“The nexus between foreign direct investment and environmental sustainability in North Africa”

This paper provides a study of the relationship between sustainable development and foreign direct investment (FDI) from an empirical point of view in the case of the North African countries during the period from 1985 to 2005. The researchers use the cointegration test, the FMOLS (Fully Modified Ordinary Least Squares) model and the Granger causality test to examine this relationship. According to the empirical results, we confirm the existence of a cointegration relationship between the different series studied in this paper. Based on the cointegration test we can use the error correction model. Also, to test the effect of FDI on sustainable development in the North African countries, we make an estimate by FMOLS method. We found that the foreign direct investment has a positive impact on CO2 emissions. Also, the Granger causality test confirms the presence of a bidirectional relationship between FDI and CO2 emissions (carbon dioxide). That is to say, the FDI can cause CO2 emissions and CO2 emissions can cause FDI based on the Granger causality.

Introduction  Regarding the relationship between FDI and the environment, a lot of literature focuses on their potential link. For example, Hoffmann et al. (2005) use the Granger causality test based on data from 112 countries to make sure that the relationship between FDI and pollution depends on the development of the host countries. Cole et al. (2006) develop a model of political economy and conclude that when the degree of corruptibility of the government is weak, FDI leads to a stricter and cleaner environmental policy.
Hitam and Borhan (2012) use a data for Malaysia from 1965 to 2010 to examine the impact of FDI on the quality of the environment and conclude that FDI can increase environmental pollution. Therefore, FDI should be incorporated as an independent variable in the regression model of the environmental Kuznets curve (EKC). The estimated coefficients from the regression of the EKC equation will be biased by omitted variable. Grossman and Krueger (1995) establish a relationship between economic growth and environmental pollution. Their conclusion shows that the relationship between environmental pollution and income per capita is an inverted U˗ shape, which is known as environmental Kuznets curve (EKC). The quality of the environment does not deteriorate with both economic growths beyond the turning point.
According to the study by Grossman and Krueger (1995), some studies (Selden & Song, 1995 In these models, they assume that individual utility is a function of the normal quality of goods and the environment, resulting in a compromise between the normal property and environmental quality to maximize the utility level when resource constraints are imposed. An important difference between these theoretical models is that they offer different mechanisms to explain the survival of an inverted U-shaped pattern. Then, Stocky (1998) concludes that the choice of optimal production technology in diverse periods of development resulted in the EKC. Jones and Manuelli (2001) change the outlook from technology to political factors; they show that the pollution tax and/or regulations may interpret the formation of the EKC.
For most of the existing literature, they neglect one significant feature that the effect of FDI on environmental pollution depends on the level of economic development, in other words, the impact of FDI on environmental quality varies according to the development period. The pollution is based on gross domestic product (GDP) and should be considered as a function of GDP.
In addition, most empirical research using the quadratic term and the cubic term to capture the nonlinear effect of GDP and/or FDI on the environment, prior specification of the regression function may bias the results as mentioned by Harbaugh et al. (2002).
This paper provides a study on the impact of foreign direct investment (FDI) on sustainable development from an empirical point of view in the case of the North African countries during the period from 1985 to 2005.
Then, we use the estimation FMOLS and causality test. According to the empirical findings, we show the existence of a cointegration relationship between the different variables used in this paper. With the cointegration test, we can determine the use of an error correction model. Also, to test the effect of FDI on sustainable development in the countries of North Africa, we will make an estimate by FMOLS method. We conclude that the FDI has a positive impact on sustainable development. In addition, we notice that there is a bidirectional relationship between FDI Granger and CO2 emissions (0.0000 < 5% and 0.0000 < 5%). That is to say, the FDI can cause Granger emissions of CO2 and CO2 emissions can cause Granger FDI.
The rest of the paper is organized as follows. In section 1, we present a literature review. Second section summarizes the econometric methodology. Data are presented in section 3. Section 4 was dedicated to the interpretation of results. The conclusion is made in the last section. Still, the study of Pegkas (2015) including its goal is twofold: first, analyzes the relationship between foreign direct investment and economic growth, and, second, estimates the effect of FDI on economic growth using panel data for the euro area countries over the period from 2002 to 2012 and applying the method of OLS completely changed FMOLS and Dynamic Ordinary Least Squares (DOLS). The empirical analysis reveals that there is a lasting positive co-integration relationship between the stock of FDI and economic growth, and the results show that the stock of foreign direct investment is a significant factor that positively affects economic growth.  The data used in this paper are of annual frequency for all variables. These data come from the World Bank database and the International Monetary Fund for the period from 1985 to 2015. We will estimate the models chosen by referring to an analysis of panel data.

