Determinants of load capacity factor in South Korea: does structural change matter?

Abstract

By likening biocapacity and ecological footprint, the load capacity factor follows a specified ecological threshold, permitting for an in-depth analysis of ecological damage. It can be seen that as the load capacity factor is reduced, the ecological damage intensifies. Until now, scholars have used carbon dioxide, ecological footprint, nitrogen oxide, sulfur dioxide, and other indices to objectively examine ecological problems. The utilization of these metrics can cause the supply side of ecological concerns to be overlooked. To make up for this weakness, this paper evaluates the impact of structural change and trade globalization on the load capacity factor. The research also considers other drivers of load capacity factors such as economic growth and energy. We utilized the nonparametric such as nonparametric causality and quantile-on-quantile (QQ) regression approaches to scrutinize these interconnections for South Korea between 1970 and 2018. The findings from the QQ approach disclosed that in the majority of the quantiles, the influence of economic growth, structural change, energies (renewable and nonrenewable), and trade globalization mitigate the load capacity factor. Moreover, the nonparametric causality test divulged that in variance and mean, all the independent variables can predict the load capacity factor. Policy proposals for South Korea’s sustainable development are offered based on the findings.

Full text

Vol.:(0123456789)
1 3
Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-022-20676-2
RESEARCH ARTICLE
Determinants ofload capacity factor inSouth Korea: does structural
change matter?
TahaAbdulmagidBasheerAgila1· WagdiM.S.Khalifa1· SeyiSaintAkadiri2· TomiwaSundayAdebayo3 ·
MehmetAltuntaş4
Received: 23 December 2021 / Accepted: 3 May 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
By likening biocapacity and ecological footprint, the load capacity factor follows a specified ecological threshold, permit-
ting for an in-depth analysis of ecological damage. It can be seen that as the load capacity factor is reduced, the ecological
damage intensifies. Until now, scholars have used carbon dioxide, ecological footprint, nitrogen oxide, sulfur dioxide, and
other indices to objectively examine ecological problems. The utilization of these metrics can cause the supply side of
ecological concerns to be overlooked. To make up for this weakness, this paper evaluates the impact of structural change
and trade globalization on the load capacity factor. The research also considers other drivers of load capacity factors such
as economic growth and energy. We utilized the nonparametric such as nonparametric causality and quantile-on-quantile
(QQ) regression approaches to scrutinize these interconnections for South Korea between 1970 and 2018. The findings from
the QQ approach disclosed that in the majority of the quantiles, the influence of economic growth, structural change, ener-
gies (renewable and nonrenewable), and trade globalization mitigate the load capacity factor. Moreover, the nonparametric
causality test divulged that in variance and mean, all the independent variables can predict the load capacity factor. Policy
proposals for South Korea’s sustainable development are offered based on the findings.
Keywords Load capacity factor· Structural change· Trade globalization· Renewable energy use· Economic growth
Introduction
Over time, enormous pressure has been mounted on the
environment due to structural changes amid trade globali-
zation as a result of the financial integration of the world
economies (Acheampong etal. 2022). South Korea (SK),
as a developed economy, is blessed with a huge and ris-
ing population. The economy has also become one with
high production and consumption activities over the last
two decades. The industrial framework of the SK economy
has changed persistently since the 1980s. This is the period
where industrialization of the economy sprouted, the manu-
facturing sector became matured, and the service sectors
conjointly contributed hugely to the gross domestic product.
Following the study of Suh (2004), the manufacturing sector
outperforms the service sector. Overall productivity in the
manufacturing sector was found to be larger when compared
with the output in the service sector. Although, in terms of
employment, the service sector do better than the manufac-
turing sector (the automobiles and electronics sectors hugely
contribute to the growth rate recorded in the manufacturing
Responsible Editor: Ilhan Ozturk
* Tomiwa Sunday Adebayo
tadebayo@ciu.edu.tr
Taha Abdulmagid Basheer Agila
wonderfull_forever@yahoo.com
Wagdi M. S. Khalifa
wagdikalifa@gmail.com
Seyi Saint Akadiri
ssakadiri@cbn.gov.ng
Mehmet Altuntaş
mehmet.altuntas@nisantasi.edu.tr
1 University ofMediterranean Karpasia, NorthernCyprus,
Turkey
2 Research Department, Central Bank ofNigeria, Abuja,
Nigeria
3 Faculty ofEconomics andAdministrative Science,
Department ofBusiness Administration, Cyprus International
University, Mersin 10, Nicosia, NorthernCyprus, Turkey
4 Faculty ofEconomics, Administrative andSocial Sciences,
Department ofEconomics, Nisantasi University, Istanbul,
Turkey

