En la gestión del liderazgo y la comunicación

Umar Tijjani Babuga Department of Economics, Yusuf Maitama Sule University, Kano.

PMB 3220, Kano – Nigeria.

[email protected]

Abstract: This study is aimed at examining the long-run effect that changes in oil price play in economic growth-CO2 emission relationship for Sub-Saharan Africa (SSA) oil exporters in (Angola, Cameroon, Chad, The Republic of Congo, Equatorial Guinea, Gabon, Ghana, Nigeria and Sudan). Based on the estimation carried out using a dynamic heterogenous panel

„Pooled Mean Group‟(PMG) technique, the results show that economic growth, oil price increase, interaction of oil price and economic growth, and trade openness are all positive and statistically significant, which means they are all contributing to the increase in CO₂ emissions on the SSA oil exporting countries. The finding suggests that increase in economic growth can bring a negative effect on CO2 emission and increase in oil price reinforces the adverse effect. The Environmental Kuznet (EKC) does not exist for this model, as the short run income elasticity is found to be insignificant while the long run is significant.

Accordingly, checkmating the proliferation of CO2 emission that is aggravated by the increase in oil price, is through investing the proceeds from the oil revenue in provision of cleaner and efficient energy sources such solar, wind and so on. Also, policies like carbon trading scheme and imposition of taxes to the polluters can be used to control the rate of increase in CO2 emissions in the sub region.

Keywords: CO2 emissions, economic growth, oil price, interaction model JEL Classifications: O4, Q4, Q50, Q56

Introduction

A carbon dioxide (CO2) emission is now raising a public concern worldwide because of the potential dangers it has to the environment and human life. CO₂ emission which accounts for more than 60% of the total greenhouse gasses according to Ozturk and Acaravci (2010) is everyday increasing. The CO2 emission deteriorates the environment and affect the human life negatively (Sirag, Matemilola, Law & Bany-Ariffin, 2017). A report by International Energy Agency (I.E.A), 2018 on global energy and CO2 emission status report, 2018 indicated that energy-related CO2 emissions raised by 1.7% reaching a historic high of 33.1 Gigaton (Gt) CO₂. This was the highest since 2013, and it is 70% more than the average growth since 2010. The increased in the CO₂ emissions was generally triggered by a robust global energy demand and other weather conditions that led to the increased in energy demand for cooling and heating.

Global warming and climate change are the most intimidating environmental challenges facing the planet. This has to do with growing economic activities that require the use of more energy. And, the challenge is what prompted for the deep investigation into relationship

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among the economic growth, energy consumption and increase in CO2 emission. For instance, studies such as Al-mulali (2012); AlZgool, Shah and Ahmed (2020); Andjarwati et al. (2020); Khan, Khan and Rehan, (2020) all find the significant relationship among the income, energy consumption and changes in CO₂ emission. Economic growth-pollution relation starts gathering attention right from the beginning of the twentieth century due to the increase in global warming and its consequences to the environment and mankind. The CO2

emission is one of the great contributor to that, and extensive literature were conducted on that, mostly addressing the effect of income on pollution (Narayan and Narayan, 2010). The proposition theoretically is that, during the early stage of economic development, the pressure on the environment tends to be higher, and as the time goes-on the attention on the environment is likely going to be increased and the societal concern would then be much on protecting the environment as the income grows (Kais and Sami, 2016). The environmental kuznet curve (EKC) hypothesis postulates that an inverted u-shaped relationship exists between the economic growth and pollution.

In the region of SSA, the rate of CO2 emissions increased by 20% from 2000 – 2012 (Odugbesan and Murad, 2019). Within the same time (2000 – 2012), the price of crude oil – West Texas Intermediate (WTI) rises by about 211.13% (EIA, 2019). Also, as at 2014, the rate of CO2 stood at 822,819.03 kiloton in the region, an increase of 4.94% from 2013. This consists of CO₂ emission stemming from burning of fossil fuels, cement manufacturing, (consumption of liquid, solid and gas fuels) and flaring of gasses (macrotrend, 2019), this coincides with the period of high oil price, $92.91 per barrel (EIA, 2019). CO2 emission is no doubt one of the consequences of economic growth (Alshehry and Belloumi, 2015; Sirag et al., 2017). Figure 1 indicates how the GDPC and CO₂ are in tandem for the study area, in fact their correlation is about 82%.

