The Impact of Macroeconomic Variables on Carbon Dioxide Emissions in Mena Countries

Abstract
Introduction
In 2023, global carbon dioxide (CO₂) emissions reached a record high of 37.4 billion metric tons, according to the International Energy Agency (IEA). This alarming trend underscores the urgent need to identify the key drivers of CO₂ emissions, representing a critical step toward effective air pollution mitigation and climate action. Among the various determinants, foreign direct investment (FDI) plays a pivotal role in shaping the environmental consequences of economic development. However, the existing literature provides mixed-and often contradictory-evidence regarding FDI’s impact on emissions. In developing countries, inward FDI frequently leads to higher CO₂ emissions, primarily through the expansion of energy-intensive industrial sectors. Conversely, developed economies often relocate their polluting industries to developing countries, a phenomenon commonly referred to as the “pollution haven hypothesis.” This offshoring of carbon-intensive production significantly elevates emissions in host emerging economies, particularly in nations with lax environmental regulations.
Beyond FDI, population growth and rapid urbanization have emerged as defining features of the contemporary global landscape. Owing to their profound economic and infrastructural implications, these demographic trends are widely recognized as major contributors to rising CO₂ emissions. This effect is particularly pronounced in fossil fuel-dependent economies, such as many in the Middle East and North Africa (MENA) region, where national income relies heavily on non-renewable energy resources. In these contexts, the combustion of oil, gas, and coal remains the dominant source of carbon emissions, positioning fossil fuel-rich countries at the center of the global climate debate. Against this backdrop, the present study aims to investigate the principal determinants of CO₂ emissions in MENA countries over the period 2010-2023. To account for dynamic interdependencies and address potential endogeneity among explanatory variables, the analysis employs the Generalized Method of Moments (GMM) estimation technique-specifically, the system GMM approach-which is well suited for panel data with persistent lagged dependent variables.
Research Method
The key distinction between the two GMM estimators lies in how they handle unobserved individual-specific effects. The difference GMM (D-GMM) eliminates these effects by first-differencing the variables, thereby removing time-invariant heterogeneity. In contrast, the orthogonalized-or system-GMM (S-GMM) combines moment conditions from both the differenced and level equations, leveraging additional orthogonality conditions between the instruments and the error terms.
Compared to difference GMM, system GMM offers several important advantages. It produces more efficient and less biased estimates, particularly in contexts characterized by small sample sizes, high persistence in the dependent variable (i.e., strong autoregressive dynamics), or weak instrument relevance. By incorporating information from both levels and differences, system GMM mitigates finite-sample bias and enhances estimation precision, making it the preferred approach in contemporary empirical studies employing dynamic panel data models.
Results and Discussion
The estimation results obtained via the Generalized Method of Moments (GMM) reveal several key determinants of CO₂ emissions in the MENA region. First, the lagged dependent variable is positive and statistically significant: a 1% increase in CO₂ emissions in the previous period is associated with a 0.061% rise in current emissions. This finding underscores the strong persistence of environmental degradation over time, reflecting path dependency in emission trajectories.
Population growth also exerts a positive and significant effect on emissions. Specifically, a 1% increase in the population growth rate leads to a 0.112% increase in CO₂ emissions, highlighting how demographic expansion intensifies demand for energy, infrastructure, and resources-thereby exacerbating environmental pressures.
Foreign direct investment (FDI) is found to significantly increase carbon emissions: a 1% rise in FDI inflows corresponds to a 0.124% increase in CO₂ emissions. This suggests that, in the MENA context, inward FDI is largely channeled into energy-intensive and pollution-prone sectors, consistent with the “pollution haven” hypothesis.
Similarly, economic growth-measured by GDP-has a positive and statistically significant impact on emissions. A 1% increase in GDP is associated with a 0.102% rise in CO₂ emissions, indicating that economic expansion in the region remains heavily reliant on carbon-intensive production processes and fossil fuel-based energy systems.
Trade openness also shows a positive and significant relationship with emissions: a 1% increase in the degree of openness leads to a 0.003% increase in CO₂ emissions. While modest in magnitude, this effect reflects how greater integration into global markets may facilitate the transfer of pollution-intensive goods and production activities across borders.
Finally, non-renewable energy consumption emerges as the most potent driver of emissions. A 1% increase in the use of non-renewable energy sources-such as oil, gas, and coal-is associated with a substantial 0.652% increase in CO₂ emissions. This result underscores the central role of fossil fuel dependence in shaping the region’s environmental footprint and confirms that the energy mix remains the primary lever for emission mitigation in MENA countries.
Conclusion
Therefore, governments in these countries should formulate and implement comprehensive policies aimed at developing and expanding renewable energy infrastructure. Such policies may include tax reductions, financial incentives to support the adoption and advancement of modern clean technologies, and the provision of low-interest loans to encourage investment in the renewable energy sector.