Econometric analysis of the influence of factors on the share of energy
from renewable sources in the EU
ANDREI V. ORLOV
Nizhny Novgorod State Technical University n.a. R.E. Alekseev
603950, Russia, Nizhniy Novgorod, st Minin, 24
RUSSIA
ORCID ID: 0000-0002-5440-7370
Abstract - The use of renewable energy is at the core of EU energy policy, reducing dependence on fossil fuels
imported from non-EU countries, reducing greenhouse gas emissions and decoupling energy costs from oil prices.
Currently, 22.5% of energy consumed in the EU comes from renewable sources. This increase over 2021 is due
to strong growth in solar energy. This share is also increasing due to the consumption of non-renewable energy
sources in 2022. According to expert forecasts, the share of renewable energy sources in Europe will continue to
grow. Achieving the 42.5% target by 2030 will require a deep transformation of the European energy system.
The very strong climate and energy policies pursued in the EU for about a dozen years and aimed at limiting
emissions of harmful substances into the environment have led to a gradual abandonment of traditional energy
sources. Increasing demand for energy while reducing its supply from traditional sources means that in order not
to make the economy too dependent on energy imports, the dynamic development of renewable energy is
necessary. The EU is therefore taking very extensive operational and strategic actions to use other sources for
production, such as wind, solar energy, mechanical water energy, biomass and geothermal energy, as well as tidal
waves, ocean heat, wave energy and sea currents.
In this study, we assess the impact of energy, economic and environmental factors on the share of renewable
energy in the EU. The aim of these studies was to identify the energy, economic and environmental indicators
that have the greatest impact on the share of energy from renewable sources in the European Union. The study
was conducted using the Statgraphics Centurion software package. The source data for the study was data from
the official Eurostat website for the period from 2012 to 2022. The results of this study show that changes in
gross domestic product in market prices per capita have a positive impact, as do changes in greenhouse gas
emissions per capita negatively affect the share of energy from renewable sources in the EU in the period 2012-
2022. This may most likely be due to the fact that EU countries are more likely to invest in renewable energy as
they can afford to invest in the development of expensive renewable energy technologies and support subsidies
for the promotion and regulation of renewable energy. The negative impact of per capita greenhouse gas
emissions on renewable energy development is due to the high share of coal in the EU energy mix, meaning that
coal not only has negative environmental impacts, but also negative environmental impacts. development of
renewable energy.
Key-Words - European Union; renewable energy; greenhouse gas emissions; gross domestic product; correlation;
regression
Received: June 15, 2023. Revised: March 19, 2024. Accepted: April 21, 2024. Published: June 3, 2024.
1 Introduction
Currently, the European Union imports up to 60% of
its energy, which costs more than 350 billion euros.
To solve the problem of ensuring energy security and
reducing the dependence of the European energy
market on energy imports, especially from the
Russian Federation, the EU Energy Union was
created. In January 2015, priority areas of its activity
were outlined: creation of an integrated energy
market, decarbonization of the economy, security of
energy supply, increasing energy efficiency,
development of scientific research and innovation.
And although in 2019, with a change in the leadership
of the European Commission, the post of European
Commissioner for the Energy Union ceased to exist,
energy security problems were not removed from the
agenda, but were combined with climate change
problems.
It is predicted that by 2030, the generation of
electricity from renewable sources will be 46 - 50%,
which may aggravate the problem of uncontrolled
production, since with the current level of technology
International Journal of Environmental Engineering and Development
DOI: 10.37394/232033.2024.2.13
Andrei V. Orlov
E-ISSN: 2945-1159
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Volume 2, 2024
development, the existence of a parallel traditional
energy supply system is necessary. Currently,
renewable energy receives government subsidies and
priority access to the electrical grid, thereby
crowding out other sources, which, due to
insufficient load, reduces the efficiency of nuclear
and fossil fuel power plants. Energy transmission
systems also suffer from uneven generation and
power surges. Thus, the most pressing issue is
balancing generation, energy storage and rapidly
changing demand. In addition to those mentioned
above, the following disadvantages of renewable
energy can be noted: high level of capital costs, low
efficiency, dependence on climatic conditions,
inability to recycle wind generator blades and
photovoltaic panels. It is important that the
production of components of generating plants
requires a large amount of metals, the main producer
of which is currently China, and in order to reduce
dependence on China it is necessary to have
alternative sources of raw materials.
