Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf

In response to the damage caused by a growth-led global economy, researchers
across the world started investigating the association between environmental
pollution and its possible determinants using different models and techniques.
Most famously, the environmental Kuznets curve hypothesizes an inverted
U-shaped association between environmental quality and gross domestic
product (GDP). This book explores the latest literature on the environmental
Kuznets curve, including developments in the methodology, the impacts of
the pandemic, and other recent findings.
Researchers have recently broadened the range of the list of drivers of
environmental pollution under consideration, which now includes variables
such as foreign direct investment, trade expansion, financial development,
human activities, population growth, and renewable and nonrenewable
energy resources, all of which vary across different countries and times. And in
addition to CO2
emissions, other proxies for environmental quality – such as
water, land, and ecological footprints – have been used in recent studies. This
book also incorporates analysis of the relationship between economic growth
and the environment during the COVID-19 crisis, presenting new empirical
work on the impact of the pandemic on energy use, the financial sector, trade,
and tourism. Collectively, these developments have improved the direction
and extent of the environmental Kuznets curve hypothesis and broadened the
basket of dependent and independent variables which may be incorporated.
This book will be invaluable reading for researchers in environmental
economics and econometrics.
Muhammad Shahbaz, School of Management and Economics, Beijing
Institute of Technology, China.
Daniel Balsalobre Lorente, Department of Political Economy and Public
Finance, Economic and Business Statistics and Economic Policy, University of
Castilla La Mancha, Spain.
Rajesh Sharma, SCMS, Nagpur, and Constituent of Symbiosis International
University, Pune, India.
Economic Growth and Environmental
Quality in a Post-pandemic World

Routledge Explorations in Environmental
Economics
Edited by Nick Hanley, University of Stirling, UK
Economics of International Environmental Agreements
Environmental and Economic Impacts of Decarbonization
Input-Output Studies on the Consequences of the 2015 Paris Agreements
Edited by Óscar Dejuán, Manfred Lenzen and María-Ángeles Cadarso
Advances in Fisheries Bioeconomics
Theory and Policy
Edited by Juan Carlos Seijo and Jon G. Sutinen
Redesigning Petroleum Taxation
Aligning Government and Investors in the UK
Emre Üşenmez
National Pathways to Low Carbon Emission Economies
Innovation Policies for Decarbonizing and Unlocking
Edited by Kurt Hübner
The Economics of Renewable Energy in the Gulf
Edited by Hisham M. Akhonbay
Pricing Carbon Emissions
Economic Reality and Utopia
Aviel Verbruggen
Economics and Engineering of Unpredictable Events
Modelling, Planning and Policies
Edited by Caterina De Lucia, Dino Borri, Atif Kubursi and Abdul Khakee
Environmental Finance and Green Banking
Contemporary and Emerging Issues
Edited by Sergey Sosnovskikh and Samsul Alam
Economic Growth and Environmental Quality in a Post-Pandemic
World
New Directions in the Econometrics of the Environmental Kuznets Curve
Edited by Muhammad Shahbaz, Daniel Balsalobre Lorente, and Rajesh Sharma
For more information about this series, please visit www.routledge.com/series
/REEE

Edited by
Muhammad Shahbaz, Daniel Balsalobre
Lorente, and Rajesh Sharma
Economic Growth and
Environmental Quality in a
Post-pandemic World
New Directions in the Econometrics of the
Environmental Kuznets Curve

First published 2023
by Routledge
4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
605 Third Avenue, New York, NY 10158
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 selection and editorial matter, Muhammad Shahbaz, Daniel
Balsalobre Lorente and Rajesh Sharma; individual chapters, the
contributors
The right of Muhammad Shahbaz, Daniel Balsalobre Lorente and Rajesh
Sharma to be identified as the authors of the editorial material, and of the
authors for their individual chapters, has been asserted in accordance with
sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or
utilised in any form or by any electronic, mechanical, or other means, now
known or hereafter invented, including photocopying and recording, or in
any information storage or retrieval system, without permission in writing
from the publishers.
Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-1-032-37350-8 (hbk)
ISBN: 978-1-032-37351-5 (pbk)
ISBN: 978-1-003-33656-3 (ebk)
DOI: 10.4324/9781003336563
Typeset in Bembo
by Deanta Global Publishing Services, Chennai, India
Access the Support Material: www.routledge.com/9781032373508

Contents
List of Contributors viii
1 Examination of the Environmental Kuznets Curve
Hypothesis with Quadratic and Cubic Functional
Models: An Econometric Analysis of European Countries 1
MUHAMMAD SHAHBAZ, KENAN İLARSLAN, AND MÜNEVVERE YILDIZ
2 The Effect of Trade, Renewable Energy, and
Economic Growth on CO2
Emissions in Central and
Eastern Europe 34
NUNO CARLOS LEITÃO, DANIEL BALSALOBRE-LORENTE, AND
MUHAMMAD SHAHBAZ
3 Air Pollution and COVID-19 Nexus: Insights from
Wavelet Approach for Selected Groups of Countries 49
MUHAMMAD IBRAHIM SHAH, AVIK SINHA, ARSHIAN SHARIF, AND
SOLOMON PRINCE NATHANIEL
4 The Impact of Economic Growth, International
Trade, and Carbon Dioxide Emissions on Portuguese
Energy Consumption 61
NUNO CARLOS LEITÃO, CLARA CONTENTE DOS SANTOS PARENTE, AND DANIEL
BALSALOBRE-LORENTE
5 Renewable Energy, Carbon Emissions, and Economic
Growth: The Comparison between EKC and RKC 81
CONGYU ZHAO, XIUCHENG DONG, AND KANGYIN DONG

vi Contents
6 COVID-19 and Energy Transition: A Review 107
DANIEL BALSALOBRE-LORENTE, WALTER FERRARESE, ISMAEL GÁLVEZ-INIESTA,
MONICA A. GIOVANNIELLO, AND ELPINIKI BAKAOUKA
7 EKC Modelling in a Post-pandemic Era: A Policy
Note on Socio-ecological Trade-offs 121
AVIK SINHA AND NICOLAS SCHNEIDER
8 Sustainable Development through Carbon Neutrality:
A Policy Insight from Foreign Direct Investment and
Service Policy 149
EDMUND NTOM UDEMBA
9 Reset the Industry Redux through Corporate Social
Responsibility: The COVID-19 Tourism Impact on
Hospitality Firms through Business Model Innovation 177

JAFFAR ABBAS, KHALID AL-SULAITI, DANIEL BALSALOBRE LORENTE,
SYED ALE RAZA SHAH, AND UMER SHAHZAD
10 Addressing the Nexus between Economic Growth
and Environmental Pollution in a Small Petroleum-
Exporting Transition Economy 202
ELKHAN RICHARD SADIK-ZADA, ANDREA GATTO, AND MUBARIZ MAMMADLI
11 Revising the Environmental Kuznets Curve in the
Post-COVID-19 Era from an SDGs Perspective 217
MUHAMMAD AZAM, AHMED IMRAN HUNJRA, MAHNOOR HANIF, AND
QASIM ZUREIGAT
12 Revisiting the Environmental Kuznets Curve (EKC):
An Analysis Using the Sectoral Output and Ecological
Footprint in India 233
MUHAMMED ASHIQ VILLANTHENKODATH
13 The Contribution of Transport Modes to Carbon
Emissions in Turkey 251
MUHAMMAD SHAHBAZ, TUĞRUL BAYAT, AND MEHMET TANYAŞ

Contents vii
14 The Roles of Education and Export Diversification
in the Improvement of Environmental Quality:
A Comparison between China and India 275
MUHAMMAD SHAHBAZ, MANTU KUMAR MAHALIK, SHUJAAT MUBARAK,
AND SHAWKAT HAMMOUDEH
15 Are Economic Advancements Catalysts for Carbon
Emissions? Depicting the Indian Experience 301
NIKUNJ PATEL, YASWANTH KAREDLA, ROHIT MISHRA, AND PRADEEP KAUTISH

Khalid Al-Sulaiti
Al Rayyan International University College
in partnership with University of Derby UK - Doha, Qatar
Muhammad Azam
Department of Economics, Ghazi University,
Dera Ghazi Khan, Pakistan
Elpinik Bakaouka
Department of Business Economics, Universitat de les Illes Balears (UIB), Spain
Daniel Balsalobre Lorente
Department of Applied Economics I
University of Castilla-La Mancha, Cuenca, Spain
Department of Applied Economics
University of Alicante, Spain
Tuğrul Bayat
Bolvadin Faculty of Applied Sciences
Afyon Kocatepe University, Turkey
Kangyin Dong
School of International Trade and Economics, University of International
Business and Economics, Beijing, China
UIBE Belt Road Energy Trade and Development Center, University of
International Business and Economics, Beijing, China
Xiucheng Dong
Walter Ferrarese
Department of Applied Economics, Universitat de les Illes Balears, Palma de
Mallorca, Spain
List of Contributors

List of Contributors ix
Ismael Gálvez-Iniesta
Mallorca, Spain
Andrea Gatto
Center for Economic Development and Social Change, Napoli, Italy
Wenzhou-Kean University, CBPM, Wenzhou, Zhejiang Province, China
Natural Resources Institute, University of Greenwich, Chatham Maritime, UK
Monica A. Giovanniello
Mallorca, Spain
Shawkat Hammoudeh
Drexel University, United States
Mahnoor Hanif
University Institute of Management Sciences – PMAS – Arid Agriculture
University Rawalpindi, Pakistan
Ahmed Imran Hunjra
Rabat Business School,
International University of Rabat, Morocco
Kenan İlarslan
Yaswanth Karedla
Student, Institute of Management,
Nirma University, S.G. Highway, Ahmedabad, India
Pradeep Kautish
Associate Professor, Institute of Management,
Nuno Carlos Leitão
Polytechnic Institute of Santarém, Center for Advanced Studies in Management
and Economics, Évora University, and Center for African and Development
Studies, Lisbon University, Portugal
Mantu Kumar Mahalik
Department of Humanities and Social Sciences
Indian Institute of Technology, Kharagpur
West Bengal, India
Mubariz Mammadli
Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan

x
List of Contributors
Rohit Mishra
Student, Institute of Management,
Shujaat Mubarak
Muhammad Ali Jinnah University, Karachi, Pakistan
Solomon Prince Nathaniel
Department of Economics,
University of Lagos, Akoka, Nigeria
Nikunj Patel
Associate Professor, Institute of Management, Nirma University,
S.G. Highway, Ahmedabad, India
Elkhan Richard Sadik-Zada
Institute of Development Research and Development Policy, Ruhr-University,
Bochum, Germany
Centre for Environment, Resources and Energy, Faculty of Management and
Economics, Bochum, Germany
Clara Contente dos Santos Parente
University of castilla-La Mancha
Nicolas Schneider
The London School of Economics and Political Science (LSE),
Department of Geography and Environment, Houghton Street, London, UK
Muhammad Ibrahim Shah
Independent researcher, Edmonton, Canada
Alma mater Department of Economics, University of Dhaka, Bangladesh
Syed Ale Raza Shah
School of Economics and Finance,
Xi’an Jiaotong University, Xian, China
Muhammad Shahbaz
School of Management and Economics
Beijing Institute of Technology, China
Umer Shahzad
Research Institute of the University of Bucharest, Social Sciences Division,
University of Bucharest, Romania
Arshian Sharif
Department of Economics and Finance
Sunway University, Malaysia

List of Contributors xi
Avik Sinha
Centre for Excellence in Sustainable Development,
Goa Institute of Management, Goa, India
Adnan Kassar School of Business, Lebanese American University, Beirut,
Lebanon
Mehmet Tanyaş
Maltepe University, Faculty of Business and Management Sciences, Turkey
Edmund Ntom Udemba
Faculty of Economics Administrative and Social sciences
Istanbul Gelisim University, Istanbul, Turkey
Muhammed Ashiq Villanthenkodath
School of Social Sciences and Humanities
B.S. Abdur Rahman Crescent Institute of Science and Technology
Chennai, India
Münevvere Yıldız
Congyu Zhao
Qasim Zureigat
School of Business, Sulaiman AlRajhi University, Saudi Arabia

