Economic Impacts of Climate Change

sparingal Impacts of clime ChangeEconomic Impacts of Climate Change and discrepancy on Agricultural Production in the Middle east approximately and North Africa Region1. IntroductionThe accumulation of scientific evidences indicating that growing greenhouse gases will warm our planet becomes cle ber. Higher temperature and limitings in venturesomeness level will shrinkage crop yield in many countries. IPCC (2007) reported that most land aras will experience an addition in average temperature with more than frequent heat waves, more tonic water resources and desertification. Stern and Treasury (2006) noted, that the the poorest countries and populations will bear the grea tribulation cost of mood miscellanea. in that locationfore, the encounter of temper metamorphose on agriculture has certain summation attention in the last decade literatures. Climate change coupled with population growth will deeply affect the handiness and quality of water resources in the Middle East and North Africa (MENA) percentage (Alpert, Krichak, Shafir, Haim, Osetinsky, 2008 Evans, 2010 Gao Giorgi, 2008). In a similar way, Sowers and Weinthal (2010) argued that since most of the MENA persona is arid and hyper-arid, rebuff changes in water accessibility and arable land have straightforward consequences for human security.It is worth to take into account the climaticalal variableness in addition to climate change in order to provide an integrate compendium of the impact of climate variables. Selvaraju and Baas (2007) stated that climate variation is the way climate fluctuates yearly above or below a semipermanent average value while climate change is the long-term continuous change ( annex or decrease) to average weather conditions or the tell of weather. In this study, we think the possible impacts of climate changes and climate disagreement on bucolic yield, with a focus on the region of Middle East and North Africa, where the deleterious impacts of climate change are generally communicate to be greatest. In order to achieve such objective, strict final result Regression (FER) is used to Estimate the clownish achievement portion utilize cross-section clock time series data of MENA countries. The advantages of panel data outline are getting actual responses is more in strainative to policy makers than results from orbital cavity trials. Second, boorish fixed centers capture all additive differences mingled with various countries (Stock Watson, 2003).2. Data SourcesIn order to estimate the performance function, cross-section(a) time series (panel data) are used. The panel set consists of 20 MENA countries for the time period between 1961 and 2009 including Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, Turkey, United Arab Emirates, and Yemen. flurry 1 shows the data description and data sources. Due to unavailab ility of the data for a few(prenominal) countries, some observations are missing therefore panel data in the mannikin are unbalanced. The data set consists of two variables group. The first is political economy variables such as net agrarian yield index number in international dollar, pastoral machinery, total fertilizers consumed, labors, and land. The number data subset is climatic variables manage temperature and precipitation. The monthly climatic data were available by meteorological stations rather than by country as shown in Table 2. Therefore, it was necessary to calculate monthly country averages of climate variables and summed up into seasonal data.Table 1 Data description and sourcesVariableUnit explanationSourceAgricultural production1000 I$Net hoidenish Production Index Number (2004-2006 = 100)FAO statisticsAgricultural machinery (tractors)NumberAgricultural tractors, constitute to total wheel, crawler or track-laying type tractors and pedestrian tractors used in agriculture.FAO statistics plant foods consumptionTon nutrientsTotal consumption of chemical fertilizers (N+P2O5+K2O)International Fertilizer Industry AssociationLivestockHeadBuffaloes + cattleFAO statisticsLabor jillionTotal economically active populationInternational Labor physical composition (LABORSTA)Land1000 HectareTotal area of cultivated landFAO statisticsTemperatureCelsius periodic correspond temperatureFAOClim-NET Agroclimatic database management system fallmillimeterMonthly entertain precipitationFAOClim-NET Agroclimatic database management system3. Climate change and agriculture in Mena countriesAccording to the World Bank, The Middle East and North Africa is one of the regions that is most vulnerable to climate change, with the highest level of water scarcity in the world. The region has a total area of about 14 million km2, of which more than 87 per cent is desert. It is characterized by a high dependency on climate-sensitive agriculture and a whopping share of its population and economic activities are located in flood-prone urban coastal zones.Bucknall (2007) classify the MENA countries into three groups on the subject of water source and availability. First group is countries have decorous quantities of renewable water, but the at bottom-country and within year variations are problematically large including Iran, Lebanon, Morocco, and Tunisia. Second group is countries that have low levels of renewable water resources and super dependent on non-renewable groundwater sources and supplies by desalination of sea water