International Journal of Energy Sector Management [629413]

International Journal of Energy Sector Management
The Renewable Energy- Economic Growth Relationship in
the Long-Term: Empirical Analysis for Russia
Journal: International Journal of Energy Sector Management
Manuscript ID IJESM-09-2019-0022
Manuscript Type: Research Papers
Subject Area: Natural gas, Policy, Renewable energies
Methods/ Tools / Analytical
Frameworks:Econometric, Time series analysis

International Journal of Energy Sector Management

International Journal of Energy Sector Management
1The Renewable Energy- Economic Growth
Relationship in the Long-Term: Empirical
Analysis for Russia
Abstract
Reducing external dependence on energy imports or exports by increasing domestic
resources is very important for economic growth. Considering that fossil resources will be
depleted, the necessity of seeking alternative energy in ensuring security of energy supply has
been proven in many scientific studies. The political crises that may occur will bring the
economies of the country to a crisis in terms of both energy imports and energy exports. In this
context, diversification of energy resources, ensuring sustainable energy use and energy
consumption as a result of minimizing damage to the environment is extremely important.
Population growth rate, industrialization, technological developments and energy demand
increase lead the country's strategies to energy policies. One of these policies is sustainable
development-oriented energy supply policies. The most important factor in the determination
of energy policies is the determination of the relationship between energy and economic
growth. Determining the type, direction and effect size of energy-growth relationship can be
prevented to determine the wrong policies.
In this study, energy-growth relationship for Russia between 1990 and 2015 period by
using ARDL Bound test, energy consumption, renewable energy production, natural gas prices,
oil prices and economic growth.
Keywords: Russia, Economic Growth, Renewable Energy Consumption, Renewable Energy
Production, ARDL Bound Test
I. Introduction
Energy prices are among the factors affecting direct economic growth. Along with the
increase in oil and gas prices, possible fluctuations in prices have increased production losses.
In addition, energy policy is difficult to develop. The impact of energy prices on production has
been the subject of several studies. Ogunleye (2008) investigated the effect of Nigeria's oil
revenues on the country's economic growth and revealed that oil revenues had a negative impact
on economic growth as it increased the private consumption and electricity production and
caused regressions on agriculture and manufacturing industry. Coloni and Manera (2008) As a
result of their study using the VAR model for the years 1980-2003, it was concluded that the
effects of oil prices on inflation for G7 countries in all countries except Japan and the UK were
negative. Mehrara (2009) concluded that, as a result of panel data analysis for the oil exporting
countries, the increase in oil revenues positively affected the economy to some extent but after
a certain level this effect was turned into a negative direction. Iwayemi and Fowowe (2011),
using data from the period 1985-2007, found that the VAR model and oil prices had no
significant impact on the many macroeconomic variables (real gross domestic product, Page 1 of 26 International Journal of Energy Sector Management
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2inflation, real exchange rate, net exports) of Nigeria. As a result of the study conducted by
Hassan and Zaman (2012) 1975-2010 period, ARDL test was conducted and a significant
negative relationship was found between oil prices, exchange rate and trade balance.
Within the scope of energy supply security, energy production, transmission and
consumption activities, it is very important to realize energy supply, transportation and demand
in sufficient quantity and quality, at reasonable cost / prices, in a continuous and
environmentally sensitive manner. Many countries adopt this strategy to ensure economic
development. The most important starting point in the implementation of this strategy can be
seen as an examination of the energy-growth relationship.
This study which examines the relationship between energy and growth in Russia and its
characteristics that make it different from other studies can be listed as follows:
In this study, it is aimed to examine the effects of renewable energy production and
consumption on the economy of the country, considering that Russia's fossil-based
energy resources will be exhausted.
Investigation of the effect of oil and gas prices on growth.
Determining the effect of renewable energy use on growth
In the absence of the study that examined the relationship between the specified period
and the specified variables and the energy consumption economic growth in Russia, it
was aimed to eliminate the shortcomings in the literature. Through implications, it is
aimed to ensure that policy makers make easier inferences.
Within the framework of Russia's energy potential, which has a strong raw material for
fossil-based sources, the impact of resource diversification on the country's economy,
awareness of the impact of investments and strategies for renewable energy on the
country has been sought.
Considering Russia's economic position in the world, making it easier for other
countries to make inferences about Russian energy strategies in their energy strategies.
The importance of energy infrastructure investments for economic growth was emphasized. In
order for the producer country to achieve high energy prices, it is necessary to complete the
energy infrastructure investments. Despite the fact that Russia is the country with the highest
amount of natural gas and significant oil reserves in the world and its huge size hydraulic
capacity, in the early 1990s, the electricity sector has stated that there is a need for large-scale
investments in energy production, transmission and distribution. In the long term, high energy
prices make positive contributions to the country's economy by increasing productivity, saving
and research and development activities for the consumer or importer country. However, it is
suggested that high energy prices will negatively affect the economic growth and international
bargaining power, which will reduce the long-term relationship between the growth of the
exporting country's GDP and global energy consumption (Umbach, 2008).
II. Theoretical Framework
The effects of energy on economic growth have been tried to be explained by various
models. According to the growth hypothesis, it can be said that energy consumption has a
significant effect on economic growth. In such cases, it can be said that there is a one-way
causality relationship from energy consumption to economic growth. It is possible to explain Page 2 of 26 International Journal of Energy Sector Management
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3this one-way causality with the protection hypothesis. As economic growth leads to an increase
in energy use, energy conservation policies may need to be implemented. On the other hand, if
the relationship between energy consumption and economic growth and vice versa mirrors each
other, it is possible to talk about two possibilities. When there is a two-way dynamic
relationship, the feedback hypothesis is supported, it can be said that the neutrality hypothesis
is supported if there is no dynamic link between the two variables (Balcılar et al. 2018).