Figure 1. Total GDP per capita and CO2 emissions metric ton per capita (1990 –2016) for 9 SSA Oil Exporting Countries - Angola, Cameroon, Chad, Congo Republic, Equatorial Guinea, Gabon, Ghana, Nigeria and Sudan.

Source: Computed from the data procured from WDI and EIA.

Oil plays an important and strategic role to the GDP and other economic-activities of these countries, and proliferation in economic growth is always seen as the major agent contributing to the increase in CO₂ emission. Figure 2 shows how the oil price and CO₂ emission is in tandem with the study area, their correlation coefficient is around 67%. So also, figure 3 shows the trend at which the two variables oil price and economic growth for these countries are highly related, in fact they are about 91% correlated.

0 5 10 15 20

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total GDPC Total CO2 emission

Correlation coefficient = 0.82

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Figure 2. Oil price (WTI) and CO2 emissions per capita (1990 –2016) for 9 SSA Oil Exporting Countries - Angola, Cameroon, Chad, Congo Republic, Equatorial Guinea, Gabon, Ghana, Nigeria and Sudan. Source:

Computed from the data procured from WDI and EIA

Figure 3. Total GDP per capital and Oil price (WTI) within (1990 –2016) for 9 SSA Oil Exporting Countries - Angola, Cameroon, Chad, Congo Republic, Equatorial Guinea, Gabon, Ghana, Nigeria and Sudan. Source:

Computed from the data procured from WDI and EIA

The rate of CO₂ emission in SSA oil exporting countries is persisting with the rate of increase in oil price and economic growth, for instance, around 2000 -20012 the price of crude oil – West Texas Intermediate (WTI) rises by about 211.13% (EIA, 2019), and the period coincides with the rate of increase in CO₂ emissions by 20% in the region (Odugbesan and Murad, 2019). Also, as at 2014, the price of crude oil (WTI) was as high as $92.91 per barrel and at that time the rate of CO2 emission in the region of SSA stood at 822,819.03 kiloton, an increase of 4.94% from 2013. This consists of CO₂ emission stemming from burning of fossil fuels, cement manufacturing, (consumption of liquid, solid and gas fuels) and flaring of gasses (macrotrend, 2019). The uproar in the rate of CO2 emission increase is growing with the rate of increase in GDP as a result of increase in oil price. One of the great effects of CO2

emission is climate change, the repercussion is felt in many ways in the SSA sub region including both natural and human systems. The projection of climate change in SSA is in warning trend, especially in the sub-tropics inland, changes in rainfall, recurrent occurrence of excessive heat, growing aridity and so on. SSA countries could also face as much as 1 meter of the rise in sea level by the end of the century under 4 C warming situation. The SSA region is already facing problem of higher rate of undernutrition and prevalence of infectious diseases which are anticipated to grow as a result of climate change brought immensely by the increase in the rate of CO₂ emission (Serdeczny et al., 2016).

0 5 10 15 20

0 20 40 60 80 100 120

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Oil price (WTI) Total CO2 emission

0 20 40 60 80 100 120

0 10000 20000 30000 40000 50000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Total GDPC Oil price (WTI)

Correlation coefficient = 0.67

Correlation coefficient = 0.91

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Against this background, the objective of this study is aimed at investigating the long run effect of economic growth on CO2 emission, and the moderating role that changes in oil price play in the economic growth-CO₂ emission relationship for the SSA oil exporting countries.