1
1.1 Introduction
According to a report published by the World Health Organization in 2020,1
preventable environmental risks cause about a quarter of all deaths and disease
burden worldwide, and at least 13 million deaths occur yearly. Air pollution,
one of the most significant risks posed to health, causes 7 million preventable
deaths annually; worse, more than 90% of people breathe polluted air.
Industrialization, population growth, urbanization, hazardous wastes, fossil
fuel consumption, and technological developments can be listed as the main
factors that increase global carbon emissions. The carbon emission caused by
fossil fuels used in energy production, which is necessary for activities carried
out to enhance and improve human comfort and quality of life, also leads
to major problems. According to Heydari et al. (2021), greenhouse gases in
the atmosphere have increased significantly due to the use of fossil fuels inci-
dental to the Industrial Revolution. Consequently, global warming and cli-
mate change have been the main concerns, especially in the past two decades.
According to assessments made by Dettner and Blohm (2021) and Gul et al.
(2015), one of the prime movers and propellants of global warming is the use
of fossil fuels, which add significant amounts of greenhouse gases to the atmos-
phere. Therefore, the common and extensive use of these resources contrib-
utes to greenhouse gas emissions, especially carbon dioxide emissions, which
are shown as the main factor in global warming and ozone layer depletion.
The increase in carbon emissions brings along several diversified problems.
The most important of these problems is global warming due to the green-
house gas effect caused by increased carbon emissions (Ustaoglu et al., 2021).
As a consequence of global warming, profound climate changes (Macrina
and Zurbenko, 2021) and natural disasters due to climate change (Rossati,
2017) create critical uncertainties regarding the world being a liveable place
in the future. The climate change caused by greenhouse gas emissions causes
Examination of the Environmental
Kuznets Curve Hypothesis
with Quadratic and Cubic
Functional Models
An Econometric Analysis of
European Countries

Muhammad Shahbaz, Kenan İlarslan, and
Münevvere Yıldız
DOI: 10.4324/9781003336563-1

2 Examination of the Environmental Kuznets
reductions in agricultural production (Kalakoti, 2021), health problems con-
cerning well-being of humans, especially chronic obstructive pulmonary
disease (COPD), asthma, shortness of breath, and cardiovascular diseases
(Manisalidis et al., 2020) and has potential adverse effects such as constraints
on access to water resources (Demirhan, 2020), impairments on biodiversity,
and forest ecosystem (Velepucha et al., 2016; Shahbaz et al., 2013). Another
consequence of carbon emissions is air pollution. As Tahir et al., (2021) stated,
air pollution raises the temperature of the earth, and this situation adversely
affects human life. Toxic chemicals and toxic compounds such as sulphur
oxide, nitrous oxide, carbon dioxide and carbon monoxide are added to the
air we breathe, reducing air quality. These toxic compounds threaten life by
reducing air quality and causing harmful environmental changes on a global
scale. The destruction of the ecosystem as a consequence of the biological
and chemical deterioration of the atmosphere caused by air pollution threatens
human health and is also a global problem with serious economic and financial
implications throughout the world. In this context, air pollution has a negative
effect on the sustainable economic growth of countries (Dong et al., 2021),
creates a significant burden on the budget by increasing health expenditures
(Shen et al., 2021a), negatively affects stock returns and investor behaviour
(Wu et al., 2020; Lepori, 2016), causes strong anomalies in the stock mar-
ket (Nguyen and Pham, 2021), reduces agricultural productivity (Koondhar
et al., 2020; Wang et al., 2020c; Giannadaki et al., 2018), negatively affects the
employment of qualified workers (Wang et al., 2021; Li et al., 2020; Liu and
Yu, 2020), poses a threat to tourism activities (Zhang et al., 2020; Churchill
et al., 2020; Lapko et al., 2020), increases internal migration (Cui et al., 2019),
and adversely affects employee productivity (Chen and Zhang, 2021; Yang and
Xu, 2020; Neidell, 2017).
Although various strategies have been implemented worldwide to reduce
carbon emissions in line with the Kyoto Protocol and the Paris Agreement,
the use of fossil fuels to promote economic development continues to contrib-
ute significantly to carbon dioxide (CO2) emissions. Therefore, the relation-
ship between economic growth and environmental quality, as captured by
the Environmental Kuznets Curve (EKC), has been the subject of ongoing
high-pitched debate (Churchill et al., 2018). In other words, most of the stud-
ies between per capita income and carbon emissions are handled in the context
of the EKC hypothesis. EKC is the theory that deals with the relationship
between per capita income and environmental degradation. Accordingly, there
is an “inverted U” relationship between environmental pollution and per cap-
ita income. In other words, when economic growth increases, environmental
pollution initially increases. After a certain threshold value, the trend reverses,
and, as the level of economic development increases, environmental pollution
decreases with the increase in environmental awareness (Tenaw and Beyene,
2021; Adebayo, 2021; Leal and Marques, 2020; Shahbaz, 2019; Wang et al.,
2015). The theoretical background of the “inverted U”-shaped EKC describ-
ing a changing relationship between economic growth and environmental stress

Examination of the Environmental Kuznets 3
(Churchill et al., 2018; Canas et al., 2003) is justified as follows. Environmental
quality behaves like any other economic good that people are willing to pay
more for as income increases – increasing levels of welfare place environmental
concerns higher on the political agenda. As income rises, the economic struc-
ture changes as the economy shifts toward services and light manufacturing
industries, and higher income levels and technological eco-efficiency caused by
more or less voluntary changes in consumption patterns pollute the environ-
ment less and, thus, improve environmental quality. However, the empirical
studies show that the EKC curve has “U” shaped or other formations (Minlah
and Zhang, 2021; Akadırı et al., 2021; Alola and Donve, 2021; Işık et al.,
2021; Yilanci and Pata, 2020), suggesting that researchers contextualize and
address the issue under different scenarios. Therefore, the controversial results
about traditional second-order EKC have led researchers to explore different
third-order (cubic) EKC patterns. Considering the cubic effect, an N-shaped
relationship is expected for EKC if the coefficients of gross domestic prod-
uct (GDP) or per capita income, which are β1
0, β2
0, and β3
0, and
an inverse N-shaped relationship if β1
0, β2
0, and β3
0 are expected.
An N-shaped relationship was found in most studies dealing with EKC in
cubic form. These include studies conducted by Hasanov et al. (2021) He
et al. (2021), Moriwaki and Shimizu (2021), Xu et al. (2020), Shahani and
Raghuvansi (2020), Ahmad et al. (2019), Pala (2018), Allard et al. (2018),
Murthy and Gambhir (2018), Sinha and Bhatt (2017), Moutinho et al. (2017),
Balsalobre and Herranz (2016), Balın and Akan (2015), and Balibey (2015),
which can be cited as examples.
According to Liu and Song (2020), there are three mechanisms by which
financial development can affect carbon emissions: the consumption effect,
the production expansion effect, and the technological innovation effect. The
consumption effect occurs when a sound financial sector can provide con-
sumers with sufficient credit to purchase enough consumables such as houses,
cars, and air conditioners. Thus, the increase in financial resources improves
citizens’ living standards and human activities, ultimately increasing energy
consumption and carbon emissions (Neog and Yadava, 2020; Acheampong et
al., 2019). Instead of the individual and/or family perspective, the production
expansion effect and the technological impact occur in line with the perspec-
tive of the firm. When a sound financial system can channel sufficient funds
to firms’ production processes, firms can expand their production scales as a
result of sufficient credit required for production (Gök, 2020; Samour et al.,
2019; Li and Ouyang, 2019; Acheampong et al., 2019). When more products
are produced, more energy is consumed and more carbon is released (Shen
et al., 2021b). This is the production expansion effect. Finally, technological
innovations and firms’ research and development (RD) require a large num-
ber of financial resources that can be provided and guaranteed by an advanced
financial system. Thus, an advanced financial system can promote new tech-
nologies to conserve non-recyclable resources and reduce emissions (Shahbaz
et al., 2020; Acheampong, 2019; Abbasi and Riaz, 2016). This reduction in

emissions is a result of the innovation effect. Moreover, the well-developed
financial sector provides access to low-cost capital or creates incentives for
firms and governments to invest in environmentally friendly projects (Lv and
Li, 2021; Lahiani, 2020; Tsaurai, 2019).
Another factor affecting carbon emissions is renewable energy production.
Renewable energy has low or even zero carbon emissions compared to fossil
fuels. Using renewable energy instead of fossil fuels effectively reduces carbon
emissions (Yao et al., 2019). Stating that renewable energy consumption is
an important driving force for a quality environment (Bekun et al., 2021),
they emphasized the role of renewable energy consumption in reducing car-
bon emissions in their studies. There is a clear consensus in the empirical lit-
erature that renewable energy production and use have a reducing effect on
carbon emissions, and studies in this direction may be exemplified as follows:
Balsalobre et al. (2021), Azam et al. (2021), Radmehr et al. (2021), Yuping et
al. (2021), Kirikkaleli and Adebayo (2021), Vo et al. (2020), Saidi and Omri
(2020), Akram et al. (2020), Mert et al. (2019), Charfeddine and Kahia (2019),
Pata (2018), and Mert and Bölük (2016).
Numerous studies in the literature are trying to reveal the factors affect-
ing carbon emissions in different countries. At this point, the study aims to
comprehensively reveal the relationship between per capita income, financial
development level, renewable energy production, and carbon emissions and
the aspects of this relationship. In the study, in which the 1990–2019 period
data regarding 19 European countries2
were used, the EKC hypothesis was
examined for quadratic and cubic structures by using the fixed-effect panel
quantile regression method. Moreover, the obtained results were confirmed by
panel cointegration regressions. The period of the study, the method used, and
the countries included distinguish the study from other studies in the literature
and add originality. According to the main result obtained from the study, we
have reached strong empirical evidence that EKC has an “inverted U” image
in the quadratic model and an “N shape” in the cubic model.
The study is comprised of four parts. In the introduction (Section 1.1), the
subject’s importance and the problem’s introduction within the context of the
theoretical background are addressed. In Section 1.2, the research hypotheses
are developed in the light of the theoretical discussions on the subject, and in
Section 1.3, the practical econometric methodology is introduced, and the
findings are presented. In the fourth and last section (Section 1.4), the findings
obtained from the research, results, and suggestions for decision-makers are
shared.
1.2
Literature Review and Hypothesis Development
The rise in carbon emissions has been threatening our world from past to
present, and human effort to reduce emissions has not yet been successful. For
this reason, the scientific world constantly examines the factors that affect car-
bon emissions in different dimensions by separating them at the periodic and

national levels. In this regard, many studies in the field have taken their place
in the literature. The place of the variables used in this part of the study in the
literature and the studies on the results obtained are summarized below.
1.2.1
Studies Examining the Relationship between
Carbon Emissions and Economic Growth
Most of the studies examining the relationship between carbon emission and
GDP have focused on the EKC hypothesis. Based on the EKC hypothesis,
it is stated that, in the early times, when the economic growth in the coun-
tries increased, the carbon emission level increased in parallel with the increase
in the amount of production and energy consumption. However, with the
increase in the welfare level of the country, policies for environmental factors
came to the fore, and they gravitated toward clean energy sources. Studies
by Grossman and Krueger (1991), Shafik and Bandyopadhyay (1992), and
Panayotou (2003) were the first studies in the literature to examine the EKC
hypothesis. In studies based on the EKC hypothesis, it is seen that the effect of
economic growth on carbon emissions is negative in some studies and positive
in others, depending on the level of economic development of the countries.
As it is understood from the studies in the literature, different results emerge
on whether economic growth and financial development have a positive or
negative effect on the environmental factors of the countries. Most of the stud-
ies have investigated the validity of the EKC hypothesis by considering differ-
ent countries. Table 1.1 lists some of the studies examining the relationship
between economic growth and carbon emissions in Panel A, and the results
obtained in these studies are summarized. Following the study’s aims and in
line with the theoretical expectations obtained from the literature review, the
hypotheses developed to examine the effect of national income per capita on
carbon emissions are specified below.
H1a
: According to EKC in quadratic form, the coefficients of per capita income have the
values of β1
0, β2
0.
H1b
: According to EKC in cubic form, the coefficients of per capita income have the val-
ues of β 1
0, β2
0, β3
0.
1.2.2
Studies Examining the Relationship between Carbon
Emissions and Financial Development Level
Financial development is generally evaluated in two dimensions. The first
dimension is the expansion of the financial institutions in the country, and
the other dimension is the increase in the share of financial assets in income
(Aslan and Korap, 2006). When both dimensions of financial development
are considered together, it can be said that it is an essential indicator of eco-
nomic growth. Expansion and growth in the economy inevitably lead to an
increase in production opportunities; thus, the level of carbon emissions will