like Bahrain, Jordan, Kuwait, Libya, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Yemen. The last group is countries that mainly dependent on the inflow of transboundary rivers such as the Nile, the Tigris, and the Euphrates including Syria, Iraq, and Egypt.Table 2 Descriptive Statistics for aggregate climatic variables during the Period 1961-2009No.Metrological stationsTemperature (c) rush (mm/year) conceive Std. Dev.MeanStd. Dev.Algeria9519.910.9923.985.99Bahrain126.620.918.517.74Egypt5222.420.634.142.15Iran6717.312.7020.039.05Iraq2922.352.8213.627.98Israel1319.801.5329.3314.31Jordan1518.951.0815.775.04Kuwait1525.911.2313.737.49Lebanon1218.491.7856.5817.08Libya2721.140.8014.744.12Morocco3418.030.7132.2910.95Oman2726.780.608.005.34Qatar227.460.706.405.05Saudi Arabia6725.190.915.933.73Sudan4728.300.8948.5157.62Syrian Arab Republic2018.300.9021.617.26Tunisia2519.350.9830.308.34Turkey31513.030.8951.317.73United Arab Emirates1327.561.335.475.11Yemen1225.523.529.707.444. MethodologyThere are various models can be employed to assess the impact of climate change on agricultural production. Ricardian model, Agronomic model, and crop model models are most widely adopted models for the climate impact studies (Lee, Nadolnyak, Hartarska, 2012). The Ricardian model estimates the examines the impact of climate and other variables on land values and get revenues using cross-sectional data (Mendelso hn, Nordhaus, Shaw, 1994). Crop Simulation Models (CSM) restrict the compendium to crop physiology and compare crop productivity for different climatic conditions (Salvo, Begalli, Signorello, 2013). Because of the country level panel psycho compendium, the production function model is adopted for the abridgment in the present study.ModelTo estimate the impact of climatic change on agriculture production in MENA countries, an empirical production function for country i at time t net agricultural production index is a function of some economic infixs (Frisvold Ingram, 1995) and climatic variables . Y construes the net agricultural production index, M, F, L, A, and V are economic inputs which include agricultural machinery, fertilizer consumption, labor, cultivated area, and descent respectively. T and represent temperature and precipitation. Number of agricultural tractors is used as proxy of agricultural bang-up stock and number of cattle and buffaloes is used as proxy of l ivestock production. For climatic variables temperature and precipitation, plastered of the wintertime season (January, February, and March) , spring (April, May, and June), pass (July, August, and September), and personal identification number (October, November, and December) are involved in the model. Following (Barrios, Ouattara, Strobl, 2008 Belloumi, 2014 Lee et al., 2012), The agricultural production model in the present study has the following specification form(1)By taking the log on both sides, the fixed effect panel model is (2)According to the fixed effect model, i (i=1.n) is the unknown intercept for each country that absorb unabsorbed time confused effectuate and is a time varying effects. For climatic variables, both the analogue and quadratic forms are integrated into the model in order to consider the nonlinear carnal knowledgeship between agricultural production and climatic variables.VariabilityAs it is also sensible to estimate the impact of the diverge nce of climatic variable along with the seasonal deviation and the mean temperature and precipitation, the square up of the mean differences of temperature and precipitation for each season observation is used in the second model. Then, This variant was measured by the seasonal coefficient of variation (CV) calculated as the seasonal ratio of the standard deviation to the mean of each climate variable for each country.5. Results and discussionReview different papers to spike the discussion Table 3 shows the results of fixed effects regression analysis in which we estimated the impact of agricultural inputs and climatic variables on agricultural production in MENA countries. The results show that the regression coefficient of temperature is authoritative and statistically significant in spring, summer, and fall seasons. By contrast, temperature in winter has oppose coefficient at signification level of 0.01. Regarding the estimated parameters of precipitation, precipitation du ring spring showed negative impact at significance level of 1%.The estimated parameters of nonlinear climatic variables indicated that each of the shape summer temperature has positive coefficient at significance level 0.05 while squared winter temperature has negative and significant impact at level of 0.05. In addition, squared spring precipitation showed positive influence.As expected, production inputs showed significant and positive tattle with agricultural production except machinery and fertilizers consumption. As inputs and agricultural production are in logarithmic form, the regression coefficients reflect the production picnic of each input. Therefore, 1 percent increase in each input of livestock, labor, and land, with keeping all other inputs the same, leads to increase in agricultural production by 0.16%, 0.98%, and 0.91% respectively.Table 3 Fixed Effects Regression analysis of climate changeVariablesCoefficientsS.E.P valueIntercept-0.05820.0160-0.058Winter Temperat ure-0.0582**0.01600.000 source Temperature0.0431*0.02120.042 