Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth in Selected OPEC Countries: A Panel ARDL Analysis

Abstract

This study uses the panel ARDL method to analyze the relationship between oil exports, renewable energy consumption, agricultural industry, and economic growth in selected OPEC member countries. The study examined data from 9 OPEC countries from 1990 to 2024. The findings showed that while oil exports in OPEC countries have a positive long-term effect, they have a negative short-term impact in Nigeria and a positive short-term impact in other countries. The consumption of renewable energy has been found to have a negative long-term effect. However, the short-term impact varies from country to country, with the observation that the short-term effect is insignificant in most countries. The long-term effect of the food production index is statistically insignificant. On the other hand, the short-term effect varies significantly among countries, with a notable negative impact in some countries. Notably, Equatorial Guinea stands out from other variables, as all variables have insignificant effects in the short term. Research findings have demonstrated that oil exports play a supportive role in the economic growth of OPEC countries.

Full text

International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024
344
International Journal of Energy Economics and
Policy
ISSN: 2146-4553
available at http: www.econjournals.com
International Journal of Energy Economics and Policy, 2024, 14(6), 344-352.
Relationship between Oil Exports, Renewable Energy
Consumption, Agriculture Industry, and Economic Growth in
Selected OPEC Countries: A Panel ARDL Analysis
Aina B. Aidarova1*, Gulzada Mukhamediyeva2, Aizhan A. Yessentayeva1, Guliya Utemissova1,
Karlygash Tastanbekova3, Bagila Mustafayeva4, Kundyz Myrzabekkyzy5
1M. Auezov South Kazakhstan University, Shymkent, Kazakhstan, 2Q University, Almaty, Kazakhstan, 3International Taraz
Innovative Institute named after Sherhan Murtaza, Taraz, Kazakhstan, 4International University of Tourism and Hospitality,
Turkestan, Kazakhstan, 5Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan.
*Email: aidarovaaina07@gmail.com
Received: 15 July 2024 Accepted: 06 October 2024 DOI: https://doi.org/10.32479/ijeep.17201
ABSTRACT
This study uses the panel ARDL method to analyze the relationship between oil exports, renewable energy consumption, agricultural industry, and
economic growth in selected OPEC member countries. The study examined data from 9 OPEC countries from 1990 to 2024. The ndings showed
that while oil exports in OPEC countries have a positive long-term eect, they have a negative short-term impact in Nigeria and a positive short-term
impact in other countries. The consumption of renewable energy has been found to have a negative long-term eect. However, the short-term impact
varies from country to country, with the observation that the short-term eect is insignicant in most countries. The long-term eect of the food
production index is statistically insignicant. On the other hand, the short-term eect varies signicantly among countries, with a notable negative
impact in some countries. Notably, Equatorial Guinea stands out from other variables, as all variables have insignicant eects in the short term.
Research ndings have demonstrated that oil exports play a supportive role in the economic growth of OPEC countries.
Keywords: OPEC, Oil Exports, Renewable Energy Consumption, Agricultural Industry, GDP, Panel ARDL
JEL Classications: C13, C20, C22
1. INTRODUCTION
The idea of establishing OPEC emerged in 1949 when Venezuela
proposed regular cooperation with other oil-producing countries
(Iran, Iraq, Kuwait, and Saudi Arabia). However, the real push
came from the multinational oil companies, known as the “seven
sisters,” which began to control oil and set prices in 1959.
This led to the First Arab Petroleum Congress held in Cairo in
April. Congress demanded that the oil companies consult with
governments when making decisions, but the companies rejected
this request. Delegates gathered in Baghdad on September 10,
1960, and on September 14, 1960, OPEC was established with
the founding members of Iran, Iraq, Kuwait, Saudi Arabia,
and Venezuela. Over time, Qatar, Indonesia, Libya, the UAE,
Algeria, Nigeria, Ecuador, Angola, Gabon, and Guinea also
joined the organization. OPEC’s rst headquarters in Geneva
was transferred to Vienna in 1965 (OPEC, 2024a). OPEC aims
to coordinate the oil policies of member countries to ensure
fair and stable prices, provide regular oil supply to consumer
countries, and ensure fair returns to those who invest in the
sector (Fattouh and Mahadeva, 2013). In 1960, OPEC’s share of
world oil production was 38.2%. By 1973, it had reached a peak
of 52.8%, but then decreased to 24.9% in the 1980s. In 2004,
the production was recorded as 29.6 million barrels per day, and
This Journal is licensed under a Creative Commons Attribution 4.0 International License

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024 345
34.6% in 2012. Their share rose to 42.4% in 2015 and 44.1% in
2016, then fell to 26.5% in 2023. OPEC’s crude oil production
in 2023 averaged 27.0 million barrels per day with a fall of 700
thousand barrels per day compared to the previous year (OPEC,
2024b). Oil remains the most critical energy source for modern
economies and maintains its importance despite the increasing
focus on alternative energy sources. Fluctuations in oil prices
have far-reaching eects on world economies and can lead to
economic recession or expansion (Basher and Sadorsky, 2006).
