Towards carbon neutrality: asymmetric impact of financial development and digitalization on carbon dioxide emissions in Mediterranean countries

Abstract

The current research investigates the impact of financial development, digitalization, green trade, manufacturing, and national income on carbon dioxide (CO 2 ) emissions of six Mediterranean countries (MEDIT-6). The study uses a nonlinear panel quantile regression model with panel data of MEDIT-6 countries from 1994 to 2022. The study asserts that higher financial development will reduce CO 2 emissions for MEDIT-6 countries, as it provides more financing options to invest in green energy and potentially curb excessive energy consumption which in turn reduces CO 2 emissions. The study also provides evidence that digitalization in MEDIT-6 countries has led to dematerialization, thereby reducing CO 2 emissions. Digitalization makes trade and commerce platforms more efficient by facilitating the smooth flow of information and enhancing the efficiency of production processes. The positive relationship between manufacturing and national income and CO 2 emissions exhibits a U-shaped pattern, which supports the existence of Environmental Kuznets Curve (EKC) hypothesis. The study shows how the MEDIT-6 countries have been successful in promoting financial development and digitalization, which helps reduce their CO 2 emissions. However, it also raises concerns for policymakers as promoting developmental activities such as manufacturing is inevitable, but it comes with environmental challenges such as higher CO 2 emissions. The current study contributes to the reservoir of existing literature by providing fresh evidence from the Mediterranean region on the impact of financial development and digitalization on CO 2 emissions.
Graphical Abstract

Full text

Mehta Carbon Research (2024) 3:76
https://doi.org/10.1007/s44246-024-00161-w
ORIGINAL ARTICLE Open Access
© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
Towards carbon neutrality: asymmetric
impact ofnancial development
anddigitalization oncarbon dioxide emissions
inMediterranean countries
Dhyani Mehta1*
Abstract
The current research investigates the impact of financial development, digitalization, green trade, manufacturing,
and national income on carbon dioxide (CO2) emissions of six Mediterranean countries (MEDIT-6). The study uses
a nonlinear panel quantile regression model with panel data of MEDIT-6 countries from 1994 to 2022. The study
asserts that higher financial development will reduce CO2 emissions for MEDIT-6 countries, as it provides more financ-
ing options to invest in green energy and potentially curb excessive energy consumption which in turn reduces CO2
emissions. The study also provides evidence that digitalization in MEDIT-6 countries has led to dematerialization,
thereby reducing CO2 emissions. Digitalization makes trade and commerce platforms more efficient by facilitat-
ing the smooth flow of information and enhancing the efficiency of production processes. The positive relation-
ship between manufacturing and national income and CO2 emissions exhibits a U-shaped pattern, which supports
the existence of Environmental Kuznets Curve (EKC) hypothesis. The study shows how the MEDIT-6 countries have
been successful in promoting financial development and digitalization, which helps reduce their CO2 emissions.
However, it also raises concerns for policymakers as promoting developmental activities such as manufacturing
is inevitable, but it comes with environmental challenges such as higher CO2 emissions. The current study contrib-
utes to the reservoir of existing literature by providing fresh evidence from the Mediterranean region on the impact
of financial development and digitalization on CO2 emissions.
Highlights
• Policymakers have difficulties since encouraging developmental activities is unavoidable, but it leads to increased
CO2 emissions.
• Study examines the asymmetric impact of digitization and financial development on CO2 emissions of MEDIT-6
countries.
• The MEDIT-6 countries have been effective in advancing digitization and financial development, which lowers their
carbon footprints.
Keywords Financial development, Digitalization, CO2, EKC, Mediterranean countries
Handling Editor: Hwai Chyuan Ong.
*Correspondence:
Dhyani Mehta
dhyani.mehta@sls.pdpu.ac.in
Full list of author information is available at the end of the article

Page 2 of 14
Mehta Carbon Research (2024) 3:76
JEL Q5, Q53, Q58, G2, G23, O3, O32
Graphical Abstract
1 Introduction
e increase in human population is an organic con-
sequence of life’s inherent drive to expand and thrive.
However, with this expansion comes a host of challenges
that have profound implications for our planet’s delicate
ecosystems such as rising global temperatures, chang-
ing natural balances, and worsening environmental con-
ditions (Aydin and Esen 2018; Mehta and Derbeneva
2024). As the global population grows, so do the demands
and human-made pollutants that disrupt the ecologi-
cal balance on earth (Aydin and Esen 2018). Among the
human-made pollutants, the most significant source of
environmental contamination is CO2 emissions (Majeed
and Ozturk 2020; Zhao etal. 2023a, 2023b). Most nations
are aware of the adverse effects of increasing CO2 levels in
the atmosphere and are taking measures to brace for the
risks and severe conditions linked to climate change (Li
and Haneklaus 2022; Ullah etal. 2020).
Energy consumption in developing nations is more
due to its development-related activities (Saidu Musa
and Maijama’a 2020). According to the Environmental
Kuznets Curve (EKC) theory, economic growth and car-
bon emissions are positively correlated (Grossman and
Krueger 1995). e developing countries have higher
emission levels because their per capita energy consump-
tion is higher, which is required to support their activities
such as industrialization (Pata 2018; Ullah et al. 2020),
urbanization (Pata and Caglar 2021), financial develop-
ment, tourism, and digital infrastructure among others.
Even developed countries, such as those in the European

Page 3 of 14
Mehta Carbon Research (2024) 3:76
Union and the USA, are among the highest emitters of
CO2 emissions (Fig. 1); however, the cost of environ-
mental conservation is often passed on to industrialized
countries (Goldstein et al. 2022). e 13th Sustainable
Development Goal (SDG) also emphasizes the need
for quick action to lessen the consequences of climate
change. CO2 emissions are a primary focus of this pur-
pose because of their significant role in climate change
and global warming (Mehta and Prajapati 2024).
e Mediterranean region, which consists of 25 nations
with varied levels of economic development and energy
usage, is characterized primarily by diversity and inequal-
ity (Ben Abdeljelil etal. 2023). Primary energy consump-
tion in the Mediterranean region has nearly doubled
due to population expansion and economic progress. It
increased from around 393 million tons of oil equivalent
in 1971 to over 978 million tons in 2016 (Bartoletto 2020).
At present, more than 40% of the emissions in the Medi-
terranean area are produced in Slovenia, Greece, Italy, and
Spain (Figs.1 and2). When it comes to CO2 growth rates,
there has been a decline, mostly in European nations, but
there has also been a significant increase in other regions.
e need for energy is predicted to increase drastically
over the next several decades, particularly in the nations
around the southern Mediterranean (Bartoletto 2021; Ben
Abdeljelil etal. 2023). erefore, more efforts are needed
to hasten the shift to a low-carbon economy.
ree primary factors contribute to development among
others: financial development, digital infrastructure, and
trade (Dauda etal. 2021; Zhao etal. 2023a, 2023b). e
development of capital markets plays a pivotal role in
channeling investments into the economy, and fostering
economic development. Moreover, the developed capi-
tal markets aids in the redirection of financial resources
towards greener, more environmentally friendly, and eco-
logically sustainable avenues (W.-X. Zhao et al. 2023a,
2023b). To connect developing countries with the global
economy and boost their economic potential, international
trade and information and communication technology
(ICT) have always been considered crucial foundations
(Herman and Oliver 2023). Potential benefits of trade
include competition, greater access to goods and services,
increased integration of new technologies, and enhanced
efficiency (Freund and Weinhold 2004; Yuan etal. 2024).
Utilizing e-commerce platforms and global supply chains,
the ICT can effectively link small businesses, low-skilled
workers, and informal laborers to international markets,
thus promoting the expansion of trade (Freund and Wein-
hold 2004; Herman and Oliver 2023).
e nations situated within the Mediterranean region
are confronted with a challenging decision-making
process, wherein they must carefully manage the com-
peting priorities of curbing their CO2 emissions while
ensuring the continuation of their economic growth.
is requires a nuanced approach that considers both
Fig. 1 Annual CO2 Emissions. Source: Global Carbon Budget 2022; complied by Our World in Data, retrieved from: https:// ourwo rldin data. org/
co2- emiss ions

