Agricultural Technology and Carbon Dioxide Emissions: Evidence from Jordanian Economy

Agricultural Technology and Carbon Dioxide Emissions: Evidence from Jordanian Economy Mohanad Ismael, Fathi Srouji y, Mohamed Amine Boutabba z September 25, 2015 Abstract This paper aims to study the impact of agricultural technologies on the emissions of carbon dioxide (CO2) in Jordan using annual data from 1968 to 2012. In order to realize our purpose, we pass through unit-root test, Johansen co-integration test, causality test and variance decomposition analysis. Our results state that using unit-root test GDP growth and fertilizer are stationary at levels (no unit root) while CO2 emissions, agricultural tractors and cereal lands are not stationary at levels but at rst di erence. The presence of non-stationary variables allows us to test for the long term relationship. Based on Johansen cointegration test there appears one co-integrating equation across the non-stationary variables. It is shown also that CO2 emission can be explained in the short run by its own shock while in the medium run fertilizers, agricultural tractors, cereal lands and GDP growth have some contributions to the emissions of CO2 in the atmosphere. Keywords: carbon dioxide, tractors, agricultural technology, economic growth. JEL Classi cation: C10; C51; Q16; Q54; University of Birzeit, Economic Department, maburjaile@birzeit.edu. y University of Birzeit, Economic Department, fsrouji@birzeit.edu. z Université d Evry Val D Essonne, EPEE, mohamedamine.boutabba@univ-evry.fr. 1

1 Introduction In the last decade, there exist huge attentions toward developing activities and regulations which involved at environment protections and sustainable development. In order to realize the challenges of development and environment issues, nancial resources are required to increase the capacity of institutions for implementation and all countries must cooperate to accelerate the sustainable development processes. Global warming and climate change are now key sustainable development issues. Companies around the world must be able to understand the risks beyond the emissions of greenhouse gases (carbon dioxide, methane and nitrous oxide). Most of governments are taking steps toward reducing the emissions of greenhouse gases throughout introducing carbon and energy taxes and regulations on energy e ciency and emissions. Agriculture and natural environment are closely related to each other. Mainly, carbon dioxide is directly generated from agricultural productions. In agricultural production process, high amounts of CO2 (and thus greenhouse gases) emissions caused by irrational utilization such as inappropriate land use and chemical fertilizer have an impact on climate warm change and therefore the polar ice caps and glaciers would melt. Further, using energy as initial inputs in agriculture enhances the magnitude e ect of agriculture production on environment quality. In 2010, CO2 emissions from agricultural sector represent about 2% of total CO2 emissions in Jordan. In particular, it arrives to around 397.6 thousands metric tons in 2010 comparing to 303.3 thousands metric tons in 1990 with an increase of 31% (World Economic Indicator, 2015). According to 2014 data, the contribution of agricultural output in total GDP in Jordan is 2.7% (Bank Audi 2014). Despite its small share, it is considered as a major source of food and a major source of foreign currency. In addition, around 25% of Jordanian poor families are relying on agriculture where 2% of total employees in 2012 are working in agriculture sector (World Bank data). In this paper, we match agriculture sector together with environment and focus on the in uence of agricultural technology on carbon emissions in Jordan in both short run and long run by using annual time series data from 1968 2012. This paper does not include all factors that a ect the CO2 emissions (Kt) but limited to the amount of fertilizers (in $ U.S.), the number of agricultural tractors, GDP ($ U.S.), value added of agricultural sector (% of GDP), value added of industrial sector (% of GDP), land under cereal production (Hectares). To realize this objective, we apply unit-root test to examine the stationarity properties of the data by applying three di erent tests namely, (Augmented) Dickey Fuller (hereafter, DF/ADF) test, Phillips and Perron (hereafter, PP) (1988) test and Ng and Perron (hereafter, NP) (2001) test. In addition, the co-integration framework of Johansen (1988) is applied to test the multivariate co-integrating relationship. We also perform Vector Error Correction Model (VECM) to study the short term and long term relationship between agriculture technology and CO2 emissions. 2