Yet the oil-exporting and oil-importing countries are aected by
these changes dierently. Therefore, it is crucial to examine the
changes in oil prices in more detail (Bekzhanova et al., 2023).
Renewable energy is energy obtained from natural processes,
including solar, wind, geothermal, hydro, and some types of
biomass. According to the International Energy Agency (IEA),
renewable energies provided approximately 13.2% of the world’s
total primary energy use in 2012, and this rate increased to 22%
in 2013. In 2015, the share of renewable energy in total electricity
production was over 23%, and it is expected to approach 28% by
2021 (IEA, 2015). Solar energy is the most popular renewable
energy source, generating electricity through solar panels. In
2014, two of the largest solar energy markets were in Asia, and
new markets continued to emerge all around the world by 2015.
The wind has been used as an energy source throughout history
and reached a global capacity of 197,039 megawatts at the end
of 2010. In 2013, China became the leader in the wind energy
market (Shahan, 2014). Hydroelectric energy is generated by the
gravitational force of water turning turbines to produce electricity.
By 2016, the global installed hydroelectric energy capacity reached
1,064 GW (Svendsen, 2013). Geothermal energy, generated from
approximately 4,000 miles below the earth’s surface, yielded
electricity production of 151 TWh in 2015, with the global total
geothermal capacity reaching 13.2 GW. Renewable energy has
seen signicant growth and progress in the world’s energy sector,
with IEA statistics showing that renewable energy reached its
fastest level in 2015, representing more than half of the additional
electricity capacity worldwide. Eorts to develop these energy
sources and reduce their costs indicate that renewable energy will
gain an important place in the energy world (IEA, 2015).
The food production index (2014-2016=100) measures the
production of edible and nutritious food products. It does
not include non-nutritious items like coffee and tea. The
index, calculated by the Food and Agriculture Organization
of the United Nations (FAO), compares the total agricultural
production volume for each year to the base period of 2014-2016.
This index is based on the price-weighted sum of production
quantities of dierent agricultural commodities, with seed and
feed quantities weighted similarly. FAO calculates indices at
the national, regional, and global levels using the Laspeyres
formula. The production quantity of each commodity is weighted
by the average international commodity prices for the years
2014-2016 and summed for each year. This sum is compared
with the average total for the base period of 2014-2016 for a
given year to obtain the index (World Bank, 2024; Sartbayeva
et al., 2023).
Economic growth is the continuous increase in production factors,
such as the labor force, natural resources, domestic capital
structure, foreign trade policy, banking and nancial infrastructure,
energy production and consumption, and foreign direct investment.
This gradual increase provides higher returns from 1 year to the
next (Neelankavil et al., 2012). Gross Domestic Product (GDP) is
typically used as a measure of economic growth (Dyussembekova
et al., 2023). GDP refers to the total value of nal goods and
services produced in an economy in a year (Somel, 2014). When
this total value is calculated based on the prices of the production
year, it is called Nominal GDP. The signicance of economic
growth has increased due to globalization, advancements in
information technologies, and easier access to nancial markets.
Changes in economic growth reect the economic development
of a country during a specic period and provide a basis for
comparison with countries with similar characteristics. As a
result, economic growth has become a critical subject of analysis
for both policymakers and academics (Sartbayeva et al., 2023;
Issayeva et al., 2023; Abdibekov et al., 2024; Ibyzhanova et al.,
2024; Abdulsahib, 2024).
Recently, the visible negative eects of climate change have
led to increasing concerns about environmental sustainability.
Governments worldwide are now encouraging and supporting
the use of renewable energy sources rather than fossil fuels to
promote environmental sustainability. This shift in energy sources
has been the focus of academic studies from various perspectives.
In this study, we used the Panel ARDL method to analyze the
relationship between oil exports, renewable energy consumption,
the agricultural industry, and economic growth in selected OPEC
countries.
