Energy Diversification and Resilience: An Empirical Analysis of OECD
Countries
Empirical Research Paper for Economics and Business Economics
Group 6 Team 3
Eva van Eck: S4823753
Martijn Termijtelen: S4568060
Yang Yang:S5699142
Supervisor: L. (Lingxiu) Zhu
May 16, 2025
University of Groningen
Faculty of Economics and Business
Groningen, the Netherlands
Abstract
This study investigates the relationship between energy diversification and energy
resilience in OECD countries, focusing on the role of diversified energy sources in reducing the
frequency of power outages. Using a fixed-effects panel regression model, the analysis
demonstrates that the diversification index (HHI) does not significantly reduce energy supply
disruptions, suggesting that diversification alone may not enhance resilience. Robustness checks,
including models omitting the first 7 and 9 years, confirm the consistency of these findings. This
finding implies that policymakers could explore the interplay between energy diversification and
policy frameworks to better understand the conditions under which diversification can reduce the
frequency of energy supply disruptions.
1. Introduction
In recent years, energy resilience has emerged as a critical concern for policymakers and
economists alike. Global energy systems face increasing vulnerability due to climate change,
geopolitical instability, and volatile fossil fuel markets. The importance of ensuring a stable,
affordable, and secure energy supply has never been more pressing. Recent shocking
developments such as the COVID-19 pandemic, and the Russia-Ukraine conflict, have exposed
the fragility of global energy supply chains, prompting renewed interest in strategies that can
strengthen the ability of energy systems to withstand and adapt to disruptions (Streimikiene,
Siksnelyte-Butkiene, & Lekavicius, 2023).
One of the most frequently proposed mechanisms for enhancing energy system resilience
is diversification of energy sources. A diversified energy portfolio, incorporating a mix of fossil
fuels, renewable sources, and nuclear energy, may reduce reliance on any single energy input or
supplier. This diversification is theorized to buffer economies from external shocks, such as price
volatility, supply chain interruptions, or geopolitical tensions, by increasing the flexibility and
adaptability of the energy system (Nguyen, Nghiem, & Doan, 2025). Within the Organisation for
Economic Co-operation and Development (OECD), where energy consumption patterns are both
complex and resource-intensive, the role of energy diversification is particularly prominent in
ensuring long-term energy security and economic stability.
Despite the intuitive appeal of energy diversification as a resilience strategy, empirical
evidence on its effectiveness remains limited. While some studies suggest a positive link
between diversification and resilience, others emphasize the importance of additional factors
such as energy infrastructure, policy environments, and technological readiness. Furthermore,
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most existing research focuses on energy security in terms of access or pricing, rather than
measurable outcomes of supply disruptions.
This study seeks to address this gap by empirically examining whether energy source
diversification contributes to greater resilience against energy supply disruptions in OECD
countries. The central research question is as follows:
"To what extent does energy source diversification reduce the frequency or severity
of energy supply disruptions in OECD countries?"
To investigate this question, the study will construct a panel dataset covering a selection
of OECD member states and apply econometric techniques to assess the relationship between
energy mix diversification and indicators of energy supply resilience. In doing so, the research
aims to contribute to the academic literature on energy economics and provide relevant insights
for policymakers tasked with designing robust and adaptive energy systems.
2. Literature Review and Hypothesis Testing
2.1 Review of Theoretical and Empirical Literature
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The relationship between energy diversification and energy system resilience has gained
increasing attention in the field of energy economics, particularly due to recent shocks such as
the COVID-19 pandemic, the Russia–Ukraine conflict, and the broader energy transition.
Drawing from portfolio theory, it is theorized that diversification reduces systemic risk. Investors
mitigate volatility through diversified financial portfolios, and countries can similarly reduce
vulnerability in their energy systems by relying on a broader mix of energy sources.
Streimikiene, Siksnelyte-Butkiene, and Lekavicius (2023) conducted a comparative assessment
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of energy diversification and security across various regions of the European Union. They
developed an energy import diversification and security index to evaluate energy supply risk,
highlighting how diversification can mitigate both volume and price shocks. The authors
emphasized that high energy import dependency significantly undermines national energy
security and that tailored diversification strategies are essential in the context of regional and
geopolitical variations.
In addition, Nguyen, Nghiem, and Doan (2025) investigated the convergence of energy
diversification, financial development, and per capita income across OECD countries. Their
findings revealed no overall convergence, but rather the formation of “convergence clubs”,
suggesting heterogeneity among countries. Importantly, their panel regression analysis
established a two-directional relationship between financial development and energy
diversification, supporting the notion that financial systems both influence and are influenced by
energy portfolio choices.
