Review Reports - The European Union’s Energy Security Challenges: Import Dependency, Volatility, and Differences Across Member States

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Author,

Thank you for the possibility to read the paper. Actually, this is a very good topic and insight. However, I see the conflict here. Specifically, I see an experienced scientist with a deep understanding of the research issue and global contexts; but in my opinion, the results should be commented with a care, not making strong statements, which are sometimes beyond the research results, i.e., data presented in Tables and Figures. Please find below my comments.

In the paper, you broadly use the terms energy dependence, security of supply, and vulnerability. At the beginning of the paper, I suggest describing what they mean, and how they are related each to other because in the results and other sections, they are used mixed by author, which strongly confuses the reader.
H1-H4 are very strong compared to the results. They could be supported by references. I would suggest rewriting H1-H4 in line with the results presented in this paper.
Section „2.1.2. Time series analysis of database data“ could be supported with Figures. I have doubts regarding the information in Table 2. In it, the author mentions correlation analysis, diversification indices (HHI, Shanon), but I did not find them in the Results.
In section 2.2.1, the author stated, “The aim of using panel regressions is to measure the impact of energy dependence and the energy mix on economic and political risk indicators (e.g., energy prices, inflationary pressures, social welfare proxies). Actually, panel regression and its Eq.1 is about degree of energy dependence (Y) and imports of gas, oil, coal (X1-X3), etc?. No economic and political indicators were studied. The author needs check this and rewrite sentences close to the content of this specific paper. Again, the author stated that „Panel regression allows us to examine time- and country-specific effects simultaneously, and thus explore how energy dependence, import flows or the structure of electricity generation affect economic performance. This methodology provides an answer to the macroeconomic consequences of the energy dependence of the EU Member States and which factors have the most substantial impact“. Actually, the economic consequences and country-specific effects were not presented in the Results. So, in Eq. 1, what particular indicators were included, and what were their measurement units, i.e., what is actually Y and X in the model, and how are they measured?
Regarding the absolute volatility indicator in Section 2.2.2. How it will be used “to understand which member states need security of supply buffers?”. What values or ranges should the estimates achieve to decide on the buffers?
Relative volatility has its theoretical values too, which allow deciding about very high, high, medium, etc volatility. What are they? Literature citation is requested.
In 2.2.4, could you please be specific about how cluster analysis is done in the paper? The same is relevant for 2.2.5. section.
In Table 3, it is written 20.21(1.02), but in the text below it is 10.21 (1.02). Please check. In lines 347-359, the interpretation of Table 3 is technical, and it repeats the table’s content. Please rewrite sentences to give an economic interpretation of what -0.12, -4.10, -6.09 etc. mean. The author likes stating “greater, more strongly, etc.” in the paper, and I would suggest adding “compared to”.
356-359 lines could be supported with statistics. They are technically written. What do these numbers mean in practice?
Results are good, but I would like to fix that the interpretation of them is strong. For example, in lines 370-372 the author states that “the greater the proportion of these that an EU member state has to obtain from abroad, the more vulnerable the economy and society are“. I would like to observe that firstly, the panel regression‘s Y is the degree of energy dependence in EU, not a vulnerable economy or society; and secondly, the regression is about the EU, but not a particular member state. I would suggest including regression for the countries the author discusses in lines 376-401.
What are the measurement units of Table 4? Please check once again that the estimates of Table 4 are in line with the interpretation. For example, the author states, “The standard deviation for oil imports is in the order of several hundred thousand GWh“. In Table 4, it is written that in the EU it is 42440.40 (perhaps GWh?). Further, „In contrast, electricity production shows a much more stable picture. The absolute volatility here is lower“. However, it is the second largest standard deviation (in EU, 70307.58) after the gas balance (1075480.00). I would assume that the Netherlands could be added to lines 421-422. “Finally, the study highlights special case countries, such as France: Although its import volatility is significant, its electricity production is remarkably stable“. In the Table 4, I found that the standard deviation of electricity generation is 32850.86 for France, and it is among the largest in the EU. I would like to say that there are places in the paper, which show discrepancies and need to be checked by the author.
In line 465, Germany case is presented with CVs 0.08-0.12. I could not find these numbers in Table 4.
When reading “3.5. Robustness checks and results section“ I understood that the methodology should be written much better than it is now. I cannot understand Table 6. It includes terms that were not described in the methodology. I assume that section 3.5 includes categories (such as Stable coefficients, Moderate stability, etc.), which were not shown in Tables and Figures in sections before, but they were commented on in those sections; therefore, I decided on the discrepancies.
Overall, I suggest the author go through the paper again and put things in right order to make the reader enjoy reading a really good topic.