2. LITERATURE REVIEW
Given that oil and petroleum products continue to dominate
the world energy market, there is a plethora of literature on
oil production and the world oil market. In addition, there are
numerous academic studies regarding the dierent aspects of
the economies of OPEC and its member countries, which play
a crucial role in determining the global oil market. Considering
the extensive nature of the academic literature, this study will
reference only specic studies on the topic at hand.
Ftiti et al. (2016) analyzed the relationship between crude oil
prices and economic growth in selected OPEC countries (United
Arab Emirates, Kuwait, Saudi Arabia, and Venezuela) during
the period from September 3, 2000 to December 3, 2010. They
used the frequency approach of Priestley and Tong (1973) and
the cointegration procedure developed by Engle and Granger
(1987) to conduct their analysis. Their ndings revealed that oil
price shocks during uctuations in the global business cycle and/
or nancial turmoil have an impact on the economic growth in
OPEC countries.
Ghalayini (2011) investigated whether changes in oil prices can
account for world economic growth and whether the eects of oil
prices on economic growth vary among dierent countries and

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024
346
country groups. The study also discussed the reasons for these
dierences between oil-importing and exporting countries. The
study covered the G-7 group, OPEC countries, Russia, China,
and India during the period 1986-2010. Granger causality tests
indicated a one-way relationship between oil prices and economic
growth for G-7 countries.
Nusair (2016) analyzed the eects of oil price shocks on the real
GDP of the Gulf Cooperation Council (GCC) countries using a
Nonlinear Autoregressive Distributed Lag (NARDL) model. The
study found asymmetry in all cases, with positive oil price changes
leading to increases in real GDP, while negative oil price changes
were signicant only for Kuwait and Qatar, causing decreases in
real GDP in these countries. Additionally, panel data analyses
revealed that positive price changes had a greater eect than
negative changes.
Sonmez (2016) analyzed the relationships among various
economic factors (oil prices (real and nominal), real government
expenditures, real exchange rates, GDP (real and nominal), M2,
ination, foreign exchange reserves, and reserve money) in Gulf
Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman,
Saudi Arabia, UAE, and Qatar) and non-GCC oil exporters
(Canada, Norway, Iran, Russia, Nigeria, and Venezuela) between
1970 and 2013. The study used a restricted Vector Error Correction
Model (VECM) with the cointegration of macroeconomic
variables. The ndings indicated that real oil prices had a greater
positive eect on the real output of the GCC countries compared to
non-GCC countries due to their higher oil dependency. It was also
observed that an increase in real oil prices led to a real exchange
rate appreciation in GCC countries with minimal impact on the
nominal exchange rate, which diered from the results for non-
GCC oil exporters.
Agboola et al. (2024) examined the asymmetric and empirically
significant effects of unexpected changes in oil prices on
oil-exporting emerging markets using nonlinear models for
eight oil-exporting countries. Various nonlinear models were
developed and estimated for eight oil-exporting countries. The
ndings demonstrated signicant evidence of asymmetry in some
countries and highlighted the role of government expenditures
in propagating these eects. Robustness checks with dierent
models, including nominal oil price changes, conrmed the
validity of the ndings. The study also emphasized the signicant
eects of the asymmetric nature of GDP responses to oil price
shocks on policy eectiveness.
Alam and Quazi (2003) used the Bounds Test and Autoregressive
Distributed Lag procedures to investigate the long-term
equilibrium relationship between capital ight and its determinants
and to estimate the behaviors of capital ight from Bangladesh
in both the long-term and short-term. Their ndings revealed
that political instability is the primary reason for capital ight.
Moreover, increases in corporate income taxes, higher real interest
rate dierences between capital haven countries and Bangladesh,
and decreasing GDP growth rates also signicantly contribute to
capital ight.
Zaidi and Saidi (2018) conducted a study to model the
relationship between health expenditures (HE), environmental
pollution (carbon dioxide emissions and nitrous oxide
emissions), and economic growth in Sub-Saharan African
countries using annual data for the period 1990-2015. They
utilized the ARDL estimation method to analyze the long-term
and short-term eects and applied the VECM Granger causality
test to ascertain the direction of causality. The research ndings
demonstrated that economic growth has a positive long-term
eect on health expenditures. However, CO2 emissions and
nitrous oxide emissions (NOE) were found to have a negative
eect on health expenditures. The results indicated that a 1%
increase in GDP per capita would enhance health expenditures
by 0.332%, while a 1% increase in CO2 emissions and NOE
would reduce health expenditures by 0.066% and 0.577%,
respectively. Furthermore, the VECM Granger causality test
results revealed a unidirectional causality relationship moving
from health expenditures to GDP per capita, as well as a
bidirectional causality relationship between CO2 emissions
and GDP per capita, and between health expenditures and CO2
emissions.
