Revealing the dynamic effects of fossil fuel energy, nuclear energy, renewable energy, and carbon emissions on Pakistan's economic growth

Abstract

The primary goal of this study was to examine the relationship between fossil fuel energy, electricity production from nuclear sources, renewable energy, CO2 emissions, and economic growth in Pakistan. Data ranging from 1975 to 2019 were utilized, and the stationarity of this data was verifed through the unit root testing. The dynamic connections between variables were investigated by utilizing the linear autoregressive distributed lag technique. Long-run analysis results uncover that fossil fuel energy, renewable energy use, CO2 emissions, and GDP per capita have a productive relationship with economic progress in Pakistan, whereas electric power consumption, electricity produced from nuclear sources, and energy utilization have an adverse efect on economic growth. Furthermore, the consequences revealed that fossil fuel energy, renewable energy consumption, carbon dioxide emissions, and GDP per capita have a signifcant linkage to Pakistan’s economic growth via short run, whereas we revealed that the variables electric power consumption, electricity produced from nuclear sources, and energy usage have an adversative linkage to Pakistan’s economic growth. Feasible progressive policies are required from the Pakistani government to pay more attention for tackling the energy and power sectors’ issues in terms of fulflling the country’s energy requirements.

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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-022-19317-5
RESEARCH ARTICLE
Revealing thedynamic effects offossil fuel energy, nuclear energy,
renewable energy, andcarbon emissions onPakistan’s economic
growth
AbdulRehman1· HengyunMa1· IlhanOzturk2,3· MagdalenaRadulescu4
Received: 10 January 2022 / Accepted: 16 February 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
The primary goal of this study was to examine the relationship between fossil fuel energy, electricity production from nuclear
sources, renewable energy, CO2 emissions, and economic growth in Pakistan. Data ranging from 1975 to 2019 were utilized,
and the stationarity of this data was verified through the unit root testing. The dynamic connections between variables were
investigated by utilizing the linear autoregressive distributed lag technique. Long-run analysis results uncover that fossil fuel
energy, renewable energy use, CO2 emissions, and GDP per capita have a productive relationship with economic progress
in Pakistan, whereas electric power consumption, electricity produced from nuclear sources, and energy utilization have
an adverse effect on economic growth. Furthermore, the consequences revealed that fossil fuel energy, renewable energy
consumption, carbon dioxide emissions, and GDP per capita have a significant linkage to Pakistan’s economic growth via
short run, whereas we revealed that the variables electric power consumption, electricity produced from nuclear sources,
and energy usage have an adversative linkage to Pakistan’s economic growth. Feasible progressive policies are required
from the Pakistani government to pay more attention for tackling the energy and power sectors’ issues in terms of fulfilling
the country’s energy requirements.
Keywords Fossil fuel energy· Carbon dioxide emissions· Economic growth· Environmental sustainability· ARDL model
Introduction
Environmental sustainability and economic development
play an essential role in the energy field. However, promot-
ing economic success, without harming the climate, is a
requirement for the sustainable development. Developing
nations typically confront several difficulties in fostering
the economic development (Fang etal. 2018; Roson and
Van der Mensbrugghe 2012; Rehman etal. 2021g). Obso-
lete infrastructure guarantees that even the industrialized
economies continue to use fossil fuel energy to meet the
growing demand for more power consumption. Conse-
quently, the economic development in such economies stag-
nates, increasingly contributing to the generation of harm-
ful gases and excessive fossil fuel use and aggravating the
environmental air and land deterioration (Hanif 2018). The
global companies use renewable and non-renewable energy
sources; however, the paradigm of the sustainable growth
that substituted the conventional model of growth over time
has diversified the energy needs of the economy. The key
energy sources in conventional growth models involve fossil
Responsible Editor: Philippe Garrigues
* Abdul Rehman
abdrehman@henau.edu.cn
Hengyun Ma
h.y.ma@163.com
Ilhan Ozturk
ilhanozturk@cag.edu.tr
Magdalena Radulescu
magdalena.radulescu@upit.ro
1 College ofEconomics andManagement, Henan Agricultural
University, Zhengzhou450002, China
2 Faculty ofEconomics andAdministrative Sciences, Cag
University, 33800Mersin, Turkey
3 Department ofMedical Research, China Medical University
Hospital, China Medical University, Taichung406040,
Taiwan
4 Department ofFinance, Accounting andEconomics,
University ofPitesti, 110040Pitesti, Romania

Environmental Science and Pollution Research
1 3
fuel energy including gasoline, biomass, and natural gas.
It has started to substitute the blueprint for the sustainable
development through alternative energies such as wind,
solar, and geothermal resources (Akadiri etal. 2019; Tuna
and Tuna 2019; Alper and Oguz 2016).