Environmental Science and Pollution Research
1 3
sector) which maintained a stable level in terms of value-
added. This leads to one fact that escalating productivity in
the service sector is inevitable for economic growth and/or
development in the region. The widening gaps via the nature
and the size of the sector are the most obvious in inter-sec-
toral disparities recorded in total factor productivity and the
economic growth rate at large. On this basis, one will be
theoretically right to assume that structural variations over
time have in one way or another impact on economic growth
and, as a result, influence the environment via consumption
and/or production activities (Lin 2011).
Furthermore, the economic phenomenon in terms of
the widening gaps among businesses and economic sec-
tors which Suh (2004) terms polarization or bifurcation is
evident in the financial system in this region. SK economy
started its journey to financial liberation in the 1990s. This
transition intensifies healthy competition among financial
institutions and also leads to the upwards movement in for-
eign borrowings, alongside short termination of both foreign
debt and domestic financing that makes the SK economy
vulnerable and prone to external shocks; all of these led
to the financial crisis experienced in 1997. With the help
of President Kim Young-sam (1993–1998), the financial
transformation program, the progress of trade liberalization
was intensified. Several constraints on financial and foreign
exchange markets were abolished, while some were relaxed
to pave the way for “globalization” of the SK economy. This
is coupled with the region becoming an OECD member in
1996 which also fueled the financial liberalization transition.
Having established how structural changes and trade glo-
balization evolve in the region, we believe that these series
might have in one way or the other promoted environmental
degradation or environmental quality of the region, which
this current study seeks to investigate while controlling for
other drivers. Although, the drivers of environmental degra-
dation, specifically CO2 calculated in metric tons per capita
in South Korea, have been extensively examined (see Ade-
bayo etal. 2021; Alvarado etal. 2021; Ivanovski etal. 2021;
Koc and Bulus 2020; Marchenko etal. 2019; Chen etal.
2019; Lee and Jung 2018; Destek and Aslan, 2017; Adewuyi
and Awodumi 2017). However, Wu etal. (2019) and Solarin
and Bello (2018) argued that CO2 emissions, though they
constitute a large portion of global GHGs emissions, may
be inadequate to replicate and explore the entire ecological
dilapidation. In the light of this argument, researchers have
turned the searchlight to investigate the factors responsible
for environmental degradation from the angle of structural
changes and financial globalization.
As a result of the inability of some environmental pollu-
tion proxy such as ecological footprint and biocapacity that
only captures the demand and the supply side of the environ-
ment to fully capture and replicate environmental degrada-
tion, Siche etal. (2010) proposed a better environmental
assessment indicator known as the “load capacity factor.”
According to Siche etal. (2010), the load capacity factor
(LCF) expresses the strength of an area, region, or country
to use the present lifestyle to sustain the population of such
a region or country. The LCF is measured as the supply
side (biocapacity) divided by the demand side (ecological
footprint) of the ecosystem. Also, Pata (2021) and Siche
etal. (2010) stated that a load capacity less than 1 shows
that the existing environmental situation is not sustainable,
while a value greater than 1 is an indication that the exist-
ing system is sustainable. Thus, the sustainability limit is
equal to 1. South Korea, at present, is reported as one of
the nations with poor ecological sustainability in terms of
both the demand side (ecological footprint) and the supply
side (load capacity factor) of the ecological system. Recent
statistics on biocapacity and ecological footprint for South
Korea indicate that its ecological footprint surpassed its bio-
capacity with about 852% in 2021 (Global Footprint Net-
work 2021). A glimpse of the trend of ecological footprint
and biocapacity in South Korea for the period 1970–2018
is shown in Fig.1.
The present paper contributes to the literature as follows:
First, methodologically, we contribute to the body of lit-
erature by applying the QQR developed by Sim and Zhou
(2015). The QQR novelty lies in its ability to amalgamate
the essentials of nonparametric estimation and quantile
regression (QR) analysis. In doing so, the technique inclines
to regress one variable quantile into another, and the results
have the probability to disclose the impact of endogenous
variables on exogenous variables across all quantiles. Taking
into account the diversity of structural change and trade glo-
balization, the use of the Q-Q regressions can provide a use-
ful insight into empirical findings compared to those derived
from conventional quantile regression and the OLS method.
Furthermore, we applied the nonparametric causality in
quantiles suggested by Balcilar etal. (2017) to capture the
causal association between the exogenous variables and the
endogenous variables. Unlike other causality tests, the non-
parametric causality can capture causality at different quan-
tiles (0.1–0.90) in both the mean and variance. Secondly, we
contribute theoretically by using the load capacity factor as
environmental degradation proxy. By likening biocapacity
and ecological footprint, the load capacity factor follows a
specified ecological threshold, permitting for an in-depth
analysis of ecological damage. Unlike proxies such as carbon
dioxide, ecological footprint, nitrogen oxide, sulfur dioxide,
and other indices that ignore the supply side of the ecologi-
cal issue, the load capacity factor captures both the demand
and supply side of environmental issues. To the understand-
ing of the authors, this is the first study that assesses the
effect of structural change and trade globalization on the
load capacity factor. Lastly, the current research tends to
identify how existing structural frameworks alongside trade

Environmental Science and Pollution Research
1 3
globalization can be accommodated in environmental policy
decision-making toward reducing environmental degrada-
tion. Thus, the empirical findings would assist governments
and policymakers to identify the most effective structural
framework and trade liberalization policy when making pol-
icy decisions toward curtailing environmental degradation.
The remaining segments of the research are designed as
follows: The “Synopsis of studies” section unveils a sum-
mary of studies. The “Theoretical framework, data, and
methodology” section presents the methods and data of the
paper. The discussions of the empirical findings are pre-
sented in the “Findings and discussion” section. The “Con-
clusion and policy suggestions” section offers conclusions
with policy recommendations.
Synopsis ofstudies
This portion offers a summary of the related studies on the
drivers of ecological quality. Though there are several driv-
ers of environmental deterioration, this present research only
focuses on structural change, economic growth, renewable
energy, trade globalization, and nonrenewable energy use.
Over the years, numerous works have been conducted on
drivers of environmental quality (Pata & Samour, 2022;Sal-
eemet al. 2022;Acheampong etal. 2021; Shahzad etal.
2021; Samour etal. 2022; Xia etal. 2022;Adebayo, 2022b;
Sharma etal. 2021; Habeşoğluet al. 2022;He etal. 2021;
Chen etal. 2021; Twum etal. 2021; Long et al. 2021,
2020; Rehman etal. 2021; Farooq etal. 2022; Samour
& Pata, 2022;Ozturk and Acaravci 2010; Le and Ozturk
2020; Akadiri etal. 2022b;Muhammad and Long 2021);
nevertheless, their conclusions are diverse founded on the
nation/nations of investigation, method(s), and period. For
instance, in Japan, Ikram etal. (2021) evaluated drivers of
environmental degradation using a dataset from 1965Q1 to
2017Q4 and the novel QARDL approach. The study out-
comes disclosed that GDP impacts environmental deteriora-
tion positively. Furthermore, Shahzad etal. (2021), in their
work on the effect of growth and coal energy usage using the
Pairwise Granger causality and ARDL approaches from 1979
to 2016, reported that GDP and coal consumption trigger
CO2 emissions positively. Using a dataset between 1971 and
2018, the study of Xia etal. (2022) on the effect of energy use
and growth on CO2 for 67 developing and developed nations
disclosed that an increase in CO2 is caused by an upsurge
in utilization of energy and GDP. Moreover, Sharma etal.
(2021) investigated the drivers of emissions in the BIMSTEC
region using a dataset from 1985 to 2019. The finding from
the research disclosed that in all tails (0.1–0.90), the effect of
GDP on CO2 is positive. Likewise, the research of He etal.
(2021), in their research on the effect of GDP on CO2 in
the top 10 energy transition nations utilizing the CS-ARDL
between 1990 and 2018, reported that growth stimulates CO2.
To handle the issue of mounting emissions levels, author-
ities are promoting the use of renewable energy options.
Studies on the linkage between renewable energy use (REC)
and ecological deterioration have been done with most
establishing that renewable energy mitigates environmental
deterioration. For example, Abbasi etal. (2021) scrutinized
the nexus between renewable energy (REC) and CO2 in
Thailand utilizing the dynamic ARDL between 1980 and
2018. The research finding disclosed that REC curbs emis-
sions of CO2. Similarly, Radmehr etal. (2021) explored the
nexus between REC and emissions in EU countries using
the GS2SLS approach, and the outcome unveiled a negative
association between REC and CO2. This infers that REC aid
in abating emissions in EU nations. Likewise, the research
of Leitão and Lorente (2020) in EU nations on the drivers
of emissions using GMM and FMOLS from 1995 to 2014
Fig. 1 Trend of ecological
footprint and biocapacity for
South Korea

