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253
Energy Efficiency, Renewable Energy and Economic Growth
Nexus on CO2 Emission: Evidence from MINT Countries
Abner Ishaku Prince1 Inim Victor Edet 2 Boniface L. Akpan3
Emmanuel Samuel Udo4 Ekaetor, Enobong Akpan5
1University of Abuja Business School
2Department of Economics Nile University of Nigeria Abuja
3&5Department of Economics Akwa Ibom State University
5Department of Banking and Finance University of Nigeria Enugu
Campus
Abstract
This study scrutinises the energy efficiency (EEF), renewable energy
(REN), and economic growth (GDP) nexus on CO2 emissions in the
MINT countries of Mexico, Indonesia, Nigeria, and Turkiye from 1990-
2022. Using the novel asymmetric technique of the non-linear panel
ARDL on the nexus between the EEF as CO2 emission stimulator, with
REN, GDP, nuclear energy (NUE), and urbanization (URB), which
previous studies ignored to use the symmetric model predominantly.
Despite the importance of EEF in ecological policy formulation and
management, its mitigating influence on CO2 emissions is yet to be
expansively examined in the ecological literatures in MINT countries.
Results and findings revealed an asymmetric long-short nexus between
EEF, REN, through green energy sources, reducing the CO2 emission
effect in MINT countries. The GDP-CO2 emission nexus supports the
environmental Kuznets curve (EKC) hypothesis. The nuclear energy-
CO2 emission nexus is negative and non-significant. Indicating that
MINT countries at present are not generating significant mega electron
volts of nuclear energy to reduce CO2 emissions. The study recommends
prioritising REN policies through EEF, advancement in energy
technology, and easing of the legal requirements for EEF, particularly
NUE technology adoption, and implementation, to achieve the 2030 UN
SDGs of environmental quality sustainability.
ISSN: 1533 - 9211
CORRESPONDING
AUTHOR:
Emmanuel Samuel Udo
emmanuelsamueludo@gmial.c
om
KEYWORDS:
Energy efficiency, renewable
energy, MINT countries,
Asymmetric panel ARDL,
CO2 emission.
Received: 26 November 2023
Accepted: 19 December 2023
Published: 30 December 2023
TO CITE THIS ARTICLE:
Prince, A. I., Edet , I.
V., Akpan, B. L., Udo,
E. S., & Akpan, E. E.
(2023). Energy
Efficiency, Renewable
Energy and Economic
Growth Nexus on
CO2 Emission:
Evidence from MINT
Countries. Seybold
Report Journal,
18(10), 253-273. DOI:
10.5110/77. 1091
Seybold Report Journal Vol. 18. No. 10. 2023
254
1. INTRODUCTION
Globally the increasing energy demand for socio-economic development and the supply gap
thereof, is one of the core burdens of the 21st century due to its impact on environment quality.
The decline in environmental quality and energy demand-supply gap impact on climate change is
trace to the continuous consumption of fossil energy which contains about 75%-85% of carbon
(CO2) (Ahmed, et, al 2019; Abner, et, al 2021; Ahmed, et, al 2020; Omojolaibi, et, al 2020).
Developing economies particularly high-income oil-producing contribute about 60%-67% of CO2
due to population growth rates and high energy demands for rapid economic industrialization.
The energy sector through fossil fuel provides 80% of global energy needs, which contributes
66.667% to total greenhouse gas (GHG) CO2 emissions globally (International Energy Agency
(IEA), 2013; Umar et al., 2021).
Efficient energy generation and distribution through green sources is key to improving
environmental quality, plummeting energy-related CO2 emissions and stimulating green economic
and financial development in particularly in MINT countries endowed with immense REN and
green energy sources. Mexico and Turkiye are blessed with significant solar and wind energy due
to geographical location; Indonesia’s abundant geothermal resource offers a colossal green energy
production opportunity and Nigeria’s substantial sunlight also offers a solar energy production
opportunity.
According to Economist Jim ONeill (2013), the MINT countries are the budding and evolving
economic bloc of the world economy taking over from BRIC countries as a result of their rapid
economic growth stimulated by their growing young population, and remittances inflow among
others. Nigeria and Mexico in MINT nations fall within the top 10 remittances-receiving nations
(Odugbesan et al., 2021). According to 2023 World Bank statistics report, MINT nations roughly
account for an estimated 720 million populations; Nigeria (223.8 million), Mexico (126.60
million), Indonesia (284.3 million), and Turkiye (85.3) million. The stable and healthy growth
trend in MINT countries can be attributed to individual country proximity to developed countries.
Nigeria is globally regarded as the economic hub of Africa, economic and social development in
America influences the Mexican economy, China influences Indonesia, and the European Union
influences Turkiye.
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255
The positive and significant impact of this proximity to developed countries on the individual
MINT countries is evident World Bank economic ranking of 2018, ranking Mexico 15th, Indonesia
16th, Turkiye 18th, and Nigeria 31st. In June 2021, based on GDP, ranking Mexico 15th, Indonesia
16th, Turkiye 19th, and Nigeria 27th (World Bank, 2021).
However, in light of these distinctive economic traits, this study envisages that by the end of 2023,
the MINT countries will rank among the top 20 economies in the world for the next three decades,
with Mexico ranked 15th, Indonesia 16th, Turkiye 17th, and Nigeria 20th. This prediction is
supported by the findings of Odugbesan and Rjoub (2020) and others and further collaborate the
2014 Goldman Sachs stable growth progression forecast for MINT countries till 2020.
Similarly, the United States report investment according to Dogan et al., (2019) forecast a 5%
annual growth in MINT. Despite these distinctive economic traits and forecast for
industrialization, human capital development, political stability, population and urbanization
growth rates, resource endowment, trade and export diversification among others. Various country
specific heterogeneous factors group under; economic, income per capita, energy, finance and
sociopolitical significantly impedes their individual and collective economic expansion, and also
truncate the achievement of UN 2030 SDGs of environmental quality, clean energy consumption
and climate action as indicated in Goals 7; 13; 12 and 17 and Millennium Development Goals
(MDGs) (Akram et al., 2020a; Dogan et al., 2019; Ahmad, et al., 2020a; Jakada et al., 2020a;
Abner, et, al., 2021).