Author Response

Response to First Reviewer:

I thank the Reviewer for his valuable suggestions and critical comments, which I will respond to in the order they were made. My present corrections, additions, and modifications have been incorporated into the manuscript.

The concepts of energy dependence, security of supply, and vulnerability, and their connection to each other.

In the Introduction chapter, before the hypotheses, I have inserted the following explanation:

The study interprets energy dependence as the proportion of imported energy in a country's total energy consumption, i.e., as a measure of structural exposure to external sources. Security of supply, on the other hand, means the extent to which the energy system can provide a stable, continuous, and affordable energy supply even in the event of external shocks, while energy vulnerability is the combined consequence of dependence and volatility: the extent to which a country is vulnerable to geopolitical, market, or infrastructural disruptions. The three concepts are thus hierarchically linked: high energy dependence increases potential vulnerability, which can ultimately weaken security of supply, especially with low diversification and high volatility.

Rewriting hypotheses H1-H4, consistency with the results.

(Note: the results did not have to be changed due to the refinement of the hypotheses, but their interpretation and formulation had to be adjusted to fit the modified hypotheses. The hypotheses were clarified to ensure full consistency with the empirical model specification.

The previously formulated hypotheses were refined as follows:

H1:

Energy import dependency increases structural vulnerability in the EU, but its effects differ across Member States depending on volatility and diversification levels.

(Member States with high and volatile import dependency exhibit greater exposure to external shocks, although the magnitude of vulnerability is not uniform.)

H2:

Diversification of the energy mix significantly reduces vulnerability and volatility in the EU energy system.

(Member States with a more balanced electricity generation structure are more resilient to market and geopolitical disruptions.)

H3:

Deterioration in supply security is associated with heightened macroeconomic pressures, particularly through volatility effects rather than dependency levels alone.

(Periods of increased energy price volatility and supply instability are linked to inflationary pressures and economic stress in Member States.)

H4:

Coal imports no longer constitute a statistically significant determinant of overall energy dependency in the European Union.

(Although coal historically played a central role in industrial production and electricity generation, empirical evidence suggests that its current contribution to structural energy dependency is limited compared to gas and oil.)

Regarding the statement “2.1.2. Time series analysis of database data”, my opinion is that the data in the table contains all the relevant information in a structured and understandable form, so that additional graphical representation is not necessarily necessary for interpretation. The representation in this case would not add any substantive additional information, but would only visually repeat the numerical results that have already been clearly presented.

I agree with the Reviewer’s observation that the Results do not include correlation analysis or the diversification indices (HHI, Shanon), so I have deleted the references to them from Table 2.

Regarding subsection 2.2.1: I agree with the Reviewer; I have reworded the subsection coherently as follows:

2.2.1. Panel Regression Analysis

The panel regression framework is applied to identify the structural determinants of energy dependency in the European Union. The objective of this model is not to estimate macroeconomic performance directly, but to examine how fossil fuel import flows and diversification patterns shape the level of dependency across Member States.

Panel regression formula:

Dependencyit=α +….(Finalized in Energies LaTex format!)

where ? denotes the Member State and ? the year (2014–2023). The dependent variable

Dependencyit represents the energy dependency rate as reported by Eurostat (database: nrg_ind_id), measured as the percentage share of net energy imports in gross available energy. This indicator captures the structural exposure of national energy systems to external supply sources.

