A different look at the environmental Kuznets curve from the perspective of environmental deterioration and economic policy uncertainty: evidence from fragile countries

Abstract

Environmental degradation is one of the most significant issues that developing nations confront and needs to be resolved right away in order for them to achieve sustainable development. Government policies are crucial in this situation since emerging nations frequently struggle with the issue of policy ambiguity, which can result in environmental deterioration. In this context, this study investigates how policy uncertainty affects environmental degradation in the five fragile emerging economies known as the Fragile Five—Brazil, India, Indonesia, South Africa, and Turkey. Using data from 1996 to 2019, we estimate a Panel Quantile Regression analysis. The empirical findings indicate that economic policy uncertainty and technology innovation increases the environmental degradation whereas environmental degradation is slowed down by financial development and renewable energy consumption. Empirical evidence also confirms the presence of EKC hypothesis in fragile economies. Based on the findings, we suggest both a policy and an environmental framework for achieving sustainable development in fragile economies.

Graphical Abstract

Full text

Vol.:(0123456789)
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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-023-28761-w
ECONOMIC UNCERTAINTY, (GEO)POLITICAL RISK, ANDSUSTAINABLE
DEVELOPMENT GOALS
A different look attheenvironmental Kuznets curve
fromtheperspective ofenvironmental deterioration andeconomic
policy uncertainty: evidence fromfragile countries
AhsanAnwar1 · AbdulkadirBarut2· FahrettinPala3· NurcanKilinc‑Ata4,5· EmineKaya6· DuongThiQuynhLien7
Received: 11 April 2023 / Accepted: 8 July 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023
Abstract
Environmental degradation is one of the most significant issues that developing nations confront and needs to be resolved
right away in order for them to achieve sustainable development. Government policies are crucial in this situation since
emerging nations frequently struggle with the issue of policy ambiguity, which can result in environmental deterioration.In
this context, this study investigates how policy uncertainty affects environmental degradation in the five fragile emerging
economies known as the Fragile Five—Brazil, India, Indonesia, South Africa, and Turkey. Using data from 1996 to 2019,
we estimate a Panel Quantile Regression analysis. The empirical findings indicate that economic policy uncertainty and
technology innovation increases the environmental degradation whereas environmental degradation is slowed down by
financial development and renewable energy consumption. Empirical evidence also confirms the presence of EKC hypothesis
in fragile economies. Based on the findings, we suggest both a policy and an environmental framework for achieving
sustainable development in fragile economies.
Keywords Economic policy uncertainty· Environmental degradation index· Panel quantile regression· Fragile countries
Introduction
In terms of achieving sustainable development, developing
economies are still facing many issues. Environmental
degradation is one of the major issues experienced by these
Responsible Editor: Arshian Sharif
* Duong Thi Quynh Lien
liendtq@vinhuni.edu.vn
Ahsan Anwar
ahsananwar786@gmail.com
Abdulkadir Barut
kadirbarut@harran.edu.tr
Fahrettin Pala
fahrettinpala@gumushane.edu.tr
Nurcan Kilinc‑Ata
nurcankilinc@yahoo.com
Emine Kaya
emine.kaya001@hotmail.com
1 Business Administration Department, Faculty
ofManagement Sciences, ILMA University, Karachi,
Pakistan
2 Siverek Vocational School, Department ofAccounting
andTaxation, Harran University, Sanliurfa, Turkey
3 Kelkit Vocational School, Department ofAccounting
andTaxation, Gümüşhane University, Gümüşhane, Turkey
4 College ofEconomics andManagement, Al‑Qasimia
University, Sharjah, UnitedArabEmirates
5 Research Laboratory forScience andTechnology Studies
andEconomics ofKnowledge, National Research University
“Higher School ofEconomics”, Moscow, Russia
6 Faculty ofSocial Sciences andHumanities, Department
ofAccounting andFinance, Malatya Turgut Özal University,
Malatya, Turkey
7 College ofEconomics, Vinh University, Vinh, Vietnam

Environmental Science and Pollution Research
1 3
countries and must be resolved by reducing carbon dioxide
(CO2) emissions on a worldwide scale. If not, the trend
of increasing CO2 emissions might be more troublesome
(IPCC, 2018). As a result, there has been an uptick in
recent years in the conversation concerning environmental
degradation, climate change, and global warming. One of the
effects of energy generation is environmental deterioration
(Mukhtarov 2022). Given that there is a high demand for
energy and a limited supply (Mutezo and Mulopo 2021),
environmental deterioration accelerates as a result of the use
of fossil fuels, which results in CO2 emissions, which have
the highest rate of all greenhouse gas emissions (Depren
etal. 2022). Olivier etal. (2012) show that industrialized
nations contribute significantly to environmental
deterioration on a worldwide scale. At the same time, this
scenario is changing, with emerging countries like the
fragile five economies beginning to contribute more to
environmental degradation.
The literature discusses various factors that contribute
to environmental degradation, including renewable energy
(RE), technological innovation (Wen etal. 2021; Wen etal.
2022; Wang etal. 2022; Kirikkaleli etal. 2022), and eco‑
nomic policy uncertainty (EPU) (Candau and Dienesch
2017; Masron and Subramaniam 2018; Su etal. 2021; Sekraf
and Sghaier 2018; Adebayo etal. 2022a, b and c). EPU can
be attributed to factors such as the COVID‑19 pandemic, the
US‑China trade war, the financial crisis, and Brexit, which
result in ambiguous economic policies. The International
Monetary Fund (IMF) identifies EPU as a cause of envi‑
ronmental destruction and slow economic expansion. This
is because EPU reduces energy consumption and economic
growth, leading to an increase in environmental degradation.
Additionally, EPU can hinder R&D, technological innova‑
tion, and the development of RE sources, further contrib‑
uting to environmental degradation (Anser etal. 2021).
Corruption in the government can also undermine effective
direction and control to confirm ecological sustainability, as
highlighted by Biswas etal. (2012) and Sekraf and Sghaier
(2018). Conversely, political stability may prevent environ‑
mental degradation by adopting effective policies, which in
turn can help reduce waste and pollution and safeguard the
environment (Helland and Whitford 2003). Therefore, the
link between EPU and environmental degradation is a topic
of debate, and it is essential to determine the link between
the two (Vu and Huang 2020) to recommend policies to
combat environmental degradation (Farooq etal.2021).
Over the past few decades, economic expansion has had
a negative effect on both the environment and the quality
of life (Charfeddine and Mrabet 2017; Canaj etal. 2021).
The exploitation of natural resources resulting from eco‑
nomic activities is one of the leading causes of environ‑
mental degradation (Cronin and Pandya 2009; Gutti etal.
2012). Alvarado and Toledo (2017) suggest that there is a
negative correlation between real gross domestic product
(GDP) income and environmental degradation. The EKC is
a theoretical and empirical concept that claims that environ‑
mental degradation primarily rises during the early phases
of economic growth but subsequently declines as incomes
rise (Stern etal. 1996; Stern 2004). The EKC is based on an
inverted U‑shape, which suggests that higher levels of pro‑
duction during the initial stages of economic development
lead to more environmental degradation, but as a country
develops, technology improves, efficiency increases, and the
population becomes more concerned with the environment,
environmental degradation begins to decline (Stern 2004).
The influence of technological innovation on CO2 emis‑
sions is a topic of ongoing debate, but according to new
growth theories, technological innovation has a direct posi‑
tive impact on both economic expansion and the environ‑
ment (Xinmin etal. 2020). While technological innovation is
crucial for the development of economies and firms, it heav‑
ily relies on energy use, which can lead to environmental
pollution (Omri etal., 2020). However, Asongu etal. (2018)
argue that technological innovation can also serve as a tool
for improving environmental quality by reducing CO2 emis‑
sions. This suggests that while technological innovation has
the potential to contribute to environmental degradation, it
can also be used to mitigate the negative impact of economic
expansion on the environment.
Financial development is a factor that may have an impact
on environmental degradation. Some studies, such as that
of Islam etal. (2013), indicate that financial development
can reduce environmental degradation by promoting energy
efficiency. On the other hand, Dogan and Kurkekul (2016)
discuss how financial development can increase investments
in equipment and lead to more energy consumption and
CO2 emissions. As a result, there is no consensus on the
link between financial development and environmental
degradation, and it can have positive, negative, or neutral
effects (Ahmad etal. 2023). RE consumption and production
are now generally considered significant factors for the
environment and the growth of the economy (Raza etal.
2019). In addition, policymakers need to understand the
connection between environmental degradation and RE
consumption to plan the path of economic growth (Adebayo
and Kirikkaleli 2021), since RE consumption can be seen as
a significant factor in the environment and growth (Ocal and
Aslan 2013a, Raza etal. 2015). Krewitt etal. (2007), Guoyan
etal. (2022), Abulfotuh (2007), Chien and Hu (2008), Raza
etal. (2022), and Raza and Shah (2018) advocate RE usage,
which supports a decrease in emissions of greenhouse gases,
air population, total energy consumption, and economic
growth (Dai etal. 2016). Studies like that of Dong etal.
(2018) suggest that increasing RE consumption and reducing
reliance on fossil fuels can help reduce environmental
degradation. Furthermore, RE consumption can contribute