Table
1.1
E
mpirical
Literature
Summary
Study
Period
Countries
Method
Findings
Panel
A:
Certain
studies
examining
the
relationship
between
GDP/per
capita
income
and
carbon
emissions
Cole
et
al.
(1997)
1970–1992
OECD
countries
Generalized
least
squares
They
demonstrated
the
existence
of
the
EKC
hypothesis
for
local
pollutants.
However,
they
stated
that
indicators
with
global
or
indirect
effects
show
a
monotonous
increase
in
income,
and
they
predict
turning
points
in
per
capita
income
levels
with
high
standard
errors.
Song
et
al.
(2008)
1985–2005
China
Panel
cointegration
test
They
revealed
the
existence
of
a
long-term
relationship
between
GDP
and
waste
gas,
wastewater
and
solid
waste.
In
addition,
the
results
showed
that
all
three
pollutants
exhibit
an
inverted
U
shape.
Wang
et
al.
(2011)
1995–2007
China
Panel
cointegration
and
panel
vector
error
correction
modelling
There
is
a
cointegration
relationship
between
carbon
emissions,
energy
consumption,
and
economic
growth.
Energy
consumption
and
economic
growth
are
the
cause
of
carbon
emissions
in
the
long
run.
It
has
been
stated
that
it
is
difficult
to
reduce
the
carbon
emission
level
in
China
in
the
long
term
and
that
the
policies
to
be
implemented
in
the
direction
of
reduction
may
cause
a
certain
level
of
inhibition
of
economic
growth
in
China.
Jaunky
(2011)
1980–2005
36
high-Income
countries
Generalised
methods
of
moments
A
one-way
relationship
from
GDP
to
CO2
emissions
in
the
short
and
long
term
has
been
demonstrated.
The
increase
in
GPD
increases
the
carbon
emission
level
in
the
short
and
long
term.
However,
it
has
also
been
revealed
that
there
are
stabilizations
in
economically
rich
countries.

Ozcan
(2013)
1990–2008
12
Middle
Eastern
countries
Panel
cointegration,
FMOLS,
panel
causality
It
revealed
the
validity
of
the
U-shaped
EKC
hypothesis
for
five
countries,
and
an
inverted
U
curve
was
defined
in
three
countries.
In
the
short
run,
a
unidirectional
causality
running
from
economic
growth
to
energy
consumption
was
found.
In
the
long
run,
there
is
a
unidirectional
causality
relationship
from
energy
consumption
and
economic
growth
to
carbon
emissions.
Robalino-López
et
al.
(2015)
1980–2025
Venezuela
Seemingly
unrelated
regression
The
study
tried
to
reveal
the
forecasts
for
the
future
years
within
the
framework
of
different
economic
scenarios
by
using
the
past
data.
The
results
show
that
Venezuela
does
not
provide
the
EKC
hypothesis,
but
it
can
stabilize
its
environmental
effects
in
the
medium
term
by
turning
to
renewable
energy
with
economic
growth.
Begum
et
al.
(2015)
1970–2009
Malaysia
ARDL
bounds
test,
dynamic
ordinary
least
squared
They
revealed
that
the
EKC
hypothesis
was
not
valid
in
Malaysia
during
the
study
period.
While
the
increase
in
energy
consumption
and
GDP
has
a
positive
effect
on
CO2
in
the
long
run,
no
significant
effect
of
population
growth
has
been
found.
Apergis
and
Öztürk
(2015)
1990–2011
14
Asian
countries
Generalized
method
of
moments
(GMM)
The
relationship
between
emissions
and
per
capita
income
emerged
in
an
inverted
U
shape,
which
provided
evidence
for
the
existence
of
the
EKC
hypothesis
for
countries.
Shahbaz
et
al.
(2013)
1965–2008
South
Africa
ARDL
bounds
test,
Granger
causality
The
results
of
the
study
reveal
that
economic
growth
increases
energy
emissions,
while
financial
development
decreases
it.
This
indicates
the
validity
of
the
EKC
hypothesis
for
South
African
countries.
(Continued)

Shahbaz
et
al.
(2016)
1972–2013
Next
11
countries
Granger
causality
They
determined
that
economic
growth
is
the
cause
of
carbon
emissions
for
Bangladesh
and
Egypt.
In
addition,
there
is
a
unidirectional
causality
relationship
from
economic
growth
to
carbon
emissions
for
Indonesia
and
Turkey,
which
ultimately
proves
the
validity
of
the
EKC
hypothesis.
Saidi
and
Hammami
(2016)
1990–2012
58
countries
GMM
For
all
panel
data,
it
shows
that
energy
use
increases
carbon
emissions,
and
the
increase
in
GDP
has
a
positive
significant
effect
both
in
the
whole
panel
and
in
Europe,
demonstrating
the
validity
of
the
EKC
hypothesis.
Bakirtas
and
Cetin
(2017)
1982–2011
Mexico,
Indonesia,
South
Korea,
Turkey
and
Australia
(MIKTA)
countries
Panel
vector
autoregression
(VAR)
It
has
been
determined
that
the
EKC
hypothesis
is
not
valid
for
MIKTA
countries.
Can
and
Gozgor
(2017)
1964–2014
France
Dynamic
ordinary
least
squares
In
France,
the
EKC
hypothesis
is
valid.
Energy
consumption
has
a
positive
effect
on
carbon
emissions.
High
economic
complexity
suppresses
the
carbon
emission
level
in
the
long
run.
Alshehry
and
Belloumi
(2017)
1971–2011
Saudi
Arabia
ARDL,
Granger
causality
The
EKC
hypothesis
is
not
valid
for
Saudi
Arabia.
In
the
long
run,
there
is
a
unidirectional
causal
relationship
from
economic
growth
to
transport
carbon
emissions
and
road
transport
energy
consumption.
Table
1.1
Continued
Study
Period
Countries
Method
Findings

Jiang
et
al.
(2021)
1985–2018
China
ARDL
bounds
test,
FMOLS,
canonical
cointegration
regression
(CCR)
The
validity
of
the
EKC
hypothesis
in
China
has
been
demonstrated
with
the
relationship
in
the
form
of
an
inverted
U
curve
obtained
in
the
short
and
long
term
based
on
electricity
production
and
consumption.
Ozturk
and
Acaravci
(2013)
1960–2007
Turkey
ARDL
bounds
test,
Granger
causality
They
showed
that
the
carbon
emission
level
increased
in
line
with
the
expansion
in
income
in
the
beginning
in
Turkey
but
showed
a
decreasing
trend
when
it
reached
the
stationary
point,
and
the
validity
of
the
EKC
hypothesis
was
revealed.
Panel
B:
Certain
studies
examining
the
relationship
between
financial
development
and
carbon
emissions
Shahbaz
et
al.
(2021)
1870–2017
United
Kingdom
Bootstrapping
ARDL
bounds
test
Financial
development
and
energy
consumption
lead
to
environmental
degradation.
It
has
been
stated
that
there
is
a
U-shaped
relationship
between
financial
development
and
CO2
emissions.
Gozbası
et
al.
(2021)
1995–2017
American,
Asian,
and
European
continent
(34
countries)
Panel
quantile
regression
According
to
the
results
obtained
for
the
whole
sample,
financial
development,
fossil
fuel
energy
consumption,
and
tourism
revenues
increase
pollution.
This
effect
of
financial
development
continues
up
to
high
quantile
levels.
Guo
(2021)
1988–2018
China
Bayer–Hanck
and
Maki
cointegration
tests,
FMOLS,
DOLS
According
to
the
results
obtained
from
the
analyses,
the
level
of
financial
development
in
China
has
a
reducing
effect
on
carbon
emissions.
Bui
(2020)
1990–2012
100
countries
Two-stage
least
squares
(2SLS)
and
three-
stage
least
squares
(3SLS)
The
empirical
results
confirm
the
positive
direct
impact
of
financial
development
on
environmental
degradation.
The
development
of
the
financial
system
also
leads
to
more
energy
demand
and,
consequently,
more
pollutant
emissions.
Liu
ve
Song
(2020)
2007–2016
China
Spatial
econometric
method
It
is
stated
that
the
overall
effect
is
that
financial
development
will
reduce
carbon
emissions.
(Continued)

Acheampong
et
al.
(2020)
1980–2015
83
countries
Generalised
methods
of
moments
Financial
development
has
had
a
reducing
effect
on
the
emission
intensity
in
developed
and
developing
countries.
It
has
been
revealed
that
the
non-linear
and
regulatory
effects
of
the
development
in
financial
markets
differ
in
terms
of
countries.
Boutabba
(2014)
1971–2008
India
ARDL
bounds
test,
Granger
causality
It
has
been
seen
that
financial
development
has
a
positive
effect
on
carbon
emissions
in
the
long
run
and
is
the
cause
of
environmental
degradation
in
India.
With
the
Granger
causality
test,
a
unidirectional
causal
relationship
from
financial
development
to
carbon
emissions
was
determined.
Cetin
and
Ecevit
(2017)
1960–2011
Turkey
ARDL
bounds
test,
Granger
causality
Financial
development,
economic
growth,
and
trade
openness
have
a
positive
long-term
impact
on
carbon
emissions
in
Turkey.
The
study
revealed
the
validity
of
the
EKC
hypothesis
for
Turkey
and
that
economic
and
financial
development
affect
environmental
degradation.
Ehigiamusoe
and
Lean
(2019)
1990–2014
122
countries
FMOLS,
DOLS
In
analyses
that
consider
all
countries,
energy
consumption,
economic
growth,
and
financial
development
have
harmful
effects
on
carbon
emissions.
In
the
analyses
made
by
disaggregating
the
countries
according
to
their
income
level,
it
was
observed
that
economic
growth
and
financial
development
reduced
CO2
emissions
in
high-
income
countries,
while
the
opposite
effect
was
observed
in
low-
and
middle-income
countries.
Table
1.1
Continued
Study
Period
Countries
Method
Findings

Jiang
and
Ma
(2019)
1990–2014
155
countries
Generalized
method
of
moments
Globally,
financial
development
has
an
increasing
effect
on
carbon
emissions.
On
the
other
hand,
although
this
situation
is
similar
in
emerging
markets
and
countries,
the
effect
of
financial
development
on
carbon
emissions
is
not
significant
in
developed
countries.
Tsaurai
(2019)
2003–2014
W.
African
countries
Classic
Panel
regression
In
the
study,
it
was
seen
that
only
local
bank
loans
provided
to
the
financial
sector
had
a
significant
effect
on
increasing
carbon
emissions.
Wang
et
al.
(2020b)
1990–2017
N-11
countries
Westerlund
cointegration
test
It
has
been
stated
that
economic
growth
and
financial
development
have
a
positive
effect
on
carbon
emissions
in
the
relevant
countries,
while
technological
innovation
and
renewable
energy
consumption
have
a
negative
effect.
Zaidi
et
al.
(2019)
1990–2016
APEC
countries
Westerlund
cointegration
test,
FMOLS
The
results
show
that
globalization
and
financial
development
have
a
decreasing
effect
on
carbon
emissions
for
Asia-Pacific
Economic
Cooperation
(APEC)
countries,
while
economic
growth
and
energy
density
have
an
increasing
effect.
Zhang
(2011)
1980–2009
China
Cointegration
test,
Granger
causality
It
has
been
determined
that
financial
development
is
the
driving
force
behind
carbon
emissions
in
China.
Panel
C:
Certain
studies
examining
the
relationship
between
renewable
energy
and
carbon
emissions
Acheampong
et
al.
(2019)
1980–2015
46
sub-Saharan
African
countries
Fixed
and
random
effect
Panel
regression
Renewable
energy
reduces
carbon
emissions.
Financial
development
and
population
growth
cause
an
increase
in
emissions.
(Continued)