In addition, not all EU member states have reached a
consensus on what types of energy generation are
considered “green”, what production methods they
are willing to abandon, and cross-border regulatory
schemes have not been worked out everywhere.
For example, Germany plans to completely abandon
nuclear energy by 2023, Austria and Luxembourg are
also against nuclear energy, and France, Finland and
the Czech Republic are not ready to give it up.
In this regard, of great interest is the “Additional
climate change law aimed at accelerating
decarbonization” published by the European
Commission on February 2, 2022, according to
which, subject to certain conditions, projects in the
field of gas and nuclear energy are recognized as
environmentally friendly and included to the EU
Taxonomy, which is a list of environmentally
sustainable economic activities that need priority
investment to achieve climate neutrality by 2050.
The Commission's proposal also seeks to clarify
metering and billing rules for consumers. Shadow
negotiations began in February 2018 and led to a
partial agreement between EU institutions on 19 June
2018. The final text was approved by the Parliament
(13 November 2018) and the Council (4 December
2018). It was published in the Official Gazette on
December 21, 2018 and came into force three days
later. Although measurement and payment
requirements can be implemented by 25 October
2020 [1, 2], Member States must comply with the
revised guidelines by 25 June 2020.
Since the introduction of the 27% target in 2014,
many changes have taken place in the energy sector.
Key renewable energy technologies such as solar PV
and offshore wind have achieved significant cost
reductions beyond expectations given their speed and
scale. As these technologies develop, so will low-cost
renewable energy.
Technological development in end-use sectors has
also increased rapidly; Electric vehicles, for example,
are achieving rapid sales growth and could play a key
role in providing a large proportion of renewable
energy in the EU's driving and electricity categories
by 2030. At the same time, new information and
communication technologies are changing the way
we create and consume energy. Due to this positive
development, the 27% increase target agreed in 2014
can be considered as the EU target [3].
In 2015, the share of renewable energy in EU
member states varied from 5% to 54%. Diversity will
remain until 2030, reflecting many factors such as
different starting points, current capacities, existing
and planned policies and market conditions for
renewable energy in each country; However, this gap
may narrow by 2030 as member countries with lower
shares can grow faster. By 2030 the total share of
renewable energy likely to result from Member
States' plans and estimates will not reach the EU's
27% target; Therefore, further commitments from
Member States are required to meet or exceed the
2030 target. In 2050, renewable energy will be the
main additional energy source, accounting for two-
thirds of the energy mix. This would require an
increase in renewable energy stocks of around 1.2%
per year, a sevenfold increase over recent years. If the
world increases its share of renewable energy by
2030, the European Union (EU) will account for 14%
of the world's renewable energy and become the third
largest consumer of renewable energy after China
and the United States. [3]
2 Review of literature
Studies on the influence of certain factors of the
development of renewable energy sources usually
analyze the period since 1990 and consider different
groups of countries. Marques et al. [4], Marques and
Fuinhas [5], Cadoret and Padovano [6], Lucas et al.
[7] and Papiez et al. [8] evaluated the impact of
selected factors on the development of renewable
energy in EU countries. Studies by Popp et al. [9];
Polzin et al. [10] and Biresselioglu et al. [11]
analyzed European and non-European countries
belonging to the OECD. Aguirre and Ibikunle [12]
explore the EU and OCED countries along with five
BRICS countries, while Kilinc-Ata's [13] study also
includes 50 US states.