There are numerous literatures which focus on the relationship between agriculture and CO2 emissions. Among others, Zaman et al. (2012) investigate the in uence of agriculture technologies on carbon emissions in Pakistan using annual data from 1975 to 2010. Using co-integration test and Granger causality test, it is shown that agriculture technology increases the emissions of CO2. Moreover, agriculture irrigated land seems relatively the least contributors on CO2 emission changes. Further, Soni et al. (2013) present an energy input-output analysis on different agricultural activities in Thailand. In particular, the study relates energy consumption in agriculture production systems associated with their corresponding greenhouse gases. It is shown that transplanted rice provides the highest CO2 emission among crops. Based on tillage technologies in maize cultivation, Šarauskis et al. (2014) assess the energy e ciency of maize cultivation technologies in di erent systems of reduced tillage in Lithuania. The study considers ve di erent tillage systems: Deep ploughing, shallow ploughing, deep cultivation, shallow cultivation and no tillage. It is shown that the greatest amount of fuel was used in the traditional deep ploughing. The reduced tillage systems required 12 58% less fuel and the lowest energy input was associated with no tillage technology. Lower fuel consumption reduces the technology costs and thus the emissions of CO2. In addition, Buragiene et al. (2011) study the impact of previously mentioned tillage machines on the emission of CO2 from soil. The highest CO2 gas emissions were found in the case of intensive ploughing and the lowest emissions were observed from no tillage soil. Similarly, Silva-Olaya, La Scala, Dias and Cerri (2013) use di erent tillage methods in Brazilian sugarcane elds to study their e ects on CO2 emissions. They indicate that conventional tillage method produces CO2 more than both reduced and minimum methods. In particular, 350.09 g/m 2 of CO2 is generated in conventional method while 51.7 and 5.5 g/m 2 are produced by using the reduced and minimum methods respectively. Zaman et al. (2011) investigate the relationship between electricity consumption and technological factors in the agricultural sector of Pakistan. By applying techniques of co-integration and causality tests, it is found that agricultural technology causes energy consumption. From animal sources point of view, Carlsson-Kanyama and Gonzalez (2009) conclude that the total GHG emissions in Sweden for beef measured about 30 kg CO2-eq. / kg beef and therefore they encourage protein productions from vegetables rather than from animal sources. So, in order to keep the amount of CO2 emissions as that of the 2000, each person around the world should daily consume at most 70 to 90 grams of meat (McMichael et al. 2007, Barclay 2011). Our results state that using unit-root test GDP growth and fertilizer are stationary at levels (no unit root) while CO2 emissions, agricultural tractors and cereal lands are not stationary at levels but at rst di erence. The presence of non-stationary variables allows us to test for the long term relationship. Based on Johansen cointegration test there appears one co-integrating equation across the non-stationary variables. 3

According to the variance decomposition analysis, it is shown that CO2 emission can be explained in the short run by its own shock (own innovation) while in the medium run fertilizers, agricultural tractors, cereal lands and GDP growth can contribute to the emergence of CO2 in the atmosphere. The outline of the paper is the following: section 2 presents the data and the methodology, section 3 presents the model. Section 4 presents the results and we conclude in section 5. Finally, the appendix is presented in section 6. 2 Methodology and data The majority of empirical studies use cross-sectional data to analyze the economic relationship. However, in some circumstances, cross-sectional data fails to establish a long term relationship between the relevant variables included in the model. For this reason, we adopt time-series data to study the impact of the independent variables on the dependent variable as well as the direction of causality among variables in a closed form model. In order to achieve the impact of agriculture technology on CO2 emissions in Jordan, the researchers use multivariate analysis framework adopted by Zaman et al. (2012). In other words, we consider the following model CO2 = + 1 GGDP + 2 T rac + 3 F ert + 4 land + " (1) where a. CO2 is the amount of CO2 emissions in (Kilo tonne), b. GGDP is the gross domestic product (in %) c. T rac is the number of tractors used in agricultural production, d. F ert is the value (in $US) of fertilizers used in agricultural production, e. land measures the land under cereals production (in hectares) While ; 1 ; :::; 4 are parameters (intercept and the coe cients) to be estimated and " is the error term. The annual data is obtained from World Development Indicators published by the World Bank (2013) and covers the period from 1968 to 2012. 2.1 Unit-root test In applied economic literature, the aim of applying the unit-root test is to examine whether the data series are stationary or not. In particular, an Augmented Dickey-Fuller (ADF) unit root test is used, Dickey and Fuller (1979, 1981). The null hypothesis in ADF test is that the data series is not stationary, or there exists a unit root. While the alterative hypothesis is that the series is stationary. There are three cases used to show the existence of unit-root test: without intercept and trend, with intercept only and with both intercept and trend as shown in the following formulas: 1. without intercept and trend X t = X t 1 + u t (2) 4