Further supporting this line of research, a study published in Energy Economics
developed a energy diversification index and examined its broader macroeconomic implications
(Gozgor & Paramati, 2022). While the primary focus was on whether energy diversification
leads to economic slowdowns, the study’s methodological innovation in quantifying
diversification is relevant to understanding its role in resilience. Their findings suggest that while
energy diversification supports long-term economic sustainability, it may introduce short-term
inefficiencies, particularly in less advanced economies.
Despite these contributions, there remains a notable gap in the empirical literature
regarding the direct link between energy diversification and energy resilience, specifically in
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terms of a system’s ability to maintain supply during disruptions. Much of the existing research
has focused on energy security more broadly: encompassing access, affordability, and
geopolitical risks, rather than the capacity to absorb and recover from external shocks. Moreover,
there is a lack of comparative studies across OECD countries that specifically test the resilienceenhancing effects of diversified energy mixes.
The underlying theoretical framework for the development of the central hypothesis
stems from the application of portfolio theory to energy systems, which states that greater
diversification reduces exposure to risks associated with any single energy source or supplier.
Just as diversified financial portfolios are less volatile, diversified energy portfolios are
presumed to be more stable, especially during periods of geopolitical or market-related
turbulence. This forms the basis for the following hypothesis:
H01: Countries with more diversified energy sources experience fewer energy supply
disruptions.
Empirical studies provide indirect but compelling support for this. Streimikiene et al.
(2023) demonstrated that EU member states with higher energy diversification scores tended to
exhibit stronger energy security, particularly in the face of shocks such as the COVID-19
pandemic and the Russia–Ukraine war. Similarly, Nguyen et al. (2025) identified a statistically
significant, two-directional relationship between energy diversification and financial
development in OECD countries, implying that broader energy portfolios may play a role in
enhancing system stability and reducing exposure to external shocks. Although their study did
not directly measure resilience outcomes such as supply disruption frequency or volatility, their
findings reinforce the importance of diversification in managing systemic risks.
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However, the direct empirical relationship between energy source diversification and
energy resilience, defined as the ability to maintain a secure, stable, and uninterrupted energy
supply during external disturbances, remains underexplored. This gap will be addressed by
explicitly examining whether greater energy source diversification is associated with fewer or
less severe supply disruptions in OECD countries. This hypothesis will be tested using panel data
econometrics, allowing for control of country-specific fixed effects, time trends, and potential
confounding factors such as GDP per capita, energy import dependency, and renewable energy
share.
3. Data
To investigate the relationship between energy source diversification and energy supply
chain disruptions in OECD countries, this study utilizes a unbalanced panel dataset spanning the
period from 2006 to 2022. The dataset is constructed using data from the World Bank database,
which provides consistent and comprehensive information on key variables related to energy
systems, economic indicators, and demographic factors.
3.1 Variables
The dependent variable in this study is the frequency of energy supply disruptions. The
indicator of frequency is the number of power outages in firms per year. The independent
variable is energy diversification, measured using the Herfindahl-Hirschman Index (HHI), which
quantifies the concentration of energy sources within a country’s energy mix (Nguyen et al.,
2025). A lower HHI indicates a higher level of diversification. In this study, the HHI is
calculated by squaring the shares of fossil energy, renewable energy, and nuclear energy in the
total primary energy supply (TPES) as follows:
HHI =¿
5
Where s fossil is the share of fossil energy in the total energy mix; srenewable is the share of
renewable energy in the total energy mix; snuclear is the share of nuclear energy in the total energy
mix.
To isolate the effect of energy diversification on energy supply disruptions, several
control variables are included. First, GDP per capita is used to control for economic
development, as wealthier countries tend to invest more in energy infrastructure and resilient
systems (Nguyen et al., 2025; Gozgor & Paramati, 2022). Countries with higher income levels
often demonstrate greater capacity to integrate diversified energy sources and develop robust
supply chains, which can mitigate the effects of external disruptions. Moreover, energy import
dependency is included as a measure of a country’s reliance on external energy sources. Previous
studies (Streimikiene et al., 2023) highlight that high dependency on energy imports can increase
vulnerability to supply disruptions, especially during geopolitical crises or sudden market shifts.
By controlling for import dependency, the analysis accounts for the extent to which external
shocks directly impact national supply chain disruptions. Renewable energy share (%), defined
as the contribution of renewables to the total primary energy supply (TPES), is added to capture
the influence of renewable uptake on resilience. The transition to renewable energy can both
enhance and challenge resilience, as higher shares of variable energy sources (like wind and
solar) may introduce intermittency issues (Nguyen et al., 2025; Gozgor & Paramati, 2022).
