This study aimed to investigate the effect of economic growth, agricultural growth and energy use on methane (CH 4) and nitrous oxide (N 2O) emissions in Sudan. Within the context of the EKC, the study applies the OLS, cointegration, vector error correction modelling (VECM) and Granger causality methods. The study has established a long run equilibrium relationship for both CH 4 and N 2O in their relation to economic growth, agricultural growth and energy use in presence of trade openness (TOP) and inflows of foreign direct investments (FDI). The estimated VECM shows that emissions of CH 4 are significantly affected by economic growth, TOP, and FDI with no effect of agricultural growth in the short run while CH 4 is found to be significantly affected by economic growth, agricultural growth, TOP and FDI in the long run. The estimated VECM for N 2O shows that N 2O emissions are more significantly affected by energy use, agricultural growth and FDI with no effect of economic growth in the short run, while N 2O is found to be significantly affected by economic growth, agricultural growth, TOP and FDI in the long run. Consistently, findings from the estimated OLS and VECMs show that the EKC does not hold for either CH 4, or N 2O emissions, and that N 2O emissions are more significantly affected by economic growth, agricultural growth and energy use than emissions of CH 4. Findings from impulse response and variance decomposition analysis confirm that emissions of N 2O are more responsive to economic growth, agricultural growth and energy use than emissions of CH 4. Granger causality analysis shows existence of bidirectional relationship between CH 4 and agricultural growth, but a unidirectional relationship from CH 4 to FDI. For N 2O, the study finds a unidirectional relationship running from agricultural growth to N 2O, while N 2O emissions are found to cause GDP per capita, the squared GDP per capita, OIL consumption and FDI. In terms of causality, these results suggest that emissions of CH 4 and N 2O have been generated more by agricultural activities than by overall economic activity, and that activities generating N 2O emissions in particular have been contributing significantly to economic growth. Within the context of the country’s intended nationally determined contributions, the findings of this study suggest that policies should be directed cautiously but more effectively to control N 2O than CH 4 emissions. Economic growth could be pursued without significant environmental harm from both CH 4 and N 2O emissions. However, Sudan should expand adoption of energy efficiency measures, expansion of renewable energy use, place restrictions on production and use of fuel woods and charcoal for low carbon economy and green growth.
Global warming and climate changes are international environmental problems caused by emissions and concentration of greenhouse gases (GHGs) chief among them is carbon dioxide (CO2). Globally, in 2016, about 72% of GHG emissions consist of CO2, but non-CO2 emissions, namely methane (CH4) and nitrous oxides (N2O) make up shares of 19% and 6% and that CH4, N2O and Chlorofluorocarbons (CFCs) are the main GHGs contributing to global warming potential (WGP) (Olivier et al., 2017). CH4 was the largest contributor to non-CO2 GHG emissions, from non-dairy and dairy cattle and cattle manure management, while collectively coal mining, oil and natural gas production and distribution, and rice cultivation accounted for 25% and 10% of CH4 emissions respectively (Olivier et al., 2017). N2O emissions both anthropogenic and natural originate from soils which account for over half of total atmospheric N2O inputs, (Parton et al., 2001). Emissions of N2O are directly and indirectly related to land use and land cover changes (LULCC) and thus to agricultural growth and development. Sources emitting N2O directly include cultivated soils and fertilized and/or grazed grassland systems, while indirect emissions result from transport of nitrous from agricultural systems into ground and surface waters, or emission as ammonia or nitrogen oxides (Smith et al., 1999). N2O emissions stem also from agricultural biomass burning, industrial activities. According to the Intergovernmental Panel on Climate Change, IPCC, Climate Change (1995), compared with CO2 heat trapping, a kilogram of CH4 is 21 times as effective at trapping heat in the earth’s atmosphere as a kilogram (kg) of carbon dioxide within 100 years, while the per kg global warming potential (GWP) of N2O is nearly 310 times that of CO2 within 100 years. The same report (IPCC, Climate Change, 1995) attributes growth of CO2, CH4 and N2O to fossil fuel use, land use change and agriculture. Moreover, it has been documented that one pound of N2O warms the atmosphere almost 300 times more than one pound of CO2 (United States Environmental Protection Agency (EPA), 2018). Furthermore, releasing 1 kg of CH4 into the atmosphere is about equivalent to releasing 84 kg of CO2, while releasing 1 kg of N2O into the atmosphere is about equivalent to releasing about 298 kg of CO2 (Climate Change Connection, 2015). Most of CH4 and N2O emissions are related to LULCC, and intensive statistical analysis of these emissions can be found in Parton et al. (2001) and Houghton et al. (2012). Importantly, it has been recognized that policy targeting reduction of CH4 and N2O emissions result in higher social benefits compared with reductions of CO2 emissions based on the GWP of these trace gases (Marten & Newbold, 2014).
From 1997 to 2019, a number of conferences of Parties (COPs) have been held to reach a consensus and agreement to cut GHG emissions. The most remarkable achievement was Paris Agreement adopted at COP-21 in 2015, which replaced the Kyoto Protocol (KP) signed in 1997. Analytical and empirical efforts to analyze determinants of GHG emissions have first focused on CO2 emissions as the main cause of global warming and have preceded formal climate agreements including the United Nations Framework Convention on Climate Change (UNFCCC), entered into force on 21 March 1994. Earlier studies such as of Grossman & Krueger (1991) found that concentrations of sulfur dioxide and smoke increase with per capita gross domestic product (GDP) at low levels of national income, but decrease with GDP growth at higher levels of income in 42 countries. Then, Panayotou (1993) has invented the so-called environmental Kuznets curve (EKC). These are fundamental examples of such earlier investigations of the relationships between economic growth and trade on CO2 emissions. Analysis of non-CO2 GHGs, namely CH4 and N2O emissions is important for climate change policy that can be taken by countries, particularly in the context of the intended nationally determined contributions (INDCs) anchored in Paris 2015 Agreement. Yet, where climate change policy should be directed needs to be grounded on sound findings from rigorous empirical studies. Both mitigation and adaptation policies to climate change have costs, and thus only the most cost effective should be designed and implemented to control GHG emissions. In general, cross-sectional studies of agriculture using the Ricardian method suggest that efficient adaptation will reduce damages from climate change (Mendelsohn et al., 1994, 1996; Mendelsohn & Dinar, 1999). However, Mendelsohn (2000) upon analysis of different types of adaptations both private and public in agriculture, forestry and the energy sectors, states that adaptations against increases in climate variance are difficult to identify and are likely to have only modest net benefits.
Sudan is a low-income country; member of the UNFCCC, signed and ratified the KP and submitted its INDCs to Paris 2015 Agreement. Although mitigation is not a priority and it’s not binding with specific emission cut targets for Sudan, adaptation measures are extremely important since Sudan is vulnerable to different impacts of climate change including drought, flooding and reduced agricultural products (Nimir & Ismail, 2013). Nonetheless, for effective climate change adaptation, the driving forces of CH4 and N2O emissions as the main GHGs in Sudan need to be identified and their relative importance needs to be well understood. Emissions of these gases in Sudan stem mainly from agriculture, deforestation, energy use and municipal wastes. Yet, commercial energy in terms of oil production and consumption has been steadily increasing since the late 1990s and contributing increasingly to GHGs in general and CO2 emissions in particular. In Sudan, N2O increased by 439.29% and CH4 increased by 242.44% over the period 1970-2017 amounting to an annual rate of increase of 9.15% and 5.05% for N2O and CH4 respectively. Meanwhile, CO2 emissions have increased by 301.33% at an annual rate of 6.28%. Emissions from agriculture account for an average of 84% and 91% form total emissions for CH4 and N2O respectively (calculated from World Bank, 2017). The second source of CH4 and N2O emissions in Sudan is the energy sector. Since agriculture also accounts for more than 35% of GDP over the period 1970-2017, GDP separately and its agricultural component jointly could be major determinants of CH4 and N2O emissions in Sudan.
