A new study published in the journal Energy, Sustainability and Society reveals that geopolitical risks emanating from the Russian Federation and the Middle East are significantly exacerbating energy and carbon-emission disparities among European countries.
The study, by researchers Sakeni Ojimehir, Mehdi Emami Meybodi and Zainab Yadgar, analyzes data spanning three decades, from 1993 to 2022, to understand the impact of political tensions on disparities related to energy access and carbon emission levels. By focusing on a specific set of geopolitical risks rather than overall global indicators, the researchers offer a more detailed view of how regional conflicts are affecting the European energy landscape. The study identifies a strong correlation between these two types of inequality, suggesting that the factors that lead to one often contribute to exacerbating the other in parallel.
The primary finding of the study is that geopolitical risks associated with specific regions affect inequality more than global geopolitical risks. And researchers have created a weighted composite index, called MGPR, that includes data from Israel, Turkey, the Russian Federation, Saudi Arabia and Egypt. This index allows for a more accurate measurement of the risks directly affecting European energy markets.
The analysis showed that the higher risks in these regions are related to the widening differences between European countries in energy consumption and carbon footprint, which highlights the extent to which the continent is affected by external political shocks.
To monitor the temporal developments of these effects, the study used the Panel ARDL framework, a statistical model that distinguishes short-term and long-term impacts, a key point for understanding how geopolitical shocks evolve over time.
And the researchers adopted the estimates of the Mean Group and Pooled Mean Group, in order to take into account the differences between the 39 European countries included in the sample. This systematic selection allows for the distinction between immediate market reactions and long-term structural changes in energy-related inequalities, providing a solid basis for policy analysis.
And the findings suggest that geopolitical threats, such as threats of war, military build-ups and nuclear tensions, are having a more lasting impact on energy disparities than events that actually occur, such as conflict and terrorism.
And in the long run, the risks associated with these threats create uncertainty that discourages investment in energy infrastructure. And a lack of investment could widen the gap between countries with strong energy systems and those with weak infrastructure.
The study finds that the coefficients of geopolitical risk variables are systematically larger in the long term than in the short term, suggesting that the effects of political instability are persistent and cumulative over time.
Renewable Energy and Inequalities among European Countries
Renewable energy consumption plays a complex and time-frame-varying role in reducing these inequalities. In the short term, higher renewable energy consumption is associated with lower energy and carbon inequalities. This suggests that renewable energy could form a barrier that mitigates the immediate effects of geopolitical risks.
But this relationship is reversed in the long run, as renewable energy consumption becomes a contributor to rising inequalities. The researchers attribute this to the uneven distribution of renewable energy infrastructure among European countries, with some countries investing heavily in the field, while others are still lagging behind.
The study highlights the concept of “double disparities,” noting that energy and carbon emissions disparities have followed strikingly similar trajectories over the past three decades.
Between 1993 and 2007, both types of inequality declined, before rising again from 2007 to 2022. A marked divergence emerged between 2017 and 2022, as geopolitical tensions escalated.
And the correlation between energy inequality and carbon emissions inequality was 0.89, a statistically significant correlation, reinforcing the conclusion that these two types of inequality are deeply interconnected and react similarly to external shocks.
Economic growth and urbanization and their impact on inequality
Controlling variables, such as GDP and urbanization, showed different patterns in their relationship to inequalities.
GDP has had a statistically significant negative impact on energy inequality in the short term, suggesting that higher income levels help to reduce disparities. However, in the long run, this effect is fading, which means that economic growth alone is not enough to address structural inequalities.
Urbanization has not shown a significant short-term impact, but it has had a statistically significant positive long-term impact on energy inequality. This is consistent with projections that urban population concentration leads to energy disparities, given that urban energy needs and infrastructure are different from rural areas.
The researchers acknowledge a number of limitations in their study, most notably the level of data collection. At its core, energy inequality is a resource-allocation phenomenon, while macroeconomic data provide only an overview of these disparities.
The availability of household-level data would have provided a more detailed understanding of how disparities affect different income groups and geographic regions, but such data were only available until 2020, which does not allow for full monitoring of the newer geopolitical landscape.
Nor does the study explicitly address potential problems with endogeneity within the framework of the distributed gap self-regression model (ARDL), which may affect the interpretation of causal relationships between variables.
Despite these limitations, the study provides important insights for policymakers seeking to strengthen Europe's ability to confront geopolitical risks.