Page 4 of 14
Mehta Carbon Research (2024) 3:76
environmental sustainability and economic prosperity,
as these countries seek to find a harmonious balance
between the two. is study examined the impact of
financial development, ICT, and low-carbon technol-
ogy trade on reducing CO2 emissions in Mediterranean
countries. It contributes to the literature by providing
insights into these factors’ influence on emissions and
offers lessons for other nations, addressing a crucial gap
with empirical data. e study uses selected countries
such as Croatia, France, Greece, Italy, Malta, and Slo-
venia (collectively referred to as the MEDIT-6) due to
their substantial contributions to the Mediterranean
region’s GDP, to examine the relationship between eco-
nomic growth and CO2 emissions. ese countries col-
lectively make up over 70% of the Mediterranean GDP,
exceeding the contributions of other countries in the
region. Spain, despite contributing approximately 13%
to the Mediterranean GDP, was excluded due to incon-
sistent data availability for some of the variables under
study.
2 Literature review
Economic development and CO2 emissions are linked,
according to the ground-breaking research of Grossman
and Krueger (1995), which created the EKC hypoth-
esis. Numerous studies have examined the relationships
between environmental sustainability and economic
growth since the EKC (Agrawal and Mehta 2016; Saidu
Musa and Maijama’a 2020; Song 2021; Zaidi and Saidi
2018). Most studies found a connection between emis-
sions and economic growth. Research has also shown that
increased per capita energy consumption will result in
higher CO2 emissions as a result of financial development,
urbanisation, and industrialization (Pata and Caglar 2021;
Shahbaz etal. 2021; Sharma etal. 2020; Song 2021).
2.1 CO2 Emissions andnancial development
Energy consumption has increased in both developed
and emerging nations due to the quick advancement of
finance and technology (Grossman and Krueger 1995;
Omri etal. 2015). Although energy use is directly related
to economic growth, it also plays a major role in degra-
dation of the environment (Ibrahim and Ajide 2021). e
relationship between financial development and environ-
mental performance has been the focal point of numer-
ous research, although the results are not always uniform.
However, some studies confirm the negative impact
of financial development on environmental (Le et al.
2020; Ozturk and Ullah 2022; Zhao etal. 2023a, 2023b).
Whereas, certain studies support that financial develop-
ment reduces environment degradation (Mahalik and
Mallick 2014; Shahbaz etal. 2013; Tamazian etal. 2009).
Fig. 2 Per Capita CO2 Emissions of Mediterranean countries and world. Source: Global Carbon Budget 2022; complied by Our World in Data,
retrieved from: https:// ourwo rldin data. org/ co2- emiss ions

Page 5 of 14
Mehta Carbon Research (2024) 3:76
Financial expansion and its impact on CO2 emissions
can be defined using the stock market worth to GDP
ratio; however, financial development can also be meas-
ured through various metrics, such as stock market per-
formance, liquidity, and credit availability (Zhao et al.
2023a, 2023b). ere are different measures of finan-
cial development, on the scale of financial development
which can be measured by stock market, liquidity, and
credit availability. Research has shown that financial
development contributes to a decrease in CO2 emis-
sions, with studies finding that financial development,
measured by stock market turnover, stock market capi-
talization, total credit, and private sector credit, leads to a
reduction in CO2 emissions (Abbasi and Riaz 2016; Shah-
baz etal. 2013). Similarly, financial development has been
demonstrated to enhance environmental quality, and
financial stability has been found to exhibit unidirectional
causality to CO2 emissions (Nasreen etal. 2017; Shahbaz
etal. 2018). Concurrently, efficient financial systems offer
loans for alternative energy systems and provide funding
for environmentally friendly projects at a discounted rate.
ese affordable financing options can potentially curb
excessive resource and energy consumption by enhanc-
ing innovation within domestic energy-intensive sectors
(Khan etal. 2020; Tao etal. 2023).
Numerous studies challenge the claim that financial
development reduces CO2 emissions (Table 1). While
some research suggests that financial development
enhances environmental quality, others conclude the
opposite, finding that financial development can amplify
CO2 emissions. Increased investment in carbon-inten-
sive industries may overshadow potential environmental
benefits, highlighting the complex relationship between
financial development and environmental outcomes. e
impact on CO2 emissions likely depends on how financial
resources are allocated within an economy (Raihan 2023;
Shahbaz etal. 2015). Research using the NARDL model
has demonstrated that both stock market and bank-based
financial development indices negatively impact envi-
ronmental quality (Shahbaz et al. 2016). Additionally,
evidence from developing countries, such as India and
China, shows that increased financial development is
associated with higher CO2 emissions (Maji etal. 2017;
Sunday Adebayo etal. 2023).
2.2 CO2 emissions anddigitalization
e production function introduced by Cobb and Doug-
las (1928) comprises three fundamental elements: capital,
labor, and technology. e digitization of the economy
stands as a pivotal factor in its expansion (Cardona etal.
2013); nonetheless, its impact on the environment remains
a subject of debate. Certain literature posits that the transi-
tion from a physical to a digital economy can lead to dema-
terialization, thereby enhancing environmental quality
(Bastida etal. 2019; Ozcan and Apergis 2018; Zhang and
Liu 2015). e advancement of Information and Commu-
nication Technology via e-commerce platforms is poised
to complement the international markets and trade by
smooth flow of information and enhancing the efficiency
of production processes (Zafar et al. 2022). Internet and
ICT infrastructure are crucial for digitalization, providing
the foundation for efficient technology implementation.
High-speed connectivity and robust ICT infrastructure
support advanced technologies, enhance productivity,
and foster innovation, driving economic growth and soci-
etal development. Without these infrastructures, the ben-
efits of digitalization cannot be fully realized (Morley etal.
2018; Preston and Rogers 2012). Bastida et al. (2019)
investigates the impact of digitalization in the energy sec-
tor on the electricity consumption patterns of residents;
and found that ICT enabled consumers to make more
informed and efficient energy choices.
On the flip side, some studies suggest that making ICT-
related products requires more energy, which can harm
the environment (Salahuddin and Alam 2015, 2016; Zhao
et al. 2023a, 2023b). e rise of digital technologies,
Table 1 Literature on financial development and CO2 emissions
Recent studies on nancial development
and CO2 emissions Finding on the impact of nancial development on CO2emissions
Mixed
impact Positive impact Negative impact Conclusion
(Dong et al. 2024; Habiba et al. 2023; Patel
and Mehta 2023; Zhou and Zhang 2024)–- YES – Found that financial development is a crucial
driver of economic growth. Economic growth
is fueled by more non-renewal energy leading
to higher emissions.
(Aslan et al. 2014; Le et al. 2020; Ozturk
and Ullah 2022; Tao et al. 2023; Wang et al.
2024; Zhao et al. 2023)
–- –- YES Financial development helps channel invest-
ments and makes funding more accessible
for green technology, leading to a reduction
in CO2 emissions.
(Wu et al. 2023) YES – – In the short run, financial development
has a positive impact on CO2 emissions,
but in the long run, it has a negative impact.