The causality relationship obtained from studies examining the relationship between
economic growth and energy consumption is summarized by four hypotheses:
Hypothesis Description
The neutrality hypothesis No causality between E and GDP
The conservation hypothesis Uni-directional causality: E←GDP
The growth hypothesis Uni-directional causality: E→GDP
The feedback hypothesis Bi-directional causality: E↔GDP
i. neutrality hypothesis
The small share of energy consumption in total production means that there is no causal
relationship between energy consumption and economic growth. It means that energy
consumption is not related to GDP. Small or large-scale energy policies are considered to have
no impact on economic growth. In other words, there is no causal relationship between GDP
growth and energy consumption (Menegaki, 2011; Menyah and Wolde-Rufael, 2010; Abosedra
et al. 2015; Odhiambo, 2009; Stern & Enflo 2013, Warr & Ayres 2010). According to this
hypothesis, no energy savings nor expansion policies would affect economic growth and vice
versa. According to this hypothesis, energy expenditures represent a rather small fraction of
GDP. Even significant changes in consumption are not expected to have very significant effects
on GDP. According to this hypothesis with time (and higher levels of development), the
production structure will be shifting towards the service sector (that is typically less energy
intensive) and the production structure will be shifting towards the service sector (that is
typically less energy intensive). Such a structural change, typically evidenced by significant
energy intensity changes, may lead to what is often labeled as decoupling of the energy use and
GDP, and would speak for the absence of evidence for the causality (especially if considered
only on the aggregate levels). The last type of arguments favoring this hypothesis focuses on
the absence of the actual physical link between the variables. It points out to the limited
possibility to store the energy consumed in previous periods (especially valid with the use of
relatively low-frequency data, such as the typical annual panels). Then the volumes of the past
energy consumption cannot have a reasonable physical linkage to the current output.
Some of the studies that support the neutrality hypothesis in the literature which means
no relationship between EC to GDP as follows:
Akarca and Long (1980) using the data from 1950 to 1970 for USA by using Sims
causality. Yu and Jin (1992) from 1974 to 1990 for USA by using Cointegration and Granger
causality. Fatai et al. (2002) from 1960 to 1999 for New Zeland by using TY Granger causality
and ARDL. Bowden and Payne (2009) from 1949 to 2006 for USA by using TY causality test.
ii. conservation hypothesis Page 3 of 26 International Journal of Energy Sector Management
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4According to this hypothesis, it is assumed that there is a one way causality relationship
from economic growth to energy consumption. The increase in real GDP leads to an increase
in energy consumption. The protection hypothesis is based on a one-way causal relationship. It
is accepted that there is a causality relation from growth to energy consumption. It is envisaged
that GDP growth causes energy. It can be said that an economy operating in such a causal
relationship is dependent on less energy. It is expected that the conservation policies on energy
consumption will have little or no impact on economic growth (Gurgul and Lach, 2012;
Sadorsky, 2009; Esso, 2010; Apergis and Payne, 2009; Baranzini et al. 2013, Damette & Seghir
2013, Ouedraogo 2013, Azlina & Mustapha 2012, Haghnejad & Dehnavi 2012, Adom 2011,
Abbasian, Nazary & Nasrindoost 2010, Jamil & Ahmad 2010) and many other studies. It is
foreseen that energy conservation plans can be implemented without affecting the impacts on
economic growth. Usually in this hypothesis, the policy proposal advocates the implementation
of energy conservation policies. This type of hypothesis is usually favored by economists, who
consider the energy primarily as the intermediate product. Therefore, with the increasing level
of output there will be an increasing demand for goods and services, including the derived
demand for energy.
Some of the studies that support the conservation hypothesis in the literature which
provide an one way causality relationship running from EC to GDP as follows:
Ang (2008) using the data from 1971 to 1999 for Malaysia by using Johansen
cointegration and VECM approaches. Zhang and Cheng (2009) from 1960 to 2007 for China
by using Granger causality. Souhila and Kourbali (2012) from 1965 to 2008 for Algeria by
using the threshold cointegration and Granger causality tests. Herrerias et al. (2013) from 1995
to 2009 for China by using panel cointegration techniques.
iii. growth hypothesis
From these hypotheses, it is assumed that there is a unidirectional causality relationship
from energy consumption to economic growth according to growth hypothesis. The growth
hypothesis reveals that energy consumption is a very important component in economic growth.
It means that energy consumption causes GDP growth. In such cases, it is assumed that the
slowing down and decrease of energy consumption policies will have a negative impact on
economic growth. Energy is a growth-limiting factor, the policy of increasing investments in
industrial sectors, especially electrification is expected to stimulate economic development.
While increases in energy consumption may contribute to more economic growth, the decrease
in energy consumption may have negative effects on growth. (Bowden and Payne, 2010; Belke
et al. 2011; Payne, 2011; Salahuddin and Gow, 2014; Charfeddine and Khediri, 2016). In this
hypothesis, energy is considered a necessary production factor. According to this hypothesis,
the reduction in energy consumption (or energy supply) is predicted to will affect economic
growth. This type of hypothesis would also advocate the inclusion of energy in the
macroeconomic production function.
Some of the studies that support the economic growth hypothesis in the literature and
that causality is towards growth from energy consumption are as follows:
Ang (2007) using the data from 1960 to 2000 for by using cointegration and VECM
approach in his analysis. Ho and Siu (2007) using the data from 1966 to 2002 for Hong Kong
by using Cointegration and VECM. Tsani (2010) using the data from 1960 to 2006 for Greece
by using Toda Yamamoto causality test. Warr and Ayres (2010) using the data from 1946 to
2000 for USA by using the Johansen cointegration causality and VECM. Hossain and Saeki Page 4 of 26 International Journal of Energy Sector Management
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5(2011) using the data from 1971 to 2007 for several Asian countries by using Granger causality,
Engle-Granger (EG) and generalized method of moments (GMM). Dergiades et al. (2013) using
the data from 1960 to 2008 for Greece by using Parametric and non-parametric test.
iv. feedback hypothesis
According to this hypothesis, the causality relationship between energy consumption
and economic growth is considered bidirectional. The increase in energy consumption
positively affects economic growth. Likewise, the increase in economic activity is considered
to increase energy consumption. Energy consumption and economic growth trigger each other
(Shahbaz et al. 2011; Al Mulali et al. 2014; Belaid & Abderrahmani 2013; Hu & Lin 2013,
Tang & Tan 2013; Shahbaz & Lean 2012; Zhang & Yang 2013; Kouakou 2011; Ouédraogo,
2013) and many others. Policy practices should take into account the expected behavior of the
economy according to the relationship between the design and the effects of the proposed
energy policy. According to this hypothesis, it often requires additional policy research or
policy design, which should determine the hierarchy of targets.
Some of the studies that support the feedback hypothesis in the literature and that
causality is bidirectional between growth and energy consumption are as follows:
Hwang and Gum (1992) using the data from 1961 to 1990 for Taiwan by using Granger
causality method. Zarnikau (1997) using the data from 1970 to 1992 for USA by using Granger
causality. Belloumi (2009) using the data from 1971 to 2004 for Tunisia by using Granger
causality and vector error correction model (VECM) approaches. Zhang (2011) using the data
from 1970 to 2008 for Russia by using time-varying cointegration and Toda Yamamoto (TY)
causality test. Zhang and Xu (2012) using the data from 1995 to 2008 for China by using panel
causality tests. Shahiduzzaman and Alam (2012) using the data from 1960 to 2009 for Australia
by using Johansen co-integration and VECM causality tests. Wesseh Jr and Zoumara (2012)
using the data from 1980 to 2008 for Australia by using parametric and non-parametric Granger
causality approaches.