Theoretically, the EKC hypothesis propounded by Grossman and Krueger (1995) support country
specific heterogeneous factor under income per capita revealing an inverted U-shaped nexus. The
EKC hypothesis states a rise income per capita of a nation increases CO2 emissions at the initial
stage of development to a slanting point, from which CO2 emissions diminishes to improve
environmental quality (Jakada et al., 2022a). Similarly, the inverse U-shaped model suggests a unit
increase in economic prosperity causes environmental quality decline through increased in
greenhouse emission (GHG) and CO2 emissions (Jakada et al., 2022b).
In the bid to reduce CO2 emissions by caused fossil energy consumption for industrialisation, and
reduce the GHG caused by population and urbanisation growth rates. It’s vital for MINT
economies to tailor their economic agenda towards green economic industrialisation, population
and urbanisation growth rates, to mitigate the effects of global warming (Akram, et, al 2020;
Seybold Report Journal Vol. 18. No. 10. 2023
256
Dogan, et, al 2019).
Empirically, the European Commission report, reveals that a unit in EEF has the potential to boost
natural resource sustainability, enhance the realisation of the SDGs and MDGs, reduce GHG and
CO2 emissions, diminish the over-dependence on fossil fuels to bridge the energy demand-supply
gap and improve energy security (European Commission, 2016; Bayar & Gavriletea, 2019;
Shahbaz et al., 2019).
Interestingly, factors instigating environmental changes have been an active research area. Cheng
et al., (2019); Danish Baloch et al., (2019); among others, revealed that efficient management of
climate change and ecological quality improvement anchor on energy efficiency. Energy
efficiency denote the capacity to increase or retain production level using the same Joule (J) of
energy. Investment in energy efficiency through green sources of energy, has colossal ecological
and economic sustainability growth benefit (Huang et al., 2021; Dong et al., 2018a,b). Energy
efficiency through the development of the abundant green energy sources embedded in MINT
countries and the implementation of an all-inclusive environmental regulations is aim at closing
the energy gap, and spur raid green industrialization.
Empirical studies examining the three constructs of EEF, REN and GDP on CO2 emissions in
MINT nations are scanty. Extant ecological literatures have largely regarded economic growth,
agricultural activities, financial development and foreign direct investment as prime stimulant of
CO2 emission (Nwabueze, et, al, 2023; Salman, et al., 2019; Liu et al., 2017), while Udo, et, al
(2012); Abner, et, al (2021) Shao et al., (2019) Haug and Ucal, (2019) and other examined the
energy consumption, trade openness nexus. This studies relatively omitted in ecological literatures
the contributive influence of energy efficiency and green energy development. As such, their
contributive influence on environmental quality in MINT countries is yet to be broadly
investigated in detail. This study is one of the very few empirical studies in MINT countries
investigating these constructs to bridge the knowledge gap in the previous ecological literatures.
Extant ecological literature based their findings on various linear modelling techniques such as the
classical linear regression while others adopted the dynamic ordinary least square (Balsalobre-
Lorente et al., (2019a,b; Dong et al., 2018a,b) and the fully modified ordinary least square, Dong
et al., (2018a,b); Shao et al., (2019) Dong et al., 2018a,b; Pata, (2018a,b) autoregressive
distributive lag (ARDL); Udo et, al (2020); Abner et, al (2021) Dong et al., (2018a,b); Pata,
Seybold Report Journal Vol. 18. No. 10. 2023
257
(2018a,b), among other.
Extant studies have criticise the predominate used of the linear estimation technique for neglecting
operational fluctuations and the short-run differences in their studies. Gunst and Mason, (1980:
167–206) upheld that, it is statistically untenable to draw inferences based on a single strand. Nam,
et, al (2002), recommended the adoption of an alternative model to provide an all-encompassing
inference. On this nexus a non-linear model was adopted. Time series are typically leptokurtic and
skewed (Brooks, 2014). The spikes and the oscillatory movement accompanying them renders the
linear model inept for a conclusive estimation.
This study adds to the extant studies by employing the asymmetric model of ‘‘non-linear panel
ARDL (NPARDL)’’. According to Kumar (2017), the asymmetric behaviour of economic time
series can be trace to economic uncertainty. The asymmetric model, specifically the NPARDL, is
a novel methodology in this study area that is highly dominated by linear models. It addresses the
asymmetry and heterogeneity influence on the long-short run panel dynamics caused by country-
specific effect. This study is significant in the context of MINT countries given their abundant
green energy resources to reduce GHG emission and manage climate change which has not been
extensively examined in extant ecological literatures. This study introduced energy efficiency and
green energy as core factors of GHG and CO2 emission. The study findings and results
significantly add to developing apt energy policies for MINT nations to improve environmental
quality, enhance economic growth through green energy generation and consumption.
2. Literature Review
Generally, extant ecological literature focuses on four literatures classifications of; economic
growth, renewable energy, energy consumption, and CO2 emissions. The first school of thought
discourses CO2 emissions-economic growth (income per capita) nexus, with Grossman and
Krueger (1995) EKC hypothesis of an inverted U-shaped nexus. The findings of Soytas et al.
(2007), Dinda (2004); Iwata et al. (2010), among others, revealed that the EKC hypothesis upholds
three diverse inferences on the CO2 emissions-economic growth nexus. According to Dietz and
Rosa (1994) and Özokcu and Özdemir (2017), there is an "inverted U-shape theory". Friedl and
Getzner (2003) and Holtz-Eakin, et, al (1995) reported a N or other shape in the CO2 emissions-
per-capita income long-run nexus and not an inverted U nexus. According to Stern (1993), the
major hindrance associated with previous EKC studies is that of potential variable bias. Instigated
Seybold Report Journal Vol. 18. No. 10. 2023
258
by a statistical model variable omission.
Kraft, et, al (1978), advanced the second school of thought arguing on energy consumption-
economic growth link. Ozturk (2010) revealed that the energy consumption-economic growth link
can be assess under four premises: a) the growth hypothesis, envisages that energy consumption
through energy guidelines may throttle economic growth (Stem, 1993; Damette, et, al 2013); b)
the protection hypothesis reveals a non-energy consumption-economic growth effect, as such
energy conservation policies have no negative effect on actual GDP (Jamil & Ahmad, 2010; Lee,
2005); c) the feedback hypothesis, school of thought revealed complementary interaction (Tang,
et,al, 2014; Belloumi, 2009); d) neutral hypothesis revealed a non-causal nexus, arguing that the
influence of energy conservation policies on economic growth is limited (Ozturk, 2010; Agras &
Chapman, 1999; Doğan;2018).