Environmental Science and Pollution Research
1 3
disclosed that REC curbs emissions in the selected nations.
Moreover, using the G11 countries and dataset from 1990
to 2019, the research of Mehmood (2021) reported that
CO2-REC negative interconnectedness.
Trade globalization can increase/decrease pollution. For
example, Wang etal. (2020) assessed the effect of trade glo-
balization on emissions using the ASEAN nations and dataset
from 1985 to 2017. Their finding showed that trade globaliza-
tion abates CO2 emissions in the ASEAN nations. Further-
more, the work of Guo etal. (2021) on the trade and emis-
sions nexus in the leading emitting nations using the GMM
approach revealed that trade curbs CO2. Likewise, using a
dataset from 1986 to 2014, Xu etal. (2020) scrutinized the
trade and CO2 nexus in China, utilizing 116 economies as a
case study. The investigators use a panel approach, and the
result uncovered that trade surges CO2. Furthermore, using a
dataset from 1990 to 2016, Dauda etal. (2021) investigated
the trade openness-emissions nexus for selected African
nations, and their outcome uncovered that trade openness
hinders environmental quality, therefore validating the Pol-
lution haven hypothesis. Likewise, the study by Essandoh
etal. (2020) reported that for developed countries, the effect
of trade on CO2 is negative, while the effect is positive for
emerging nations for 52 countries between 1991 and 2014.
Furthermore, it is claimed that when the level of income
is low, there is a propensity for people to migrate from agri-
culture to the industrial sector leading to enhanced earnings
in jobs in manufacturing, which are more hazardous and
might contribute to an upsurge in contamination. On the
flip side, when income levels are high, a significant move-
ment from the manufacturing sector to the service sector,
which is generally low in pollution-linked operations, can
be noticed. As a result, this shift can be termed structural
change in GDP acquisition. Studies on the structural change
and CO2 interconnectedness have been conducted; however,
mixed empirical outcomes have surfaced. For instance, Ali,
etal. (2020b, a), using a dataset from 1971 to 2014, assessed
the interconnectedness between structural change (STC) and
CO2 in 63 countries. The investigators used GMM, and their
result uncovered that STC impacts CO2 positively. Likewise,
using the ARDL approach, Ali, etal. (2020b, a) investigated
the interconnection between STC and CO2 using data from
1985 to 2016, and their empirical outcome unveiled that
STC does not have a significant influence on Malaysia’s
CO2 emissions. Moreover, Villanthenkodath etal. (2021),
in their research on the STC-CO2 nexus utilizing data from
1995Q1 to 2016Q4, disclosed that STC improves the qual-
ity of the environment in India. Likewise, the study of Alia
etal. (2021), using data from 1980 to 2017 for the case of
Pakistan, reported that STC impacts CO2 positively.
Based on the reviewed literature, it is clear that the out-
comes of the interrelationship between CO2 emissions and
its determinants are mixed. Therefore, it is vital to take a
step further by assessing these associations using advanced
econometric techniques. As stated by Adebayo etal. (2021),
using recent econometric techniques is vital to ensure impar-
tial results. As a result, we utilized the recent quantile-on-
quantile regression to assess these associations.
Theoretical framework, data,
andmethodology
Theoretical framework
The current energy consumption trend in Asian nations such
as South Korea has a negative environmental externality since
fossil fuel-based energy offers a vital stimulus to economic
progress. South Korea’s increased economic growth has led
to an increase in job prospects. South Korea is significantly
reliant on nonrenewable energy sources to meet its energy
needs. South Korea can maintain its growth agendas thanks to
cross-border transfers of energy resources. As a corollary, par-
ticularly in the last two decades, South Korea has seen unprec-
edented economic development. Nevertheless, this approach
has increased environmental costs in the context of ecological
preservation (Akadiri etal. 2022a; Adebayo, 2022a;Ozturk
and Acaravci 2016). The rising pressure of GHGs caused by
the usage of nonrenewable energy resources demonstrates this.
To handle the issue of expanding emissions levels, author-
ities are promoting the use of renewable energy options.
While South Korea lags behind its developed counterparts,
it may be assumed that the dissemination and development
of these renewable energy-generating technologies across
countries will begin to coopt the negative environmental
externalities produced by the current economic expansion
trend. As a consequence, dealing with the issue of ecologi-
cal damage in South Korea warrants the creation of policy
initiatives that address these concerns.
Moreover, it is claimed that when the level of income is
low, there is a propensity for people to migrate from agri-
culture to the industrial sector leading to enhanced earnings
in jobs in manufacturing, which are more hazardous and
might contribute to an upsurge in contamination. On the flip
side, when income levels are high, a significant movement
from the manufacturing sector to the service sector, which is
generally low in pollution-linked operations, can be noticed.
As a result, this adjustment can be regarded as a structural
change in GDP acquisition (Alia etal. 2021).
Trade globalization can either increase or decrease emis-
sions. Moreover, Dinda (2004)’s inspiring study reveals
that trade openness can boost pressure on the environ-
ment through the composition effect since the emphasis on
expanding output simultaneously increases input, result-
ing in increased CO2 emissions and waste. Nevertheless,
the following two phases of trade, notably technique and

