The impact of financial innovation, green energy, and economic growth on transport-based CO2 emissions in India: insights from QARDL approach

Abstract

Concerns about a sustainable environment are increasing and have attained significant attention among policy experts worldwide. Therefore, the current research investigated to what extent financial innovations, green energy, and economic growth impacted Indian transport-based carbon (TCO2) emissions from 1990 to 2018. This research applied quantile autoregressive distributed lag (QARDL) model and the Wald test for parameter consistency. The QARDL approach proves valuable as it illustrates the causal patterns across different quantiles of financial innovations, green energy, economic growth, and environmental degradation. It offers a more comprehensive understanding of the overall relationships among these variables, which conventional methods such as ARDL and OLS often overlook. The outcomes reveal that financial innovation and green energy negatively affect TCO2 emissions, suggesting that transportation sector emissions will likely decline because of a rise in green energy and financial innovation. In contrast, GDP positively affects TCO2 emissions which deteriorate the environment. Furthermore, the findings of GDP² found significant and negative effects on TCO2 for all quantiles, affirming the inverse U-shaped curve for the Indian economy. Overall, the study suggests that Indian governments should promote the development of green financial innovation and focus their priorities on sustainable energy to attain carbon neutrality and sustainable development goals.

Full text

Vol.:(0123456789)
Environment, Development and Sustainability
https://doi.org/10.1007/s10668-023-03843-4
1 3
The impact offinancial innovation, green energy,
andeconomic growth ontransport‑based CO2 emissions
inIndia: insights fromQARDL approach
SnoviaNaseem1· UmairKashif1· YasirRasool2· MuhammadAkhtar2
Received: 28 May 2023 / Accepted: 23 August 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
Concerns about a sustainable environment are increasing and have attained significant
attention among policy experts worldwide. Therefore, the current research investigated
to what extent financial innovations, green energy, and economic growth impacted Indian
transport-based carbon (TCO2) emissions from 1990 to 2018. This research applied quan-
tile autoregressive distributed lag (QARDL) model and the Wald test for parameter con-
sistency. The QARDL approach proves valuable as it illustrates the causal patterns across
different quantiles of financial innovations, green energy, economic growth, and environ-
mental degradation. It offers a more comprehensive understanding of the overall relation-
ships among these variables, which conventional methods such as ARDL and OLS often
overlook. The outcomes reveal that financial innovation and green energy negatively affect
TCO2 emissions, suggesting that transportation sector emissions will likely decline because
of a rise in green energy and financial innovation. In contrast, GDP positively affects TCO2
emissions which deteriorate the environment. Furthermore, the findings of GDP2 found
significant and negative effects on TCO2 for all quantiles, affirming the inverse U-shaped
curve for the Indian economy. Overall, the study suggests that Indian governments should
promote the development of green financial innovation and focus their priorities on sus-
tainable energy to attain carbon neutrality and sustainable development goals.
Keywords Financial innovation· Green energy· Transportation CO2 emission· QARDL·
India
* Umair Kashif
kashifumair@ujs.edu.cn
Snovia Naseem
snovia.naseem@yahoo.com; snovia.naseem@ujs.edu.cn
Yasir Rasool
yasirrasool3@gmail.com
Muhammad Akhtar
muhammadakhtar22@outlook.com
1 School ofFinance andEconomics, Jiangsu University, Xuefu Road 301, Zhenjiang212012,
Jiangsu, China
2 School ofManagement, Jiangsu University, Xuefu Road 301, Zhenjiang212012, Jiangsu, China

S.Naseem et al.
1 3
1 Introduction
Carbon neutrality has gained significant recognition as a pivotal measure in the ongoing
battle against the repercussions of global warming (Xia, 2023). This initiative aims to
establish regulations that effectively reduce both direct and indirect emissions of green-
house gases (hereafter GHGs), such as carbon dioxide (CO2), methane (CH4), and nitrous
oxide (N2O) (Parravicini et al., 2022). These GHGs contribute considerably to climate
catastrophe and threaten human survival (Jianguo etal., 2022; Liu et al., 2023; Sofuoğlu
& Kirikkaleli, 2023). In 2021, the United Nations Climate Change Conference (UNCCC)
urged countries worldwide to address environmental concerns and take decisive actions to
cut CO2 emissions and keep global warming to 1.5°C by 2050 (Dwivedi etal., 2022). In
this regard, UN General Assembly embraced the 2030 agenda, including 17 sustainable
development goals (SDGs) that tackle various socioeconomic and climate-related develop-
ment challenges. However, this study focuses on constructing a comprehensive measure-
ment frameworkby considering carbon neutrality goals and SDG indicators, such as UN-
SDGs (7, 13), by analyzing transportation’s carbon (TCO2) emissions.
The transportation industry significantly contributes to global warming, as millions of
automobiles release huge GHGs into the environment (Zahoor etal., 2023). Since 1990,
this sector has been involved in an eightfold rise in the ultimate oil use, which comprises
17% of the total GHG emissions produced worldwide (Javed & Cudjoe, 2022). Likewise,
road transportation accounted for 7.3 billion metric tons of CO2 in 2020, which makes up
41% of worldwide emissions (Zahoor etal., 2023). If there is no curative intervention, it is
anticipated that CO2 emissions from transportation will experience a 60% increase by the
year 2050; hence, evaluating transportation sector emissions is crucial and a substantial
contribution to the existing literature of environmental sustainability (Luderer etal., 2022).
Financial innovation (hereafter FI) is related to novel financial instruments designed to
observe and control risks, insecurities, credit, and liquidity (Fareed etal., 2022). The pres-
ence of financing and innovation is crucial for the economy’s advancement. Although the
contribution of FI to environmental sustainability is limited and controversial, it is antici-
pated as a way to fund projects that are good for the environment (Chishti & Sinha, 2022).
Several studies have demonstrated a correlation (either positive or negative) between FI and
sustainable development. For example, Kirikkaleli etal. (2022) and Adebayo etal. (2022)
found that FI had a decelerating effect on environmental degradation. In contrast,the out-
comes of Jianguo etal. (2023) suggest that FI damages the environment by increasing CO2
emission in BRICS nations; while, Ahmad and Zheng (2021) demonstrated that FI may
indirectly impact climate change. Some other researchers also reported that environmental
regulations in the financial sector haveled FI to favor green activities.
Energy Agency report (2021) stated that reducing fossil fuels in energy generation
is a practical approach to mitigate the release of pollutants and curbing global warm-
ing. According to the International Renewable Energy Agency (IRENA), the amount of
green energy (hereafter GRE) produced worldwide is expected to rise by fifty percent
between 2019 and 2024. Similarly, International Energy Agency (IEA) predicted that
GRE consumption would be the fastest growing component of global energy demand.
GREsignificantly improves environmental quality as these resources have no emissions
and are used as a substitute for conventional fuels (Danish Godil etal., 2021). There-
fore, reducing CO2 emissionsthrough the development of GRE sources can mitigate
the adverse effects of climate change and assist in achieving SDGs (Belaïd & Zrelli,
2019). Further, the graphical trends of variables are mentioned in Fig.1.The graphical