Global prosperity and long-term growth are the ultimate
goals of both industrialized and emerging countries. This
objective is hampered by a number of obstacles. Even while
the degradation of the environment is the most often debated
danger to the planned step of sustainable development, there
is a complicated link between economic growth and envi-
ronmental degradation that has to be understood (Alvarado
and Toledo 2017; Ahmed etal. 2015; Halkos and Bampat-
sou 2019; Jamel and Derbali 2016). Global warming and
environmental degradation are two important development
issues in reaching the sustainable global output and develop-
ment. Therefore, preserving the environmental quality has
been the focus of the national and foreign policy debates in
the background of attaining the global imperishable progress
in the last few decades. In addition to global warming and air
pollution, carbon dioxide emissions have been identified as
the primary causes of climate change and are generally rec-
ognized as major factors influencing both issues (Başarir and
Çakir 2015; Ali etal. 2017; United Nations 2016; Kwakwa
etal. 2014).
Pakistan’s dependence on thermal energy, which includes
imported coal as well as local coal and natural gas, has
decreased in the recent years as far as the energy mix is
concerned. Pakistan gets two-thirds of its energy from fuel
oil and natural gas. Natural gas made up 34.6% of the energy
mix, with fuel oil coming second with a share of 31.2%.
Renewable energy has a very low share in the energy mix
(around 1.1%). In reality, the nuclear energy accounts for
2.7% of the total energy consumption. Coal is another fos-
sil fuel in the Pakistan’s energy mix, and it is becoming
more important as a source of energy, accounting 12.7% of
the total energy mix (GOP 2019). In the Pakistan’s electric-
ity sector, petroleum-fired power plants have a life expec-
tancy of more than 30years and will be removed during
the next several years. It is anticipated that in the next few
years, furnace oil–based energy will account for less than
1% of the total energy consumption of the country. There
are about 186 billion tonnes of coal reserves in Pakistan,
which are sufficient to meet the country’s long-term energy
needs (GOP 2020). In terms of GDP per capita, it presented
large fluctuations during the decades, from high growth
rates down to negative rates. Pakistan steadily grew after
the international financial crisis from 2007–2009, up to 2018
(+ 3.68%), but then collapsed in 2019 (− 1.03%), before the
pandemic erupted in 2020 (− 1.44%) (GOP, 2020).
In terms of contribution to manufacturing, energy use
may result in large carbon emissions that affect the environ-
ment. Similarly, the economic development should stimulate
innovation in a healthier world according to the energy con-
sumption needs, because an unhealthy environment will only
delay the development (Adewuyi and Awodumi 2017). Vari-
ous studies have been carried to expose the link among eco-
nomic progress, CO2 emission, foreign trade, environmental
degradation, environmental pollution and trade openness,
urban agglomeration, economic globalization, sustainable
development, environmental-related technologies, com-
mercial energy distribution, food production, electricity and
renewable energy usage, carbonization and atmospheric pol-
lution, and coal energy in power sector (Magazzino 2016;
Obradović and Lojanica 2017; Moreau and Vuille 2018;
Ssali etal. 2019; Alam and Murad 2020; Rehman etal.
2021a; Murshed etal. 2021; Rehman etal. 2021b; Khan
etal., 2022; Rehman etal. 2021c; Rehman etal. 2021d; Cao
etal. 2021; Rehman etal. 2021e; Rehman etal. 2021f), but
this study’s main aim was to explore the association among
fossil fuel energy, electricity generated from nuclear sources,
renewable energy usage, electric power consumption, CO2
emissions, energy utilization, and economic growth in Paki-
stan by utilizing the ARDL model. This research makes a
significant addition to the current literature in the areas of
energy utilization, carbon emission, environmental sustain-
ability, and economic development, among others.
As a consequence, the remaining sections of the paper
are organized as follows: the section “Literature review”
presents the findings of previously conducted studies that
are relevant to the topic, while the section “Methods and
data” uncovers the study methodology and data collection
that were used in the analysis of the data. The findings of
the research, as well as their interpretation, are provided
under the heading “Results and discussion.” “Conclusion
and policy recommendations” is the last portion of the paper,
where ideas and the policy implications are discussed.
Literature review
Sustainable development and climate change mitigation
are gravely endangered by the use of fossil fuels, accord-
ing to several investigations. While the collapse in inter-
national fuel prices has boosted a political will to imple-
ment reforms in order to subsidize the fossil fuel energy,
these recent reforms can be reversed because the fuel prices
have bounced. Moreover, they can be reversed particularly
if they fail to address the basic mechanisms that cause the
low demand for fossil fuel (Schmidt etal. 2017). The core
source of atmospheric pollution is carbon dioxide emissions,
and CO2 emissions are mainly caused by the fossil fuel com-
bustion. Environmental degradation is increasing because
of the carbon dioxide emissions and climatic variation cre-
ates a variety of issues in the emerging economies including
inadequate quality of air and water, desert deforestation, and

Environmental Science and Pollution Research
1 3
poor quality of survival (Nathaniel etal. 2019; Xue etal.
2014; Pan etal. 2018; Heydari etal. 2019).