Despite their importance to global warming and climate change, relatively less analytical and empirical attention has been paid to determinants of CH4 and N2O emissions, globally and at country level. For Sudan, no such study on determinants of CH4 and N2O exists, and the country’s INDCs have not been grounded on findings of rigorous empirical studies. Since the early 1990s, Toth (1995) notes that although CO2 continues to be the single most important GHG, contributions of other trace gases to the aggregated global warming potential are sufficiently significant to justify their more equitable treatment in climate policy analysis. Even more recently, Sinha & Sengupta (2019) note that there are a limited number of studies on the estimation of EKC hypothesis for N2O emissions, though it is one of the most harmful GHGs present in ambient atmosphere.
Upon this introduction and background information, the aim of this study is to contribute to this literature and knowledge twofold. First, how differently economic growth, agricultural growth and energy use affect emissions of CH4 and N2O in Sudan. Secondly, the study comparatively addresses the two most important non-CO2 emissions with the same explanatory variables, in a context of a single country. Findings from such comparative analysis guide which and where policies can be directed effectively toward control of these emissions, and thus serves as evidence-based framework to climate change policy in Sudan in the context of its INDCs.
Huge empirical literature has been developing to investigate what development factors that lead to increases in GHGs emissions at the global and national levels. Studies from the field of environmental economics in the early 1990s the focus was on determining a level of per capita income at which GHGs generally and CO2 emissions in particular start a reversion from upward to downward slopping in the context of the EKC hypothesis (Panayotou, 1993). Empirical tests of the EKC were earlier undertaken by Selden & Song (1994) and Grossman & Krueger (1995), with focus on economic growth, trade liberalization and GHGs emissions. But, over time and across countries CO2 emissions per capita have been found to strongly correlate with GDP per capita (Stern, 2011). The focus has shifted to investigate probable determinants of GHGs emissions but focusing mostly on CO2 emissions, so as to design environmental and climate change policies compatible with economic development objectives.
In empirical literature, identified factors affecting GHGs emissions in general include the scale and rate of economic growth, energy use, openness to trade and flows of capital, urbanization and the contribution of the services sector to total economic output. Technological changes in the energy sector play an important role in GHGs emissions (Wheeler, 2012; Wang et al., 2012). Population density which is important factor in energy distribution and use by sectors has also been identified as important factor affecting GHGs emissions. Empirical literature on CO2 determinants of emissions can be found in Omri (2009), with reference to the Middle East and North Africa (MENA) countries, Kerkhof et al. (2009) for Sweden, Norway, United Kingdom and the Netherlands, Musolesi et al. (2010) for a panel of countries, Iwata et al. (2010) for the Organization of Economic Cooperation and Development (OECD) countries, Sharma (2011) for 69 countries, Stolyarova (2013) for 93 countries, clustered into 7 groups, Beck & Prathibha (2015) for OECD and non-OECD countries, Heidari et al. (2015) for The Association of Southeast Asian Nations (ASEAN) countries, Lin at al. (2016) for Africa, Maryam et al. (2017) for Brazil, Russia, India, China and South Africa (BRICS) countries, Jeremiás & Attila (2017) for 164 countries, Adewuyi & Awodumi (2017) for West African countries, Bekhet et al. (2017) for the Gulf Cooperation Council (GCC) countries, and Apergis et al. (2018) for 19 developed economies. Some of these studies find support for the EKC, while many of them find no support the hypothesis that CO2 emissions start a reversion trend with higher level of income over time.
At country level, there are numerous studies on economic growth and CO2 emissions, including, Soytas et al. (2007) for the United States, Zhang & Cheng (2009) and Wang et al. (2012) for China, Halicioglu (2009), Kargi (2014) and Bozkurt & Yusuf (2014) for Turkey, Iwata et al. (2009) for France, Tiwari (2011) for India, Banerjee & Rahman (2012) for Bangladesh, Odhiambo (2012) for South Africa, Birgit & Getzner (2013) for Austria, Bento (2014) for Italy, Nuno (2014) for Portugal. In addition, Lau et al. (2014) who tested the EKC between economic growth and CO2 emissions in Malaysia with incorporation of effects from foreign direct investment (FDI) and trade openness, and also Begum et al. (2015) for Malaysia, Mahmood & Shahab (2014) and Mirza & Kanwal (2017) for Pakistan, Kang et al. (2016) on EKC for China, Shahbaz et al. (2017) for Australia, Shmelev & Speck (2018) for Sweden, Mikayilov et al. (2018) for Azerbaijan, Elwasila (2018) who finds no support of EKC for Sudan, and Zheng et al. (2019) for regional development and carbon emissions in China, among others. Systematic review of studies on CO2 emissions was also undertaken by Mardani et al. (2019).
This study rather focuses on reviewing some of the literature investigating non-CO2 emissions, namely CH4 and N2O emissions. Managi et al. (2009) find an inverse U-shaped relationship between trade openness and sulphur dioxide (SO2) emissions in the case of OECD countries, but not for non-OECD countries. Zhang & Chen (2010) present inventories for CH4 emissions and embodied emissions in production, consumption, and international trade for the Chinese economy in 2007, documenting that the total CH4 emissions by Chinese economy in 2007 were 39,592.70 Greenhouse gas (Gg-CO2 equivalent), three quarters of China’s CO2 emissions from fuel combustion and greater than CO2 emissions from fuel combustion of many developed countries. They identified agriculture and coal mining as the dominant direct sources of emissions, and the construction sector holds the top embodied emissions in production and consumption. They state that China is a net exporter of embodied CH4 emissions with the emission embodied in exports represented 35.42% of the total direct emissions, mostly from exports of textiles, industrial raw materials, and primary machinery and equipments. They call for agricultural carbon-reduction strategies, coal bed methane recovery, export-oriented and low value added industry adjustment, and low carbon energy policies to address methane emissions. Also, Wang et al. (2016) find evidence in favour of the EKC hypothesis between economic growth and urbanization on SO2 emissions in China.
For the case of Turkey, Bölük & Mert (2015) examine the effect of electricity generated from renewable energy sources in reducing GHG emissions over the period 1961-2010 using the autoregressive distributed lag (ARDL) approach. Their results show that the coefficient of electricity production from renewable sources with respect to CO2 emissions is negative and significant in the long run and found a U-shaped (EKC) relationship between per capita GHGs and income, with a peak point of GDP per capita of $9920 which according to them was outside the observed sample period. Vavrek & Chovancova (2016) evaluate the relationship between economic development and GHGs emissions based on decoupling theory in the case of the Czech Republic, Hungary, Poland and Slovakia over the period of 1991-2012. Their results suggest that observed partial variables indicate strong decoupling of economic growth and GHGs emissions. The authors state that in order to meet their 2050 objectives to reduce GHG emissions, these countries need to accelerate restructuring the ways how they meet their demand for energy, food, transport and housing.