The impact offinancial innovation, green energy, andeconomic…
1 3
demonstrations show a rising trend for all variables indicating a good sign for India,
except for CO2 emission because it can cause damage to the environment. However, the
figure shows a declining trend in financial innovation at the end of the study period.
This study mainly focuses on the goals outlined in SDGs (7 and 13) and made efforts
to examine the most efficient methods for achieving carbon neutrality. In the context of
India, several studies were conducted on CO2 emission efficiency in different sectors
(Rao & Sekhar, 2022; Rasoulinezhad & Taghizadeh-Hesary, 2022; Zhang etal., 2022a,
2022b); however, mostly ignored the transportation sector. India is the third largest CO2
emitter in the world, while the transport sector is the fourth most significant contributor
to the economy. However, being the fifth largest economy, they require a vast transporta-
tion network to support their economic setup and activities. The transportation arrange-
ments in India are still not extensively sustainable, producing harmful air and pollutant
gases to degrade the environment. Therefore, it is essential to examine transportation
emissions, and the current study formulated two research questions in this regard. (1) Is
it worthwhile to examine the relationship between FI, GRE, GDP, and TCO2 for India
Fig. 1 Trends of the study variables

S.Naseem et al.
1 3
in achievingcarbon neutrality and SDGs? (2) In which direction and to what extent FI,
GRE, and GDP can influence the TCO2 emissionof India?
Furthermore, the present research empirically answers the above research questions and
examines the relationship between mentioned variables from 1990 to 2018 using quantile
autoregressive distributed lag (QARDL) approach. This method was established by Cho
etal. (2015) to show quantile asymmetries in the long- and short-run adjustments between
variables. QARDL model allows the cointegrating coefficient to vary across different levels
of innovation quantiles caused by shocks. The QARDL model is superior to other lin-
ear (autoregressive distributed lag) and nonlinear (nonlinear autoregressive distributed lag)
models since nonlinearity is exogenously defined and the threshold is set to zero instead
of being determined by a data-driven process. The advantage of acknowledging quantile-
based asymmetric relationships is that they capture the evolving impact within the time-
series spanning low, medium, and high quantiles. The stated benefits establish the QARDL
approach’s suitability to effectively capture the nonlinear and asymmetric relationships
between FI, GRE, GDP, and TCO2 for India.
Moreover, the relationship between FI and TCO2 emissions provides uncertain and
inconclusive findings, thus provide motivation for further research. In this regard, the cur-
rent study aims to provide substantial contributions to the existing knowledge by examin-
ing the influence of FI on TCO2 emissionin the Indian context. FI is used as the proxy of
broad money (M3) and narrow money (M1) and is calculated as the ratio of M3:M1. At the
same time, prior studies measured FI using automated teller machines and debit and credit
cardsetc. In addition, environmental Kuznet curve (EKC) hypothesis was also examined
through GDP and its square term. The present analysis offers comprehensive guidelines
and suggestions for implementing environmentally sustainable policies and measures to
reduce carbon intensity in India.
The remaining sections of this manuscript are as follows: The preceding literature is
reviewed in Sect.2. Datasources and econometric model isdescribed in Sect.3. Estima-
tionsand discussion ofthe study are presented in Sect.4. Section5 concludes the article
and offers some helpful recommendations.
2 Literature review
2.1 Financial innovation andtransport‑based CO2 emissions
FI has recently attracted legislators’ and researchers’ interest as a sustainable planning tool,
and its role in promoting financial inclusion is indispensable. The outcome of previous
researchers that investigated the link betweenFI, financial development, and environmen-
tal degradation can be divided into two categories. The first category claimsthat FI may
helpto reduce environmental corrosion. The second one asserts that it drives ecological
deterioration and stimulates its progression. Likewise,Ali and Kirikkaleli (2022) evaluated
the link between FI growth and China’s transport sector by applying wavelet techniques
from 1971 to 2018. They found that increased development in financials may efficiently
decline CO2 emissions. Shabir et al. (2022) used AMG methodology to investigate the
link between FI, technological innovation, and other environmental characteristics for the
Asia–Pacific region from 2004 to 2018. The findings demonstrate that expanding access
to financial services improves ecological sustainability in the region. Chishti and Sinha
(2022) used AMG and most miniature squares models to analyze the link between FI and

The impact offinancial innovation, green energy, andeconomic…
1 3
ecological deterioration in BRICS nations. According to their research findings, positive
FI shocks can decrease CO2 emissions, while the adverse impacts of the FI lead to a rise in
emissions.
On the contrary, according to the findings of Lebdaoui etal. (2021), FI contributes to
environmental degradation in the West African Economic Community States block. Simi-
larly, using OLS and GMM for BRICS countries from 2007 to 2019, Rehman etal. (2022)
found that increasing access to digital financial services boosts economic development and
negatively impacts environmental sustainability. Fareed etal. (2022) examined the impact
of FI on ecological sustainability and the moderating effect of technological innovation
in 27 European nations using the MMQ method from 1995 to 2018. They discovered that
FI exacerbates environmental deterioration; however, the moderating effect shows that FI
mitigates ecological sustainability. In addition, several studies shed light on the distinction
between financial inclusion and financial development, while the interaction between FI
and TCO2 emissions has received limited attention. A few studies were conducted on FI
in the financial sector and relatively found it a new idea that describes the process through
which innovation is utilized to enhance financial operations (Palmié et al., 2020). Con-
sequently, evaluating FI concerning transport sector CO2 emissions in the Indian context
becomes imperative, contributing valuable insights to the existing body of literature.
2.2 Green energy andtransport‑based CO2 emissions
Green energy (GRE), called renewable energy, is produced from sources without adverse
environmental impact. It includes hydro, bioenergy, geothermal, solar, wind, and ocean
energy which have grown in popularity as an environmentally acceptable substitute for tra-
ditional fuels. Multiple studies have analyzed the impact and potential of GRE in mitigat-
ing CO2 emissions. Raihan and Tuspekova (2022) examine the correlation between GRE
and CO2 emissions using data from 1990 to 2018 and show a negative correlation between
GRE and CO2 emissions in Malaysia. Sharif et al. (2019) claim that GRE is responsi-
ble for CO2 reduction in a panel of 74 countries from 1990 to 2015. Accordingly, Sharif
etal. (2020) examine the impact of GRE on CO2 emission in Turkey using the QARDL
approach from 1965 to 2017. Their findings reveal that GRE significantly reduces the
ecological footprint for each quantile. Likewise, Jamil etal. (2022) found a negative cor-
relation between GRE and CO2 emissions in G-20 countries from 1970 to 2013. On the
contrary, GRE positively impacts CO2 emissions in some countries because such coun-
tries have not yet reached the threshold at which renewables may effectively cut CO2 emis-
sions (Ben Jebli etal., 2015). This argument is consistent with the findings of Apergis etal.
(2010). Thus, this study seeks to make significant contributions to the existing knowledge
by examining the impact of GRE on TCO2 emissions. Doing so enhances understanding
of the relationship between GRE deployment and TCO2 emissions, particularly within the
Indian context.
2.3 Economic growth andtransport‑based CO2 emissions
Various researchers assessed the GDP and CO2 emission relationship, which signifies
the importance of this association. For example, Wan etal. (2022) used a novel bootstrap
ARDL approach to evaluate the GDP relationship with CO2 emissions for China during
2000–2019 and indicated GDP as a contributor to emissions. Kirikkaleli et al. (2023)
explored the asymmetric and long-term impact of CO2 intensity on GDP in Portugal