According to the 3Es “energy consumption (ENC), economic growth, and CO2 emissions” school
of thought, the incorporation of this variables is to circumvent potential variable bias problem
associated with the first school of thought. The 3Es results show that income per capita in the US
causes ENC and not CO2 emissions. In 6 Central American countries from 1971–2004, Apergis
and Payne (2009) observed a positive energy consumption–CO2 emissions long-term equilibrium
nexus, while the EKC hypothesis support an inverted U-shape nexus with real GDP. In BRIC from
1971–2005, Pao and Tsai (2010) observed both a strong and mild bidirectional causal nexus in
Brazil, India, and China between ENC and CO2 emissions; ENC and economic growth, except for
Russia from 1990–2005. Similarly, a short-run unidirectional link between CO2 emissions, ENC,
and economic growth was also observed.
In China, Brazil, India, and Indonesia, Alam ,et, al (2016), using the ARDL model from 1970–
2012, observed that significant caused nexus between income and energy consumption increasing
CO2 emissions. Waheed, et, al (2018), using the ARDL model from 1990–2004, observed that
renewable energy and forest areas significantly influence CO2 emissions in the long run in
Pakistan. Dong, Sun, and Hochman (2017) revealed that a unit increase in REN usage and natural
gas usage decreases environmental quality by 0.2601% and 0.1641%, respectively in BRICK
countries.
Using the fixed effect and GMM estimators, Khan et al. (2021) observed that REN improves
ecological quality. This finding was upheld in the findings of Mohsin et al. (2021) in 25 Asian
Seybold Report Journal Vol. 18. No. 10. 2023
259
countries. On the contrary, in Brazil, Hdom and Fuinhas, (2020) revealed that REN, hydropower,
and GDP negatively affect CO2 emissions. CO2 emissions impact positively on GDP using the
FMOLS model.
In Brazil, Magazzino et al. (2021), amidst COVID-19, observed economic growth via REN
consumption. The findings of Magazzino and Mele (2022) using the LSTM model, collaborate
with the claims of Magazzino et al. (2021) on renewable energy. In Pakistan, using symmetric and
asymmetric models’ results indicates that in the long-short run economic growth and FDI upsurges
CO2 emissions symmetrically. In the short-run, oil prices upsurge CO2 emissions and reduce them
in the long-run. The asymmetric result shows that in the long run, oil prices reduce CO2 emissions,
and the decrease in oil prices intensifies CO2 emissions (Malik et al. 2020).
3. Methodology
This study assesses the asymmetric nexus between the three constructs of EEF, REN, and GDP on
CO2 Emissions in MINT nations from 1990-2022. Within the sample period of this study, several
global events such as Covid-19 pandemic wielded shock, that spread to MINT nations. The shock
moments are not stationary, as they are felt in diverse front.
The study dataset was extracted and collated from the WDI. Extant ecological literature over the
decades has widely explored this nexus, however, these studies attached less or no importance to
energy efficiency in managing climate change in MINT countries. This study expands the frontiers
of the study of Dong et al. (2017) to capture energy efficiency measured by energy intensity as a
contributing factor to CO2 emissions. Economic growth is empirically considered one of the prime
instigators of CO2 emissions. Table 1 describes the designated study variables.
3.1 Cross-sectional Dependency Test
To determine whether relevant variables exhibit cross-sectional dependence (CD), the Breusch-
Pagan Lagrange multiplier and the Pesaran-scaled Lagrange multiplier were performed as a result
of nations' interconnection through globalisation triggered by economic, social, and cultural
networks. The second-generation unit root was conducted using the cross-sectionally augmented
IPS (CIPS) and cross-sectionally augmented ADF to ascertain stationarity of the series. The
equation is given:
ΔSi,t = φi + φiSi,t-1 + φiꟐt-1 +
φiIꟐt-1 +
φiIꟐt-1 +μit…………………….. (Eq 1)
Where: Ꟑ = cross-sectional averages.
Seybold Report Journal Vol. 18. No. 10. 2023
260
CIPS test statistic: CIPS =
CDFi…………………….. (Eq 2)
Where: CDF = cross-sectionally augmented Dickey–Fuller.
3.2 Model Specification
The present study introduced the asymmetric model to questioned the symmetric assumption that
saturates the pervious ecological literatures. The linear specification of the variables is expressed
as
CO2 = f (GDP, EEF, REN, URB, NUE)…………………………. (Eq 3)
The variables in (Eq1) are transformed into natural logarithm forms and expressed as:
LCO2it = β0 + β1LGDPit + β2LEEFit + β3LRENit + β4LNUEit + β4LURBit + εit………….(Eq 4)
Where: t = time; I = cross-section unit; CO2 = carbon emission; GDP = economic growth; EEF =
energy efficiency; REN = renewable energy; NUE; nuclear energy; URB = Urbanization and ε =
error term.
3.3 Non-linear Panel Autoregressive Distributed Lag (NPARDL)
Shin, et, al (2014) developed the NPARDL model and was employed to examine the asymmetric
effect of EEF, REN and GDP on CO2 emissions in the long-short run. Empirical studies employing
the linear combination, revealed that yt and χt result in a long-short run symmetric-change. Where
yt and χt become non-linear, χt initiates an asymmetric impact on yt. The NPARDL revealed
asymmetries in panel, as a result of heterogeneous and heterogeneity traits, triggered by country-
specific effects, in contrast to asymmetric effects in prior studies.
The model is linear ARDL expansion initiated by disaggregating χt into positive and negative
partial sums as: χt = χ0 + χt+ + χt- ……………….(Eq 5)
Where: χt+ and χt- = partial sum processes of positive and negative changes in χ1
χt+ =
j+ =
(ΔRj, o) ……………….(Eq 6) and
χt- =
j- =
(ΔRj, o) ……………….(Eq 7)
The NPARDL Equation is specified as:
ΔYit = α0 + α1Yit-1 + α2+GDP+it-1 + α2-GDP-it-1 + α3+EEF+it-1 + α3-EEF-it-1 + α4+REN+it-1 + α4-REN-
it-1 + α5+URB+it-1 + α5-URB-it-1 + α6+NUE+it-1 + α6-NUE-it-1 +
βkΔYit-k +
(
ΔGDP+it-k +
ΔGDP-it-k) +
(
ΔEEFP+it-k +
ΔEFF-it-k) +
(
ΔREN+it-k +
ΔREN-it-k) +
(
ΔURB+it-k +
ΔURB-it-k) +
(
ΔNUE+it-k +
ΔNUE-it-k) + μi + εit …………………………...(Eq 8)
where p and q = the respective lags; μi = country-wise effect and εit = error term; the coefficients
α1- α6+ and – and
,
,
,
,
,
,
,
, =and short-run asymmetries. Equation (6) is re-
Seybold Report Journal Vol. 18. No. 10. 2023
261
expressed in the form of an error correction model (ECM):
ΔYit = α0 + ρεit-1 +
βk ΔYit-k +
Χ (
ΔGDP+it-1 +
ΔGDP-it-1) +
(
ΔEEF+it-1 +
ΔEEF-it-1) +
(
ΔREN+it-1 +
ΔREN-it-1) +
(
ΔURB+it-1 +
ΔURB-it-1) +
(
ΔNUE+it-1 +
ΔNUE-it-1) + μi + εit …………………………...(Eq 7)
where εit = non-linear ECM term; ρ = speed of convergence to long-run equilibrium from
equilibrium deviation. The pooled mean group ARDL model was adopted as the most suitable
model for this study as it offers the short-long-term coefficients for every cross-sectional unit.