Environmental Science and Pollution Research
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to economic development without significantly increasing
environmental degradation (Mehmood et al. 2023).
Therefore, policymakers aim to reduce the use of fossil fuels
and increase the use of RE sources to combat environmental
pollution (Sharif etal. 2020).
The main challenges faced by developing countries,
particularly those identified as the "fragile five"—Turkey,
South Africa, Brazil, Indonesia, and India —include
sustainable economic growth, climate change, health
issues, and the use of clean energy resources (Barut etal.
2023). These countries are experiencing environmental
degradation, accounting for 9.22% of global degradation
(Gao etal. 2022). These fragile economies also suffer from
high inflation, large current account deficits, fast capital
outflows, and weak economic growth. Given these economic
and environmental factors, it is likely that the fragile five
economies will continue to be susceptible to environmental
degradation (Unver and Dogru, 2015).
Table1 summarizes the regional CO2 emissions for the
world. According to Table1, there is a decrease in CO2
emissions in North America, South and Central America, and
Europe. On the other hand, in the rest of the world, which
includes the Commonwealth of Independent States, the
Middle East, Africa, and Asia Pacific, there is an increase in
CO2 emissions. Also, Table1 indicates that the CO2 emissions
for Indonesia, Turkey, and India are high when compared to
other regions and fragile countries, and that, except for South
Africa, the rest of the fragile economies, which are Argentina
and Brazil, do not have low CO2 emissions.
In this context, the current paper explores the impact of
GDP, EPU, financial development (FD), RENE and techno‑
logical innovation (TIN) on environmental degradation for
the period 1996–2019 using data from five fragile countries
(Brazil, India, Indonesia, Turkey and South Africa). For
this purpose, firstly, the environmental degradation index is
measured with various environmental variables using prin‑
cipal component analysis (PCA), and then panel quantita‑
tive regression analysis is used to determine the relationship
between the effects of GDP, EPU, FD, RENE. In this context,
the study seeks answers to the following research questions:
“Is there a connection between the variables selected in the
study (GDP, FD, TIN, RENE) and environmental degrada-
tion? If yes, what is the nature of this relationship?”, and “Is
the EKC hypothesis valid for Five Fragile countries?”.
Answers to these research questions are sought in this
study, which is anticipated to make numerous contributions
to the literature. Firstly, the present study is a pioneering
attempt to investigate the impact of economic policy
uncertainty, technological innovation, RE consumption,
and financial development on environmental degradation
under the EKC framework for fragile countries (Turkey,
South Africa, Brazil, Indonesia, and India). In the previous
studies, the link between variables such as RENE, EPU, FD,
and environmental degradation was separately discussed;
however, the combined role of RENE, GDP, FD, TIN,
and EPU on environmental degradation was not widely
discussed. This study examines the effects of RENE, GDP,
FD, TIN, and EPU variables in the same model. Secondly,
the existing literature has used CO2 emissions or ecological
footprints as a measure of environmental degradation,
whereas this study calculates and uses a comprehensive
index of environmental degradation by incorporating
different environmental indicators by following the
method of Barut etal. (2023). Thirdly, most of the time,
the macroeconomic variables have the issue of data non‑
normality. Therefore, in cases of data non‑normality, the
use of linear average‑based econometric techniques may
provide inconclusive and spurious outcomes; therefore,
this study uses panel quantile regression to control the
issue of data non‑normality and address the impact of
outliers. The current study focuses on quantile regression
analysis, unlike the previous studies. A lot of the studies
on the relationship between energy consumption and CO2
emissions include the top 10 emitting countries (Nejat
etal. 2015), China (Bai etal. 2019), Brazil (Gioda 2019),
low and lower middle income, upper middle income, and
Table 1 Growth Rate of CO2 Emissions in Different Regions and Countries of the World
Source: BP Energy Outlook (2022)
CO2 Emissions 2011 2021 Growth rate per
annum from 2011 to
2021
CO2 Emissions 2011 2021 Growth rate per
annum from 2011 to
2021
Total North America 6352.2 5602.2 ‑1.2% Argentina 173.1 181.7 0.5%
Total South & Central America 1225.1 1213.1 ‑0.1% Brazil 424.2 436.6 0.3%
Total Europe 4599.1 3793.7 ‑1.9% Turkey 298.8 403.3 3.0%
Total Commonwealth of Inde‑
pendent States
2046.5 2132.5 0.4% South Africa 466.3 438.9 ‑0.6%
Total Middle East 1764.5 2117.2 1.8% India 1728.4 2552.8 4.0%
Total Africa 1103.6 1290.7 1.6% Indonesia 470.6 572.5 2.0%
Total Asia Pacific 14,813.5 17,734.6 1.8% Total World 31,904.6 33,884.1 0.6%