Page 6 of 14
Mehta Carbon Research (2024) 3:76
including advancements in ICT, can have both posi-
tive and negative effects on emissions (Ha 2023). While
going digital can improve how efficiently we use energy
and even reduce CO2 emissions in some cases (Karlilar
etal. 2023), it also means thatweare using more energy
and creating more emissions with all our gadgets and
online services (Liu and Zhang 2023). A review of the lit-
erature reveals mixed results on how digitalization and
financial development affect CO2 emissions due to var-
ied methods, periods, and samples. Notably, no studies
haveexamined their combined impact across Mediter-
ranean countries. Our study addresses this gap, offering
new insights into their nexus with emissions.
3 Methodology
3.1 Data description
is study attempts to quantify the effect of financial
development and digitalization on the CO2 emissions of
the MEDIT-6 countries (selected based on their share to
the total GDP of Mediterranean countries).1 e list of
the MEDIT-6 nations and their percentage share of Med-
iterranean GDP are shown in Table2.
Study uses annual panel data of MEDIT-6 countries
from 1994 to 2022 of CO2 emissions, financial develop-
ment, digitalization, trade in low carbon technology prod-
ucts, contribution of manufacturing sector, and GDP. e
data sources and description are mentioned in Table3.
3.2 Model
Equation (1) represents the long-run unidirectional
model having CO2 emissions (
∅
) as dependent vari-
able and asymmetric independent variables like financial
development (

), digitalization (

), green trade (

),
manufacturing value added (
χ
) and national income (
ϒ
)
to measure the economic development.
Equation (2), presents the liner relationship between
CO2 emissions, digitalization, financial development and
green trade.
where i = 1, …, N denotes the country and t = 1, …, T
denotes the time period.
e Eqs.(3), and (4) represent the positive and negative
partial sum decomposition of

and

.
e
+
and
−
, and
+
and
−
represent the asym-
metric impact (positive and negative shock) of financial
development, and digitalization, respectively. To meas-
ure the relationship between CO2 emissions and other
explanatory variables in Eq. (1), the study usesd cross
sectional asymmetric quantile regression model. Asym-
metric quantile regression was used because it will help
to estimate the impact which varies across conditional
quantiles. e Eqs.(5), (6a), and (6b) present mathemati-
cal form of the cross section (panel data).
where
ℵi
represents dependent variables,
∀i
(positive
shocks
∀+
i
and negative shocks
∀−
i)
is the vector of asym-
metric independent variable and
∁i
is the vector of sym-
metric independent variables.
εi
is homoscedastic and
random error term.
QRϑ(ℵi/∀i)
, and
QRϑ
ℵ
i
/
∁i
denote
ϑ
th quantile of dependent variable and
ρϑ
represents
ϑ
th quantile regression estimator and Eq.(7) presents the
solution.
where
ϑ
represents different orders of quantiles for esti-
mating parameters of the independent variables. e
(1)
φit
=
f
(
+
it
,
−
it
,
+
it
,
−
it
,
it
,χ
it
,ϒ
it
,
)
(2)
φit
=
β0
+
β1ti
+
β2it
+
β3it
+
β4χit
+
β5ϒit
+
νit
(3)

+
i=

t
k=1+
ik =

T
k=1Max(ik ,0
)
−
i=

t
k=1−
ik
=

T
k=1Min(ik ,0)
(4)

+
i=

t
k=1+
ik =

T
k=1Max(ik ,0
)
−
i
=

t
k=1−
ik
=

T
k=1Min(ik ,0
)
(5)
ℵi=∀
iρϑ+∁iρϑ+εi;
0
<ϑ<1
(6a)
QRϑ(ℵi/∀i)=∀
iρϑ
(6b)
QRϑ
ℵ
i
/
∁i
=
∁i
ρ
ϑ
(7)
min

ℵ
i
≥∀+
∁
ρ
ϑ


ℵi−∀
iρ−
∁
iρ


+
ℵ
i
≥∀+
∁
ρ
(1−0)|ℵi−ℵ
i−∀
iρ−
∁
i
ρ
Table 2 List top 6 Mediterranean countries (MEDIT-6)
Source: World Development Indicator, World Bank Database, retrieved
from:https:// datab ank. world bank. org/ source/ world- devel opment- indic ators#
Serial
Number Country Share in total
European GDP in
2022
GNI per capita
(constant 2015 US$)
in 2022
1 France 19.4% 38963.78
2 Italy 16.2% 32580.62
3 Malta 13.5% 27248.69
4 Slovenia 11.9% 23982.82
5 Greece 9.6% 19430.73
6 Croatia 8.3% 16756.90
1 e MEDIT-6 nations (Croatia, France, Greece, Italy, Malta, and Slove-
nia) were chosen based on their contribution to the overall GDP of Medi-
terranean countries. Together, these countries account for over 70% of the
total Mediterranean GDP, surpassing the contributions of other nations in
region. Spain, despite representing about 13% of the Mediterranean GDP,
was not included due to inconsistent data availability for certain variables
being studied.

Page 7 of 14
Mehta Carbon Research (2024) 3:76
study measured the asymmetric impact of financial
development and digitalization along with symmet-
ric impact of green trade, growth of manufacturing and
national income on CO2 emissions at 10th to 90th quan-
tiles (Xu and Lin 2020). To incorporate heterogeneity
among MEDIT-6 countries, thisstudy used fixed effect
quantile panel regression model (Eq.(8a)).
To account for an unobservable fixed effects as param-
eters that need to be evaluated for different quantiles in
addition to covariate effects. is method is unique in
that it directly addresses the computational challenge of
estimating a large number of parameters by including a
penalty element into the minimization process (Abid
et al. 2023; Koenker 2004). is is how the parameter
estimate is computed:
(8a)
RQϑ
ℵ
i
/α
i
∀
i
,
∁i
=α
i
+∀
i
(ℵ
i
)+
∁i
(ℵ
i)
where i is the index for MEDI-6 countries (N), T is the
index of number of observations, k is quantile index,
∀
and
∁
are dependent variable indexes,
wk
is weights of kth
quantile,
∃Tk
is loss function of quantile. Study assumes
that equal weights to each quantile, i.e.,
wk
=
1
k
(Abid
et al. 2023; Lamarche 2011). e

improves the esti-
mates of
δ
,and current study fixes
=1
. Because if

is
zero it is a fixed effect estimator (as it makes the penalty
term disappear) and if

is infinite its individual effect
(Abid et al. 2023; Damette and Delacote 2012). Equa-
tions (8a) and (8b) represent the conditional quantile
function for

quantile,
where
φit
is CO2 emissions (dependent variable),
+
it
and
−
it
are asymmetric variables for financial development,
+
it
and
−
it
are asymmetric variables,
it
is green trade,
(8b)
min
α
,
δk
k=1
T
t=1
N
n=1wk∃Tk

ℵit −αi−∀
T
it δ(Tk)−∁T
it δ(Tk)

+
N
i|αi
|
(9)
QR
∅
it 
T/α
i
,ϕ
i
,∀
i
,
∁i
=α
i
+ϕ
i
+δ1
i

+
it
+δ2
i

−
it
+δ3
i

+
it
+δ4
i

−
it
+δ5
i

it
+δ6
i
χ
it
+δ7
i
ϒ
it
Table 3 Data description and sources
Compiled by author and retrieved from: https:// clima tedata. imf. org/ pages/ go- indic ators# gp1 and https:// datab ank. world bank. org/ source/ world- devel opment- indic
ators
Variable Variable
representation Description and measure Source
Dependent variable
CO2 emissions
∅
Measured:
∅
=
CO
2
emission met ric tonsti
Countr y Populati on
ti
Description: This shows the per capita emission of the carbon
dioxide in metric tons
World Development Indicators, World Bank
Independent variables
Financial development