S.Naseem et al.
1 3
from 1990 to 2019. Their results showed a positive relationship between GDP and CO2
intensity, suggesting that it can contribute to higher CO2 emissions. Jian & Afshan (2023)
investigated the effect of GDP on CO2 emissions in G10 countries using the CS-ARDL
approach from 2000 to 2018. The outcomes of their study found a positive impact of GDP
on CO2 emissions in the short and long run. In contrast, Naseem etal. (2020) examined
the relationship between GDP and CO2 emissions in India. Their findings revealed that
GDP significantly reduces CO2 emissions. Liobikienė & Butkus (2019) used the system
GMM approach to assess the relationship between GDP and CO2 emissions for 147 coun-
tries. Their findings demonstrated that improvements in energy efficiency could lead to
decreased CO2 emissions.
Likewise, Grossman & Krueger (1991) established the EKC hypothesis and argued
that GDP and CO2 emissionsposit a nonlinear and inverse U-shaped link. Dogan & Seker
(2016) examined the EKC theory and suggested no connection between CO2 emission and
GDP. Suki etal. (2020) researched the existence of EKC in Malaysia using QARDL meth-
odology from 1970 to 2018 and endorsed an inverted U-shaped curve. In another study,
Dogan & Turkekul (2016) found bidirectional causality between CO2 emission and GDP.
The present study investigates the relationship between GDP and TCO2 emissions by its
square term as independent variables. This analysis allows us to examine the potential
impact of GDP growth on TCO2 emissions and assess whether it aligns with the theoretical
expectations of the EKC theory.
2.4 Knowledge gap
Various scholars have researched CO2 emissions with different macroeconomic factors
such as energy consumption, technology innovation, population, urbanization, and eco-
nomic growth and discovered that CO2 emissions are a global concern. Studies are yet to
arrive at a conclusive argument regarding the relationship between these factors. The find-
ings were vulnerable in study design, data, and methodology, and the results were highly
responsive to the specific energy-related practices of each nation. Previous studies have
predominantly focused on different regions and subgroups for their evaluation with other
proxies and periods (Jianguo et al., 2023; Pholkerd & Nittayakamolphun, 2022; Rao &
Sekhar, 2022; Sofuoğlu & Kirikkaleli, 2023). However, the role of TCO2, FI, GRE, and
GDP as environmental parameters in achieving carbon neutrality and SDGs (7, 13) goals
has not been investigated, especially in the context of India. Therefore, this study is needed
to fill this void and shed light on the particular dynamics and difficulties encountered by
the country on its path to carbon neutrality and SDGs. To conclude, there is a need to
conduct this research to contribute toward a more nuanced comprehension of the intercon-
nections within India’s distinctive economic and environmental context. By doing so, it can
facilitate targeted insights and solutions to address the specific challenges and opportuni-
ties present in the country.
3 Methodology
3.1 Data source
The present research examines the association between financial innovation, green energy,
economic growth, and CO2 emission from transportation in India 1990 to 2018. Barut

The impact offinancial innovation, green energy, andeconomic…
1 3
(2023) and Jianguo et al. (2023) utilized CO2 emissions of total GDP ratio and green
logistics in G7, E7, and BRICS nations, respectively. However, in the present research, we
examine the country-specific effect, which is rare in the prior literature. We used the inten-
sity index of all GHGs, i.e., CO2, N2O, CH4, and various hydrofluorocarbons, to generate
TGI (transportation gases index). We have used the carbon neutrality goal in our study as
a dependent indicator and adopted transportation emissions as a proxy, measured by met-
ric tonne (mt) per capita. The independent variable involves financial innovation (FI), and
M1–M3 is used as a proxy by following the study of Chishti and Sinha (2022) and Jianguo
etal. (2023), where M1 is the narrow money and M3 broad money, a seasonally adjusted
index based on 2015 = 100. Green energy (GRN) measures as a tonne of oil equivalent,
including hydro, bioenergy, solar, wind, and ocean energy sources, and economic develop-
ment is measured as GDP per capita growth (annual %). The data on variables were gath-
ered from the OECD, World Bank (WDI), and Climate Watch. The details of the variables
used in the study are explained in Table1.
This study converts data into quarterly form to enable a more comprehensive analysis.
Quarterly data provide a higher frequency of data points, allowing for a closer examina-
tion of short-term fluctuations and trends. By capturing more frequent observations, we
can better identify seasonality effects that may be present in specific sectors or industries.
Moreover, the conversion to quarterly data enhances the accuracy and reliability of econo-
metric models and statistical analyses by reducing potential biases associated with annual
data. Overall, the utilization of quarterly data offers a more detailed and nuanced under-
standing of the underlying dynamics and variations in the variables under investigation.
The variables were transformed into natural log form to eliminate the chances of uneven-
ness and scale discrepancies in data. The flowchart of the study is presented in Fig.2.
3.2 Econometric specification
This study utilized the QARDL cointegration approach to analyze the relationship between
FI, GRE, GDP, and TCO2, and the primary form of simulation is:
where
𝜖t
is the white noise error illustrated through the lowest ground made by
(TCO
2
t
,FI
t
,GRE
t
,GDP
t
,GDP
2
t
,TCO2
t−
1,FI
t−
1,
…)
and j, k, l, m, and n represent Schwarz
information criterion lag orders. Further, i represents India, t is 1990–2018; and TCO2,
GRE, FI, GDP, and GDP2 denote transportation carbon emission, green energy, financial
innovation, economic growth, and its square term, respectively.
(1)
TCO
2t=𝛼+
j
∑
i
𝛽1TCO2t−1+
k
∑
i
𝛽2FIt−i+
l
∑
i
𝛽3GREt−1+
m
∑
i
𝛽4GDPt−1+
n
∑
i
𝛽5GDP2
t−1+𝜖
t
Table 1 Variable explanation
Variables Sign Measurement Data sources
Transportation carbon emissions TCO2Mt per capita https:// www. clima tewat chdata. org
Financial innovation FI Seasonally adjusted index https:// data. oecd. org/ money
Green energy GRE Tonne of oil equivalent https:// data. oecd. org/ energy
Economic growth GDP GDP (constant 2015 US$) https:// data. world bank. org/ count ry