Empirical methodology
The choice of panel data is based on the two dimensions of the used data: the first dimension is time (a period of 31 years) and the second is individual (employee sample consists of 6 countries of North Africa).

Data
In this section, we present the sample and the model used in our paper.
Our objective in this paper is tostudy of the impact of FDI on sustainable development in the case of the North African countries during the period from 1985 to 2015.
In Table 2, we expose the different countries employed in our paper. In this section, we will try to make a descriptive analysis of the different results for the study of the impact of FDI on sustainable development in the North African countries.
First, let's define the type of assessment which is a regression on panel data. Our choice is justified by the presence of two dimensions in the data used:the first is time (a period of 31 years) and the second is individual (our sample is made up of 6 North African countries).
This section is dedicated to the interpretation of results for the descriptive statistics and Pearson correlation matrix for the variables used in our study.
All descriptive statistics of the variables used in our paper are summarized in Table 3.
According to the results of Table 3, we find that the LCO2 variable, which expresses logarithm of CO2 emissions, can reach a maximum value of 12.30497. As its minimum value is 7.975197, its risk is measured by the standard deviation whichis 1.022934.
The LGINI variable, which measures the logarithm of the GINI Index, can reach a maximum value of 4.146937. While its minimum value is 3.425890, its risk is measured by the standard deviation which is 0.192268.
Using both statistics of asymmetry (skewness) and kurtosis, we can conclude that all variables used in this paper are characterized by non-normal distribution. Then, the asymmetry coefficients indicate that all variables are shifted to the left (negative sign of asymmetry coefficients) and are far from symmetrical except for LGINI, LFDI, LINF, LGDP, LUP, LEU, LGCF and LU variables, which are oriented to the right (positive sign of asymmetry coefficients).
Also, the kurtosis coefficient shows that leptokurtic for all variables used in this paper indicate the presence of a high peak or a large tail in their volatilities (leptokurtic the coefficients are more than 1).
In addition, the positive sign of estimation coefficients of Jarque-Bera statistics indicates that we can reject the null hypothesis of the normal distribution of the variables used in our paper. In fact, the high value of the coefficients of the Jarque-Bera statistic shows that the series are not normally distributed at a level of 1 percent.
The results shown by the three statistics, namely skewness, kurtosis and Jarque-Bera, suggest that all variables used in our paper are not normally distributed for the case of the countries of North Africa and during the study period from 1985 to 2015.
Thus, we conduct a test of the correlation between the different variables used in the case of the North African countries during the study period from 1985 to 2015.  Table 5 for the North African countries.
The acceptance or rejection of the null hypothesis of the different tests is based on the value of probability and the indicated statistics test. These probabilities are compared with a 10% threshold. If these probabilities are less than 10%, then we reject the null hypothesis and if these probabilities are more than 10%, then we accept the null hypothesis.      Table 6.
Indeed, the Pedroni test demonstrates the longterm relationship between the FDI and sustainable development.
Thus, Kao test confirms the long-term relationship between the different variables used in this paper, mainly between FDI and sustainable development.
In addition, Fisher's test results confirm the presence of a long-term relationship between FDI and sustainable development in the North African countries for the period from 1985 to 2015.
According to the results in Table 6, we confirm the existence of a cointegration relationship between the different series studied in this paper. The results of the null hypothesis test of no cointegration were rejected at the 5% threshold, which explains the presence of a cointegration relationship.
The results of these tests can determine the use of an error correction model. Also, to test the effect of FDI on sustainable development in the North African countries, we will perform a FMOLS estimate.