Measured:

=
Total V al ue of Stock T r aded
ti
RealGDPti
X
100
Description: The value of shares traded is the total number
of shares traded, both domestic and foreign, multiplied
by their respective matching prices
World Development Indicators, World Bank
Digitalization

Measured:

=
Number of Internet User s
ti
Total Polulation
ti
X
100
Description: Internet users are individuals who have used
the Internet via a computer, mobile phone, personal digital
assistant, games machine, digital TV, etc.
Green trade

Measured:

=
Trad e of LowCar bon Tec hnology
ti
Total T radeti
X
100
Description: Trade in low carbon technologies include mechan-
ics like wind turbines, solar panels, biomass systems and car-
bon capture equipment
Government Policy Indicators, Climate
change Dashboard
IMF
Manufacturing
χ
Measured:
χ
=
Manufact uring V alue add ed
ti
RealGDPti
X
100
Description: The share of the manufacturing output out of the
total economy of India is represented by this variable
World Development Indicators, World Bank
National income
ϒ
Measured:
ϒ
=
RealGDP
ti
Country Populat ionti
Description: This represents the total economic output per indi-
vidual belonging to the country

Page 8 of 14
Mehta Carbon Research (2024) 3:76
χit
is manufacturing value added, and
χit
is national
income. And i = 1, …, N denotes the country and t = 1, …,
T denotes the time period.
4 Results anddiscussion
Descriptive statistics for CO2 emissions (
∅
), finan-
cial development (

), digitalization (

), green trade
(

), manufacturing (
χ
) and national income (
ϒ)
for
MEDIT-6 countries are presented in Table 4. e
average CO2 emissions (per capita in metric ton) for
MEDIT-6 countries during 1994–2022 was 5.77, with
highest emissions of 9.44. e average value of stock
traded to GDP ratio (measure for financial develop-
ment) was 21.63 and average number of internet user to
population ration (measure of digitalization) was 53.66
for MEDIT-6 countries. e value of Jarque–Bera (JB)
statistics confirmed normality for CO2 emissions (
∅
),
green trade (

), and manufacturing (
χ
). Whereas, JB
statists failed to confirm normality for other variables,
i.e., financial development (

), digitalization (

) and
national income (
ϒ)
.
Pairwise correlation estimates confirmed the posi-
tive relationship between CO2 emissions, manufac-
turing and national income. Furthermore, there is
negative impact of financial development, digitaliza-
tion, and green trade on CO2 emissions (Table4). Panel
data analysis can be challenging due to cross-section
dependence (CSD), where observations from various
countries are interrelated because of shared economic
traits (Gaibulloev etal. 2014). is poses a significant
problem for panel data analysis since it assumes that
observations across different cross-sections are inde-
pendent. erefore, it is essential to examine the exist-
ence of cross-sectional dependency in empirical studies
that utilize panel data (Breusch and Pagan 1980; Pesa-
ran 2004).
e CSD test estimates revealed a cross-sectional
dependency, indicating a similarity in the way they
changed across all MEDIT-6 countries (Table 5). e
estimates reject the null hypothesis of cross-sectional
independence. e study examines the null hypothesis
of non-stationarity by assessing unit root in the panel
Table 4 Descriptive statistics and pairwise correlation
a and b indicate signicant at 1% and 5% level of signicance, respectively
Source: authors’ calculations
∅



χ
ϒ
Mean 5.772883 21.63566 53.66419 0.045486 13.38525 1.755810
Median 5.916383 4.219195 59.36315 0.026546 13.55802 1.969661
Maximum 9.441115 108.8391 91.54076 1.336612 20.60883 12.54750
Minimum 2.969764 0.189622 0.514029 -1.177850 6.667995 -10.64179
Std. Dev 1.483920 28.98619 26.42999 0.515320 3.854584 4.020689
Skewness 0.156825 1.238362 -0.615084 0.362852 0.080492 -0.496475
Kurtosis 2.338699 3.274138 2.225608 2.936332 2.133004 4.181729
Jarque–Bera 3.169530 36.73841a12.50190a3.139981 4.600789 14.09606a
Observations 142 142 142 142 142 142
Pairwise correlation
∅
1.0000 – – – – –

-0.2303a1.0000 – – – –

-0.3174a0.1240 1.0000 – – –

-0.3197a0.2716a0.1810a1.0000 – –
χ
0.3558a0.0548 0.3740a0.6312a1.0000 –
ϒ
0.2097b-0.1129 0.2040a-0.0626 0.9059a1.0000
Table 5 CSD test
a indicates signicant at 1% level of signicance
Source: authors’ calculations
Cross-section
∅



χ
ϒ
LM Breusch-Pagan 266.92a71.803a417.394a95.999a283.494a165.979a
LM Pesaran scaled 45.995a10.370a73.466a14.788a49.020a27.565a
CD Pesaran 15.682a7.082a20.428a2.968a16.669a12.657a

Page 9 of 14
Mehta Carbon Research (2024) 3:76
data (Breitung 2000; Im etal. 2003; Levin etal. 2002;
Mehta and Derbeneva 2024; Patel etal. 2023).
Second generation unit root test shows that CO2 emis-
sions (
∅
), green trade (

), manufacturing (
χ
), financial
development (

), digitalization (

), and national income
(
ϒ)
are stationary at I (1) level (Table6). Table7 presents
the results of the panel cointegration tests conducted by
Pedroni (1999) and Westerlund (2007). e findings con-
firm that the variables are cointegrated in the long term
and indicate that the estimated statistics were significant.
After establishing the long-term relationship, the study
used nonlinear panel quantile regression to investigate
how financial development (

), digitalization (

) green
trade (

), manufacturing (
χ
), and national income (
ϒ)
affected the CO2 emissions (
∅
) of MEDIT-6 countries
within the 10th to 90th quantiles. Table 8 presents the
panel quantile regression estimates for the panel data of
MEDIT-6 countries during 1994–2022.
4.1 Asymmetric impact ofnancial development (
+
it
and
−
it
) on CO2 emissions (
∅
)
e coefficients of positive shocks of financial develop-
ment (
+
it
) were negative and significant (at 1% and 5%
significance level) from q(10) to q(90), which indicates
that an increase in financial development results in lower
emissions. e coefficients were -0.3412 in q(10) and pro-
gressing gradually to -0.9374 in q(90). It can be inferred
that a 1 percent increase in financial development (
+
it
)
will reduce CO2 emissions (
∅
) by 0.34 percent in q(10)
and further reduces at a higher rate of 0.93 percent by
q(90). Similarly, the coefficient of negative shock of fiscal
development (
−
it
) was significant and positive from q(10)
to q(90). It can be inferred that any reduction in financial
development will increase the CO2 emissions. e coef-
ficients were 0.1013 in q(10) and progressing gradually to
0.9264 (though coefficients of q[30] and q[40] are insig-
nificant) which means 1 percent decrease in financial
development (
−
it
) will increase the CO2 emissions (
∅
)
by 0.10 percent in q(10), 0.79 percent in q(50) and 0.92
percent in q(90), respectively. e insignificant and small
coefficient (
+
it
= -0.0068 and
−
it
= 0.0001) of scale indi-
cated stable variability of estimated effect of positive and
negative shocks of financial development on CO2 emis-
sions across quantiles. Furthermore, the location vari-
ables estimates for
+
it
and
−
it
which measure the median
(50th percentile) were significant. It asserts that 1 percent
increase in financial development will reduce CO2 emis-
sions by 0.19 percent and decrease in financial develop-
ment will increase CO2 emissions by 0.79 percent. e
impact of negative shock of financial development was
higher compared to the positive shock of financial devel-
opment on CO2 emissions (Fig.3). e results asserts that
higher the financial development will reduce CO2 emis-
sions by providing affordable financing options to invest
into more green energy and potentially curb excessive
resource and energy consumption by enhancing innova-
tion within domestic energy-intensive sectors. e results
support the previous research (Khan et al. 2020; Raihan
2023; Shahbaz et al. 2018; Sunday Adebayo etal. 2023;
Tao etal. 2023; Zhao etal. 2023a, 2023b).
4.2 Asymmetric impact ofdigitalization (
+
it
and
−
it
)
on CO2 emissions (
∅
)
Digital economy can lead to dematerialization, thereby
enhancing environmental quality. It also enables e-com-
merce platforms to be poised to complement the
Table 6 Second-generation unit root test
a and b indicate signicant at 1% and 5% level of signicance respectively
Source: authors’ calculations
Variables CADF CIPS
∅
3.4081 3.4605
∅
-3.8626a-3.8044a