Insofar as energy consumption promotes the economic
prosperity, increases sales and employment, and improves
safety and services, it may become a positive element in
achieving the sustainability goals such as poverty eradication.
This argument clearly implies that energy utilization leads
to the economic progress, and therefore the energy conser-
vation measures would restrict income (Asafu-Adjaye etal.
2016). The primary goal of the industrialized and emerging
countries is to promote global prosperity and sustainable
development. Many challenges prohibit the achievement of
that aim. Although the most prominent contentious challenge
is to achieve the targeted level of the sustainable progress,
the linkage between the economic development and environ-
mental destruction is complicated. Greenhouse gas (GHG)
pollution in the presence of CO2 emissions is rising and the
environmental deterioration is owing to the climatic variation
and global warming (Cowan etal. 2014; Gasimli etal. 2019).
Decoupling the increased CO2 emissions from the global
economic growth indicates lower fossil fuel activity. It also
reflects the renewable energy usage and energy transition.
Indeed, most CO2 emissions derive from the burning of fos-
sil fuel and hence are dictated by the electricity consump-
tion or energy-intensive practices. Thus, the high demand for
energy forecasts high use levels in the electricity generation,
industry, and road transport. Nonetheless, changes in the fuel
process, including medium carbon or low carbon natural coal,
nuclear, or renewable energies, would typically reduce the
environmental warming (Apergis etal. 2010; Barreto 2018;
Wesseh and Zoumara, 2012; Rehman etal. 2019). Economic
development determines climatic variation. Global prosperity
encourages industrialization and enhances the utilization of
natural resources. All these commercial practices determine
natural resource degradation and increase waste volume and
threats (Dong etal. 2017; Ahmad and Zhao 2018).
In emerging economies, energy consumption tends to
expand at the same pace as demand. According to predictions,
this increase will continue. Aside from the need to react to
nations’ consumption requirements and adapt to the techno-
logical advancements, the energy consumption will rise. While
a significant percentage of fossil energy sources are used to
generate electricity, meeting low levels of renewable energy
demand poses possible difficulties in the area of clean energy.
In addition, much energy research and national energy policy
are moving in this direction. Oil and oil-related instability,
import dependence, global crises, and the severe environmen-
tal consequences of fossil fuel usage are the most urgent issues
(Can and Korkmaz 2019; Bekareva etal. 2017; Amri 2017).
Electricity is essential for economic progress, and ensuring
that everyone has access to inexpensive, reliable electricity is
a significant development goal. In the previous few decades,
fossil fuels have exceeded the largest need for energy, but in
the future, they would have to offer minimal carbon and poten-
tially zero carbon framework. Decarbonization takes effect in
all countries at different rates, based on regional situations
(Fankhauser and Jotzo 2018).
Reversing the negative environmental change remains one
of the world’s biggest problems. GHG and CO2 emissions
have increased annually, because of the huge usage of fossil
fuels. The significance of fossil fuels has been recognized
both in historical and contemporary growth drivers. Cur-
rent energy is recognized as a core component of the global
development, which provides exposure to accessible, secure,
renewable, and efficient electricity.
Methods anddata
The study variables used in this analysis include economic
growth, fossil fuel energy, renewable energy consumption,
electric power consumption, electricity produced from the
nuclear sources, CO2 emission, GDP per capita, and energy
utilization. Time series data range is 1975–2019 which is
taken from the WDI (World Development Indicators). Fig-
ure1 plot depicts the production and consumption scenario
of all variables.
Specification ofeconometric model withARDL
technique
In order to encounter the relation amid variables, the follow-
ing model can be stated as:
In Eq.(1),
ECGt
indicates the economic growth,
FOFECt
displays the fossil fuel energy consumption,
REECt
indicates
renewable energy consumption,
EPNSt
signifies electric
power consumption,
CO2et
shows the carbon dioxide emis-
sions,
EPNSt
indicates the electricity produced from nuclear
sources,
GDPPCAt
displays the GDP per capita, and
ENUSt
shows the energy usage.
Equation(1) can also be written as:
The logarithmic version of the variables is described in
the log-linear model as:
The logarithmic forms of variables are demonstrated in
Eq.(3) including fossil fuel energy consumption, electricity
produced from nuclear sources, renewable energy consump-
tion, CO2 emissions, electric power consumption, GDP per
(1)
ECGt=f(FOFECt, REECt, ELPCt, CO2et, EPNSt, GDPPCAt, ENUSt)
(2)
ECG
t
=β
0
+β
1
FOFEC
t
+β
2
REEC
t
+β
3
ELPC
t
+β
4
CO2et
+β
5
EPNS
t+β
6
GDPPCA
t+β
7
ENUS
t+ε
t
(3)
ECG
t
=β
0
+β
1
LnFOFEC
t
+β
2
LnREEC
t
+β
3
LnELPC
t
+β
4
LnCO2et
+β
5
LnEPNS
t+β
6
LnGDPPCA
t+β
7
LnENUS
t+ε
t

Environmental Science and Pollution Research
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capita, energy use, and economic growth. t is showing the
time dimension and
𝛽1
to
𝛽7
are the model coefficients where
𝛽0
is considered a constant interrupt.
