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# Estimating the dynamic relationship between Energy Use, Economic activity and CO2 emission by using a Panel ARDL Model on SADC countries

Tojonirina Miada Zafindraibe Ranaivoson, Pierre Lazamanana

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Tojonirina Miada Zafindraibe Ranaivoson, Pierre Lazamanana. Estimating the dynamic relationship between Energy Use, Economic activity and CO2 emission by using a Panel ARDL Model on SADC countries. 2025. hal-05355880

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Distributed under a Creative Commons Attribution 4.0 International License
Estimating the dynamic relationship between Energy Use,
Economic activity and CO2 emission by using a Panel ARDL
Model on SADC countries

Tojonirina RANAIVOSON Tojonirina M.Z¹ and Lazamanana

André Pierre²

¹,²EGS Faculty, University of Antananarivo, Madagascar

**Abstract**

This paper examines not only the dynamic relationship between
CO₂ emission, energy use and economic activity but also their long
run relationship via the use of Panel Autoregressive Distributed Lag
Model ARDL Model where the CO₂ emission as the dependent variable.
For testing the direction of the causality, Granger Causality test has
been employed. We used pooled mean group method to estimate our
equation for a panel data that consist of 8 African countries within the
Southern African Development Community between 1990 and 2014.

Empirical findings display a significant causality running from eco-
nomic activity and energy use to CO2 during this period. The long run
equation coefficients are all significant at 1% suggesting the presence
of a cointegration relationship. These coefficients are expressed in
logarithm, meaning that they can be interpreted as an elasticity. In
addition, CO2 emission reacts negatively to GDP, suggesting a deterio-
ration of environment in case of a rise in economic activity. Moreover,
the error correction term is significant and negative, as expected. In-
deed, according to our estimates, the presence of deviation from long
equilibrium is corrected at 73% in each period.

**keywords :** cointegration, Panel ARDL, economic activity
# 1 Introduction

Recently, global warming and environmental issue has been a growing interest because of its negative and considerable effect both on economic activity and social welfare. Not only developed countries, but also developing ones are facing climatic-based problems but at different scale and, moreover, the way they dealt with such a problem depends on the instruments they are holding.

The principal hypothesis over which climate change debate rely on alluded to the Environmental Kuznets Curve (EKC) assumption. In this respect, one certainly knows that economic activity is obviously in correlation with pollution and degradation of the environment, but to a certain level, this is not yet verified when refering to this (EKC) Hypothesis (Kaika & Zervas, 2013).

Hence, according to Alagidede et al., two school of thoughts emerged by showing a real contradiction: the one stating that the sustainability of environment needs to slow down economic growth and those that confirm technological breakthrough and application of new techniques could mitigate the undesirable effect of economic activity. However, the empirical findings resulting from these two assumptions depends on the country or the group of country, the period of study and the methodology adopted. So, as pointed out by Amjath-Babu et al., the tendency towards the environmental and social degradation, due to economic activity, is evident. In fact, the more economic activity that uses energy, the greater the environmental degradation. Economic growth definitely requires lots of resources but at the cost of the environment destruction where impacts are non-reversible.

As one knows, among of the principal greenhouse gases contributor of climate change is the CO₂ emissions because it is mainly considered as the products of economic activities that drive to growth and development (Mladenović et al., 2016). Nevertheless, there are other greenhouse gases that can have an impact on economic growth, namely methane (NH₄)
and nitrous ($NO_2$) (Yusuf et al., 2020). In this study, evidence showed the impact of other greenhouse gases on economic growth and energy consumption.

Concerning the nexus between $CO_2$ emission, Energy consumption and economic growth, extent literature has contributed to many different points of view. With this in mind, Energy consumption got a great attention due to its impact on environment and economic growth and also because of its characteristics as renewable or non-renewable resources. Carbon Dioxide ($CO_2$) emission, recognized as the major pollutant among the greenhouse gases emission, has also a direct linkage with energy use and economic activity (Acaravci & Ozturk, 2010).