III. Literature
There are many studies in the literature on the relationship between energy and
economic growth. For example; Nachane et al. (1988) using the data from 1950 to 1985 for 16
countries and Co-integration, Sims and Granger Casuality method found that thera was a two
way relationship between energy consumption and economic growth. Ebohon (1996) using the
data from 1960 to 1984 for Nigeria and Tanzania and Granger Casuality approach found that
there was a two way relationship between energy consumption and economic growth. Cheng
(1997) using the data from 1949 to 1993 for Mexico, Brazil and Venezuela and Hsiao’s version
of Granger Causality method found that there was a no casuality between energy consumption
and economic growth. Glasure and Lee (1998) using the data from 1960 to 1984 for South
Korea and Cointegration, ECM method found that there was no casuality between energy
consumption and economic growth. Lee (2005) using the data from 1975 to 2001 for 18
developing countries and Panel VECM Methodology found that there was an one way
relationship from energy consumption to economic growth. Chang et al. (2006) using the data
from 1997 to 2006 for G7 countries and Threshold estimation found that there was an one way
relationship from economic growth to energy consumption. Al-Iriani (2006) using the data from
1970 to 2002 for the six countries of the Gulf Cooperation Council (GCC) and and Panel co-
integration, GMM found that there was an one way relationship from economic growth to
energy consumption. Mehrara (2007) using the data from 1971 to 2002 for the 11 Oil Exporting
countries and Panel cointegration technique found that there was an one way relationship from Page 5 of 26 International Journal of Energy Sector Management
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6economic growth to energy consumption. Huang et al. (2008) using the data from 1972 to 2002
for 82 countries and GMM system approach found that there was an one way relationship from
economic growth to energy consumption. Chontanawat et al. (2008) using the data from 1971
to 2000 for 30 OECD and 78 non OECD countries and Granger Casuality Test found that there
was an one way relationship from energy consumption to economic growth. Narayan & Smyth
(2008) using the data from 1972 to 2002 for G7 countries and Panel co-integration and Granger
causality found that there was an one way relationship from energy consumption to economic
growth. Narayan & Smyth (2009) using the data from 1974 to 2002 for 6 MENA countries and
and Panel cointegration, VECM methodology found that there was a two way relationship
between electricity consumption to economic growth. Sadorsky (2009) using the data from
1980 to 2005 for G7 countries and Panel cointegration found that there was a two way
relationship from renewable energy consumption to economic growth. Wolde-Rufael (2009)
using the data from 1971 to 2004 for Algeria, Benin, South Africa and Toda and Yamamoto
test procedure found that there was an two one way relationship betweeneconomic growth to
energy consumption. Sadorsky, P. (2009a) using the data from 1994 to 2003 for 18 emerging
countries and Bivariate panel error correction model found that there was a one way relationship
from economic growth to renewable energy consumption. Apergis and Payne (2010) using the
data from 1985 to 2005 for 20 OECD countries and Panel cointegration and error correction
found that there was a two way relationship between economic growth to renewable energy
consumption. Belke (2011) using the data from 1981 to 2007 for 25 OECD countries and Panel
co-integration and VECM methodology found that there was a two way relationship between
energy consumption and economic growth. Eggoh et al. (2011) using the data from 1970 to
2006 for 21 African countries and Panel co integration and Panel Causality tests found that
there was a two way relationship between energy consumption and economic growth. Tiwari
(2011) using the data from 1965 to 2009 for 6 European and Eurasian countries and Panel VAR
approach found that there was a two way relationship between energy consumption and
economic growth. Aïssa et al. (2013) using the data from 1980 to 2008 for 11 African countries
and Panel error correction model found that there was no casuality. Damette and Seghir (2013)
using the data from 1990 to 2010 for 12 oil-exporting countries and Panel cointegration
techniques found that there was an one way relationship from energy consumption to economic
growth. Mohammadi and Parvaresh (2014) using the data from 1990 to 2008 for 14 oil-
exporting countries and Panel estimation techniques, dynamic fixed effect, found that there was
a two way relationship between energy consumption and economic growth. Smiech and Papiez
(2014) using the data from 1993 to 2011 for 25 european union member states and Bootstrap
Granger Panel Casuality approach found that there was no casuality between energy
consumption and economic growth.
It can be said that Russia has some of the largest natural gas, oil and other raw materials
in the world, which is vital for many industrialized countries. Also, most European countries
and former Soviet states are highly dependent on Russian natural gas. In addition, Russia may
be seen as a key player and determinant in some issues critical to the United States, such as the
spread of Iran and North Korea's nuclear weapons (Goldman, 2007). Russia is a major natural
gas supplier of many US European allies. For this reason, energy-related studies about Russia
are very important.The literature studies examining the relationship between energy and
economic growth for Russia are as follows:
Zhang (2011) using the data from 1970 to 2008 for Russia and the state space model,
time-varying cointegration approach, and causality perspectives found that there was a two way
casuality relationship between energy consumption and economic growth. Pao et al. (2011)
using the data from 1990 to 2007 for Russia and Granger causality VEC, JJ cointegration found Page 6 of 26 International Journal of Energy Sector Management
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7that there was casuality relationship from the economic growth to CO 2 emissions and from
energy consumption to CO 2 emissions. It was concluded that both economic growth and energy
conservation policies could reduce emissions and no negative impact on economic
development. Burakov ve Freidin (2017) using the data from 1990 to 2014 for Russia and the
Granger causality test VEC in domain found that economic growth is granger cause of changes
in renewable energy consumption. Wolde-Rufael (2014) examined the relationship between
electricity consumption and economic growth for the 15 transition economy in the period of
1975 – 2010 and a brand-new and strong analysis found that there was a one-way causality
relationship from economic growth to electricity consumption for Russia. Cowan (2014) for
the 5 countries of BRIC examined the relationship between electricity consumption and
economic growth by using Panel Causality analysis for the period 1990-2010 and found that
there was a two way causality relationship between economic growth and electricity
consumptionfor Russia.
According to the literature studies on the relationship between energy and economic
growth, it can be said that renewable energy has a role in ensuring energy security and climate
change problems (Ristinen and Krushaar, 2006; Sims et al., 2007). Krewitt et al. (2007) In line
with the 2050 focus plans, it is foreseen that renewable energy sources will be able to supply
half of the energy needs of the world by 2050. It can be said that the issue of energy supply
security has led many countries to seek alternative energy sources. In addition, the negative
impact of greenhouse gas (GHG) emissions associated with the danger of extinction of fossil
resources is increasing day by day in order to determine climate change mitigation policies,
CO 2 emission reduction strategies and energy sector should be re-established accordingly.