Environmental Science and Pollution Research
1 3
composition effects, reduce emissions because structural
changes in the economy favor cleaner operations and the
technique effect contributes to the substitution of technolo-
gies that are polluting green technology. Predicated on this
detailed debate, it is clear that trade globalization can have a
negative/positive impact on environmental quality.
Drawing inspiration from this short description, it can be
assumed that South Korea’s economic progress is exerting
an ecological strain via causing ambient air pollution. As a
result, economic expansion and its prospective contributors
can be blamed for the increase in CO2 in South Korea. In
furtherance of this reasoning, the following functional form
of this relationship can be hypothesized:
Data
In this empirical analysis, we assessed drivers of load capac-
ity factor (LCF) in South Korea. The drivers of LCF used in
this study comprise nonrenewable energy use (NREC), eco-
nomic growth (GDP), structural change (STC), trade globali-
zation (TGLO), and renewable energy use (REC). The data-
set used in this research spans between 1970Q1 and 2018Q4.
The dependent variable is LCF which is measured by divid-
ing biocapacity by ecological footprint. The independent
(1)
LCF
t=
(
GDPt,STCt,RECt,NRECt,TGLOt
)
variables are economic growth (GDP) which is calculated
as GDP per capita (constant 2015 US$), renewable energy
(REC) is calculated as KWh, nonrenewable energy use is
measured as fossil fuels (TWh), trade globalization (TGLO)
is calculated as trade globalization index, and structural
change (STC) is calculated as service value-added percent
of GDP. Moreover, LCF is gathered from Global Footprint
Network, NREC and REC are obtained from the British petro-
leum database, TGLO is obtained from KOF, and STC and
GDP are gathered from World Bank Database. To guarantee
that there are no abnormalities during estimation, the reported
variables were transfigured into their natural logarithms. Fur-
thermore, the flow of analysis is depicted in Fig.2.
Methodology
We employed the PP and ADF tests to identify the sta-
tionarity attribute of the variables which is followed by
the BDS test initiated by Broock etal. (1996) to identify
the nonlinearity attributes of the variables of the research.
Furthermore, we check the cointegration feature between
the load capacity factor and each independent variable.
The cointegration vector appears to shift across the dis-
tribution, according to empirical literature in finance and
economics. As a result, the quantile cointegration offered
by Xiao (2009) is used in this research. The beauty of this
approach is that it allows one to investigate the effect of
Fig. 2 Flow of analysis

Environmental Science and Pollution Research
1 3
conditioning variables on the scale, location, and shape of
the feedback variable’s conditional distribution. Moreover,
Xiao (2009) splits the errors of the cointegrating equation
into lead-lag components using the approach proposed by
Saikkonen (1991), and in this way, Xiao (2009) employs
a new model to address the endogeneity difficulties that
plague classic cointegration frameworks.
Moreover, we evaluate the interrelationship between load
capacity factor and its drivers using Sim and Zhou (2015)’s
quantile-on-quantile (QQ) technique. This method is a
refinement of normal quantile regression (QR), which inves-
tigates the impact of the independent variable’s quantiles on
the distinct quantiles of the endogenous variable. The utiliza-
tion of nonparametric estimations and QR is the foundation
of this approach. To begin, traditional QR is used to explore
the impact of exogenous variables on the endogenous vari-
able. The standard QR proposed by Bassett and Koenker
(1978) is a modification of the standard OLS. Unlike the
linear regression (LR) model, QR investigates the impact of
a variable not only on the conditional mean of the depend-
ent variable but also on distinct quantiles. In this sense,
the QR, as compared to the least square method, shows a
more complete association. Furthermore, conventional lin-
ear regression (LR), as advocated by Cleveland (1979) and
Stone (1977), is employed to analyze the exogenous variable
quantiles’ effect on endogenous variable quantiles. Merging
the two techniques (traditional LR and standard QR) permits
scholars to explore the various quantiles of the exogenous
variable influence on various endogenous variable quantiles.
As a consequence, blending these two methodologies can
allow us to understand the fundamental interrelationship bet-
ter than utilizing either OLS or traditional QR alone.
Thus, we applied the QQ estimation initiated by Sim
and Zhou (2015) to assess the influence of different quan-
tiles of X on the various quantiles of Y.
where the load capacity factor in period t is depicted by
Yt
, and
Xt
stands for the exogenous variable in time t. More-
over,
𝝈
is the
𝝈th
quantile on the independent variables. Fur-
thermore,
𝝁𝝈
t
denotes quantile error-term, where
𝝈th
quantile
estimated is 0. Furthermore,
∝𝝈(.)
is unidentified because
of no availability of information on the interrelationship
between LCF and the independent variables. Finally, it is
crucial to comprehend bandwidth determination while per-
forming nonparametric analysis. This bandwidth has the
benefit of aiding with the target point’s simplicity, the size
of the quarter background, and, as a consequence, band-
width gearshifts the conclusion’s speed. A high bandwidth,
h, reduces variability while increasing estimate deviation
and vice versa. We use a bandwidth value of h = 0.05 in this
investigation, as advised by Sim and Zhou (2015).
(2)
Y
t=𝜸𝝈
(
Xt
)
+𝝁
𝝈
t
Lastly, we utilized the novel nonparametric causality
initiated by Balcilar etal. (2018) centered on the mod-
els of Jeong etal. (2012) and Nishiyama etal. (2011).
The three novelty of the causality-in-quantile method are
as follows: First, it is resistant to errors of misspecifica-
tion because it recognizes the underpinning dependence
structure between the variables under consideration. This
is extremely significant because financial and economic
variables are well recognized to have nonlinear dynamics
(Shahbaz etal. 2017), which we demonstrate to be true in
the current research. Second, we can use this approach to
test not only for causation in the mean (1st instant) but also
for causality in the tails of the variables’ joint distribution.
Finally, we can look into causality-in-variance and hence
look into higher-order dependence. This research is essen-
tial because, at times, causation in the conditional mean
may not occur, whereas higher-order interdependencies
may become relevant.
Findings anddiscussion
Pre‑estimation tests
The present research assesses the effect of structural change
(STC) on load capacity factor (LCF) as well as other driv-
ers of LCF in South Korea, utilizing a dataset spanning
from 1970Q1 to 2018Q4. We commenced by presenting the
data summary which is reported in Table1. The mean of
GDP (13,475.9) is the highest which ranges from 1903.0
to 30,649.8, NREC (1408.9) which ranges from 157.30
to 3037.8, STC (48.2800) which ranges from 38.5977 to
56.3192, and TGLO which ranges from 21.1441 to 69.4876.
All the variables are skewed positively with the exemption
of structural change which is negatively skewed. Further-
more, GDP, NREC, STC, and TGLO are leptokurtic, while
LCF and REC are platykurtic. In addition, the p-value of the
JB revealed that STC, TGLO, REC, NREC, LCF, and GDP
do not affirm normality. Based on this understanding, using
a linear technique(s) to assess these interconnections will
produce biased outcomes. Furthermore, stationarity attrib-
utes of the indicators are assessed using PP and ADF tests,
and the outcomes are reported in Table4. The outcomes
unveiled that STC, TGLO, REC, NREC, LCF, and GDP are
I(1) variables. Furthermore, we utilized the BDS test initi-
ated by Broock etal. (1996) to catch the linearity attributes
of the variables of investigation. The outcome of the BDS
test is reported in Table2 which uncovered that all the vari-
ables are nonlinear. These results align with the Jarque–Bera
outcomes in Table1. Based on this knowledge, we utilized
nonparametric techniques to assess the drivers of load capac-
ity factor in South Korea.