2. with intercept 3. with intercept and trend 2.2 Co-integration test X t = + X t 1 + u t (3) X t = + t + X t 1 + u t (4) The concept of co-integration was initially introduced by Granger (1981) followed by Engle and Granger (1987), Phillips and Ouliaris (1990) and Johansen (1991). Most of macroeconomic variables are non-stationary where Engle and Granger (1987) show that a linear combination of two or more non-stationary series can be stationary. If this linear combination exists, these non-stationary series are said to be co-integrated and can be interpreted as a long-run relationship among the variables. Practically, researchers usually use Johansen co-integration test to test the co-integration since it can check the existence of more than one co-integration relationship if data contains more than two series. Let M be a P x 1 vector that contains: M t = (CO2; GDP; Agri%; Indus%; F ert; land; T ract) (5) where all variables in this vector are in rst-di erenced stationary I (1). Once rst-order stationary variables exists, then according to Johansen (1991) M t has a vector autoregressive (VAR) representation taking the following form: M t = + 1 M t 1 + 2 M t 2 + ::: + k M t k + t (6) where is the intercept and t is a vector of white noise processes together with zero mean. All information regarding the long term relationship between variables exists in matrix. This VAR equation can be written as: M t = + 1M t 1 + 2M t 2 + ::: + km t k + t (7) where the rank of the parameter k represents the number of co-integrating vectors. 2.3 The VECM regression Initially, Sargan (1964) presents the ECM approach followed by Engle and Granger (1987). This approach is generated from the co-integrated equations after including the lagged error correction term to reconsider the long run information lost through taking the di erence of relevant variables. Therefore, if the model contains n co-integrated variables, then the error correction model can be written as: qx X t = ' + X t i + ecm t 1 + t (8) i=0 5

Where X t represents the n co-integrated variables, q is the number of lag periods, ecm is called the residual from the co-integration equation and is a vector of white noise residuals. Further, measures the short run e ects of the long run dynamics, in particular, this coe cient denotes the short run adjustment representing the proportion by which the long run disequilibrium in the dependent variable is being corrected for in each time period. 3 Data analysis and results The research applies unit-root tests, co-integration tests to study the empirical impact of GDP, agricultural production, industrial production, fertilizers, tractors and land with cereal productions on the emissions of carbon dioxide CO2. 3.1 Unit-root test The purpose of unit-root test is to check the stationary properties of the variables, and this is necessary to conduct the co-integration test. In particular, Augmented Dickey-Fuller test was employed on each relevant variable in the dataset. Table (1) and table (2) summarize the outcomes of ADF test at level variables as well as at rst-di erence variables. Table (1) ADF test at levels (at constant). Variable t-statistics Critical Value at 5% CO2_MTPK -1.592890-3.520787 GDP_Growth -4.294622-2.931404 Agri -3.216673-3.544284 Indus -4.178569-3.515523 Fert -3.524434-2.929734 Tract -2.457042-3.518090 Land_Cereal -2.645274-3.518090 Table (2) ADF test. at rst di erence (at constant). 1 Variable t-statistics Critical Value at 5% CO2_MTPK -3.407559-2.933158 GDP_Growth - - Agri -7.628302-2.933158 Indus - - Fert - - Tract -4.015611-2.931404 Land_Cereal -6.789587-2.935001 Table (1) shows that only GDP growth, the share of industrial production in GDP and the fertilizers are stationary variables (do not have unit-roots) at levels, in the sense that the absolute values of the t-statistics are higher than 6