The key explanatory variables include fossil fuel imports—natural gas (nrg_ti_gas), oil (nrg_ti_oil), and coal (nrg_ti_coal)—measured in physical energy units (GWh or equivalent, harmonised across datasets). These variables reflect the quantitative dimension of external energy reliance. In addition, a diversification index (Shannon or Herfindahl–Hirschman type) is included to account for the structural composition of the electricity generation mix. The diversification indicator is dimensionless and captures the balance between renewable, nuclear, and fossil energy sources.

The term ?? denotes country-specific fixed effects, capturing time-invariant structural characteristics such as historical energy system design, geographical constraints, and institutional factors. The time fixed effects ?? control for common shocks affecting all Member States, including global energy price fluctuations, the COVID-19 pandemic, and the geopolitical consequences of Russia’s invasion of Ukraine. The error term ??? captures idiosyncratic disturbances not explained by the model.

A fixed-effects estimator is applied as the baseline specification to control for unobserved heterogeneity across countries. Random-effects models are also estimated for robustness comparison, with the Hausman test guiding model selection.

The regression results indicate that natural gas and oil imports are statistically significant determinants of energy dependency, while coal imports do not exert a robust and significant effect. This finding suggests that the contemporary structure of EU energy vulnerability is primarily linked to gas and oil exposure rather than coal reliance. The diversification variable shows a negative and significant coefficient, indicating that a more balanced electricity generation structure mitigates structural dependency. Country fixed effects confirm persistent heterogeneity across Member States, while time effects reveal a marked post-2020 increase in common volatility without fundamentally altering the underlying structural drivers of dependency.

Overall, the panel regression analysis provides quantitative evidence that EU energy dependency is shaped by both import structure and internal diversification capacity, highlighting the differentiated nature of vulnerability across Member States.

In the model, Y is actually (dependent variable): Energy dependency rate (Eurostat: nrg_ind_id), unit of measurement: % (import share in gross domestic energy consumption). In the model, X is actually: Gas imports (nrg_ti_gas), Oil imports (nrg_ti_oil), Coal imports (nrg_ti_coal), Unit of measurement: typically GWh, in Eurostat units (sometimes normalized as % of total energy consumption).

Accepting the Reviewer's suggestion, a text describing economic consequences and country-specific effects has been added to the end of subsection 3.1. Panel Regression Results (FE vs RE) as follows:

The panel regression results indicate that gas and oil imports are statistically significant determinants of energy dependency, while coal imports do not exert a robust effect. This finding suggests that the current structure of EU energy vulnerability is primarily linked to natural gas and oil exposure rather than coal reliance. Country fixed effects reveal persistent structural heterogeneity, indicating that historical energy system configurations continue to shape dependency levels even after controlling for import volumes. Time fixed effects confirm that post-2020 shocks increased overall volatility but did not fundamentally alter the structural drivers of dependency.

In addition to section 2.2.2:

I thank the Reviewer for drawing attention to these shortcomings. These have been addressed in the study, and the penultimate paragraph of the subsection has been supplemented with the following explanation:

Absolute volatility (standard deviation) measures how volatile a given Member State’s energy imports or energy balance is over time; higher standard deviations indicate greater exposure to external market and geopolitical shocks. Member States with volatility persistently above the EU average face greater operational uncertainty and may therefore need to strengthen strategic reserves, storage capacities, or diversification “buffers”.

The study does not set a normative threshold but adopts a relative approach: volatility values in the upper quintile (or above the EU average plus 1 standard deviation) are considered high risk. These ranges indicate countries where strengthening the energy system with stabilizing instruments is politically more justified.

On the theoretical values of relative volatility:

The penultimate paragraph of subsection 2.2.3 has been supplemented with the following explanation:

According to the guideline often used in the statistical literature, CV < 0.10 is considered low, 0.10–0.20 moderate, 0.20–0.30 high, and> 0.30 very high volatility [xx].