The study is important as it would give light on the economic growth-CO₂ emission relationship for those small countries that relied on oil as a major contributor to GDP in the region. It would be significant to learn if the increase in oil price and economic growth for those oil exporting countries are impacting the CO₂ emission or not. Even though, Chai et al.

(2016), Sirag et al. (2017), Begum et al. (2015), (Alshehry & Belloumi, 2015) and Alam et al.

(2016) have studied the economic growth-CO2 relation, this study is improved on their study by introducing the interactive term of oil price and GDP to explore the moderating role that changes in oil price play in the economic growth-CO2 emission relationship, and to the best of author‘s knowledge, this is the first of its kind since the focus is on the SSA oil exporting countries that heavily relied on oil revenue for their total exports, government revenue and GDP.

The rest of the paper is organized as follows: section 2 reviewed the past related literature, section 3 explains the empirical model, methodology and data; section 4 reports the estimated results of panel unit root test, panel cointegration estimations and the interpretations of the findings while the last section 5 concludes the discussion.

Literature Review

The argument on the link between CO2 emission and economic growth/development has been discussed extensively among scholars in the literature. It is argued that that relationship between the CO₂ emission and economic growth is positive, that is an increase in economic growth is affecting the rate of CO2 emission positively. For instance, Chai, Zhou, Liang, Xing, and Lai (2016) find that an increase in income (GDP per capita) tend to cause increase energy consumption per GDP and CO₂ emission. In a similar manner, Sirag, Matemilola, Law, and Bany-Ariffin (2017) find a positive and significant relationship between GDP and CO2 emissions for developing countries (low and middle income). In the same vein, Begum, Sohag, Abdullah and Jaafar (2015) find a positive relation of economic growth and CO₂ emissions and established that an inversed u shape hypothesis is not valid for the study area (Malaysia). Also, Alshehry and Belloumi (2015) established that there is positive and long run relationship between economic growth and CO₂ emission. In another study, Alam, Murad, Noman and Ozturk (2016) discover that for India, that an increase of income over time is found to be not reducing CO2 emission (no inverted u shape). A study by Mirza and Kanwal (2017) on energy consumption, CO2 emission and economic growth in Pakistan using Johansen-Julius cointegration to test for the presence of bivariate long run relationship between the variables was also included. The results indicate that there is positive and significant long run relationship between GDP and CO2 emission. Contrarily, there are views for the argument that economic growth is negatively affecting the CO₂ emissions, a situation where the rate of CO2 is falling while economic growth is happening. For instance, on the link between oil prices and CO2 emissions, Groot (2015) established that an increase in oil price is reducing the rate of CO₂ emission for now and future. Although, studies that find the existence of relationship between economic growth and CO₂ emission seems to be much in the previous studies, but at the same time, there are still some researches that find no-relation between the two. For instance, Zhang and Cheng (2009) using multivariate model of economic growth on China between (1960-2007) find that economic growth do not affect CO2 emission, therefore reduction in CO2 emission will not affect economic growth. In brief, the previous studies have proved various relationship exist between economic growth and CO₂ emission which consists of positive, negative and even no-relationship.