The objective of this paper is twofold: the first one is to investigate the dynamic relationship of $CO_2$ emission level on energy use and economic activity both at long and short term and, the second one, to estimate a long run relationship through a Panel Autoregressive Distributed Lag Model. To test the direction of the relationship, Granger non-causality test will be performed. Then the main contribution of this survey is the use of such techniques on a few groups of African countries in the assessment of the effect of Energy use and growth on CO2 emission.

This paper is organized as followed: in section 2, a brief literature will be introduced and followed by the methodology and results in section 3. Finally, section 4 will end up with a concluding remark.

# 2 Literature review

By considering a panel data of 30 regions in China, the studies of dynamic relationship between economic growth, CO2 emission and energy consumption through the spatial Durbin model displays a significant and positive impact of economic growth on carbon dioxide emissions. Inversely, the investigation found that the reduction of carbon dioxide emission does not have a significant impact on economic growth (Zou & Zhang, 2020).
In their paper, they found a unidirectional linkage running from GDP to energy consumption and from energy consumption to carbon emission as well, in the long run. In the same way as Zou and Zhang, Empirical investigation exhibit an insignificant impact of both carbon emission and energy consumption on economic activity(Zhang & Cheng, 2009). Moreover, regarding the effect of GDP (proxy of economic activity) on Carbon emission, Balibey (2015), Zaman et al. (2016), Chaabouni and Saidi (2017), Saidi and Hammami (2015) found a positive result according to empirical evidence. Hence, as the economy activity extends, the environmental effects deteriorates. Contrarily to that, GDP has a decreasing effect on carbon emission Kasman and Duman (2015).

Concerning the effect of energy consumption on carbon emission, a positive impact has been pointed out by Arouri et al. (2012), Salahuddin and Gow (2014), Masih and Masih (1998), Pao and Tsai (2010), Saidi and Hammami (2015), Vickers (2017), Apergis and Payne (2010). Results shows that non-renewable, industrial and residential energy consumption has a positive correlation with on CO₂ emission while renewable has not (Salari et al., 2021). By using static and dynamic model, they found a long run relationship among various types of energy consumption and CO₂ emissions at the state level.

Their work provides a reflexion of the empirical validation of the well-known Environmental Kuznets Curve hypothesis (EKC). So, in the perspective of African context, some evidence has been found out on the support of this EKC hypothesis. For example, those of Sulemana et al. (2016) and Osabuohien et al. (2014) use Panel data to state the relevance of EKC on African Economy but only for oil-producing country. however, this was not confirmed according to Effiong and Oisaozoje (2016). Similarly, Ogundipe et al. (2014), Yaduma et al. (2015), Adu and Denkyirah (2019), examined the EKC for African Economies but discovered no proof for its presence.

In terms of causality, the empirical studies on Turkey about the rela-
tionship among Energy Consumption, Economic growth and carbon emissions, Soytas and Sari (2009) showed evidence that carbon emissions cause energy consumption, but the contrary did not. According to the empirical evidence of Alam et al. (2012) on the existence of the dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth, an unidirectional causality exists from energy consumption to economic growth both in long and short run. They also found a unidirectional causality that goes from energy consumption to CO₂ emission in the short run and CO₂ emissions to economic growth in both short and long run.

In the same manner, studies carried out on Italy by Magazzino (2016) over the period 1970-2006 showed that there is not a cointegration relationship between CO₂ emissions, energy consumption and economic activity. However, according to Toda and Yamamoto approach (Toda & Yamamoto, 1995), Granger non-causality test exhibit a bidirectional causality between CO₂ emissions and economic growth as well as between CO₂ emissions and energy consumption.

# 3 Methodology

In this paper, one use the Non-Granger-causality approach to test the direction of the causality relationship (Seth, 2007). Then, proceed to the panel unit root test following the Pedroni (1995) approach in order to check the order of the integration of each variable. Before running our Panel ARDL model, we need to make sure that these order of integration does not exceed 2(Pesaran et al., 2001), so either I(0) or I(1). According to Pesaran et al.'s paper, Panel ARDL can combine such order of integration difference. For the estimation method, we run a pooled mean group model in order to consider a homogenous model.
## 3.1 Data description