Table 1: Variable description and Unit.
Variables
Unit
Source
Carbon Emission (CO2)
Mt
World bank development indicator
(WDI)
Economic Growth (GDP)
Constant US$ 2015
Energy Efficiency (EEF)
Terawatt hour (TWh)
International energy agency (IEA)
Renewable Energy (REN)
%
World Development Indicators
Urbanization (URB)
%
Nuclear energy (NUE)
Source: Author, (2023)
4. RESULTS AND DISCUSSION
The descriptive statistics output of the study variables is reported in Table 2. The table show the
panel and country-specific results. The mean and median values of the observations, are not far
from each other. Indicating no extreme projection. The mean values in all cases show a positive
mean return, indicating a positive increasing propensity effect of CO2. The low standard deviation
values compared to the mean values indicate that the variables are not highly volatile around the
mean. The kurtosis of the series is platykurtic (<3).
Table 2: A Descriptive Summary of the Variables
Panel
CO2
EEF
GDP
NUE
REN
URB
Mean
2.414373
4.443953
4582.884
2.194157
38.26185
57.82992
Median
2.430808
3.720000
3399.603
2.179286
23.98000
58.56850
Maximum
5.066379
10.01000
12507.59
6.654301
88.68000
81.30000
Minimum
0.491388
2.490000
270.0275
0.274464
8.970000
29.68000
Std. Dev.
1.428488
1.838587
3644.866
1.822680
29.86250
16.35514
Skewness
0.065671
1.200684
0.603842
0.492656
0.680358
-0.176607
Kurtosis
1.450192
3.570844
1.944413
1.900953
1.813935
1.611349
Turkiye
CO2
EEF
GDP
NUE
REN
URB
Seybold Report Journal Vol. 18. No. 10. 2023
262
Mean
3.630098
2.866818
7020.584
4.705316
16.84194
68.36385
Median
3.397843
2.920000
7686.445
4.695301
15.34000
68.45000
Maximum
5.066379
3.270000
12507.59
6.654301
24.37000
77.02200
Minimum
2.562358
2.490000
2241.290
3.686443
11.40000
59.20300
Std. Dev.
0.762448
0.238655
3552.940
0.752722
4.417619
5.476959
Skewness
0.258965
0.051113
0.014877
0.671840
0.493154
-0.030732
Kurtosis
1.828935
1.713972
1.366007
3.215137
1.750453
1.729012
Indonesia
CO2
EEF
GDP
NUE
REN
URB
Mean
1.503207
4.164762
2063.770
0.677244
40.26290
45.84230
Median
1.503529
4.260000
1411.098
0.664507
41.46000
46.73800
Maximum
2.299258
5.420000
4332.709
0.947815
59.18000
57.93400
Minimum
0.815391
3.120000
459.1919
0.408720
19.77000
30.58400
Std. Dev.
0.388363
0.832488
1366.144
0.132313
11.39554
8.201688
Skewness
0.021504
0.115180
0.369080
0.098053
-0.120769
-0.298911
Kurtosis
2.252458
1.529652
1.425662
2.468091
2.035559
1.923122
Mexico
CO2
EEF
GDP
NUE
REN
URB
Mean
3.834793
3.575909
7812.123
2.895868
10.99581
76.55809
Median
3.863596
3.680000
8213.381
2.866289
10.27000
76.61600
Maximum
4.220763
4.010000
11076.09
3.517211
14.41000
81.30000
Minimum
3.298753
3.040000
3196.919
2.095645
8.970000
71.41900
Std. Dev.
0.270406
0.317005
2322.557
0.367749
1.662973
2.930232
Skewness
-0.268156
-0.452619
-0.467796
-0.275293
0.437443
-0.059864
Kurtosis
2.003003
1.820419
1.992318
2.497797
1.745744
1.834453
Nigeria
CO2
EEF
GDP
NUE
REN
URB
Mean
0.689395
7.284762
1435.057
0.369686
84.94677
40.55542
Median
0.707257
6.840000
1451.280
0.350200
84.67000
39.94300
Maximum
0.916428
10.01000
3200.953
0.462855
88.68000
53.52100
Minimum
0.491388
6.040000
270.0275
0.274464
80.64000
29.68000
Std. Dev.
0.122509
1.178077
929.6829
0.055246
2.349114
7.558128
Skewness
0.217515
1.112608
0.229683
0.173346
-0.218425
0.188245
Kurtosis
1.786132
3.169703
1.591907
1.927411
1.917926
1.701737
Source: Author, (2023)
4.1 Unit Root Test
Table 3: Second generational Panel Unit Root Test for MINT countries.
Panel A: Second generational Panel Unit Root
Panel B; Cross-Sectional
Dependence
CIPS
CADF
Breusch–
Pagan LM
Pesaran-
scaled LM
Variables
Level I(0)
1st Difference
I(1)
Level
I(0)
1st Difference
I(1)
CO2
-4.345*
-7.879**
-2.901**
-3.341*
101.314*
(0.0000)
27.514*
(0.000)
EEF
-1.876
-5.812**
-3.901**
-4.998**
78.074*
(0.000)
20.8060*
(0.000)
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263
GDP
-3.993*
-5481*
-2.100
-4.101*
154.189*
(0.0000)
42.778*
(0.0000)
NUE
-4.981**
-5.120*
-2.082
-4.019*
17.985**
(0.0006)
3.459**
(0.0005)
REN
-2.351
-4834**
-4.808**
-3.998*
99.562*
(0.0000)
27.009*
(0.0000)
URB
-3.879*
-6.872**
-5.940**
-6.933**
195.292*
(0.0000)
54.643*
(0.0000)
*Depicts 1% significance and ** 5% significance.