-2.3185b-2.2044a

-6.6936a-7.1484a

1.7978 1.7183

-4.5015a-4.4564a

-0.1725 -0.1281

-68,089a-7.6068a
χ
-0.6850 -0.6584
�χ
-5.9846a-6.3616a
ϒ
-4.1211a-4.0756a
�ϒ
-6.9775a-7.7232a
Table 7 Panel cointegration test
a and b indicate signicant at 1% and 5% level of signicance respectively
Pv
,
Prho
,
Ppp
, and
PADF
denotes panel statistics respectively and
Grho
,
Gpp
,
and
GADF
denotes group statistics respectively
Source: authors’ calculations
Panel: Pedroni Cointegration Common AR coecients (within-
dimension)
Statistic Weighted Statistic
Pv
-1.2489a-1.0387a
Prho
1.9891b2.1080b
Ppp
0.8429a0.9659a
PADF
1.8422a1.0837a
Group : Pedroni Cointegration Individual AR coecients
(between-dimension)
Statistic
Grho
2.8410a
Gpp
0.5725a
GADF
1.2128a
Westerlund (2007) Cointegration
Variance Ratio 2.1375a

Page 10 of 14
Mehta Carbon Research (2024) 3:76
Table 8 Nonlinear panel quantile regression estimates
a , b, and c indicates signicant at 1%, 5% and 10% level of signicance, respectively, VL denotes variable location
Source: authors’ calculations
Dependent Variable: CO2 emissions (
∅
)
Quantiles
+
it
−
it
+
it
−
it

χ
ϒ
Constant
VL -0.1953a0.7907b-0.6376a0.5857a-0.4566a0.2556a0.0204a2.5275a
Scale -0.0068 0.0001 -0.0021 0.0037 -0.0004 0.0025 0.0204 0.0114
q(10) -0.3412a0.1013b-0.1577a0.3894b-0.4259a0.1963c0.0200a4.7617a
q(20) -0.4398a0.0848c-0.1828b0.3118b-0.4403b0.0996 0.0140 4.6222a
q(30) -0.4497a0.6515 -0.1606b0.4816a-0.3537c0.0697 0.0066 3.9601a
q(40) -0.2336b0.6709 -0.1602b0.4900a-0.3795 0.1423b0.0017 3.9601a
q(50) -0.1953a0.7907b-0.6376a0.5857a-0.4566a0.2556a0.0204a2.5275a
q(60) -0.7851a0.7010b-0.1185 0.5914a-0.3833 0.2636a0.0658a2.6704a
q(70) -0.9146a0.8651b-0.8789 0.6877a-0.9230a0.2940a0.0842a2.5285a
q(80) -0.9061a0.8448b-0.9120a0.6794a-0.6177a0.1888a0.0620a5.6367a
q(90) -0.9374a0.9264b-0.9812a0.8459a-0.8444a0.2311a0.0649a7.5217a
Fig. 3 Asymmetric and Symmetric Relationship Plot. Source: Authors’ Calculation using EViews

Page 11 of 14
Mehta Carbon Research (2024) 3:76
international markets and trade by smooth flow of infor-
mation and enhancing the efficiency of production pro-
cesses. e significant and negative coefficient of positive
shock of digitalization (
+
it
) asserts that increase in digi-
talization will reduce the CO2 emissions (
∅
). e coef-
ficient value of
+
it
for q(10) was -0.1577 which gradually
improved till q(90) to -0.9812, it can be inferred that 1
percent increase in digitalization will reduce CO2 emis-
sions by 0.15 percent in q(10), 0.63 percent in q(50) and
0.98 percent in q(90), respectively (Fig. 3). e positive
and significant negative shock coefficient of digitalization
(
−
it
) shows that 1 percent decrease in digitalization will
increase CO2 emissions (
∅
) by 0.38 percent in q(10), 0.48
percent in q(30), 0.58 percent in q(50) and 0.84 percent by
q(90), respectively. e insignificant and small coefficient
(
+
it
= -0.0021 and
−
it
= 0.0037) of the scale indicated
stable variability of the estimated effect of positive and
negative shocks of digitalization on CO2 emissions across
quantiles. Furthermore, the location variables estimates
for
+
it
and
−
it
which measure the median (50th percen-
tile) were significant. It asserts that 1 percent increase in
digitalization will reduce CO2 emissions by 0.63 percent
and a decrease in digitalization will increase CO2 emis-
sions by 0.58 percent, the results are in line with the pre-
vious studies (Bastida etal. 2019; Ha 2023; Karlilar etal.
2023; Liu and Zhang 2023).
4.3 Symmetric impact ofgreen trade (
)
, manufac turing
(
χ
), andnational income (
ϒ
) on CO2 Emissions (
∅
)
e negative and significant coefficients of green trade
(