Most studies use panel data to conduct an
econometric analysis of factors that influence the
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DOI: 10.37394/232033.2024.2.13
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development of renewable energy sources. Their
authors use the following methods for analyzing
panel data, such as: fixed effects with vector
decomposition estimator (FEVD) [4,12], the panel
corrected standard error (PCSE) estimator (PCSE)
[6,10,12,14], the Feasible Generalized Least Squares
(FGLS) estimator [9] or the estimate of dummy
variables with least squares (LSDV) [5, 6]. Marques
and Fuinhas [5] apply panel dynamic evaluations
such as GMM-dif and GMM-sys. In his work,
Biresselioglu et al. [11] similarly uses the system for
estimating the generalized method of moments
(GMM). Marques et al. [15] apply the quantile
method to study the factors contributing to the
development of renewable energy in European
countries, while Menz and Vachon [23] use the least
squares method to study the development of wind
power.
In most studies that analyze the influence of various
factors on the development of renewable energy, the
share of renewable energy in the total primary energy
supply (TPES) is used as the dependent variable
[4,5,7,12,14,15]. Dependent variables used by other
authors include: the share of renewable electricity in
the total supply of electricity from non-water
renewable sources [13], the number of installed wind
power generators [11] or the total newly introduced
capacity indicating the country and year in a
particular type of renewable energy sources (solar
energy, wind, biomass) [10].
Many studies show different factors influencing the
development of RES. Marquez and his friends.
[4,15], Marques and Fuenhas [5,9], and Aguirre and
Ebikunli [12] consider three important aspects of this
development. The first group includes political
factors such as a complete reform to identify EU
countries, unusual ways of ratifying the Kyoto
agreement, government policies that help improve
energy efficiency, research and development
programs, financial incentives and taxes. The second
group includes social and economic factors such as
oil, gas and coal prices, carbon dioxide (CO2)
emissions (carbon footprints per capita), coal, oil,
natural gas and nuclear energy. Production, energy
expenditure, income (GDP or GDP growth), energy
consumption and primary energy. The third group
dealing with renewable energy potential includes
national factors such as renewable energy
contribution, electricity market regulation and
renewable energy potential (calculation of biomass,
as well as solar/wind/hydropower). Lucas et al. [7]
distinguish three indicators and groups according to
their relevance to each aspect of energy policy:
environmental sustainability (signing the Kyoto
Protocol, energy intensity, emission levels), supply
dependence (total dependence on energy supply,
degree of diversity for energy sources and various
type of electricity generation) and competition (coal,
gas and oil prices, GDP per capita). Polzin et al. [10],
Bircelioglu et al. [11] and Kilink-Ata [13] consider
economic, energy security, environmental and
energy market data as dynamic factors and
investigate their effects on energy efficiency. Cadoret
and Padovano [6] analyze the political aspects of the
development of renewable energy sources. It divides
variables into three categories: political economy,
economy, energy and environment. Political units are
also used by Marques and Fuenhas [5], Polzin et al.
[10], Aguirre and Ibikunle [12], Nesta et al. [17] and
Zhao et al. [18]. Pope and others. [9] and Johnston et
al. [19] estimate technological progress as measured
by the number of patents per technology in
investment in renewable energy. Their variables are
the share of energy exports from total electricity and
per capita production of coal, natural gas, and oil.
Marquez and his friends [4, 15], Marques and
Fuenhas [5, 9] and Lucas et al. [7] argue that as CO2
emissions increase, renewable energy consumption
decreases, and therefore environmental pollution
does not play a large enough role in encouraging
renewable energy development. In contrast to
previous work, Cadoret and Padovano [6], together
with Aguirre and Ebikunli [12] confirm a positive
relationship between CO2 emissions and energy
production.
Marquez and his friends. [4, 15], Marques and
Fuenhas [5, 9] and Lucas et al. [7] found that per
capita energy consumption has a significant effect on
energy production from renewable energy sources at
home. Aguirre and Ebikunli [12] show that energy
consumption is negatively correlated with renewable
energy contribution, meaning that countries use more
renewables and more fossil fuels because these are
cheaper.