Source: Author, (2023)
The second-generation unit root results presented in Panel A of Table 3 show that the series is
stationary at (1) and I (0) order of integration, thus giving creditability to our adopted model. The
CD test results in Panel B of Table 3 designate evidence of CD. By implication, shocks to EFF,
REN, GDP, NUE, and URB from any country under investigation have a lifelong influence on the
ecosystem. The null hypothesis of ‘‘No CD’’ was rejected. However, policy actions cannot be
deduced at this point.
Table 4 Non-linear panel ARDL
Variable
Coefficient
Long Run Equation
EEF
0.158036 (21.08802)**
LOGGDP
-0.629431 (-35.28668)**
LOGREN
0.291188 (3.040891)***
LOGURB
41.70791 (37.90307)**
NUE
-0.154501 (-12.95905)**
Short Run Equation
COINTEQ01
-0.823129 (9.146690)**
D(CO2(-1))
0.556121 (0.864059)
D(CO2(-2))
0.089065 (1.253174)
D(EEF)
0.232650 (1.151602)
D(LOGGDP)
-0.228724 (-0.436219)
D(LOGREN)
0.902707 (0.855377)
D(LOGURB)
-2761.722 (-1.033508)
D(NUE)
-0.309089 (-1.454755)
C
157.5026 (1.042695)
Log-likelihood
197.9649
Source: Author, (2023)
The NPARDL results in Table 4 show that the level I(0) variables explain the behavioural pattern
of the series in the long run, while the I (1) series describe the short-run effect adjustment for 1
year by taking the variance. The ECM is rightly signed that is negative and significant. Inferring
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264
converge to equilibrium from short-run shock. Presenting a non-linear nexus and a long-term
asymmetric equilibrium link. A significant positive influence on CO2 emissions ensues due to a
positive shock in energy efficiency, urbanisation, and economic growth stimulated by
advancement in technology, industrialization, and urban immigration in MINT nations.
The findings show that a unit improvement in energy efficiency and renewable energy reduces the
use of unsustainable energy sources and also reduces CO2 emissions and climate change by
0.158% and 0.291% in the long and short run, respectively (0.232% and 0.902%). These results
substantiate the findings of (Akram et al. 2019; Liobikiene and Butkus, 2017; Ahmed and Wang,
2019; Abner, Ogbodo, Eneoli, and Udo (2021); among others, attributing the increasing
environmental deterioration and climate change to unsustainable energy generation and
distribution sources. The findings of Jacobs (1993) also substantiate the study result stating that
between 2010 and 2020, CO2 emissions are estimated to be reduced by 0.4–0.9 billion tonnes. The
Intergovernmental Panel report on Climate Change (IPCC) in 2019 also substantiates the study
result noting that 80% penetration of REN sources by 2050 will aid in combating climate change
(Masson-Delmotte, et, al 2018).
The empirical findings of Cheng et al., (2019); Danish Baloch et al., (2019) among others, posit
that efficient management of climate change and ecological quality improvement anchor on
efficient energy and renewable energy generation, distribution, and consumption. Economic
growth in the long-run is a key factor in reducing CO2 emissions. A 1% decrease in economic
growth through unsustainable energy sources in the long run reduces CO2 emissions for every 1%
increase in GDP through REN and EEF. MINT countries showed signs of a U-shaped curve.
These results, support the EKC hypothesis, and the findings of Marques et al. (2019), in MENA
region. As such, natural and man-made catastrophes instigate climate change (Udemba, 2020)
Carbon dioxide (CO2), sulphur hexafluoride (SF6), nitrous oxide (N2O), and methane (CH4) are
some of the gases that contribute to global warming because of human activities such as
deforestation, industrial smoke, and fossil fuel burning. From the results, we can infer that EEF,
and REN are the fulcrum for CO2 emissions in MINT countries, largely due to increasing energy
demand for industrialization and their other unique economic features to achieve their economic
vision for the next three decades.
Urbanisation through population growth and economic growth in MINT nations is expected to
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265
significantly impact energy efficiency and CO2 emissions through renewable energy. A 1%
increase in economic expansion and population growth rate requires excessive EEF and REN
resources to reduce CO2 emissions. This is evident in Mexico's rise from 19th in ranking in the
energy efficiency IEA scorecards of 25 nations in 2016 to 12th position in the 2018 IEA
scorecards. In the industrial energy efficiency programme, Mexico saved 3%; Indonesia saved 7%;
and Turkiye, collaborating with the IEA to reduce energy consumption, implemented the National
Energy Efficiency Action Plan to save $30.2 billion in energy consumption by 2023 through an
investment plan of approximately $11 million in energy efficiency (Presidency of the Republic of
Turkiye, I. O. (Producer), 2019).
Energy efficiency implementation in Nigeria is still very much at the primary stage due to non-
existing regulations spurred by a lack of commitment. However, the government is exerting efforts
to meet the growing energy demand through diversification of energy sources and adopting newly
available technology to cut energy wastage and save costs. The Council of Renewable Energy of
Nigeria revealed that power outages led to an income loss of about N126 billion (US$ 984.38
million) annually and also increased health hazards through CO2 emissions. The renewable
energy-CO2 emission results from this study clarify the asymmetric nexus within the MINT
countries. A 1% rise in renewable energy sources through technological advancement and
favourable eco-friendly government policy reduces CO2 emissions by 0.291% in the long run and
0.902% in the short run.
In Turkiye, the findings of Sugiawan and Managi (2016) collaborate the study results, upholding
that REN through green energy sources reduces CO2 emissions and enhances MINT counties'
environmental standards. The availability of green energy sources places the MINT countries in
an advantageous position. This is evident in Mexico's 2012 energy reform, which increased green
and nuclear energy from 35% by 2024 to 50% by 2050 (Defilippe, 2018). Also, the launch of
online green energy certificates is considered a key policy path to green energy and renewable
energy transformation.
Turkiye accounts for high renewable energy sources to increase green energy generation to 30%
by 2027. The IEA, 2019 report revealed that Turkiye is projected to rank among Europe’s top 5
renewable energy countries with 50% existing capacity, to reach 63 GW by 2024 (IEA, 2019).