In their 2018 study, Da Silva et al. highlighted the signicance of
renewable energy in discussions about a reliable and sustainable
energy future. They stressed the importance of understanding
the primary factors inuencing renewable energy and drawing
conclusions for energy policies. The study utilized panel data
covering the period 1990-2014 and specifically employed
the panel-ARDL model. The analysis revealed that economic
development (GDP per capita) and increased energy use promoted
renewable energy development, while population growth
hindered this progress. Additionally, the article examined the
renewable energy potential and status in Sub-Saharan Africa.
It was determined that while the region has great potential for
developing renewable energy sources such as wind, biomass,
solar, and hydroelectric power, this potential has not been fully
utilized. It has been determined that many resources are not
suciently utilized even though they are abundant and have high
economic potential.
3. METHODS
The panel ARDL test, proposed by Pesaran et al. (1997) and
Pesaran et al. (2004), analyzes the relationship between variables
in two steps. In the rst step, the model investigates the long-
term relationship. If a long-term relationship (cointegration)
between the dependent and independent variables is established,
the second step involves estimating the short-term and long-term
coecients using the ARDL method. During the analysis phase,
the choice between the within-group estimator (MG) and the
pooled within-group estimator (PMG) is determined through
the Hausman test (1978). The advantages of the panel analysis
method, as explained by Hsiao (2007), include using a larger
dataset, which provides more eective estimation values. The
ARDL (Autoregressive Distributed Lag) test has the advantage
of not requiring variables in the model to be stationary at the
same level. This exibility allows variables to be included in the

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024 347
model as stationary at level I(0) or rst dierence I(1) (Pesaran
et al., 2001). Furthermore, the ARDL method can be applied to
datasets with small sample sizes, which is important considering
that macro indicators for countries are usually published in
annual periods.
The Panel ARDL method was utilized by Binder and Oermanns
(2007) to analyze purchasing power parity in Europe, and by
Da Silva et al. (2018) to study the factors inuencing renewable
energy use in Africa, as well as the relationship between renewable
energy consumption and economic growth. Zaidi and Saidi (2018)
employed the method to investigate the connection between
environmental pollution, health expenditures, and economic
growth in African countries.
When performing analysis on time series data, the rst step is
to examine the stationarity of the series. The panel data method
assesses cross-sectional dependence before conducting the
stationarity test. When the number of periods (T) for the data set
is greater than the number of cross-sectional units (N) (T>N),
cross-sectional dependence is examined with the Breusch and
Pagan (1980) LM test and Pesaran et al. (2008) 𝐿𝑀𝑎𝑑𝑗 tests.
But when the number of periods (T) is smaller than the number
of cross-sectional units (N), the examination is performed
using the Pesaran (2004) 𝐶𝐷𝐿𝑀 test and Pesaran (2004) CD
tests. Depending on the result, you can then proceed with rst-
generation unit root tests or one of the second-generation unit
root tests (Baltagi, 2008).
If there is no cross-sectional dependence, the rst-generation unit
root tests employed are Levin et al. (2002), Breitung (2005), Hadri
(2000), Maddala and Wu (1999), and Choi (2001). The common
second-generation unit root tests include Bai and Ng (2004),
Taylor and Sarno (MADF, 1998), Breuer et al. (SURADF, 2002),
Pesaran (CADF, 2007), and Carrion-i-Silvestre et al. (PANKPSS,
2005) (Pesaran, 2006).
4. DATA AND FINDINGS
The OPEC countries constitute a signicant group in the world’s
oil-based energy supply. Because these economies heavily rely
on oil production and exports, it is expected that oil plays a key
role in their economic growth, regardless of their geographical
locations. With rich oil reserves, it is anticipated that oil exports
have a profound impact on their economic growth. However
econometric analyses indicate the existence of various other
factors that inuence economic growth. Therefore, this study
included two additional variables representing renewable
energy and the agricultural industry. The primary research
question of the study pertains to the inuence of oil production
and exports on economic growth in OPEC countries. The
secondary research question explores how the utilization of
renewable energy aects economic growth when examined
in conjunction with oil exports. While analyzing these
eects together, the study also examines the inuence of the
agricultural industry, which is deemed signicant for these
countries’ economies.
