journal 1(1) © 2016 SAMR 40 Khalid, Mahmood and Rukh H1: We expect the negative influence of climate changes on Agriculture Value Added (AVAGDP). H2: We expect the negative impact of climate changes on Gross Domestic Product (GDP). Empirical Models We have developed the following two models for investigation of the effect of climate changes on GDP and Agriculture value added share in GDP. We have also controlled the factors such as FDI, Government expenditures, fertilizers use, capital stock and net production index as these factors influence the dependent variables but we are not interested in exploring these factors. Baseline Model (Climate Change and GDP) (1) Where; In the above baseline model of equation (1) dependent variables is LNGDP that is Natural log of Gross Domestic Product (US$, 2005),CO2EM is main explanatory variable that shows Carbon dioxide emissions (kg per 2005 US$ of GDP), The control variables includes, AFDI that depict FDI inflows to Agriculture, Forestry and Fishing (Share of total FDI), LNCST is Natural log of Net Capital Stock (constant 2005 prices), AGEXP show central government expenditures on agriculture, forestry, fishing (Share of total government expenditures), AGARE is percentage annual change in agricultural area, ANPI show Agricultural Net Production Index Number (2004-2006 = 100), LNFCONSP is the Nitrogen + Phosphate Fertilizers use on permanent and arable crops, µi,t =is error term and α represents country specific individual fixed effects. Climate Change and Agriculture Value Added Share in GDP (2) In the above baseline model of equation (2) dependent variables is AVAGDP is value Added, agriculture, forestry and fishing (Share of GDP), CO2EM is main independent variable that shows Carbon dioxide emissions (kg per 2005 US$ of GDP). The control variables includes, AFDI that depict FDI inflows to Agriculture, Forestry and Fishing (Share of total FDI), LNCST is Natural log of Net Capital Stock (constant 2005 prices), AGEXP show central government expenditures on agriculture, forestry, fishing (Share of total government expenditures), AGARE is percentage annual change in agricultural area, ANPI show Agricultural Net Production Index Number (2004-2006 = 100), LNFCONSP is the Nitrogen + Phosphate Fertilizers use on permanent and arable crops, µi,t =is Asian Management Research Journal 1(1) © 2016 SAMR 41 Khalid, Mahmood and Rukh error term and α represents country specific individual fixed effects. RESULTS AND DISCUSSION The following tables shows the results of the study for testing of hypothesis about climate changes and GDP and Agriculture value added share in the GDP. Table 1 gives summary statistics including mean, median, max, smallest value and standard deviation of the variables used in this study. The average Agricultural value added share in the GDP is 0.17 through mean and median is 0.16 that shows no outliers in the data. The standard deviation is 0.07 that shows the deviation of values from mean. This variable has 238 observations and maximum share in GDP is 0.35. The average GDP is 10.86 and its standard deviation shows value 1.68 that shows less deviation from mean. Table 1: Descriptive Statistics Variable Agri Value Added (Share in GDP) (AVAGDP) Natural Log GDP (Current US$ 2005) (LNGDP) CO2 Emissions (KG per GDP) (CO2EM) Agriculture FDI (AFDI) Natural Log Capital Stock (LNCST) Agri Expenditures Agricultural Area (AGARE) Agri Net Production Index Natural Log Fertilizer Consumption (LNFCONSP) Mean Median Std. Dev. Min Max N 0.17 0.16 0.07 0.04 0.35 238 10.86 10.84 1.68 8.12 14.20 239 1.05 0.73 0.70 0.29 3.20 210 2.46 0.68 4.31 0.00 29.51 89 10.30 10.29 1.47 8.21 12.78 178 3.76 3.41 2.48 0.28 11.08 82 0.35 0.00 2.07 -7.4 11.91 217 93.64 92.85 21.51 46.5 147.5 237 4.19 4.39 0.93 2.04 5.46 81 Source: Own composition from Food and Agriculture Organization Database Carbon emissions are on average 1.05 KG per GDP share although less than developed countries but these countries are affected by the emissions from other developed countries. The mean agricultural foreign direct investment is 2.46 and Asian Management Research Journal 1(1) © 2016 SAMR 42 Khalid, Mahmood and Rukh its standard deviation is 4.31 that shows higher variation in the level of Agricultural FDI share in total FDI. The Net Capital stock is showing the average of 10.36 that means capital stock is higher than less developed economies but its standard deviation is not higher and showing the value of 1.47. Finally, net production index is showing the mean of 83.64 and its standard deviation is 21.51 that shows higher volatility in the production of agricultural products in these countries. Table 2: OLS Results of Climate Change and Gross Domestic Product and Agricultural Value CO2EM AFDI LNCST AGEXP AGARE ANPI LNFCONSP Constant Observations F-test value R-squared LNGDP 1.55** (0.67) 0.00 (0.02) 0.14 (0.27) -0.11 (0.16) 0.16* (0.09) 0.03* (0.01) 0.30 (0.47) 4.87** (2.32) 22 41.38 0.95 AVAGDP 0.00 (0.02) -7.65 (0.00) 0.01** (0.00) -0.00 (0.00) -0.00 (0.00) -0.00 (0.00) 0.06*** (0.01) -0.23*** (0.07) 22 106.80 0.982 Table 2 shows the OLS Pooled regression results (column 2 and 3) results for both LNGDP and AVAGDP. The control variables includes, AFDI that depict FDI inflows to Agriculture, Forestry and Fishing (Share of total FDI), LNCST is Natural log of Net Capital Stock (constant 2005 prices),AGEXP show central government expenditures on agriculture, forestry, fishing (Share of total government expenditures), AGARE is percentage annual change in agricultural area, ANPI show Agricultural Net Production Index Number (2004-2006 = 100), LNFCONSP is the Nitrogen + Phosphate Fertilizers use on permanent and arable crops, µi,t =represents error term, Standard errors are in parentheses and *, **, *** indicate one tail statistical significance at the 1, 5, and 10% levels, respectively. Source: Own composition from Food and Agriculture Organization Database Table 2 displays the results of OLS estimation using the dependent, independent and control variables. Mainly we have checked the impact of climate changes on the GDP and Agricultural value added to GDP. Table 2 shows the results in two columns. First column shows the impact of climate changes on GDP in these countries. The coefficient of our main independent variable CO2E is Asian Management Research Journal 1(1) © 2016 SAMR 43 Khalid, Mahmood and Rukh 1.552 that is significant at 5% level. Its lower coefficient shows less economic significance in the results and the direction of relationship is also against our expectations in hypothesis 1. Table 3 displays the results of fixed estimation using the dependent, independent and control variables. Mainly we have checked the impact of climate changes on the GDP and Agricultural value added to GDP. Table 3 shows the results in two columns. First column shows the impact of climate changes on GDP in these countries. The coefficient of our main independent variable CO2E is -0.747 that is significant at 1% level. Its high coefficient indicates higher economic significance in the result supports our hypothesis 1. In the second column in which we used Agriculture Value Added in GDP proxy as dependent variable it displays the coefficient of 0.0553 that is against our hypothesis but significant at 5% level. We have controlled the effect of other variables in this study such as, LN fertilizer consumption have positive influence on GDP and its coefficient is 0.135 but statistically insignificant. In the second column with AVAGDP proxy LN Capital stock have surprisingly negative influence on agricultural value added into share of GDP. In sum, we find support to our hypothesis 1 that climate changes have adverse impact on the overall GDP as because of these natural disasters these countries bear handsome amount as losses but, this decrease in the GDP due to losses is not transferred to agricultural sector as its value added share in the GDP is not reduced that may be due to nature of these countries as they are prone to these changes in the external environment. Table 3: Hausman Test Results of Climate Change and Gross Domestic Product and Agricultural Value Added (Share in GDP) Dependent Variable CO2EM AFDI LNCST AGEXP AGARE ANPI LNFCONSP Constant Observations LNGDP AVAGDP Fixed Effects -0.74*** 0.05 (0.21) (0.03) 0.00 -3.27 (0.00) (0.00) 2.08** -0.15 (0.90) (0.15) -0.00 -0.00 (0.02) (0.00) 0.00 -0.00 (0.02) (0.00) 0.00 0.00 (0.00) (0.00) 0.13 -0.00 (0.11) (0.01) -11.13 1.76 (9.59) (1.64) 22 22 Asian Management Research Journal 1(1) © 2016 SAMR LNGDP AVAGDP Random Effects 1.55** 0.00 (0.67) (0.02) 0.00 -7.65 (0.02) (0.00) 0.14 0.01* (0.27) (0.00) -0.11 -0.00 (0.16) (0.00) 0.16* -0.00 (0.09) (0.00) 0.030 -0.00 (0.01) (0.00) 0.30 0.06*** (0.47) (0.01) 4.87** -0.2*** (2.32) (0.07) 22 22 44 Khalid, Mahmood and Rukh Hausman P-Value R-squared 0.0000 0.940 0.47 0.039 - - Table 5 shows the fixed effects results (column 2 and 3) and random effects (column 4 and 5) results for both LNGDP and AVAGDP. These fixed and random effects tests are applied for Hausman test calculation and selection of random or fixed effects method. In this table dependent variables are AVAGDP is value Added, agriculture, forestry and fishing (Share of GDP, 2005 prices) and LNGDP is Natural log of Gross Domestic Product (US$, 2005),CO2EM is main explanatory variable that shows Carbon dioxide emissions (kg per 2005 US$ of GDP), The control variables includes, AFDI that depict FDI inflows to Agriculture, Forestry and Fishing (Share of total FDI), LNCST is Natural log of Net Capital Stock (constant 2005 prices),AGEXP show central government expenditures on agriculture, forestry, fishing (Share of total government expenditures), AGARE is percentage annual change in agricultural area, ANPI show Agricultural Net Production Index Number (2004-2006 = 100), LNFCONSP is the Nitrogen + Phosphate Fertilizers use on permanent and arable crops, µi,t =represents error term, Standard errors are in parentheses and *, **, *** indicate statistical significance at