Similarly, Indonesia's energy reform targets 788,000 MW in renewable energy generation and a
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266
23% renewable energy increase by 2025 to close the energy demand-supply gap for their budding
population. Renewable energy generation, distribution, and consumption in Nigeria are in the
developmental phase due to limited funds. Notwithstanding, the financial challenges hampering
the effective implementation of renewable energy programmes in Nigeria, investment in solar
energy in recent times has stood at approximately 20 million US dollars. The Nuclear energy-CO2
emission nexus in MINT countries within the review period of this study is negative and non-
significant. Hence, there is no asymmetric nexus, as MINT countries at present are not generating
significant mega electron volts (meV) of nuclear energy to reduce CO2 emissions.
4.2 Country-Specific Asymmetric Effects
Table 5 Non-Linear panel ARDL Asymmetric Effects
Indonesia
Mexico
Nigeria
Turkiye
Log-Run
Variable
Coefficient
Coefficient
Coefficient
Coefficient
GDP
-0.000184
(-10.94706)
-0.004363
(-0.033347)
-6.57E-05
(-3.162827)
0.261741
(2.055742)
EEF
0.110420
(4.934837)
6.017589
(0.033347)
0.022106
(1.710433)
0.581012
(10.72278)
NUE
-0.615061
(-13.76609)
-37.81883
(-0.035127)
-0.414607
(-2.143587)
-0.223516
(-4.650069)
REN
0.000436
(0.268014)
18.47957
(0.035123)
0.002283
(0.284262)
0.165506
(3.252301)
URB
0.025899
(9.926183)
-0.980708
(-0.043469)
0.011732
(1.252631)
0.322075
(11.63200)
C
0.094567
(0.363433)
65.55707
(0.025501)
4.804761
(10.27587)
24.06755
(7.159049)
Short-Run
COINTEQ01
-0.829731 (-
102.4395)**
-0.030811
(-17.97617)**
-0.586933
(-87.68135)**
-0.555653
(-33.02299)**
** at 0.05 level of significance.
Source: Author, (2023)
The country-specific results demonstrate the presence of asymmetric effects. EEF and REN had a
nonlinear impact on CO2 emission. In Nigeria, the energy efficiency-CO2 emission nexus is low
due to Nigeria’s inability to generate, distribute and consume efficient energy to achieve its
environmental goals in the short term. Similarly, Nigeria ranks low in renewable generation this
is evident in the REN and CO2 emission nexus, the result validates the proficiency of ecological
policies in nations with high CO2 emission. Mexico, Turkiye, and Indonesia are way ahead of
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267
Nigeria in renewable energy generation, distribution, and consumption. Goals 7; 12 and 13 of the
UN 2030 SDGs are all directly relevant to this study. Notably, the (COINTEQ01) results show the
speed of convergence from disequilibrium in the energy sector to long-run equilibrium in MINT
countries.
5. Conclusion
This study empirically assesses the asymmetric between EEF, REN and GDP on CO2 emission in
MINT countries using the NPARDL model via the PMG model. The finding of this study revealed
that EEF and REN through green energy sources reduces CO2 emission and improve the quality
of MINT countries' eco-system. Contrarily, the nexus between EEF, REN, GDP, CO2 emission,
and NUE within the period of this study negatively and non-significantly influenced CO2
emissions. Suggesting the insufficient generation and consumption of NUE in each MINT country.
The results support the U-shaped curve of the EKC hypothesis. Economic growth through
stainable energy sources in the long-short run reduces CO2 emissions for every 1% increase in
GDP through REN and EEF sources.
In specific MINT country estimate the nexus varies as heterogeneous properties among the MINT
economies are observed. The study findings revealed a vital policy inference for the MINT
countries. To prioritize their renewable procedures through energy efficiency, advancement in
energy technology, and easing of the legal requirements for energy efficiency particularly nuclear
energy technology adoption and implementation to achieve the NUN 2030 SDGs in MINT
economies. Similarly, this study recommends government policy on non-renewable energy
consumption reduction along with a micro-finance proposal for hydrological and biomass
generation to increase the green energy ratio in MINT countries. This study also recommends the
inclusion of cultural variables such as social, institutional, and political indicators, to assess this
nexus and their impact on CO2 emission in emerging economies or economic blocs for future
research. These variables have different preferences in specific countries.
268
COMPETING INTERESTS
The authors have no competing interests to declare.
Author’s Affiliation
Abner Ishaku Prince1 Inim Victor Edet 2 Boniface L. Akpan3 Emmanuel Samuel Udo4
Ekaetor, Enobong Akpan5
1University of Abuja Business School
2Department of Economics Nile University of Nigeria Abuja
3&5Department of Economics Akwa Ibom State University
5Department of Banking and Finance University of Nigeria Enugu Campus
COPYRIGHT:
© 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original author and source are credited. See http://creativecommons.org/
licenses/by/4.0/. Seybold Report is a peer-reviewed journal published by Seybold Publications.
HOW TO CITE THIS ARTICLE:
Prince, A. I., Edet , I. V., Akpan, B. L., Udo, E. S., & Akpan, E. E. (2023). Energy Efficiency,
Renewable Energy and Economic Growth Nexus on CO2 Emission: Evidence from MINT
Countries. Seybold Report Journal, 18(10), 253-273. DOI: 10.5110/77. 1091
Seybold Report Journal Vol. 18. No. 10. 2023
269
REFERENCES
Abner, I., P.,, Izuchukwu, O., Eneoli O., C., & Udo E., S., (2021) Energy Consumption Effect On
Economic Growth In Nigeria: Multivariate Framework. International Journal of
Economics, Management and Accounting 29, 2 (2021): 519-542.
Ahmad, A.U., Ismail, S., Ahmad, I.M., Adamu, I.M., Jakada, A.H., Farouq, I.S., & Danmaraya,
I.A. (2020a), Pollutant emissions, renewable energy consumption and economic growth:
An empirical review from 2015-2019. Journal of Environmental Treatment Techniques,
8(1), 323-335.
Ahmed, Z., & Wang, Z. (2019). Investigating the impact of human capital on the ecological
footprint in India: An empirical analysis. Environmental Science and Pollution Research,
26(26), 26782–26796. https://doi.org/10. 1007/s11356-019-05911-7.
Ahmed, Z., Zafar, M. W., Ali, S., & Danish. (2020). Linking urbanization, human capital, and the
ecological footprint in G7 countries: An empirical analysis. Sustainable Cities and Society,
55, 102064. https://doi.org/ 10.1016/j.scs.2020.102064.
Akram, R., Chen, F., Khalid, F., Huang, G., & Irfan, M., (2020). Heterogeneous effects of energy
efficiency and renewable energy on economic growth of BRICS countries: A fixed effect
panel quantile regression analysis. Energy 215, 119019.