Our sample consists of a balanced panel data in which one have 8 cross-section composing by the Southern African Development Community (SADC) countries, namely Angola, Botswana, Mozambique, Namibia, Tanzania, Zimbabwe, Congo Democratic Republic and Mauritius over the period 1990-2014. All data are provided by the World Bank Open database (WDI,2023) along with its description<fnref n="1" />:

<table><caption>Table 1: data description</caption><thead><tr><td>Variables</td><td>description</td><td>Description</td></tr></thead><tbody><tr><td>CO<sub>2</sub> in Kt</td><td>Carbon Dioxide emission</td><td>Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.</td></tr><tr><td>GDP in US$</td><td>Gross Domestic Product</td><td>GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products</td></tr><tr><td>EU kt of oil equivalent</td><td>Energy use</td><td>Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport</td></tr></tbody></table>

Sources: World Bank, World Development Indicators

Overall we have 200 observations, that is 25 times 8.

<footnote n="1">¹https://data.worldbank.org/</footnote>
## 3.2 Granger Causality test

Applied on a Panel data, this test is an extension of “Granger Causality” method developed by Granger (1969), where instead of testing causal relationship among two variables, it enables the assessment of causal interactions among multiple variables in a system. The null hypothesis for each pair of variables is that no variable “Granger-causes” the other to end up with a conclusion of the direction and strength of the causality

<table><caption>Table 2: Pairwise Granger Causality Tests</caption><thead><tr><th>Null Hypothesis</th><th>Obs</th><th>F-Statistic</th><th>Prob.</th></tr></thead><tbody><tr><td>logeu does not Granger Cause loggdp</td><td>181</td><td>0.57</td><td>0.57</td></tr><tr><td>loggdp does not Granger Cause logeu</td><td></td><td>2.78*</td><td>0.07</td></tr><tr><td>logCO<sub>2</sub> does not Granger Cause loggdp</td><td>182</td><td>0.22</td><td>0.80</td></tr><tr><td>loggdp does not Granger Cause logCO<sub>2</sub></td><td></td><td>6.19***</td><td>0.00</td></tr><tr><td>logCO<sub>2</sub> does not Granger Cause logeu</td><td>182</td><td>3.21*</td><td>0.04</td></tr><tr><td>logeu does not Granger Cause logCO<sub>2</sub></td><td></td><td>2.89*</td><td>0.06</td></tr></tbody></table>

Source: Author's calculation/Eviews

NB: the sign *,**,*** denote significance at 10%, 5% and 1%.

The test shows that the null hypothesis of the "does not Granger-cause" (Seth, 2007) is rejected for both `loggdp` to `logeu` at 10% and for `logCO<sub>2</sub>` to `logeu` at 5%. Likewise, we observe the rejection of this Null hypothesis for `logdp` to `logCO<sub>2</sub>` and `logeu` to `logCO<sub>2</sub>` respectively at 1% and 10%. However, it is accepted for `logeu` to `loggdp` and for `logCO<sub>2</sub>` to `loggdp`.

## 3.3 Unit Root Test

The unit root test according to Levin et al. (2002) is carried out to determine the order of integration of our individual series. For the common unit root test, Breitung (2002) approach is used, knowing that the null hypothesis is the non-stationarity. One select Akaike Information Criterion (AIC) as an information criterion.
<table><caption>Table 3: Unit Root test</caption><thead><tr><th colspan="4">Newey-West automatic bandwidth selection and Bartlett kernel</th></tr><tr><th></th><th>Method</th><th>Stat in level</th><th>stat in first diff</th></tr></thead><tbody><tr><td></td><td>Null: Unit root</td><td></td><td></td></tr><tr><td>loggdp</td><td>Levin, Lin & Chu t*</td><td>-1.24</td><td>-7.14***</td></tr><tr><td></td><td>Breitung t-stat</td><td>-0.94</td><td>-6.58***</td></tr><tr><td>logeu</td><td>Levin, Lin & Chu t*</td><td>-1.05</td><td>-6.98***</td></tr><tr><td></td><td>Breitung t-stat</td><td>1.25</td><td>-3.45***</td></tr><tr><td>logCO<sub>2</sub></td><td>Levin, Lin & Chu t*</td><td>0.62</td><td>-3.45***</td></tr><tr><td></td><td>Breitung t-stat</td><td>0.30</td><td>-2.19***</td></tr></tbody></table>

Source: Author's calculation/Eviews

The null hypothesis of unit root test (Pesaran et al., 2001) is rejected if the computed statistical value is greater than the critical value. Our findings show that all variables are stationary at 1% in first difference. Even if all variables are I(1) but not a mixture (I(0) and I(1)), Panel ARDL techniques could be used to estimate our Model.