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high‑income countries (Narayan and Doytch 2017), BRICS,
and N‑11 countries (Raza etal. 2020a, b). However, there
is no study on the fragile five economies to determine
the effect of economic policy uncertainty, technological
innovation, RE consumption, and financial development
on environmental degradation. The final contribution of
this study is focusing on the fragile five countries, which
have high EPU. Thus, the findings of this study may shed
light on recommendations for policymakers since the
nexus between natural resources, economic expansion,
and environmental degradation is not only beneficial for
government and policymakers but also important for the
growth of RE. The findings of this study not only support
ensuring the Sustainable Development Goals, which
contain decreasing green gas emissions by 2050, but also
help reach the goal of a robust and sustainable economy as
expressed by Zhang etal. (2023).
The current paper consists of five parts. The second
part gives information about the literature summary, and
the third part introduces data and techniques. Also, the
findings are given in the fourth part, and the conclusion is
in the final part.
Literature review
It is important to see that the literature on the link between
economic expansion and environmental degradation has
many diverse and contradictory conclusions. While Wang
etal. (2022a, b), Boukhelkhal (2022), Al‑mulali etal.
(2013), Wang etal. (2012), Wang etal. (2013), Elif etal.
(2011), Acaravcı and Ozturk (2010) discovered that eco‑
nomic growth increases environmental degradation, studies
such as Salazar‑Nunez etal. (2022), Adebanjo and Shakiru
(2022), Weimin etal. (2022), Heidari etal. (2015), Narayan
and Pop (2012), and Jaunky (2010) have identified that
economic expansion reduces environmental degradation in
the long term. The EKC model, which states that the CO2
emissions curve in the graph rises until it reaches a specific
income level, at which time it turns downward and forms an
inverted "U" curve, clarifies this ambiguity (Arouri etal.
2012). In other words, despite a short‑term increase, long‑
term CO2 emissions decrease as income increases. Using
historical, relatively short‑term data series and environ‑
mental quality measures that can be acknowledged as sub‑
par, it is possible to test the possibility of an EKC (Chen
and Taylor 2020).
According to the literature review, studies such as Raza
etal. (2020a, b), and Suki etal. (2020) support the EKC
hypothesis. However, studies such Erdogan etal. (2020)
did not reach the results predicted by the EKC hypoth‑
esis. Murshed etal. (2022), and Gormus and Aydin (2020)
found that while the EKC is valid for several countries,
it is invalid for others. Furthermore, Ansari (2022) con‑
cluded that the EKC hypothesis is valid for ecological
footprints but invalid for CO2 emissions. Conversely,
Dogan etal. (2020) showed that the EKC hypothesis for
ecological footprints is invalid.
Environmental degradation is affected by many factors
besides economic growth. The following studies on the
independent factors impacting environmental deterioration
are some of those cited in this study. For example, Anser
etal. (2021), and Adedoyin and Zakari (2020) indicate that
while the link between uncertainty in economic policies and
environmental degradation is optimistic in the short term,
it is adverse in the long term. On the contrary, Syed and
Bouri (2022) concluded that while uncertainty in economic
policies negatively affects environmental degradation in the
short run, it has a positive effect in the long run. Abbasi
and Adedoyin (2021), on the other hand, found a signifi‑
cant difference between the two variables, Mukhtarov etal.
(2023), Adebayo etal. (2022b), Khan etal. (2022), Kirik‑
kaleli etal. (2022), Khan etal. (2020a, b), Wolde‑Rufael
and Weldemeskel (2020), Zafar etal. (2020) discovered that
there is a negative link between RE usage and environmental
degradation.
Khan and Ozturk (2021), Zhao and Yang (2020), and
Rafique (2020) found that environmental degradation and
financial development are mutually contradictory. In con‑
trast, Anwar etal. (2021a, b, c, d), Le and Ozturk (2020),
and Qayyum etal. (2021) discovered a positive link between
financial development and environmental degradation.
Along with identifying a link between financial develop‑
ment and environmental degradation, Lv and Li (2021) also
made the remarkable finding that the financial development
levels of its neighbors influence a country’s CO2 emis‑
sions, which decline as the financial development levels of
its neighbors rise. Shahbaz etal. (2020), and Cheng etal.
(2021) assert that there is a negative link between technolog‑
ical innovation and environmental degradation. In contrast,
Adebayo etal. (2021) find that environmental deterioration
and technological progress are positively correlated. Raza
etal. (2019) support the positive effect of economic growth
and transportation energy consumption on environmental
degradation in the US. Zhang etal. (2023) research glo‑
balization, EPU, ecological innovation, and RENE in the
1990–2019 period and assert that EPU and RENE decrease
CO2,and ecological innovation supports reducing CO2
emissions. Kirikkaleli etal. (2023) study the relationship
between GDP, globalization, and the carbon intensity of
GDP on consumption‑based CO2 emissions (CO2E) by
using the nonlinear autoregressive distributed lag bound
test (ARDL) and Fourier ARDL and claim that there is a
significant long‑run relationship between variables. Also,
the findings prove that positive environmental innovations
have a negative effect on the CO2E, positive and negative

