**5. Results and discussion**

Before applying dynamic ARDL simulations, it is crucial to demonstrate that any series are not I (2). The Unit root test results reported in **Table 1** indicate that the variables are integrated either in levels or at first differences. So, the estimated results of the unit root tests confirm that dynamic ARDL model can be applied with the used series.

### **5.1 The direct effect of ICT and GDP on environmental quality**

First, we will select the required number of lags of dependent variable and the regressors by applying the information criteria such as Criterion (HQ ). Second, the estimation of the model should be carried out based on the number of lags.

According to the results obtained from the AIC and HQ information criteria, one lag is the optimum number to be incorporated in the analysis.

To examine the direct impact of ICT and GDP on CO2 emissions both in Tunisia and in Morocco, we will estimate Eqs. (1) and (2). So, we will find for each country five regressions. In the four first regressions, we examine the direct effect of ICT on carbon emissions, so, we will introduce each time one of the four measures of ICT in Eq. (1). In the last regression, we will introduce in Eq. (2) the variable GDP in order to examine the direct impact of GDP on environmental quality.

To test the cointegration of variables, the ARDL bounds test was used. This test was used to check the long-term relationship among the study series. **Table 2** indicates the results of F-statistics which is applied to decide the cointegration. The calculated F-statistics value is greater than the upper bounds value at 10% and 5% level of significance that indicates that cointegration exists among the study variables, in both Tunisia and Morocco for all our models.

This section consists of describing the short-term estimators and presenting an error correction model corresponding to the established cointegration or the longterm equilibriums. The notion CointEq (−1) defines the delayed residue originating from our long-term equilibrium equation. The negative sign of its estimated coefficient and its significance for both countries thus confirm the presence of an error correction tool. The coefficient of the cointegration equation explains the order in which the variable CO2 will be mobilized towards the long-term target.

Findings presented in **Table 4** confirm that ECT is negative and significant which proves that there is a cointegration relationship between the variables of the model. In fact, a negative sign of ECT is essential for a stable error correction mechanism.

With regard to the GDP variable, the short-run results presented in **Table 4** demonstrate that the coefficient of the present value of the GDP is significant and positive in both Tunisia and Morocco. That is means that economic growth was found to have increased the quantity of CO2 emissions in the case of Tunisia and Morocco. The positive impact of GDP on CO2 emissions can be explained, essentially, by the fact that when economic growth improves, the use of energy increases. So the increase in CO2 emissions is mainly due to the combustion of petroleum, coal, and natural gas for energy purposes. Also, the environmental


*Global Trade in the Emerging Business Environment*

**Table 1.** *Unit root test results.*

*Does the Interaction between ICT Diffusion and Economic Growth Reduce CO2 Emissions?… DOI: http://dx.doi.org/10.5772/intechopen.102945*


**Table 2.** *Bounds test results.*


**Table 3.**

*Short-run estimation and cointegration form.*

**52**


*Does the Interaction between ICT Diffusion and Economic Growth Reduce CO2 Emissions?… DOI: http://dx.doi.org/10.5772/intechopen.102945*

> **Table 4.**

*Long runform.*

quality can be deteriorated by certain industrial processes namely the production of cement, the manufacture of clothing, the alcohol factories, etc. Then, the increase in the number of economic activities has a detrimental effect on the environmental quality.

It is not surprising that there is a strong negative relationship between ICT diffusion and environmental quality, in the short-run model. In fact, the coefficients of each measure of ICT (fbs, fts, internet, and mcs) are significant and positive, implying that fts, fbs, internet, and mcs negatively influence the Moroccan and Tunisian environmental quality. It means that the ICT diffusion increases the CO2 emissions. Concerning the variable find, we note that both present and first delayed value have positive effects on carbon emissions of the two countries.

Besides, the findings presented in **Table 4** confirm that in the short run, the openness deteriorates the Moroccan and Tunisian environmental quality. In fact, the coefficient of the variable open is positive and significant in all models.

As apparent in **Table 4**, the coefficient of the variable inf is negative and significant in all models in short run. Therefore, inflation ameliorates the environmental quality in both countries. This negative relationship between carbon emissions and inf can be explained by the fact that when prices increases, energy consumption decreases; as a result the CO2 emissions decrease. Also, we can see that the first delayed value of inf affect negatively the Tunisian CO2 emissions in the first model when we introduce the variable fts as a measure of ICT.

Finally, the variable invst has not an impact on environmental quality in both countries. In fact the coefficient of this variable is insignificant, except, in the case of Tunisia, when we introduce the variable internet in our model invst affects positively and significantly the CO2 emissions. That is means that invst deteriorates the Tunisian environmental quality. The positive relationship between CO2 emissions and invst can be explained, especially, by the fact that invst increases energy consumption that deteriorates the environmental quality.

We can deduce from **Table 3** that there is substantial evidence that the variables ICT and GDP have long-run effects on CO2 emissions. Therefore, we will examine the long-run direct impact of ICT and GDP on CO2 emissions for both countries. So, we will find for each country five regressions.

Findings presented in **Table 4** confirm that in the long term all explanatory variables have the same signs of the coefficients in both countries. Similarly the GDP and ICT conserve the same sign in the short run. In other words, in the long run, the economic growth and the ICT diffusion deteriorate the environmental quality in both countries. In fact the coefficient of the variables fbs, fts, internet, mcs, and GDP are positive and significant, so when ICT and GDP increase, the CO2 emissions increase.

### **5.2 The effect of the interaction between ICT and GDP on environmental quality**

To examine the role of the interaction between ICT diffusion and economic growth on enhancing the environmental quality, we will estimate in this section Eq. (3) where we will introduce in our model the variable (GDP\*ICT). Because the ICT is measured by four measures (fbs, fts, internet and mcs), we will find for each country four regressions.

The ARDL bounds test results are presented in **Table 5** which demonstrates that the calculated F-statistics value is greater than the upper bounds value at 10% and 5% level of significance that indicates that cointegration exists among the study variables, both in Tunisia and Morocco for all our models.

*Does the Interaction between ICT Diffusion and Economic Growth Reduce CO2 Emissions?… DOI: http://dx.doi.org/10.5772/intechopen.102945*


**Table 5.** *Bounds test results.*