Including this variable ensures that the model accounts for the dual role of renewables in
stabilizing and complicating energy supply systems. Moreover, population density is
incorporated to control for the scale of energy demand. Larger populations often face greater
pressure on energy infrastructure, increasing the risk of disruptions during peak consumption
periods (Streimikiene et al., 2023). By including population size as a control, the model adjusts
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for demographic factors that may affect resilience outcomes. Furthermore, government
expenditures are included to capture the role of public investment in enhancing energy
infrastructure and diversification. Higher levels of government spending are often associated
with better maintenance of critical energy systems and the development of alternative energy
projects (Cooray et al., 2025). Finally, government effectiveness is considered to reflect the
quality of public services, policy implementation, and the efficiency of government institutions.
Effective governance is crucial for planning and managing diversified energy projects, ensuring
that energy infrastructure investments are both strategic and sustainable (Cooray et al., 2025).
Additionally, country and time fixed effects are included to account for structural differences
across countries and temporal variations due to global events.
4. Methodology and econometric models
This study employs a quantitative approach to analyze the relationship between energy
diversification and resilience, using panel data econometrics to account for both cross-sectional
and temporal variations.
4.1 The multiple linear regression model
The primary model used in this study is a fixed effects unbalanced panel regression,
chosen for its ability to control for unobserved heterogeneity across countries. The fixed effects
model is preferred over random effects due to potential endogeneity issues related to unobserved
heterogeneity. Country-specific characteristics, such as energy policy frameworks, are controlled
through fixed effects, minimizing bias. The model is specified as follows:
Y ¿ =β 0 + β 1 Diversificatio n¿ + β2 GD P¿ + β3 Populatio n¿ + β 4 EnergyImport s¿ +¿
β 5 GovernmentEffectivenes s ¿ + β 5 GovernmentExpenditur e ¿ + μi + λ t + ε ¿
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Where Y ¿ represents the frequency of supply chain disruptions (power outages) in
country i at time t , measured as the frequency of energy supply disruptions; Diversificatio n¿ is
the energy diversification index, calculated using the Herfindahl-Hirschman Index (HHI) (in
logarithmic form); GD P¿ denotes gross domestic product per capita (in logarithmic form),
serving as a proxy for economic development; Populatio n¿ represents the population density (in
logarithmic form), accounting for the scale of energy demand; EnergyImport s ¿ measures the
share of imported energy in total energy consumption, measuring import dependency;
GovernmentEffectivenes s¿ reflects the quality of public services, policy implementation, and the
efficiency of government institutions.; GovernmentExpenditur e ¿ represents the amount of public
spending on energy infrastructure and diversification projects; μidenotes country fixed effects; λ t
represents time fixed effects to account for global shocks and time trends; and ε ¿ is the error
term.
This model enables testing H01 through β 1. A significant negative β 1 would indicate that
greater energy source diversification reduces the frequency of energy disruptions.
Table 1: Descriptive statistics and correlation matrix of the variables
Notes: The variables represented below are all the variables used in this study, covering the period from 2006 to
2022. Power Outages, expressed as the number of power outages per year in firms, are used as the frequency of
energy supply disruptions. Energy diversification (HHI) is calculated using the Herfindahl-Hirschman Index (HHI),
indicating the concentration of the energy mix; GDP per capita represents the level of economic development within
a country; Population Density is measured as the number of people per square kilometer, also in logarithmic form, to
account for the scale of energy demand; Energy Imports capture the share of imported energy in total energy
consumption, reflecting a country’s import dependency; Government effectiveness is represented as an index
reflecting the quality of public services, policy implementation, and the efficiency of government institutions;
Government expenditure represents the proportion of GDP spent on public services, infrastructure, and development
projects.
Panel A: Descriptive statistics of variables (1-7)
(1)Power
N
Mean
SD
Min
Median
Max
Skewness
Kurtosis
4522
1.179
5.002
0
0
26.4
4.446
21.638
8
Outages
(2)HHI
(3)GDP
(4)Population
Density
(5)Energy
Imports
(6)Governmen
tEffectiv~s
(7)Governmen
tExpendit~e
2823
4403
4455
8.874
8.74
4.356
0.236
1.465
1.460
7.139
5.087
-1.991
8.86
8.715
4.343
9.647
12.329
9.971
-.261
.033
.122
4.248
2.15
5.419
3097
-22.418
118.342
-427.523
11.022
109.23
-1.879
6.396
3456
-.031
0.994
-2.44
-.152
2.47
.257
2.351
3696
4.727e+11
1.567e+12
55623315
1.014e+10
1.670e+1
3
5.248
35.612
(4)
(5)
(6)
(7)
1.000
0.370***
0.238***
-0.051***
1.000
0.280***
0.075***
1.000
0.269***
1.000
Panel B: Matrix of correlation between variables (1-7)
Variables
(1)
(2)
(3)
(1) Power Outages
1.000
(2) HHI
-0.023
1.000
(3) GDP
-0.175***
0.103***
1.000
(4) Population Density
-0.033**
0.021 0.159***
(5) EnergyImports
0.008 -0.306***
0.037**
(6) GovernmentEffe~s -0.137*** -0.065*** 0.847***
(7) GovernmentExp~e -0.072***
-0.024 0.195***
Panel A (Table 1) displays the descriptive statistics of the variables. To mitigate the
impact of outliers, Power Outages and Energy Imports are winsorized at the 3% level.
