A new study published in Energy, Sustainability and Society reveals that geopolitical risks originating from the Russian Federation and the Middle East significantly exacerbate both energy and carbon inequalities across European nations. The research, conducted by Sakine Owjimehr, Mehdi Emami Meybodi, and Zeinab Yadegar, analyzes three decades of data from 1993 to 2022 to understand how political tensions influence disparities in energy access and carbon emissions. By focusing on a specific subset of geopolitical risks rather than global aggregates, the authors provide a nuanced view of how regional conflicts impact the European energy landscape. The study identifies a strong correlation between these twin inequalities, suggesting that factors driving one often drive the other in parallel.
The central finding is that region-specific geopolitical risks exert a stronger influence on inequality than global geopolitical risks. The researchers constructed a weighted composite index, referred to as MGPR, which incorporates data from Israel, Turkey, the Russian Federation, Saudi Arabia, and Egypt. This approach allows for a more precise measurement of the risks that directly affect European energy markets. The analysis demonstrates that heightened risks in these specific regions are associated with increased disparities in energy consumption and carbon footprints among European countries, highlighting the vulnerability of the continent to external political shocks.
To capture the temporal dynamics of these effects, the study employed a Panel Auto Regressive Distributed Lag framework. This statistical model distinguishes between short-term and long-term impacts, which is crucial for understanding how geopolitical shocks evolve over time. The authors utilized Mean Group and Pooled Mean Group estimators to account for heterogeneity across the 39 European countries included in the sample. This methodological choice enables the differentiation of immediate market reactions from longer structural changes in energy inequality, providing a robust foundation for policy analysis.
The results indicate that geopolitical threats, such as war threats, military buildups, and nuclear tensions, have a more persistent impact on energy inequality than realized acts like conflicts or terrorism. In the long run, these threat-related risks create an atmosphere of uncertainty that discourages investment in energy infrastructure. This lack of investment can widen the gap between countries with robust energy systems and those with weaker infrastructure. The study finds that the coefficients for geopolitical risk variables are consistently larger in the long run than in the short run, suggesting that the effects of political instability are durable and cumulative.
Renewable energy consumption plays a complex and time-dependent role in mitigating these inequalities. In the short term, higher renewable energy consumption is associated with a reduction in both energy and carbon inequality. This suggests that renewable energy can serve as a buffer against the immediate impacts of geopolitical risks. However, in the long term, the relationship reverses, with renewable energy consumption contributing to increased inequality. This divergence is attributed to the uneven distribution of renewable energy infrastructure across European countries, where some nations have made substantial investments while others lag behind.
The study highlights the concept of “twin inequalities,” noting that energy and carbon inequalities follow remarkably similar trajectories over the three-decade period. Both metrics declined from 1993 to 2007 before reversing course and increasing from 2007 to 2022. A substantial divergence between the two inequalities emerged during the period from 2017 to 2022, coinciding with heightened geopolitical tensions. The correlation coefficient between energy and carbon inequality is 0.89 and statistically significant, reinforcing the idea that these two forms of inequality are deeply interconnected and respond similarly to external shocks.
Control variables such as GDP and urbanization also exhibit distinct patterns in their relationship with inequality. GDP shows a significant negative effect on energy inequality in the short run, indicating that higher income levels help mitigate disparities. However, this effect disappears in the long run, suggesting that economic growth alone is insufficient to address structural inequalities. Urbanization, on the other hand, has no significant short-run impact but demonstrates a positive and significant long-run effect on energy inequality. This aligns with the expectation that urban concentration creates disparities in energy consumption, as urban areas often have different energy demands and infrastructure needs compared to rural regions.
The authors acknowledge several limitations in their study, primarily related to the level of data aggregation. Energy inequality is fundamentally a distributive phenomenon, and macro-level data provide only a broad picture of disparities. Household-level data would offer more granular insights into how inequality affects different income groups and regions, but such data were only available up to 2020, which does not fully capture the recent geopolitical landscape. Additionally, the study does not explicitly address potential endogeneity issues within the ARDL framework, which could affect the interpretation of the causal relationships between variables.
Despite these limitations, the study offers valuable insights for policymakers seeking to enhance Europe’s resilience to geopolitical risks. The findings suggest that while renewable energy can help mitigate short-term impacts, long-term strategies must focus on harmonizing energy infrastructure across the continent. Policymakers are advised to invest in renewable energy infrastructure, particularly in resource-constrained areas, to prevent the widening of energy gaps. Additionally, promoting energy efficiency and public engagement in the transition to a low-carbon economy can help address the structural drivers of inequality.