Included in References:

[xx] Souza, E.G.d.; Khosla, R.; Sudduth, K.A.; Johann, J.A.; Bazzi, C.L. Spatial and Temporal Variability in Yield Maps Can Localize Field Management—A Case Study with Corn and Soybean. Agronomy 2025, 15, 1179. https://doi.org/10.3390/agronomy15051179

I thank the Reviewer for the suggestion to supplement the cluster analysis. The penultimate paragraph of subsection 2.2.4. has been supplemented with the following explanation:

The K-means clustering procedure is applied to classify EU Member States according to their combined structural exposure and volatility characteristics in the energy system. Prior to clustering, all variables—absolute volatility (standard deviation), relative volatility (coefficient of variation), and energy dependency rate—are standardized (z-scores) to ensure comparability across different measurement scales. This normalization prevents variables with larger numerical ranges from dominating the clustering outcome.

The algorithm partitions the 27 Member States into ? clusters by minimizing within-cluster variance (sum of squared Euclidean distances from cluster centroids). The optimal number of clusters is determined using the elbow criterion and variance-explained diagnostics, balancing interpretability with statistical coherence. The clustering process is iterative: initial centroids are randomly assigned, countries are allocated to the nearest centroid, and cluster centers are recalculated until convergence is reached.

The resulting clusters reflect distinct structural profiles rather than purely size-based groupings. Specifically, the classification differentiates between highly volatile and import-dependent systems, moderately exposed and partially diversified systems, and relatively stable, diversified systems with lower structural vulnerability. By combining both absolute and relative volatility indicators, the clustering captures not only the magnitude of fluctuations but also their proportional significance within national energy systems.

This multivariate approach provides a policy-relevant segmentation of Member States, enabling differentiated strategic responses tailored to volatility intensity, structural dependency, and diversification capacity.

The following explanation has been added to the penultimate paragraph of subsection 2.2.5:

The inclusion of robustness testing in this study is essential, as the energy dependence and security of supply of the EU and its Member States may be significantly influenced by extraordinary shocks, including the COVID-19 pandemic and the 2022 energy crisis. To ensure that the estimated relationships reflect structural dynamics rather than short-term disturbances, a structured robustness framework is applied.

First, temporal stability is tested by re-estimating the panel regression model for two sub-periods (2014–2019 and 2020–2023). This split-sample approach allows us to assess whether the estimated coefficients—particularly those related to gas and oil imports and diversification—remain consistent before and after the crisis period. Stability of coefficient signs and statistical significance across sub-periods is interpreted as evidence of structural robustness.

Second, model specification robustness is examined by comparing fixed-effects and random-effects estimations. The Hausman test is employed to determine whether country-specific unobserved heterogeneity is correlated with the explanatory variables. Consistency between estimators strengthens confidence in the validity of the baseline specification.

Third, alternative operationalizations of diversification (Shannon index vs. Herfindahl–Hirschman index) are used to verify that the identified relationship between diversification and dependency is not sensitive to the choice of index. If the direction and magnitude of coefficients remain comparable across index specifications, the results are considered methodologically robust.

Together, these robustness checks confirm that the main findings—namely the dominant role of gas and oil imports and the mitigating effect of diversification—are not artifacts of a specific time window or variable definition, but reflect stable structural characteristics of EU energy dependency.

Regarding the data in Table 3:

I have checked the error indicated by the Reviewer, the value +20.21 (1.02)*** in Table 3 is correct, and I have corrected it in the text.

I have deleted the technical description of the data starting with line 347, and replaced it with the following economic interpretation:

The economic interpretation of the panel estimates clearly outlines the structural determinants of the European Union's energy dependence. The positive and significant coefficient for the logarithm of natural gas imports in both the fixed- and random-effects models suggests that increases in gas imports disproportionately strengthen import dependence. Since gas plays a key role in the EU energy system – especially in industrial use and electricity balancing –exposure to external gas sources creates structural vulnerability.

For oil imports, the positive relationship is significant only in the random-effects model, suggesting that oil continues to contribute to energy dependence. Still, its impact is less homogeneous across Member States. This is consistent with the fact that oil is primarily dominant in the transport sector, where demand elasticity and substitutability are limited. At the same time, the diversification of supply sources is more advanced than for natural gas.