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shocks in GDP and carbon intensity of GDP have a posi‑
tive effect on the CO2E, and a negative shock in globaliza‑
tion increases the CCO2E. Ramzan etal. (2023) explore the
effect of financial depth, information communication tech‑
nology, technological innovation, and green innovation on
CO2 emissions and ecological footprint in the 1980–2019
period for the 10 greenest economies by using the Method
of Moments Quantile Regression (MMQR) and causality
test. The findings of Ramzan etal. (2023) demonstrate that
green innovation and financial depth significantly decrease
CO2 emissions and ecological footprints, but information
communication technology increases CO2 emissions and
ecological footprint.
Data, model specications,
andmethodology
Data
The current paper aims to measure environmental
degradation in five countries, namely Turkey, South Africa,
Brazil, Indonesia, and India, using an environmental
degradation index proposed by Barut etal. (2023). This
study studies the five fragile economies. The CO2 emission
for fragile countries, particularly Indonesia, Turkey, and
India, is high when compared to other regions in the world,
according to the BP Energy Outlook Report (2022), which
is given in Table1. Also, these countries are experiencing
environmental degradation, accounting for 9.22% of global
degradation (Gao etal.2022), and suffer from high inflation,
large current account deficits, fast capital outflows, and weak
economic growth. Due to the availability of data, this study
spans the years 1996 through 2019. For the vulnerable five
economies in particular, earlier environmental data are not
available.
The environmental degradation index takes into account
various environmental dimensions, and the study cov‑
ers comprehensive indicators to measure environmental
degradation since there is no consensus on how to meas‑
ure it in the literature (Nyugen, 2020). To construct the
environmental degradation index, the study uses several
variables, such as usage of coal per inhabited area, CO2
emissions per GDP, vehicle GDP per capita, nitrous oxide,
and methane emissions, fertilizer use per hectare of ara‑
ble land, population density, CO2 emissions per person,
number of threatened birds, and number of threatened
mammals. These variables are in line with the studies of
Anwar and Malik (2021), Anwar etal. (2023), Sun etal.
(2023), Anwar etal. (2021, 2021a, 2021b), Anwar etal.
(2022), Esmaeili etal. (2023), Liu etal. (2022), Habiba
etal. (2022), Salem etal. (2021), Cai etal. (2022), Chien
etal. (2021), and Farooq etal. (2021),. The environmental
deterioration index is created by the study using principal
component analysis (PCA), as suggested by Jha and Mur‑
thy (2003). PCA is a standard analysis for simplifying data
(Le etal. 2019) and is widely employed in the literature
(Jolliffe 2011). The advantages of PCA are low noise sen‑
sitivity, decreased requirements for capacity and memory,
and increased efficiency in smaller dimensions (Karami‑
zadeh etal. 2013). Thus, this study uses PCA analysis to
measure environmental degradation. Before running the
PCA, all variables are standardized, following the sug‑
gestion of Le etal. (2019). The PCA gives the weight of
the environmental degradation index, which is presented
in Table2. Finally, the environmental degradation index
is constructed using the weights. Table2 indicates that
the first two modules clarify 79% of the total variance
of the environmental degradation index. Furthermore, the
Kaiser–Meyer–Olkin analysis reports that the model is
sufficient, and Bartlett's analysis indicates that the vari‑
ables are related to each other for PCA. Finally, the values
of the environmental degradation index differ between 0,
in which the environmental degradation level is low, and
100, in which the environmental degradation level is high.
This study investigates the influence of GDP, EPU, FD,
RENE, FD, and Tin using data from five fragile nations
(Brazil, India, Indonesia, Turkey, and South Africa) between
1996 and 2019. The selection of data period is based on the
availability of the data, as the data of few variables of the
study is not available before the year 1996. The authors
created EDI, which is the dependent variable of the paper,
by combining ten different variables. Information about GDP,
EPU, RENE, FD, and TIN data are given in Table3, and a
graphical presentation is given in sub‑sections (a, b, c, d, and
e) of Fig.1.
GDP per capita (GDP) is the first independent variable
in the study because it impacts energy consumption, which
Table 2 PCA findings for measuring environmental degradation
index
Total Variance for Components
Components Eigen Values % of Variance Cumula‑
tive Vari‑
ance
1 6.14 0.68 0.68
2 0.99 0.11 0.79
3 0.66 0.07 0.87
4 0.47 0.05 0.92
5 0.34 0.04 0.96
6 0.26 0.03 0.98
7 0.08 0.01 0.99
8 0.06 0.01 0.99
9 0.03 0.00 1

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Fig. 1 Data trends of fragile five
economies
b)
1996
1997
1998
1999
2000
2001
20
02
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Brazil
EDI
TIN
FD
RENE
GDP
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Indonesia
EDI
TIN
FD
RENE
GDP
a)
Table 3 Description of data
Sign Variables Proxy Data Source
EDI Environmental Degradation Index % Created by the authors
GDP GDP per capita Current US$ WDI‑2023
EPU Economic Policy Uncertainty World uncertainty Index https:// world uncer
taint yindex. com/
TIN Technological Innovation Patent applications residents + non‑residents WDI‑2023
FD Financial Development Domestic credit to private sector (% of GDP) WDI‑2023
RENE RE Consumption % of total final energy consumption WDI‑2023

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c)
d)
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
India
EDI
TIN
FD
RENE
GDP
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Turkey
EDI
TIN
FD
RENE
GDP
e)
1996
1997
1998
1999
2000
2001
20
02
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
South Africa
EDI
TIN
FD
RENE
GDP
Fig. 1 (continued)

Environmental Science and Pollution Research
1 3
in turn affects environmental degradation by creating CO2
emissions (Zafar etal.2020). The study's other independent
variable, financial development, was chosen since it is pre‑
sumable that it can both raise investments in clean energy
and green technologies while also reducing environmental
deterioration. On the other hand, Zafar etal. (2020) assert
that financial development can both boost economic activ‑
ity and worsen the environment. As a result, it has been
decided that the financial development variable (FD) is
the most relevant in terms of environmental deterioration.
Technology has significant effects on economic growth and
environmental degradation.
Model specification
The non‑linear link between environmental indices (like air
pollution) and welfare measurements (like income per capita)
is typically modelled using a quadratic equation in traditional
testing for the EKC hypothesis (Wang 2013). Thus, the
quadratic functional form is estimated using the methods in the
study, which excludes cubic functional form. Kuznets (1955),
using the income distribution data of two states in America,
England, and Germany, estimated how income inequality will
move in the economic growth process with Eq.(1).
In Eq.(1), GINI represents income inequality, and
GDPPER represents GDP per capita. GDPPER2 is the
square form of GDP per capita. According to Kuznets
(1955), income inequality increases at first, but later on,
with industrialization (Fig.2), individuals’ incomes will
increase and income inequality will decrease.
Following Eq. (1), Grossman and Krueger (1991)
developed Eq.(2) by adapting the Kuznets Curve to the
environment(Fig.3).
(1)
GINI = f (
GDPPER,GDPPER
2)
Equation(2) states that countries choosing a more
aggressive growth strategy during the early phases
of industrialization disregard the environment, which
results in an increase in CO2 emissions. However, it
has been stated that in the following periods, with the
increase in industrialization and the increase in the
income of individuals, their sensitivity to the environ‑
ment will increase, which will lead to a decrease in CO2
emissions.
In this study, Eq.(3) was established by following the
studies of Citil etal. (2023) and Liu and Zhang (2023).
In Eq.(3), the dependent variable is the Environmental
Degradation Index (EDI), and the independent variables are
GDP per capita, EPU, RE Consumption (RENE), Financial
Development (FD), and Technological Innovation (TIN).
GDP2 is included in the model to test whether the EKC
hypothesis is valid.The next section explain the methodol‑
ogy, which we providedin Fig.4.
Methodology
Cross‑section dependency test andunit root test
Cross‑sectional dependence must be assessed when panel
data are utilized to analyze the presence of a unit root. First
generation unit root tests can be applied if the panel data set
rejects the existence of cross‑section dependence. However,
utilizing 2nd generation unit root tests can help us produce
more reliable, effective, and potent estimates if there is
cross‑sectional dependence in the panel data.
(2)
CO2
=
(
GDPPER,GDPPER
2)
(3)
𝐄𝐃𝐈 −
𝐆𝐃𝐏
+
𝐆𝐃𝐏
2+
𝐄𝐏𝐔
+
𝐑𝐄𝐍𝐄
+
𝐅𝐂
+𝐓𝐈𝐍
Fig. 2 Traditional Kuznets
Curve
GDPPER
Income
Per Capita
0
Income Inequality
Inflection Point