Additionally, HHI, GDP and Population Density are log-transformed. Both were used to reduce
the skewness and kurtosis of the data. Furthermore, Panel B (Table 1) presents the correlations
between the variables. The table shows that Power Outages are negatively correlated with GDP,
Population Density, Government Effectiveness, and Government Expenditure, indicating that
countries with higher economic development, higher population density, and more effective
governance tend to experience fewer power outages. The correlation between Power Outages
and HHI is negative but very small and statistically insignificant, suggesting that energy
diversification alone does not directly correlate with the frequency of outages.
5. Results
good!
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5.1 Panel data regression
To examine the relationship between energy diversification and energy supply chain
disruptions, a fixed-effects panel regression was conducted. The model includes the
diversification index (HHI) as the independent variable while controlling for GDP, population
density, energy imports, government effectiveness, and government expenditure. The dependent
variable is the frequency of power outages in OECD countries. The results, presented in Table 2,
reveal that the coefficient of the diversification index (HHI) is negative but not statistically
significant. This indicates that, on average, energy diversification does not significantly reduce
the frequency of power outages across OECD countries. This finding cannot reject the null
hypothesis (H01), which posited that greater diversification leads to fewer energy supply
disruptions. The lack of significance could suggest that other factors, such as infrastructure
quality or policy frameworks, may play a more substantial role in reducing energy supply chain
disruptions.
The control variables show different levels of significance. GDP per capita shows
significance at the 1% level and Energy Imports show significance at the 10% level. The other
variables do not show significance. Furthermore, most coefficient signs are consistent with prior
research, while others diverge from expected outcomes. GDP is negatively correlated with power
outages, meaning that a higher GDP will reduce the frequency of supply disruptions (Power
Outages) in a year. This is consistent with prior work by Cooray et al. (2025) and Nguyen et al.
(2025). The sign of Energy Imports is positive, indicating that more energy-import-dependent
countries have more power outages, in line with Streimikiene et al. (2023). However, population
density is negative, which is not in line with prior studies that suggest when population density
increases, energy risk increases as well (Cooray et al., 2025; Nguyen et al., 2025). Government
10
effectiveness is negatively correlated, which suggests that higher government effectiveness will
reduce the frequency of supply chain disruptions (Power Outages) (Cooray et al., 2025).
Additionally, government expenditure shows a negative coefficient, but the effect is so small that
the table presents zero.
Table 2: The effect of energy diversification on the number of power outages
Notes: In the table, column (1) presents the results of the regression between energy diversification and power
outages. Power Outages represent the frequency of energy supply disruptions in country i at time t; Diversification
denotes the Herfindahl-Hirschman Index (HHI), which measures the concentration of energy sources within a
country’s energy mix; GDP represents the GDP per capita of country i at time t, serving as a proxy for economic
development; Population Density represents the number of inhabitants per square kilometer in country i at time t;
Energy Imports indicate a country’s share of imported energy in total energy consumption at time t; Government
Effectiveness reflects the quality of public services, policy implementation, and institutional efficiency of country i
at time t; Government expenditure captures a country’s level of public investment at time t. μi denotes the country
fixed effects; λt denotes the year fixed effects.