Environmental Science and Pollution Research
1 3
Quantile cointegration outcomes
Table 3 reports the quantile cointegration (QC). The
observed variable stability is illustrated by the Supremum
norm values of coefficients (β and γ). As shown in Table3,
all of the coefficients (β and γ) exhibit larger values of supre-
mum norm than all of the critical values, indicating the pres-
ence of a strong nonlinear or asymmetric long-run connec-
tion between load capacity factor and trade globalization,
economic growth, renewable energy, nonrenewable energy,
and structural change.
QQR results
The present research employed the novel QQR approach
to capture the effect of economic growth, trade globaliza-
tion, renewable energy use, structural change, and nonre-
newable energy use on the load capacity factor. The QQR
results are reported in Fig.3. Figure3a depicts the effect of
GDP on LCF. In all tails (0.1–0.90) of GDP and LCF, the
effect of GDP on LCF is negative; however, in the lower tail
(0.1–0.30), the effect is more pronounced. Specifically, in
all quantiles (0.1–0.90) of both GDP and LCF, GDP impacts
LCF negatively. Therefore, an upsurge in GDP mitigates
LCF in all quantiles (0.1–0.90). Furthermore, Fig.3b pre-
sents the effect of REC on LCF in South Korea. In the lower
tail (0.1–30) of the combination of both REC and LCF, the
effect of REC on LCF is positive which implies that in the
lower tail (0.10–0.30), an upsurge in REC increases LCF. In
addition, in the middle and higher tails (0.40–0.90) of the
combination of both REC and LCF, the effect of REC on
LCF is negative which implies that in the middle and upper
quantiles (0.45–0.90), an upsurge in REC decrease LCF.
Moreover, Fig.3c presents the TGLO effect on LCF in
South Korea. In the lower tail (0.1–0.40), the effect of TGLO
on LCF is negative which illustrates that an upsurge in
TGLO mitigates LCF in the lower tail (0.10–0.40); however,
between 0.45 and 0.50 quantiles, there is a slight positive
Table 1 Descriptive statistics and unit root tests outcomes
* P < 1%
** P < 5%
*** P < 0.10%
JB, Jarque–Bera; SD, standard deviation; ADF, augmented Dickey-Fuller; PP, Phillip-Peron
Descriptive statistics Stationarity tests
Mean Median Maximum Minimum SD Skewness Kurtosis JB
Δ
ADF
Δ
PP
NREC 1408.9 1374.12 3037.8 157.30 958.96 0.1741 1.5175 18.5518* − 3.4583** − 6.9876*
GDP 13,475.9 11,856.08 30,649.8 1903.0 9147.9 0.3537 1.7237 17.0341* − 4.1097* − 6.8092*
LCF 0.2761 0.1889 0.8425 0.1026 0.1821 1.0756 3.2271 37.4365* − 4.8058* − 6.8931*
REC 0.2754 0.2047 1.2662 0.0990 0.2175 2.3805 8.8748 457.4524* − 4.9756* − 7.0946*
STC 48.2800 49.1379 56.3192 38.5977 5.8998 − 0.1781 1.5314 18.2700* − 3.4441** − 6.5229*
TGLO 42.7075 40.6664 69.4876 21.1441 14.6698 0.1887 1.5641 17.6323* − 4.1790* − 6.5313*
Table 2 BDS test
* 1% level of significance
Z-statistics
Dimension GDP REC NREC STC TGLO LCF
M2 60.072* 18.786* 67.226* 67.221* 63.268* 35.947*
M3 63.997* 19.623* 72.033* 72.318* 67.609* 38.414*
M4 69.143* 20.817* 78.248* 78.729* 73.305* 41.559*
M5 76.793* 22.721* 87.324* 88.006* 81.670* 46.116*
M6 87.399* 25.419* 99.891* 100.738* 93.352* 52.443*
Table 3 Quantile cointegration test outcomes
Model Coefficient
Sup𝜏|Vπ(τ)|
CV1 CV5 CV10
LCFtVsGDPt
β 4998.31 3797.49 2180.51 913.503
𝛾
539.523 311.759 224.895 150.265
LCFtVsRECt
β 3807.54 2283.09 1543.82 870.854
𝛾
404.043 305.682 202.731 125.439
LCFtVsNRECt
β 6926.53 5345.46 4092.63 2992.63
𝛾
701.252 570.76 410.276 231.238
LCFtVsTGLOt
β 2389.92 1631.62 1140.28 804.028
𝛾
374.289 284.234 192.463 89.9544
LCFtVsSTCt
β 7377.35 5012.52 3125.77 1102.76
𝛾
559.78 345.421 284.735 194.422