(Abulfotuh, 2007; Apergis ve Payne, 2012; Balsalobre-Lorente et al., 2018).
The relationship between renewable energy and economic growth is as follows:
Apergis et al. (2010) using the data from 1984 to 2007 for Developed and Developing
19 countries countries and Panel Cointegration, Panel Causality, Panel Error Correction Model
techniques found that there was a two way relationship between renewable energy consumption
and economic growth. Menyah and Wolde Rufael (2010) using the data from 1960 to 2007 for
Granger Causality Test, Variance Decomposition techniques found that there was an one way
relationship from economic growth to renewable energy consumption. Pao and Fu (2013) using
the data from 1980 to 2010 for Brasil and Johansen Cointegration Test, Granger Causality Test
found that there was a two way relationship between total renewable energy consumption and
economic growth. Sebri and Ben Salha (2014) using the data from 1971 to 2010 for BRICS
countries and ARDL Bound Test, VECM Granger Causality found that there was a two way
relationship between total renewable energy consumption and economic growth. Bloch (2015)
using the data from 1977 to 2013 and from 1965 to 2011 for China and Structural Fracture Test,
ARDL Cointegration, Causality test based on VECM found that there was a two way
relationship between total renewable energy consumption and economic growth. Ibrahiem
(2015) using the data from 1980 to 2011 for Egypt and ARDL Bound Test, Granger Causality
found that there was a two way relationship between renewable electricity and economic
growth.Bhattacharya (2016) using the data from 1991 to 2012 for 38 countries of Renewable
Energy Country Attractiveness Index (RECAI) and Panel Cointegration, DOLS, Panel Data
FMOLS, Panel Causality found that % 57 of selected countries for long term; the increase in
renewable energy consumption has a significant and positive effect on the economic output.
Inglesi- Lotz (2016) using the data from 1990 to 2010 for 34 countries of OECD and Least
Squares with Fixed Effects and Panel Cointegration Test (Pedroni) found Renewable energy
consumption has a positive and significant effect on economic growth.Page 7 of 26 International Journal of Energy Sector Management
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8IV.Data and Methodology
i.Data
It is known that oil and gas prices directly affect economic growth. Similarly, there are
many studies examining the impact of renewable energy on growth. In this study, we have
examined the effects of fossil-based and renewable energy sources on growth. In this way, we
tried to identify the effects of the transition from fossil sources to renewable energy sources.
The studies examining the relationship with similar variables are as follows. Zhang (2011)
examined the dynamic nexus between Russia’s energy consumption and economic growth
according to the state space model and time-varying cointegration approach for Russia by using
the state space model, time-varying cointegration approach, and causality perspectives found
that there was a two way casuality relationship between energy consumption and economic
growth. Pao et al. (2011) examined the relationship between GDP, EC, CO2 variables for
Russia and Granger causality VEC, JJ cointegration Burakov ve Freidin (2017) examined the
relationship between financial development, economic growth and renewable energy
consumption for Russia with Granger causality test VEC in domain Wolde-Rufael (2014)
examined the relationship between electricity consumption and economic growth for the 15
transition economy with a brand-new and strong analysis. Cowan (2014) for the 5 countries of
BRICS examined the relationship between the causal link between electricity consumption,
economic growth and CO2 emissions variables by using Panel Causality.
In this study, fossil fuel, renewable energy and economic growth relations discussed for
Russia by using renewable energy consumption (REC), renewable energy production (REO),
natural gas prices (GAS), oil prices (OIL) and economic growth variables.
The variables discussed in this study were obtained from World Bank. Only the natural
logarithm of the GDP in constant prices (GDP) in US Dollars in taken in this study. The
descriptive stats of the variables discussed in the study are given in Table 1.
Table 1: Decriptive Stats
GDP REO REC GAS OIL
Mean 38.8945 17.6240 3.6002 8.2040 3.6670
Median 39.2257 17.5823 3.6073 9.2660 3.3139
Maximum 39.6783 20.4266 4.0380 14.3226 8.5057
Minimum 34.9981 15.3381 3.2277 1.4005 1.7100
Std. Dev. 1.1085 1.4708 0.2241 3.5073 1.4121
Skewness -2.2691 0.0754 0.0917 -0.2226 1.8084
Kurtosis 7.5028 1.9536 2.0981 1.9981 6.5907
Jarque-Bera 44.2783 1.2107 0.9175 1.3021 28.1394
Probabilty 0.0000c0.5458 0.6320 0.5214 0.0001c
Observations 26 26 26 26 26
The symbols a,b,c denote significance at %10, %5 and %1 levels, respectively.
According to the results of Table I, it can be said that all variables (except OIL and GDP)
have normal distribution.
ii.Methodology Page 8 of 26 International Journal of Energy Sector Management
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9The first step is to test for the presence of cointegration among the variables by employing
the bounds testing procedure (Pesaran and Pesaran, 1997; Pesaran, Shin and Smith, 2001).
Following Pesaran et al. (2001), we can write an ARDL bound model as the follows:
∆𝑌𝑡=𝑐+𝛼𝑌𝑡―1+𝛽𝑋𝑡―1+∑𝑝―1
𝑖=1𝜃𝑌𝑡―𝑖+∑𝑝―1
𝑖=1𝛿𝑋𝑡―𝑖+𝑢𝑡
(1)
There is no need feedback from Y to X for Equation (1). Thus we cannot allow two or
more variables to be (weakly) endogenous. This violates the assumptions underlying the
distributions of the test statistics (Pesaran et al., 2001). It considers weak exogeneity of the
regressors. The dependent variable not effects these regressors in the long run, but this doesnt
not preclude the existence of cointegration among the regressions, nor does it assume the
absence of (short run) Granger causality from the dependent variable to the regression. Many
studies ignore this assumption in the empirical results of the ARDL boundary test (Lin et al.,
2018). Pesaran et al. (2001) procedure to investigate the existence of a long-run relationship in
the form of the unrestricted error correction model for each variable as follows regarding our
issues:
+ ∆𝐺𝐷𝑃 𝑡= 𝛿0+ 𝛿1𝐺𝐷𝑃 𝑡―1 𝛿2𝑅𝐸𝐶 𝑡―1+ 𝛿3𝑅𝐸𝑂 𝑡―1+ 𝛿4𝐺𝐴𝑆 𝑡―1+ 𝛿3𝑂𝐼𝐿 𝑡―1+
+ + + ∑𝑝―1
𝑖=1𝜃1∆𝐺𝐷𝑃 𝑡―𝑖 ∑𝑞―1
𝑖=1𝜃2∆𝑅𝐸𝐶 𝑡―𝑖∑𝑠―1
𝑖=1𝜃3∆𝑅𝐸𝑂 𝑡―𝑖+∑𝑡―1
𝑖=1𝜃4∆𝐺𝐴𝑆 𝑡―𝑖 ∑𝑟―1
𝑖=1𝜃5∆
(2) 𝑂𝐼𝐿 𝑡―𝑖+𝜀𝑡
Where GDP is economic growth, REC is renewable energy consumption, REO is
renewable energy production, GAS is natural gas prices, OIL is oil prices, while is error term 𝜀
in the models. The bounds test procedure is based on the common F statistic (or Wald statistic)
for cointegration analysis. The asymptotic distribution of F-statistics is non- standard under the
null hypothesis that there is no cointegration between the examined variables. The null
hypothesis of no cointegration among the variables in Eq. (2) is ( 𝐻0:𝛿1=𝛿2=𝛿3=𝛿4=𝛿5
) against the alternative hypothesis ( ). Pesaran and Pesaran =0 𝐻0:𝛿1≠𝛿2≠𝛿3≠𝛿4≠𝛿5≠0
(1997) and Pesaran et al. (2001) report two sets of critical values for a given significance level.