S.Naseem et al.
1 3
Using standard econometric techniques, such as the linear ARDL model and the
Johansen cointegration test, Danish Iqbal Godil et al. (2020) identify the direction of a
lack of cointegration among various time series. In contrast, the current analysis used the
QARDL model, where shocks cause the cointegrating coefficient to change across the
innovation quantile. Regarding quantiles, Eq.(1) is represented by the QARDL model as:
where
𝜖t
(τ) =
TCOt−QTCOt(𝜕∕∀t−1)
(Kim and White, 2003) and 0 < τ < 1 simplifies
quantile. The present research uses successive pair of quantiles (τ) links to (0.10, 0.20,
0.30, 0.40, 0.50, 0.60, 0.70, 0.80, and 0.90). The QARDL approach, suggested by Cho
etal. (2015), is an appropriate way to inspect nonlinear and asymmetric links between the
(2)
Q
TCO2t=𝛼(𝜏)+
j
∑
i
𝛽1(𝜏)TCO2t−1+
k
∑
i
𝛽2(𝜏)GREt−1+
l
∑
i
𝛽3(𝜏)FIt−
1
+
m
∑
i
𝛽4(𝜏)GDPt−1+
n
∑
i
𝛽5(𝜏)GDP2
t−1+𝜖t(𝜏)
Introduction
Literature review
Methodology
Statistical analyses QARDL model
BDS test
Wald test
Unit root tests
Descriptive stat
DOLS and FMOLS
Background of GHGs
Financial innovation
Green energy
Contribution
FI and TCO2
GRE and TCO2
Econometric Modeling
GDP and TCO2
Knowledge gap
Variable description
TCO2, FI, GRE, GDP, GDP2
Data (1990-2018)
OECD, WDI, Climate Watch
Conclusions
Limitations and
future directions
Economic growth
Fig. 2 Study framework flowchart

The impact offinancial innovation, green energy, andeconomic…
1 3
variables. It is an expansion of the ARDL model that allows for the assessment of lin-
ear and nonlinear nexus between the above-mentioned variables. QARDL model counts
locational asymmetries, where results and variables may vary according to the dependent
variable (Wang, 2019). Due to the probability of serial correlation in Eq.(2) error term,
QARDL can be rewritten as:
Equation (3) can be reviewed and updated after considering the error correction meas-
urement of QARDL as follows:
The short-term influence of the previous TCO2 on current TCO2 was measured using
the delta technique and shown through
𝜏
. Likewise, the aggregate short-run influence
of previous and prevailing GRE, FI, and GDP on the current level of TCO2 was gauged
𝜏
oGRE∗=
∑
k
i=1
𝜏GRE
o
,δ
FI
∗
o
=
∑
l
i=1
δFI
o
,𝜑
GDP
∗
o
=
∑
m
i=1
𝜑
GDP
i
o
,𝜔oGDP2
∗=
∑
n
i=1
𝜔oGDP2
i
. In
Eq.(4), the sign of the parameter must be adverse and substantial. The parameter for the
long-run cointegration of GRE, FI, and GDP is denoted by
𝛽
. There has been the use of the
following formula:
Moreover, before examining the long-run quantile effect, we summarized the dataset
through descriptive statistics and checked the correlation between the study variables. Con-
sequently, this study utilized ADF, PP, and KPSS unit root tests to check the integration
level. Check data stationarity before the main results are critical because non-stationary
data can provide misleading outcomes, which may not be appropriate for policy sugges-
tions. Further, Brock, Dechert, and Scheinkman (BDS) test introduced by Brock et al.
(1996) is applied, in which frequency was assessed using the correlation integral approach.
This method helps to distinguish between chaotic and nonlinear processes. The test is
referred to as having a greater accuracy relative to linear chaos; nonetheless, it was noticed
that it might be used to analyze several different forms of nonlinearity. The BDS test statis-
tic can be written as follows:
(3)
Q
ΔTCO2t=𝛼(𝜏)+𝜌TCO2t−1+𝛿1FIt−1+𝛿2GREt−1+𝛿3GDPt−1+𝛿4GDP2
t−1+
j
∑
i
𝛽1(𝜏)TCO2t−
i
+
k
∑
i
𝛽2(𝜏)FIt−i+
m
∑
i
𝛽3(𝜏)GREt−i+
n
∑
i
𝛽4(𝜏)GDPt−i+
o
∑
i
𝛽5(𝜏)GDP2
t−i+𝜖t(𝜏)
(4)
Q
ΔTCO2t=𝛼(𝜏)+𝜌(𝜏)
(
TCO2t−i−𝜐1(𝜏)FIt−i−𝜐2(𝜏)GREt−i−𝜐3(𝜏)GDPt−i−𝜐4(𝜏)GDP
2
t−i
)
+
j−1
∑
i=1
𝛽1(𝜏)ΔTCO2t−i+
k−1
∑
i=0
𝛽2(𝜏)ΔFIt−i+
m−1
∑
i=0
𝛽3(𝜏)ΔGREt−i
+
n−1
∑
i=0
𝛽4(𝜏)ΔGDPi−i+
o−1
∑
i=0
𝛽5(𝜏)ΔGDP2
i−i+𝜖t(𝜏)
𝛽
GRE∗=−
𝛽GRE
𝜌
,𝛽FI∗=
𝛽FI
𝜌
,𝛽GDP∗=−
𝛽GDP
𝜌
,𝛽GDP2∗=−
𝛽GDP2
𝜌
BDS
(𝜀,m)=(
√
N[C𝜀,m−(C𝜀,1)m
]
√
V𝜀,m

S.Naseem et al.
1 3
where
[
C
𝜀,m
−
(
C
𝜀,1)m
denote asymptotic normal distribution with zero mean and
V𝜀,m
is
the variance. Subsequently, the Wald test checks the integration and reliability factors that
change over time for short- and long-term equilibrium. For instance, if ρ represents the
speed of adjustment parameter, then the null assumption is ρ*(0.10)…… = ρ*(0.90). The
same approach was used for βFI, βGRE, and βGDP indicators and short-run components
for lags exhibiting
𝜗FI
o
,
𝜗GRE
o
,
𝜔GDP
o
, and
𝜇
GDP
2
o
. Lastly, the robustness of main estimations
was examined using DOLS and FMOLS methods to warrant the sensitivity estimation of
the main results.
4 Results anddiscussion
Table2 shows descriptive statistics of the study variable. All mean values have a positive
indication, and GRE reported the highest value, 11.897, ranging from11.645 to 12.264.
GDP2 has the second highest value, 2.311, with a minimum and maximum of 0.002 and
3.794, respectively. The mean value of TCO2 is 2.170, with the lowest and highest values
being 1.488 and 2.607, respectively. It is followed by a GDP value of 1.435, with a mini-
mum of 0.044 and a maximum of 1.948. The FI has the lowest value, 0.883, ranging from
0.620 to 1.010. Furthermore, the Jarque–Bera test results show that all the variables were
not normally distributed at a significance level of 1%. It demonstrates that more analysis
can be conducted using the QARDL model. The values of SD show how firmly data are
adjacent to the mean; a lesser SD indicates a higher concentration. This interpretation pro-
vides that FI is more adjacent to its mean, followed by GRE, TCO2, GDP, and GDP2.
In Fig.3, the statistical parameters are described through box plots, where 25, 50, and
75% were characterized across all graphs correspondingly. The circles and squares denote
median and mean values, whereas the bottom and top small lines exhibit the minimum and
maximum values, respectively.
Next, the ADF, PP, and KPSS unit root test results are displayed in Table3. In the ADF
test, all the variables are stationary at the first difference form apart from FI, which is sta-
ble at the level. PP test results stated GDP2 is stationary at level form, but all other param-
eters are statistically stable at the first difference. Furthermore, KPSS findings depict that
variables are stationary at the level form. Unit root outcomes specify that the data are sta-
tistically stable and no second difference variable is involved; thus, the main model will
provide accurate results.
Table 2 Descriptive statistic
Source: Authors calculated
Variable TCO2FI GRE GDP GDP2
Mean 2.170 0.883 11.897 1.435 2.311
SD 0.376 0.111 0.178 0.504 1.201
Skewness − 0.479 − 0.863 0.425 − 1.082 − 0.516
Minimum 1.488 0.620 11.645 0.044 0.002
Maximum 2.607 1.010 12.264 1.948 3.794
Kurtosis 1.681 2.627 2.063 3.242 1.877
JB 12.843 15.076 7.742 22.921 11.249
Prob 0.002 0.001 0.021 0.000 0.004