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and significant connection between TGLO and LCF. In addi-
tion, in the middle and higher tails (0.55–0.90), the effect of
TGLO on LCF is negative which unveils that TGLO miti-
gates LCF in the middle and higher quantiles (0.55–0.90). In
summary, we established that TGLO influences LCF nega-
tively. Figure3d illustrates the effect of NREC on LCF. In
all tails (0.1–0.90), the effect of NREC on LCF is negative,
suggesting that an upsurge in NREC in all tails (0.10–0.90)
influences LCF negatively. Therefore, an upsurge in NREC
decreases LCF in all quantiles (0.10–0.90).
Lastly, Fig.3e unveils the effect of STC on LCF in South
Korea. In the lower tail (0.1–0.30) of the combination of
STC and LCF, the influence of STC on LCF is positive. This
implies that in the lower quantiles (0.1–0.35), STC improves
LCF. In addition, in the middle and upper tails (0.40–0.90),
the negative and dominance effect of STC on LCF is estab-
lished, suggesting that an upsurge in STC mitigates LCF in
the middle and upper tails (0.40–0.90).
Robustness check
The quantile regression (QR) is utilized as a robustness
check for the QQR. The summary of the slope coefficient
of both QR and QQR of the effect of GDP on LCF in South
Korea is presented in Fig.4a. Figure4a affirmed the nega-
tive effect of GDP on LCF across all quantiles (0.1–0.90).
Furthermore, there is a similar trajectory between the slope
coefficient of QR and QQR which substantiates our find-
ings. Moreover, Fig.4b unveiled the influence of REC on
LCF in each quantile. The QQR outcomes are affirmed by
Fig.4b which disclosed the negative effect of REC on LCF.
In addition, we affirmed a similar slope coefficient between
the outcomes of QR and QQR. Figure4c presents the out-
come of the effect of TGLO on LCF in South Korea which
affirmed the QQR outcomes. It is observed that in each
quantile (0.1–0.90), the effect of TGLO on LCF is negative.
Furthermore, there is a similar trajectory between the slope
coefficient of QR and QQR. Moreover, Fig.4d unveiled the
influence of NREC on LCF in each quantile. The QQR out-
comes are affirmed by Fig.4d which disclosed the negative
effect of NREC on LCF. In addition, we affirmed a similar
slope coefficient between the outcomes of QR and QQR.
Lastly, the summary of the slope coefficient of both QR and
QQR of the effect of STC on LCF in South Korea is pre-
sented in Fig.4e. Figure4e affirmed the negative effect of
STC on LCF across all quantiles (0.1–0.90). Furthermore,
there is a similar trajectory between the slope coefficient of
QR and QQR which substantiates our findings.
Nonparametric causality outcomes
In this study, we assessed the effect of structural change
(STC), economic growth (GDP), trade globalization
(TGLO), nonrenewable energy use (NREC), and renew-
able energy use (REC) on load capacity factor (LCF) in
each quantile. In doing so, we utilized the innovative non-
parametric causality in mean and variance, as suggested by
Baciliar etal. (2016). The causality outcomes are presented
in Fig.5a–e and Tables4 and 5, respectively. In Fig.5a–e,
vertical and horizontal axes illustrate the T-statistics and
quantiles. The causality in mean and variance are depicted
by thick green and think purple, respectively. The 5% and
10% levels of significance are illustrated by thick and broken
purple and yellow lines, respectively. Furthermore, the thick
red line stands for causality in mean, and the thick green line
stands for causality in variance.
The causality in variance and mean from GDP to LCF in
South Korea is illustrated in Fig.5a. At 5% and 10% levels of
significance in the lower and middle quantiles (0.15–0.55),
GDP granger causes LCF at the conditional distribution of
LCF. In addition, the effect is stronger and more persistent
in the lower tail with a T-statistic of approximately 2.288.
Furthermore, the volatility is observed from GDP to LCF in
Fig.5a at middle and lower quantiles (0.1–0.65) at 5% and
10% levels of significance. Moreover, causality in mean and
variance from REC to LCF in South Korea is illustrated in
Fig.5b. At 5% and 10% significance levels in the lower and
middle tails (0.15–0.65), REC granger causes LCF at the
conditional distribution of LCF. Additionally, the effect is
stronger and more persistent in the lower tail with a T-sta-
tistic of approximately 2.08. Furthermore, the volatility is
observed from REC to LCF in Fig.5b at the majority of the
quantiles at 5% and 10% levels of significance.
Figure5c presents causality in variance and mean from
TGLO to LCF in South Korea. At 5% and 10% levels of
significance in the lower and middle quantiles (0.20–0.75),
TGLO granger causes LCF at the conditional distribution
of LCF. Moreover, the effect is stronger and persistent in
the middle tail with a T-statistic of approximately 2.45.
Furthermore, the volatility is observed from TGLO to LCF
in Fig.5a at middle and lower quantiles (0.1–0.80) at 5%
and 10% levels of significance. Furthermore, causality in
mean and variance from NREC to LCF in South Korea is
illustrated in Fig.5d. At 5% and 10% significance levels in
the lower and middle tails (0.10–0.60), REC granger causes
LCF at the conditional distribution of LCF. Additionally, the
effect is stronger and more persistent in the lower tail with a
T-statistic of approximately 2.19. Furthermore, the volatility
Fig. 3 Impact of renewable energy, nonrenewable energy, economic
growth, structural change, and trade globalization on load capac-
ity factor. (a) Impact of economic growth on load capacity factor
(b) Impact of renewable energy on load capacity factor (c) Impact of
trade globalization on load capacity factor (d) Impact of nonrenew-
able energy on load capacity factor (e) Impact of structural change on
load capacity factor
◂

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Fig. 4 Impact of renewable energy, nonrenewable energy, economic growth, structural change, and trade globalization on load capacity factor (a)
GDP effect on LCF (b) REC effect on LCF (c) TGLO effect on LCF (d) NREC effect on LCF (e) STC effect on LCF

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Fig. 5 Causality in mean and variance from renewable energy, non-
renewable energy, economic growth, structural change, and trade
globalization to load capacity factor (a) Causality from GDP to LCF
(b) Causality from REC to LCF (c) Causality from TGLO to LCF (d)
Causality from NREC to LCF (e) Causality STC to LCF