The study combined the oil production with the oil export
variable. The oil data was retrieved from the https://asb.opec.
org/data/ASB_Data.php webpage (Accessed on: June 01, 2024).
Renewable energy consumption was represented as the share of
renewable energy in total energy consumption. The agricultural
industry was represented by the food production index. The
study’s target variable was economic growth, dened as the
annual increase in national income. Data for renewable energy,
food production index, and economic growth were obtained from
https://datacatalog.worldbank.org/(Accessed on: June 01, 2024).
Nine countries with complete data were selected from OPEC
countries, covering the period from 1990 to 2024. Table 1 provides
a summary of the research variables and countries.
The study began with descriptive statistical analyses for each
variable, providing descriptive statistics and line graphs illustrating
changes in each variable over the research period. The stationarity
of the variables was then examined using an appropriate panel
unit root test, considering cross-sectional dependency. Lastly, the
panel ARDL test was used to analyze the eect of the independent
variables on the dependent variable. The Housman test was used
to determine the appropriate econometric model. Additionally,
short-term variable eects were analyzed by country and the
results were interpreted.
The panel ARDL model for the relationship between renewable
energy consumption, food production index, oil exports, and
economic growth was described in this study.
01 , 2 ,
10
3 , 4 , 1 ,1
00
2 ,1 3 ,1 4 ,1
1
23
123
αα α
ααβ
ββ βε
−−
= =
− −−
= =
−−−
∆= + ∆ + ∆
+∆ + ∆ +
++ + +
∑∑
∑∑
pp
it j it j
jj
pp
it j it j ii
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XX X (1)
In the model, the constant term
α
0 represents the error term
(residual), long-term eects β1, β2, β
3, β4
ββ ββ
12
34
,,,, and
eects α1, α2, α3, α4
αα αα
12
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,,,
Table 1: Codes for research variables and countries, and
explanations
Country
Code
Country Variable Description
COG Congo X1 Renewable energy
consumption
(% of total nal energy cons.)
DZA Algeria X2 Food production
index (2014-2016=100)
GAB Gabon X3 OPEC Members’ crude oil
exports (1,000 b/d)
GNQ Equatorial
Guinea
Y GDP per capita growth
(annual %)
IRN Iran
IRQ Iraq
LBY Libya
NGA Nigeria
VEN Venezuela

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024
348
Table 2 provides descriptive statistics on renewable energy
consumption in OPEC countries. The average values show that
Congo, Gabon, and Nigeria have very high renewable energy
consumption rates. However, Iran, Iraq, Algeria, and Libya exhibit
very low renewable energy consumption rates. These results indicate
a signicant disparity in renewable energy consumption among
countries during the research period. According to World Bank data,
the overall average for all countries is 17.34 (https://datacatalog.
worldbank.org/), while the average for OPEC countries stands at
31.52, surpassing the global average. Skewness and kurtosis values
suggest that the distribution of renewable energy production rates
closely resembles a normal distribution for all countries.
Graph 1 presents a thorough analysis of the changes in renewable
energy consumption among OPEC countries during the research
period. The graph indicates that, aside from Equatorial Guinea,
the renewable energy consumption rates of the countries are
relatively stable. Notably, Equatorial Guinea experienced a
signicant decline from 80% to 10% between 1995 and 2005.
Apart from this anomaly, it can be surmised that the overall trend
in renewable energy consumption among OPEC countries has
remained consistent.
Table 3 presents the food production index values for OPEC
countries. The average value for OPEC countries is 86.29.
When looking at individual countries, Iraq has the highest food
production index value, while Algeria has the lowest. Unlike the
variable for renewable energy consumption, the countries do
not dier signicantly in the food production index value. The
skewness and kurtosis values indicate that the food production
index follows a distribution close to normal for all countries.
An analysis of the change in the food production index for OPEC
countries during the research period is provided in Graph 2. The
graph reveals uctuating trends for Iraq, decreasing trends for
Venezuela and Iran after 2010, and increasing trends for other
countries with low uctuation.
In Table 4, statistical information on the oil export variable of
OPEC countries shows that Iran, Iraq, and Nigeria have high
average exports, while Equatorial Guinea, Congo, and Gabon have
the lowest. Additionally, the minimum statistics indicate that Iran
and Iraq had very low oil export years.