The impact offinancial innovation, green energy, andeconomic…
1 3
Fig. 3 Box chart, scatterplots, and distribution of variables
Table 3 Unit root test
***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively
Variable ADF I(0) ADF I(1) PP I(0) PP I(1) KPSS I(0) KPSS I(1)
TCO20.391 − 2.891** − 1.088 − 12.111*** 1.199*** 0.341*
FI − 2.745* − 2.402 − 4.460*** − 11.955*** 1.161*** 0.715**
GRE 2.026 − 2.972** 3.822 − 13.976*** 1.248*** 0.638**
GDP − 3.599*** − 8.927*** − 3.309*** − 19.75*** 0.651** 0.207
GDP2− 3.435*** − 10.586*** − 3.286*** − 15.038 0.668** 0.114

S.Naseem et al.
1 3
The outcomes of the BDS test are depicted in Table4, which indicates the non-accept-
ance of the null assumption that the series is linearly correlated. Since the BDS test
reported significant results for all integrating parameters, it validates nonlinearity for the
variables incorporated. The BDS stats increased as the integrating parameters rose, indicat-
ing that large dimensions had substantial nonlinearity.
Short-run results from the QARDL model are depicted in Table 5. The φ represents
the results of a short-term relationship between independent variables (FI, GRE, GDP, and
GDP2) and dependent variables (TCO2). The outcomes of FI quantiles were significant and
negative, except for 0.80 and 0.90. It illustrates that FI does not affect TCO2 at the upper
quantiles but substantially affects ranging from 0.10 to 0.70 quantiles. Furthermore, the
value of GRE is significant and negative for all quantiles, which vary from 0.10 to 0.90.
According to this result, if there is an upsurge in GRE, there will be a reduction in TCO2 in
India. These results are consistent with Lak Kamari etal. (2020) and aligned with what we
already know about this type of energy and how it affects the environment. Moreover, the
GDP results showed significant positive effects from quantiles 0.10 to 0.60, while the later
quantiles indicated insignificant outcomes. However, GDP2 outcomes were reported as sig-
nificant and negative for all quantiles. These findings are consistent with the past studies of
Shahnazi & Shabani (2021) and Jamshidi etal. (2023).
Long-run results from the QARDL model are reported in Table6. The
α
indicates con-
stant term value, which is positive and significant for all quantiles apart from 0.30. The
FI findings showed a significant negative relation with TCO2 for all quantiles, suggesting
that FI is better and more helpful for India in improving its environment. An increase in
Table 4 BDS results
***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively
Dimension TCO2FI GRE GDP GDP2
2 0.200*** 0.204*** 0.198*** 0.134*** 0.143***
3 0.335*** 0.347*** 0.332*** 0.210*** 0.225***
4 0.428*** 0.448*** 0.423*** 0.246*** 0.265***
5 0.492*** 0.519*** 0.488*** 0.254*** 0.277***
6 0.536*** 0.569*** 0.535*** 0.262*** 0.284***
Table 5 Short-run QARDL statistics
() shows SE. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively
Quantiles (τ)φ1 (τ) ωoFI (τ) λoGRE (τ) δoGDP (τ) ϑoGDP2 (τ)
0.10 3.24*** (1.31) − 5.38*** (2.81) − 1.66*** (− 3.02) 5.94*** (2.05) − 3.01*** (1.22)
0.20 3.89*** (1.20) − 6.78*** (3.62) − 1.65*** (− 3.95) 4.95** (2.15) − 2.62*** (1.09)
0.30 4.16** (1.49) − 4.93** (3.69) − 2.24*** (− 3.28) 5.77** (2.70) − 3.04*** (1.15)
0.40 6.79*** (1.96) − 4.18** (4.16) − 2.46*** (− 3.90) 4.89** (2.23) − 2.26** (0.99)
0.50 5.61*** (2.07) − 3.83** (4.52) − 2.56*** (− 4.27) 5.71** (2.81) − 2.89* (1.49)
0.60 3.53** (2.01) − 3.89* (4.00) − 2.98*** (− 4.47) 4.33* (2.29) − 1.98** (0.81)
0.70 4.84*** (1.94) − 1.31* (9.75) − 3.13*** (− 5.81) − 1.43 (8.43) − 5.34** (2.46)
0.80 4.08*** (1.78) − 1.28 (8.19) − 3.64*** (− 5.57) − 1.15 (7.33) − 4.33* (2.47)
0.90 4.71* (3.17) − 1.72 (9.49) − 4.49*** (− 7.07) − 4.71 (7.76) − 5.94* (3.18)

The impact offinancial innovation, green energy, andeconomic…
1 3
the FI ratio will decrease TCO2 emissions, and the outcomes are consistent with the past
studies of Chishti & Sinha (2022) for BRICS countries and Tian etal. (2017) for China. In
the meantime, the results of Jianguo etal. (2023) determined that FI causes environmental
degradation in BRICS countries. The contradiction reported is due to methodology or vari-
able index difference or because of panel analysis. By creating innovative financial instru-
ments such as green bonds, carbon credits, and clean energy investment funds, financial
institutions can attract investment toward sustainable projects and help accelerate the tran-
sition to low-carbon energy sources. Further, FI can promote energy efficiency initiatives
by developing innovative financing models and mechanisms. Energy performance contract-
ing and green leasing enable businesses and individuals to access capital for energy-effi-
cient upgrades and technologies. This encourages the adoption of energy-saving measures
and reduces the overall energy consumption and associated emissions.
In addition, the output of GRE is similar to the short-run findings; it shows a substan-
tial negative relationship with TCO2 emissions for all quantiles ranging from 0.10 to 0.90.
Considering the role of GRE in the environment, Wang (2019) explained that renewables
might play a dominant role in creating an eco-friendly economy. It helps to achieve sus-
tainability by enhancing resource efficiency, conserving energy, and reducing CO2 emis-
sions. The deployment of green technologies can contribute to decarbonizing other sec-
tors of the economy, such as transportation. Electric vehicles powered by green energy can
reduce or eliminate tailpipe emissions associated with conventional internal combustion
engines. GRE is seen as an essential element in forming policies related to environmental
safety, which may decrease CO2 emissions and ultimately improve the environment’s qual-
ity (Naseem & Ji, 2020).
In contrast, GDP reported significant and positive findings regardless of the quantile
difference. The output illustrates that as the economy grows, there is a corresponding
increase in industrial activity and energy consumption, adding pollutants to the environ-
ment. Economic growth typically accompanies increased transportation demand, includ-
ing increased road traffic, air travel, and shipping activities. Transportation significantly
contributes to CO2 emissions, mainly when fossil fuels are the primary energy source. The
increased movement of goods, services, and people associated with economic growth leads
to higher emissions from the transportation sector. Addressing this issue requires a com-
prehensive approach, including the adoption of sustainable and low-carbon technologies,
energy efficiency measures, and policies that promote decoupling economic growth from
Table 6 Long-run QARDL statistics
() shows SE. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively
Quantiles (τ) α* (τ)βFI(τ)βGRE (τ)βGDP (τ)βGDP2 (τ)
0.10 3.24*** (3.28) − 3.84 (6.57) − 3.23*** (2.73) 0.23*** (5.80) − 0.09*** (2.01)
0.20 3.89*** (6.29) − 0.23*** (1.15) − 2.48*** (1.10) 0.03*** (1.81) − 0.01*** (5.67)
0.30 − 1.60** (3.01) − 0.41*** (4.75) − 1.79*** (2.35) 0.03*** (1.33) − 0.01*** (9.24)
0.40 8.79*** (5.17) − 0.22*** (3.38) − 1.37*** (1.48) 0.03*** (1.62) − 0.02*** (5.67)
0.50 1.61*** (1.29) − 0.13*** (7.76) − 1.33*** (1.03) 0.00*** (1.30) − 0.01*** (1.11)
0.60 1.53*** (1.77) − 0.06*** (4.81) − 0.98*** (8.02) 0.01*** (5.24) − 0.01*** (2.39)
0.70 1.84*** (9.13) − 0.49*** (1.17) − 0.73*** (5.01) 0.04*** (7.86) − 0.02*** (3.04)
0.80 1.08*** (1.01) − 0.72*** (2.07) − 0.61*** (8.14) 0.05*** (1.46) − 0.02*** (5.24)
0.90 2.71*** (6.29) − 0.80*** (3.99) − 0.56*** (3.95) 0.06*** (3.14) − 0.02*** (1.12)