This analysis is established on Pesaran etal. (2001) and
Pesaran etal. (1999) ARDL approach to solve the variable
interactions by using the long- and short-run estimates. The
ARDL method provides more compensation than other tech-
niques and makes no mandatory assumptions. In contrast to
other integration methods, all variables must be combined
in the same order during the investigation process. In other
words, the ARDL process can be used independently. The
basic return system is separated in the I(2) and coincidence
in I(0) or I(1) order. Secondly, despite the small sample size,
the ARDL test is appropriate. The sample size is extremely
important. The UECM (unrestricted error correction model)
technique validates the ARDL model in both long- and short-
term implementations. This paradigm is discussed in two parts:
short-term interactions and long-term interactions. The general
classification of the variables in the model can be written as:
(4)
Δ
LnECGt=π
0+
P
∑
A=1
π1AΔLnECGt−i+
P
∑
A=1
π2AΔLnFOFECt−i
+
P
∑
A=1
π3AΔLnREECt−i+
P
∑
A=1
π4AΔLnELPCt−i
+
P
∑
A=1
π5AΔLnCO2et−i+
P
∑
A=1
π6AΔLnEPNSt−i
+
P
∑
A=1
π7AΔLnGDPPCAt−i
+
P
∑
A=1
π8AΔLnENUSt−i+𝛼1LnECGt−1
+𝛼2LnFOFECt−1
+𝛼3LnREECt−1
+𝛼4ELPCt−1+𝛼5CO2et−1+𝛼6EPNSt−1+𝛼7GDPPCAt−
1
+𝛼
8
ENUS
t−1
+ε
t
Δ
shows the difference operator and P denotes the equa-
tion sequence of lags. The description of the long-run rela-
tion amid variables can also be stated as:
In Eq.(5), T represents the order of the lags; furthermore,
given the variables involved, the description of short-run
interactions through ECM can be demonstrated as:
The short-run estimation amid variables is stated in
Eq.(6), where R shows the lag order.
Results anddiscussion
Summary statistics ofthevariables
Table1 uncovers the summary findings of skewness, Kurto-
sis, Jarque–Bera, probability, and sum of squares.
(5)
Δ
LnECGt=β
0+
T
∑
G=1
β1GΔLnECGt−i+
T
∑
G=1
β2GΔLnFOFECt−i+
T
∑
G=1
β3GΔLnREECt−
i
+
T
∑
G=1
β4GΔLnELPCt−i+
T
∑
G=1
β5GΔLnCO2et−i+
T
∑
G=1
β6GΔLnEPNSt−i
+
T
∑
G=1
β7GΔLnGDPPCAt−i+
T
∑
G=1
β8GΔLnENUSt−i+ε
t
(6)
Δ
LnECGt=𝜗0+
R
∑
K=1
𝜗1KΔLnECGt−i+
R
∑
K=1
𝜗2KΔLnFOFECt−i
+
R
∑
K=1
𝜗3KΔLnREECt−i
+
R
∑
K=1
𝜗4KΔLnELPCt−i+
R
∑
K=1
𝜗5KΔLnCO2et−i
+
R
∑
K=1
𝜗6KΔLnEPNSt−i+
R
∑
K=1
𝜗7KΔLnGDPPCAt−
i
+
R
∑
K=1
𝜗8KΔLnENUSt−i+𝛼ECMt−1+ε
t
Fig. 1 Plot of variables trend
-5.0
-2.5
0.0
2.5
5.0
7.5
10.
0
12.5
1975 1980 1985 1990 1995 20002005 2010 2015
LnECGLnFO FECLnREEC
LnELPCLnCO2e LnEPNS
LnGDPPCALnENUS

Environmental Science and Pollution Research
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Correlation amongvariables
The correlation amid variables including economic growth,
fossil fuel consumption, electricity produced from nuclear
sources, renewable energy consumption, carbon emissions,
energy use, electric power consumption, and GDP per capita
are depicted in Table2. The outcomes exposed that all vari-
ables are linked with one another.
Unit root testing technique
The unit root tests, such as the Phillips-Perron (Phillips and
Perron 1988) and augmented Dickey-Fuller (Dickey and
Fuller 1979) unit root tests, were used in this work to vali-
date the normality of the variables. The period 1975–2019
has been chosen as the data range for stationary purposes.
Both tests certify that in the order of two, none of the vari-
able is integrated. Table3 displays PP and ADF unit root test
consequences at level and at the first difference.
Cointegration test forthevalidation ofbounds
testing
The ARDL technique is used in this study to examine the
connection between study variables by using the annual
data from 1975 to 2019. To perform the ARDL bounds
testing for integration valuation, we must choose a suitable
lag time by measuring the F-statistic based on the Akaike
Information Criterion (AIC) lowest value. The consequences
of the bounds testing are shown in Table4. The findings
indicate that the measured F-statistic assessments are more
than 10%, 5%, 2.5%, and 1% of the crucial upper limits in
the sequences of 0 and 1.