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On the other hand, several studies have argued that CO2 emission-economic growth relationship is initially positive then turns negative, that is as economic growth is at its initial stage the rate of CO₂ emission will tend to be higher but later falls as the economy grows further, that an inverse u shape curve or Kuznet hypothesis. For instance, Zaghdoudi (2017) find that, non-linear relationship exists between carbon emission and GDP, therefore the existence of EKC was confirmed. The conclusion finally was, an increase in the price of oil decreases the rate of carbon emission in OECD. Additionally, Narayan, Saboor and Soleymani (2016) investigate the dynamic relationship between economic growth and carbon emissions for 181 countries using cross-correlation estimate, the result shows the existence of EKC for 21 countries, where the carbon emission is increasing with the increase in income but subsequently fall as the economy grown well (income outgrown environmental pollution). For 49 countries, it was found that an increase in income will reduce the rate of carbon emission in the future. Also, a study by Narayan and Narayan (2010) using Income Elasticity as a yards stick to check for the existence of Kuznet curve hypothesis for 49 developing countries find that, the income elasticity is smaller in the long run, which is indicating CO2 emission is falling with a rise in income and the turning point exists. The study concludes that in long run elasticity of income has fallen with the increase in income, therefore there is existence of turning point. And, Heidari et al., (2015) use a Panel Smooth Threshold Regression (PSTR) to test the validity of the EKC on five ASEAN countries (Indonesia, Malaysia, Singapore, Philippines and Thailand) between 1980-2008. The findings confirm the existence of turning point as income grows further to a certain limit in relation to CO2 emission. Furthermore, a study by Kais and Sami (2016) using a system Generalize Method of Moment (GMM) on 58 countries between the period 1990-2012 to identify the impact of CO₂ emission on economic growth. The findings indicate that, per capita GDP has a positive and statistically significant impact on CO2 emission for the global panel, Europe and North Asia, Middle East and Sub-Saharan Africa (SSA). Moreover, the result shows the presence of an inverted U-shape curve between CO₂ emission and per capita GDP. In a nutshell, the inverted u shape relationship which is signifying a Kuznet hypothesis were found in many of the previous studies across different continents, regions, sub-regions and countries of the world.

Equally important, there is another phase of argument on trade openness relation with CO2

emission or simply, trade openness is an important factor that could affect the quality of environment. According to Ertugrul et al., (2016) using unrestricted error correction (UECM) model to examine the relationship between CO2 emission, trade openness, real income and energy consumption (between 1971-2011) on top-ten emitters among the developing countries which are India, China, Brazil, South Korea, South Africa, Mexico, Indonesia, Turkey, Thailand and Malaysia. The findings indicate that trade openness is significantly among the determinants of CO2 emission. In contrast, Shahbaz, Nasreen, Ahmed and Hammoudeh (2016) used Fully Modified OLS (FMOLS) to study global impact of trade openness on CO2 emission. The result indicates that trade openness reduces CO2 emission in most of the countries. it was indicated that trade openness contributes to emission in all income levels but with different turning points and the inverted u-shaped curve exists. For the middle-income countries, they have the highest environmental deterioration than low income but require less time to improve the problem than the low income. The low income requires long time period to reach the turning-point but its environment deterioration is less than that of the middle-income countries. However, Mutascu (2018) used a wavelet econometric method to discover the co-movement between trade openness and CO2 emission for France (1960-2016). The main finding of the study indicates that no co-movement between and CO2

emission at high frequency. The study also confirms the neutral hypothesis in the short term,

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and CO2 emission drives trade openness at the medium frequency. On the contrary, the trade openness also exhibits a character of inverse u shape (EKC), where the rate of CO2 emission as a result of increase in trade openness is increasing up to a certain level then start diminishing as trade openness grows further. Zhang and Zhang (2018) used autoregressive distributed lag (ARDL) model. The findings show that as income increases CO2 emission increases also but up to certain level then start declining as income grows further, therefore, an inverted u-shaped curve exists. Nonetheless, the impact of service trade on China‘s CO₂ emission was found to be negative.

In summary, from the strand of literature, it is indicated that economic growth affects CO₂ emission in different ways. Various studies came out with different findings, some arrived at a positive relation, some negative, some both positive and negative. This is showing mixed results with no consensus in findings, and that makes it necessary to conduct such research to see a particular impact the economic growth has on CO₂ emission. The study in relation to economic growth-CO2 emission is generally scanty on Africa and the SSA countries especially the oil exporting ones among them.