Environmental Science and Pollution Research
1 3
Cross‑sectional dependence can be demonstrated using
a variety of tests in the literature. For instance, the CD test
created by Pesaran (2004) gives reliable results in panel data
where the time dimension is larger than the unit size or the
time dimension is smaller than the unit size. Due to this, the
cross‑sectional dependence of the study's data was examined
using Pesaran's (2004) CD test.
One of the most important issues to be considered to reach
the right result while performing econometric analysis is that
the series are stationary. Since panel data models also include
(4)
CD
LM =√1
N(N−1)
N−1
∑
i=
1
.
N
∑
J=

I+1
ij2−
1
a time dimension, stationarity analysis should be done first.
To determine if the series are stationary or not, certain tests
must be performed. The unit root test, which checks for the
existence of a unit root in the data, is one method for figur‑
ing out whether a time series is stationary. Since the work by
Levin, Lin (1992, 1993), and Quah (1994), the unit root panel
has also taken an important place in the empirical analysis of
data. Thus, the Peseran (2007) CIPS method and the Peseran
(2003) CADF test were applied in this study's context.
Panel cointegration test
In Pedroni cointegration analysis, there are seven cointe‑
gration statistics. Three of these statistics are between the
Fig. 3 Environmental Kuznets
Curve
Income Per Capita
0
Environmental
Degradation
Inflection Point
Panel Estimation (Quantile regression)
Panel Cointegration Test
Unit Root Test ( CADF and CIPS)
Cross-Section Dependency Test( CD Test)
Literature Review
Determining the Variables and Establishing the Model
Fig. 4 Flow Chart of the Methods and Methodology of the Study

Environmental Science and Pollution Research
1 3
groups (between dimensions), while the remaining four are
within the group (within dimensions) (Asteriou and Hall,
2007). Non‑parametric tests make up the first three statistics
of in‑group statistics. A variance ratio‑type statistic is used
in the first test. Both the second and third are comparable
to the Phillips Peron (PP) (rho) and PP (t) statistics, respec‑
tively. The Augmented Dickey‑Fuller (ADF) (t) statistic
is a parametric statistic that makes up the fourth statistic.
Cointegration tests are based on the group mean technique
in statistics between groups. The first test in the group is
comparable to PP (rho) statistics, while the other two tests
are comparable to PP (t) and ADF (t) data (Pedroni 2004).
Panel cointegration tests from Kao (1999) and Johansen
Fisher (1999) were utilized to assess the dependability of
Pedroni's (2004) tests.
Panel estimation techniques
The quantile regression analysis approach created by
Koenker and Bassett (1978) is more flexible than the
EKC method and was developed based on median
regression to overcome the difficulty that arises when
the dependent variable is asymmetrically distributed.
Binder and Coad (2011) stated that the panel quantile
method gives more reliable results because it ignores
the mean effects of other coefficient estimation methods.
The quantile analysis provides a great advantage over
classical regression methods as it can perform regression
conditional quantitative estimation and predict the behavior
of each particular point in the conditional distribution
(Alharthi etal. 2021). In essence, the quantile regression
approach allows the distribution of the effect of a change
in the independent variable on the dependent variable to
be interpreted in different slices. The quantile regression
model is a layout model, and the simple location model is
as in Eqs.4 and 5;
(5)
Yi=Xi𝛽𝜏 +𝜀i
𝜏
Here, βτ; the τ'th quantile represents the anticipated
coefficient for the regression [ 0 < τ < 1], and εiτ represents
the error term.
where, i shows the unit (country, business) and t represents
time.
Results anddiscussions
Statistical information about the variables included in the
research is given in Table3. Table4 demonstrates that
technological innovation (TIN), with an average of 9.574,
is the variable with the highest average, and the variable
with the lowest average is EPU with an average of ‑1.542.
Economic growth (GDP) is shown to have the highest
standard deviation with a value of 2.254, and the variable
with the lowest is financial development (FD) with 0.556.
Among other variables, the average change in economic
growth (GDP) is 7.548, the average change in environmen‑
tal degradation (DGI) is 0.161, the average change in RE
consumption is 3.269, and the average change in financial
development (FD) is 3.859.
Skewness, Kurtosis, and Jargua‑Bera analyses are used
to test whether the series has a normal distribution. When
the values of Skewness and Kurtosis fall within the range
of ‑1.5 and + 1.5, the series is described as having a regular
distribution (Tabachnick and Fidell 2013). The coefficient
values of all the variables fall within the range of ‑1.5
and + 1.5, as mentioned in the literature, and when the
Skewness test results are analyzed, it can be argued that
all the variables exhibit a normal distribution because their
coefficient values fall within this range. When the Kurtosis
values are examined, it can be said that the series shows
(6)
Qτ(
y
i
X
i)
=X
i
τ
β
τ
(7)
Yi,t=a
i
+β
τ,1X1i,t+β
τ,2X2i,t……….+β
τ,mXmi,t+ε
i,τ
Table 4 Summary statistics DGI GDP EPU RENE FD TIN
Mean 0.161 7.548 ‑1.542 3.269 3.859 9.574
Median 0.002 8.889 ‑1.505 3.497 3.784 8.933
Maximum 2.507 9.629 0.599 3.984 4.958 14.24
Minimum ‑4.135 3.063 ‑3.579 2.280 3.044 6.729
Std. Dev 1.566 2.254 0.796 0.592 0.556 1.743
Skewness ‑0.302 ‑1.085 ‑0.040 ‑0.387 0.531 1.161
Kurtosis 2.582 2.594 2.793 1.487 2.100 3.769
Jarque–Bera 2.615 23.59 0.236 13.95 9.373 28.92
Probability 0.270 0.000 0.888 0.000 0.009 0.000
Observations 120 120 120 120 120 120