S.Naseem et al.
1 3
carbon emissions. Most scholars previously narrated similar results for GDP and TCO2
nexus (Akalpler & Hove, 2019; Aslam etal., 2021; Namahoro etal., 2021).
In addition, GDP2 detailed opposite results to GDP, indicating a substantial negative
link with TCO2 for all quantiles. The positive and negative GDP measures align with the
EKC hypothesis, which recommends economic activities raise pollution initially, and then
decline after reaching a specific level. It can cause an improvement in the environment,
and the relationship becomes negative. The findings of the EKC assumption are coherent
with Zhaomin Zhang etal. (2022a, 2022b). Figure4 depicts the EKC demonstration, which
shows an upward trend for pollution and growth in the initial phase. However, growth con-
tinuously rises after the turning level, but pollutants decline.
The results of the Wald test on parameter consistency for short and long-term param-
eters are presented in Table7. The findings of the Wald test do not support the validity of
the null assumption for any of the variables considered in the long run, including FI, GDP,
and GRE. The Wald test rejects the null assumption in the short run for all variables except
GDP2. It indicates that GRE, FI, and GDP have an asymmetric or nonlinear link, so the
Wald test accepts the null assumption and shows a symmetric link in the short term.
Fig. 4 EKC hypothesis
Table 7 Wald test
Null hypothesis: parameters are constant over quantiles
*** and ** indicate significance at the 1% and 5% levels, respectively
Variables Stat. (p–v)
Long run
p* 1.15***(.000)
𝛿GRE
3.07***(.000)
𝛿FI
2.47***(.000)
𝛿GDP
3.17***(.000)
𝛿GDP2
2.02***(.000)
Short run
α1 4.44***(.000)
𝛾O
GRE
1.64***(.000)
𝜔O
FI
8.21***(.000)
∅O
GDP
7.69**(.000)
𝜕O
GDP
2
3.25(.000)

The impact offinancial innovation, green energy, andeconomic…
1 3
The robustness outcomes using DOLS and FMOLS methodologies are shown in
Table8. It confirms the baseline findings and demonstrates the dependability of the analy-
sis. The coefficients of FI and GRE are negative and significant at a 1% level, indicating
that both improve environmental health. Positive and significant results of GDP suggest
it damages the environment. India must take swift measures to combine their goals with
environmentally friendly procedures. The graphical interpretation of the main findings
using QARDL and robustness methods are depicted inFig.5.
Table 8 Robustness check
***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively
Variable DOLS FMOLS
Coefficient Prob Coefficient Prob.
GRE − 1.32*** 0.000 − 1.33*** 0.000
FI − 0.25*** 0.001 − 0.25** 0.051
GDP 0.15* 0.082 0.01* 0.092
GDP2− 0.01** 0.054 − 0.00* 0.046
C 1.23** 0.033 0.74*** 0.010
Fig. 5 Graphical interpretation of results

S.Naseem et al.
1 3
5 Conclusion andpolicy implications
This research contributes to the literature on the interrelation between financial innovation
(FI), green energy (GRE), economic growth (GDP), and transport-based carbon (TCO2)
emissions in India. The present research used quantile autoregressive distributed lag
(QARDL) approach and Wald test to analyze the long- and short-run asymmetries between
the variables, using quarterly data from 1990 to 2018. The QARDL technique assesses how
different FI, GRE, and GDP quantiles influence TCO2, offering a more detailed insight into
the overall relationship among these variables. The results of QARDL indicate that FI has
a negative significant association with TCO2 emission for all quantiles, while GRE has the
same relationship with TCO2 emission for each quantile (0.10–0.90). The result of GDP
has a favorable effect on TCO2, while GDP2 shows a significant negative impact on TCO2
for all quantiles, which supports the environmental Kuznet curve hypothesis. In addition,
the null assumption was rejected for all variables in the Wald test for the long run, while
the null assumption was unable to deny in the short run for GDP2.
This paper focuses on achieving carbon neutrality and SDGs (7 and 13) through imple-
menting sustainable practices and procedures for India. Government and policymakers
should consider the findings of this study and adopt more sustainable strategies in their
decision-making processes. Educating the general population about environmentally
friendly and sustainable options is essential to attain carbon neutrality and SDGs. More-
over, lowering CO2 emissions can be accomplished by investing more in financial inno-
vation, such as green infrastructure, and embracing new technologies. These innovations
have the potential to enable the use of less energy while still achieving the same level of
output. If India focuses on implementing financial innovation through environmentally
friendly initiatives, it can effectively enhance energy efficiency and reduce its carbon foot-
print. Similarly, the Indian government should enhance its transportation infrastructure and
drive innovation by introducing nonpolluting and hybrid vehicles. Encouraging the devel-
opment of electric cars and electrified railway systems among the population can substan-
tially reduce CO2 emissions in the transportation sector. The implementation of alternative
carbon policies has the potential to play a significant role in curtailing emissions within
this sector.
Further, the Indian government should promote the adoption of renewable energy across
all sectors by increasing investments in advanced technologies for a more effective and
efficient energy generation system. This approach would aid in decreasing the levels of
CO2 emissions. Moreover, shifting the economic growth paradigm toward a strategy that
transitions from nonrenewable to renewable energy sources holds significant advantages.
This transformation meets energy requirements more sustainably and effectively reduces
CO2 emissions. Consequently, it becomes imperative for India to embrace climate change-
focused policies, especially targeting pivotal sectors like transportation, which is a key con-
tributor to CO2 emissions. Such policies would reduce energy consumption while aligning
with environmental preservation goals. Therefore, the Indian government must prioritize
implementing effective environmental policies to mitigate the adverse impact of economic
advancement on the environment.
Apart from contributions, we also encountered limitations. This study utilized the maxi-
mum available data and converted it to a quarterly form for a comprehensive assessment.
However, the study period can be extended through monthly or daily datasets, which could
provide improved outcomes. Moreover, we utilized M1 and M3 for financial innovation
proxy, while upcoming studies can make more indexes by incorporating M2 or M4, or