## 3.4 Panel cointegration test

As a dependent variable, one have CO₂ emission whereas the independent variables are energy consumption and the economic activity which is a proxy of GDP. To test a long run relationship, we will use the Panel cointegration test method as suggested by Pedroni (1995), the null hypothesis being the "no cointegration".
<table><caption>Table 4: Panel cointegration test</caption><thead><tr><th colspan="3">Null Hypothesis: No cointegration</th></tr><tr><th colspan="3">Alternative hypothesis: common AR coefs. (within-dimension)</th></tr><tr><th></th><th>Statistic</th><th>Prob.</th></tr></thead><tbody><tr><td>Panel v-Statistic</td><td>4.95</td><td>0.00</td></tr><tr><td>Panel rho-Statistic</td><td>-1.10</td><td>0.14</td></tr><tr><td>Panel PP-Statistic</td><td>-3.56</td><td>0.00</td></tr><tr><td>Panel ADF-Statistic</td><td>-4.31</td><td>0.00</td></tr></tbody><tbody><tr><th colspan="3">Alternative hypothesis: individual AR coefs. (Between-dimension)</th></tr><tr><th></th><th>Statistic</th><th>Prob.</th></tr><tr><td>Group rho-Statistic</td><td>0.47</td><td>0.68</td></tr><tr><td>Group PP-Statistic</td><td>-2.94</td><td>0.00</td></tr><tr><td>Group ADF-Statistic</td><td>-3.81</td><td>0.00</td></tr></tbody></table>

Source: Author's calculation/Eviews

In the first block of the test where the alternative hypothesis is "common AR coefficient", 3 out of the 4 statistics reject the null hypothesis of "no cointegration" at 1% while 2 out of 3 is observed in the "individual AR coefficients" block and still at 1%.

According to the rule of democracy, there is likely a long run relationship between $logCO_2$, $logeu$ and $loggdp$

## 3.5 The Panel ARDL Model

In this survey, we are using a Panel ARDL model to estimate the long run equilibrium relationship, taking $CO_2$ as the dependent variable and Energy use and Economic activity as the regressors. The above causality test showed a two pairwise causality, but for our purpose we will only retain that of energy use and economic activity to $CO_2$ emission. Then, even if our variables are all I(1), we may use such model as the presence of variables with an order of integration of either I(0) or I(1), and both I(0) and I(1) is allowed (Pesaran et al., 2001). In terms of estimation method, this study will use the Pooled Mean Group (PMG) Method developed by Pesaran et al. (1999). In their paper, Pesaran, Shin, and Smith introduced
the PMG method as an econometric technique for the estimation of a dynamic panel data models by capturing the heterogeneity across individual units. Hence, the long run coefficient is the same (homogeneity) across the group (cross-section) while the short run and cointegrating coefficient vary. Anyway, their contribution to the economic research was a point of departure for the extension of the empirical cointegration analysis.