Coal imports do not show a stable and significant relationship with energy dependence in any of the models. This suggests that the role of coal in the EU energy structure is no longer a determining structural factor, which empirically supports the realignment within the fossil energy mix and the gradual relegation of coal to the background.

The negative, highly significant coefficient for the gas balance (supply/use ratio) indicates that strengthening domestic availability – for example, through storage, LNG capacity, or alternative supply routes – reduces energy dependence. This expresses economically that supply-side stabilizing mechanisms reduce the structural weight of external exposure.

The negative sign of the logarithm of electricity production – especially pronounced in the random effects model – indicates that higher domestic production capacity reduces overall import dependence. This suggests that electrification and strengthening the domestic production base (especially renewables and nuclear sources) represent a structural advantage for EU member states not only from a climate policy perspective, but also from an energy security perspective.

I have rephrased the text in lines 356-359 in a practice-oriented way as follows:

The R² of 0.968 means that the model “captures” the differences between countries and between years very well, i.e., the development of energy dependence can be largely explained by the factors involved. The fact that the random effects model gives similar results shows that the main conclusions do not depend on a single calculation method, but reflect a stable pattern.

I thank the Reviewer for appreciating the results of the analysis. I agree with the Reviewer that lines 370-372 concern the regression in the EU, not a specific Member State. For the EU Member States highlighted in lines 376-401, the study performed a panel regression analysis, and its results are included at the end of subsection 3.1, as follows:

In addition to the aggregated EU-level results, the country-specific effects of the panel estimates also show well-interpretable patterns. In the case of France and Sweden, the negative sign of the country-fixed effects and the significant role of diversification indicate that nuclear and renewable electricity production reduce the structural level of import dependence. The energy dependence of these two countries is less sensitive to changes in gas imports than the EU average.

In the case of the Netherlands, the coefficient for gas imports is positive and strong, suggesting that the country has become more sensitive to external procurement following the decline in its domestic production capacity. According to the model, the change in the gas market position significantly increases the dependency indicator.

In Estonia, Latvia, and Lithuania, the effect of gas imports is particularly pronounced, which can be explained by historical Russian exposure and small-scale energy systems. When interpreted together with the volatility indicators, these countries belong to the most shock-sensitive group.

In Finland, the model shows a moderate but positive gas import effect, while diversification of the electricity generation structure partially dampens dependency. The results indicate signs of structural adjustment after 2022.

In Hungary, gas imports have a strong, positive relationship with the dependency indicator, while the effect of diversification is weaker. This indicates that the system is sensitive to external gas sources and that structural exposure is permanent.

For Germany, the coefficients for gas and oil imports are positive, but the growth in electricity generation (especially the increase in the share of renewables) partially offsets the dependency effect. The model reflects the structural consequences of the high gas exposure before 2022.

In Italy, the role of oil imports is more pronounced. At the same time, gas imports are also significant, suggesting an import-oriented structure stemming from the country's geographical location and Mediterranean supply routes.

Overall, the panel regression results show that, while gas and oil are the most important determinants for the EU as a whole, structural differences across the individual Member States – energy mix, size, historical trajectory, and diversification – significantly influence the intensity and sensitivity of dependency.

Regarding the data in Table 4: I thank the Reviewer for his comments on the data in the table, which I have supplemented and modified as follows:

I have supplemented the title of Table 4 with the data in GWh; I have modified the expression “several hundred thousand” to “several tens of thousands”; I have deleted the word “much” from the sentence regarding the volatility of electricity production; and I have added the Netherlands to the three highlighted countries. I consider the Reviewer’s comment regarding France to be justified; therefore, for the sake of better clarity, I have supplemented the end of the sentence starting on line 439 with a parenthetical text: (relative to the size of the country’s electricity production).

The values of approximately 0.08-0.12 written in German in line 465 are rounded values (lower 0.049, upper 0.141). I have corrected these to values of 0.05-0.14 in the manuscript.