Adams
and
Acheampong
(2019)
1980-2015
46
sub-Saharan
African
countries
GMM
Democracy
and
renewable
energy
have
revealed
effects
that
reduce
carbon
emission.
Although
foreign
direct
investments,
trade
openness,
population,
and
economic
growth
are
the
driving
forces
behind
carbon
emissions
for
these
countries,
it
is
also
noteworthy
that
economic
growth
reduces
carbon
emissions
at
the
point
where
democracy
emerges.
Akram
et
al.
(2020)
1990–2014
66
developing
countries
Classic
Panel
regression,
fixed-
effect
panel
quantile
regression
Renewable
energy
has
a
statistically
significant
and
negative
effect
on
carbon
emissions
at
all
quantile
levels.
The
increase
in
renewable
energy
in
developing
countries
will
be
effective
in
reducing
carbon
emissions.
Dogan
and
Seker
(2016)
1985–2011
40
top
renewable
energy
countries
FMOLS,
DOLS
According
to
FMOLS
and
DOLS
results,
the
increase
in
renewable
energy
consumption,
trade
openness,
and
financial
development
cause
a
decrease
in
carbon
emissions.
Hanif
et
al.
(2019)
1990–2015
25
Asian
countries
Two-step
system
GMM
The
use
of
renewable
energy
in
Asian
countries
avails
the
control
of
carbon
emissions,
while
the
use
of
non-renewable
energy
increases
carbon
emissions
as
expected.
Lu
(2017)
1990–2012
24
Asian
countries
Panel
cointegration,
Granger
causality
In
some
of
the
countries
examined,
it
has
been
determined
that
carbon
emission
has
a
positive
effect
on
renewable
energy
consumption,
and
there
is
a
causal
relationship
between
carbon
emission
and
renewable
energy
consumption.
Table
1.1
Continued
Study
Period
Countries
Method
Findings

Nguyen
and
Kakinaka
(2019)
1990–2013
107
countries
Panel
cointegration,
FMOLS,
DOLS
The
study
examined
the
relationship
between
renewable
energy
consumption
and
carbon
emissions,
taking
into
account
the
development
level
of
countries.
It
is
seen
that
renewable
energy
has
positive
effects
with
carbon
emissions
for
low-income
countries,
while
the
relationship
is
negative
in
high-income
countries.
Leitao
and
Balsalobre
(2020)
1995–2014
28
European
Union
countries
Panel
FMOLS,
Panel
DOLS,
System
GMM
Econometric
results
prove
that
trade
openness
and
renewable
energy
reduce
climate
change
and
environmental
degradation.
Empirical
study
has
found
that
economic
growth
also
has
an
increasing
effect
on
carbon
dioxide
emissions.
Saidi
and
Omri
(2020)
1990–2014
15
major
renewable
energy-
consuming
countries
FMOLS,
Granger
causality
The
efficiency
of
renewable
energy
reduces
carbon
emissions.
There
is
no
causal
relationship
between
renewable
energy
and
carbon
emissions
in
the
long
run,
and
there
is
a
bidirectional
relationship
in
the
short
run.
Vural
(2020)
1980–2014
8
sub-Saharan
African
countries
Panel
cointegration,
DOLS
While
non-renewable
energy
and
trade
have
a
significant
impact
on
carbon
emissions,
renewable
energy
emissions
have
a
mitigating
effect.
Yuping
et
al.
(2021)
1970–2018
Argentina
ARDL,
Gradual
Shift
Causality
Renewable
energy
consumption
and
globalization
reduce
emissions
in
the
short
and
long
term.

increase due to the increase in energy demand. However, on the other hand,
the increasing financial development of countries can bring forth the use of
clean energy technologies in production processes, and in such cases, financial
development can also reduce carbon emissions. In this regard, some of the
studies in the literature found the relationship positive, while others found it
negative. Studies in the literature examining the effects of financial develop-
ment on carbon emissions can be seen in Panel B in Table 1.1. The hypotheses
developed to examine the effect of financial development level on carbon
emissions under the study’s aims and in line with the theoretical expectations
obtained from the literature review are given below.
H2
: There is a significant relationship between the level of financial development and
carbon emissions.
1.2.3
Studies Examining the Relationship between
Carbon Emissions and Renewable Energy
Energy resources have an important place in the turning of the economic
wheels of countries. Especially the increase in the amount of energy used in
production is associated with economic growth. However, the important
point here is the environmental pollution that occurs as a result of the use of
energy while the countries are growing. A significant portion of the world’s
energy needs is met by using fossil fuels. However, the need for clean energy
sources is increasing day by day due to the scarcity of these fuels and their
negative effects on the environment. The duty of the countries is to gravitate
toward clean energy sources that will take environmental factors into account
while establishing the balance between energy consumption and economic
welfare. Efforts of economically developed countries, unions, and organiza-
tions with environmental policies to increase the use of renewable energy
sources increase social awareness daily. A large number of funding sources
are offered by the European Union, especially for developing countries, in
order to encourage the use of renewable energy sources. As stated by Shahbaz
et al. (2021), because of the environmental damage and limited availability
of fossil fuels, countries are increasingly striving to find and expand renew-
able energy sources and are becoming less dependent on non-renewable fuels.
Resources such as solar energy, the kinetic energy of streams and waves, wind
energy, geothermal and biomass energy, and the power of sea waves are called
renewable energy sources. They are considered difficult to run out in terms
of ease of manufacture, low costs, and energy production after a short invest-
ment period. It is expected that the increase in the production of renewable
energy sources will reduce carbon emissions. Most of the studies examining the
effect of renewable energy on carbon emissions in the literature have focused
on the consumption of renewable energy sources. A few studies have exam-
ined the relationship between renewable energy production and carbon emis-
sions. However, all countries are faced with the following reality: almost all of

the renewable energy sources produced are consumed. All studies examining
renewable energy and carbon emissions are expected to produce similar results
in this context. The results of some studies examining renewable energy and
carbon emissions are given in Panel C in Table 1.1. The hypotheses developed
to examine the effect of renewable energy production on carbon emissions in
accordance with the aims of the study and in line with the theoretical expecta-
tions obtained from the literature review are given below.
H3
: There is a negative relationship between renewable energy production and carbon
emissions.
1.3 Econometric Methodology
1.3.1 Data
The data used in the study are annually based and cover the period 1990–2019.
In the study, the dependent variable is carbon emission, and the independent
variables are per capita income, renewable energy production, and financial
development level. All data were included in the analysis over their natural
logarithms. A data set consisting of a total of 570 observations for the 30-year
period (T = 30) of European countries (N = 19) was used. However, the
data set shows unbalanced panel data characteristics due to the missing data
in some years. Eviews 11 SE and Stata 16.1 programs were used for statistical
and econometric analysis. Abbreviations, definitions, and sources related to the
variables are presented in Table 1.2.
1.3.2 Econometric Method
In the study, the fixed-effect panel quantile regression analysis method will be
used in order to measure the effect of financial development level, renewable
energy production, per capita income, and the quadratic and cubic form of
GDP on carbon emissions.3
Using panel quantile regression methodology, we
can examine the determinants of carbon emissions across European countries
across the conditional distribution. The use of traditional regression method-
ology may lead to over- or underestimation of relevant coefficients, or these
techniques may not be successful in detecting a significant relationship, as they
Table 1.2
Definition of Variables
Variable Definition Source
lnco2
Carbon emission amount www.globalcarbonatlas.org
lnpgdp GDP per capita www.worldbank.org
lnfindev Financial development index www.imf.com
lnrenew Renewable energy production www.ourworldindata.org

focus on average effects (Khan et al., 2020). Therefore, in this study, a panel
quantile method with fixed effects will be used, which makes it possible to
estimate the conditional heterogeneous covariance effects of carbon emis-
sion causes and thus to control for unobserved individual heterogeneity. The
panel econometrics specification used in this study is the Method of Moments
Quantile Regression (MM-QR) developed by Machado and Silva (2019).
The advantages of this method, which has become a central study subject and
widely used in recent years (Akram et al., 2021; Cheng et al., 2021; Halliru et
al., 2020; Wang et al.,, 2020a; Salehnia et al., 2020; Huang et al., 2020), are
highlighted and specified as follows. In this respect, quantile regression is more
reliable because the classical ordinary least squares (OLS) assumptions of error
terms with zero mean, constant variance, and normal distribution are difficult
to meet. Therefore, OLS can provide robust results even when classical econo-
metric assumptions fail. Compared to OLS regression, quantile regression can
select any quantile point for parameter estimation. Because it does not make
any specific assumptions about the distribution of error terms, its sensitivity to
outliers is much less than mean regression, so it can provide more accurate and
robust regression results. This method is preferred, as it captures all significant
variation between predicted and observed variables and, thus, avoids errone-
ous regression coefficients. The pantile quantile regression method does not
follow any distribution assumptions. While classical regression methods do not
consider differential heterogeneity, this method deals with differential hetero-
geneity of panel data along with distribution heterogeneity. The panel quantile
regression method also looks for unobserved heterogeneity for each cross-
section and measures various parameters at different quantiles. In short, panel
quantile regression analysis was used in the study because it provides more
informative data, greater variability, and degrees of freedom, thus, increasing
the efficiency of parameter estimations. Machado and Silva (2019) expressed
the quantile regression for the X variable belonging to the position-scale family
in estimating the conditional quantiles as follows:
Y X Z U
it i it i it it
= + + +
a b d g
’ ’
( )
P Z
i it
d g a b d g
+
{ }= ( )
’ ’ ’
’
. , , ,
0 1 is the probability of the predicted parameters
here. Moreover, as shown by a d
i i i n
, , ,...,
( ) = 1 individual fixed effects i, and Z
is a k-vector expressed by the l element of differentiable transformations of the
components of X.
Z Z X l a k
l l
= =
( ), ,...,
Xit is independent and uniformly distributed within the framework of each
constant i and the independent time (t) element. On the other hand, Uit is inde-
pendent and identically distributed over individual i and time and orthogonal

with respect to Xit . Based on this information, the quantile regression of
moments is expressed as follows:
Q X q X Z q
Y i i it it
( | ) ( ) ( )
’ ’
t a d t b g t
= +
( )+ +
Q X
Y ( | )
t shows the quantile distribution of the dependent variable Y, whereas
a d t
i iq
+
( )
( ) shows scalar influence. q( )
t as being the t -th quantile, the estima-
tion is solved by optimization of the following problem:
min ( )
’
q
t
i
it i it
R Z q
r d g
t
å
å - +
( )
r t
t ( ) ( )
A AI A TAI A
= - £
{ }+
{ }
1 0 0 denotes the check function.
1.3.3
Empirical Analysis and Findings
1.3.3.1 Descriptive Statistics
The results of the basic statistical tests performed to obtain preliminary infor-
mation about the variables used in the study and to understand the relationship
between them are presented in Table 1.3.
An a priori clue as to whether the data has a normal distribution is that the
skewness and kurtosis values are close to 0 and 3 (You et al., 2017). However,
as seen in Table 1.3, the skewness and kurtosis values of all the variables are far
from these values. In addition, according to the results of the Jarque-Bera test,
not all variables are normally distributed at the 1% significance level. The fact
that the data do not satisfy the assumption of normality is an obstacle to the use
of least squares regression. For this reason, the quantile regression approach,
which stretches this assumption, was used in the study.
Table 1.3
Basic Statistical Tests
lnco2 lnpgdp lnpgdp2
lnpgdp3
lnfindev lnrenew
Mean 4.746 10.265 105.930 1098.195 −0.457 7.505
Median 4.528 10.333 106.782 1103.445 −0.393 8.835
Maximum 6.958 11.685 136.548 1595.623 0.000 11.172
Minimum 2.040 7.456 55.600 414.589 −1.970 −1.532
Std. Dev. 1.132 0.738 14.598 218.769 0.275 3.599
Skewness −0.027 −1.077 −0.793 −0.526 −1.181 −1.426
Kurtosis 2.272 4.737 4.063 3.601 5.227 3.630
Jarque-Bera 12.628 181.951 86.621 34.952 250.542 202.688
Probability (0.001)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
Notes: Significance: ***1%.