Marquez and his friends. [4, 15], Marques and
Fuenhas [5, 9] also believe that increasing fuel
efficiency will reduce renewable energy
consumption. As observed by Sovakul [20], the
effect inhibits RES development.
The impact of GDP on renewable energy generation
is not perfect. Articles examining the relationship
between RES use and economic growth (for
European countries: [8, 20–23], for OECD countries:
[24–27]) consider different perspectives
(conservation, feedback, growth and moderation). By
analyzing the impact of different factors on
renewable energy development in all EU countries,
Marques et al. [4] Income growth appears to support
renewable energy investment, but find a different
relationship with non-EU countries in the 2000s.
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DOI: 10.37394/232033.2024.2.13
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Similarly, Marques et al. [15], Cadoret and Padovano
[6] and Lucas et al. [7] argue that per capita income
has a negative effect on RES development.
Marquez and his friends. [4, 15], and Cadoret and
Padovano [6] prove that external energy dependence
has a positive effect on renewable energy
development, but Marques and Fuenhas [5, 14] and
Lucas et al. [7] show that excessive dependence on
energy imports inhibits the installation of renewable
energy sources. This dependence is mainly
associated with traditional energy sources, and the
production processes in the analyzed countries show
that they depend on petroleum resources, which is a
major obstacle to the development of renewable
energy.
Finally, many authors [4-7, 14, 15, 20, 28, 29]
confirm that growth decreases with the increasing
contribution of natural energy sources (coal, oil,
natural gas and nuclear power) to electricity
generation. . From renewable energy. In their
opinion, this is the presence of the industrial
influence that prevents the development of renewable
energy sources.
3 Material and methods
In this study, we assess the impact of energy,
economic and environmental factors on the share of
renewable energy in the EU.
Energy consumption has traditionally been used as an
indicator of development. It is also used to identify
the country's energy needs. Large consumption needs
put strong pressure on energy consumption. Energy
consumption can be met by traditional energy
sources, clean sources, and using a combination of
traditional and clean sources. In this case, the factor
of influence on renewable energy is the energy
consumption per capita.
High energy dependence on energy imports has an
impact on the development of RES, but also high
energy dependence on imports impedes the
introduction of RES. This dependence is mainly
associated with traditional energy sources, which is a
sign that the production infrastructure in the EU
countries depends on fossil energy sources, which is
a significant obstacle to the development of
renewable energy sources.
In this case, the factors influencing the development
of renewable energy are import dependence on
traditional energy sources (coal, oil, natural gas).
The economic factors selected in this study is the
prices of conventional forms of energy, such as
natural gas, oil, coal, and GDP per capita.
Climate change is associated with emissions of large
amounts of greenhouse gases such as carbon dioxide
(hereinafter CO2), chlorofluorocarbons, methane,
nitric acid and ozone. This phenomenon is called the
greenhouse effect, which is caused by these gases.
When this effect is not controlled, it leads to a
significant and continuous increase in the average
temperature of the planet. The most common factor
responsible for these climate changes is CO2. We
suggest that environmental concerns are an incentive
for the widespread use of RES instead of traditional
energy sources. We chose per capita CO2 emissions
as a factor, expecting more CO2 to mean a greater
incentive to develop RES.
The purpose of these studies was to identify the
factors that have the greatest impact on the share of
renewable energy in the European Union. The study
was conducted using the Statgraphics Centurion
software package. The initial data for the study were
data from the official Eurostat website for the period
from 2012 to 2022 [30].
As a method of econometric modeling, we chose
correlation and regression analysis, which allows you
to choose from the whole set of factors considered the
most significant.
This study examined the influence of factors on the
share of renewable energy in the European Union
from 2012 to 2022. When analyzing the influence of
independent variables on the dependent variable (the
share of energy from renewable sources),
multivariate regression analysis was used.
Share of energy from renewable sources (%) was
taken as the dependent indicator (Y).