Graph 3 shows the time path of crude oil exports for OPEC
members. A detailed analysis of the oil export data reveals that
most countries have a uctuating trend. The period between 1990
and 2000 shows signicant uctuations for Iraq. It’s worth noting
that Iraq’s oil exports have increased steadily and rapidly after
2010. In contrast, Iran’s oil exports, which had been stable until
2010, have been declining with uctuations since then. Venezuela
also exhibits a similar pattern of volatility.
The Table 5 provides descriptive statistics for the GDP change data
in OPEC countries. It shows that Equatorial Guinea experienced
the highest average GDP increase during the analysis period,
while Venezuela had the lowest GDP change, indicating annual
negative economic growth. Interestingly, apart from Iraq, other
countries showed low economic growth. The minimum and
maximum growth values of the countries indicate a highly volatile
economic structure.
Graph 4 presents the time path for the GDP per capita growth of
OPEC countries. It is noteworthy that Equatorial Guinea exhibited
a volatile structure between 1990 and 2000, Libya between 2010
and 2015, and Iraq between 2000 and 2005. Additionally, there
was low economic growth in Iraq in 1991, Libya in 2011 and 2020,
and Venezuela in 2020. However, the time path graph indicates a
relatively stable economic growth pattern in other OPEC countries.
Graph 1: The time path of renewable energy consumption
Table 2: Descriptive statistics for renewable energy consumption
Country Code Mean Median Maximum Minimum Standard deviation Skewness Kurtosis
COG 67.2085 67.6500 80.2000 54.5000 6.2979 0.1219 2.6989
DZA 0.3013 0.3000 0.6000 0.1000 0.1560 0.0606 1.6960
GAB 81.2092 81.7000 91.3000 69.7000 7.3469 −0.0962 1.5859
GNQ 29.8500 7.3000 84.7000 3.7000 31.5702 0.7388 1.8271
IRN 0.9572 0.9056 1.5000 0.4000 0.2523 0.2192 3.0434
IRQ 0.9180 0.8500 2.6000 0.3000 0.6062 1.1321 3.7247
LBY 2.7157 2.9000 3.2000 2.0000 0.3729 −0.6451 2.1470
NGA 84.2729 84.5000 88.6000 79.9000 2.7237 −0.1436 1.6691
VEN 16.2118 14.4500 33.7000 12.0000 5.5158 1.2851 2.7914
ALL 31.5161 7.3000 91.3000 0.1000 35.8561 0.5555 1.4689

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024 349
Graph 2: The time path of the food production index
Table 4: Descriptive statistics for OPEC Members’ crude oil exports
Country Code Mean Median Maximum Minimum Standard Deviation Skewness Kurtosis
COG 236.1659 243.9194 309.3590 144.9200 43.0574 −0.6605 2.7141
DZA 592.1614 568.6000 1253.5000 279.4000 231.3743 0.7305 3.1890
GAB 245.3432 226.7057 352.0161 174.0678 52.0642 0.7783 2.4571
GNQ 127.2772 120.4279 275.1369 0.7140 87.7704 −0.0159 1.7166
IRN 2036.0650 2269.3600 2684.1000 404.4894 669.1379 −1.1223 2.8824
IRQ 1929.6240 1895.4100 3968.2450 39.0000 1268.4540 0.0242 1.9810
LBY 1005.0190 1100.3100 1399.5400 288.3879 320.9006 −1.2289 3.4905
NGA 1930.2930 1929.3690 2464.1200 1388.2600 298.7144 0.0292 1.8662
VEN 1567.7340 1602.3550 2243.9000 438.1734 500.9581 −1.0216 3.3344
ALL 1074.4090 841.9100 3968.2450 0.7140 923.2683 0.7703 2.8099
Table 3: Descriptive statistics for food production index
Country Code Mean Median Maximum Minimum Standard Deviation Skewness Kurtosis
COG 75.5202 75.2950 106.1300 43.3400 22.1699 −0.0140 1.4515
DZA 69.2399 59.6050 112.5000 31.0800 28.9814 0.2629 1.4550
GAB 84.7478 79.1100 105.7600 63.2200 14.0163 0.2109 1.5675
GNQ 86.0847 85.8350 104.5400 67.4700 13.1757 0.0696 1.4426
IRN 80.5904 84.5500 101.7500 50.8200 15.0262 −0.4810 2.0689
IRQ 114.4601 114.6600 149.1300 77.3100 18.4389 −0.3711 2.7824