1.3.3.2
Unit Root Test Results
When panel data is used to test for the existence of a unit root, cross-section
dependency needs to be tested. If the cross-sectional dependency is rejected in
the panel data set, first generation unit root tests can be used. If there is a cross-
section dependency, using the second generation unit root tests can enable
us to make more consistent, efficient, and powerful estimations (Bojnec and
Fertö, 2020; Cai and Menegaki, 2019).
According to the results of the cross-sectional dependency tests presented in
Table 1.4, cross-section dependency exists for all variables, as all probabilities
obtained under the p-value are less than 0.01. According to this result, it is
appropriate to apply second generation panel unit root tests to test the station-
arity of the variables. In the study, the unit root test statistics (CADF) of each
cross-section (country) were averaged, and the Cross-Sectionally Augmented
Im, Pesaran and Shin (CIPS) test, which is the unit root test statistic for the
entire panel, was used. The CIPS statistic can be expressed as follows:
CIPS N CADFi
t
n
= -
=
å
1
1
The results of the CIPS unit root test (see Table 1.5) show that the null hypoth-
esis of all variables, except for the lnco2 variable, is rejected at first-degree dif-
ferences in the trend-free model. Therefore, the CIPS unit root test results
indicate that the lnco2 variable is stationary at the level, whereas the other vari-
ables are unstable at the level and are stationary at the first-degree difference.
1.3.3.3
Panel Cointegration Analysis Results
At this stage of the study, whether there is a long-term equilibrium relationship
between the variables was investigated with the Westerlund panel cointegra-
tion test, which is one of the second-generation tests. This cointegration test
was preferred in analyses because it can be used for unbalanced panel data, and
Table 1.4
Cross-section Dependency Test Results
Models Breusch-Pagan LM Test Pesaran CD Test
Variables Statistic p-Value Statistic p-Value
lnco2
1883.437 (0.000)*** 24.684 (0.000)***
lnpgdp 4622.685 (0.000)*** 67.944 (0.000)***
lnpgdp2
4625.255 (0.000)*** 67.961 (0.000)***
lnpgdp3
4625.482 (0.000)*** 67.960 (0.000)***
lnfindev 3343.990 (0.000)*** 56.288 (0.000)***
lnrenew 354.149 (0.000)*** 6.962 (0.000)***
Notes: Significance: ***1%.

they want the time dimension (T) to be larger than the unit size (N) (Tatoğlu,
2018). Table 1.6 shows the cointegration test results of the models.
According to the results of the Westerlund panel cointegration test, the H0
hypothesis stating that there is no cointegration was rejected, and it was accepted
that there was a cointegration relationship between the variables. Accordingly,
in both models, it has been observed that there is a cointegration relationship
between carbon emissions and per capita income, financial development level,
and renewable energy production. Greene (2019) and Gujarati (2011) stated
that, when there is a cointegrated relationship between the variables, it would
be inefficient to take the differences in the data because it would hide the long-
term relationship between the variables. For these reasons, the variables were
included in the panel quantile regression analysis with their level values.
1.3.3.4
Panel Quantile Regression Analysis Results
Quantitative regression is frequently used, especially when the assumptions required
for least squares regression are not met. In the study, the panel quantile regression
models were established to investigate the per capita income, financial develop-
ment level, and the effect of renewable energy production on carbon emissions and
to show the fixed effects of bi and mt , country, and time are as follows:
Table 1.5
CIPS Panel Unit Root Test Results
Level First Difference
Model Variable Test statistics Probability Variable Test statistics
Without trend lnco2
−2.171 0.015** −0.150 0.441
lnpgdp −1.400 0.081* −3.449 0.000***
lnpgdp2
−1.126 0.130 −3.432 0.000***
lnpgdp3
−0.893 0.186 −3.404 0.000***
lnfindev −5.597 0.000*** −3.960 0.000***
lnrenew −12.438 0.000*** −6.897 0.000***
With trend lnco2
−2.701 0.003*** −2.952 0.002***
lnpgdp 1.045 0.852 −1.103 0.135
lnpgdp2
1.198 0.885 −1.226 0.110
lnpgdp3
1.312 0.905 −1.331 0.092*
lnfindev −5.021 0.000*** −2.509 0.006***
lnrenew −10.594 0.000*** −4.293 0.000***
Notes: Significance: *** 1%, ** 5%, * %10.
Table 1.6
Westerlund Panel Cointegration Test Results
Test Statistics p-Value
Quadratic function 1.885 0.029**
Cubic function 3.021 0.001***
Notes: Significance: *** 1%, ** 5%.

Model Q pgdp pgdp fi
co i t i t
1 2 1 2
2
3
: ( |.) ln ln ln
ln , , , , ,
t a a a
t t t
= + + n
ndev
renew
Model Q pg
i t
i t i t
co
,
, ,
ln ,
ln
: ( |.) ln
+ + +
=
a b m
t a
t
t
4
1
2 2
d
dp pgdp pgdp
findev
i t i t i t
i t
, , , , ,
, , ,
ln ln
ln l
+ +
+ +
a a
a a
t t
t t
2
2
3
3
4 5 n
n ,
renewi t i t
+ +
b m
Table 1.7 shows the results of the fixed-effect panel quantile approach pre-
sented by Machado and Silva (2019), and the results are discussed below.
Accordingly, it has been seen in both quadratic and cubic form models that
the level of financial development has a significant effect on increasing carbon
emissions in European countries. This effect has an increasing effect at differ-
ent quantile levels. Therefore, it can be interpreted that the financial ecosystem
in Europe allocates resources to economic units in a way that increases carbon
emissions. The carbon emission reduction effect of renewable energy produc-
tion has been demonstrated in both models. However, this reducing effect
acts within very small limits. In the quadratic model, per capita income has
a positive and significant effect on carbon emissions, but this effect is gradu-
ally decreasing. After a certain level, this effect was found to be negative and
significant. This effect also demonstrates a decreasing trend. Therefore, the
coefficients of national income per capita in quadratic form EKC have val-
ues corresponding to lnpgdp 0, lnpgdp2
0 and are statistically significant.
Accordingly, it has been empirically proven that the EKC is valid as an inverted
U shape in quadratic form. On the other hand, in the cubic form, an increas-
ing, then decreasing, and then increasing trend in per capita carbon emissions
is displayed. According to this result, it has been empirically revealed that the
Circumferential Kuznets Curve tends to be N shape in cubic form.
The results in cubic form are statistically significant from the 1st to the 5th
quantile level but not after the median quantile level. These results show that
EKC has an N-shaped appearance at low and medium quantile levels. The tests
were performed to understand whether the coefficient values obtained at dif-
ferent quantile levels differ, and their results are presented in Table 1.8.
According to the information presented in Table 1.8, the Delta test and
heteroscedasticity and autocorrelation consistent (HAC) robust test statistics
are statistically significant at the 1% level. Therefore, the H0
hypothesis, which
is expressed as equal slopes along the quantiles, is rejected. This finding can be
interpreted as evidence that the relationship between explained and explana-
tory variables varies across different quantiles.
1.3.3.5
Robust Check and Determination of Turning Points
At this stage of the study, the validity of the findings obtained from the panel
quantile regression analysis was tested with the Panel-FMOLS and Panel-
DOLS models. In addition, in the context of the findings obtained from these

Table
1.7
P
anel
Quantile
Regression
with
Fixed
Effects
Estimation
Results
Model
1:
Quadratic
Function
Quantile
Levels
Model
2:
Cubic
Function
lnpgdp
lnpgdp
2
lnfindev
lnrenew
lnpgdp
lnpgdp
2
lnpgdp
3
lnfindev
lnrenew
1.641
(0.000)***
−0.082
(0.000)***
0.114
(0.162)
−0.011
(0.006)***
10
18.224
(0.003)***
−1.812
(0.004)***
0.059
(0.005)***
0.196
(0.037)**
−0.011
(0.010)***
1.510
(0.000)***
−0.075
(0.000)***
0.131
(0.047)**
−0.011
(0.001)***
20
14.574
(0.002)***
−1.440
(0.003)***
0.046
(0.004)***
0.199
(0.006)***
−0.010
(0.001)***
1.318
(0.000)***
−0.069
(0.000)***
0.148
(0.004)***
−0.010
(0.000)***
30
11.758
(0.002)***
−1.155
(0.003)***
0.037
(0.005)***
0.202
(0.000)***
−0.010
(0.000)***
1.285
(0.000)***
−0.060
(0.000)***
0.161
(0.000)***
−0.010
(0.000)***
40
9.110
(0.005)***
−0.882
(0.007)***
0.028
(0.011)**
0.205
(0.000)***
−0.010
(0.000)***
1.189
(0.000)***
−0.055
(0.000)***
0.174
(0.000)***
−0.010
(0.000)***
50
7.077
(0.019)**
−0.674
(0.028)**
0.021
(0.040)**
0.206
(0.000)***
−0.010
(0.000)***
1.099
(0.000)***
−0.055
(0.000)***
0.186
(0.000)***
-0.010
(0.000)***
60
4.806
(0.124)
−0.442
(0.164)
0.013
(0.214)
0.209
(0.000)***
−0.010
(0.000)***
1.026
(0.000)***
−0.052
(0.000)***
0.196
(0.000)***
−0.009
(0.000)***
70
2.680
(0.448)
−0.225
(0.530)
0.005
(0.622)
0.211
(0.000)***
−0.009
(0.000)***
0.939
(0.001)***
−0.047
(0.001)***
0.208
(0.000)***
−0.009
(0.001)***
80
0.469
(0.910)
0.000
(1.000)
−0.001
(0.908)
0.213
(0.001)***
−0.009
(0.000)***
0.849
(0.015)**
−0.043
(0.010)***
0.220
(0.001)***
−0.009
(0.005)***
90
−2.165
(0.670)
0.269
(0.603)
−0.010
(0.538)
0.215
(0.006)***
−0.009
(0.008)***
Notes:
Significance:
***
1%,
**
5%,
*
10%.
Figures
in
parentheses
are
p-values.

models, the first and second turning points of the inverted U and N-shaped
EKC were calculated. Accordingly, the panel cointegration regression analysis
results for quadratic and cubic forms are given in Table 1.9.
The results of the panel cointegration regression analysis shown in Table 1.9
show that the EKC hypothesis is valid in the quadratic form in the European
countries as of the examined period in an inverted U shape. As a matter of
fact, lnpgdp 0, lnpgdp2
0 is verified in both panel cointegration regression
models, and it is statistically significant. The turning point in quadratic form
averages at $25,222. The fact that 15 European countries, except for Greece,
Poland, Portugal, and Turkey, have a per capita income higher than the turn-
ing point suggested by the results in Table 1.9 increases the probability that
the EKC curve may display an N-shaped image. Accordingly, the coefficients
calculated for the cubic form are statistically significant in both models, and
values of lnpgdp 0, lnpgdp2
0, and lnpgdp3
0 indicate that the EKC has
an N shape. In cubic form, EKC’s first turning point averages at $10,351, and
Table 1.8
Results of Quantile Slope Equality Tests
Model 1: Quadratic Function Model 2: Cubic Function
Delta Test HAC Robust Test Delta Test HAC Robust Test
Test Statistic 18.315
(0.000)***
17.256
(0.000)***
16.640
(0.000)***
9.618
(0.000)***
Adj. Test Statistic 20.476
(0.000)***
19.293
(0.000)***
19.004
(0.000)***
10.985
(0.000)***
Notes: Significance: *** 1%, ** 5%, * 10%. Figures in parentheses are p-values.
Table 1.9
Prediction Results of Panel Cointegration Regressions
Model 1: Quadratic Function Model 2: Cubic Function
Panel FMOLS Panel DOLS Panel FMOLS Panel DOLS
lnpgdp 1.207
(0.000)***
1.338
(0.000)***
12.307
(0.001)***
23.245
(0.000)***
lnpgdp2
−0.187
(0.000)***
−0.066
(0.000)***
−1.206
(0.002)***
−2.298
(0.000)***
lnpgdp3
0.039
(0.004)***
0.075
(0.000)***
lnfindev 0.146
(0.000)***
0.282
(0.000)***
0.230
(0.001)***
0.206
(0.049)**
lnrenew −0.072
(0.000)***
−0.018
(0.000)***
−0.013
(0.000)***
−0.014
(0.013)**
Turning Points4
X1
: 25.208 $ X1
: 25.236 $ X1
:10.688 $
X2
: 83.810 $
X1
: 10.015 $
X2
: 74.065 $
Notes: Significance: *** 1%, ** 5%.