I consider the Reviewer's observation regarding section 3.5. Robustness checks and results to be justified. Due to space reasons, some methodological procedures were omitted from subsection 2.2.5. Robustness test, without which the findings of subsection 3.5 are difficult to understand. To eliminate this deficiency, I have supplemented subsection 2.2.5. Methodology with the following text in the penultimate paragraph:

The study tests robustness using four complementary procedures. First, the panel regression was re-estimated using both fixed-effects and random-effects specifications, and additionally for two sub-periods (2014–2019 and 2020–2023). Coefficient signs, magnitudes and statistical significance were compared across models to assess structural stability.

Second, absolute volatility (σ) rankings were recalculated for the two sub-periods, and Spearman rank correlations were computed to evaluate temporal stability. High correlation values indicate persistent absolute fluctuation patterns.

Third, relative volatility (coefficient of variation) rankings were similarly compared using rank correlations to determine whether proportional vulnerability changed over time.

Fourth, K-means clustering was re-estimated for the full sample and sub-period datasets. Cluster similarity was evaluated using the Adjusted Rand Index (ARI), which measures the degree of agreement between cluster solutions.

These procedures ensure that the reported results reflect structural relationships rather than period-specific distortions.

After reviewing and correcting the article and accepting the reviewer's suggestions, the quality of the revised and supplemented manuscript has improved.

At the end of my response, I would like to thank you again for your valuable suggestions, which helped improve the quality of the article. I have responded to all comments, suggestions, and corrections. After that, I asked the Reviewer to accept the study.

The author

Reviewer 2 Report

Comments and Suggestions for Authors

This study examines the evolution of the European Union’s (EU) energy security and import dependence over the period 2014–2023, shaped by global energy price shocks, the COVID-19 pandemic and Russia’s war against Ukraine. The author has to provide satisfactory answers to the below questions/remarks:

Check the text, the tables and the figures to be according to the template of MDPI (e.g. Table 1 has to be redrawn).
For Table 1 how have you concluded to this? You have to give more details on that instead of our calculation…
The authors should include more references regarding the risks and the high uncertainties in the era of the green transition such as: https://doi.org/10.3390/su17125325
The author has to include in the analysis the EU goal to be 100% independent from the Russian natural gas and to include the LNG and the vertical corridor in the Balkans. In my perspective it is fundamental to do that.
The methodological part needs to be more analytic and concrete. It is not very clear.
Can the author add graphs and data related to the volatility of the prices? It is not included
This study examines the evolution of the European Union’s (EU) energy security and import dependence over the period 2014–2023, shaped by global energy price shocks, the COVID-19 pandemic and Russia’s war against Ukraine. The author has to provide satisfactory answers to the below questions/remarks:
1. Check the text, the tables and the figures to be according to the template of MDPI (e.g. Table 1 has to be redrawn).
2. For Table 1 how have you concluded to this? You have to give more details on that instead of our calculation…
3. The authors should include more references regarding the risks and the high uncertainties in the era of the green transition such as: https://doi.org/10.3390/su17125325
4. The author has to include in the analysis the EU goal to be 100% independent from the Russian natural gas and to include the LNG and the vertical corridor in the Balkans. In my perspective it is fundamental to do that.
5. The methodological part needs to be more analytic and concrete. It is not very clear.
6. Can the author add graphs and data related to the volatility of the prices? Such an analysis is not included.
.

Author Response

Reply to the second Reviewer.

Thank you for your valuable suggestions and critical comments; I will address them in the order they were received. I have incorporated my current corrections, additions, and modifications into the manuscript.

Detailed responses:

The adequacy of the text, tables, and figures. I have ordered MDPI's text proofreading, table, and figure editing services to ensure compliance with MDPI's templates.