Utilizing time series data between 1970 and 2012 and the ARDL approach, Manuel & Mario (2017) analyze the relationship between N2O emissions, economic growth, agricultural land used and exports in Germany. Their results show a quadratic long run relationship between N2O emissions and economic growth, confirming the existence of an EKC for Germany. They estimated a turning point at $27,880 which according to them was within the sample and implies that Germany was in the decreasing part of the curve of environmental degradation. They also find that agricultural land area affects N2O emissions positively, whereas exports affect emissions negatively. The paper shows that, contrary to testing the EKC in less developed countries, mitigation of N2O emissions does not negatively affect growth in Germany and as such, it is feasible to undertake any conservative policy in order to reduce emissions without major consequences on economic sectors. Using panel unit root and cointegration tests, Cho et al. (2014) analyze determinants of total GHG, CH4 and N2O emissions. They found a quadratic relationship in the long run for OECD countries. They also found effects of trade openness on GHG emissions. Although exports may not affect N2O emissions according to Kearsley & Riddel (2010) the existence of an EKC for exports may be because of the pollution haven hypothesis (PHH) asserting transfer of pollution across countries instead of mitigating it which could be described as an inverse U-shaped relationship between exports and economic growth. For the European countries, Gielen & Kram (1998) find evidences that CH4, N2O and CFCs play a significant role in meeting Kyoto targets by EU member states, and that their emission reductions were forecast to contribute one quarter to the total emission reduction in 2010, given the emission reduction goals of individual European countries. Furthermore, Gambhir et al. (2017) find non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005$100/tCO2e or less throughout the 21st century compared to a marginal CO2 mitigation cost which was estimated to be already greater than this by 2030 in the most stringent mitigation scenario.
Benavides et al. (2017) use the ARDL method to investigate the relationship between emissions of CH4, GDP, electricity production from renewable energy sources (excluding hydro) and trade openness in Austria. They find an inverted U-shaped between GDP per capita and CH4 emissions as well as a unidirectional causality between CH4, GDP per capita and electricity production. Applying econometric methods on annual data for 1981-2012, Maralgua (2017) investigates the EKC hypothesis for N2O emissions, income, exports, urbanization, and growth in different sectors of the economy for Mongolia. The author finds a highly significant and robust long-run U-shaped relationship between N2O emissions and income. Furthermore, exports, urbanization, and growth in the industrial and services sectors were found to decrease N2O, while growth in the agricultural sector was found to increase N2O emissions. The author found significant short- and long-run Granger causal relationships amongst the variables. However, until recently, Sinha & Sengupta (2019) argue for better understanding of the impact of energy consumption pattern on N2O emissions and revision of energy policies. They analyzed the impact of renewable and fossil fuel energy consumption on N2O emissions for Asia Pacific Economic Cooperation (APEC) countries over the period of 1990-2015 in the context of the EKC hypothesis. They indicate that the efficacy of the renewable energy solutions help to reduce the level of N2O emissions, and energy policies should be designed to be compatible with objectives of sustainable development goals (SDGs) in these nations.
In conclusions, the findings of the above reviewed studies indicate that policy measures could be effective and less costly to be directed to non-CO2 emissions that directly targeting reduction of CO2 emissions.
This study investigates the effects of economic growth, agricultural growth and energy use with separate inclusion of oil consumption (OIL) on emissions of CH4 and N2O in Sudan in presence of trade openness (TOP) and inflows of foreign direct investments (FDI). The study utilizes annual time series data over the period 1970-2016. Within the context of the EKC, the study applies the ordinary least squares (OLS), cointegration, vector error correction modelling (VECM) and Granger causality methods. The study departs with a graphical analysis of total GHGs, CH4 and N2O emissions over time.
General two OLS models to analyze how differently economic growth, agricultural growth and energy use affect CH4 and N2O emissions in presence of (TOP) and (FDI) in natural logarithms (L) are written as follows:
L ( CH 4 ) = α + β 1 L ( GDPP ) + β 2 L ( GDPP ) 2 + β 3 L ( CPIN ) + β 4 L ( EUP ) + β 5 L ( OIL ) + β 7 L ( TOP ) + β 8 L ( FDI ) + μ (1)
L ( N 2 O ) = α + β 1 L ( GDPP ) + β 2 L ( GDPP ) 2 + β 3 L ( CPIN ) + β 4 ( EUP ) + β 5 L ( OIL ) + β 7 L ( TOP ) + β 8 L ( FDI ) + μ (2)
The variables are defined as follows:
CH4 is Methane emissions measured in Kiloton (Kt) of CO2 equivalent.
N2O is Nitrous Oxide emissions measured in thousand metric tons of CO2 equivalent.
GDPP is GDP per capita measured in current US Dollars.
EUP is the energy use per capita measured in kg of oil equivalent per capita.
Oil is oil consumption measured in kg per capita.
CPIN is crop production index which shows agricultural production for each year relative to the base period 2004-2006. It includes all crops except fodder crops.
TOP is trade openness measured as exports plus imports as percentage of GDP.
FDI is the inflow of foreign direct investments to Sudan, measured in current US Dollars.
The study departs with descriptive statistical analysis for both CH4 and N2O emissions together with the set of explanatory variables, as presented in
The correlation matrix in
The correlation matrices give an initial idea that N2O emissions are more affected by economic growth and agricultural growth than CH4 emissions.
Equation (1) is estimated by the method of OLS to investigate the relationship between CH4 and economic and agricultural growth. The OLS results indicate that CH4 is positively and significantly affected by economic growth in terms of the squared value of GDPP, agricultural growth and to less extent by trade openness, while negatively and significantly affected by the actual value of GDPP and energy use. OIL consumption and FDI have no significant effects on CH4 emissions. For N2O, the OLS results indicate that N2O emissions are positively and significantly affected by the squared value of GDPP, agricultural growth and trade openness, while negatively and significantly affected by the actual value of GDPP and energy use. OIL and FDI have no significant effects on N2O emissions. For both CH4 and N2O emissions, the OLS results are summarized in
The OLS estimates show that the coefficients of N2O emissions with respect to economic growth are much higher than the coefficients of CH4 with respect to economic growth. Results of the two models for both types of emissions show that low level of economic growth is also associated with low level of emissions, and emissions increase monotonically with increases in GDP per capita over time. But, both of the OLS models for CH4 and N2O appear unstable on the basis of the plots of cumulative sum of squares of CUSUM of the recursive residuals. Therefore the OLS estimators for both CH4 and N2O emissions are not reliable as shown by Figures 2-5.