The impact offinancial innovation, green energy, andeconomic…
1 3
other instruments. This study tried to explore the damaging impact of CO2 from the trans-
portation sector, while other sectors, such as energy and agriculture, can also be examined.
The country-specific effect can be replaced with a panel analysis of SAARC, OECD, EU,
G7, E7, or BRICS countries. We used the QARDL method, which may be substituted with
the latest models, i.e., CS-ARDL, MMQR, bootstrap ARDL, or spatial. The current issues
have a tremendous perspective for further research from different dimensions and models.
Author contribution SN was involved in conceptualization; writing—original draft preparation; formal
analysis and investigation; and writing—review and editing; UK was involved in conceptualization; meth-
odology; formal analysis and investigation; writing—original draft preparation; funding acquisition; and
supervision; YR was involved in formal analysis and investigation and writing—review and editing; and
MA was involved in methodology and writing—review and editing.
Funding No funding was received to assist with the preparation of this manuscript.
Data availability All data generated or analyzed during this study are included in this published article, and
sources are mentioned in Table1 of the manuscript.
Declarations
Conflict of interest The authors declare no competing interests.
Ethical approval The authors all agree to ethical approval and understand its related rules and content.
Consent to participate The authors of this manuscript are all aware of the journal to which the manuscript
was submitted, and all agree to continue to support the follow-up work.
Consent to publish This manuscript has not been submitted or published in other journals, and the authors
agree to consent to publish.
References
Adebayo, T. S., AbdulKareem, H. K., Kirikkaleli, D., Shah, M. I., & Abbas, S. (2022). CO2 behavior amidst
the COVID-19 pandemic in the United Kingdom: The role of renewable and non-renewable energy
development. Renewable Energy, 189, 492–501.
Ahmad, M., & Zheng, J. (2021). Do innovation in environmental-related technologies cyclically and asym-
metrically affect environmental sustainability in BRICS nations? Technology in Society, 67, 101746.
Akalpler, E., & Hove, S. (2019). Carbon emissions, energy use, real GDP per capita and trade matrix in the
Indian economy-an ARDL approach. Energy, 168, 1081–1093.
Ali, M., & Kirikkaleli, D. (2022). The asymmetric effect of renewable energy and trade on consumption-
based CO2 emissions: The case of Italy. Integrated Environmental Assessment and Management,
18(3), 784–795.
Apergis, N., Payne, J. E., Menyah, K., & Wolde-Rufael, Y. (2010). On the causal dynamics between emis-
sions, nuclear energy, renewable energy, and economic growth. Ecological Economics, 69(11),
2255–2260.
Aslam, B., Hu, J., & Shahab, S. (2021). The nexus of industrialization, GDP per capita and CO2 emission in
China. Environmental Technology & Innovation, 23, 101674.
Barut, e. a. (2023). How do economic and financial factors influence green logistics? A comparative analy-
sis of E7 and G7 nations. Environmental Science and Pollution Research, 30(1), 1011–1022.
Belaïd, F., & Zrelli, M. H. (2019). Renewable and non-renewable electricity consumption, environmental
degradation and economic development: Evidence from Mediterranean countries. Energy Policy, 133,
110929.
Ben Jebli, M., Ben Youssef, S., & Ozturk, I. (2015). The role of renewable energy consumption and trade:
Environmental kuznets curve analysis for sub-saharan Africa countries. African Development Review,
27(3), 288–300.

S.Naseem et al.
1 3
Brock, W. A., & ScheinkmanDechert, J. A. (1996). A test for independence based on the correlation dimen-
sion. Econometric Reviews, 15(3), 197–235.
Chishti, M. Z., & Sinha, A. (2022). Do the shocks in technological and financial innovation influence the
environmental quality? Evidence from BRICS economies. Technology in Society, 68, 101828.
Cho, J. S., Kim, T.-H., & Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag mod-
eling framework. Journal of Econometrics, 188(1), 281–300.
Dogan, E., & 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 Sus-
tainable Energy Reviews, 60, 1074–1085.
Dogan, E., & Turkekul, B. (2016). CO2 emissions, real output, energy consumption, trade, urbanization and
financial development: Testing the EKC hypothesis for the USA. Environmental Science and Pollution
Research, 23(2), 1203–1213.
Dwivedi, Y. K., Hughes, L., Kar, A. K., etal. (2022). Climate change and COP26: Are digital technologies
and information management part of the problem or the solution? An editorial reflection and call to
action. International Journal of Information Management, 63, 102456.
Fareed, Z., Rehman, M. A., Adebayo, T. S., Wang, Y., Ahmad, M., & Shahzad, F. (2022). Financial inclu-
sion and the environmental deterioration in Eurozone: The moderating role of innovation activity.
Technology in Society, 69, 101961.
Godil, D. I., Sharif, A., Afshan, S., Yousuf, A., & Khan, S. A. R. (2020). The asymmetric role of freight and
passenger transportation in testing EKC in the US economy: Evidence from QARDL approach. Envi-
ronmental Science and Pollution Research, 27(24), 30108–30117.
Godil, D., Yu, Z., Sharif, A., Usman, R., & Khan, S. A. R. (2021). Investigate the role of technology innova-
tion and renewable energy in reducing transport sector CO2 emission in China: A path toward sustain-
able development. Sustainable Development, 29(4), 694–707.
Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agree-
ment. National Bureau of economic research Cambridge Mass.
Jamil, K., Liu, D., Gul, R. F., Hussain, Z., Mohsin, M., Qin, G., & Khan, F. U. (2022). Do remittance and
renewable energy affect CO2 emissions? An empirical evidence from selected G-20 countries. Energy
& Environment, 33(5), 916–932.
Jamshidi, N., Owjimehr, S., & Etemadpur, R. (2023). Financial innovation and environmental quality:
Fresh empirical evidence from the EU Countries. Environmental Science and Pollution Research, 30,
73372–73392.
Javed, S. A., & Cudjoe, D. (2022). A novel grey forecasting of greenhouse gas emissions from four indus-
tries of China and India. Sustainable Production and Consumption, 29, 777–790.
Jian, X., & Afshan, S. (2023). Dynamic effect of green financing and green technology innovation on carbon
neutrality in G10 countries: Fresh insights from CS-ARDL approach. Economic Research-Ekonomska
Istraživanja, 36(2), 2130389.
Jianguo, D., Ali, K., Alnori, F., & Ullah, S. (2022). The nexus of financial development, technological inno-
vation, institutional quality, and environmental quality: Evidence from OECD economies. Environ-
mental Science and Pollution Research, 29(38), 58179–58200.
Jianguo, D., Cheng, J., & Ali, K. (2023). Modelling the green logistics and financial innovation on carbon
neutrality goal, a fresh insight for BRICS-T. Geological Journal, 58(7), 2742–2756.
Kirikkaleli, D., Awosusi, A. A., Adebayo, T. S., & Otrakçı, C. (2023). Enhancing environmental quality in
Portugal: Can CO2 intensity of GDP and renewable energy consumption be the solution? Environmen-
tal Science and Pollution Research, 30(18), 53796–53806.
Kirikkaleli, D., Güngör, H., & Adebayo, T. S. (2022). Consumption-based carbon emissions, renewable
energy consumption, financial development and economic growth in Chile. Business Strategy and the
Environment, 31(3), 1123–1137.
Lak Kamari, M., Isvand, H., & Alhuyi Nazari, M. (2020). Applications of multi-criteria decision-making
(MCDM) methods in renewable energy development: A review. Renewable Energy Research and
Applications, 1(1), 47–54.
Lebdaoui, H., Asri, M. E., & Chetioui, Y. (2021). Environmental Kuznets curve for CO2 emissions-an
ARDL-based approach for an emerging market. Interdisciplinary Environmental Review, 21(3–4),
269–289.
Liobikienė, G., & Butkus, M. (2019). Scale, composition, and technique effects through which the economic
growth, foreign direct investment, urbanization, and trade affect greenhouse gas emissions. Renewable
Energy, 132, 1310–1322.
Liu, M., Chen, Z., Sowah, J. K., Jr., Ahmed, Z., & Kirikkaleli, D. (2023). The dynamic impact of energy
productivity and economic growth on environmental sustainability in South European countries.
Gondwana Research, 115, 116–127.