the MacKinnon (or ADF) critical value at 5%, implying that we reject the null hypothesis (there exists a unit-root). However, Table (2) states that all variables, including CO2_MTPK emissions, the share of agricultural production in GDP, number of tractors and the size of lands with cereal production become stationary at rst-di erence, where the absolute values of t-statistics are greater than the ADF critical values at 5%. 3.2 Co-integration test In general, if two variables have long run relationship, then these variables are co-integrated. If two variables are integrated of order one I(1), there could be a linear combination between them and integrated of order zero, I(0) (Green, 2002). Therefore, we have to check the possible co-integration relationship between CO2_MTPK, Agri, Tract and Land_Cereal. In order to conduct cointegration test, we perform the Johansen co-integration test. the null hypothesis states that there is no co-integration. According to the maximum eigenvalue co-integration test (Table (3)) and Trace test (Table (4)), it is shown that there is one co-integration equation at 5% signi cant level. This implies a long term relationship between the variables. Table (3) Unrestricted Co-integration Rank Test (Maximum Eigenvalue) Hypothesized no. of CE(s) Eigenvalue Max-Eigen Statistics 0.05 Critical Value Prob.** None* 0.595669 38.93740 32.11832 0.0063 At most 1 0.258886 12.88284 25.82321 0.8124 At most 2 0.233594 11.43988 19.38704 0.4690 At most 3 0.101182 4.587002 12.51798 0.6563 Max-eigenvalue test indicates 1 co-integrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level ** MacKinnon-Haug-Michelis (1999) p-value Table (4) Unrestricted Co-integration Rank Test (Trace) Hypothesized no. of CE(s) Eigenvalue Trace Statistics 0.05 Critical Value Prob.** None* 0.595669 67.84711 63.87610 0.0223 At most 1 0.258886 28.90972 42.91525 0.5674 At most 2 0.233594 16.02688 25.87211 0.4904 At most 3 0.101182 4.587002 12.51798 0.6563 Trace test indicates 1 co-integrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level ** MacKinnon-Haug-Michelis (1999) p-value 7

3.3 Causality test In this section, we study the causality between agricultural technology variables, GDP growth and CO2 emissions in Jordan using Granger causality test. Table (5) shows that the null hypothesis "Agri_Tractors does not granger cause CO2_emissions_MTPK" and vice versa is accepted at 5% signi cant level. Similarly, there exist no causality between fertilizer, GDP growth and CO2 emissions, whilst causality only occurs between CO2 emissions and cereal land at 5% signi cant level. Accordingly, this test implies that agricultural technology variables fail to associate with the emissions of carbon dioxide. Table (5) Causality test results between agricultural technology variables and CO2 emissions Null Hypothesis: Obs F-Statistic Prob. AGRI_TRACTORS does not Granger Cause CO2_EMISSIONS_MTPK 43 0.88609 0.4206 CO2_EMISSIONS_MTPK does not Granger Cause AGRI_TRACTORS 2.32368 0.1117 LAND_CEREAL does not Granger Cause CO2_EMISSIONS_MTPK 43 0.40958 0.6668 CO2_EMISSIONS_MTPK does not Granger Cause LAND_CEREAL 3.39541 0.0440 FERTILIZER does not Granger Cause CO2_EMISSIONS_MTPK 43 1.36983 0.2664 CO2_EMISSIONS_MTPK does not Granger Cause FERTILIZER 2.25238 0.1190 GDP_GROWTH does not Granger Cause CO2_EMISSIONS_MTPK 42 0.40266 0.6714 CO2_EMISSIONS_MTPK does not Granger Cause GDP_GROWTH 2.00320 0.1493 3.4 Variance decomposition analysis The variance decomposition analysis is used to compare the contribution of di erent indicators of agricultural technology in Jordanian economy to the variation in CO2 emissions over the entire period. Table (6) summarizes the contribution of each independent variables on CO2 emissions. It is found that in the short run, that is in the second year, shocks to CO2_emissions account for 98.25278 percent variation in uctuation in CO2 emissions, in other words, own shock. In addition, shock to agricultural tractors can cause 1.362483 percent variation of the uctuation in CO2 emissions, shock to land cereal generate 0.052507 percent uctuation in CO2 emissions, while shock to fertilizers and GDP growth account for 0.148834 percent and for 0.183399 percent respectively variation in CO2 emissions uctuations. 8