Environmental Science and Pollution Research
1 3
a normal distribution since the Kurtosis value of all the
variables except the RENE variable is greater than the
values of ‑1.5 and + 1.5 expressed in the literature. The
Jarque–Bera test, which displays the statistical outcomes
of the error terms, was once again used to determine
whether the series demonstrated a normal distribution.
The H0 hypothesis, which claims that the error terms are
regularly distributed, was not disproved by the test because
all of the variables' Chi(2) values were greater than 0.05,
which implied that the series had a normal distribution.
When the series is normally distributed, traditional regres‑
sion methods can be used, as well as the quantile regres‑
sion method.
When Table5 is examined, Pesaran's (2004) CD test sta‑
tistic values are seen for the inter‑unit correlation of the data.
In light of the test results, it was determined that there was
a correlation between units for all variables except the EPU
variable, and thus the H0 hypothesis that "there is no inter‑
unit correlation" was invalid.
Table6 displays the variables' stationarity at the I[0] and
I[1] levels based on the outcomes of the CIPS and CADF unit
root tests. Upon looking at Table3, it is clear that the GDP
and RENE data are not stationary at the I[0] level. Yet, when
calculating their first‑order differences, the CIPS unit root test
result demonstrates that they are stationary at the I[1] level.
Other variables can be demonstrated to be stationary at the
I[0] level. Similarly, the findings of the CADF unit root test
indicate that the GDP, RENE, and FD variables are not sta‑
tionary at the I[0] level but become stationary at the I[1] level
when their first‑order differences are taken into account. At
the I[0] level, several variables can be seen to be stationary.
Table7 shows the panel and group statistics and
probability values of the Pedroni (2004) panel cointe‑
gration test. The panel v and panel rho statistics are not
significant; however, the panel PP and panel ADF are
statistically significant at the 1% level when the table
is examined. The group statistics show that, with the
exception of group ADF, all data are statistically sig‑
nificant at the 1% level. When the results of the Pedroni
cointegration test are taken as a whole, four of the seven
tests that make up the panel and group statistics show
significant cointegration between the series. The Pedroni
cointegration test rejected the H0 claim that "there is no
cointegration between the series".
Table8 provides the outcomes of Johansen Fisher
and Kao cointegration test.The null hypothesis "there
is no cointegration between the series" was rejected at a
statistical significance level of 1% when the results of the
Johansen‑Fisher panel cointegration test were assessed
according to the probability values of trace and max‑eigen
statistics. As a result, it was discovered that the variables
have a long‑term link and that the alternative theory, "there
is cointegration between the series," is true.
Table 5 Results of the cross‑
sectional dependency test Indicator CD test
EDI (11.139)***
GDP (13.138)***
EPU (0.526)
RENE (6.112)***
FD (11.209)***
TIN (8.947)***
Table 6 Unit root test
Indicator CIPS test CADF test
I[0] I[1] I[0] I[1]
EDI ‑2.750*** ‑ ‑3.090*** ‑
GDP ‑1.399 ‑3.500*** ‑1.423 3.359***
EPU ‑3.673*** ‑ ‑2.877*** ‑
RENE ‑1.074 ‑3.343*** ‑1.385 ‑3.343***
FD ‑2.356** ‑ ‑2.356* ‑3.457***
TIN ‑3.030*** ‑ ‑3.030*** ‑
Table 7 Panel Cointegration (Pedroni 2004)
Estimates Stats Prob
Panel v Statistics ‑2.395 0.991
Panel rho Statistics 1.886 0.970
Panel PP Statistics ‑3.552 0.000
Panel ADF Statistics ‑2.854 0.002
Alternative hypothesis: individual AR coefficient
Group rho Statistic 2.617 0.995
Group PP Statistic ‑4.496 0.000
Group ADF Statistic ‑3.916 0.000
Table 8 Cointegration test by Johansen fisher panel
Hypothesized
No of CE(s)
Fisher Stat.*
From trace test p‑value From max‑eigen
test p‑value
None 450.3 0.0000 577.3 0.0000
At most 1 166.9 0.0000 106.7 0.0000
At most 2 79.38 0.0000 44.01 0.0035
At most 3 46.66 0.0016 32.06 0.0764
At most 4 29.00 0.1448 21.91 0.4654
At most 5 22.31 0.4415 22.31 0.4415
Kao‑cointegration Test
ADF t‑statistics p‑value
‑1.860 0.031

Environmental Science and Pollution Research
1 3
The H0 hypothesis, which states " Between the series,
there is no cointegration," was rejected at the 5% level of
significance when the Kao cointegration test results were
analyzed since the probability value was less than 0.05.
(0.031). In light of this, it was decided to accept the alter‑
native hypothesis that "there is cointegration between the
series".
Figure5 shows the frequency distribution of the data,
which confirms the data’s non‑normality, so we apply panel
quantile regression. In Table9, the low (25%), medium
(50%), and high (75%) panel quantile regression coeffi‑
cients, standard error, and probability values of the vari‑
ables included in the research are given. When the table
is examined, it can be observed that all other variables are
statistically significant at a 1% level of significance at all
quantile levels, despite the fact that the influence of techno‑
logical innovation (TIN) on environmental degradation is
not significant at a high (75%) quantile level.
Examining how economic expansion affects environmental
deterioration, low (25%), medium (50%), and high (75%)
quantiles are positive and statistically significant at a 1%
level of significance. As a result of this finding, a 1% rise
in economic growth at a low (25%) quantile level causes a
growth of 0.194% in environmental degradation. Likewise,
a 1% rise in economic growth at the medium (50%) quantile
level causes a rise of 0.447 units in environmental degradation
and an increase of 0.250 units at the high (75%) quantile
level. Additionally, this outcome shows that the EKC is
valid. So, until economic growth hits a "turning point,"
environmental degradation will continue to rise along with
economic development in the early phases of economic
development. Because in this process, new technologies
will be used in production, new supply chain methods and
distribution channels will be used, and nature will be abused
at an undesirable level, which will positively affect economic
growth and negatively affect environmental quality. However,
0
4
8
12
16
20
-5 -4 -3 -2 -1 0 1 2 3
Frequency
EDI
0
5
10
15
20
25
34 5 6 7 8 9 10
Frequency
GDP
0
5
10
15
20
25
-4.0 -3.5 -3.0 -2 .5 -2.0 -1.5 -1 .0 -0.5 0.00.51.0
Frequency
EPU
0
4
8
12
16
2.22.42.62.83.03.23.43.63.84.0
Frequency
RENE
0
2
4
6
8
10
3.03.23.43.63.84.04.24.44.64.85.0
Frequency
FD
0
4
8
12
16
20
24
28
6 7 8 9 10 11 12 13 14 15
Frequency
TIN
Fig. 5 Frequency distribution of the data