| Mean | Maximum | Minimum | Std. Dev. | J-B | Prob. | Obs. | |
|---|---|---|---|---|---|---|---|
| CH4 | 59,487.03 | 125,045.0 | 30,622.10 | 25,947.86 | 6.12 | 0.000 | 47 |
| N2O | 50,978.73 | 130,632.7 | 20,251.59 | 35,263.29 | 12.54 | 0.002 | 47 |
| GDPP | 713.28 | 2684.63 | 175.63 | 606.57 | 34.19 | 0.000 | 47 |
| GDP2 | 868,869.2 | 7,207,238 | 30,844.57 | 1,556,282.0 | 131.82 | 0.000 | 47 |
| CPIN | 70.78 | 108.26 | 38.06 | 24.68 | 5.37 | 0.068 | 47 |
| EUP | 410.22 | 491.38 | 350.84 | 36.33 | 3.41 | 0.182 | 47 |
| OIL | 0.07 | 0.09 | 0.04 | 0.02 | 2.29 | 0.319 | 47 |
| TOP | 26.87 | 47.58 | 11.09 | 9.62 | 1.27 | 0.529 | 47 |
| FDI | 556.36 | 2311.46 | 0.000 | 764.57 | 7.90 | 0.019 | 47 |
| CH4 | ||||||||
|---|---|---|---|---|---|---|---|---|
| CH4 | GDPP | (GDPP)2 | CPIN | EUP | OIL | TOP | FDI | |
| CH4 | 1.00 | |||||||
| GDPP | 0.80 | 1.00 | ||||||
| (GDPP)2 | 0.72 | 0.97 | 1.00 | |||||
| CPIN | 0.91 | 0.70 | 0.63 | 1.00 | ||||
| EUP | −0.68 | −0.40 | −0.25 | −0.67 | 1.00 | |||
| OIL | 0.67 | 0.65 | 0.57 | 0.66 | −0.22 | 1.00 | ||
| TOP | 0.27 | −0.01 | −0.07 | 0.32 | 0.03 | 0.59 | 1.00 | |
| FDI | 0.90 | 0.75 | 0.65 | 0.92 | −0.60 | 0.83 | 0.44 | 1.00 |
| N2O | ||||||||
| N2O | GDPP | (GDPP)2 | CPIN | EUP | OIL | TOP | FDI | |
| N2O | 1.00 | |||||||
| GDPP | 0.89 | 1.00 | ||||||
| (GDPP)2 | 0.82 | 0.97 | 1.00 | |||||
| CPIN | 0.83 | 0.70 | 0.63 | 1.00 | ||||
| EUP | −0.56 | −0.40 | −0.25 | −0.67 | 1.00 | |||
| OIL | 0.71 | 0.65 | 0.57 | 0.66 | −0.22 | 1.00 | ||
| TOP | 0.23 | −0.01 | −0.07 | 0.32 | 0.03 | 0.59 | 1.00 | |
| FDI | 0.87 | 0.75 | 0.65 | 0.92 | −0.60 | 0.83 | 0.44 | 1.00 |
| CH4 | N2O | |||||
|---|---|---|---|---|---|---|
| Variable | Coefficient | t-Statistic | Prob. | Coefficient | t-Statistic | Prob. |
| L(GDPP) | −1.52 | −2.937 | 0.006* | −4.03 | −5.633 | 0.000* |
| L(GDP)2 | 0.13 | 3.285 | 0.002* | 0.34 | 6.205 | 0.000* |
| L(CPIN) | 0.51 | 5.332 | 0.000* | 0.41 | 3.059 | 0.004* |
| L(EUP) | −1.63 | −4.687 | 0.000* | −2.30 | −4.773 | 0.000* |
| L(OIL) | −0.17 | −1.562 | 0.126 | −0.23 | −1.553 | 0.129 |
| L(TOP) | 0.12 | 1.657 | 0.106 | 0.31 | 3.057 | 0.004* |
| L(FDI) | 0.004 | 0.973 | 0.336 | −0.000 | −0.030 | 0.976 |
| C | 22.03 | 6.091 | 0.000* | 32.76 | 6.564 | 0.000* |
| R-squared = 0.94; Adjusted R-squared = 0.93; SER = 0.107; SSR = 0.443; LL = 42.940; AIC= −1.487; SC = −1.172; HQC = −1.368; F-statistic = 92.24 (P. 0.000); DW = 1.76 Diagnostic Tests Stat. Prob. DW Normality (J-B) (9.04) 0.011 1.76 Autocorrelation F (0.28) 0.754 1.98 Heteroskedasticity F (0.82) 0.578 2.46 Stability RESET F (1.15) 0.259 1.87 | R-squared = 0.95; Adj. R-squared = 0.94; SER = 0.147; SSR = 0.843; LL = 27.80; F. Stat. = 96.06 (P. 0.000); AIC = −0.843; SC = −0.528; HQC = −0.724; DW = 1.73 Diagnostic Tests Stat. Prob. DW Normality (J-B) (3.32) 0.190 1.73 Autocorrelation F (0.41) 0.667 2.00 Heteroskedasticity F (1.09) 0.390 2.61 Stability RESET F (1.12) 0.276 1.68 | |||||
* indicates significance at 1%level.
As some of the main variables of the study, i.e., CH4, N2O and GDPP are found to follow non-normal distribution, and the likelihood that the variables may not follow an autoregressive (AR), and a first differenced random walk I(1), which in fact were reflected by the unreliability of the above estimated OLS models, the study proceeds to dynamic econometric methods of cointegration, VECM and Granger causality analysis.
Stationarity and Cointegration1) Stationarity Analysis
For meaningful estimation of time series econometric models and for reliable results, the stationarity of the variables must be established otherwise, estimations may be spurious (Granger, 2001). The non-stationarity and presence of a unit root in the series implies that and any shock will have a permanent effect on the system of variables. Unit root tests, namely Augmented Dickey-Fuller (ADF), Dickey & Fuller (1981), and Philips Perron (PP), Phillips & Perron (1988) tests are commonly used for establishment of stationarity. This study uses the ADF and PP unit root tests. The general ADF test equation with p lags is written as:
Δ y t = γ y t + β 1 Δ y t − 1 + ⋯ + β p Δ y t − p + ε t (3)
The null hypothesis (non-stationarity) is that H0: γ = 0, and the alterative (stationarity) AR process H1: γ < 0. Assuming a drift (a random walk with a nonzero mean period-to-period change) against stationarity, the ADF function is written as:
Δ y t = α + γ y t + β 1 Δ y t − 1 + ⋯ + β p Δ y t − p + ε t (4)
With a linear trend in order to test whether the series is I(1) against the alternative that it is stationary around a fixed linear trend, a trend term component needs to be added along with the constant, i.e.,
Δ y t = α + δ t + γ y t + β 1 Δ y t − 1 + ⋯ + β p Δ y t − p + ε t (5)
The lag p should be large enough to make the error term ε white noise. The estimated ADF statistics is compared with the simulated MacKinnon (2010) critical values, which employ a set of simulations to derive asymptotic results and to simulate critical values for arbitrary sample sizes. The ADF statistics must be larger than critical values in absolute value and have a minus sign. For CH4, using the ADF and PP unit root tests, with the assumption of intercept only, and with trend and intercept, all variables included in the study are found to be nonstationary at level I(0), but they all turn to be stationary at first difference I(1) at 5 percent level of significance as presented in
2) Cointegration
Given that all variables are found to be integrated at the first order, the long run nature of the relationships between CH4, N2O and their explanatory variables included in the model are tested by the Johansen cointegration method. Applying this method, for CH4 with the assumption of intercept only, the maximum Eigen value statistics indicate existence of at least three cointegrating vectors. With the assumption of intercept and trend in data, the maximum Eigen value statistics indicate existence of at least two cointegrating vectors as summarized in
Thus, stationarity of the study variables is established by the unit root tests and a long run equilibrium relationship is confirmed for both CH4 and N2O with economic growth, agricultural growth and energy use in presence of trade openness and inflows of FDI. Moreover, it appears that emissions of N2O have more and persistent co-movements with economic growth, agricultural growth and energy use than emissions of CH4. Upon establishment of stationarity at I(1) and the cointegration of the variables, the study proceeds to estimate an unrestricted vector autoregressive VAR model upon which an optimal lag length is selected for estimation of VECM. Results of the optimal lag length are summarized in
| CH4 Stationarity | |||||
|---|---|---|---|---|---|
| Variable | Intercept | Trend & Intercept | Intercept | Trend & Intercept | Order of integration |
| ADF I(1) | ADF I(1) | PP I(1) | PP I(1) | ||
| L(CH4) | −8.176* | −8.110* | −8.971* | −8.971* | I(1) |
| L(GDPP) | −6.149* | −6.129* | −6.129* | −6.129* | I(1) |
| L(GDPP)2 | −6.006* | −6.056* | −6.056* | −6.056* | I(1) |
| L(EUP) | −8.887* | −9.501* | −9.510* | −9.510* | I(1) |
| L(OIL) | −5.496* | −5.595* | −5.578* | −5.578* | I(1) |
| L(CPIN) | −10.295* | −10.188* | −10.546* | −10.546* | I(1) |
| L(TOP) | −8.381* | −8.284* | −8.171* | −8.171* | I(1) |
| L(FDI) | −7.266* | −7.190* | −25.252* | −25.252* | I(1) |
| N2O Stationarity | |||||
| Intercept | Trend & Intercept | Intercept | Trend & Intercept | Order of integration | |
| ADF I(1) | ADF I(1) | PP I(1) | PP I(1) | ||
| L(N2O) | −7.443* | −7.528* | −7.443* | −7.535* | I(1) |
| L(GDPP) | −6.149* | −6.129* | −6.149* | −6.129* | I(1) |
| L(GDPP)^2 | −6.006* | −6.056* | −6.004* | −6.056* | I(1) |
| L(EUP) | −8.887* | −9.501* | −9.046* | −9.501* | I(1) |
| L(OIL) | −5.496* | −5.595* | −5.482* | −5.578* | I(1) |
| L(CPIN) | −10.295* | −10.188* | −10.659* | −10.546* | I(1) |
| L(TOP) | −8.381* | −8.284* | −8.260* | −8.171* | I(1) |
| L(FDI) | −7.266* | −7.190* | −22.946* | −25.252* | I(1) |
* indicates stationary at 1% level; for the sake of space and ease of exposition only ADF and PP statistics for I(1) are reported.