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is observed from NREC to LCF in Fig.5d at the majority of
the quantiles at 5% and 10% levels of significance.
Lastly, Fig.5e presents the causality in variance and
mean from STC to LCF in South Korea. At 5% and
10% levels of significance in the lower and middle tails
(0.20–0.60), STC granger causes LCF at the conditional
distribution of LCF. Moreover, the effect is stronger and
more persistent in the lower tail with a T-statistic of
approximately 2.36. Additionally, the volatility is observed
from STC to LCF in Fig.5e at the majority of the quan-
tiles. The outcomes of the nonparametric causality dis-
closed that all the independent variables can significantly
predict load capacity factor across all quantiles. These
outcomes are significant for policymakers in formulating
Table 4 Causality in mean
* 5% level of significance
** 10% level of significance
Quantiles CV 5% CV 10% REC TGLO GDP STC NREC
0.10 1.96 1.65 1.6008 1.3522 1.5906 1.3911 1.9911*
0.15 1.96 1.65 1.7903** 1.6126 1.8033** 1.5607 2.2607*
0.20 1.96 1.65 1.9366** 1.7644** 1.9167** 1.7440** 2.2440*
0.25 1.96 1.65 1.9414** 1.8110** 1.9746* 1.8978** 2.1978*
0.30 1.96 1.65 2.0853* 1.9343** 2.2835* 2.0200* 2.0200*
0.35 1.96 1.65 1.9735* 2.1386* 2.0830* 1.9370** 1.9959*
0.40 1.96 1.65 1.8971** 2.3935* 1.9738* 2.0785* 1.8852**
0.45 1.96 1.65 1.7921** 2.4554* 1.8483** 1.9304** 1.8604**
0.50 1.96 1.65 1.6836** 2.1505* 1.7854** 1.8782** 1.7782**
0.55 1.96 1.65 1.5260 1.9428** 1.6542** 1.7669** 1.7269**
0.60 1.96 1.65 1.6836 1.8265** 1.4314 1.7463** 1.6929**
0.65 1.96 1.65 1.5281 1.7834** 1.2188 1.8066** 1.5066
0.70 1.96 1.65 1.3853 1.6748** 1.3990 1.7487** 1.4487
0.75 1.96 1.65 1.2253 1.5086 1.1946 1.6851** 1.2393
0.80 1.96 1.65 0.9145 0.9500 1.1186 1.6474 0.9851
0.85 1.96 1.65 0.6570 0.7488 0.8301 1.3458 0.9738
0.90 1.96 1.65 0.5199 0.6246 0.6364 1.1539 0.7458
Table 5 Causality in variance
* 5% level of significance
** 10% level of significance
Quantile CV 5% CV 10% REC TGLO GDP STC NREC
0.10 1.96 1.65 2.3813* 2.1265* 2.3313* 2.1074* 1.8810**
0.15 1.96 1.65 2.5219* 2.2042* 2.3820* 2.2499* 1.9606*
0.20 1.96 1.65 3.2864* 2.9155* 3.3610* 2.9107* 2.1440*
0.25 1.96 1.65 3.2119* 2.9826* 3.0587* 3.3993* 2.3977*
0.30 1.96 1.65 3.1512* 3.3325* 2.8800* 3.0393* 2.5852*
0.35 1.96 1.65 3.0268* 3.2626* 2.6611* 3.0137* 2.4200*
0.40 1.96 1.65 2.9337* 3.6786* 2.5434* 3.1411* 2.1730*
0.45 1.96 1.65 2.9337* 3.5374* 2.5237* 3.2208* 2.2878*
0.50 1.96 1.65 2.7822* 3.1457* 2.4876* 3.1144* 1.9668*
0.55 1.96 1.65 2.4541* 2.5184* 2.1653* 2.9934* 1.9095**
0.60 1.96 1.65 2.3025* 2.9078* 1.9807* 2.8540* 1.8506**
0.65 1.96 1.65 1.8456** 2.9064* 1.7565** 2.5429* 1.9728*
0.70 1.96 1.65 1.6749** 2.2332* 1.1985 2.0203* 1.6948**
0.75 1.96 1.65 1.5659 1.8409** 1.1752 1.9018** 1.9535**
0.80 1.96 1.65 1.2512 1.5110 0.8835 1.7693** 1.6898**
0.85 1.96 1.65 1.1124 1.0894 0.7639 1.6685** 1.6438
0.90 1.96 1.65 0.6956 0.8547 0.6774 1.2975 1.3745

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policy regarding environmental degradation in South
Korea.
Discussion
The discussion from the findings above is presented here.
We assess the effect of structural change (STC), economic
growth (GDP), trade globalization (TGLO), renewable
energy use (REC), and nonrenewable energy use (NREC)
on load capacity factor (LCF) utilizing nonparametric tech-
niques (quantile-on-quantile regression and nonparamet-
ric causality). The outcomes of the quantile-on-quantile
regression revealed the following interesting findings: In
all quantiles (0.1–0.90), the effect of economic growth on
the load capacity factor is negative. This implies that across
all quantiles (0.10–0.90), the economic progress of South
Korea worsens the quality of the environment. Japan’s rapid
growth came at the expense of environmental pollution. The
findings go on to show that GDP per capita is one of Japan’s
leading sources of CO2 emissions. Another plausible justi-
fication for economic growth’s positive impact on environ-
mental deterioration is that fossil fuels are the key inputs for
industry, which affect both economic expansion and ecologi-
cal deterioration (Akadiri etal. 2021). Moreover, increased
CO2 is a result of Japan’s economic progress being linked
to trade expansion, economic capitalization, and infrastruc-
ture development, all of which have a positive impact on
economic activity and investment, as a result, increased uti-
lization of energy (Adebayo and Kirikkaleli 2021). Further-
more, as manufacturing sectors expand, increased economic
expansion will result in increased environmental damage at
high levels of income. This outcome complies with the stud-
ies of Fareed etal. (2021), Lin etal. (2021), Ahmed etal.
(2021), and Akinsola etal. (2021) who reported positive
growth-emissions interconnections.
Furthermore, across all quantiles (0.1–0.90), the effect
of structural change on the load capacity factor is negative
which implies that in each quantile, the structural change
reduces the quality of the environment. The findings suggest
that an increase in industrial value-added in all quantiles
(0.1–0.90) would result in an increase in CO2 in all quan-
tiles in Japan. As a result of this finding, one of the primary
factors of CO2 in Japan is the move from basic agricultural
to more manufacturing-based economic activity. Industrial
operations are more energy-intensive, and as a result, they
generate a large amount of CO2 into the environment. The
study of Ali, etal. (2020b, a) for Pakistan reported a similar
finding; however, the finding contradicts the studies of Ali
etal. (2017) for Malaysia and Hashmi etal. (2020) who
reported negative interconnectedness between structural
change and environmental degradation.
Likewise, across all quantiles (0.1–0.90), the effect of
nonrenewable energy use on load capacity factor is negative.
This finding coincides with the results of Alola etal. (2021),
Kirikkaleli and Adebayo (2021), Odugbesan etal. (2021),
Miao etal. (2022), and Adebayo (2022a) who reported that
nonrenewable energy use worsens the quality of the envi-
ronment. This is not surprising considering that fossil fuels
and nonrenewable energy sources would enhance industrial
activities, which might also result in a rise in ecological
pollution. Moreover, this outcome is not surprising given
the fact that the share of fossil fuels in South Korea’s over-
all energy mix is approximately 66% (EIA 2020). South
Korea’s extensive usage of nonrenewable energy, as well
as the wastes that follow, adds to ecological strain by con-
taminating the air, decreasing soil quality, and damaging
aquatic bodies.
Moreover, in each quantile (0.1–0.90), the effect of trade
globalization on the load capacity factor is negative. This
demonstrates that in each quantile, trade globalization wors-
ens the quality of the environment. Trade openness can
boost pressure on the environment through the composition
effect since the emphasis on expanding output simultane-
ously increases input, resulting in increased CO2 emissions
and waste. The studies of Leal and Marques (2021), Shah-
baz etal. (2018), Kirikkaleli etal. (2021), Yameogo etal.
(2021), and Dingru etal. (2021) reported similar findings.
The outcomes of our research reveal that South Korea has
permissive environmental rules, which entice international
investors, and in this situation, trade globalization has a
detrimental influence on ecological damage. Based on the
ecological effects of trade globalization, we also believe that
policymakers should not overlook the role of trade globaliza-
tion in the dynamics of CO2 in South Korea when develop-
ing a sustainable and comprehensive environmental policy
framework.
In the majority of the quantiles, the effect of renewable
energy use on load capacity factor is negative which dem-
onstrates that an upsurge in renewable energy mitigates the
quality of the environment. A similar result is reported by
Pata (2018) for the case of Turkey and Alola etal. (2021)
for the case of China. However, these findings contradict
the research of Miao etal. (2022), Adebayo (2022a), and
Awosusi etal. (2022) who established that surge in renew-
able energy use enhances the quality of the environment.
This result is as expected for the case of South Korea, given
the fact that renewables constitute approximately 6.4% of
South Korea’s energy mix which is the lowest among mem-
bers of the OECD.1 Renewables can only flourish if they
are cheaper than fossil fuels in terms of relative pricing. To
encourage the use of renewable energy sources, the govern-
ment can implement a number of R&D policies that would
1 https:// energ ytrac ker. asia/ the- main- barri ers- to- the- renew able-
energy- trans ition- in- south- korea/