VARIABLES
Diversification
GDP
Population Density
Energy Imports
Government Effectiveness
Government Expenditure
Constant
Observations
Adjusted R-squared
fe
(1)
Power Outages
-0.0730
(0.0798)
-0.0795***
(0.0256)
-0.0136
(0.0140)
0.000301*
(0.000161)
-0.0408
(0.0387)
-0
(0)
1.917***
(0.699)
2,011
0.118
yes
11
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
5.2 Robustness check
To assess the robustness of the results, alternative models were tested by omitting the initial
years of the dataset. Two additional regressions were performed: one omitting the first 7 years
(model 1) and another omitting the first 9 years of data (model 2). This approach accounts for
potential biases arising from early-year variability or inconsistencies in data collection. The
results from both models remained consistent with the primary findings, as the coefficient of the
diversification index (HHI) remained statistically insignificant, although the sign has changed to
positive. This indicates that the observed relationship between energy diversification and power
outages is not sensitive to the inclusion or exclusion of early-year data, which makes the model
more robust. The coefficients in both models have the same signs as in the initial model,
although the significance levels of some variables differ. GDP remains significant at the 1%
level in the first model but loses significance entirely when two additional years are omitted in
the second model. A possible explanation for this could be that omitting two additional years (for
a total of 9 years) results in a smaller and more homogeneous sample, potentially reducing the
variability required to detect a significant effect. Additionally, energy imports show increased
significance in the first model, reaching the 5% level, and remain significant at the 10% level in
the second model.
Table 3: The effect of energy diversification on the number of power outages
Notes: In the table, column (1) presents the results of the regression between energy diversification and power
outages using data from 2013 until 2022 and column (2) presents the results of the regression between energy
diversification and power outages using data from 2015 until 2022. Power Outages represent the frequency of
energy supply disruptions in a given country at time t; Diversification denotes the Herfindahl-Hirschman Index
(HHI), which measures the concentration of energy sources within a country’s energy mix; GDP represents the GDP
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per capita of country at time t, serving as a proxy for economic development; Population Density represents the
number of inhabitants per square kilometer in a country at time t; Energy Imports indicate a country’s share of
imported energy in total energy consumption at time t; Government Effectiveness reflects the quality of public
services, policy implementation, and institutional efficiency of a country at time t; Government expenditure captures
a country’s level of public investment at time t. μi denotes the country fixed effects; λt denotes the year fixed
effects.
VARIABLES
Diversification
GDP
PopulationDensity
EnergyImports
Government Effectiveness
Government Expenditure
Constant
Observations
Adjusted R-squared
fe
(1)
Power Outages
(2)
Power Outages
0.0584
(0.0988)
-0.0869***
(0.0321)
-0.00857
(0.0171)
0.000487**
(0.000197)
0.00670
(0.0478)
-0
(0)
(0.364)
1.173
(0.860)
0.0431
(0.100)
-0.0403
(0.0322)
-0.0149
(0.0172)
0.000358*
(0.000197)
-0.00737
(0.0480)
-0
(0)
(0.323)
0.117
(0.869)
1,148
0.140
yes
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
895
0.074
yes
6.
Conclusion
- Limitation: choice of dependent variable to describe energy supply disruptions
extend your conclusion.
This research aimed to explore whether using a more diversified energy mix in OECD countries
results in less energy supply interruptions. To test this, panel data for the period from 2006 to
2022 was employed together with a fixed-effects model, focussing on the frequency of power
outages as the main premise for resilience. The results demonstrated that the use of a variety of
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energy had a negative coefficient, without a statistically significant impact. This has led to a
conclusion that diversification of energy supplies still may not be an appropriate measure for
ensuring stable energy supplies.
At the same time, some other factors stand out more clearly. A higher GDP per capita leads to
fewer disruptions, but being more energy import-dependent stands for a higher vulnerability.
These outcomes are in line with the existing literature and thereby stress the relevance of
economic capacity and reliance on external sources in the determination of energy resilience.
However, it’s important to note some limitations. Power outages may be a inadequate measure of
the complexity of supply disruptions, and the measure of diversification may not properly reflect
the differences inside energy categories. Future research could aim at investigating other
indicators of resilience and at investigating how specific types of diversification, especially
within renewables, impact stability.
Thus, while diversification remains an important aspect of an energy policy, it should be seen as
an element of a broader strategy that also consists of strong infrastructure, efficient governance
and lower import dependency. Making an energy system more resilient goes beyond variety, it
also comes from coordination, investment and flexibility.
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References
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Cooray, A., Shahbaz, M., Kuziboev, B., & Çatık, A. N. (2025). Mitigating energy risk through
energy sources diversification. Journal of Environmental Management, 380, 124955.
Gozgor, G., & Paramati, S. R. (2022). Does energy diversification cause an economic
slowdown? Evidence from a newly constructed energy diversification index. Energy Economics,
109, 105970.
Nguyen, T., Nghiem, S., & Doan, A. T. (2025). Energy diversification, financial development
and economic development: an examination of convergence in OECD countries. China Finance
Review International.
Streimikiene, D., Siksnelyte-Butkiene, I., & Lekavicius, V. (2023). Energy diversification and
security in the EU: comparative assessment in different EU regions. Economies, 11(3), 83.
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