The robustness among all study variables is determined
by using the cointegration test (Johansen and Juselius 1990)
with having trace test, max eigenvalue test, and outcomes
depicted in Table5.
Short‑ andlong‑run estimations
Table6 illustrates the ARDL model’s short- and long-term
results.
Table6 presents the results of the ARDL model. Out-
comes reveal that via short run, the coefficient (3.420) of
fossil fuel energy has positive linkage with the economic
growth with p-value (0.520). Similarly, outcomes also
expose that renewable energy consumption, carbon emis-
sions, and GDP per capita have coefficients of 2.607, 0.596,
and 0.442 with p-values of 0.606, 0.707, and 0.508 that
indicates a significant linkage to the economic growth of
Pakistan. Furthermore, during the analysis, we found that
variables such as electric power consumption, electricity
produced from the nuclear sources, and energy usage expose
an adversative linkage to the economic growth in Paki-
stan. Moving to the outcomes of the long-run estimations,
they expose that the fossil fuel energy, renewable energy
Table 1 Summary statistics
results LnECG LnFOFEC LnREEC LnELPC LnCO2e LnEPNS LnGDPPCA LnENUS
Mean 1.468 3.978 3.954 5.699 11.337 0.458 6.300 6.027
Median 1.578 4.043 3.950 5.872 11.458 0.624 6.166 6.087
Maximum 2.323 4.229 4.140 6.307 12.211 1.805 7.301 6.278
Minimum − 0.011 3.597 3.743 4.681 10.036 − 4.315 5.124 5.700
Std. Dev 0.525 0.176 0.119 0.489 0.683 1.116 0.596 0.172
Skewness − 1.134 − 0.738 − 0.092 − 0.785 − 0.448 − 2.058 0.148 − 0.524
Kurtosis 4.055 2.479 1.702 2.354 1.975 9.187 2.033 2.072
Jarque–Bera 11.738 4.597 3.220 5.414 3.476 103.566 1.918 3.680
Probability 0.002 0.100 0.199 0.066 0.175 0.000 0.383 0.158
Table 2 Correlation among
variables LnECG LnFOFEC LnREEC LnELPC LnCO2e LnEPNS LnGDPPCA LnENUS
LnECG 1.000 − 0.369 0.366 − 0.363 − 0.374 − 0.182 − 0.286 − 0.377
LnFOFEC − 0.369 1.000 − 0.930 0.992 0.978 0.265 0.902 0.993
LnREEC 0.366 − 0.930 1.000 − 0.925 − 0.965 − 0.407 − 0.959 − 0.954
LnELPC − 0.363 0.992 − 0.925 1.000 0.983 0.281 0.902 0.990
LnCO2e − 0.374 0.978 − 0.965 0.983 1.000 0.350 0.953 0.984
LnEPNS − 0.182 0.265 − 0.407 0.281 0.350 1.000 0.386 0.304
LnGDPPCA − 0.286 0.902 − 0.959 0.902 0.953 0.386 1.000 0.913
LnENUS − 0.377 0.993 − 0.954 0.990 0.984 0.304 0.913 1.000

Environmental Science and Pollution Research
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consumption, carbon dioxide emissions, and GDP per capita
have positive coefficients of 3.411, 2.600, 0.594, and 0.441
that show the productive linkage with the economic growth,
while the variables electric power consumption, electricity
produced from the nuclear sources, and energy utilization
have an adversative linkage to the economic growth. Paki-
stan belongs to those economies that deal with electricity
deficits, with no influence on the expansion of the nuclear
energy and clean energy use. Therefore, the exposure to
electricity is a significant problem for the rural and urban
communities because of the absence or rather limited access
of less than half of the rural population. Pakistan relies on
Table 3 Results of PP and ADF unit root test
*, **, and *** signify the level of significant at 10%, 5%, and 1%; “n0” denotes not significant
LnECG LnFOFEC LnREEC LnELPC LnCO2e LnEPNS LnGDPPCA LnENUS
P-P unit root test at level
[With Constant] t-statistic values
(Prob. values)
− 4.074
(0.002)
***
− 1.470
(0.539)
n0
− 0.125
(0.940)
n0
− 2.484
(0.126)
n0
− 3.519
(0.012)
**
− 3.649
(0.008)
***
− 1.564
(0.491)
n0
− 1.091
(0.710)
n0
[With Constant and Trend] t-statistic values
(Prob. values)
− 4.752
(0.002)
***
− 1.396
(0.848)
n0
− 2.895
(0.173)
n0
− 1.139
(0.910)
n0
0.207
(0.997)
n0
− 4.400
(0.005)
***
− 2.400
(0.374)
n0
− 1.634
(0.762)
n0
[Without Constant and Trend] t-statistic values
(Prob. values)
− 1.177
(0.214)
n0
3.729
(0.999)
n0
− 3.358
(0.001)
***
3.549
(0.999)
n0
5.921
(1.000)
n0
− 3.373
(0.001)
***
3.654
(0.999)
n0
3.5598
(0.999)
n0