Environmental Science and Pollution Research
1 3
as a result of the developments in the economy, environmental
pollution and/or environmental degradation will tend to
decrease, and environmental quality will improve by using
environmentally friendly new technologies, supply chain
methods, distribution channels, and configurations. In other
words, economic development and environmental quality will
start to resemble an inverted U. These findings concur with
findings from previous research published in the literature
(Yan etal. 2022; Martins etal. 2021; Ahmad etal. 2021a,
b; Salari etal. 2021; Khan etal. 2020a, b; Dong etal. 2018).
When the effect of economic expansion squared on
environmental degradation is considered, the low (25%),
medium (50%), and high (75%) quantiles are negative and
statistically significant at a 1% level. As a result of this find‑
ing, a 1% increase in the square of economic growth at a low
(25%) quantile level causes a 0.626% decrease in environ‑
mental degradation. Likewise, a 1% increase in the square of
economic growth at the medium (50%) quantile level causes
a 0.731 unit decrease in environmental degradation and a
0.448 unit decrease at the high (75%) quantile level. Exam‑
ining the research's findings reveals that the EKC is valid
because of both the positive impact of economic growth on
environmental deterioration and the detrimental effect of its
square. These findings concur with those of research that has
been published in the literature (Sultana etal. 2022; Yeter
etal. 2021; Sarac and Yaglikara, 2017; Gunduz 2014).
Examining how RE affects environmental deterioration,
low (25%), medium (50%), and high (75%) quantiles are
all statistically significant and negative at a 1% significance
level. According to this finding, environmental deteriora‑
tion is reduced by 0.604% for every 1% increase in RE at
a low (25%) quantile level. Similarly, at the medium (50%)
quantile level, a 1% increase in RE results in a 0.502% drop
in environmental deterioration and a 0.557% decrease at the
high (75%) quantile level. Energy from renewable sources
can help enhance environmental quality, whereas non‑RE
sources are widely acknowledged to be the primary cause of
environmental degradation (Majeed and Lui, 2019). Since
RE will not release polluting gases into the environment,
it may increase environmental quality. Likewise, RE can
reduce environmental degradation when used as a substitute
for fossil fuels. RE also has a positive impact on environ‑
mental quality since it conserves resources from mining and
resource extraction because it is non‑depletable, unlike fos‑
sil fuels. Furthermore, by creating dynamic effects through
economies of scale and spillover effects, RE enhances envi‑
ronmental quality. Using RE sources to generate electric‑
ity allows for the avoidance of thermal pollution brought
on by conventional power sources, which is another way
that RE improves environmental quality (Majeed and Lui,
2019). The theoretical literature largely indicates that RE has
a positive impact on improving environmental quality. The
result of the study also supports this theoretical literature.
These findings concur with findings from previous research
in the literature (Namahoro etal. 2021; Majeed and Lui,
2019; Charfeddine and Kahia 2019; Adams and Nsiah 2019;
Sharif etal. 2019; Haseeb etal. 2018).
When the link between financial development and envi‑
ronmental deterioration is examined, the low (25%), medium
(50%), and high (75%) quantiles are all statistically signifi‑
cant at a 1% level of significance for being negative. The pre‑
sent study indicates that a 1% rise in financial development
at a low (25%) quantile level results in a 0.446 unit reduction
in environmental deterioration. Likewise, a 1% increase in
financial development at the medium (50%) quantile level
causes a 0.946 unit reduction in environmental degradation
and a 0.570% unit decrease at the high (75%) quantile level.
Financial development can support firms in expanding their
economies of scale, enhancing new manufacturing methods
or innovations, encouraging investment in environmental
projects, and promoting environmentally responsible con‑
duct. The introduction of more ecologically friendly tech‑
nologies in place of polluting ones can be accelerated by
financial development to reduce environmental damage (Liu
etal. 2022). The study's findings also imply that, for the
Table 9 The findings of panel quantile regression
“***” shows to 1 percent and “**” shows to 5 percent level of signifi‑
cance
Indicators Outcome of Quantile
25th 50th 75th
Coefficients of
GDP
0.194*** 0.447*** 0.250***
St. Error 0.056 0.066 0.060
P‑value 0.000 0.000 0.000
Coefficients
of GDP2
-0.626*** -0.731*** -0.448***
St. Error 0.219 0.237 0.200
P‑value 0.005 0.002 0.000
Coefficients
ofEPU
0.904*** 0.535*** 0.429***
St. Error 0.160 0.164 0.150
P‑value 0.000 0.001 0.005
Coefficients
ofRENE
-0.604*** -0.502*** -0.557***
St. Error 0.170 0.183 0.174
P‑value 0.000 0.000 0.001
Coefficients
ofFD
-0.446*** -0.946*** -0.570***
St. Error 0.151 0.189 0.183
P‑values 0.004 0.000 0.002
Coefficients
ofTIN
0.315*** 0.215*** 0.039
St. Error 0.074 0.078 0.069
P‑values 0.000 0.007 0.5731