) imply that a 1 percent increase in the green trade
will reduce the CO2 emissions (
∅
) by 0.42 percent
in q(10), 0.45 percent in q(50), and 0.84 percent in
q(90) (Fig.3). e negative impact of green trade has
improved from q(10) to q(90). Furthermore, the coeffi-
cients of manufacturing and national income were pos-
itive and significant which supports the EKC hypothesis
(Grossman and Krueger 1995; Mehta and Derbeneva
2024; Mehta and Prajapati 2024; Patel and Mehta 2023).
e manufacturing (
χ
) increases CO2 emissions by 0.19
percent in q(10), 0.25 percent in q(50) and 0.23 per-
cent in q(90) respectively (Fig.3). Similarly, change in
national income also increased CO2 emissions by 0.02
percent in q(10) to 0.6 percent in q(90). Furthermore,
the insignificant and small coefficient green trade, man-
ufacturing and national income of the scale indicated
stable variability of the estimated on CO2 emissions
across quantiles. Furthermore, the location variables
estimate for all three variables also asserts negative
impact of green trade, and positive impact of manufac-
turing as well as national income on CO2 emissions.
5 Conclusion
is study aims to investigate the relationship between
CO2 emissions, financial development, digitaliza-
tion, green trade, manufacturing, and national income
for the panel of six Mediterranean countries (France,
Italy, Malta, Slovenia, Greece, and Croatia). e study
used a nonlinear panel quantile regression model with
panel data from 1994 to 2022. e findings indicate
that higher financial development will reduce CO2
emissions for MEDIT-6 countries by providing afford-
able financing options to invest in more green energy
and potentially curb excessive resource and energy
consumption by enhancing innovation within domes-
tic energy-intensive sectors. e study also shows that
digitalization of the economy can lead to demateriali-
zation, thereby reducing CO2 emissions in MEDIT-6
countries. Digitalization makes trade and commerce
platforms more efficient by facilitating the smooth flow
of information and enhancing the efficiency of pro-
duction processes. e positive relationship between
manufacturing and national income and CO2 emissions
exhibits a U-shaped pattern, which supports the exist-
ence of the EKC hypothesis. e study shows how the
MEDIT-6 countries have been successful in promot-
ing financial development and digitalization, which
helps reduce their CO2 emissions. However, it also
raises concerns for policymakers as promoting devel-
opmental activities such as manufacturing is inevita-
ble, but it comes with environmental challenges such
as higher CO2 emissions. e current study contributes
to the reservoir of existing literature on the relation-
ship between financial development, digitalization, and
CO2 emissions. Fresh evidence from the Mediterranean
region is presented, and this opens up opportunities for
further research on comparable and industrial coun-
tries. e study can be expanded by incorporating more
explanatory variables. Additionally, quantifying the bal-
ance between economic development and environmen-
tal sustainability using benchmarking methodology can
help determine the level of economic growth needed
for a sustainable environment, and vice versa.
Abbreviations
AR Autoregressive
CADS Cross-Sectionally Augmented Dicky-Fuller Test
CDS Cross-section Dependence
CIPS Cross-Sectionally Augmented Test
CO2 Carbon Dioxide
EKC Environmental Kuznets Curve
GDP Gross Domestic Product
ICT Information and Communication Technology
JB Jarque–Bera
LCTT Low-Carbon Technology Trade
MEDIT Mediterranean
NARDL Non-Linear Autoregressive Distributed Lag
SDG Sustainable Development Goal

Page 12 of 14
Mehta Carbon Research (2024) 3:76
Authors’ contributions
Dhyani Mehta, as a single author, has contributed to the study’s conception
and design, Material preparation, data collection, analysis, writing, review, and
editing of the drafts of the manuscript.
Funding
The authors declare that no funds, grants, or other support were received dur-
ing the preparation of this manuscript.
Data availability
The data will be made available on request from the corresponding author.
The data of all the variables used in the study was taken from World Develop-
ment Indicators Data from World Bank Database 2023, retrieved from: https://
datab ank. world bank. org/ source/ world- devel opment- indic ators and Govern-
mentPolicy Indicators, Climate change Dashboard IMF https:// clima tedata. imf.
org/ pages/ go- indic ators# gp1.
Declarations
Ethics approval and consent to participate
This article does not contain any studies with human participants or animals
performed by any of the authors. The submitted work is original and it is not
published elsewhere in any form of language. No data, text, or theories by
others are presented as if they were the author’s own.
Consent for publication
Not applicable.
Competing interests
The author have no relevant financial or non-financial interests to disclose.
Author details
1 Department of Economics, School of Liberal Studies, Pandit Deendayal
Energy University, Gandhinagar, Gujarat 382426, India.
Received: 6 May 2024 Revised: 7 October 2024 Accepted: 7 October 2024
References
Abbasi F, Riaz K (2016) CO2 emissions and financial development in an emerg-
ing economy: An augmented VAR approach. Energy Policy 90:102–114.
https:// doi. org/ 10. 1016/j. enpol. 2015. 12. 017
Abid N, Ahmad F, Aftab J, Razzaq A (2023) A blessing or a burden? Assessing
the impact of Climate Change Mitigation efforts in Europe using Quantile
Regression Models. Energy Policy 178:113589. https:// doi. org/ 10. 1016/j.
enpol. 2023. 113589
Agrawal V, Mehta D (2016) Proposal for Energy Social Responsibility. Journal of
Social and Development Sciences 7(2):14–19. https:// doi. org/ 10. 22610/
jsds. v7i2. 1301
Aslan A, Apergis N, Topcu M (2014) Banking development and energy con-
sumption: Evidence from a panel of Middle Eastern countries. Energy
72:427–433. https:// doi. org/ 10. 1016/j. energy. 2014. 05. 061
Aydin C, Esen Ö (2018) Reducing CO 2 emissions in the EU member states: Do
environmental taxes work? J Environ Planning Manage 61(13):2396–2420.
https:// doi. org/ 10. 1080/ 09640 568. 2017. 13957 31
Bartoletto, S (2020). Economy and energy in Mediterranean COuntries. In
Renewables and CO2 emissions (1st ed., pp. 84–113). Edward Elgar.
https:// books. google. co. in/ books? id= 1sgBE AAAQB AJ& lpg= PP1& pg=
PA4#v= onepa ge& q&f= false
Bartoletto, S (2021). IEMed Mediterranean Yearbook 2021. https:// www. iemed.
org/ publi cation/ a- post- carbon- energy- econo my- impli catio ns- for- the-
medit erran ean- count ries/
Bastida L, Cohen JJ, Kollmann A, Moya A, Reichl J (2019) Exploring the role of
ICT on household behavioural energy efficiency to mitigate global warm-
ing. Renew Sustain Energy Rev 103:455–462. https:// doi. org/ 10. 1016/j.
rser. 2019. 01. 004
Ben Abdeljelil M, Rault C, Belaïd F (2023) Economic growth and pollutant
emissions: new panel evidence from the union for the Mediterranean
countries. Econ Chang Restruct 56(3):1537–1566. https:// doi. org/ 10. 1007/
s10644- 022- 09476-3
Breitung, J (2000). The local power of some unit root tests for panel data. In
Baltagi, B.H., Fomby, T.B. and Carter Hill, R. (Ed.) Nonstationary Panels,
Panel Cointegration, and Dynamic Panels (Advances in Econometrics, Vol.
15) (pp. 161–177). Emerald Group Publishing Limited. https:// doi. org/ 10.
1016/ S0731- 9053(00) 15006-6
Breusch TS, Pagan AR (1980) The Lagrange Multiplier Test and its Applications
to Model Specification in Econometrics. Rev Econ Stud 47(1):239. https://
doi. org/ 10. 2307/ 22971 11
Cardona M, Kretschmer T, Strobel T (2013) ICT and productivity : conclusions
from the empirical literature. Inf Econ Policy 25(3):109–125. https:// doi.
org/ 10. 1016/j. infoe copol. 2012. 12. 002
Cobb CW, Douglas PH (1928) A Theory of Production. Am Econ Rev 18(1):139–
165 (http:// www. jstor. org/ stable/ 18115 56)
Damette O, Delacote P (2012) On the economic factors of deforestation:
What can we learn from quantile analysis? Econ Model 29(6):2427–2434.
https:// doi. org/ 10. 1016/j. econm od. 2012. 06. 015
Dauda L, Long X, Mensah CN, Salman M, Boamah KB, Ampon-Wireko S, Kofi
Dogbe CS (2021) Innovation, trade openness and CO2 emissions in
selected countries in Africa. J Clean Prod 281:125143. https:// doi. org/ 10.
1016/j. jclep ro. 2020. 125143
Dong K, Wang S, Hu H, Guan N, Shi X, Song Y (2024) Financial development,
carbon dioxide emissions, and sustainable development. Sustain Dev
32(1):348–366. https:// doi. org/ 10. 1002/ sd. 2649
Freund CL, Weinhold D (2004) The effect of the Internet on international
trade. J Int Econ 62(1):171–189. https:// doi. org/ 10. 1016/ S0022-
1996(03) 00059-X
Gaibulloev K, Sandler T, Sul D (2014) Dynamic Panel Analysis under Cross-
Sectional Dependence. Polit Anal 22(2):258–273. https:// doi. org/ 10. 1093/
pan/ mpt029
Goldstein B, Reames TG, Newell JP (2022) Racial inequity in household energy
efficiency and carbon emissions in the United States: An emissions
paradox. Energy Res Soc Sci 84:102365. https:// doi. org/ 10. 1016/j. erss.
2021. 102365
Grossman GM, Krueger AB (1995) Economic Growth and the Environment. Q J
Econ 110(2):353–377. https:// doi. org/ 10. 2307/ 21184 43
Ha LT (2023) Scrutinizing interlinkages between digitalization, economic
complexity, green technologies, green energy consumption and
CO2 emission by quantile spillovers in Vietnam. Environ Sci Pollut Res
30(33):81073–81092. https:// doi. org/ 10. 1007/ s11356- 023- 28114-7
Habiba U, Xinbang C, Ali S (2023) Investigating the impact of financial devel-
opment on carbon emissions: Does the use of renewable energy and
green technology really contribute to achieving low-carbon economies?
Gondwana Res 121:472–485. https:// doi. org/ 10. 1016/j. gr. 2023. 05. 013
Herman PR, Oliver S (2023) Trade, policy, and economic development in the
digital economy. J Dev Econ 164:103135. https:// doi. org/ 10. 1016/j. jdeve
co. 2023. 103135
Ibrahim RL, Ajide KB (2021) Nonrenewable and renewable energy consump-
tion, trade openness, and environmental quality in G-7 countries:
the conditional role of technological progress. Environ Sci Pollut Res
28(33):45212–45229. https:// doi. org/ 10. 1007/ s11356- 021- 13926-2
Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous
panels. Journal of Econometrics 115(1):53–74. https:// doi. org/ 10. 1016/
S0304- 4076(03) 00092-7
Karlilar S, Balcilar M, Emir F (2023) Environmental sustainability in the OECD:
The power of digitalization, green innovation, renewable energy and
financial development. Telecommunications Policy 47(6):102568. https://
doi. org/ 10. 1016/j. telpol. 2023. 102568
Khan MI, Teng JZ, Khan MK (2020) The impact of macroeconomic and financial
development on carbon dioxide emissions in Pakistan: evidence with
a novel dynamic simulated ARDL approach. Environ Sci Pollut Res
27(31):39560–39571. https:// doi. org/ 10. 1007/ s11356- 020- 09304-z
Koenker R (2004) Quantile regression for longitudinal data. J Multivar Anal
91(1):74–89. https:// doi. org/ 10. 1016/j. jmva. 2004. 05. 006
Lamarche C (2011) Measuring the incentives to learn in Colombia using new
quantile regression approaches. J Dev Econ 96(2):278–288. https:// doi.
org/ 10. 1016/j. jdeve co. 2010. 10. 003
Le TH, Le HC, Taghizadeh-Hesary F (2020) Does financial inclusion impact
CO2 emissions? Evidence from Asia Finance Research Letters 34:101451.
https:// doi. org/ 10. 1016/j. frl. 2020. 101451