| CH4 cointegration | ||||||
|---|---|---|---|---|---|---|
| H0 | Intercept | Trend & Intercept | ||||
| Eigen Value | Trace Statistic | Max-Eigen Statistic | Eigen Value | Trace Statistic | Max-Eigen Statistic | |
| r = 0 | 0.823 | 269.837* | 76.083* | 0.823 | 293.710* | 76.256* |
| r ≤ 1 | 0.781 | 193.755* | 66.783* | 0.781 | 217.454* | 66.788* |
| r ≤ 2 | 0.600 | 126.972* | 40.267* | 0.621 | 150.667* | 42.636 |
| r ≤ 3 | 0.472 | 86.705* | 28.135 | 0.549 | 108.030* | 35.021 |
| r ≤ 4 | 0.382 | 58.570* | 21.200 | 0.392 | 73.009* | 21.903 |
| r ≤ 5 | 0.34 | 37.370* | 18.694 | 0.358 | 51.106* | 19.510 |
| r ≤ 6 | 0.306 | 18.676* | 16.055 | 0.315 | 31.596* | 16.617 |
| r ≤ 7 | 0.058 | 2.621 | 2.621 | 0.289 | 14.980* | 14.980 |
| N2O Cointegration | ||||||
| Intercept | Trend & Intercept | |||||
| Eigen Value | Trace Statistic | Max-Eigen Statistic | Eigen Value | Trace Statistic | Max-Eigen Statistic | |
| r = 0 | 0.869 | 284.211* | 89.313* | 0.869 | 300.806* | 89.385* |
| r ≤ 1 | 0.727 | 194.898* | 57.067* | 0.727 | 211.421* | 57.096* |
|---|---|---|---|---|---|---|
| r ≤ 2 | 0.651 | 137.832* | 46.301* | 0.658 | 154.325* | 47.243* |
| r ≤ 3 | 0.565 | 91.530* | 36.586* | 0.591 | 107.082* | 39.354* |
| r ≤ 4 | 0.370 | 54.945* | 20.351 | 0.371 | 67.728* | 20.394 |
| r ≤ 5 | 0.338 | 34.594* | 18.136 | 0.348 | 47.335* | 18.844 |
| r ≤ 6 | 0.267 | 16.458* | 13.688 | 0.331 | 28.490* | 17.706 |
| r ≤ 7 | 0.061 | 2.770 | 2.770 | 0.217 | 10.785 | 10.785 |
Note: * denotes rejection of the null hypothesis at the 0.05 level.
| CH4 | ||||||
|---|---|---|---|---|---|---|
| Lag | LL | LR | FPE | AIC | SC | HQ |
| 1 | 168.081 | NA | 1.26e-12 | −4.731 | −2.136* | −3.769* |
| 2 | 236.975 | 87.684* | 1.29e-12 | −4.953 | 0.237 | −3.029 |
| 3 | 324.441 | 79.515 | 9.71e-13* | −6.020* | 1.766 | −3.133 |
| N2O | ||||||
| Lag | LL | LR | FPE | AIC | SC | HQ |
| 0 | −85.378 | NA | 9.63e-09 | 4.244 | 4.569 | 4.365 |
| 1 | 167.445 | 402.218 | 1.89e-12 | −4.338 | −1.419* | −3.256* |
| 2 | 237.800 | 86.345* | 1.89e-12 | −4.627 | 0.887 | −2.582 |
| 3 | 325.599 | 75.826 | 1.56e-12* | −5.709* | 2.401 | −2.701 |
Note: LR: sequentially modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion; SC: Schwarz information criterion; HQ: Hannan-Quinn information criterion.
A VECM version of Equation (1) is estimated for CH4 emissions and of Equation (2) for N2O emissions, both on lag length of 2 according to LR criterion as in
The VECM models estimates show that the coefficients of N2O emissions with respect to economic growth and agricultural growth are much higher and more significant than the coefficients of CH4 with respect to economic growth and agricultural growth. Results of the two models for both types of emissions show that low level of economic growth is also associated with low level of emissions, but while it is found that emissions of CH4 increase significantly with the squared value of the GDP per capita, this does not hold for emissions of N2O in the long run. Positive coefficients for both CH4 and N2O emissions with respect to the squared value of GDP per capita contradict predications of the EKC in the case of Sudan, more strongly and clearly for CH4 emissions. Also, N2O emissions are better correctly adjusting back to equilibrium than CH4 emissions in response to shocks in the system. The VECM models for both types of emissions proved to be stable and not miss-specified as depicted by
| CH4 VECM | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | Short Run | Variable | Long Run | ||||||||
| Coefficient | T. stat. | P. value | Coefficient | T. stat. | |||||||
| ECTt−1 | −0.16 | −2.063 | 0.040* | L(CH4)t−1 | 1.00 | ||||||
| dL(CH4)t−1 | −0.18 | −1.032 | 0.303 | L(GDPP)t−1 | −4.17 | −4.828** | |||||
| dL(CH4)t-2 | 0.00 | 4.9e-05 | 1.000 | L(GDPP)2t−1 | 0.32 | 4.743** | |||||
| dL(GDPP)t−1 | −1.45 | −1.260 | 0.209 | L(CPIN)t−1 | −1.88 | −8.371*** | |||||
| dL(GDPP)t−2 | 0.75 | 0.600 | 0.549 | L(EUP)t−1 | −0.27 | −0.394 | |||||
| dL(GDPP)2t−1 | 0.12 | 1.293 | 0.198 | L(OIL)t−1 | −0.09 | −0.584 | |||||
| dL(GDPP)2t−2 | −0.08 | −0.742 | 0.459 | L(TOP)t−1 | −0.53 | −4.547** | |||||
| dL(CPIN)t−1 | −0.47 | −3.145 | 0.002** | L(FDI)t−1 | 0.07 | 6.515*** | |||||
| dL(CPIN)t−2 | −0.24 | −1.611 | 0.109 | C | 13.35 | ||||||
| dL(EUP)t−1 | 0.97 | 2.202 | 0.029* | ||||||||
| dL(EUP)t−2 | 0.19 | 0.439 | 0.661 | ||||||||
| dL(OIL)t−1 | 0.18 | 1.199 | 0.232 | ||||||||
| dL(OIL)t−2 | −0.20 | −1.309 | 0.192 | ||||||||
| dL(TOP)t−1 | 0.08 | 0.762 | 0.447 | ||||||||
| dL(TOP)t−2 | 0.03 | 0.279 | 0.781 | ||||||||
| dL(FDI)t−1 | 0.01 | 1.245 | 0.215 | ||||||||
| dL(FDI)t−2 | 0.01 | 1.344 | 0.180 | ||||||||
| C | 0.06 | 3.237 | 0.001** | ||||||||
| N2O VECM | |||||||||||
| Variable | Short Run | Variable | Long Run | ||||||||
| Coefficient | T. stat. | P. value | Coefficient | T. stat. | |||||||
| ECTt−1 | −0.34 | −3.404 | 0.001** | L(N2O)t−1 | 1.00 | ||||||
| d(L(N2O)t−1 | 0.06 | 0.334 | 0.739 | L(GDPP)t−1 | −1.55 | −1.879* | |||||
| d(L(N2O)t−2 | 0.08 | 0.514 | 0.608 | L(GDPP)2t−1 | 0.09 | 1.341 | |||||
| d(L(GDPP)t−1 | 0.60 | 0.341 | 0.733 | L(CPIN)t−1 | −2.03 | −9.512*** | |||||
| d(L(GDPP)t−2 | 1.42 | 0.744 | 0.458 | L(EUP)t−1 | 0.59 | 0.902 | |||||
| d(L(GDPP)2t−1 | −0.06 | −0.428 | 0.669 | L(OIL)t−1 | 0.15 | 1.010 | |||||
| d(L(GDPP)2t−2 | −0.14 | −0.917 | 0.365 | L(TOP)t−1 | −1.01 | −9.070*** | |||||
| d(L(CPIN)t−1 | −1.01 | −4.855 | 0.000*** | L(FDI)t−1 | 0.09 | 8.742*** | |||||
| d(L(CPIN)t−2 | −0.55 | −2.485 | 0.014** | C | 4.08 | ||||||
| d(L(EUP)t−1 | 1.82 | 2.766 | 0.006** | ||||||||
| d(L(EUP)t−2 | 0.69 | 1.080 | 0.282 | ||||||||
| d(L(OIL)t−1 | 0.38 | 1.689 | 0.095* | ||||||||