Figure (1) Variance Decomposition Analysis of CO2 emissions However, in the long run, for instance 10 years, it is shown that shocks to CO2_emissions account for 87.93385 percent variation in uctuation in CO2 emissions. In addition, shocks to agricultural tractors can cause 6.688580 percent of the uctuation in CO2 emissions, shock to land cereal area can generate 1.436775 percent uctuation in CO2 emissions, while shock to fertilizers and GDP growth account for 3.719491 percent and for 0.221306 percent respectively variation in CO2 emissions. If we compare short run contribution to the long run contribution, it is clearly shown that CO2 emissions shock can contribute less in the long run than in the short run, while agricultural tractors, cereal lands, and fertilizers have slightly higher contributions for CO2 emissions in the long run than in the short run. Notice also that the contribution of each explanatory variable to explain the variance in CO2 emissions is steady for longer periods (10 years and more). Accordingly, CO2 emissions can be highly explained by its own innovation. The graph shows that initially CO2 emissions are exogenous and independent of the other variables. However, in the medium run and the long run all variables 9

are responsible for explaining at least 12% of the variation in CO2 emissions. Comparing to Pakistan (Zaman et al. 2012) where tractors contribute 13.76%, in this paper, tractors share around 6.69% of CO2 emissions, while fertilizers generate 3.72% in Jordan comparing to 5.29% in Pakistan, while cereal lands shares 1.43% in Jordan comparing to 4.03% in Pakistan economy. 4 Conclusion The purpose of this research is to investigate the relationship between agricultural technologies and CO2 emissions in Jordanian economy using annual data from 1968 to 2012. In particular, the paper focuses on fertilizers, tractors, cereal land as proxies to agricultural technology. The contribution of this paper to the literature is the analysis of the role of fertilizers, tractors, GDP growth and cereal land in carbon emissions. To do so, we apply unit-root test, co-integration test, causaity test and the variance decomposition analysis. The empirical tests achieve our objective and show that GDP growth has the highest in uence on CO2 emissions followed by the number of tractors used in agricultural productions, the fertilizers and the cereal land areas. The variance decomposition analysis shows that in the short run the variation in CO2 emissions are mostly explained by CO2 own shocks, while in both medium run and long run, tractors, fertilizers as well as lands can slightly contribute to the emissions of CO2 in the atmosphere. For policy implications, when Jordanian policy makers want to perform a strategy to meet the planned level of CO2 emissions in Jordanian economy, they do not have to focus on agricultural technologies. Instead, they have to consider among others, industrial sector and transportation sector which might generate most of the emissions. Furthermore, policy makers have to wait for long periods so that the implementations of targeted CO2 level arise and the negative impacts of tractors as well as fertilizers on the environment do appear in the medium run and in the long run. 5 References References [1] Bank Audi Group. (2014). Annual Report 2014. Lebanon. [2] Barclay, J. M. G. (2012). Meat, a Damaging Extravagence: A Response to Grumett and Gorringe. The Expository Time 123. PP. 70-73. [3] Buragiene, S., Sarauskis, E., Romaneckas, K., Sakalauskas, A., Uzupis, A. and Katkevicius, E. (2011). Soil Temperature and Gas (CO2 and O2) emissions from Soil under Di erent Tillage Machinery Systems. Journal of Food, Agriculture and Environment 9. PP. 480-485. 10

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