Page 13 of 14
Mehta Carbon Research (2024) 3:76
Levin A, Lin CF, James Chu CS (2002) Unit root tests in panel data: asymptotic
and finite-sample properties. Journal of Econometrics 108(1):1–24.
https:// doi. org/ 10. 1016/ S0304- 4076(01) 00098-7
Li B, Haneklaus N (2022) Reducing CO2 emissions in G7 countries: The role of
clean energy consumption, trade openness and urbanization. Energy
Rep 8:704–713. https:// doi. org/ 10. 1016/j. egyr. 2022. 01. 238
Liu T, Zhang K (2023) Effects of the digital economy on carbon emissions in
China: an analysis based on different innovation paths. Environ Sci Pollut
Res 30(32):79451–79468. https:// doi. org/ 10. 1007/ s11356- 023- 27975-2
Mahalik MK, Mallick H (2014) Energy Consumption, Economic Growth and
Financial Development: Exploring the Empirical Linkages for India. The
Journal of Developing Areas 48(4):139–159. https:// doi. org/ 10. 1353/ jda.
2014. 0063
Majeed MT, Ozturk I (2020) Environmental degradation and population
health outcomes: a global panel data analysis. Environ Sci Pollut Res
27(13):15901–15911. https:// doi. org/ 10. 1007/ s11356- 020- 08167-8
Maji IK, Habibullah MS, Saari MY (2017) Financial development and sectoral
CO2 emissions in Malaysia. Environ Sci Pollut Res 24(8):7160–7176.
https:// doi. org/ 10. 1007/ s11356- 016- 8326-1
Mehta D, Derbeneva V (2024) Impact of environmental fiscal reforms on car-
bon emissions of EURO-4 countries: CS-NARDL approach. International
Journal of Thermofluids 21:100550. https:// doi. org/ 10. 1016/j. ijft. 2023.
100550
Mehta D, Prajapati P (2024) Asymmetric effect of environment tax and
spending on CO2 emissions of European Union. Environ Sci Pollut Res
31(18):27416–27431. https:// doi. org/ 10. 1007/ s11356- 024- 32990-y
Morley J, Widdicks K, Hazas M (2018) Digitalisation, energy and data demand:
The impact of Internet traffic on overall and peak electricity consump-
tion. Energy Res Soc Sci 38:128–137. https:// doi. org/ 10. 1016/j. erss. 2018.
01. 018
Nasreen S, Anwar S, Ozturk I (2017) Financial stability, energy consumption
and environmental quality: Evidence from South Asian economies.
Renew Sustain Energy Rev 67:1105–1122. https:// doi. org/ 10. 1016/j. rser.
2016. 09. 021
Omri A, Daly S, Rault C, Chaibi A (2015) Financial development, environmental
quality, trade and economic growth: What causes what in MENA coun-
tries. Energy Economics 48:242–252. https:// doi. org/ 10. 1016/j. eneco.
2015. 01. 008
Ozcan B, Apergis N (2018) The impact of internet use on air pollution: Evidence
from emerging countries. Environ Sci Pollut Res 25(5):4174–4189. https://
doi. org/ 10. 1007/ s11356- 017- 0825-1
Ozturk I, Ullah S (2022) Does digital financial inclusion matter for economic
growth and environmental sustainability in OBRI economies? An empiri-
cal analysis. Resour Conserv Recycl 185:106489. https:// doi. org/ 10. 1016/j.
resco nrec. 2022. 106489
Pata UK (2018) The effect of urbanization and industrialization on carbon
emissions in Turkey: evidence from ARDL bounds testing procedure.
Environ Sci Pollut Res 25(8):7740–7747. https:// doi. org/ 10. 1007/
s11356- 017- 1088-6
Pata UK, Caglar AE (2021) Investigating the EKC hypothesis with renewable
energy consumption, human capital, globalization and trade openness
for China: Evidence from augmented ARDL approach with a structural
break. Energy 216:119220. https:// doi. org/ 10. 1016/j. energy. 2020. 119220
Patel N, Mehta D (2023) The asymmetry effect of industrialization, financial
development and globalization on CO2 emissions in India. International
Journal of Thermofluids 20:100397. https:// doi. org/ 10. 1016/j. ijft. 2023.
100397
Patel N, Kautish P, Shahbaz M (2023) Unveiling the complexities of sustainable
development: An investigation of economic growth, globalization and
human development on carbon emissions in 64 countries. Sustain Dev.
https:// doi. org/ 10. 1002/ sd. 2846
Pedroni P (1999) Critical Values for Cointegration Tests in Heterogeneous
Panels with Multiple Regressors. Oxford Bull Econ Stat 61(s1):653–670.
https:// doi. org/ 10. 1111/ 1468- 0084. 0610s 1653
Pesaran, M H (2004). General diagnostic tests for cross-section dependence in
panels (IZA Discussion Paper 1240; Cambridge Working Papers Econ.).
Preston P, Rogers J (2012) Crisis, digitalisation and the future of the internet.
Info 14(6):73–83. https:// doi. org/ 10. 1108/ 14636 69121 12712 44
Raihan A (2023) Nexus between economic growth, natural resources rents,
trade globalization, financial development, and carbon emissions toward
environmental sustainability in Uruguay. Elec J Educ, Soc Econ Technol
4(2):55–65. https:// doi. org/ 10. 33122/ ejeset. v4i2. 102
Saidu Musa K, Maijama’a R (2020) Economic growth, energy consumption
and environmental pollution in Nigeria: evidence from ARDL approach.
Energy Econ Letters 7(2):61–73. https:// doi. org/ 10. 18488/ journ al. 82. 2020.
72. 61. 73
Salahuddin M, Alam K (2015) Internet usage, electricity consumption and
economic growth in Australia: A time series evidence. Telematics Inform
32(4):862–878. https:// doi. org/ 10. 1016/j. tele. 2015. 04. 011
Salahuddin M, Alam K (2016) Information and Communication Technology,
electricity consumption and economic growth in OECD countries: A
panel data analysis. Int J Electr Power Energy Syst 76:185–193. https:// doi.
org/ 10. 1016/j. ijepes. 2015. 11. 005
Shahbaz M, Solarin SA, Mahmood H, Arouri M (2013) Does financial develop-
ment reduce CO2 emissions in Malaysian economy? A time series
analysis. Econ Model 35:145–152. https:// doi. org/ 10. 1016/j. econm od.
2013. 06. 037
Shahbaz M, Mallick H, Mahalik MK, Loganathan N (2015) Does globalization
impede environmental quality in India? Ecol Ind 52:379–393. https:// doi.
org/ 10. 1016/j. ecoli nd. 2014. 12. 025
Shahbaz M, Shahzad SJH, Ahmad N, Alam S (2016) Financial development
and environmental quality: The way forward. Energy Policy 98:353–364.
https:// doi. org/ 10. 1016/j. enpol. 2016. 09. 002
Shahbaz M, Nasir MA, Roubaud D (2018) Environmental degradation in France:
The effects of FDI, financial development, and energy innovations. Energy
Economics 74:843–857. https:// doi. org/ 10. 1016/j. eneco. 2018. 07. 020
Shahbaz M, Sharma R, Sinha A, Jiao Z (2021) Analyzing nonlinear impact of
economic growth drivers on CO2 emissions: Designing an SDG frame-
work for India. Energy Policy 148:111965. https:// doi. org/ 10. 1016/j. enpol.
2020. 111965
Sharma R, Kautish P, Uddin GS (2020) Do the international economic endeav-
ors affect CO2 emissions in open economies of South Asia? An empirical
examination under nonlinearity. Management of Environmental
Quality: an International Journal 31(1):89–110. https:// doi. org/ 10. 1108/
MEQ- 05- 2019- 0099
Song Z (2021) Economic growth and carbon emissions: Estimation of a
panel threshold model for the transition process in China. J Clean Prod
278:123773. https:// doi. org/ 10. 1016/j. jclep ro. 2020. 123773
Sunday Adebayo T, Saint Akadiri S, Haouas I, Rjoub H (2023) A Time-Varying
Analysis between Financial Development and Carbon Emissions: Evi-
dence from the MINT countries. Energy & Environment 34(5):1207–1227.
https:// doi. org/ 10. 1177/ 09583 05X22 10820 92
Tamazian A, Chousa JP, Vadlamannati KC (2009) Does higher economic and
financial development lead to environmental degradation: Evidence
from BRIC countries. Energy Policy 37(1):246–253. https:// doi. org/ 10.
1016/j. enpol. 2008. 08. 025
Tao M, Sheng MS, Wen L (2023) How does financial development influence
carbon emission intensity in the OECD countries: Some insights from the
information and communication technology perspective. J Environ Man-
age 335:117553. https:// doi. org/ 10. 1016/j. jenvm an. 2023. 117553
Ullah S, Ozturk I, Usman A, Majeed MT, Akhtar P (2020) On the asymmetric
effects of premature deindustrialization on CO2 emissions: evidence from
Pakistan. Environ Sci Pollut Res 27(12):13692–13702. https:// doi. org/ 10.
1007/ s11356- 020- 07931-0
Wang Q, Hu S, Li R (2024) Could information and communication technology
(ICT) reduce carbon emissions? The role of trade openness and financial
development. Telecommunications Policy 48(3):102699. https:// doi. org/
10. 1016/j. telpol. 2023. 102699
Westerlund J (2007) Testing for Error Correction in Panel Data. Oxford Bull Econ
Stat 69(6):709–748. https:// doi. org/ 10. 1111/j. 1468- 0084. 2007. 00477.x
Wu L, Adebayo TS, Yue XG, Umut A (2023) The role of renewable energy
consumption and financial development in environmental sustainability:
implications for the Nordic Countries. Int J Sust Dev World 30(1):21–36.
https:// doi. org/ 10. 1080/ 13504 509. 2022. 21155 77
Xu B, Lin B (2020) Investigating drivers of CO2 emission in China’s heavy indus-
try: A quantile regression analysis. Energy 206:118159. https:// doi. org/ 10.
1016/j. energy. 2020. 118159
Yuan H, Liu J, Li X, Zhong S (2024) The impact of digital economy on
environmental pollution: Evidence from 267 cities in China. PLoS ONE
19(1):e0297009. https:// doi. org/ 10. 1371/ journ al. pone. 02970 09