The impact offinancial innovation, green energy, andeconomic…
1 3
Luderer, G., Madeddu, S., Merfort, L., etal. (2022). Impact of declining renewable energy costs on elec-
trification in low-emission scenarios. Nature Energy, 7(1), 32–42.
Namahoro, J., Wu, Q., Zhou, N., & Xue, S. (2021). Impact of energy intensity, renewable energy, and
economic growth on CO2 emissions: Evidence from Africa across regions and income levels.
Renewable and Sustainable Energy Reviews, 147, 111233.
Naseem, S., & Ji, T. G. (2020). A system-GMM approach to examine the renewable energy consump-
tion, agriculture and economic growth’s impact on CO2 emission in the SAARC region. GeoJour-
nal, 86, 1–13.
Naseem, S., Ji, T. G., Kashif, U., & Arshad, M. Z. (2020). Causal analysis of the dynamic link between
energy growth and environmental quality for agriculture sector: A piece of evidence from India.
Environment, Development and Sustainability, 23, 7913–7930.
Palmié, M., Wincent, J., Parida, V., & Caglar, U. (2020). The evolution of the financial technology eco-
system: An introduction and agenda for future research on disruptive innovations in ecosystems.
Technological Forecasting and Social Change, 151, 119779.
Parravicini, V., Nielsen, P. H., Thornberg, D., & Pistocchi, A. (2022). Evaluation of greenhouse gas
emissions from the European urban wastewater sector, and options for their reduction. Science of
the Total Environment, 838, 156322.
Pholkerd, P., & Nittayakamolphun, P. (2022). Nexus financial innovation and economics growth in Thai-
land. ABAC Journal, 42(3), 148–161.
Raihan, A., & Tuspekova, A. (2022). Toward a sustainable environment: Nexus between economic
growth, renewable energy use, forested area, and carbon emissions in Malaysia. Resources, Conser-
vation & Recycling Advances, 15, 200096.
Rao, V. T., & Sekhar, Y. R. (2022). Comparative analysis on embodied energy and CO2 emissions for
stand-alone crystalline silicon photovoltaic thermal (PVT) systems for tropical climatic regions of
India. Sustainable Cities and Society, 78, 103650.
Rasoulinezhad, E., & Taghizadeh-Hesary, F. (2022). Role of green finance in improving energy effi-
ciency and renewable energy development. Energy Efficiency, 15(2), 14.
Rehman, A., Ma, H., Ozturk, I., & Ulucak, R. (2022). Sustainable development and pollution: The
effects of CO2 emission on population growth, food production, economic development, and energy
consumption in Pakistan. Environmental Science and Pollution Research, 29(12), 17319–17330.
Shabir, M., Gill, A. R., & Ali, M. (2022). The impact of transport energy consumption and foreign direct
investment on CO2 emissions in ASEAN countries. Frontiers in Energy Research, 10, 1–12.
Shahnazi, R., & Shabani, Z. D. (2021). The effects of renewable energy, spatial spillover of CO2 emis-
sions and economic freedom on CO2 emissions in the EU. Renewable Energy, 169, 293–307.
Sharif, A., Baris-Tuzemen, O., Uzuner, G., Ozturk, I., & Sinha, A. (2020). Revisiting the role of renewa-
ble and non-renewable energy consumption on Turkey’s ecological footprint: Evidence from Quan-
tile ARDL approach. Sustainable Cities and Society, 57, 102138.
Sharif, A., Raza, S. A., Ozturk, I., & Afshan, S. (2019). The dynamic relationship of renewable and
nonrenewable energy consumption with carbon emission: A global study with the application of
heterogeneous panel estimations. Renewable Energy, 133, 685–691.
Sofuoğlu, E., & Kirikkaleli, D. (2023). The effect of mineral saving and energy on the ecological foot-
print in an emerging market: Evidence from novel Fourier based approaches. Letters in Spatial and
Resource Sciences, 16(1), 3.
Suki, N. M., Sharif, A., Afshan, S., & Suki, N. M. (2020). Revisiting the environmental Kuznets curve
in Malaysia: The role of globalization in sustainable environment. Journal of Cleaner Production,
264, 121669.
Tian, Y., Chen, W., & Zhu, S. (2017). Does financial macroenvironment impact on carbon intensity: Evi-
dence from ARDL-ECM model in China. Natural Hazards, 88, 759–777.
Wan, Q., Miao, X., & Afshan, S. (2022). Dynamic effects of natural resource abundance, green financ-
ing, and government environmental concerns toward the sustainable environment in China.
Resources Policy, 79, 102954.
Wang, Z. (2019). Does biomass energy consumption help to control environmental pollution? Evidence
from BRICS countries. Science of the Total Environment, 670, 1075–1083.
Xia, Q. (2023). Does green technology advancement and renewable electricity standard impact on car-
bon emissions in China: Role of green finance. Environmental Science and Pollution Research,
30(3), 6492–6505.
Zahoor, A., Mehr, F., Mao, G., Yu, Y., & Sápi, A. (2023). The carbon neutrality feasibility of worldwide
and in China’s transportation sector by E-car and renewable energy sources before 2060. Journal of
Energy Storage, 61, 106696.

S.Naseem et al.
1 3
Zhang, K., Lau, H. C., Bokka, H. K., & Hadia, N. J. (2022a). Decarbonizing the power and industry sectors
in India by carbon capture and storage. Energy, 249, 123751.
Zhang, Z., Bashir, T., Song, J., Aziz, S., Yahya, G., Bashir, S., & Zamir, A. (2022b). The effects of Envi-
ronmental Kuznets Curve toward environmental pollution, energy consumption on sustainable eco-
nomic growth through moderate role of technological innovation. Environmental Science and Pollu-
tion Research, 29, 405–416.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under
a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of such publishing agreement and applicable
law.