LBY 94.3294 96.7850 109.3500 71.4000 11.1207 −0.7280 2.5709
NGA 79.7164 79.0200 119.8500 39.1500 23.5211 0.1957 1.9394
VEN 91.9322 90.4300 119.4100 70.9000 12.0103 0.0958 2.3841
ALL 86.2912 88.3850 149.1300 31.0800 22.0775 −0.1908 2.8427
Table 5: Descriptive statistics for GDP per capita growth
Country Code Mean Median Maximum Minimum Standard deviation Skewness Kurtosis
COG 2.0724 2.3005 11.6370 −6.6140 4.9554 −0.0236 2.2504
DZA 2.5927 3.0675 7.2000 −5.1000 2.3831 −1.0919 4.8975
GAB 2.2288 2.6515 7.0920 −8.9430 3.4110 −1.0806 4.5268
GNQ 17.1494 7.3670 147.9730 −9.1100 33.3524 2.5140 9.5218
IRN 3.4799 2.9745 18.0800 −3.7470 4.5783 0.9501 4.3206
IRQ 7.2611 5.5030 81.7870 −50.6000 22.1772 0.8638 6.5540
LBY 1.8738 0.0580 86.8270 −50.3390 21.1388 1.5066 9.7789
NGA 4.6314 3.8495 14.6040 −1.7940 3.9891 0.6153 2.8867
VEN −0.9729 1.1715 18.2870 −29.9950 10.7745 −1.0678 3.8709
ALL 4.4796 3.0860 147.9730 −50.6000 16.3569 3.8754 31.4362
Graph 3: The time graph of OPEC Members’ crude oil exports
Table 6 presents the research data’s cross-sectional dependency
and unit root test results. The Breusch-Pagan LM test revealed
cross-sectional dependency for all four variables. To check for
stationarity, the CADF test was used, determining that variable Y

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024
350
Table 6: The cross-section dependence and unit root test results
Variable Cross-Section Dependence Level First diference
t-Statistics P-value t-Statistics P-value t-Statistics P-value
X1 202.9224 0.0000 21.2278 0.2681 112.323 0.0000
X2 652.3821 0.0000 21.6796 0.2465 100.826 0.0000
X3 270.9916 0.0000 16.5936 0.5512 93.4941 0.0000
Y 66.4189 0.0015 95.9222 0.0000 212.098 0.0000
Table 7: The panel ARDL analysis ndings for the research model
Variable Coecient Standard Error t-Statistic Prob
Long run equation
DX1 −0.8385 0.2589 −3.2388 0.0014
DX2 0.1238 0.1218 1.0166 0.3104
DX3 0.0106 0.0032 3.3166 0.0011
Hausman testi: Chi-square (2) =4,2311; P=0.2376
Short run equation
COINTEQ01 −0.6483 0.0686 −9.4553 0.0000
D(DX1) −0.9892 1.9946 −0.4960 0.6204
D(DX1(−1)) −0.1897 1.5791 −0.1201 0.9045
D(DX2) 0.5381 0.4740 1.1354 0.2574
D(DX2(−1)) 0.4276 0.5100 0.8383 0.4027
D(DX3) 0.0031 0.0172 0.1800 0.8574
D(DX3(−1)) 0.0060 0.0045 1.3359 0.1829
C 2.4540 0.7127 3.4431 0.0007
Mean dependent var −0.0992 SD, dependent var 19.1305
SE, of regression 12.1664 Akaike info criterion 6.1849
Sum squared resid 32860.5600 Schwarz criterion 7.1176
Log likelihood −843.4515 Hannan-Quinn criter, 6.5583
Graph 5: The line graph of the observation, estimation, and residual
values according to the research model
Graph 4: The time graph for the GDP per capita growth
was stationary at the level, while variables X1, X2, and X3 were
stationary at the rst dierence.
Table 7 presents the panel ARDL analysis ndings for the research
model. The Hausman test was conducted to choose between
the within-group estimator (MG) and the pooled within-group
estimator (PMG), and the PMG estimator was chosen as the more
suitable one. Based on the AIC ndings for lag number selection,
an ARDL (1, 2, 2, 2) lag structure was deemed appropriate. The
error correction coecient of −0.6483 indicates a long-term
relationship between the variables, with the error correction term
being between 0 and −1 pointing to convergence towards the
equilibrium. This suggests that 64.8% of short-term shocks can be
eliminated within a year, and equilibrium is reached approximately
1.52 years (about 1 year 6 months) after a short-term shock.