One set of critical values assumes that all variables included in the ARDL model are I(0), while
the other is calculated on the assumption that the variables are I(1). If the computed test statistic
exceeds the upper critical bounds value, then the H 0 hypothesis is rejected. If the F-statistic falls
into the bounds then the cointegration test becomes inconclusive. If the F-statistic is lower than
the lower bounds value, then the null hypothesis of no cointegration cannot be rejected
(Odhiambo, 2009).
The second step is to estimate the coefficient of the long run relationships identified in
the first step. Having found long run relationships (i.e. cointegration) among the variables, in
the next step the long run relationship are estimated using the following selected
ARDL(m,n,p,r,s) models :
+ + 𝐺𝐷𝑃 𝑡= ∅0+ ∑𝑙
𝑖=0𝛼𝑖𝐺𝐷𝑃 𝑡―𝑖 ∑𝑚
𝑗=0𝛽𝑖𝑅𝐸𝐶 𝑡―𝑗∑𝑛
𝑘=1𝜃𝑖𝑅𝐸𝑂 𝑡―𝑘+∑𝑝
𝑙=1𝜇𝑖𝐺𝐴𝑆 𝑡―𝑙+
(3) ∑𝑠
𝑙=1𝜌𝑚𝑂𝐼𝐿 𝑡―𝑚+𝜀𝑡
The lag lengths l,m,n,p, r and s are determined by Akaike Information Criteria (AIC) or
Schwarc Information Criteria(SIC) criterion following the suggestion of Pesaran and Pesaran
(1997). If there is a cointegration between the variables, Eq. (3) presents the long-run model
and Eq. (4) shows the short-run Dynamics.Page 9 of 26 International Journal of Energy Sector Management
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10+ + ∆𝐺𝐷𝑃 𝑡= ∅0+ ∑𝑎
𝑖=0𝛼𝑖∆𝐺𝐷𝑃 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑖∆𝑅𝐸𝐶 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑖∆𝑅𝐸𝑂 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑖∆𝐺𝐴𝑆 𝑡―𝑙
+∑𝑓
𝑚=1𝜌𝑖∆𝑂𝐼𝐿 𝑡―𝑚+𝜓𝐸𝐶𝑀 𝑡―1+𝑣𝑡
(4)
where is the coefficient of error correction term (hereafter ECM). It shows how quickly 𝜓
variables converge to equilibrium and it should have a statistically significant coefficient with
a negative sign. Once the estimating the long-run model in equaiton (3) in order to obtain the
estimated residuals, the next step is to estimate a VEC model, i.e. with the variables in first
differences and including the long-run relationships as error correction terms in the system.
Thus, the following dynamic VEC model is estimated to investigate the Granger causality
between variables:
+ + + ∆𝐺𝐷𝑃 𝑡= ∅1+ ∑𝑎
𝑖=0𝛼𝑖∆𝐺𝐷𝑃 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑗∆𝑅𝐸𝐶 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑘∆𝑅𝐸𝑂 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑙∆𝐺𝐴𝑆 𝑡―𝑙
(5) ∑𝑓
𝑚=1𝜌𝑖∆𝑂𝐼𝐿 𝑡―𝑚+𝜓1𝐸𝐶𝑀 𝑡―1+𝑣1𝑡

+ + + ∆𝑅𝐸𝐶 𝑡= ∅2+ ∑𝑎
𝑖=0𝛼𝑖∆𝑅𝐸𝐶 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑗∆𝐺𝐷𝑃 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑘∆𝑅𝐸𝑂 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑙∆𝐺𝐴𝑆 𝑡―𝑙
+ (6) ∑𝑓
𝑚=1𝜌𝑖∆𝑂𝐼𝐿 𝑡―𝑚𝜓2𝐸𝐶𝑀 𝑡―1+𝑣2𝑡
+ + + ∆𝑅𝐸𝑂 𝑡= ∅3+ ∑𝑎
𝑖=0𝛼𝑖∆𝑅𝐸𝑂 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑗∆𝑅𝐸𝐶 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑘∆𝐺𝐷𝑃 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑙∆𝐺𝐴𝑆 𝑡―𝑙
(7) ∑𝑓
𝑚=1𝜌𝑖∆𝑂𝐼𝐿 𝑡―𝑚+𝜓3𝐸𝐶𝑀 𝑡―1+𝑣3𝑡

+ + ∆𝐺𝐴𝑆 𝑡= ∅4+ ∑𝑎
𝑖=0𝛼𝑖∆𝐺𝐴𝑆 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑖∆𝑅𝐸𝐶 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑘∆𝑅𝐸𝑂 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑙∆𝐺𝐷𝑃 𝑡―𝑙
+∑𝑓
𝑚=1𝜌𝑖∆𝑂𝐼𝐿 𝑡―𝑚+ 𝜓4𝐸𝐶𝑀 𝑡―1+𝑣4𝑡
(8)
+ + + ∆𝑂𝐼𝐿 𝑡= ∅4+ ∑𝑎
𝑖=0𝛼𝑖∆𝑂𝐼𝐿 𝑡―𝑖 ∑𝑏
𝑗=0𝛽𝑖∆𝑅𝐸𝐶 𝑡―𝑗∑𝑐
𝑘=1𝜃𝑘∆𝑅𝐸𝑂 𝑡―𝑘+∑𝑑
𝑙=1𝜇𝑙∆𝐺𝐷𝑃 𝑡―𝑙
(9) ∑𝑓
𝑚=1𝜌𝑖∆𝐺𝐴𝑆 𝑡―𝑚+𝜓4𝐸𝐶𝑀 𝑡―1+𝑣5𝑡

Residual terms, (n=1,2,3,4,5), are independently and normally distributed with zero mean 𝑣𝑛𝑡
and constant variance. An appropriate lag selection based on a criterion such as AIC and SIC.