Akram, R., Chen, F., Khalid, F., Ye, Z., & Majeed, M.T., (2019). Heterogeneous effects of energy
efficiency and renewable energy on carbon emissions: Evidence from developing
countries. Journal of Clean Production. 1 (19) 12-20.
Alam MM, Murad MW, Noman AHM, & Ozturk I ()2016. Relationships among carbon emissions,
economic growth, energy consumption and population growth: Testing Environmental
Kuznets Curve hypothesis for Brazil, China, India and Indonesia. Ecological Indicators.
2016; 70:466–79.
Azevedo, V.G., Sartori, S., & Campos, L.M., (2018). CO2 emissions: A quantitative analysis
among the BRICS nations. Renewable Substantiable Energy Review. 81, 107–115.
Balsalobre-Lorente, D., Driha, O.M., Bekun, F.V., & Osundina, O.A., (2019). Do agricultural
activities induce carbon emissions? The BRICS experience. Environment Science
Pollution. Research. 26 (24), 25218–25234.
Balsalobre-Lorente, D., Gokmenoglu, K.K., Taspinar, N., & Cantos-Cantos, J.M., (2019). An
approach to the pollution haven and pollution halo hypotheses in MINT countries. Environ.
Sci. Pollut. Res. 1–17.
Bayar, Y., & Gavriletea, M.D., (2019). Energy efficiency, renewable energy, economic growth:
evidence from emerging market economies. Qual. Quant. 1–14.
Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model
Seybold Report Journal Vol. 18. No. 10. 2023
270
specification in econometrics. The Review of Economic Studies, 47(1), 239.
https://doi.org/10.2307/2297111
Cheng, C., Ren, X., Wang, Z., & Yan, C., (2019). Heterogeneous impacts of renewable energy
and environmental patents on CO2 emission-Evidence from the BRIICS. Sci. Total
Environ. 668, 1328–1338.
Defilippe, M., 2018. Mexico grows as world leader on energy reform and renewables. Retrieved
fromhttps://www.renewableenergyworld.com/2018/04/12/mexico-reform-and-
renewables/
Dogan, E., Taspinar, N., & Gokmenoglu, K.K., (2019). Determinants of ecological footprint in
MINT countries. Energy Environ. 0958305X19834279.
Dong K, Sun R, & Hochman G (2017). Do natural gas and renewable energy consumption lead to
less CO emission? 2 Empirical evidences from a panel of BRICS countries. Energy.
2017:2–50.
Dong K, Sun R, Jiang H, & Zeng X (2018). CO2 emissions, economic growth, and the
environmental Kuznets curve in China: What roles can nuclear energy and renewable
energy play? Journal of Cleaner Production. 2018; 196:51–63.
Dutta, A., Bouri, E., & Roubaud, D., (2019). Nonlinear relationships amongst the implied
volatilities of crude oil and precious metals. Resour. Policy 61,473–478.
European Commission, (2016). The macroeconomic and other benefits of energy efficiency.
Retrieved from https://ec.europa.eu/energy/sites/ener/files/
documents/final_report_v4_final.pdf.
Fazli, P., & Abbasi, E. (2018), Analysis of the validity of kuznets curve of energy intensity among
D-8 countries: Panel-ARDL approach. International Letters of Social. and Humanistic
Science, 81, 1-12.
Haug, A.A., & Ucal, M., (2019). The role of trade and FDI for CO2 emissions in Turkey: Nonlinear
relationships. Energy Econ. 81, 297–307.
Hdom H,A,D, & Fuinhas J,A (2020). Energy production and trade openness: Assessing economic
growth, CO2 emissions and the applicability of the cointegration analysis. Energy Strategy
Reviews. 2020;30:100488.
Huang, Y., Xue, L., & Khan, Z. (2021). What abates carbon emissions in China: Examining the
impact of renewable energy and green investment. Sustainable Development.
https://doi.org/10.1002/SD.2177
IEA, (2019). Renewables 2019. Retrieved from https://www.iea.org/reports/renewables-2019.
Ikram, M., Zhang, Q., Sroufe, R., & Shah, S.Z.A., (2020). Towards a sustainable environment: the
nexus between ISO 14001, renewable energy consumption, access to electricity,
agriculture and CO2 emissions in SAARC countries. Sustain. Prod. Consum..
Jacobs M (1993). The green economy: Environment, sustainable development and the politics of
Seybold Report Journal Vol. 18. No. 10. 2023
271
the future: UBC press; 1993.
Jakada, A.H., & Mahmood, S. (2020), An asymmetric effect of economic growth, foreign direct
investment and financial development on the quality of environment in Nigeria. The
Journal of Management Theory and Practice, 1(1), 5-13.
Jakada, A.H., Mahmood, S., Ahmad, A.U., Muhammad, I.G., Danmaraya, I.A., & Yahaya, N.S.
(2022a), Driving forces of CO2 emissions based on impulse response function and variance
decomposition: A case of the main African countries. Environmental Health Engineering
and Management Journal, 9(3), 1-11.
Jim O Neill, (2013). Who You Calling a BRIC? Retrieved from https://
www.bloomberg.com/opinion/articles/2013-11-12/who-you-calling-a-bric-.
Khan, Z., Sisi, Z., & Siqun, Y., (2019). Environmental regulations an option: Asymmetry effect
of environmental regulations on carbon emissions using nonlinear ARDL. Energy Sources
Part A. Recovery Util. Environ. Eff. 41 (2), 137–155.
Kouton, J., (2019). The asymmetric linkage between energy use and economic growth in selected
African countries: Evidence from a nonlinear panel autoregressive distributed lag model.
Energy Econ. 83, 475–490.
Kumar, S., (2017). On the nonlinear relation between crude oil and gold. Resour. Policy 51, 219–
224.
Liobikiene, G., & Butkus, M., (2017). Environmental Kuznets Curve of greenhouse gas emissions
including technological progress and substitution effects. Energy 135, 237–248.
Liu, X., Zhang, S., & Bae, J., (2017). The nexus of renewable energy-agriculture environment in
BRICS. Appl. Energy 204, 489–496.
Magazzino C, & Mele M (2022). A new machine learning algorithm to explore the CO2 emissions-
energy use economic growth trilemma. Annals of Operations Research. 2022.
Magazzino C, Marco M, & Morelli G (2021). The Relationship between Renewable Energy and
Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the
Brazilian Economy. Sustainability. 2021;13(3).