And the findings suggest that renewable energy could help mitigate short-term impacts, but long-term strategies should focus on balancing the distribution of energy infrastructure across the continent.
And researchers recommend investing in renewable energy infrastructure, particularly in resource-constrained areas, to avoid widening energy gaps. Enhancing energy efficiency and encouraging public participation in the transition to a low-carbon economy can also contribute to addressing the structural factors behind these inequalities.
The study emphasizes the importance of taking geopolitical risks into account when analyzing their impact on energy systems. By deconstructing global risks into region-specific components, the study provides a more nuanced picture of how political tensions affect Europe's energy disparities.
This approach could help formulate more targeted policy responses, taking into account the specific vulnerabilities in the European energy market. And as geopolitical dynamics continue to evolve, understanding the long-term effects of these risks on energy disparities and carbon emissions will become key to ensuring a sustainable and equitable future in the energy sector.
Methodology and indicators used in the study
The geopolitical risk index construction of the study is based on Text Mining techniques, applied to major newspaper archives, a methodology originally developed by researchers Caldera and Iacovello.
This methodology measures risks by monitoring the frequency of terms associated with geopolitical tensions. The index is divided into two main components:
- Geopolitical Threat Index (GPR): monitors the frequency of words and terms associated with war threats, nuclear tensions, and similar geopolitical concerns.
- Geopolitical Events Index (GPR): measures the frequency of terms related to the occurrence of wars, terrorist activities, and similar events that have actually been achieved.
By separating these two components, the researchers were able to distinguish between expectations associated with the likelihood of conflicts occurring and the occurrence of the conflicts themselves.
The Composite Index of the Middle East and the Russian Federation was created using a weighted average of individual country-specific indices. The weights used in this indicator represent the relative contribution of each country to global geopolitical risk calculations, based on the number of articles published on them. This weighting ensures that the index reflects the relative importance of geopolitical events in the media coverage of the selected countries.
States and period of time covered
The sample analysis included 39 European countries, varying in terms of their geographical locations and economic levels, namely: Albania, Austria, Azerbaijan, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, and the United Kingdom.
Other European countries were excluded from the analysis due to statistical constraints.
The data cover the period from 1993 to 2022, a period chosen based on the availability of the data. These 30 years are particularly significant because they involve major geopolitical shifts, including the post-Cold War phase and the recent escalation of the conflict between the Russian Federation and Ukraine.
The inclusion of this time period allows the model to monitor the impact of these major political shifts in energy systems and indicators of inequality among European countries.
Statistical tests and results verification
Before estimating the underlying models, the researchers conducted a series of pre-estimation tests to verify the validity of cross-section data.
And cross-sectional independence tests revealed a mutual dependence between GDP, urbanization, and renewable energy consumption. To address this, the researchers used the unit root test for cross-sectional-enhanced tablet data of variables that showed mutual dependence, and the IPS test for variables that did not show this dependence.
The results showed that all the variables were stable either at their original levels or at the first differentials.
The choice of the appropriate estimator for the tablet data was determined using the Hausman test, which assesses the null hypothesis that the estimator of the intermediate group is ineffective, versus the alternative hypothesis that assumes the consistency of the estimator of the intermediate group.
Based on the results of these tests, the mean group estimator was selected for two energy inequality models, and for all four carbon-related inequality models, while the combined mean group estimator was selected for the remaining two energy inequality models.
This process ensures that the estimation method conforms to the specific statistical properties of each model.
To validate the robustness of the preliminary results from the analysis of the tablet data, the researchers collected the data for the entire European region and reorganized them into a time series spanning from 1993 to 2022.
And then, they estimated the research models using distributed gap self-regression (ARDL) methodology within the time series framework. And the results of the durability tests were consistent with the estimates of the prototypes of the tablet data, which enhanced the reliability of the results.
Diagnostic tests were also performed, including residue screening for serial association and heterogeneity of variance, as well as stability tests using the CUSUM and CUSUM of Squares tests. All diagnostic tests confirmed the robustness of regression models.
The researchers used EViews 13 to perform pre-estimation tests, while model estimates were carried out using Stata 14, using xtpmg.
The combination of tablet and time series data methodologies provides a comprehensive view of the relationship between geopolitical risks and energy disparities, and helps ensure that the results are not just a reflection of a specific statistical method.
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