Environmental Science and Pollution Research
1 3
decrease the cost of renewable energy and encourage it over
fossil fuels. With a carbon tax, it is feasible to boost the
price of fossil fuels and so balance the economics in favor
of renewables.
Conclusion andpolicy suggestions
Conclusion
The current paper assesses the effect of structural change
on load capacity factor in South Korea utilizing a dataset
stretching between 1970Q1 and 2018Q4. We also assess
other drivers of load capacity factor such as economic
growth, nonrenewable energy, and renewable energy usage.
In doing so, we applied nonparametric techniques (quan-
tile-on-quantile and nonparametric causality) to explore this
interconnectedness. The outcomes of the BDS test validate
the use of nonlinear techniques. The outcomes of the quan-
tile cointegration unveiled evidence of cointegration between
load capacity factor and economic growth, structural, nonre-
newable energy, trade globalization, and renewable energy
usage. Furthermore, the outcomes of the quantile-on-quan-
tile regression disclosed that in the majority of the quantiles,
the effect of economic growth, nonrenewable energy use,
structural change, renewable energy use, and trade globaliza-
tion on the load capacity factor is negative which substanti-
ates the fact that all the independent variables contribute to
environmental degradation in South Korea. As a robustness
check, the quantile regression technique was utilized and
the results comply with the quantile-on-quantile outcomes.
Lastly, we applied the nonparametric causality test, and their
outcomes show that in all quantiles, the load capacity factor
can be predicted by the exogenous variables.
Policy suggestions
In terms of significant policy ramifications, we believe that
the government of South Korea can reduce the environmen-
tal cost of trade globalization through adequate and efficient
policy coordination. Considering the negative environmental
effects of trade globalization, we believe that policymakers
in South Korea should not overlook trade globalization’s
involvement in the dynamics of CO2 when constructing a
long-term and comprehensive environmental policy frame-
work. We also propose that policymakers in South Korea
embrace “trade globalization” as a crucial economic instru-
ment in their environmental policy framework in order to
enhance environmental quality.
We uncovered that structural changes worsen the qual-
ity of the environment, as South Korea’s industrial opera-
tions are primarily fossil-fuel driven. This implies that the
shift toward a service-based economy is not sustainable.
Furthermore, rather than polluting capital industries,
more attention should be focused on investments in less-
polluting industries, and ecologically friendly, informa-
tion-based, and knowledge projects by multinational cor-
porations which could also assist in curbing emissions.
Another finding from our research is as expected. That is,
nonrenewable energy usage in South Korea degrades envi-
ronmental quality. It is well documented in recent research
that South Korea’s large-scale economic dependency on
fossil fuels increases CO2. As a result, in the face of severe
global warming, policies relating to the decrease of fos-
sil fuel consumption must be adopted extensively. In this
regard, South Korea has to support a policy that encour-
ages the use of sustainable energy sources.
Additionally, it is also critical to adopt new technolo-
gies to improve the efficiency of energy, which assists in
minimizing CO2 by lowering the amount of energy needed
to produce a given output level. This form of improvement
in the energy efficiency program benefits both the environ-
ment and the economy; hence, energy efficiency initia-
tives in South Korea should be prioritized. Furthermore,
residential energy use in the transportation and industrial
sectors should be included in policy formulation. Energy-
efficient power gadgets should be utilized at the household
level to lower the amount of energy usage. At the home
level, it is also critical to deploy rooftop solar energy. Poli-
cymakers should acquire more carbon-free and efficient
industrial vehicle and machinery engines for the trans-
portation and industrial sectors. Subsidies for fossil fuels
should also be phased out via strict laws at the state and
central levels. Implementation of these strategies may help
to offset the climatic change that the research discovered
as a result of energy usage.
Finally, improvements in the economic structure can
assist in enhancing the quality of the environment. It
implies that pollution of the environment can be avoided
by emphasizing tertiary sector operations over secondary
sector associated operations. As a result, we proposed that
shifting toward service-sector-led growth aids South Korea’s
environmental conservation. From a policy standpoint, we
believe that service sector-linked trade promotion efforts,
international service sector partnership, and service sec-
tor enterprise subsidies should all be encouraged in South
Korea.
Lastly, the findings of this research revealed that encour-
aging the use of renewable energy is not an effective
approach to lowering emissions in South Korea. This con-
clusion could be attributable to the fact that South Korea
has not taken all the required efforts to promote renewable
energy, and as a result, renewable energy usage is steadily
dropping. Therefore, the study proposes that South Korea
should re-strategize its policy to encourage renewable
energy utilization.

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Caveat oftheresearch
The limitation of the current paper is that the research is
subjective toward structural change, renewable energy, and
technological innovation and does not take into account
other drivers of ecological deterioration. As a result, future
studies should use other drivers of load capacity factor using
different countries and timeframes.
Author contribution Tomiwa Sunday Adebayo collected the data and
analyzed it. Taha Abdulmagid Basheer Agila interpreted the findings.
Seyi Saint Akadiri wrote the introduction. Wagdi M. S. Khalifa wrote
the methodology. Mehmet Altuntaş wrote the literature.
Data availability Data is readily available at the request of the cor-
responding author.
Declarations
Ethics approval This research complies with internationally accepted
standards for research practice and reporting.
Consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
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