Notably, renewable energy consumption has a negative long-term
eect, while oil export has a positive eect. Short-term eects
across all OPEC countries were deemed insignicant. In the short
term, when all OPEC countries are considered collectively, it
was concluded that no variable has a signicant eect. However,
separate eect analyses were conducted for each country, and the
ndings are outlined in Table 8.

Aidarova, et al.: Relationship between Oil Exports, Renewable Energy Consumption, Agriculture Industry, and Economic Growth
in Selected OPEC Countries: A Panel ARDL Analysis
International Journal of Energy Economics and Policy | Vol 14 • Issue 6 • 2024 351
Table 8: Cross-section short run coecients
Variables DZA COG GNQ GAB IRN IRQ LBY NGA VEN
COINTEQ01 −0.9402** −0.7424** −0.4802** −0.7929** −0.5536** −0.6823** −0.8674** −0.3601** −0.4158**
D(DX1) 6.5237 0.4269** −2.3853 0.2004* 0.8864 1.302 −15.629 −0.0767 −0.1515
D(DX1(-1)) 6.1369 0.1799** −1.6838 0.1021** −2.8227 5.9885 −9.7614 −0.2304** 0.3837**
D(DX2) 0.0149 0.3176* 4.2769 0.4358** 0.2101** −0.3342** −0.0172 0.1105** −0.1713**
D(DX2(-1)) 0.0565** 0.5207** 4.3654 0.02 0.1967** −0.2581** −0.9445** 0.0862** −0.1947**
D(DX3) −0.0021** 0.0787** −0.1164 0.0228** 0.0028** 0.0005** 0.0312** −0.0001** 0.0105**
D(DX3(-1)) 0.0014** 0.0378** 0.0037 −0.0075** −0.003** 0.0021** 0.0054** −0.0013** 0.0154**
C2.3318** 1.472* 5.9503 1.9629** 1.7836** 6.0292 1.309 1.5938* −0.3469
Coecient values found to be signicant at the p˂0.01 (**) or p˂0.05 (*) levels are written in bold.
The estimation and residual value ndings of the research model
are depicted in Graph 5. Signicant uctuations are evident in
both the observation-estimation values and residual values for
Equatorial Guinea, Iraq, and Libya. This indicates the necessity of
closely monitoring the relevant periods from various perspectives
for these three countries. In contrast, the model demonstrates
successful data-estimation compatibility for the other countries.
The short-term eect coecient ndings for the countries, as per
the research model, are detailed in Table 8. Notably, the error
correction term takes a value between −1 and 0 for all countries,
indicating convergence towards the equilibrium value. The absence
of a signicant eect of any variable for Equatorial guinea raises
questions about the various factors inuencing economic growth
in the country. Additionally, it is observed that the food production
index has a negative effect on Iraq, Libya, and Venezuela,
while it has a positive eect on the other countries. Renewable
energy consumption has a positive eect for Congo, Gabon, and
Venezuela, a negative eect for Nigeria, and its eect is statistically
insignicant for Algeria, Iran, Iraq, and Libya. Furthermore, it is
noteworthy that the short-term eect of oil export is statistically
signicant for all countries.
5. CONCLUSION AND
RECOMMENDATIONS
An analysis conducted in OPEC countries found that oil exports
have a positive long-term eect on economic growth. However,
the short-term impact of oil exports varies among countries. In
Nigeria, the short-term eect is negative, while it is positive
in other OPEC countries. It has been observed that renewable
energy consumption generally has a negative long-term eect on
economic growth, but the short-term eect varies among countries
and is not statistically signicant in most cases. The long-term
eect of the food production index is not statistically signicant,
but it shows negative eects for some countries in the short
term. The insignicance of the short-term eects of all variables,
especially in Equatorial Guinea, indicates that the economic
structure of this country is dierent from others.
The ndings suggest that oil exports support economic growth
in OPEC countries. However, to comprehensively understand
this eect, a deeper analysis of causality relationships is needed.
Evaluating the relationship between oil exports and economic
growth for each country separately, considering the economic
structures and dynamics of different OPEC countries, and
developing specic analysis models for these countries can provide
more detailed and useful information. Focusing future research
on these aspects will make signicant contributions to both the
literature and the decision-making processes of policymakers
and lead to more eective design and implementation of energy
policies in OPEC countries.
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