Environmental Science and Pollution Research
1 3
reasons given below, there is a bad link between financial
development and environmental deprivation. The results of
the analysis are in line with several studies in the literature
(Amin etal. 2022; Vo etal. 2021; Koçak, 2017; Al‑mulali
etal. 2015; Shahbaz etal. 2013; Tamazian & Rao 2010).
Assessing how technological innovation affects envi‑
ronmental deterioration, low (25%) and medium (50%)
quantiles are positive and statistically significant at a 1%
level. This finding shows that a 1% rise in technological
innovation generates an increase in environmental degrada‑
tion of 0.315% at the low (25%) quantile level and 0.215%
at the medium (50%) quantile level. No significant effect
was discovered at a high (75%) quantile level. A body of
research has shown that there is a positive and negative link
between technological innovation and environmental dete‑
rioration (Adebayo etal. 2022). Environmental deterioration
is reduced if technological innovations are made toward eco‑
logically friendly technologies. Environmental deterioration
will result, however, if technical progress is not achieved
in fields that are environmentally beneficial. The results
obtained are consistent with previous research (Kirikkaleli
etal. 2022; Liv etal. 2022; Dauda etal. 2021; Ganda 2019;
Bekhet & Othman 2017; Costantini etal. 2017). Essentially,
the use of RE resources benefits from high‑volume techno‑
logical innovation, but it is obvious that many of the miner‑
als used in cutting‑edge technology have a negative impact
on the environment (Kilinc‑Ata etal. 2023). Because of this,
technological innovation is insignificant in the next quan‑
tile, even though it causes environmental harm in the first
quantile. According to several recent studies by Murgua &
Bastida (2023), Eraky etal. (2022), and Omotehinse & Ako
(2019), the usage of mineral resources in RE infrastructure
has serious negative consequences for the environment.
Conclusion andpolicy implications
In the twenty‑first century, how to prevent environmental
damage and establish a sustainable economic framework is
one of the most frequently asked questions. Many theoretical
and empirical studies have been done on this topic. The aim
of the current paper is to add to the body of literature on the
so‑called "fragile five" nations. To achieve this aim, panel
Cointegration and Quantile Regression Model were used
to examine the impact of production volume (GDP), EPU,
technological innovation (TIN), RE consumption (RENE),
and financial development (FD) on environmental degrada‑
tion (EDI).
The current paper demonstrated that the parameters had
long‑term relationships. The quantile regression findings
demonstrate that GDP increases the EDI, but the rate of
increase decreases as the quantile level increases. In con‑
trast,
GDP2
was found to increase EDI. This bidirectional
movement of GDP with EDI confirms the relationship
assumed by the EKC with an inverted "U" shape between the
two variables. Another result, which means that it increases
EDI and worsens the environment, belongs to EPU. While
EPU affects EDI at low quantile levels, this effect appears
to decrease at high quantile levels. This result may be due to
the postponement of investments in environmentally friendly
production technologies due to uncertainties in economic
policy, the more widespread use of old and environmentally
polluting production techniques, and the lack of use of RE
systems that require long‑term investments. The results show
that this hypothesis is further supported by the inverse link
between RE use and EDI. Although the effect of RENE on
EDI decreased as the quantile level increased, significant
results were obtained at all quantile levels. It is not surpris‑
ing that the increased usage of RE is reducing environmental
degradation. This result supports the findings of Sarkodie
& Adams (2018), Zhang & Liu (2019), Anwar etal. (2021),
Mehmood (2021), and Kartal (2022) studies in the literature.
One of the most important obstacles to taking environmental
protective measures and developing technologies that protect
the environment is financial constraint. Findings reveal that
FD reduces EDI. It was also revealed that FD reduces EDI
more stably at moderate and high levels of environmental
degradation. This result supports the findings of Shahbaz
etal. (2016), Acheampong (2019), Ibrahiem (2020), Ahmad
etal. (2022), and Kilinc‑Ata & Alshami (2023). It was
concluded that TIN increased EDI at low quantile levels.
Although Popp etal. (2010), Stamford & Azapagic (2018),
and Yang etal. (2021) have revealed that technological inno‑
vations have a positive effect on environmental protection,
the findings obtained in this study are also very important.
Because TIN's fueling of environmental degradation shows
that technological developments are not focused on envi‑
ronmental protection within the framework of the country
group examined.
Given these findings, we made a few policy
recommendations. Firstly, the policymakers of the countries
need to design growth‑oriented policies and strategies to
control environmental degradation in these countries.
Monetary and fiscal policies should be reconstructed, taking
into account the environmental effects of growth. Since it
is known that the increase in production and income will
bring about a structural transformation in the economy, this
transformation should be supported by environmentally
friendly green production technologies and RE adaptation.
And legal regulations, incentives, joint ventures, some
tax exemptions, and subsidies should be provided for this
transformation. It is crucial to provide companies and
individuals who invest in goods or services connected to
RE with tax exemptions, reductions, or incentives. In other
words, governments/policymakers should support both
market pull policies as well as technology push policies.

Environmental Science and Pollution Research
1 3
A number of market pull and technological push policies
were evaluated as being extremely effective overall, showing
that effective policy did actually spur private equity fund
managers to participate in emerging RE technologies.
The best technology push policies were recognized as
government subsidies, tax exemptions, joint ventures, and
incentives. Considering that the most important pollutants
are fossil fuels, energy policy will have a substantial
impact on environmental deterioration and the structural
development of the economy.
Policymakers should keep in mind the importance of
the EPU and the environmental impact of increases in the
EPU. Two different policies can be developed to adress the
link between EPU and environmental degradation. The first
of these are policies aimed directly at reducing the EPU.
Increasing economic predictability, the functioning of the
economy according to the rules, spreading transparency to
economic decisions, strong financial institutions, etc. are a
few examples of ways to reduce the EPU. The other option
is to weaken the links between the EPU and environmental
degradation. Thus, the increase in EPU will not contribute
to environmental degradation, or this contribution would be
limited. High EPU causes production by traditional methods,
the uncontrolled use of natural resources, and the withdrawal
of investments from environmentally friendly technology
areas and RE sources. Incentives and tax cuts to be given
to these areas will weaken the inverse link between EPU
and EDI.
It is necessary to create policies that promote the genera‑
tion and use of RE. The return on investment required for
RE production may take years. This situation is a deterrent
for investors to produce RE. For this reason, the govern‑
ments of the countries examined in the study are subject to
supportive positions such as tax exemptions, subsidies, land
allocation, tax refunds, joint ventures, low‑interest fund allo‑
cation, etc. International conferences and pieces of training
can be organized to increase social consciousness about RE
production and consumption. There are several unfavorable
effects of the policy suggestions to use RE resources more
frequently. For instance, encouraging the development of RE
will lead to higher demand for specific mineralresources.
For instance, several criticalminerals utilized in the con‑
struction of RE infrastructure have a severe impact on the
environment and may have supply issues in the future.
The findings of this study show that technological inno‑
vations are a variable that causes environmental degrada‑
tion. Efforts to improve technology need to be directed
towards the development of environmentally friendly tech‑
nologies. Rewarding efforts focused on the development of
labor‑saving or capital‑saving technologies, as well as the
development of technologies that save natural resources,
consume less energy, and generate less waste, maybe the
right strategy.
Financial development is located at the intersection of the
production of RE and technological innovations. Ensuring finan‑
cial development can act as a catalyst by creating momentum
for both variables. Developing financial opportunities, deepen‑
ing financial markets, increasing the number of ATMs/banks
per capita, and spreading financial literacy in society constitute
the parameters of financial development. As can be understood,
these parameters have educational, technical, and financial
aspects. In addition, environmental participants should not be
forgotten. Therefore, policies to be developed and implemented
for financial development can be realized with the participation
of different components of society. Bringing these components
together and giving them the necessary incentives can combine
financial development with environmental concerns.
Authors contributions Ahsan Anwar: Conceptualization, Writing,
Editing, Data Analysis, and Supervision. Abdulkadir BARUT: Writing,
Editing, and Review of Manuscript. Fahrettin Pala: Literature Review,
Interpretation of results. Nurcan Kilinc‑Ata: Writing original draft,
Editing, and Review of Manuscript. Emine Kaya: Literature Review,
Methodology writing. Duong Thi Quynh Lien: Writing Original Draft,
Literature Review.
Funding No funding was received from any source.
Data availability The datasets analysed during the current study are
available in the World Bank Data Bank Database repository (https://
data. world bank. org).
Declarations
Ethical approval and consent to participate Not applicable.
Consent to publish Not applicable.
Competing interests The authors declare no competing interest.
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