Strong Granger causalities are detected by testing for all j,k,l 𝐻0:𝛽𝑗=𝜃𝑘=𝜇𝑙=𝜌𝑖=𝜓1=0
and i in equations for (5),(6),(7) and (8), respectively (Ozturk and Acaravci, 2010:1941).
V.Empirical Analysis Results
a. Unit Root Analyis
In order to determine the relationship between the variables correctly, the stationary
analysis should be done. For this purpose, Augmented Dickey Fuller (ADF), Philips-Perron Page 10 of 26 International Journal of Energy Sector Management
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11(PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test were used in this study. The obtained
unit root test results are presented in Table II.
Table 2: ADF, Philips-Perron and KPSS Unit Root Tests
Level
For constant ADF PP KPSS
Variables t-Statistics p-value t-Statistics p-value t-Statistics
GDP -4.2135 0.0032c-4.1450 0.0037c0.0981c
REO -1.4149 0.5572 -2.2707 0.1885 0.3106c
REC -2.4206 0.1465 -2.4306 0.1465 0.6154c
GAS -2.0237 0.2754 -2.0343 0.2712 0.3721c
OIL -2.3410 0.1678 -2.4754 0.1331 0.1305c
For constant and trend ADF PP KPSS
Variables t-Statistics p-value t-Statistics p-value t-Statistics
GDP -4.1043 0.0179b-4.0001 0.0223b0.0765c
REO -3.2049 0.1061 -4.6996 0.0049c0.1723c
REC -4.5487 0.0068c-4.5487 0.0068c0.0994c
GAS -2.4311 0.3561 -2.5832 0.2900 0.1562c
OIL -2.2921 0.4226 -2.4294 0.3569 0.1290c
Notes: Critical value at %1 significant level in KPSS test is 0.7390 for unit root test with constant term and
0.2160 for unit root test with constant term and trend. The symbols a,b,c denote significance at %10, %5 and
%1 levels, respectively.
According to Table 2, ADF and Philips-Perron unit roots test results and KPSS test
results are contradicted. The some variables which are not stationary according to the KPSS
test is stationary at the level value according to the PP and ADF test. The conflicting results
mean that a structural breakpoint date is. In such a case, it is necessary to use unit root tests that
take into account the possibility of breakage. Therefore, in this study Perron (1997) breakpoint
unit root test used, and the results are reported in Table 3. Except for Oil and Gas, all other
variables are stationary at the level value. Oil and Gas variables are stationary at the first
difference.
Table 3: Results of Perron (1997) Breakpoint Unit Root Test
b. Cointegration AnalyisMODEL A MODEL B MODEL C
VariablesTest
statisticBreak pointTest
statisticBreak pointTest
statisticBreak point
GDP -5.0193b2008 -4.4777c2010 -7.0197c2007
REO -5.3048b1993 -6.0010c1995 -5.2548b1995
REC 5.2750b2012 -5.2830c2004 -5.3458b2003
GAS -5.9191c1999 -4.0070 2002 -6.1215c2000
GAS∆ -6.9648c2001 -4.4834c2001 -6.9307c2001
OIL -5.7079c1999 -4.0185 2003 -5.4170b1999
OIL∆ -6.4073c2000 -5.6587c1999 -6.3753c2003
The symbols are a, b, c denote, at 10%, 5% and 1% levels, respectively. Tedir means the first
difference of the variable in question. Critical values for Model A are-5.347, -4.8598, -4.6073 for
1%, 5% and 10% significance, respectively. Critical values for Model B were-4.2610, -4.5248, –
5.0674 for 1%, 5% and 10% significance, respectively. Critical values for Model C are 1%, 5% and
-4.8939, -5.1757, 5.7191, respectively.Page 11 of 26 International Journal of Energy Sector Management
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12In the study, the long-term relationship between the variables discussed with the ARDL
Bound test approach for Russia is investigated and the findings are given in Table 4. For Russia,
there is a long-term relationship between economic growth, fossil fuel and renewable energy.
Table 4. Cointegration Test Results for Russian1
Unrestricted intercept and no trend case
lags F statistic
1 9.7786
Pesaran Critical Value
%10 %5 %1
d I(0) I(1) I(0) I(1) I(0) I(1)
4 2.20 3.09 2.56 3.49 3.29 4.37
Notes: Critical boundary values are taken from Table F Case II of Pesaran and Pesaran
(2001: 300). d is independent variable number.
-0.40.00.40.81.21.6
2009 2010 2011 2012 2013 2014 2015
CUSUMofSquares 5%Significance-0.40.00.40.81.21.6
2010 2011 2012 2013 2014 2015
CUSUMofSquares 5%Significance
CUSUM of Squares Without Dummy CUSUM of Squares With Dummy
Figure 1. CUSUM of Squares for Cointegration Model
c. Selecting ARDL Model
Subsequently, the appropriate ARDL model for Russia was determined and ARDL
(1.0,0,1,0) was reported in Table 5.

1 Structural shocks are included in the model and are used dummy variable.Page 12 of 26 International Journal of Energy Sector Management
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13-4.0-3.9-3.8-3.7-3.6-3.5
ARDL(1,0,0,1,0)
ARDL(1,0,1,1,0)
ARDL(1,1,0,1,0)
ARDL(1,0,0,1,1)
ARDL(1,0,1,1,1)
ARDL(1,1,1,1,0)
ARDL(1,1,1,1,1)
ARDL(1,1,0,1,1)
ARDL(1,0,1,0,0)
ARDL(1,0,1,0,1)
ARDL(1,0,0,0,0)
ARDL(1,1,1,0,0)
ARDL(1,1,1,0,1)
ARDL(1,1,0,0,0)
ARDL(1,0,0,0,1)
ARDL(1,1,0,0,1)AkaikeInformationCriteria
Figure 2. Akaike Information Criteria for ARDL Models
Table 5. ARDL Model (1,0,0,1,0)
Dependent Variable: GDP t
Variable Coefficient Standart Error t-statistic probability
Constant 6.0419 1.5635 3.8643 0.0012c
DUMMY 0.0262 0.0134 1.9548 0.0673a
GDP t-1 0.7921 0.0539 14.6752 0.0000c
GAS t -0.0195 0.0069 -2.7930 0.0125b
REC t -0.0520 0.0406 -1.2987 0.2114
OIL t-1 0.0114 0.0026 4.2958 0.0005c
OIL t-2 0.0107 0.0038 2.8271 0.0116b
REO t -0.0107 0.0080 -1.3011 0.2106
R2= 0. 9905 F statistic = 266.0406 (0.0000c) 2= 0. 9872 𝑅
Jarque-Bera: 1.5151 (0.4687) Breusch-Godfrey LM: 6.0206 (0.0493** )
Breusch-Pagan Godfrey:4.3742 (0.7358)
The symbols are a, b, c denote, at 10%, 5% and 1% levels, respectively. Maximum lags is 3.