Empirical model, methodology and data

The empirical specification is aimed at investigating the relationship between economic growth and CO₂ emissions, and the moderating role that oil price changes play in such relationship for the selected SSA oil exporting countries. Thus, the study follow the work of Alam, Murad, Noman, and Ozturk (2016), Ertugrul, Cetin, Seker, and Dogan (2016), and Zhang & Zhang (2018), but for the purpose of this study, the model is now:

(1) where lco2 is the natural log of carbon emissions (metric tons per capita), lrop is the natural log of real oil price¹ (WTI) US$, lrgdpc is the natural log of GDP per capita (constant 2010 US$), ltr is the natural log of trade openness (sum of exports and imports of goods and services as share of GDP). The notation µi is the specific effect of country while Ɛit is the stochastic error term which is assumed to be identically distributed independently across the groups. The letters i and t are indices denoting country and time respectively.

The coefficient lrop, it is expected to be positive (β1>0) since increase in oil price will bring more income and economic activities in the economy of oil exporting countries that will lead to more CO2 emission, Groot (2015) find that oil price is an important factor determining the rate of CO2. The coefficient lrgdpc is expected to be positive (β2>0), implying that increase in real gdp will increase the activities that will lead to more CO₂ emission and pollution. For the relationship between lrgdpc and CO2 emission, for instance, Chai et al. (2016), Sirag et al.

(2017), Alshehry and Belloumi,( 2015), and Alam et al. (2016) find out that economic growth is an important factor influencing the rate CO₂ emission. For ltr, it is also expected to be positive (β3>0) as trading of goods and services is expected to increase activities that will lead to more CO2 emission. Ertugrul et al. (2016), Sun, Clottey, Geng, Fang, and Amissah (2019) find that trade openness affect the rate of CO₂ emission.

To introduce a moderating role that real oil price changes play in economic growth-CO2

emission relationship, a multiplicative interaction term of the coefficients (lrop*lrgdpc) was

1 In controlling the impact of nominal exchange rate changes on the price of oil, the oil price was transformed to real oil price by converting it to the respective countries‘ exchange rate and further scale it to their various consumer price index.

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formed as proposed by Brambor, Clark and Golder (2006) and it was placed in equation (1) to form a new equation (2) below:

( ) (2) For the multiplicative interaction term of linear oil price and real GDP per capita (lrop*lrgdpc), the expected sign for the interaction term is conditional. If β2>0 and β3<0, then this is indicating higher GDP has a negative effect on CO₂ emission even though the oil price neutralizes that negative effect. If β2<0 and β3>0, then this is showing that oil price has a diminishing effect on CO2 emission and that GDP strengthen that. Also, if β2<0 and β3<0, this is signifying that higher GDP decreases CO₂ emission and oil price reinforces that.

Finally, if β2>0 and β3>0, this signifies that higher GDP has a negative effect on CO2

emission and higher oil price reinforces the adverse effect.

Moreover, the interaction term is used to calculate the marginal effect of GDP on CO₂ emission in order to test the hypotheses whether the marginal effect of GDP on CO2 emission is higher at a time of higher oil price or otherwise. Depending on the effect of GDP on CO2

emission, the marginal effect can take either negative or positive value ((Law, Kutan and Naseem, 2018). To find the total effect which the GDP has on CO2 emission, marginal effect is calculated by taking the partial derivatives of equation (2) with respect to lrgdpc:

(3)

This relationship is showing how the changes in CO2 emission in relation to the changes in GDP relied on the changes in oil price. From the partial derivation, if β2 and β3 are positive, it indicates that a higher GDP and a higher oil prices would increase the rate of CO₂ emission.

And, if β2 and β3 are negative, it reflects that a higher GDP and a higher oil price would decrease the rate of CO2 emission.

Econometric methodology

The Pooled Mean Group estimator (PMG) as proposed by Pesaran, Shin and Smith (1999) is the econometric method employed for this study. The PMG estimator is an intermediate between the Dynamic Fixed Effect (DFE) and Mean Group (MG) because it allows both pooling and averaging. The PMG allows short-run coefficients, speed of adjustment and error variances to vary across panels and at the same time restrict the long-run coefficients to be similar across the groups. The PMG also generates consistent estimates of the mean of the short run coefficients by taking the simple average of individual unit coefficients. The MG method of estimation assumed that both slopes and intercepts are allowed to differ across panels/countries that is why the long-run coefficients are not similar across panels while the DFE assumes the slopes are fixed while the intercepts vary across panels. The Hausman (1978) test is conducted after the estimation of PMG and MG to get the appropriate method between the two. The null cannot be rejected if the long run homogeneity exists, and therefore PMG is more efficient.