The independent variable factors (X) were as
follows:
X1 - Gross inland energy consumption per capita,
toe per capita;
X2 - Import dependency of Solid fossil fuels, %;
X3 - Import dependency of Natural gas, %;
X4 - Import dependency of Crude oil, %;
X5 - Greenhouse gas emissions per capita, tonnes of
CO2 equivalent per capita;
X6 - Crude oil prices, US dollars per barrel;
X7 - Natural Gas Prices, US dollars per million Btu;
X8 - Coal Prices, US dollars per tonne;
X9 - Gross domestic product at market prices, Current
prices, euro per capita.
Table 1 summarizes the statistics for each of the
selected variables. It also includes summary statistics
for descriptive variables, including sample mean,
standard deviation, skewness, and kurtosis.
Of particular interest here are the standard deviation
and the normal kurtosis, which can be used to
determine whether a sample comes from a normal
distribution.
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Values of this statistic from −2 to +2 typically
indicate large deviations, which can interfere with
most statistical methods applied to these data.
In this study the X8 curve shows the standard
deviation and the average kurtosis lies outside this.
Table 1 presents a summary statistics for each of the
selected data variables. It includes a summary of
descriptive statistics of the variables, which include
sample mean, standard deviation, skewness and
kurtosis.
Of particular interest here are the standardized
skewness and standardized kurtosis, which can be
used to determine whether the sample comes from a
normal distribution.
Values of these statistics outside the range of -2 to +2
indicate significant departures from normality, which
would tend to invalidate many of the statistical
procedures normally applied to this data.
In this study X8 variable show the standardized
skewness and standardized kurtosis are out of this
range.
4 Results and discussion
Multicollinearity is a statistical term for the existence
of a high order linear correlation amongst two or
more explanatory variables in a regression model. In
any practical context, the regression model.
Table 1. Summary Statistics
Y
X1
X3
X4
X5
X6
Average
13,27
3,44
64,15
85,82
9,66
85,15
Standard
deviation
3,08
0,20
5,52
2,62
0,85
27,80
Coeff. of
variation
23,22%
5,73%
8,61%
3,06%
8,82%
32,65%
Minimum
8,53
3,18
53,64
80,87
8,6
45,76
Maximum
17,52
3,72
74,32
88,79
10,9
124,2
Range
8,99
0,54
20,68
7,92
2,3
78,44
Stnd. skewness
-0,21
0,31
-0,17
-0,64
0,41
0,09
Stnd. kurtosis
-1,06
-1,15
-0,08
-0,81
-1,12
-1,14
X7
X8
X9
Average
8,05
83,48
26314,3
Standard
deviation
2,35
25,25
2177,1
Coeff. of
variation
29,20%
30,25%
8,27%
Minimum
4,3
56,64
22500
Maximum
11,6
147,67
30000
Range
7,3
91,03
7500
Stnd. skewness
-0,07
2,26
0,10
Stnd. kurtosis
-0,88
1,75
-0,36
In any practical context, the correlation between
explanatory variables will be non-zero, although this
will generally be relatively benign in the sense that
a small degree of association between explanatory
variables will almost always occur but will not cause
too much loss of precision.
The presence of multicollinearity usually results in
an overstatement of the standard error, i.e. the
standard error tends to be large, leading to small “t”
value and a high coefficient of determination. The
usual procedure when multicollinearity exists is to
drop the offending variable or alternatively to drop
the variable that provides lesser contribution
towards model improvements. A simple procedure
to determine which variable to drop is to calculate
the correlation matrix. The correlation matrix on
Fig. 1 represents the correlation coefficients for the
variables used in this study.
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DOI: 10.37394/232033.2024.2.13
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E-ISSN: 2945-1159
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Volume 2, 2024
This correlation matrix shows Pearson product
moment correlations between each pair of variables.
These correlation coefficients range between -1 and
+1 and measure the strength of the linear
relationship between the variables. P-values above
0,05 indicate statistically insignificant non-zero
correlations at the 95,0% confidence level.