The expressions in parentheses indicate the probability value. * ve ** sırasıyla %1 ve %5
anlamlılık düzeyinde ilgili problemin söz konusu olduğunu ifade etmektedir.
The last issue we address is related to the goodness of fit of the ARDL models. For this
purpose we perform a series of diagnostic and stability tests. The diagnostic tests examine auto
correlation using the Breuscg-Godfrey Lagrange multiplier test of residual autocorrelation and
heteroscedasticity based on the Breusch-Pagan Godfrey test. The diagnostic tests reveal no
evidence of misspecification and, additionally, we find no evidence of autocorrelation for %1
significance. To test for structural stability we utilize the cumulative sum of recursive residuals
(CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ). The results
of CUSUM and CUSUMSQ stability test indicate that the estimated coefficients of all models
are stable.
d. Error Correction Model and Long Run Coefficient
The prediction results of the error correction model for Russia were requested to be
given in Table 6.Page 13 of 26 International Journal of Energy Sector Management
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14Table 6. Error Correction Model
Dependent Variable: GDP t
Variable Coefficient Standart Error t-statistic probability
DUMMY 0.026265 0.013436 1.954823 0.0673
GAS t ∆ -0.019521 0.006989 -2.793024 0.0125b
REC t ∆ -0.052036 0.040068 -1.298709 0.2114
OIL t ∆ 0.011481 0.002673 4.295869 0.0005c
REO t ∆ -0.010781 0.008286 -1.301146 0.2106
ECM t-1 -0.207880 0.053976 -3.851300 0.0013c
The symbols a,b,c denote significance at %10, %5 and %1 levels, respectively.
Taking into consideration the limited number of observations, a maximum of 3 lags was
used. Tests for models include minimum of 1 lag for dependent variable to ensure lagged
explanatory variables are present in the error correction model (ECM). The coefficient of error
correction term (ECM t-1) is negative and statistically significant as expected. The coefficient of
error correction term is -0.207880. The coefficient of the error correction term is negative and
significant as expected. When economic growth are far away from their equilibrium level, it
adjusts by almost 21 % within the first period (year). The full convergence to equilibrium level
takes about 5 period (years). In the case of any shock to the economic growth, the speed of
reaching equilibrium level is significant. Finally, the long-term coefficients of the study are
given in Table 7.
Table 7. Long Run Coefficients
Dependent Variable: GDP t
Variable Coefficient Standart Error t-statistic probability
Constant 29.064617 0.681373 42.655964 0.0000c
DUMMY 0.126348 0.074846 1.688106 0.1096
GAS t -0.093905 0.030041 -3.125883 0.0062c
REC t -0.250320 0.219343 -1.141228 0.2696
OIL t 0.106917 0.022375 4.778461 0.0002c
REO t -0.051861 0.032346 -1.603332 0.1273
The symbols are a, b, c denote, at 10%, 5% and 1% levels, respectively. Maximum lags is 3.
Considering Table 7, the increase in oil prices for Russia increased the rate of economic
growth, whereas the increase in natural gas prices have a negative impact on economic growth.
It was concluded that REC and REO had no significant effect on GDP. In order to examine the
findings obtained in the study, the changes of the variables in the determined periods are given
in Figure 3.Page 14 of 26 International Journal of Energy Sector Management
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1527.427.627.828.028.2
90929496980002040608101214GDP
3.23.43.63.84.04.2
90929496980002040608101214REC
15161718192021
90929496980002040608101214REO
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90929496980002040608101214GAS
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90929496980002040608101214OIL
Figure 3. Economic Growth, Renewable Energy Consumption, Renewable Energy
Production, Natural Gas Prices and Oil Prices for Russia, 1990-2015
According to the GDP graph, it is observed that GDP decreased until 1998 and then
started to rise. It can be said that the decline in GDP before 1998 and 1998 was due to the crises
and the economic policies followed in Russia. In the early 1990s, Russia experienced an
economic transformation. In this period, Russia, which has entered a free market economy, has
made the local currency Ruble a transactionable process in the international market. The issue
made Russia more vulnerable to risks. According to many researchers, the foundation of the
banking crisis in 1998 was the foundation of these years. It is possible to mention the two main
reasons of the Russian crisis. First, oil prices declined during the Asian crisis in 1997. With the
decline in oil prices, which is the most important export revenue for Russia, the current account
deficit problem has emerged in Russia. Another problem that leads Russia to the crisis can be
mentioned as high debts. Especially, as a result of the decrease in oil prices, a significant
decrease has occurred in the foreign exchange revenues of Russia. As a result of this situation,
Russia has begun to pay its short-term debts (Black et al., 1999).