The error correction model (ECM).
After testing the cointegration between FDI and sustainable development in our paper, we'll estimate the error correction model. The ECM allows to model together for short-term dynamics (represented by the variables in first differences) and long˗term dynamics (represented by the variables in level). For LFDI variable and studying the short-term dynamics, we notice that the FDI (t-2) have a positive and significant impact on a threshold of 1% of foreign direct investment at time t for the case North African countries. That is to say, if the IDE at the time (t-2) increases by one unit, then, foreign direct investment at time t increases by 0.265404 units.
Poverty measured by the GINI Index has a negative and significant impact on foreign direct investment at a 10% threshold. That is to say, if the GINI Index increases by 10 units, then, foreign direct investment fell by 3.518615 units.
The LINF variable that measures the consumer price index also has a negative and significant impact on foreign direct investment with a threshold of 5%.
That is to say, if the level of the inflation rate increases by five units, then, foreign direct investment falls by 0.016970 units.
T h e L E U v a r i a b l e t h a t m e a s u r e s t h e l e v e l o f energy consumption is statistically significant and has a positive impact on foreign direct investment at a level of 5%. So, if energy consumption increases by five units, then, foreign direct investment increases by 1.659182 units. The LGCF variable that measures the gross formation of capital stock also has a positive and significant impact on foreign direct investment with a threshold of 1%. That is to say, if the level of gross fixed capital stock increases by one unit, foreign direct investment increases by 0.059556 units.
LRE variable that measures the consumption of renewable energy has a positive and significant impact on foreign direct investment with a threshold of 1%. That is to say, if the level of consumption of renewable energy increases by one unit, foreign direct investment increases by 0.619481 units.
For sustainable development, we note that emissions of CO2 at the time (t-1) have a negative and significant effect on CO2 emissions at 1% threshold. This means that if emissions of CO2 at the time (t-1) increase by one unit,they decrease fell by 0.401891 units at time t.
The LINF variable that measures the consumer price index also has a negative and significant impact on emissions of CO2 at a threshold of 5%.
That is to say, if the level of the inflation rate increases by one unit, then the CO2 emissions decrease to 0.001444 units.
The LMC variable that measures the market capitalization of listed companies has statistically significant and positive impact on CO2 emissions a t a 1 0 % t h r e s h o l d . S o , i f t h e m a r k e t capitalization of listed companies increases by ten units, then, the CO2 emissions increase by 0.026446 units.
FDI has no effect on CO2 emissions, which measures sustainable development at short time.   The acceptance or rejection of the null hypothesis of Granger causality test is based on a threshold of 5%. If the probability of the test is less than 5% in this case, we reject the null hypothesis and if the probability is more than 5%, then, we accept the null hypothesis of no causality. According to Table 9, we notice that there is a bidirectional relationship between FDI and CO2 emissions with the Granger causality (0.0000 <5% and 0.0000 <5%). That is to say, the FDI can affect CO2 emissions and CO2 emissions can affect FDI.
Thus, we notice that there is a unidirectional relationship between sustainable development and economic growth Granger. Only CO2 emissions can affect economic growth.
In addition, we remark that there is a bidirectional relationship among the urban population and CO2 emissions. That is to say, the urban population can affect CO2 emissions and CO2 emissions can affect urban population.

Conclusion
Currently, much of the debate on foreign direct investment and the environment revolves around the assumption of "pollution havens" This essentially means that companies move their activities to less developed countries to benefit from less stringent environmental regulations. Thus, this paper provides a study on sustainable development and foreign direct point (FDI) from an empirical investigation of view in the case of the North African countries during the period from 1985 to 2005.
According to the empirical findings, we confirm the existence of a cointegration relationship between the different series studied in this paper. So, we notice that there is a bidirectional relationship between FDI and CO2 emissions with the Granger causality.
The cointegration test can confirm the use of a error correction model. Also, to test the effect of foreign direct investment on sustainable development in the North African countries, we make an estimate by FMOLS method.We find that the foreign direct investment has a positive impact on sustainable development.
The study therefore suggests the following recommendations: The North African Governments should impose stringent laws to protect their environment and regulate the activities of international corporations and ensure that these laws are adhered to. Environmental by friendly equipments should be utilized by multinational corporations and resource extracting industries. Governments should prepare policies and programs that will lessen poverty and provide to the less privileged and poor citizens. This is to make sure that natural resources are not wasted or misused by the poor. Finally, adequate lands should be provided for housing, farm and resources productivities between the less privileged to achieve environmental sustainability.