At the first difference
[With Constant] t-statistic values
(Prob. values)
− 13.141
(0.000)
***
− 6.067
(0.000)
***
− 6.726
(0.000)
***
− 5.751
(0.000)
***
− 6.774
(0.000)
***
− 11.425
(0.000)
***
− 5.528
(0.000)
***
− 6.300
(0.000)
***
[With Constant and Trend] t-statistic values
(Prob. values)
− 12.583
(0.000)
***
− 6.457
(0.000)
***
− 6.663
(0.000)
***
− 6.570
(0.000)
***
− 9.759
(0.000)
***
− 11.461
(0.000)
***
− 5.560
(0.000)
***
− 6.374
(0.000)
***
[Without Constant and Trend] t-statistic values
(Prob. values)
− 12.831
(0.000)
***
− 4.568
(0.000)
***
− 5.635
(0.000)
***
− 4.132
(0.000)
***
− 3.162
(0.002)
***
− 11.683
(0.000)
***
− 4.368
(0.000)
***
− 5.064
(0.000)
***
ADF unit root test at level
[With Constant] t-statistic values
(Prob. values)
− 4.124
(0.002)
***
− 1.498
(0.525)
n0
− 0.164
(0.935)
n0
− 2.484
(0.126)
n0
− 4.621
(0.000)
***
− 0.409
(0.896)
n0
− 1.574
(0.487)
n0
− 1.093
(0.710)
n0
[With Constant and Trend] t-statistic values
(Prob. values)
− 4.767
(0.002)
***
− 1.396
(0.848)
n0
− 2.803
(0.203)
n0
− 1.139
(0.910)
n0
− 0.213
(0.990)
n0
− 4.518
(0.004)
***
− 2.301
(0.424)
n0
− 1.586
(0.782)
n0
[Without Constant and Trend] t-statistic values
(Prob. values)
− 1.438
(0.138)
n0
4.002
(1.000)
n0
− 3.211
(0.001)
***
4.666
(1.000)
n0
7.816
(1.000)
n0
0.031
(0.686)
n0
3.801
(0.999)
n0
3.559
(0.999)
n0
At the first difference
[With Constant] t-statistic values
(Prob. values)
− 8.925
(0.000)
***
− 6.067
(0.000)
***
− 6.723
(0.000)
***
− 5.676
(0.000)
***
− 6.617
(0.000)
***
− 2.423
(0.142)
n0
− 5.536
(0.000)
***
− 6.300
(0.000)
***
[With Constant and Trend] t-statistic values
(Prob. values)
− 8.843
(0.000)
***
− 6.469
(0.000)
***
− 6.660
(0.000)
***
− 6.553
(0.000)
***
− 9.348
(0.000)
***
− 2.338
(0.403)
n0
− 5.560
(0.000)
***
− 6.374
(0.000)
***
[Without Constant and Trend] t-statistic values
(Prob. values)
− 9.022
(0.000)
***
− 4.383
(0.000)
***
− 5.561
(0.000)
***
− 2.389
(0.018)
**
− 1.116
(0.235)
n0
− 2.083
(0.037)
**
− 4.465
(0.000)
***
− 4.934
(0.000)
***
Table 4 Bounds testing for the recognition of cointegration
F-B test N-hypothesis: no-level asso-
ciation
T-S Value Signif I(0) I(1)
F-statistic value (5.582474) 10% (1.92) (2.89)
K(7) 5% (2.17) (3.21)
2.5% (2.43) (3.51)
1% (2.73) (3.9)

Environmental Science and Pollution Research
1 3
fossil fuels to meet its energy needs. However, because of
the limited exposure to these resources, it raises the seri-
ous problem of carbon emissions. Because of their non-
renewable and non-nuclear nature, such sources of energy
may be depleted in a matter of days if they are not properly
managed. Clean energy and nuclear energy initiatives, in
contrast, have a lower economic impact than the fossil fuels.
It should also be noted that nuclear power and clean energy
would decrease CO2 emissions, protect the environment, and
reduce global reliance on fossil fuels (Zhang etal. 2018;
Luqman etal. 2019; Khan etal. 2020).
The world’s robust reliance on non-renewable resources
has created significant global issues and challenges, such as
future non-renewable oil shortages, electricity stability, and
environmental concerns. The global economy confronts the
danger of increasing energy consumption in order to main-
tain sustainability and economic development. There is a
terrible misconception that carbon fuels deplete the renew-
able resources. Nevertheless, the environmental effect of
the renewable energy is shocking. The large gap between
demand and electricity generation, the growing cost, and
increased environmental pollution of fossil fuel resources
are all urgently necessary to find some cost efficient and
environmentally friendly sources of energy. Therefore, the
world has recently paid a great attention to the renewable
energy development. Power is well recognized as a source
of economic growth and social stability, and its potential for
the climate change necessitates the use of the green energy
(Inglesi-Lotz 2016; Kocaarslan and Soytas 2019; Wang etal.