The dynamic heterogeneous panel regression can be incorporated into the error-correction model using the ARDL (p, q) method, where p is the lag of the dependent variable, and q is the lag of the independent variables, the Akaike information criterion (AIC) is used for the selection of the lag order:

∑

∑ (4)

48 With interaction term

( ) ∑ ∑ ( ) (5) where δ is the long-run coefficient of the independent variables, and γ is the parameter of speed adjustment to the long-run equilibrium. µ_i is the specific for country and Ɛit is the error term. i and t represents country and time index respectively. It is assumed that the error term Ɛit in the PMG framework is distributed independently across i and t with zero mean and variance. The error term is also distributed independently of the regressors. Moreover, to capture the long-run relationship between dependent and independent variables, it is assumed that if the parameter of the speed of adjustment is less than zero (γ < 0) for all i, then panel co-integration is expressed as:

(6) ( ) (7) where are the long run coefficients of GDP per capita, oil price, interaction term of oil price and GDP, and trade openness respectively.

Data and descriptive statistics

In this study, annual data was used to estimate the relationship between economic growth and CO2 emissions for selected SSA oil exporting countries² and the moderating effect which the change in oil price plays in that relationship, covering the period of 27 years (1990-2016), subject to availability of data. This study, therefore, use CO₂ emissions (metric tons per capita) as a dependent variable, whereas the explanatory variables are oil price - West Texas Intermediate (WTI) US$, economic growth proxied by GDP per capita (constant 2010 US$), and Trade openness (sum of exports and imports of goods and services as share of GDP). The data of CO2 emissions, GDP per capita and Trade openness are sourced from world development indicators (WDI). The data of oil prices were obtained from U.S. Energy Information Administration (EIA).

Table 1 Summary of annual data set (1990-2017) N=9

Source Unit of

measurement

Mean SD Max Min

CO2 emissions WDI Metric tons per capita

1.273 2.1605 11.203 0.0084

Oil price EIA WTI (US$) 46.704 29.511 99.75 14.4

GDP per capita WDI Constant 2010 US$ 3542.596 4326.698 20532.95 462.254

Trade openness WDI % of GDP 77.916 38.321 156.861 11.087

2 The selection of countries was based on the classification of major oil exporters by Kilian, Rebucci and Spatafora (2007). That a country is classified as a major oil exporter if the fuel export of that country on the average is more than 20% of its total exports for quite a period of time.

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Summary statistics of the data for Angola, Cameroon, Chad, Congo Republic, Equatorial Guinea, Gabon, Ghana, Nigeria and Sudan

Empirical results

Panel Unitroot test

The unitroot test results are presented in table 2, Levin Lin and Chu (LLC) and Im Pesaran and Shin (IPS) were conducted. The LLC is assuming a common unitroot process whereas the LLC is assuming an individual unitroot process, so the duo was combined to strike a balance between the two processes. From the LLC, CO₂ emission and oil price were found to be significant at level (intercept). Also, from the LLC result, CO2 emission and income were found to be significant at level (intercept and trend). From the IPS result, CO2 emission and income were significant (intercept and trend). At first difference, all the variables from both LLC and IPS under intercept and intercept and trend were significant. This shows a mixture of order of integration I(0), I(1) for the variables under the study, which is a pre-condition of carrying out an ARDL, MG, PMG.