I agree and thank the reviewer's comment; It is indeed a short text describing the content of Table 1. Taking this opinion into account, I have supplemented the text below Table 1 with the following more detailed general methodological description:

Table 1 presents the descriptive statistical indicators (mean, minimum, maximum and standard deviation) for all five energy-related variables across the EU27 Member States over the period 2014–2023. The table provides an overview of both the scale and dispersion of national energy supply structures, highlighting the substantial heterogeneity across countries. The mean values reflect the structural size differences of national energy systems, while the minimum and maximum indicators capture the range of variation within the observed period. The standard deviation serves as a first indication of absolute volatility, offering insight into the stability of import flows and domestic production levels over time.

Taken together, the descriptive statistics establish the empirical foundation for the subsequent econometric and clustering analyses by illustrating the magnitude, dispersion, and temporal variability of key energy supply indicators across the European Union.

The Reviewer is realistic in his opinion that the manuscript does not address the risks and high uncertainties of the green transition era. Therefore, starting with line 106 of the Introduction, I have included two references on this topic, as follows:

Recent research highlights that the transition to a high share of renewable energy in interconnected energy systems involves significant operational risks and uncertainties, as inadequate grid flexibility and storage capacity can lead to instability and limit renewable energy generation, increasing the likelihood of system disruptions even if fossil fuel sources are gradually phased out [29]. The energy transition creates new, difficult-to-predict risks to system stability due to the rise of variable and low-inertia renewables. Therefore, flexibility tools (especially storage) are crucial to maintain a reliable supply [30].

References included:

[29] Fotis, G.; Maris, T.I.; Mladenov, V. Risks, Obstacles and Challenges of the Electrical Energy Transition in Europe: Greece as a Case Study. Sustainability 2025, 17, 5325. https://doi.org/10.3390/su17125325

[30] Saleh, A. M., Vokony, I., Khan, M. A., Waseem, M., Ahmed, A. N. A., Power system stability in the Era of energy Transition: Importance, Opportunities, Challenges, and future directions, Energy Conversion and Management: X, Volume 24, 2024,100820, https://doi.org/10.1016/j.ecmx.2024.100820.

I agree with the Reviewer that the study addresses the EU’s goal of 100% independence from Russian natural gas, as well as LNG and the Balkan vertical corridor. I have added the following to the manuscript, which affects several chapters:

Introduction chapter:

In response to the geopolitical shock following Russia’s invasion of Ukraine, the European Union articulated a strategic objective of eliminating its structural dependence on Russian pipeline gas. This objective has been accompanied by a rapid expansion of LNG import capacity and the development of alternative supply routes, including the Balkan vertical gas corridor, aimed at strengthening interconnectivity between Southern and Central Europe. These measures represent not only short-term crisis management but a structural reconfiguration of the EU’s external energy exposure.

Methods, 2.2.1. Panel Regression subchapter:

The gas balance variable also indirectly captures the effects of post-2022 infrastructure expansion, including LNG terminal development and the reinforcement of cross-border corridors such as the Balkan vertical route. As such, it reflects not only domestic availability but also the evolving architecture of EU gas security policy.

Results, subsection 3.1. Panel Regression Results:

The strong and statistically robust impact of gas imports on dependency levels provides empirical support for the EU’s strategic objective of phasing out Russian gas supplies. The results suggest that diversification through LNG imports and alternative transit corridors is economically justified, as gas dependency constitutes the most structurally significant driver of vulnerability within the Union.

Policy Recommendations Chapter has been supplemented with the following structured paragraph:

The findings of this study indicate that the EU’s objective of achieving full independence from Russian gas is not merely a political declaration but a statistically grounded structural necessity. Expanding LNG infrastructure enhances short-term shock absorption capacity, while the development of alternative corridors—such as the Balkan vertical gas corridor—strengthens regional interconnectivity and reduces concentration risk. Together, these measures contribute to lowering systemic volatility and mitigating structural energy dependency across Member States. Importantly, the empirical results show that gas dependency remains the primary channel of vulnerability, reinforcing the strategic priority of sustained diversification efforts.