2018).
For developing successful policies in consequence to
decrease the non-renewable energy usage and increase the
energy efficiency in the residential sector, the policymak-
ers must be aware of the households’ choices regarding the
home heating systems. In terms of the environmental impact,
the decisions that families make when it comes to heat-
ing may have a major impact on the environment (Laureti
and Benedetti 2021). Additionally, the local governments
and international organizations have made the sustainable
energy policy a priority. Energy strategies must address new
problems, such as energy poverty, security, justice, energy
resilience, and vulnerability, all of which are interconnected
Table 5 Cointegration test outcomes (J-J)
* Expressing the denial of hypothesis at the level (0.05). **The probability values
T-statistics Max-eigenvalue statistics
H- no. of CE(s) T-S C-V (0.05) Prob.** H- no. of CE(s) Max-eigen statistic C-V (0.05) Prob.**
None* 228.222 159.529 0.000 None* 65.249 52.362 0.001
Max 1* 162.972 125.615 0.000 Max 1* 54.938 46.231 0.004
Max 2* 108.034 95.753 0.005 Max 2 35.894 40.077 0.137
Max 3* 72.139 69.818 0.032 Max 3 29.513 33.876 0.152
Max 4 42.626 47.856 0.141 Max 4 17.669 27.584 0.522
Max 5 24.957 29.797 0.163 Max 5 11.399 21.131 0.607
Max 6 13.557 15.494 0.095 Max 6 8.599 14.264 0.321
Max 7* 4.958 3.841 0.026 Max 7* 4.958 3.841 0.026
Table 6 Results of short- and long-run estimations
* The level of significance
Variables Coefficients S-E T-S Prob.*
Short run (error correction regression)
C − 10.347 47.496 − 0.217 0.828
LnECG(− 1) − 1.002 0.183 − 5.455 0.000
LnFOFEC 3.420 5.264 0.649 0.520
LnREEC(− 1) 2.607 5.008 0.520 0.606
LnELPC(− 1) − 1.321 1.944 − 0.679 0.501
LnCO2e 0.596 1.572 0.379 0.707
LnEPNS − 0.061 0.077 − 0.793 0.433
LnGDPPCA(− 1) 0.442 0.661 0.669 0.508
LnENUS − 2.395 7.017 − 0.341 0.735
D(REEC) − 6.824 4.809 − 1.419 0.165
D(ELPC) 2.688 2.230 1.205 0.236
D(GDPPCA) 2.862 1.075 2.660 0.012
CointEq(− 1) − 1.002 0.126 − 7.924 0.000
Long run
LnFOFEC 3.411 5.121 0.666 0.510
LnREEC 2.600 5.110 0.508 0.614
LnELPC − 1.317 2.008 − 0.656 0.516
LnCO2e 0.594 1.625 0.365 0.716
LnEPNS − 0.060 0.079 − 0.769 0.447
LnGDPPCA 0.441 0.636 0.693 0.493
LnENUS − 2.388 6.944 − 0.343 0.733
C − 10.318 47.804 − 0.215 0.830
R2: 0.633
Adj-R2: 0.606
S.E. of regression: 0.377
S–S resid: 5.706
L-likelihood: − 17.498
DW-stat: 2.225
Mean-dep. var: − 0.032
S.D. dependent var: 0.601
AIC: 0.977
S-criterion: 1.139
H-Quinn criter.: 1.037

Environmental Science and Pollution Research
1 3
issues that need new solutions (Gatto and Busato 2020).
Renewable resources are less carbon-intensive and more
effective. Because of the adverse environmental effects of
GHGs caused by the volatile usage of the fossil fuels, the
new requirements in the energy field are becoming increas-
ingly common. The utilization of renewable energy has up
surged in the recent years, mostly due to the substantial drops
in solar and wind costs. Energy consumption in the develop-
ing nations is rising due to varying patterns in infrastructure
and population growth. Given the huge disparity between the
projected fossil fuel production and energy consumption, all
developing nations’ energy needs are inadequate. Given the
importance and development of the renewable energy, the
complex connection between renewable energy usage and
economic progress must be recognized as a contribution to
a green and sustainable power marketplace (Shukla etal.
2017; Kahouli 2017; Furuoka 2015; Saidi etal. 2017; Pinzón
2018). Figure2 illustrates the significant linkage among all
variables and ECG.
Figure2 uncovered that fossil fuel energy, renewable
energy utilization, CO2 emissions, and gross domestic prod-
uct per capita exposed a positive impact on the economic
progress in Pakistan. Similarly, the variables such as electric
power consumption, electricity produced from the nuclear
sources, and energy use demonstrated an adverse linkage to
the economic development in Pakistan. Furthermore, Fig.3
illustrates the plot of CUSUM, CUSUM of squares, and
recursive estimates.