the second turning point averages at $78,937. In addition, it was observed that
the level of financial development had an increasing effect on carbon emissions
and a decreasing effect on renewable energy production, and these findings are
in agreement with the panel quantile regression analysis findings. Therefore,
according to the results obtained from panel quantile regression analysis and
panel cointegration regression analysis, we have reached strong empirical evi-
dence for the effects of per capita income, financial development level, and
renewable energy production on carbon emission behaviour in European
countries.
1.4
Conclusions and Suggestions
Carbon emissions have been increasing significantly since the Industrial
Revolution and have confronted our world with a serious climate crisis. The
Kyoto Protocol in 1997 aimed to control the situation by limiting green-
house gas emissions, especially in industrialized countries. However, despite
the passing of years, the level of carbon emissions did not remain stagnant, and
the increase continued. The Paris Agreement, which aims to keep the global
temperature increase in the range of 1.5–2 degrees Celsius in 2016, was signed
by 191 countries in the first place. The main purpose of both agreements is
to reduce carbon emissions and raise public awareness about the climate cri-
sis. While individual contributions can be made to reduce carbon emissions,
it is important to control it mainly through regulations to be put forward by
policymakers. Considering that a significant part of the emission is caused by
industry, economic indicators and impacts come to the fore.
In this study, the factors affecting carbon emissions were investigated using
the panel quantile regression method for a sample consisting of 19 European
countries, using the 1990–2019 period data. With the help of descriptive statis-
tics, the general conditions of the variables were revealed, and it was observed
that the assumption of normal distribution was not met. Therefore, the panel
quantile regression model was preferred as the basic method of the study, and
in the application of the model, predictions were made with the MM-QR
method proposed by Machado and Silva (2019). Attempts to confirm the
results obtained by panel cointegration regressions were made. The contribu-
tion of this study to the literature is to use the panel quantile regression method
in the research of this relationship, and it aims to reach empirical evidence on
the extent to which per capita income, renewable energy production, and
financial development level directly affect carbon emissions from the perspec-
tive of European countries.
There are three important results obtained from the study. According to
this, (1) the financial ecosystem in 19 European countries within the scope of
the research allocates resources to economic units in a way that increases car-
bon emissions. Furthermore, it was determined that this contribution showed
an increasing trend, especially as seen from the panel quantile regression results.
Therefore, in European countries, the financial system is seen as a reason for

the increase in carbon emissions with the mechanisms it acquires. Financial
development leads to better, higher living standards. Well-developed financial
markets can provide more consumer loans. These loans will help individuals
consume more durable goods such as automobiles, electronic devices, and real
estate. Consumption will continue to increase as financial markets provide
more credit, which will further deteriorate the environment. (2) Renewable
energy production, on the other hand, has a net effect on reducing carbon
emissions. However, this effect seems to have been very limited in the past 30
years. According to this finding, we can say that more investments should be
made in renewable energy production in the fight against carbon emissions.
(3) The effect of per capita income on carbon emissions has been discussed in
the context of the EKC hypothesis, and rather explicit empirical evidence has
been obtained that the quadratic model has an inverted U-shaped view and
the cubic model has an N-shaped image. Within this framework, we con-
template that the N-shaped EKC, which appears in the cubic model, needs
explanation in the context of the theoretical background. Accordingly, the
N-shaped EKC, whose first turning point was at $10,351 on average and the
second turning point was at $78,937 on average, can be said to indicate that
environmental awareness and education levels are very high in Europe, so it
can be concluded that they make consumption choices that will contribute
to environmental protection at the income level between these two turning
points. Furthermore, it can be said that beyond the level of $10,351, indi-
viduals make consumption preferences for technological and service sector
products. The increase in carbon emissions beyond the second turning point
of $78,937 may be due to individuals’ preferences for luxurious and substitute
goods.
Notes
1 WHO (2020). Global strategy on health, environment and climate change:The trans-
formation needed to improve lives and well-being sustainably through healthy environ-
ments. Geneva:World Health Organization. https://apps.who.int/iris/bitstream/handle
/10665/331959/9789240000377-eng.pdf?sequence=1isAllowed=y
2 The sample of the study includes the following countries:Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway,
Poland, Portugal, Spain, Sweden, Switzerland,Turkey, and the United Kingdom.
3 In order to determine the most suitable econometric specification, the Hausman test
was performed and fixed effects model was preferred according to chi-square statistics
(12.84),p-value = 0.012 for quadratic form,chi-square statistics (10.35),p-value = 0.035
for cubic form.
4 Turning points were calculated for quadratic function as X1
1
2
2
= -
a
a
and as
X X
1
2 2
2
1 3
3
2
2 2
2
1 3
3
3
3
3
3
=
- - -
=
- + -
a a a a
a
a a a a
a
, for cubic function.

References
Abbasi, F. and Riaz, K. (2016). CO2 emissions and financial development in an emerging
economy: An augmented VAR approach. Energy Policy, 90, 102–114. doi: 10.1016/j.
enpol.2015.12.017.
Acheampong, A. O. (2019). Modelling for insight: Does financial development improve
environmentalquality?EnergyEconomics,83,156–179.doi:10.1016/j.eneco.2019.06.025.
Acheampong, A. O., Adams, S. and Boateng, E. (2019). Do globalization and renewable
energy contribute to carbon emissions mitigation in sub-Saharan Africa? Science of the
Total Environment, 677, 436–446.
Acheampong, A. O., Amponsah, M. and Boateng, E. (2020). Does financial development
mitigate carbon emissions? Evidence from heterogeneous financial economies. Energy
Economics, 88, 104768. doi: 10.1016/j.eneco.2020.104768.
Adams, S. and Acheampong, A. O. (2019). Reducing carbon emissions: The role of
renewable energy and democracy. Journal of Cleaner Production, 240, 118245.
Adebayo, T. S. (2021). Testing the EKC hypothesis in Indonesia: Empirical evidence from
the ARDL-based bounds and wavelet coherence approaches. Applied Economics Journal,
28(1), 78–100.
Ahmad, N., Du, L., Tian, X. L. and Wang, J. (2019). Chinese growth and dilemmas:
Modelling energy consumption, CO2
emissions and growth in China. Quality and
Quantity, 53(1), 315–338. doi: 10.1007/s11135-018-0755-0.
Akadırı, S. S., Alola, A. A. and Usman, O. (2021). Energy mix outlook and the EKC
hypothesis in BRICS countries: A perspective of economic freedom vs economic
growth. Environmental Science and Pollution Research International, 28(7), 8922–8926. doi:
10.1007/s11356-020-11964-w.
Akram, R., Chen, F., Khalid, F., Huang, G. and Irfan, F. (2021). Heterogeneous
effects of energy efficiency and renewable energy on economic growth of BRICS
countries: A fixed effect panel quantile regression analysis. Energy, 215. doi: 10.1016/j.
energy.2020.119019.
Akram, R., Chen, F., Khalid, F., Ye, Z. and Majeed, M. T. (2020). Heterogeneous
effects of energy efficiency and renewable energy on carbon emissions: Evidence
from developing countries. Journal of Cleaner Production, 247, 119122. doi: 10.1016/j.
jclepro.2019.119122.
Allard, A., Takman, J., Uddin, G. S. and Ahmed, A. (2018). The N-shaped Environmental
Kuznets Curve: An empirical evaluation using a panel quantile regression approach.
Environmental Science and Pollution Research International, 25(6), 5848–5861. doi: 10.1007/
s11356-017-0907-0.
Alola, A. A. and Donve, U. T. (2021). Environmental implication of coal and oil
energy utilization in Turkey: Is the EKC hypothesis related to energy? Management
of Environmental Quality: An International Journal, 32(3), 543–559. doi: 10.1108/
MEQ-10-2020-0220.
Alshehry, A. S. and Belloumi, M. (2017). Study of the Environmental Kuznets Curve for
transport carbon dioxide emissions in Saudi Arabia. Renewable and Sustainable Energy
Reviews, 75, 1339–1347. doi: 10.1016/j.rser.2016.11.122.
Apergis, N. and Ozturk, I. (2015). Testing Environmental Kuznets Curve hypothesis in
Asian countries. Ecological Indicators, 52, 16–22. doi: 10.1016/j.ecolind.2014.11.026.
Aslan, Ö. and Korap, L. H. (2006). Türkiye’de finansal gelişme ekonomik büyüme ilişkisi.
Muğla Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 17, 1–20.

Azam, A., Rafiq, M., Shafique, M., Zhang, H. and Yuan, J. (2021). Analyzing the effect of
natural gas, nuclear energy and renewable energy on GDP and carbon emissions: A multi-
variate panel data analysis. Energy, 219, 119592. doi: 10.1016/j.energy.2020.119592.
Bakirtas, I. and Cetin, M. A. (2017). Revisiting the Environmental Kuznets Curve and
pollution haven hypotheses: MIKTA sample. Environmental Science and Pollution Research
International, 24(22), 18273–18283. doi: 10.1007/s11356-017-9462-y.
Balıbey, M. (2015). Relationships among CO2 emissions, economic growth and foreign
direct investment and the Environmental Kuznets Curve hypothesis in Turkey.
International Journal of Energy Economics and Policy, 5(4), 1042–1049.
Balın, E. B. and Akan, H. D. M. (2015). EKC hypothesıs and the effect of innovatıon:
A panel data analysis. Journal of Business, Economics and Finance, 81–91. doi: 10.17261/
Pressacademia.201519952.
Balsalobre, L. D., Driha, O. M., Leitao, N. C. and Murshed, M. (2021). The carbon
dioxide neutralizing effect of energy innovation on international tourism in EU-5
countries under the prism of the EKC hypothesis. Journal of Environmental Management,
298, 113513. doi: 10.1016/j.jenvman.2021.113513.
Balsalobre, L. D. and Herranz, A. A. (2016). An approach to the effect of energy innovation
on Environmental Kuznets Curve: An introduction to inflection point. Bulletin of Energy
Economics, 4(3), 224–233.
Begum, R. A., Sohag, K., Abdullah, S. M. and Jaafar, M. (2015). CO2 emissions, energy
consumption, economic and population growth in Malaysia. Renewable and Sustainable
Energy Reviews, 41, 594–601. doi: 10.1016/j.rser.2014.07.205.
Bekun, F. V., Gyamfi, B. A., Onifade, S. T. and Agboola, M. O. (2021). Beyond the
Environmental Kuznets Curve in E7 economies: Accounting for the combined impacts
of institutional quality and renewables. Journal of Cleaner Production, 314, 127924. doi:
10.1016/j.jclepro.2021.127924.
Bojnec, S. and Fertö, I. (2020). Testing the validity of Gibrat’s law for Slovenian farms:
Cross-sectional dependence and unit root tests. Economic Research-Ekonomska Istraživanja,
33(1), 1280–1293. doi: 10.1080/1331677X.2020.1722722.
Boutabba, M. A. (2014). The impact of financial development, income, energy and trade
on carbon emissions: Evidence from the Indian economy. Economic Modelling, 40, 33–41.
Bui, D. T. (2020). Transmission channels between financial development and CO2
emissions: A global perspective. Heliyon, 6(11). doi: 10.1016/j.heliyon.2020.e05509.
Cai, Y. and Menegaki, A. N. (2019). Convergence of clean energy consumption—Panel
unit root test with sharp and smooth breaks. Environmental Science and Pollution Research
International, 26(18), 18790–18803. doi: 10.1007/s11356-019-04911-x.
Can, M. and Gozgor, G. (2017). The impact of economic complexity on carbon emissions:
Evidence from France. Environmental Science and Pollution Research International, 24(19),
16364–16370. doi: 10.1007/s11356-017-9219-7.
Canas, A., Ferrao, P. and Conceiçao, P. (2003). A new Environmental Kuznets Curve?
Relationship between direct material input and income per capita: Evidence
from industrialised countries. Ecological Economics, 46(2), 217–229. doi: 10.1016/
S0921-8009(03)00123-X.
Cetin, M. and Ecevit, E. (2017). The impact of financial development on carbon emissions
under the structural breaks: Empirical evidence from Turkish economy. International
Journal of Economic Perspectives, 11(1), 64–78.
Charfeddine, L. and Kahia, M. (2019). Impact of renewable energy consumption and
financial development on CO2 emissions and economic growth in the mena region:

A panel vector autoregressive (PVAR) analysis. Renewable Energy, 139, 198–213. doi:
10.1016/j.renene.2019.01.010.
Chen, S. and Zhang, D. (2021). Impact of air pollution on labor productivity: Evidence
from prison factory data. China Economic Quarterly International, 1(2), 148–159. doi:
10.1016/j.ceqi.2021.04.004.
Cheng, C., Ren, X., Dong, K.,Dong, X. and Wang, Z. (2021). How does technological
innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using
panel quantile regression. Journal of Environmental Management, 280. doi: 10.1016/j.
jenvman.2020.111818.
Churchill, S. A., Inekwe, J., Ivanovski, K. and Smyth, R. (2018). The Environmental
Kuznets Curve in the OECD: 1870–2014. Energy Economics, 75, 389–399. doi:
10.1016/j.eneco.2018.09.004.
Churchill, S. A., Pan, L. and Paramati, S. D. (2020). Air pollution and tourism: Evidence
from G20 countries. Journal of Travel Research, 1–12. doi: 10.1177/0047287520977724.
Cole, M. A., Rayner, A. J. and Bates, J. M. (1997). The Environmental Kuznets Curve: An
empirical analysis. Environment and Development Economics, 2(4), 401–416. doi: 10.1017/
s1355770x97000211.
Cui, C., Wang, Z., He, P., Yuan, S.,Niu, B., Kang, P. and Kang, C. (2019). Escaping
from pollution: The effect of air quality on inter-city population mobility in China.
Environmental Research Letters, 14(12), 12. doi: 10.1088/1748-9326/ab5039.
Demirhan, H. (2020). Impact of increasing temperature anomalies and carbon dioxide
emissions on wheat production. Science of the Total Environment, 741, 139616. doi:
10.1016/j.scitotenv.2020.139616.
Dettner, F. and Blohm, M. (2021). External cost of air pollution from energy generation
in Morocco. Renewable and Sustainable Energy Transition, 1, 100002. doi: 10.1016/j.
rset.2021.100002.
Dogan, E. and Seker, F. (2016). The influence of real output, renewable and non-renewable
energy, trade and financial development on carbon emissions in the top renewable
energy countries. Renewable and Sustainable Energy Reviews, 60, 1074–1085.
Dong, D., Xu, B., Shen, N. and He, Q. (2021). The adverse impact of air pollution on
China’s economic growth. Sustainability, 13(16), 9056. doi: 10.3390/su13169056.
Ehigiamusoe, K. U. and Lean, H. H. (2019). Effects of energy consumption, economic
growth, and financial development on carbon emissions: Evidence from heterogeneous
income groups. Environmental Science and Pollution Research International, 26(22), 22611–
22624. doi: 10.1007/s11356-019-05309-5.
Giannadaki, D., Giannakis, E., Pozzer, A. and Lelieveld, J. (2018). Estimating health and
economic benefits of reductions in air pollution from agriculture. Science of the Total
Environment, 622–623, 1304–1316. doi: 10.1016/j.scitotenv.2017.12.064.
Gozbası, O., Altınoz, B. and Ocal, O. (2021). The effect of financial development, renewable
and non-renewable energy consumptıon and international tourism on greenhouse gas
emission in high-income countries from different continents. Ekonomi, Politika Finans
Araştırmaları Dergisi, 6(2), 371–384. doi: 10.30784/epfad.959039.
Gök, A. (2020). The role of financial development on carbon emissions: A meta regression
analysis. Environmental Science and Pollution Research International, 27(11), 11618–11636.
doi: 10.1007/s11356-020-07641-7.
Greene, W. H. (2019). Econometric Analysis, 8th Edition. Essex: Pearson Education.
Grossman, G. M. and Krueger, A. B. (1991). Environmental impacts of a North American
free trade agreement. NBER Working Paper, 3914. doi: 10.3386/w3914.
Gujarati, D. (2011). Econometrics by Example. London: Palgrave Macmillan.

Gul, S., Zou, X., Hassan, C. H., Azam, M. and Zaman, K. (2015). Causal nexus between
energy consumption and carbon dioxide emission for Malaysia using maximum entropy
bootstrap approach. Environmental Science and Pollution Research International, 22(24),
19773–19785. doi: 10.1007/s11356-015-5185-0.
Guo, Y. (2021). Financial development and carbon emissions: Analyzing the role of financial
risk, renewable energy electricity, and human capital for China. Discrete Dynamics in
Nature and Society, 1025669. doi: 10.1155/2021/1025669.
Halliru, A. M., Loganathan, N., Hassan, A. A. G., Mardani, A. and Kamyab, H. (2020).
Re-examining the Environmental Kuznets Curve hypothesis in the economic
community of West African states: A panel quantile regression approach. Journal of
Cleaner Production, 276. doi: 10.1016/j.jclepro.2020.124247.
Hanif, I., Aziz, B. and Chaudhry, I. S. (2019). Carbon emissions across the spectrum of
renewable and non-renewable energy use in developing economies of Asia. Renewable
Energy, 143, 586–595.
Hasanov, F. J., Hunt, L. C. H. and Mikayilov, J. I. (2021). Estimating different order
polynomial logarithmic Environmental Kuznets Curves. Environmental Science and
Pollution Research International, 28(31), 41965–41987. doi: 10.1007/s11356-021-13463-y.
He, L., Zhang, X. and Yan, Y. (2021). Heterogeneity of the Environmental Kuznets Curve
across Chinese cities: How to dance with ‘shackles’? Ecological Indicators, 130, 108128.
doi: 10.1016/j.ecolind.2021.108128.
Heydari, A., Nezhad, M. M., Garcia, D. A., Keynia, F. and De Santoli, L.
(2021). Air pollution forecasting application based on deep learning model and
optimization algorithm. Clean Technologies and Environmental Policy. doi: 10.1007/
s10098-021-02080-5.
Huang, Y., Zhu, H. and Zhang, Z. (2020). The heterogeneous effect of driving factors
on carbon emission intensity in the Chinese transport sector: Evidence from dynamic
panel quantile regression. Science of the Total Environment, 727. doi: 10.1016/j.
scitotenv.2020.138578.
Işık, C., Ongan, S., Bulut, U., Karakaya, S., Irfan, M., Alvarado, R., Ahmad, M. and
Rehman, A. (2021). Reinvestigating the Environmental Kuznets Curve (EKC)
hypothesis by a composite model constructed on the Armey curve hypothesis with
government spending for the US States. Environmental Science and Pollution Research. doi:
10.1007/s11356-021-16720-2.
Jaunky, V. C. (2011). The CO2 emissions-income nexus: Evidence from rich countries.
Energy Policy, 39(3), 1228–1240. doi: 10.1016/j.enpol.2010.11.050.
Jiang, C. and Ma, X. (2019). The impact of financial development on carbon emissions: A
global perspective. Sustainability, 11(19), 5241. doi: 10.3390/su11195241.
Jiang, Q., Khattak, S. I. and Rahman, Z. U. (2021). Measuring the simultaneous effects of
electricity consumption and production on carbon dioxide emissions (CO2e) in China:
New evidence from an EKC-based assessment. Energy, 229, 120616. doi: 10.1016/j.
energy.2021.120616.
Kalakoti, S. R. (2021). Climate changes global warming in India – Problems and perspectives.
International Journal of Multidisciplinary Educational Research, 10(8–7), 21–27. doi: https://
ijmer.in.doi./2021/10.08.124.
Khan, K., Khan, I. and Binh, T. T. (2020). The heterogeneity of renewable energy
consumption, carbon emission and financial development in the globe: A panel quantile
regression approach. Energy Reports, 6, 859–867. doi: 10.1016/j.egyr.2020.04.002.
Kirikkaleli, D. and Adebayo, T. S. (2021). Do public-private partnerships in energy and
renewable energy consumption matter for consumption-based carbon dioxide emissions

in India? Environmental Science and Pollution Research International, 28(23), 30139–30152.
doi: 10.1007/s11356-021-12692-5.
Koondhar, M. A., Li, H.,Wang, H., Bold, S. and Kong, R. (2020). Looking back over
the past two decades on the nexus between air pollution, energy consumption, and
agricultural productivity in China: A qualitative analysis based on the ARDL bounds
testing model. Environmental Science and Pollution Research International, 27(12), 13575–
13589. doi: 10.1007/s11356-019-07501-z.
Lahiani, A. (2020). Is financial development good for the environment? An asymmetric
analysis with CO2
emissions in China. Environmental Science and Pollution Research
International, 27(8), 7901–7909. doi: 10.1007/s11356-019-07467-y.
Lapko, A., Panasiuk, A., Wojcikiewicz, R. S. and Landowski, M. (2020). The state of
air pollution as a factor determining the assessment of a city’s tourist attractiveness—
Based on the opinions of polish respondents. Sustainability, 12(4), 1466. doi: 10.3390/
su12041466.
Leal, P. H. and Marques, A. C. (2020). Rediscovering the EKC hypothesis for the 20
highest CO2 emitters among OECD countries by level of globalization. International
Economics, 164, 36–47. doi: 10.1016/j.inteco.2020.07.001.
Lepori, G. M. (2016). Air pollution and stock returns: Evidence from a natural experiment.
Journal of Empirical Finance, 35, 25–42. doi: 10.1016/j.jempfin.2015.10.008.
Leitao, N. C. and Balsalobre, L. D. (2020). The linkage between economic growth,
renewable energy, tourism, CO2 emissions, and international trade: The evidence for
the European Union. Energies, 13(18), 4838. doi: 10.3390/en13184838.
Li, P. and Ouyang, Y. (2019). The dynamic impacts of financial development and human
capital on CO2 emission intensity in China: An ARDL approach. Journal of Business
Economics and Management, 20(5), 939–957. doi: 10.3846/jbem.2019.10509.
Li, X., Chen, H. and Li, Y. (2020). The effect of air pollution on children’s migration with
parents: Evidence from China. Environmental Science and Pollution Research International,
27(11), 12499–12513. doi: 10.1007/s11356-020-07831-3.
Liu, H. and Song, Y. (2020). Financial development and carbon emissions in China
since the recent world financial crisis: Evidence from a spatial-temporal analysis and
a spatial Durbin model. Science of the Total Environment, 715, 136771. doi: 10.1016/j.
scitotenv.2020.136771.
Liu, Z. and Yu, L. (2020). Stay or leave? The role of air pollution in urban migration
choices. Ecological Economics, 177, 106780. doi: 10.1016/j.ecolecon.2020.106780.
Lu, W.-C. (2017). Renewable energy, carbon emissions, and economic growth in 24 Asian
countries: Evidence from panel cointegration analysis. Environmental Science and Pollution
Research International, 24(33), 26006–26015.
Lv, Z. and Li, S. S. (2021). How financial development affects CO2 emissions: A spatial
econometric analysis. Journal of Environmental Management, 277, 111397. doi: 10.1016/j.
jenvman.2020.111397.
Machado, J. A. F. and Silva, J. M. C. (2019). Quantiles via moments. Journal of Econometrics,
213(1), 145–173. doi: 10.1016/j.jeconom.2019.04.009.
Macrina, A. L. P. and Zurbenko, I. G. (2021). Multivariate aspects of global warming. World
Scientific News, 159, 81–94.
Manisalidis, I., Stavropoulou, E., Stavropoulos, A. and Bezirtzoglou, E. (2020).
Environmental and health impacts of air pollution: A review. Frontiers in Public Health, 8,
14. doi: 10.3389/fpubh.2020.00014.
Mert, M. and Bölük, G. (2016). Do foreign direct investment and renewable energy
consumption affect the CO2
emissions? New evidence from a panel ARDL approach to