Factors X2, X6, X7 and X8 are insignificant because
they have a low correlation with the dependent
variable Y and have P-values above 0,05 indicate
statistically insignificant non-zero correlations at the
95,0% confidence level.
When considering this matrix in order to identify
multicollinear factors, they are guided by the
following rule: if the correlation matrix of factor
variables contains pair correlation coefficients in
magnitude greater than 0.8, then it is concluded that
in this model of multiple regression there is
multicollinearity.
Fig 1. Сorrelation matrix
When considering this matrix in order to identify
multicollinear factors, they are guided by the
following rule: if the correlation matrix of factor
variables contains pair correlation coefficients in
magnitude greater than 0.8, then it is concluded that
in this model of multiple regression there is
multicollinearity.
If there is multicollinearity for its elimination or
reduction, there are a number of methods, in
particular step-by-step procedures for selecting the
most informative variables.
The most important task in the construction of
multiple linear regression is the correct selection of
factors included in this equation. In solving this
problem, the following schemes have gained the
most widespread use: the method of Forward
Stepwise Selection and the method of Backward
Stepwise Selection i.e. the elimination of factors
from its full set.
Forward Stepwise Selection is performs a forward
stepwise regression. Beginning with a model that
includes only a constant, the procedure brings in
variables one at a time provided that they will be
statistically significant once added. Variables may
also be removed at later steps if they are no longer
statistically significant.
Backward Stepwise Selection is performs a
backward stepwise regression. Beginning with a
model that includes all variables, the procedure
removes variables one at a time if they are not
statistically significant. Removed variables may also
be added to the model at later steps if they become
statistically significant.
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Fitting the model using the original data showed 3
insignificant variables. To remove them from the
model, the analysis parameters can be used to
perform the backward stepwise selection.
Backward selection begins with a model involving
all the variables specified on the data input dialog
box and removes one variable at a time based on its
statistical significance in the current model. At each
step, the algorithm removes from the model the
variable that is the least statistically significant.
Removal of variables is based on either a P-to-enter
test. In the former case, if the least significant
variable has an P-value large than 0,05, it will be
removed from the model. When all remaining
variables have less P-value, the procedure stops.
In first step the highest P-value on the independent
variables is 0,26, belonging to X4. Since the P-value
is greater to 0,05, that term is not statistically
significant at the 95,0% or higher confidence level.
Consequently, X4 must be removing from the model.
In the second step the highest P-value on the
independent variables is 0,107, belonging to X1.
Since the P-value is greater to 0,05, that term is not
statistically significant at the 95,0% or higher
confidence level. Consequently, X1 must be
removing from the model.
In the third step the highest P-value on the
independent variables is 0,084, belonging to X3.
Since the P-value is greater to 0,05, that term is not
statistically significant at the 95,0% or higher
confidence level. Consequently, X3 must be
removing from the model.
The algorithm then stops, as the highest P-value on
the independent variables is 0,0002, belonging to X9.
Since the P-value is less than 0,05, that term is
statistically significant at the 95,0% confidence
level. Consequently, it is a final model.
Table 2 shows the results of fitting a multiple linear
regression model to describe the relationship
between Y and 7 independent variables.
Table 2. Estimation results of the dependent variable: Share of energy from renewable sources
Parameter
Estimate
Standard
Error
T-Statistic
P-Value
CONSTANT
30,2887
3,3062
9,16112
0,0000
X5
-2,76445
0,171756
-16,0952
0,0000
X9
0,000367866
0,0000672072
5,47361
0,0002
Table 3 shows the statistical significance of each
variable as it was added to the model. Since the P-
value in the ANOVA table is less than 0,05, there is
a statistically significant relationship between the
variables at the 95,0% confidence level. The
estimation result of the independent variables
independent variables to the dependent variable is
shown in Table 4.
Table 3. ANOVA for Variables in the Order Fitted
Source
Sum of quares
Df
Mean Square
F-Ratio
P-Value
X5
120,456
1
120,456
1665,02
0,0000
X9
2,16747
1
2,16747
29,96
0,0002
Model
122,623
2
Table 4. Analysis of Variance
Source
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
Model
122,623
2
61,3115
847,49
0,0000
Residual
0,795792
11
0,0723447
Total (Corr.)