| d(L(OIL)t−2 | −0.33 | −1.490 | 0.138 | ||||||||
| d(L(TOP)t−1 | −0.03 | −0.198 | 0.844 | ||||||||
| d(L(TOP)t−2 | −0.17 | −1.102 | 0.272 | ||||||||
| d(L(FDI)t−1 | 0.02 | 2.241 | 0.026** | ||||||||
| d(L(FDI)t−2 | 0.01 | 2.129 | 0.035** | ||||||||
| C | 0.10 | 3.807 | 0.000*** | ||||||||
As presented in
| CH4 | N2O |
|---|---|
| Robustness R-squared = 0.48; Adj. R-squared = 0.14; SSR = 0.183; SER = 0.084; F. stat. = 1.42; LL = 58.15; AIC = −1.82; SC = −1.10 Diagnostic Tests Stat. P. value Normality 27.91 0.032 Autocorrelation 116.40 0.576 Heteroskedasticity 1237.75 0.386 Stability: VEC specification imposes 7 unit roots, none of them is outside the unit root circle | Robustness R-squared = 0.58; Adj. R-squared = 0.30; SSR = 0.400; SER = 0.124; F. stat. = 2.10; LL = 40.99; AIC = −1.05; SC = −0.32; SW = 2.02 Diagnostic Tests Stat. P. value Normality 15.41 0.495 Autocorrelation 113.06 0.661 Heteroskedasticity 1233.51 0.418 Stability; VEC specification imposes 7 unit roots; none is outside the unit circle |
Granger non-causality or exogeneity of variables is also tested through the Wald test in order to judge which variables lead and which lag the others, for both CH4 and N2O emissions. As in
In accordance with the VECM, impacts on CH4 and N2O and their volatility with economic and agricultural growth shocks are also assessed through the impulse response function (IRF). As in
The method of variance decomposition shows that variations in CH4 are much explained by CH4 itself, TOP and FDI, than by EUP and CPIN. GDPP contributes the least in explaining variations in CH4 as reported in
| CH4 Granger Causality/Block Exogeneity Wald Test | ||||
|---|---|---|---|---|
| Dependent Variable | Chi-sq | DF | Prob. | Decision |
| L(CH4)|L(GDPP), L(GDPPP)2, L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 21.05 | 14 | 0.100 | Reject |
| L(GDPP)|L(CH4), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 13.82 | 14 | 0.463 | Accept |
| L(GDPP)2)|L(CH4), L(GDPP), L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 13.97 | 14 | 0.452 | Accept |
| L(CPIN)|L(CH4), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(TOP), L(FDI) | 23.22 | 14 | 0.057 | Reject |
| L(EUP)|L(CH4), L(GDPP), L(GDPP)2, L(OIL), L(CPIN), L(TOP), L(FDI) | 23.77 | 14 | 0.049 | Reject |
| L(OIL)|L(CH4), L(GDPP), L(GDPP)2, L(EUP), L(CPIN), L(TOP), L(FDI) | 14.85 | 14 | 0.389 | Accept |
| L(TOP)|L(CH4), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(FDI) | 14.63 | 14 | 0.404 | Accept |
| L(FDI)|L(CH4), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(TOP) | 7.65 | 14 | 0.907 | Accept |
| N2O Granger Causality/Block Exogeneity Wald Test | ||||
| Dependent Variable | Chi-sq | DF | Prob. | Decision |
| L(N2O)|L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 32.40 | 14 | 0.004 | Reject |
| L(GDPP)|L(N2O), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 13.66 | 14 | 0.475 | Accept |
| L(GDPP)2)|L(N2O), L(GDPP), L(EUP), L(OIL), L(CPIN), L(TOP), L(FDI) | 13.81 | 14 | 0.464 | Accept |
| L(CPIN)|L(N2O), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(TOP), L(FDI) | 20.44 | 14 | 0.117 | Accept |
| L(EUP)|LL(N2O), L(GDPP), L(GDPP)2, L(OIL), L(CPIN), L(TOP), L(FDI) | 20.12 | 14 | 0.126 | Accept |
| L(OIL)|L(N2O), L(GDPP), L(GDPP)2, L(EUP), L(CPIN), L(TOP), L(FDI) | 13.25 | 14 | 0.507 | Accept |
| L(TOP)|L(N2O), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(FDI) | 15.19 | 14 | 0.365 | Accept |
| L(FDI)|L(N2O), L(GDPP), L(GDPP)2, L(EUP), L(OIL), L(CPIN), L(TOP) | 6.29 | 14 | 0.959 | Accept |
| Response of L(CH4) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Period | L(CH4) | L(GDPP) | L(GDPP)2 | L(CPIN) | L(EUP) | L(OIL) | L(TOP) | L(FDI) |
| 1 | 0.084 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 2 | 0.072 | 0.006 | 0.030 | −0.014 | 0.022 | 0.022 | 0.022 | −0.017 |
| 3 | 0.063 | −0.009 | 0.013 | 0.007 | 0.014 | 0.011 | 0.028 | −0.020 |
| 4 | 0.060 | 0.009 | 0.018 | 0.011 | 0.012 | 0.005 | 0.036 | −0.026 |
| 5 | 0.070 | 0.005 | 0.001 | −0.006 | 0.013 | 0.008 | 0.028 | −0.034 |
| 6 | 0.060 | 0.006 | −0.001 | 0.008 | 0.010 | 0.016 | 0.041 | −0.032 |
| 7 | 0.062 | 0.006 | −0.013 | 0.005 | 0.009 | 0.017 | 0.040 | −0.026 |
| 8 | 0.064 | 0.003 | −0.012 | 0.005 | 0.015 | 0.023 | 0.049 | −0.031 |
| 9 | 0.063 | 0.004 | −0.019 | 0.008 | 0.014 | 0.026 | 0.052 | −0.028 |
| 10 | 0.065 | 0.004 | −0.021 | 0.007 | 0.018 | 0.026 | 0.057 | −0.025 |
| Response of L(N2O) | ||||||||
| Period | L(N2O) | L(GDPP) | L(GDPP)2 | L(CPIN) | L(EUP) | L(OIL) | L(TOP) | L(FDI) |
| 1 | 0.124 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 2 | 0.113 | −0.006 | 0.036 | −0.037 | 0.025 | 0.034 | 0.035 | −0.042 |
| 3 | 0.096 | −0.017 | 0.020 | −0.006 | 0.012 | 0.008 | 0.042 | −0.054 |
| 4 | 0.086 | 0.027 | 0.030 | 0.002 | −0.013 | −0.011 | 0.068 | −0.067 |
| 5 | 0.100 | 0.029 | 0.019 | −0.030 | −0.004 | 0.006 | 0.045 | −0.092 |
| 6 | 0.094 | 0.041 | 0.019 | −0.011 | −0.012 | 0.014 | 0.077 | −0.095 |
| 7 | 0.081 | 0.054 | 0.000 | −0.015 | −0.010 | 0.019 | 0.078 | −0.077 |
| 8 | 0.094 | 0.049 | −0.002 | −0.020 | 0.001 | 0.031 | 0.090 | −0.078 |
| 9 | 0.090 | 0.053 | −0.003 | −0.012 | 0.000 | 0.037 | 0.101 | −0.075 |
| 10 | 0.092 | 0.054 | −0.007 | −0.017 | 0.007 | 0.032 | 0.107 | −0.067 |
| Variance Decomposition of L(CH4) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Period | S.E. | L(CH4) | L(GDPP) | L(GDPP)2 | L(CPIN) | L(EUP) | L(OIL) | L(TOP) | L(FDI) |
| 1 | 0.084 | 100.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 2 | 0.123 | 80.9183 | 0.254 | 6.039 | 1.365 | 3.178 | 3.155 | 3.076 | 2.015 |
| 3 | 0.145 | 77.591 | 0.554 | 5.157 | 1.186 | 3.290 | 2.830 | 5.997 | 3.395 |
| 4 | 0.165 | 73.040 | 0.746 | 5.120 | 1.346 | 3.083 | 2.269 | 9.286 | 5.112 |