Kyoto annex countries. Environmental Science and Pollution Research International,
23(21), 21669–21681. doi: 10.1007/s11356-016-7413-7.
Mert, M., Bölük, G. and Çağlar, A. E. (2019). Interrelationships among foreign direct
investments, renewable energy, and CO2
emissions for different European country
groups: A panel ARDL approach. Environmental Science and Pollution Research International,
26(21), 21495–21510. doi: 10.1007/s11356-019-05415-4.
Minlah, M. K. and Zhang, X. (2021). Testing for the existence of the Environmental
Kuznets Curve (EKC) for CO2
emissions in Ghana: Evidence from the bootstrap rolling
window Granger causality test. Environmental Science and Pollution Research International,
28(2), 2119–2131. doi: 10.1007/s11356-020-10600-x.
Moriwaki, S. and Shimizu, M. (2021). A simultaneous investigation of the Environmental
Kuznets Curve for the agricultural and industrial sectors in China. Journal of the Asia
Pacific Economy. doi: 10.1080/13547860.2020.1870068.
Moutinho, V., Varum, C. and Madaleno, M. (2017). How economic growth affects
emissions? An investigation of the Environmental Kuznets Curve in Portuguese
and Spanish economic activity sectors. Energy Policy, 106, 326–344. doi: 10.1016/j.
enpol.2017.03.069.
Murthy, K. V. B. and Gambhir, S. (2018). Analyzing Environmental Kuznets Curve and
pollution haven hypothesis in India in the context of domestic and global policy change.
Australasian Accounting, Business and Finance Journal, 12(2), 134–156. doi: 10.14453/aabfj.
v12i2.9.
Neidell, M. (2017). Air pollution and worker productivity. IZA World of Labor, 363. doi:
10.15185/izawol.363.
Neog, Y. and Yadava, A. K. (2020). Nexus among CO2 emissions, remittances, and
financial development: A NARDL approach for India. Environmental Science and
Pollution Research International, 27(35), 1. doi: 10.1007/s11356-020-10198-0.
Nguyen, H. T. and Pham, M. H. (2021). Air pollution and behavioral biases: Evidence from
stock market anomalies. Journal of Behavioral and Experimental Finance, 29, 100441.doi:
10.1016/j.jbef.2020.100441.
Nguyen, K. H. and Kakinaka, M. (2019). Renewable energy consumption, carbon
emissions, and development stages: Some evidence from panel cointegration analysis.
Renewable Energy, 132, 1049–1057.
Ozcan, B. (2013). The nexus between carbon emissions, energy consumption and economic
growth in Middle East countries: A panel data analysis. Energy Policy, 62, 1138–1147.
doi: 10.1016/j.enpol.2013.07.016.
Ozturk, I. and Acaravci, A. (2013). The long-run and causal analysis of energy, growth,
openness and financial development on carbon emissions in Turkey. Energy Economics,
36, 262–267.
Pala, A. (2018). Is there an Environmental Kuznets Curve in MENAP countries? Quadratic
and cubic polynomial random coefficient panel regression model. Journal of Clean Energy
Technologies, 6(2), 178–182. doi: 10.18178/JOCET.2018.6.2.456.
Panayotou, T. (2003). Economics growth and environment. Economic Survey of Europe, 2,
45–72.
Pata, U. K. (2018). Renewable energy consumption, urbanization, financial development,
ıncome and CO2 emissions in Turkey: Testing EKC hypothesis with structural breaks.
Journal of Cleaner Production, 187, 770–779. doi: 10.1016/j.jclepro.2018.03.236.
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section
dependence. Journal of Applied Econometrics, 22(2), 265–312. https://doi.org/10.1002/
jae.951.

Radmehr, R., Henneberry, S. R. and Shayanmehr, S. (2021). Renewable energy
consumption, CO2 emissions, and economic growth nexus: A simultaneity spatial
modeling analysis of EU countries. Structural Change and Economic Dynamics, 57, 13–27.
doi: 10.1016/j.strueco.2021.01.006.
Robalino-López, A., Mena-Nieto, Á., García-Ramos, J.-E. and Golpe, A. A. (2015).
Studying the relationship between economic growth, CO2
emissions, and the
Environmental Kuznets Curve in Venezuela (1980–2025). Renewable and Sustainable
Energy Reviews, 41, 602–614. doi: 10.1016/j.rser.2014.08.081.
Rossati, A. (2017). Global warming and its health impact. The International Journal of
Occupational and Environmental Medicine, 8(1), 7–20. doi: 10.15171/ijoem.2017.963.
Salehnia, N., Alavijeh, N. K. and Salhnia, N. (2020). Testing porter and pollution haven
hypothesis via economic variables and CO2
emissions: A cross-country review with
panel quantile regression method. Environmental Science and Pollution Research International,
27(25), 31527–31542. doi: 10.1007/s11356-020-09302-1.
Saidi, K. and Hammami, S. (2016). An econometric study of the impact of economic growth
and energy use on carbon emissions: Panel data evidence from fifty eight countries.
Renewable and Sustainable Energy Reviews, 59, 1101–1110.
Saidi, K. and Omri, A. (2020). The impact of renewable energy on carbon emissions and
economic growth in 15 major renewable energy-consuming countries. Environmental
Research, 186, 109567. doi: 10.1016/j.envres.2020.109567.
Samour, A., Isıksal, A. Z. and Resatoglu, N. G. (2019). Testing the impact of banking sector
development on Turkey’s CO2
emissions. Applied Ecology and Environmental Research,
17(3), 6497–6513. doi: 10.15666/aeer/1703_64976513.
Shafik, N. and Bandyopadhyay, S. (1992). Economic growth and environmental quality:
Time series and cross-country evidence. The World Bank Policy Research Working Paper,
1–50. http://documents1.worldbank.org/curated/en/833431468739515725/pdf/multi
-page.pdf.
Shahani, R. and Raghuvansi, K. (2020). Carbon dioxide emissions and economic growth:
A bivariate cointegration analysis for two emerging markets of India and China. Vision:
The Journal of Business Perspective, 24(1), 9–22. doi: 10.1177/0972262919855806.
Shahbaz, M. (2019). Globalization–emissions nexus: Testing the EKC hypothesis in Next-
11 countries. Global Business Review, 1–26. doi: 10.1177/0972150919858490.
Shahbaz, M., Mahalik, M. K., Shah, S. H. and Sato, J. R. (2016). Time-varying analysis of
CO2
emissions, energy consumption, and economic growth nexus: Statistical experience
in Next 11 countries. Energy Policy, 98, 33–48. doi: 10.1016/j.enpol.2016.08.011.
Shahbaz, M., Nasir, M. A., Hille, E. and Mahalik, M. K. (2020). UK’s net-zero carbon
emissions target: Investigating the potential role of economic growth, financial
development, and RD expenditures based on historical data (1870–2017). Technological
Forecasting and Social Change, 161, 120255. doi: 10.1016/j.techfore.2020.120255.
Shahbaz, M., Tiwari, A. K. and Nasir, M. (2013). The effects of financial development,
economic growth, coal consumption and trade openness on CO2 emissions in South
Africa. Energy Policy, 61, 1452–1459.
Shahbaz, M., Topcu, B. A., Sarıgül, S. S. and Vo, X. V. (2021). The effect of financial
development on renewable energy demand: The case of developing countries. Renewable
Energy, 178, 1370–1380. doi: 10.1016/j.renene.2021.06.121.
Shen, J.-S., Wang, Q. and Shen, H.-P. (2021a). Does industrial air pollution increase
health care expenditure? Evidence from China. Frontiers in Public Health, 9, 695664. doi:
10.3389/fpubh.2021.695664.

Shen, Y., Su, Z.-W., Maliik, M. Y., Umar, M., Khan, Z. and Khan, M. (2021b). Does green
investment, financial development and natural resources rent limit carbon emissions? A
provincial panel analysis of China. Science of the Total Environment, 755(2), 142538. doi:
10.1016/j.scitotenv.2020.142538.
Sinha, A. and Bhatt, M. Y. (2017). Environmental Kuznets Curve for CO2 and NOx
emissions: A case study of India. European Journal of Sustainable Development, 6(1), 267–
276. doi: 10.14207/ejsd.2017.v6n1p267.
Song, T., Zheng, T. and Tong, L. (2008). An empirical test of the Environmental Kuznets
Curve in China: A panel cointegration approach. China Economic Review, 19(3), 381–
392. doi: 10.1016/j.chieco.2007.10.001.
Tahir, S. H., Kousar, S., Ahmed, F. and Ullah, M. R. (2021). Impact of economic
freedom on air pollution: Configuration analysis of Asia-Pacific region. Environmental
Science and Pollution Research International, 28(35), 47932–47941. doi: 10.1007/
s11356-021-13681-4.
Tatoğlu, F. Y. (2018). Panel zaman serileri analizi. İstanbul: Beta Yayınları.
Tenaw, D. and Beyene, A. D. (2021). Environmental sustainability and economic
development in sub-Saharan Africa: A modified EKC hypothesis. Renewable and
Sustainable Energy Reviews, 143, 110897. doi: 10.1016/j.rser.2021.110897.
Tsaurai, K. (2019). The impact of financial development on carbon emissions in Africa.
International Journal of Energy Economics and Policy, 9(3), 144–153. doi: 10.32479/ijeep.7073.
Ustaoglu, A., Yaras, A., Sutcu, M. and Gencel, O. (2021). Investigation of the residential
building having novel environment-friendly construction materials with enhanced
energy performance in diverse climate regions: Cost-efficient, low-energy and low-
carbon emission. Journal of Building Engineering, 43, 102617. doi: 10.1016/j.
jobe.2021.102617.
Velepucha, P. A. E., Chamba, J. A. M., Mendoza, N. A. A., Luna, T. L. O., Rojas, N.
S. S., Furniss, M. J., Howe, C. and Mendoza, Z. H. A. (2016).Tropical ecosystems
vulnerability to climate change in southern Ecuador. Tropical Conservation Science, 1–17.
doi: 10.1177/1940082916668007.
Vo, D. H., Vo, A. T., Ho., C. M. and Nguyen, H. M. (2020). The role of renewable
energy, alternative and nuclear energy in mitigating carbon emissions in the CPTPP
countries. Renewable Energy, 161, 278–292. doi: 10.1016/j.renene.2020.07.093.
Vural, G. (2020). How do output, trade, renewable energy and non-renewable energy
impact carbon emissions in selected sub-Saharan African Countries? Resources Policy, 69,
101840. doi: 10.1016/j.resourpol.2020.101840.
Wang, K.-L., Pnag, S.-Q., Ding, L.-L.. and Miao, Z. (2020a). Combining the biennial
Malmquist–Luenberger index and panel quantile regression to analyze the green total
factor productivity of the industrial sector in China. Science of the Total Environment, 739.
doi: 10.1016/j.scitotenv.2020.140280.
Wang, L., Xing, F., Yu, Y. and Dai, Y. (2021). Does severe air pollution affect firm
innovation: Evidence from China. Applied Economics Letters, 28(7), 551–558. doi:
10.1080/13504851.2020.1764474.
Wang, R., Mirza, N., Vasbieva, D. G., Abbas, Q. and Xiong, D. (2020b). The nexus
of carbon emissions, financial development, renewable energy consumption, and
technological innovation: What should be the priorities in light of COP 21 agreements?
Journal of Environmental Management, 271, 111027.
Wang, S. S., Zhou, D. Q., Zhou, P. and Wang, Q. W. (2011). CO2 emissions, Energy
consumption and economic growth in China: A panel data analysis. Policy, Energy, 39(9),
4870–4875. doi: 10.1016/j.enpol.2011.06.032.

Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf

Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf

Recommended

Recommended

More Related Content

Similar to Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf

Similar to Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf (20)

Recently uploaded

Recently uploaded (20)

Economic Growth and Environmental Quality in a Post-Pandemic World_ New Directions in the Econometrics of the Environment Kuznets Curve.pdf