summer Temperature0.0730**0.02130.001 settle down Temperature0.0408**0.01540.008Winter Temperature square-0.0024*0.00100.014 springtime Temperature square up0.00020.00160.892summertime Temperature shape0.0043*0.00190.028 decease Temperature Squared-0.00050.00100.643Winter temerity-0.00060.00040.128Spring Precipitation0.0004*0.00020.050Summer Precipitation-0.00010.00020.760Fall Precipitation0.00020.00030.438Winter Precipitation Squared-5.0600E-065.1400E-060.325Spring Precipitation Squared3.8800E-066.2400E-060.535Summer Precipitation Squared1.5300E-05*7.6600E-060.047Fall Precipitation Squared-3.4000E-064.7100E-060.470Machinery-0.04710.02820.095Fertilizers Consumption-0.02690.01660.107Livestock0.1599**0.03890.000Labor0.9802**0.04810.000Land0.9128**0.10000.000R2 within0.8932R2 between0.7827R2 overall0.7917F test120.8300F-ui=0951.88**Obs. No980The results of Fixed Effects Regression analysis of climate variability as instr uctive variables and agricultural production are presented in Table 4. The results suggest that temperature variability in fall season seems to have significant and positive relation with agricultural production while it has negative relation in spring. Squared variability of temperature during winter and summer seasons have significant and negative relation. Furthermore, variability of winter precipitation have positive and significant relation. Likewise, the regression coefficient of squared variation of winter and summer precipitation showed significant and positive relation with agricultural production..Table 4 Fixed Effects Regression analysis of climate variabilityVariablesCoefficientsS.E.P valueIntercept3.8918**0.04220.000Winter Temperature-0.24510.18180.178Spring Temperature-0.5086**0.19210.008Summer Temperature0.04180.18500.821Fall Temperature0.8505**0.19290.000Winter Temperature Squared-0.0825*0.04080.044Spring Temperature Squared0.02040.03700.581Summer Temperature Squared -0.0571**0.02160.008Fall Temperature Squared-0.00710.04870.884Winter Precipitation0.0425**0.00900.000Spring Precipitation0.02690.07740.728Summer Precipitation0.17170.21380.422Fall Precipitation-0.19430.19460.319Winter Precipitation Squared0.0221**0.00620.000Spring Precipitation Squared-0.00200.00340.558Summer Precipitation Squared0.0005*0.00030.044Fall Precipitation Squared0.00560.00420.18R2 within0.793R2 between0.943R2 overall0.769F test11.620F-ui=011.330Obs. No980marginal Impact analysisThe excepted marginal effects of climatic change and variability on agricultural production appraised at the mean are calculated by the first-order differentiation of the equation 2 to temperature and precipitation respectively (3) (4)The elaticities of climate change and variability of temperature and precipitation are derived from equations (3) and (4) respectively by dividing both equation (3) on and equation (4) on . therefore, the elasticities can be computed as (5) (6)Where and refer to temperature change or variability and precipitation change or variability respectively.The marginal impact of climate change and climate variability on agricultural production in the MENA region are presented in Table 5. The impact and the elsticities of Climate change and climate variability are calculated using the regression coefficient and mean values of temperatures and precipitation. The results indicate that increase of temperature in winter season has negative impact on agricultural production as one percent increase in temperature during winter season will lead to a decrease in agricultural production value by 1.12 percent. Instead, increasing the temperature during the other seasons showed positive impact. Temperature variability negative impact on agricultural production during winter and spring as one percent increase of temperature variability, will lead to about 0.09 and 0.14 percent decrease in agricultural production.In regard to the impact precipitation changes, the results confirmed that increasing precipitation during winter and fall season have negative impact on agricultural production in MENA countries while it has positive impact in spring and summer seasons. Moreover, the results of the impact of precipitation variability showed that precipitation variability has negative impact during winter and summer seasons, whereas one percent increase of precipitation variability will lead to decrease in agricultural production in the MENA region by 0.037 and 0.013 percent respectively. However, precipitation variability showed positive impact during the season of spring and fall.Table 5 bare(a) impacts of climate change and variability on agricultural productionClimate changeClimate VariabilityMarginal impactElasticityMarginal impactElasticityTemperatureWinter-4.517-1.115-12.408-0.087Spring3.7461.567-29.211-0.139Summer4.1302.0257.0390.027Fall2.8970.92741.7130.265PrecipitationWinter-0.162-0.092-2.884-0.037Spring0.0190.0051.0380.013Summer0.2720.04 6-3.303-0.071Fall-0.040-0.0190.0710.001ReferencesAlpert, Pinhas, Krichak, Simon O, Shafir, Haim, Haim, David, Osetinsky, Isabella. (2008). Climatic trends to extremes employing regional simulate and statistical interpretation over the E. Mediterranean. Global and Planetary Change, 63(2), 163-170.Barrios, Salvador, Ouattara, Bazoumana, Strobl, Eric. (2008). The impact of climatic change on agricultural production Is it different for Africa? Food Policy, 33(4), 287-298.Belloumi, Mounir. (2014). Investig