Conclusion andpolicy recommendations
In this study, we have examined the association among fos-
sil fuel energy consumption, electricity produced from the
nuclear sources, CO2 emissions, renewable energy consump-
tion, electric power consumption, GDP per capita, energy
use, and economic growth in Pakistan. The study data range
ECG
FOFEC
(+Ve)
REEC
(+Ve)
ELPC
(-Ve)
CO
2
e
(+Ve)
EPNS
(-Ve)
GDPPC
A (+Ve)
ENUS
(-Ve)
Fig. 2 Long-term linkages of study variables with economic progress
(ECG)
-
20
-
15
-
10
-5
0
5
10
15
20
88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18
CUSUM5% Significance
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18
CUSUM of Squares 5% Significance
-
1.00
-
0.75
-
0.50
-
0.25
0.00
0.25
0.50
0.75
1.00
88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18
Recurs ive Residuals± 2 S.E.
.000
.025
.050
.075
.100
.125
.150
-1.0
-0.5
0.0
0.5
1.0
88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18
One-Step Probability
Recursiv e Residuals
.000
.025
.050
.075
.100
.125
.150
-1.0
-0.5
0.0
0.5
1.0
88 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18
N-Step Probability
Recursiv e Residuals
0
1
2
3
4
5
6
7
8
-1.0 -0.8 -0 .6 -0.4 -0.2 0.00.2 0.40.6
Series: Residuals
Sample 1976 2019
Observaons 44
Mean 4.0 4e-15
Median 0.04518 7
Maximu m 0 .581724
Minimu m -0.975337
Std. Dev. 0.364307
Skewness -0.886777
Kurtosis 3.364437
Jarque-Bera 6.010227
Probability 0.049533
Fig. 3 Recursive plots, CUSUM, and CUSUM of squares

Environmental Science and Pollution Research
1 3
is from 1975 to 2019 and variable stationarity was con-
firmed through the unit root tests including Phillips-Perron
and augmented Dickey-Fuller. The ARDL technique was
employed to rectify the dynamic association among the vari-
ables. Consequences expose that via long run, the fossil fuel
energy, renewable energy consumption, CO2 emissions, and
GDP per capita have a constructive impact on the economic
growth, while electric power consumption, electricity pro-
duced from the nuclear sources, and energy utilization have
a negative connection to the economic growth in Pakistan.
Furthermore, short-run outcomes also revealed that the fos-
sil fuel energy consumption, renewable energy consumption,
carbon dioxide emissions, and GDP per capita have a sub-
stantial positive linkage to the economic growth in Pakistan.
During analysis, the variables electricity produced from the
nuclear sources, electric power consumption, and energy
usage expose the adversative linkage with the economic
progress in Pakistan.
To address Pakistan’s energy issues, the government of
Pakistan must take the necessary efforts to implement prac-
tical policies and respond immediately. There is a need to
produce interconnected energy networks with common oper-
ating principles that offer considerable potential for strength-
ening the cooperation between new technology, increasing
the cost-effective use of the most diverse low-carbon tech-
nologies, and improving the energy system sustainability.
It was observed that energy sector is largely controlled and
operated by the government. In contrast, various policies and
efforts must be implemented in directive to expand the pres-
entation of the energy sector. Moreover, significant efforts
have been made to increase the engagement of the private
sector in the development of the energy sector in order to
improve the efficiency of public sector organizations. A
new structure can evolve to reorganize the public sector and
energy sector institutions in order to build a market where
the private firms can operate competitively for supplying
the energy.
Furthermore, Pakistan should continue to invest in the
energy industry, particularly in natural gas, coal, and hydro-
electric power production. Imports will put less of a strain
on the country’s current account as a result. In order to
alleviate the issue of energy scarcity, consumers must be
educated about the necessity of making more effective use
of the energy supply from a variety of sources. Pakistan has
been blessed with an abundance of natural resources. Solar
power and coal represent two main sources that stand out
as having an enormous potential. To satisfy the increasing
energy demand, particularly in warmer areas, the govern-
ment should prepare to implement and support the solar
power projects. Since nuclear energy is used to produce
power, the government should start new nuclear projects to
keep up with the increasing demand.
This study is not limited, and further research may be
conducted by broadening this topic in order to address Paki-
stan’s energy issue and enhance the country’s economic
growth and development. Further study may look into addi-
tional alternative energy sources by enacting new policies
and providing financial assistance, building new dams in the
country to increase energy generation and economic growth,
and installing solar systems to generate electricity from solar
panels in order to fulfill increasing demands.
Author contribution Abdul Rehman: conceptualization, investigation,
methodology, formal analysis, visualization; writing the original draft;
Hengyun Ma and Ilhan Ozturk: investigation, visualization, review and
editing; Magdalena Radulescu: editing and made suggestions of the
manuscript.
Data availability Not applicable.
Declarations
Ethics approval and consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
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