the 1, 5, and 10% levels, respectively. Source: Own composition from Food and Agriculture Organization Database Table 4: Fixed Effects Results of Climate Change and Gross Domestic Product and Agricultural Value Added (Share in GDP) CO2EM AFDI LNCST AGEXP AGARE ANPI LNFCONSP Constant Observations R-squared LNGDP -0.747*** (0.215) 0.004 (0.003) 2.088** (0.909) -0.007 (0.021) 0.002 (0.0217) 0.003 (0.0041) 0.135 (0.113) -11.13 (9.597) 22 0.954 AVAGDP 0.0553** (0.036) -3.270 (0.0006) -0.157 (0.156) -0.004 (0.003) -0.0007 (0.003) 0.0007 (0.0007) -0.006 (0.0194) 1.766 (1.645) 22 0.982 Table 4 shows the fixed effects results (column 2 and 3) results for both LNGDP and AVAGDP. In this table dependent variables are AVAGDP is value Added, agriculture, forestry and fishing (Share of GDP, 2005 prices) and LNGDP is Natural log of Gross Domestic Product (US$, 2005),CO2EM is main explanatory variable that shows Carbon dioxide emissions (kg per 2005 US$ of GDP), The control variables includes, AFDI that depict FDI inflows to Agriculture, Forestry and Fishing (Share of total FDI), LNCST is Natural log of Net Capital Stock (constant Asian Management Research Journal 1(1) © 2016 SAMR 45 Khalid, Mahmood and Rukh 2005 prices),AGEXP show central government expenditures on agriculture, forestry, fishing (Share of total government expenditures), AGARE is percentage annual change in agricultural area, ANPI show Agricultural Net Production Index Number (2004-2006 = 100), LNFCONSP is the Nitrogen + Phosphate Fertilizers use on permanent and arable crops, µi,t =represents error term, Standard errors are in parentheses and *, **, *** indicate one tail statistical significance at the 10, 5, and 1% levels, respectively. Source: Own composition from Food and Agriculture Organization Database CONCLUSION The results suggests that climate changes have adverse impacts on the GDP of these countries. But, these effects are positive with Agriculture value added GDP as dependent variable. These changes in the climate increase the frequency of floods, heat waves and storms in these countries those are most vulnerable to these events due to geographical structure. We find support to our hypothesis that climate changes reduce the GDP in these countries as they suffer the huge losses due to these natural disasters. These changes in the climate also reduce the agricultural crop production as weather patterns are changing in these days due to higher carbon emissions. However, we find no support for the effect of climate changes on Agriculture value added. Perhaps, the share of GDP may be due to lower contribution of agriculture sector to overall GDP of these countries. Mainly, we contribute towards existing literature by examining the effect of climate changes in the most affected countries by gathering data from 1990-2013. Our findings suggest that policies should be designed to increase the savings. The other policies can be adapted at regional and global level for reduction of greenhouse gas emissions as the rise in temperature is a threat for the agricultural and health sector of these countries. REFERENCES Brooks, C. (2014). Introductory econometrics for finance: Cambridge university press. Ciscar, J.-C., Iglesias, A., Feyen, L., Szabó, L., Van Regemorter, D., Amelung, B., and Dankers, R. (2011). Physical and economic consequences of climate change in Europe. Proceedings of the National Academy of Sciences, 108(7), 2678-2683. Creutzig, F., Ravindranath, N., Berndes, G., Bolwig, S., Bright, R., Cherubini, F.,Faaij, A. (2015). Bioenergy and climate change mitigation: an assessment. GCB Bioenergy, 7(5), 916-944. Darwin, R., Tsigas, M. 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Asian Management Research Journal 1(1) © 2016 SAMR 47 Khalid, Mahmood and Rukh APPENDIX Table 6: Variables Definitions Variables Definitions Agri Value Added (Share in GDP) Value Added, Agriculture, Forestry and Fishing (Share of GDP, 2005 prices) Natural Log GDP (Current US$ Natural log of Gross Domestic Product 2005) (US$, 2005) CO2 Emissions (KG per GDP) (CO2) Carbon dioxide emissions Agriculture FDI FDI inflows to Agriculture, Forestry and Fishing Natural Log Capital Stock Natural log of Net Capital Stock (constant 2005 prices) Agri Expenditures Central government expenditures on Agriculture, forestry, fishing (Share of total government expenditures) Agricultural Area Percentage annual change in agricultural area Agri Net Production Index Agricultural Net Production Index Number (2004-2006 = 100) Natural Log Fertilizer Nitrogen + Phosphate Fertilizers use on Consumption permanent and arable crops. Pairwise correlation matrix in which * indicates significance at 5% level. Table 7: List of Countries Serial# Country Names Time Span 1 Bangladesh 1990-2013 2 Dominican Republic 1990-2013 3 Guatemala 1990-2013 4 Haiti 1990-2013 5 India 1990-2013 6 Nicaragua 1990-2013 7 Pakistan 1990-2013 8 Philippines 1990-2013 9 Russian Federation 1990-2013 10 Vietnam 1990-2013 Asian Management Research Journal 1(1) © 2016 SAMR 48