123,412
13
R-squared = 99,3552 percent
R-squared (adjusted for d.f.) = 99,238 percent
Standard Error of Est. = 0,26897
Mean absolute error = 0,177641
Durbin-Watson statistic = 2,85997 (P=0,8943)
Lag 1 residual autocorrelation = -0,436458
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DOI: 10.37394/232033.2024.2.13
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E-ISSN: 2945-1159
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Volume 2, 2024
Based on the estimation results presented in Table 1,
the following equation was obtained:
Y = 30,2887 - 2,76445·X5 + 0,000367866·X9
The R-Squared statistic indicates that the model as
fitted explains 99,355% of the variability in Y. The
adjusted R-squared statistic, which is more suitable
for comparing models with different numbers of
independent variables, is 99,238%. The standard
error of the estimate shows the standard deviation of
the residuals to be 0,26897. The mean absolute error
(MAE) of 0,177641 is the average value of the
residuals. The Durbin-Watson (DW) statistic tests
the residuals to determine if there is any significant
correlation based on the order in which they occur in
your data file.
Since the P-value is greater than 0,05, there is no
indication of serial autocorrelation in the residuals at
the 95,0% confidence level.
The result of the regression estimation showed that
if greenhouse gas emissions per capita, increases by
1 tonne of CO2 equivalent per capita, share of
renewable energy sources will decrease by 2,76445
percent and if gross domestic product at market
prices increases by 1 euro per capita, share of
renewable energy sources will rise by 0,000367866
percent.
The direct correlation between the influence of gross
domestic product at market prices per capita on the
share of energy from renewable sources means that
EU countries are more likely to invest in renewable
energy sources, since they can afford to invest in the
development of expensive renewable energy
technologies and support subsidies for the
promotion and regulation of renewable energy
sources. The positive effect of gross domestic
product at market prices per capita on the promotion
of renewable energy has also been found by
Marques et al. [4].
The greenhouse gas emissions per capita negatively
affect the development of renewable energy. This is
due to the high share of coal in the EU energy
balance, which means that coal not only has a
negative impact on the environment, but also has a
negative impact on the development of renewable
energy. This is a rather unexpected effect, as one
would expect that an increase in greenhouse gas
emissions per capita would be a powerful incentive
for renewable energy investments. These results are
consistent with those from studies conducted by
Marques et al. [4], Marques and Fuinhas [5], Lucas
et al. [7], Marques and Fuinhas [14] and Marques et
al. [15].
5 Conclusions
The main objective of this study was to assess the
impact of energy, economic and environmental
factors on the share of renewable energy in the EU.
The results of this study show that changes in gross
domestic product in market prices per capita have a
positive impact, as do changes in greenhouse gas
emissions per capita negatively affect the share of
energy from renewable sources in the EU in the
period 2012-2022. This may most likely be due to
the fact that EU countries are more likely to invest
in renewable energy as they can afford to invest in
the development of expensive renewable energy
technologies and support subsidies for the
promotion and regulation of renewable energy. The
negative impact of per capita greenhouse gas
emissions on renewable energy development is due
to the high share of coal in the EU energy mix,
meaning that coal not only has negative
environmental impacts, but also negative
environmental impacts development of renewable
energy.
Large gross domestic product at market prices per
capita allows EU countries to cover the costs of
developing renewable energy technologies. The
effect of greenhouse gas emissions per capita
correlate with the decrease of share of renewable
energy sources. The effect of greenhouse gas
emissions per capita on renewable energy is
statistically significant and negative.
This result suggests that current greenhouse gas
emissions per capita levels are not enough to switch
to renewable energy sources. On the contrary, these
levels remain incentives for continued burning of
fossil fuels. It turns out that social pressure in the EU
was not enough to stimulate renewable energy.
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