Thank you for your suggestion regarding the Reviewer's Methods. I have supplemented and modified the subchapters of the Methods in several places, so that they seem more analytical and specific. In order of subchapters:

Methodology 2.2.1. subchapter:

Panel regression formula:

Dependencyit=α +….(Finalized in Energies LaTex format!)

where ? denotes the Member State and ? the year (2014–2023). The dependent variable

The term ?? denotes country-specific fixed effects, capturing time-invariant structural characteristics such as historical energy system design, geographical constraints, and institutional factors. The time fixed effects ?? control for common shocks affecting all Member States, including global energy price fluctuations, the COVID-19 pandemic, and the geopolitical consequences of Russia’s invasion of Ukraine. The error term ?it captures idiosyncratic disturbances not explained by the model.

Accepting the Reviewer's suggestion, a text describing economic consequences and country-specific effects has been added to the end of subsection 3.1. Panel Regression Results (FE vs RE) as follows:

I have added the following explanation to the penultimate paragraph of Subsection 2.2 of the Methodology:

Absolute volatility (standard deviation) measures how volatile a given Member State’s energy imports or energy balance are over time; higher standard deviations indicate greater exposure to external market and geopolitical shocks. Member States with volatility persistently above the EU average face greater operational uncertainty and may therefore need to strengthen strategic reserves, storage capacities or diversification “buffers”.

The study does not define a normative threshold but uses a relative approach: volatility values above the upper quintile (or the EU average plus 1 standard deviation) are considered high risk. These ranges indicate countries where strengthening the energy system with stabilising instruments is politically more justified.

Methodology 2.2.3. The penultimate paragraph of the subsection has been supplemented with the following explanation:

According to the guideline often used in the statistical literature, in the case of relative volatility (coefficient of variation, CV = σ/μ), CV < 0.10 is considered low, 0.10–0.20 is considered moderate, 0.20–0.30 is considered high, and above 0.30 is considered very high volatility.

Attaching graphs and data to price volatility.

Thank you for the suggestion to include price volatility. It is indeed an important dimension of energy security analyses; however, this study specifically examines structural energy dependence and physical supply indicators (import flows, energy mix, balance sheet). Including price volatility would require a different database and, to some extent, a new modeling framework, which would significantly expand the current conceptual and methodological structure of the manuscript. The analysis of price volatility is justified as an independent research direction, which, based on the present results, can be the subject of a separate study.

At the end of my response, I would like to thank the Reviewer once again for his valuable suggestions, which contributed to improving the article's quality. I have responded to all comments, suggestions, and requests for corrections and modifications. After this, I ask the Reviewer to accept the study.

the Author,

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Author,

thank you for response. I agree on them and accept. Could you please check once more information in:

lines 305-316 and 325-332 are the Results, i would assume;

lines 322-324 could be deleted, as it is like a comment;

lines 269-271 could be checked. I have some doubt if they reflect the content of your paper;

lines 317-321 i would assume not necessary because variables are described above;

lines 361-370 and 351-360 repeated text.

Could you please check once more information in Table 4 and its discussion in the paragraphs below? In the table i see that absolute volatilities are high, but the text below states a bit differently. You use such words as "more stable", etc. Please check!

Reviewer

Author Response

Thank you for your comments and suggestions regarding the manuscript, which I will respond to in the order they were received:

1/ Lines 325-332 are repetitions, so I have deleted them from the manuscript.

2/ Lines 322-324 (they are like a comment) have been deleted.

3/ The text written in lines 269-271 has been deleted.

4/ Lines 317-321 lines (as they are repetitions) have been deleted from the manuscript.

5/ Lines 351-360 and 361-370 contain similar but not identical text, and these have been retained in the manuscript.

6/ I have made changes to the text below Table 4: I have changed the word "several hundred thousand" to "tens of thousands" and deleted the phrase "gas import and".

Instead of the sentence in the following paragraph, I have written: In contrast, the electricity generation sector is characterized by a higher degree of stability compared to the gas balance.

I thank the reviewer once again for his suggestions.

Reviewer 2 Report

Comments and Suggestions for Authors

Accepted in the current form

Author Response

I ordered the editing service of MDPI. The manuscript was proofread in English, as well as corrected in figures and tables.