The Role of Intra-Industry Trade, Foreign Direct Investment, and Renewable Energy on Portuguese Carbon Dioxide Emissions

Abstract

This paper revisited the link between intra-industry trade (IIT) between Portugal and Spain and Portuguese carbon dioxide (CO2) emissions. The research also considers the effects of foreign direct investment (FDI) on CO2 emissions, pondering the arguments of the pollution haven hypothesis and the halo hypothesis. As an econometric strategy, this investigation has applied panel data, namely a Pooled Mean Group of an Autoregressive Distributed Lag (ARDL) model and Panel Quantile Regression (PQR). The preliminary unit root tests indicated that IIT, Portuguese and Spanish renewable energy, and Portuguese FDI are integrated into the first differences and stationary with the second generation test (Pesaran methodology). In the next step, this study applied the multicollinearity test and cross-dependence between the variables. The variance inflation factor test demonstrated that FDI and IIT have no multicollinear problems. However, as expected, collinearity exists between Portuguese and Spanish renewable energy. Regarding the cross-sectional dependence test, this investigation concluded that the variables have a dependence between them. The cointegration test revealed that the variables are overall cointegrated. In the econometric results with the ARDL estimator, this investigation has found that IIT between Portugal and Spain is negatively correlated with Portuguese CO2 emissions, showing that this type of trade encourages environmental improvements. However, the PQR demonstrates that there is an opposite relationship. According to this, Portuguese and Spanish renewable energy is negatively impacted by CO2 emissions, revealing that renewable energy aims to decrease pollution. Finally, Portuguese FDI reduces CO2 emissions, which is explained by product differentiation, innovation, and monopolistic competition.

A Gravity Model Analysis of Portuguese Foreign Direct Investment

“…Using the methodology of Pedroni (2001) of panel cointegration, it is possible to see in Table 5 that the variables in studies are cointegrated in the long run. In Table 6, we use the multicollinearity test based on the variance inflation factor (VIF) by the variables considered in this research (Portuguese income per capita, investors income per capita, similar language of ex-colonies (Lang), and geographical distance (LogGDIST).We can conclude that the variables have no problems of collinearity, considering the argument referred to by Leitão et al (2022), andFuinhas et al (2021), i.e., the variance inflation factor (VIF) is less to 5. Considering that the sample uses panel data, one of the appropriate tests to evaluate autocorrelation is the test proposed by Arellano and Bond (1991), determined based on the first differences.…”

“…Following the empirical works of Paniagua et al (2015), Alotaibi and Alajlan (2021), and Leitão et al (2022), we decided to apply the panel quantile regressions method suggested by Koenker and Bassett (1978) to the gravity model.…”

“…In this context, following the recommendations of Santos Silva and Tenreyro (2006) and Paniagua et al (2015), we applied the gravity model comparing the econometric results between the OLS estimator, Poisson Pseudo-Maximum-Likelihood (PPML) estimator and the Panel Quantile Regressions. For a more detailed analysis of the Panel Quantile Regression equation components, see, for example, Paniagua et al (2015) and Leitão et al (2022).…”

Comparative Analysis of the Impact of Policy Uncertainty, Agricultural Output, and Renewable Energy on Environmental Sustainability

“…Another reason for a positive association between FDI and GHG could be the role of the former in stimulating industrial activity and the surge in energy consumption leading to an increase in the latter. FDI has an adverse effect on ES by affecting carbon emission efficiency [60,61].…”

The Link between Human Development, Foreign Direct Investment, Renewable Energy, and Carbon Dioxide Emissions in G7 Economies

“…Regardless of the geographic area or the organization of the data (panel data or time series), the studies by Mahmoodi and Dahmardeh [5], Leitão et al [6], and Gyamfi et al [7] demonstrate the importance of continuing to study the EKC. Furthermore, in addition to the relationship between economic growth and polluting emissions, it is noted that in recent years, researchers have more frequently introduced into econometric models as explanatory variables not only the consumption of fossil energies but also the consumption of renewable energies and the energetic capacity.…”