Page 14 of 14
Mehta Carbon Research (2024) 3:76
Zafar MW, Zaidi SAH, Mansoor S, Sinha A, Qin Q (2022) ICT and education as
determinants of environmental quality: The role of financial development
in selected Asian countries. Technol Forecast Soc Chang 177:121547.
https:// doi. org/ 10. 1016/j. techf ore. 2022. 121547
Zaidi S, Saidi K (2018) Environmental pollution, health expenditure and eco-
nomic growth in the Sub-Saharan Africa countries: Panel ARDL approach.
Sustain Cities Soc 41(May):833–840. https:// doi. org/ 10. 1016/j. scs. 2018.
04. 034
Zhang C, Liu C (2015) The impact of ICT industry on CO2 emissions: A regional
analysis in China. Renew Sustain Energy Rev 44:12–19. https:// doi. org/ 10.
1016/j. rser. 2014. 12. 011
Zhao S, Ozturk I, Hafeez M, Ashraf MU (2023a) Financial structure and CO2
emissions in Asian high-polluted countries: Does digital infrastructure
matter? Environ Technol Innov 32:103348. https:// doi. org/ 10. 1016/j. eti.
2023. 103348
Zhao WX, Samour A, Yi K, Al-Faryan MAS (2023b) Do technological innovation,
natural resources and stock market development promote environ-
mental sustainability? Novel evidence based on the load capacity factor.
Resour Policy 82:103397. https:// doi. org/ 10. 1016/j. resou rpol. 2023. 103397
Zhou X, Zhang H (2024) Does financial development benefit carbon neutrality
in China? Pathway analysis and empirical study. Appl Econ 56(4):440–461.
https:// doi. org/ 10. 1080/ 00036 846. 2023. 21686 10
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.