<table><caption>Table 5: ARDL(4,4,4)</caption><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th></tr></thead><tbody><tr><td colspan="3">Long Run Equation/dep var: logCO<sub>2,t</sub></td></tr><tr><td>logeu<sub>t</sub></td><td>1.85***</td><td>0.12</td></tr><tr><td>loggdp<sub>t</sub></td><td>-0.23***</td><td>0.03</td></tr><tr><td colspan="3">Short Run Equation / dep var : ΔlogCO<sub>2,t</sub></td></tr><tr><td>ect<sub>t-1</sub></td><td>-0.73**</td><td>0.26</td></tr><tr><td>ΔlogCO<sub>2t-1</sub></td><td>0.33*</td><td>0.19</td></tr><tr><td>ΔlogCO<sub>2t-2</sub></td><td>0.20</td><td>0.19</td></tr><tr><td>ΔlogCO<sub>2t-3</sub></td><td>0.28</td><td>0.18</td></tr><tr><td>Δlogeu<sub>t</sub></td><td>0.29</td><td>0.77</td></tr><tr><td>Δlogeu<sub>t-1</sub></td><td>-0.85**</td><td>0.39</td></tr><tr><td>Δlogeu<sub>t-2</sub></td><td>0.06</td><td>0.46</td></tr><tr><td>Δlogeu<sub>t-3</sub></td><td>-1.11**</td><td>0.42</td></tr><tr><td>Δloggdp<sub>t</sub></td><td>0.06</td><td>0.06</td></tr><tr><td>Δloggdp<sub>t-1</sub></td><td>0.06</td><td>0.07</td></tr><tr><td>Δloggdp<sub>t-2</sub></td><td>-0.03**</td><td>0.07</td></tr><tr><td>Δloggdp<sub>t-3</sub></td><td>-0.01</td><td>0.07</td></tr><tr><td>α</td><td>0.69**</td><td>0.24</td></tr><tr><td>trend</td><td>0.02**</td><td>0.01</td></tr></tbody></table>

NB: the sign *,**,*** denote significance at 10%, 5% and 1%.

This table displays the estimates of the Pooled Mean Group (PMG) method, both at short and long run. According to the AIC criterion, an ARDL(4,4,4) model has been selected. In the long run, PMG estimates of the coefficients of GDP were found to have a significant and negative effect (1%) on CO2 emission, showing that an increase of economic activity is associated with a decrease of CO2 emission. However, energy consumption impact
positively and significantly at 1% on CO2 emission. The more the energy
consumption, the greater the environmental degradation. In short run,
the constant and trend terms are significant at 5%. The error correction
term is also significant and negative (-0.73) as expected.

# 4 Concluding remarks

In this paper, we use a Panel ARDL Model according to the work of Pesaran et al. 2001 even if the unit root test displays that all variables are non-stationary in level but stationary in first difference (I(1)). In addition, the results of the panel cointegration test following Pedroni (1995) exhibit the presence of a long run relationship between CO₂ emission, energy use and Economic activity. However, although the Granger causality test to support CO₂ emission as a dependent variable, Energy use also may be a potential candidate to play such a role.

Our findings show a significant (at 1%) and negative impact (-0.23) of
GDP on CO₂ emission in the long run, as stated by Kasman and Duman
(2015). Then a 1% increase in GDP is associated with a 0.23% fall in CO₂
emission expressed in kt. Such results imply that the SADC countries is
likely to be on the second part of the U inverted-shape curve where an addi-
tional economic activity decrease the environmental degradation (negative
slope). This negative coefficient (-0.23) witness such statement, which is
in line with Salari et al. (2021) results, but in contradiction with Ogundipe
et al. (2014), Yaduma et al. (2015) and Adu and Denkyirah (2019). This
decrease of CO₂ emission may be attributed not only to the intensity of
the technology utilization that is less harmful to the environment but also
to the abatement effort. This group of country has jumped over the early
stage of the environmental destruction of an economic growth. We also
found a significant positive impact (at 1%) of energy consumption on CO₂
like Salari et al. (2021). Yet, one record a 1.85% rise of CO₂ emission when
energy use increase by 1%. To mitigate such effect, the government has to
use more renewable energy and substitute, for instance, energy originating
from fossil fuel to like biofuel, solar power and wind energy.

However, like this latter, our findings do not coincide with that of Zou
and Zhang (2020) affirming a positive impact of GDP on CO2.

In terms of long run equilibrium, empirical findings show that the error
correction is negative and significant at 5%, that is -0.73. This represents
the speed of adjustment, meaning that the deviation from long run equi-
librium path will be corrected at a level of 73% in each period, that is to
say a full recovery at 1.37 period. We may conclude the SADC country has
high performance to absorb shock stemming from the departures of the
carbon dioxide to its equilibrium level. In fact, we can say variables are
cointegrated and the regressor - energy use and GDP - are jointly Granger
cause CO2 emission in the long run.

At the end but not the least, the Pooled mean group estimation method
clearly demonstrates heterogeneity within the SADC country in the short
run, while homogeneity is observed in the long run.
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