| 5 | 0.185 | 72.230 | 0.669 | 4.055 | 1.160 | 2.965 | 1.977 | 9.605 | 7.339 |
| 6 | 0.203 | 69.111 | 0.654 | 3.384 | 1.121 | 2.718 | 2.287 | 12.173 | 8.554 |
| 7 | 0.219 | 67.479 | 0.630 | 3.244 | 1.025 | 2.526 | 2.558 | 13.756 | 8.781 |
| 8 | 0.237 | 64.686 | 0.556 | 3.020 | 0.914 | 2.546 | 3.126 | 16.016 | 9.137 |
| 9 | 0.255 | 62.109 | 0.505 | 3.157 | 0.893 | 2.491 | 3.712 | 18.042 | 9.091 |
| 10 | 0.273 | 59.719 | 0.465 | 3.320 | 0.846 | 2.587 | 4.152 | 20.128 | 8.785 |
| Variance decomposition of L(N2O) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Period | S.E. | L(N2O) | L(GDPP) | L(GDPP)2 | L(CPIN) | L(EUP) | L(OIL) | L(TOP) | L(FDI) |
| 1 | 0.124 | 100.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 2 | 0.189 | 79.036 | 0.093 | 3.625 | 3.837 | 1.785 | 3.291 | 3.422 | 4.912 |
| 3 | 0.224 | 73.971 | 0.632 | 3.374 | 2.783 | 1.557 | 2.439 | 6.002 | 9.243 |
| 4 | 0.262 | 64.997 | 1.545 | 3.770 | 2.048 | 1.376 | 1.963 | 11.070 | 13.231 |
| 5 | 0.302 | 59.760 | 2.100 | 3.247 | 2.513 | 1.052 | 1.517 | 10.512 | 19.299 |
| 6 | 0.343 | 53.907 | 3.068 | 2.811 | 2.051 | 0.938 | 1.355 | 13.156 | 22.714 |
| 7 | 0.374 | 50.049 | 4.662 | 2.363 | 1.883 | 0.862 | 1.388 | 15.465 | 23.328 |
| 8 | 0.408 | 47.294 | 5.365 | 1.984 | 1.823 | 0.723 | 1.747 | 17.867 | 23.195 |
| 9 | 0.442 | 44.631 | 6.031 | 1.701 | 1.637 | 0.618 | 2.199 | 20.476 | 22.707 |
| 10 | 0.473 | 42.701 | 6.539 | 1.505 | 1.559 | 0.564 | 2.382 | 22.937 | 21.814 |
| Cholesky Ordering: L(CH4) L(GDPP) L(GDPP)2 L(CPIN) LEUP) L(OIL) L(TOP) L(FDI) | |||||||||
Long run causal relationships between CH4, N2O with the same set of their explanatory variables are tested by method of Granger causality. Results show a bidirectional causal relationship between agricultural growth and CH4 emissions, while a unidirectional relationship is fund to run from agricultural growth to N2O emissions. For both CH4 and N2O emissions, causal relationships between the independent variables, causal relationships are found to be concentrated around economic and agricultural growth. Granger causality results for CH4 and N2O are summarized and presented in
| CH4 | ||||
|---|---|---|---|---|
| H0: CH4 as the dependent | F-Stat. | Prob. | Decision | Direction of causality |
| H0: L(GDPP) does not Cause L(CH4) | 0.231 | 0.795 | Accept | None |
| H0: L(CH4) does not Cause L(GDPP) | 1.690 | 0.198 | Accept | None |
| H0: L(GDPP)2 does not Cause L(CH4) | 0.282 | 0.756 | Accept | None |
| H0: L(CH4) does not Cause L(GDPP)2 | 1.654 | 0.204 | Accept | None |
| H0: L(CPIN) does not Cause L(CH4) | 3.347 | 0.045 | Reject | CPIN to CH4 |
| H0: L(CH4) does not Cause L(CPIN) | 2.993 | 0.062 | Reject | CH4 to CPIN |
| H0: L(EUP) does not Cause L(CH4) | 1.016 | 0.371 | Accept | None |
| H0: L(CH4) does not Cause L(EUP) | 0.346 | 0.710 | Accept | None |
| H0: L(OIL) does not Cause L(CH4) | 0.390 | 0.680 | Accept | None |
| H0: L(CH4) does not Cause L(OIL) | 1.968 | 0.153 | Accept | None |
| H0: L(TOP) does not Cause L(CH4) | 1.201 | 0.311 | Accept | None |
| H0: L(CH4) does not Cause L(TOP) | 0.004 | 0.996 | Accept | None |
| H0: L(FDI) does not Cause L(CH4) | 0.274 | 0.762 | Accept | None |
| H0: L(CH4) does not Cause L(FDI) | 4.130 | 0.023 | Reject | CH4 to FDI |
| H0: Independents | ||||||
|---|---|---|---|---|---|---|
| H0: L(CPIN) does not Cause L(GDPP) | 3.377 | 0.044 | Reject | CPIN to GDPP | ||
| H0: L(OIL) does not Cause L(GDPP) | 5.031 | 0.011 | Reject | OIL to GDPP | ||
| H0: L(TOP) does not Cause L(GDPP) | 7.302 | 0.002 | Reject | TOP to GDPP | ||
| H0: L(OIL) does not Cause L(GDPP)2 | 4.594 | 0.016 | Reject | OIL to (GDPP)2 | ||
| H0: L(CPIN) does not Cause L(GDPP)2 | 3.449 | 0.042 | Reject | CPIN to (GDPP)2 | ||
| H0: L(TOP) does not Cause L(GDPP)2 | 7.232 | 0.002 | Reject | TOP to (GDPP)2 | ||
| H0: L(CPIN) does not Cause L(OIL) | 3.550 | 0.038 | Reject | CPIN to OIL | ||
| H0: L(FDI) does not Cause L(OIL) | 4.129 | 0.023 | Reject | FDI to OIL | ||
| H0: L(OIL) does not Cause L(FDI) | 2.430 | 0.101 | Reject | OIL to FDI | ||
| H0: L(CPIN) does not Cause L(FDI) | 8.518 | 0.001 | Reject | CPIN to FDI | ||
| N2O | ||||||
| H0: N2O as the dependent | F-Stat. | Prob. | Decision | Direction of causality | ||
| H0: L(GDPP) does not Cause L(N2O) | 0.818 | 0.449 | Accept | None | ||
| H0: L(N2O) does not Cause L(GDPP) | 2.449 | 0.099 | Reject | N2O to GDPP | ||
| H0: L(GDPP)2 does not Cause L(N2O) | 0.773 | 0.468 | Accept | None | ||
| H0: L(N2O) does not Cause L(GDPP)2 | 2.396 | 0.104 | Reject | N2O to (GDPP)2 | ||
| H0: L(CPIN) does not Cause L(N2O) | 5.826 | 0.006 | Reject | CPIN to N2O | ||
| H0: L(N2O) does not Cause L(CPIN) | 1.323 | 0.278 | Accept | None | ||
| H0: L(EUP) does not Cause L(N2O) | 0.993 | 0.380 | Accept | None | ||
| H0: L(N2O) does not Cause L(EUP) | 1.137 | 0.331 | Accept | None | ||
| H0: L(OIL) does not Cause L(N2O) | 0.953 | 0.394 | Accept | None | ||
| H0: L(N2O) does not Cause L(OIL) | 2.562 | 0.090 | Reject | N2O to OIL | ||
| H0: L(TOP) does not Cause L(N2O) | 1.139 | 0.331 | Accept | None | ||
| H0: L(N2O) does not Cause L(TOP) | 0.093 | 0.912 | Accept | None | ||
| H0: L(FDI) does not Cause L(N2O) | 0.715 | 0.495 | Accept | None | ||
| H0: L(N2O) does not Cause L(FDI) | 2.872 | 0.068 | Reject | N2O to FDI | ||
| H0: Independents | ||||||
| H0: L(CPIN) does not Cause L(GDPP) | 3.377 | 0.044 | Reject | CPIN to GDPP | ||
| H0: L(OIL) does not Cause L(GDPP) | 5.031 | 0.011 | Reject | OIL to GDPP | ||
| H0: L(TOP) does not Cause L(GDPP) | 7.302 | 0.002 | Reject | TOP to GDPP | ||
| H0: L(OIL) does not Cause L(GDPP)2 | 4.594 | 0.016 | Reject | OIL to (GDPP)2 | ||