Table 2 Levin-Lin-Chu (LLC) and Im-Pesaran-Shin Unitroot Test

Level First Difference

Intercept Intercept and trend Intercept Intercept and trend

LLC IPS LLC IPS LLC IPS LLC IPS

lco2

-5.8084***

0.0755

-10.4635***

--3.1546***

-12.7258***

-9.0702***

-8.6593***

-9.1852***

lrop -1.4815* - 0.4628

3.3691 -1.1042 -7.8051***

-8.0031***

-6.7745***

-8.1977***

lrgdpc -0.1103 3.7476 -1.5866* -1.3334* -3.5993***

-5.2138***

-2.6479***

-5.4559***

ltr 0.3617 -0.2571 0.1101 -1.1685 -6.7222***

-8.6899***

-6.3022***

-9.1812***

Notes: ***, **, * depict level of significance at 1%, 5% and 10% respectively

Dynamic heterogenous panel - Pooled Mean Group (PMG)

In this study, the PMG is the preferred technique going by the outcome of Hausman test for long run homogeneity. As reported on table (2), the coefficient oil price is positive and significant indicating oil price change has a negative effect on CO₂ emission and is an important determinant of CO2 emission, this is consistent with the findings of (Groot, 2015).

The coefficient lrgdpc (income) is positive and significant also denoting increase in income is negatively impacting the rate of CO₂ emission and it is a significant factor influencing it, this is in line with findings of Chai et al. (2016), Sirag et al. (2017), Alshehry and Belloumi,(

2015), and Alam et al. (2016). For the interaction term, the coefficient (lrop*lrgdpc) is positive and significant. The interpretation of the interaction term is conditional, since the coefficients β2>0 and β3>0 altogether are positive and significant; this signifies that higher GDP has a negative effect on CO2 emission and higher oil price reinforces the adverse effect.

The control variable (ltr) is positive showing as trading of goods and services is expected to increase activities that will lead to more CO₂ emission. Ertugrul et al. (2016), Sun, Clottey, Geng, Fang, and Amissah (2019) find that trade openness affect the rate of CO2 emission.

The co-integration relationship exists between the dependent variable and the independent variables. This is shown by the significant and negative sign of the convergence coefficient of

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the PMG result. The convergence coefficient also indicates the speed of adjustment to the long run equilibrium as 37.35% if any deviation occurs in the short run. It also denotes the duration of time it takes to converge to the long run equilibrium as 2.67 years.

Table 4 Dynamic heterogenous panel MG, PMG and DFE estimations. Dependent variable: CO2 emissions

Dependent variable:

lco2

Lag order (1,1,1,1,1) Mean Group

(MG)

Pooled Mean Group (PMG)

Dynamic Fixed Effect (DFE) Longrun coefficients

lrop 9.2744 (11.5236) -0.7394*** (0.2548) -0.0987 (0.4640)

lrgdpc 6.7516 (9.5025) 0.9735*** (0.1840) 1.1774*** (0.3571)

lrop*lrgdpc -0.9971 (1.5195) 0.1063*** (0.0388) 0.0179 (0.0632)

ltr 0.4953 (0.3196) 0.2032*** (0.0786) -0.0577 (0.2023)

Shortrun coefficients

∆lrop 3.3512 (7.0703) -1.1338 (0.8038) 0.0505 (0.6234)

∆lrgdpc 3.6193 (6.1903) -0.1005 (0.4745) 0.5424 (0.5049)

∆ lrop*lrgdpc -0.5132 (.9888) 0.1599 (0.1119) 0.0001 (0.0821)

∆ltr -0.3350* (0.1888) -0.0401 (0.1040) 0.2067 (0.1349)

Convergence Coefficient

-0.6959*** (0.1124) -0.3735*** (0.1049) -0.4595*** (0.0539)

Hausman test 7.31 (0.1204)

Log likelihood 106.4554

No of countries 9 9 9

No of observations 234 234 234

Notes: Figures in parentheses are asymptotic standard errors except for Hausman test which is its p-value. AIC criterion was used to choose the lag order. ***, **, * denote level of significance at 1%, 5% and 10%

respectively

Considering the marginal effect, from the partial derivation, both β2 and β3 coefficients are positive which indicates that a higher GDP and a higher oil prices would increase the rate of CO2 emission. The marginal effect of income on CO2 emission was measured at various levels of oil price changes, at mean, minimum and maximum. At mean level of oil price, if income is increased by 1%, the rate of CO₂ emission would increase by 1.435%. At minimum and maximum levels of oil price change, a 1% increase in income would lead to increase in CO2 emission by 0.813% and 1.632% respectively.