As a result of the investigations, it is foreseen that the Russian economy will have a
risky period in the forthcoming period. According to the situation defined as Dutch Disease, it
is stated that Russian oil reserves can be exhausted in 2044 as a result of economy based on one
type of product. Considering that nearly half of the country's income is from fossil-based
sources, it is stated that Russia should seek solutions if this resource is exhausted. Russian
Natural Resources and Environment Minister Sergey Donskoy told Russian media that oil
reserves could be exhausted by 2044. According to the explanations, it is predicted that the
conventional reserves in the country will decrease starting from 2020 and this decrease will
continue in the coming period. It is stated that the oil reserves, which are supposed to be 29
billion tons in average, are currently 14 billion tons in Russia. Russian authorities, who Page 15 of 26 International Journal of Energy Sector Management
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16described the situation as "Dutch Disease", which states that Russia is facing nearly 50% of the
country's income, says that there is a need for new solutions(Russia Today (RT) Question More
Business News 17.03.2016; Sabah, Economy News, 24.3.2016) According to Sberbank CIB
Oil and Gas Analyst Valery Nesterov, although Russia is one of the largest oil and natural gas
producers in the world, reserves have not diminished at the same rate and emphasized that the
situation in the country is not improving. In addition, approximately 20 years ago, the reserve
increase rate did not meet the petroleum production costs. According to Nesterov, who reported
problems in Russia's oil sector, the increase in reserves in the 2009-2015 period could not meet
the production costs. He also stated that one of the problems of reserve increase in his
explanations has a low share in the oil reserves available for use in industrial area. Therefore,
one of the main problems that should be emphasized is the reserve quality problem. In addition,
Nesterov stated that all of the oil gas fields could not be improved and that an average of 100
oil and gas fields in 3000 could be processed and that 11 could be considered as a very special
field and stated that these private areas have an average reserve of 300 million tons. In addition,
he pointed out that investment in Russia and low oil prices are a serious threat to the country's
oil sector. Vitaly Kazakov, Director of the Energy Economics Program at the Moscow New
Economy University, emphasized that it is possible to reduce oil production due to the taxation
system for the petroleum industry in Russia (Sabah, Economy News, 24.3.2016) According to
Kazakov, 1 $ increase in oil, Russian oil producer companies only had a 20 percent share, such
a system is not economic in most of Russia's oil field production. Noting that Russia is not the
first country in danger of "Dutch disease", Kazakov said that since ancient times every country
has always had a source to ensure that it is competitive in a subject. He added that these
resources are sometimes natural resources, and that the transition from one source to another is
a risky period. He stated that oil will be in great competition especially with renewable energy
sources in the coming years and therefore global demand for oil will decrease (Russia Today
(RT) Question More Business News 17.03.2016, Sabah, Economy News, 24.3.2016; Berezina,
2016).
According to REC and REO graphs , there is a decrease after 2000s. There may be
several reasons for this decline: Although the non-renewable energy sources of the Russia have
an important place in terms of nuclear energy and hydroelectric energy, they are extremely
inadequate in terms of production and consumption of other renewable energy resources. The
Russian Federation, which consumes approximately 100 thousand tons of petroleum equivalent
of other renewable energy sources, has a small place about 0.1% of the total world consumption
(BP, 2012). Despite the continuation of renewable energy projects, only limited development
is achieved. In particular, the Russian Federation, which has an estimated 520 million tonnes
of animal waste per year (Kalyuzhnyi, 2008: 1744), however, does not achieve a significant
improvement in the field of biofuels. The Russian Federation, which is capable of producing a
limited amount of biofuels, can only be evaluated in the overall total with the former Soviet
Union countries. In this context, together with the former Soviet Union countries, they were
able to produce approximately 197 thousand tons of oil equivalent in total (BP, 2012). The
Russian Federation is extremely inadequate compared to other countries. As a result, the data
described can be seen as reasons for the reduction of renewable energy consumption and
production.
Despite the decline in renewable energy production and consumption, the increase in
GDP value shows that there is no causality. As a result, the Russian Federation, which has rich
reserves in terms of fossil energy sources despite the insufficient data in terms of renewable
energy sources, can be defined as a strategic power in the international system due to the
potential of significant energetic potential.Page 16 of 26 International Journal of Energy Sector Management
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17e. Causality Analyis
The existence of a cointegrating relationship among all varibales in Russia suggests that
there must be Granger causality in at least one direction. There were five causal links to the
Russia. In this study we found that there is an evidence of bidirectional strong Granger causality
between all variables (see Table 8).
Table 8. Results of Strong Granger Causality
Null Hypothesis Wald (F) Q(1)2Q(12)B-P-G
Test3 Direction of Causality
REC, REO, OIL and
GAS don’t cause of GDP.-0.2078
(0.0000c)0.7681
(0.381)14.243
(0.285)1.8094
(0.1504)REC, REO, OIL and GAS →GDP
GDP, REO, OIL and
GAS don’t cause of REC.-0.5849
(0.0017c)0.6906
(0.406)8.1278
(0.774)1.5137
(0.2284)GDP, REO, OIL and GAS →REC
GDP, REC, OIL and
GAS don’t cause of REO.-0.5924
(0.0008c)0.0503
(0.823)10.737
(0.552)1.0380
(0.4416)GDP, REC, OIL and GAS →REO
REC, REO, GDP and
GAS don’t cause of OIL-0.8792
(0.0000c)6.6221
(0.010**)26.483
(0.009***)1.0725
(0.4281)GDP, REC, REO and OIL →GAS
REC, REO, OIL and
GDP don’t cause of GAS.-1.1919
(0.0000c)0.0542
(0.8160)7.8807
(0.7940)1.1000
(0.4063)REC, REO, OIL and GDP →GAS
The symbols are a, b, c denote, at 10%, 5% and 1% levels, respectively. * and **, respectively, 1% and 5% of the
level of significance is related to the problem. Probability values are given in parentheses.
Based on the findings in Table 8, it can be said that the feedback hypothesis is valid for Russia.
In other words, the causality relationship is bi-directional. Thus, the increase in energy
consumption causes growth in the Russian economy. The increase in Russian economy also
increases energy consumption. According to this result, the objectives should be determined
hierarchically and additional policy research or policy design should be made.
VI . Conclusion
When the official statements and predictions are associated with the results of the study,
it is necessary to increase the investments of renewable energy due to the danger of extinction
of fossil base reserves of Russia. The fact that approximately 50% of Russia's income is covered
by the export of oil based on one type of product and the danger of fossil resources depletion
shows how important it is to increase renewable energy investments for the country. Another
important result of the study is that the increase in fossil-based resource investments despite the
decrease in Russia's renewable energy consumption and renewable energy investments in
recent years. This result can be seen as a risk factor for the fossil-based, non-renewable
resources to be exhausted, in terms of economic energy demand and in the provision of energy
policy. It can be said that the decrease in renewable energy consumption and investments made
Russia dependent on the countries where it exported fossil resources and left it vulnerable to
political crises.
2 Problem of autocorrelation in the model invested with Q-statistics
3 Problem of heteroskedasticity in the model invested with Breush-Pagan Godfrey Test.Page 17 of 26 International Journal of Energy Sector Management
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18Some of the policies that can be developed in line with the results are as follows:
Minimizing the gap between energy demand and supply, increasing energy efficiency
and saving by reducing energy density, creating optimal energy mix, diversifying energy
supply, increasing investments in energy infrastructure, increasing strategies for alternative and
renewable energy sources, innovation and competition with research and development
activities encouraging, reducing the fragility of energy price fluctuations, ensuring good
management in the energy sector, diversifying energy resources, evaluating local resources, full
liberalization / liberalization of the domestic market, increasing cross-border investments,
improving storage capacity, increasing savings and energy efficiency, clean energy is provided
at reasonable prices and without interruption.
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