| H0: L(CPIN) does not Cause L(GDPP)2 | 3.449 | 0.042 | Reject | CPIN to (GDPP)2 | ||
| H0: L(TOP) does not Cause L(GDPP)2 | 7.232 | 0.002 | Reject | TOP to (GDPP)2 | ||
| H0: L(CPIN) does not Cause L(OIL) | 3.550 | 0.038 | Reject | CPIN to OIL | ||
| H0: L(FDI) does not Cause L(OIL) | 4.129 | 0.023 | Reject | FDI to OIL | ||
| H0: L(OIL) does not Cause L(FDI) | 2.430 | 0.101 | Reject | OIL to FDI | ||
| H0: L(CPIN) does not Cause L(FDI) | 8.518 | 0.001 | Reject | CPIN to FDI | ||
This study comparatively investigated the effects of economic growth, agricultural growth and energy use on methane and nitrous oxide emissions in Sudan using annual time series data over the period 1970-2016. The estimated OLS models for emissions of both gases indicate positively signed and statistically significant coefficients of the relationship between the squared GDP per capita and CH4 and N2O emissions. These results indicate that the EKC hypothesis does not hold for both gas emissions in Sudan. However, the study has established a long run equilibrium relationship for emissions of both CH4 and N2O in their relation to economic growth, agricultural growth and energy use in presence of trade openness and inflows of foreign direct investments. Emissions of N2O are found to have more and persistent co-movements with economic growth, agricultural growth and energy use than emissions of CH4. For CH4 emissions, the estimated VECM shows that emissions of this trace gas are significantly affected by economic growth, TOP and FDI with no effect of agricultural growth in the short run, while its emissions are found to be significantly affected by economic growth, agricultural growth, TOP and FDI in the long run. It also shows nonexistence of an EKC in the long run which is consistent with the OLS finding. For N2O, the VECM model results show that emissions of this trace gas are more significantly than CH4 affected by energy use, agricultural growth and FDI in the short run, while significantly affected by economic growth, agricultural growth, TOP and FDI in the long run. Also, no EKC is found for N2O emissions. Impulse response and variance decomposition analysis confirm that emissions of N2O are more responsive to economic growth, agricultural growth and energy use than emissions of CH4. Granger causality test shows existence of only one bidirectional relationship between CH4 and agricultural growth and only one unidirectional relationship running from CH4 to FDI. For N2O there exists only one unidirectional relationship running from agricultural growth to N2O while there is unidirectional relationship running from N2O emissions to GDPP, the squared value of GDPP, OIL consumption and FDI with no sign of significant feedback effects. These results suggest that policies toward control of N2O emissions will have significant negative effects on economic growth, and inflows of FDI may likely be discouraged with stringent environmental policies toward nitrous oxide emissions.
The study concludes that N2O emissions are more responsive to changes in economic and agricultural growth compared with CH4 emissions. Furthermore, energy use only affects N2O emissions with no effect on CH4 emissions. Oil consumption has no effect on emissions of both CH4 and N2O. Results from Granger causality analysis suggest that economic growth could be pursued without significant environmental harm from both CH4 and N2O emissions. However, policies toward control of N2O emissions in particular should be set and implemented with caution as their effects on emissions will be transmitted to negatively affecting economic growth, and inflows of FDI. Furthermore, the findings suggest that Sudan should adopt energy efficiency measures, expansion of production and use of liquefied petroleum gas and place restrictions on production and use of fuel woods and charcoal for low carbon economy and green growth. Again, such policy measures should more effective if cautiously directed to control of N2O emissions within the country INDCs for the purpose of dealing with climate change obligations, more through than to CH4 emissions control. As a least developed country (LDC) Sudan is not obliged to pursue a GHG emission reduction target. However, the country has set plans to reduce GHG emissions and pursue low-carbon development, promoting sustainable resource management in balancing national economic objectives and sustainable development requirements. Notwithstanding that Sudan INDCs give priority to energy, forestry and waste sectors in mitigation of CO2, CH4 and N2O emissions, Sudan Intended Nationally Determined Contributions (Republic of Sudan, 2015). Also, within the INDCs, the energy intensity of the economy defined as total primary energy use per unit of GDP can also be reduced by relocation of resources from energy intensive sectors to labour and capital intensive sectors. The emission intensity of energy, represented by CO2 per unit of energy can also be decreased by substitution of fuels (with lower emission factors) and through increases of renewable energies in the country’s energy mix.
I would like to express my sincere thanks to the M.Sc. Health Economics Students at the Health Economics Centre, University Khartoum for valuable discussions on an earlier version of this article. I also express my deep appreciations to the efforts of the editorial office of GEP.
The author declares no conflicts of interest regarding the publication of this paper.
Mohamed, E. S. E. (2020). Economic Growth, CH4 and N2O Emissions in Sudan: Where Should the Policy Focus Be?. Journal of Geoscience and Environment Protection, 8, 202-229. https://doi.org/10.4236/gep.2020.810015