The Environmental Kuznet hypothesis (EKC) does not exist for this model, as the short run income elasticity is found to be insignificant while the long run is significant. This notion is as suggested by Narayan and Narayan (2010), where the EKC exists when the income elasticity is less in the long run and more in the short run.

Robustness checks

For robustness checks, Fixed Effect (FE) technique was used to re-estimate the linear model and the result is found to be robust to this method. The lagged dependent variable is significant indicating the model is dynamic. And, the coefficient lrgdpc is positive and significant showing that increase in economic growth is having a deteriorating effect on CO2

emission.

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Table 5 Robustness checks. Dependent variable: lco₂ emissions (metric tons per capita) Fixed Effect

Model without interaction term Model with interaction term

Constant -5.0896***

(0.7198)

-4.6850***

(1.2532)

lco2it-1 0.5147***

(0.0525)

0.5135***

(0.0527)

lrop 0.0239

(0.0439)

-0.0559 (0.2069)

lrgdpc 0.5838***

(0.0814)

0.5251***

(0.1696)

lyop 0.0111

(0.0283)

ltr 0.0482

(0.0857)

0.0511 (0.0861) Notes: Figures in parentheses are asymptotic standard errors.

***, **, * Significant at 1%, 5% and 10% respectively

Conclusion and Policy Recommendation

This study examined the moderating effect that oil price has in economic growth-CO2

emission relationship for SSA oil exporting countries. Oil plays a significant role in the structure and economic growth of the SSA oil exporting economies. Indeed, oil is considered as the main driver of economic activity in these countries as they are highly dependent on oil revenues. This reliance is reflected in the shares of oil revenue in their respective total government revenues, exports and GDP. Even though oil is the major contributor to the GDP which bring about the increase in the economic activities and the rate of CO2 emissions, but no available study carried out to investigate the moderating role by which the oil price plays in GDP-CO₂ emission relationship for these oil exporting countries. Based on the dynamic heterogenous panel PMG estimation, the empirical results show that an economic growth brought about by the rate of increase in oil price causes an increase in CO2 emissions for these countries. The empirical result was robust to the alternative method (Fixed Effect).

As the finding suggested that increase in economic growth can bring a negative effect on CO2

emission and increase oil price reinforces the adverse effect in these oil exporting countries.

This increase in CO2 emissions especially in relation to the increase in oil price has a serious policy implication on these countries. Some of the ways to checkmate the CO2 emission is by investing the oil proceeds in providing cleaner and efficient energy sources such as solar, wind and so on. Secondly, it can also be controlled by imposing of higher taxes to the polluters. Taxes may create incentives and flexibilities for firms and other economic activities to reduce CO2 emissions which are polluting the environment. That can be imposed on firms/entities in relation to the amount of CO₂ they emit. Thirdly, a carbon trading scheme might help in reducing the rate of CO2 emissions among countries or entities. Under a carbon trading scheme, a country or entity with high emissions rate can be able to buy the right to emit more while a country or entity with low emissions rate can sell the right to countries or entities with more rate of emissions. Fourthly, highlighting the public on the dangers of climate change brought about by the increase in the rate of CO2 emission such as changes in rainfall, recurrent occurrence of excessive heat, growing aridity and so on may help in creating awareness in the minds of people generally to be concern about their environment.

In document Desempeño docente en el proceso de enseñanza aprendizaje. Estudio realizado en el 1ero, 2do y 3er año de Bachillerato de la Unidad Educativa Dr. Miguel Ángel Zambrano de la ciudad de Quito, provincia de Pichincha, en el año lectivo 2013 2014 (página 77-84)