The research underscores the importance of considering the geographic origins of geopolitical risks when analyzing their impact on energy systems. By disaggregating global risks into region-specific components, the study provides a more accurate picture of how political tensions affect European energy inequality. This approach can inform more targeted policy responses that address the specific vulnerabilities of the European energy market. As geopolitical dynamics continue to evolve, understanding the long-term effects of these risks on energy and carbon inequalities will be crucial for ensuring a sustainable and equitable energy future.
The study’s construction of the geopolitical risk index relies on text-mining techniques applied to major newspaper archives, a methodology originally developed by Caldara and Iacoviello. This approach quantifies risk by measuring the frequency of specific terminology associated with geopolitical tensions. The index is divided into two distinct components: GPR Threat and GPR Acts. The Threat component captures the frequency of words related to war threats, nuclear tensions, and similar geopolitical concerns, while the Acts component reflects the occurrence of words related to actual warfare, terrorist activities, and comparable realized events. By separating these components, the researchers were able to distinguish between the anticipation of conflict and the occurrence of conflict itself. The composite index for the Middle East and the Russian Federation was created using a weighted average of individual country indices. The weights for this composite index represent each country’s proportional contribution to global geopolitical risk calculations, determined by article frequency. This weighting ensures that the index reflects the relative prominence of geopolitical events in the news media for the selected countries.
The sample for this analysis includes 39 European countries, spanning a diverse range of geographic and economic profiles. The list of included nations comprises Albania, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, Romania, the Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, and the United Kingdom. Other European countries were excluded from the analysis due to statistical limitations. The time series data covers the period from 1993 to 2022, a selection driven by data availability. This thirty-year window is significant because it encompasses major geopolitical shifts, including the post-Cold War transition and the recent escalation of the conflict between the Russian Federation and Ukraine. The inclusion of this specific timeframe allows the model to capture the impact of these major political events on energy systems and inequality metrics across the continent.
Before estimating the primary models, the researchers conducted a series of pre-estimation tests to ensure the validity of the panel data. Cross-sectional independence tests revealed that GDP, urbanization, and renewable energy consumption exhibit cross-sectional dependence. To address this, the study employed the cross-sectionally augmented panel unit root test for variables showing dependence and the IPS test for those showing independence. The results indicated that all variables were stationary either in levels or in first differences. The selection of the appropriate panel estimator was determined using the Hausman test, which evaluates the null hypothesis that the Mean Group estimator is inefficient against the alternative that the Pooled Mean Group estimator is consistent. Based on these tests, the Mean Group estimator was selected for two of the energy inequality models and for all four carbon inequality models, while the Pooled Mean Group estimator was chosen for the remaining two energy inequality models. This careful selection process ensures that the estimation method aligns with the specific statistical properties of the data for each model.
To validate the robustness of the initial panel findings, the researchers aggregated the data for the entire European region and restructured it as a time series spanning from 1993 to 2022. They then estimated the research models using the ARDL methodology within this time series framework. The results from these robustness checks were consistent with the initial panel model estimations, reinforcing the reliability of the findings. Diagnostic tests, including residual diagnostics for serial correlation and heteroskedasticity, as well as stability diagnostics using CUSUM and CUSUM of squares tests, were conducted to assess model validity. All diagnostic checks confirmed the robustness of the ARDL models. The software used for the pre-estimation tests was EViews 13, while the model estimations were carried out using Stata 14, employing the xtpmg command. This combination of panel and time series approaches provides a comprehensive view of the relationship between geopolitical risks and energy inequalities, ensuring that the results are not artifacts of a specific estimation technique.
Subject of Research: The effect of geopolitical risks from the Russian Federation and the Middle East on carbon and energy inequalities in Europe
Article Title: The effect of geopolitical risks from the Russian Federation and the Middle East on carbon and energy inequalities in Europe
Article References: Owjimehr, S., Meybodi, M. E., & Yadegar, Z. (2026). The effect of geopolitical risks from the Russian Federation and the Middle East on carbon and energy inequalities in Europe. Energy, Sustainability and Society, 16(1), Article 26. https://doi.org/10.1186/s13705-026-00582-y
Image Credits: AI Generated
DOI: 10.1186/s13705-026-00582-y
Keywords: effect, geopolitical, risks, Russian, Federation, Middle, East, carbon, energy, inequalities, Europe, scientific research