Malik MY, KashmalaLatif, Khan Z, Butt H, Hussain M, & Nadeem MA (2202). Symmetric and
asymmetric impact of oil price, FDI and economic growth on carbon emission in Pakistan:
Evidence from ARDL and non-linear ARDL approach. Science of the Total Environment.
2020; 726:138421. https://doi.org/10.1016/j. scitotenv.2020.138421 PMID: 32481222.
Masson-Delmotte V, Zhai P, Po¨rtner H-O, Roberts D, Skea J, & Shukla PR, (2018). Global
warming of 1.5 C. An IPCC Special Report on the impacts of global warming of. 2018;
1(5).
Mohsin M, Kamran HW, Atif Nawaz M, Sajjad Hussain M, & Dahri AS (2021). Assessing the
impact of transition from nonrenewable to renewable energy consumption on economic
growth-environmental nexus from developing Asian economies. J Environ Manage. 2021;
284:111999. https://doi.org/10.1016/j.jenvman. 2021.111999 PMID: 33556829.
Seybold Report Journal Vol. 18. No. 10. 2023
272
Odugbesan, J.A., & Rjoub, H., (2020). Relationship among economic growth, energy
consumption, CO2 emission, and urbanization: evidence from MINT countries. SAGE
Open 10 (2), 2158244020914648.
Odugbesan, J.A., Sunday, T.A., & Olowu, G., (2021). Asymmetric effect of financial development
and remittance on economic growth in MINT economies: an application of panel NARDL.
Future Bus. J. 7 (1), 1–9.
Omojolaibi, J. A., & Nathaniel, S. P. (2020). Assessing the potency of environmental regulation
in maintaining environmental sustainability in MENA countries: An advanced panel data
estimation. Journal of Public Affairs. https://doi.org/10.1002/pa.2526
Pao, H.-T., & Tsai, C.-M., 2011. Multivariate granger causality between CO2 emissions, energy
consumption, FDI (foreign direct investment) and GDP (gross domestic product): evidence
from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy 36
(1), 685–693.
Pata, U.K., (2018). Renewable energy consumption, urbanization, financial development, income
and CO2 emissions in Turkey: testing EKC hypothesis with structural breaks. J. Clean.
Prod. 187, 770–779.
Pesaran, M.H., (2007). A simple panel unit root test in the presence of cross section dependence.
J. Appl. Econometrics 22, 265–312. http://dx.doi.org/10.1002/jae.951.
Presidency of the Republic of Turkey I. O. (Producer), 2019. Energy. Retrieved from
https://www.invest.gov.tr/en/sectors/pages/energy.aspx.
Salman, M., Long, X., Dauda, L., Mensah, C.N., & Muhammad, S., (2019). Different impacts of
export and import on carbon emissions across 7 ASEAN countries: A panel quantile
regression approach. Sci. Total Environ. 686, 1019–1029.
Shahbaz M, Balsalobre-Lorente D, & Sinha A (2019). Foreign direct Investment–CO2 emissions
nexus in Middle East and North African countries: Importance of biomass energy
consumption. Journal of Cleaner Production. 2019; 217:603–14.
Shao, Q., Wang, X., Zhou, Q., Balogh, L., (2019). Pollution haven hypothesis revisited: A
comparison of the BRICS and MINT countries based on VECM approach. J. Clean. Prod.
227, 724–738.
Shin, Y., Yu, B., & Greenwood-Nimmo, M., (2014). Modelling Asymmetric Cointegration and
Dynamic Multipliers in an ARDL Framework. In: Horrace, W.C., Sickles, R.C. (eds),
Festschrift in Honor of Peter Schmidt. Springer Science and Business Media, New York.
https://doi.org/10.1007/978-1-4899-8008-3_9
Soytas U, Sari R, & Ewing BT (2007). Energy consumption, income, and carbon emissions in the
United States. Ecological Economics. 2007; 62(3–4):482–9.
Sugiawan, Y., & Managi, S., (2016). The environmental Kuznets curve in Indonesia: Exploring
the potential of renewable energy. Energy Policy 98, 187–198.
Tajudeen, I.A., Wossink, A, & Banerjee, P. (2018), How significant is energy efficiency to
Seybold Report Journal Vol. 18. No. 10. 2023
273
mitigate CO2 emissions? Evidence from OECD countries. Energy Economics, 72, 200-
221.
Udemba, E. N. (2020). A sustainable study of economic growth and development amidst
ecological footprint: New insight from Nigerian perspective. Science of the Total
Environment, 732, 139270.
Udo, E., S., Idamoyibo, H., R., Inim, V., Akpan, J., E., & Ndubuaku, V., (2021). Energy
Consumption and Sectorial Value Addition on Economic Growth in Nigeria," Universal
Journal of Accounting and Finance, 9, (1), 74 - 85, 2021. DOI:
10.13189/ujaf.2021.090108.
Umar, M., Ji, X., Kirikkaleli, D., & Alola, A.A., (2021). The imperativeness of environmental
quality in the United States transportation sector amidst biomass-fossil energy
consumption and growth. J. Clean. Prod. 285, 124863.
http://dx.doi.org/10.1016/j.jclepro.2020.124863.
Waheed R, Chang D, Sarwar S, & Chen W (2018). Forest, agriculture, renewable energy, and CO2
emission. Journal of Cleaner Production. 2018; 172:4231–8.
Wang, R., Wang, Q., & Yao, S. (2021), Evaluation and difference analysis of regional energy
efficiency in China under the carbon neutrality targets: Insights from DEA and theil
models. Journal of Environmental Management, 293, 112958.
Wang, S., Zhou, D., Zhou, P., & Wang, Q., (2011). CO2 emissions, energy consumption and
economic growth in China: A panel data analysis. Energy Policy 39 (9), 4870–4875.
World Bank, 2021. Projected GDP Ranking. from World, Bank International Monetary Fund
World Economic Outlook (April - 2021).
World Bank, 2022. World Development Indicators.
Zafar, M. W., Shahbaz, M., Hou, F., & Sinha, A. (2019). From nonrenewable to renewable energy
and its impact on economic growth: The role of research & development expenditures in
Asia-Pacific economic cooperation countries. Journal of Cleaner Production, 212, 1166–
1178. https://doi.org/10.1016/j.jclepro.2018.12.081
Zhang, B., Wang, B., & Wang, Z., (2017). Role of renewable energy and nonrenewable energy
consumption on EKC: evidence from Pakistan. J. Clean. Prod. 156, 855–864.
