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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-021-16832-9
RESEARCH ARTICLE
Clean energy investment andfinancial development asdeterminants
ofenvironment andsustainable economic growth: evidence
fromChina
ZahidZahoor1· IrfanKhan1· FujunHou1
Received: 2 September 2021 / Accepted: 27 September 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Abstract
Environmental sustainability has become one of the most common phrases in discussions about climate change. This study
examines the impact of clean energy investment and financial development on environmental sustainability and China’s
economic growth, using manufacturing value-added and urbanization as moderator variables from 1970 to 2016. We used
advanced econometric methodologies for empirical estimations, used structural break unit root tests, fully modified least
square, dynamic least square, and robust least square multiple regressions for long-run estimates. Overall, the results deter-
mine that clean energy investment is negatively associated with CO2 emissions and ecological footprint while positively
associated with China’s economic growth. Financial development, manufacturing value-added, and urbanization are positively
associated with CO2 emissions, ecological footprint, and China’s economic growth. Moreover, clean energy investment
improves environmental sustainability at the expense of economic growth. Financial development, manufacturing value-
added, and urbanization encourage economic growth at the expense of environmental sustainability. We argued that the
local governments play a critical role in lifting the outstanding barriers to cleaner energy investment, addressing disincen-
tives, including pricing carbon dioxide emissions, reforming inefficient nonrenewable fossil fuel subsidies, and addressing
regulatory and market rigidities that can undesirably affect the attractiveness of clean energy investment. Policymakers are
suggested to encourage green finance strategy for the financial sector to broader sustainable development objectives. At the
heart of green manufacturing, industrialization policies are needed to integrate diverse intentions, like inclusive growth,
environmental protection, and productivity through a wider range of economic, social, and environmental policy frameworks
suitable for decoupling growthfrom social and environmental unsustainability.
Keywords Clean energy investment· Financial development· Economic growth· Environmental sustainability· China
Introduction
As renewables have become a persuasive investment propo-
sition, investment into new clean energy has grown from
less than 50 US$ billion per year in 2004 to about 300 US$
billion per year in the last decade, exceeding investments
into new fossil fuel power by a factor of three in 2018. Yet,
investments in clean energy remain beneath their potential.
On the foundation of sound enabling policy frameworks,
scaling up renewable energy investment is crucial in accel-
erating world energy transformation and reaps numerous
benefits while achieving environmental sustainability and
sustainable economic growth targets (IRENA2020). Annual
clean energy investment worldwide would need a threefold
increase by 2030 to around 4 trillion US$ to reach carbon
neutrality by 2050 (International Energy Agency2021a).
The gap between present clean energy investment pat-
terns and a sustained pathway is enormous (Lyu etal.2021).
Clean energy investment would have to double in the 2020s
to keep temperatures under 2°C rise and more than triple to
keep the door opening for a 1.5°C stabilization. Momentum
Communicated by Nicholas Apergis.
* Fujun Hou
hou@bit.edu.cn
Zahid Zahoor
Zahid.khattak@bit.edu.cn
Irfan Khan
Irfan.Khan@bit.edu.cn; Khan.Irfan4032@yahoo.com
1 School ofManagement andEconomics, Beijing Institute
ofTechnology, Beijing100081, China
Environmental Science and Pollution Research
1 3
from net zero promises, and sustainable economic growth
is yet to translate into enormous expansion in actual spend-
ing on clean energy projects. Clean energy investment is
on a moderate rise; however, it remains far short of what
will be needed to avoid the austere impacts of climate
change. The 750 billion US$ that is expected to be invested
in clean energy technologies and efficiency worldwide in
2021 remains far below what is needed in climate-driven
scenarios (International Energy Agency2021b).
Financial development, including climate finance, plays
a crucial role in bridging the financial gap and attracting
further investment from the private sector to clean renewa-
bles by addressing key barriers and risks. In 2013–2018,
onshore wind and solar PV consolidated their dominance,
attracted 46% and 29% of global renewable energy invest-
ment. Investment in offshore wind has picked up, attracted
7% of the total, followed by solar thermal at 6%. Other
renewable energy technologies, comprising biomass, hydro-
power, geothermal, biofuels, marine, and geothermal energy,
contributed only 7% of the total clean energy investment in
2013–2018, with hydropower making up a somewhat central
portion of the total (IRENA2020).
With a cleaner, more inclusive energy future and an
integrated approach to energy sustainability and sustain-
able development, the world is not making good progress in
realizing the United Nations Sustainable Development Goals
(SDGs) outcomes (Khan and Hou2021a; Lyu etal.2021;
Yang and Khan2021). In particular, the most narrowly
allied SDGs to the energy sector are SDG-7, universal
access to clean energy, part of SDG-3 decreasing the severe
health influences of air pollution, and SDG-13 addressing
climate change. The Sustainable Development Scenario
(SDS)necessitates a new annual clean energy investment
of 40 billion US$ between 2021 to 2030 to reach universal
access, decentralized, and decarbonized solutions. Getting
universal access will change the lives of hundreds of thou-
sands and condense the severe health impacts (International
Energy Agency2020).
This study’s first important research question is how
much clean energy investment influences environmental
sustainability and China’s economic growth. Implement-
ing the SDGs and the Paris Agreement meant that the
clean energy transitions to sustainable energy are crucial in
achieving the development and climate intentions. Enhanc-
ing renewable energy development will strengthen sus-
tainable economic growth, improve human welfare, create
different employment prospects, and ensure a safe climate
future (Ferroukhi etal.2016). Environmental issues directly
linked to energy consumption and production include cli-
mate change, air, water, thermal pollution, and solid waste
disposal (Tawiah et al. 2021; Zakari and Khan 2021).
Thus, energy and ecological harm are interconnected since
producing, transporting, or consuming energy devoid of
significant environmental impacts is almost impossible. Air
pollutants and nonrenewable fossil fuels’ emissions are the
primary cause of ecological degradation (European Environ-
ment Agency2021). Moreover, Ahmad and Zhao (2018)
expressed that energy investment is positively associated
with economic growth and degrading the environment. Cur-
rent net-zero pledges have created a strategic shift and have
managed a divestment away from carbon-intensive assets
and capital mobilization for low carbon energy transitions
chattel.
The second crucial research question this research
address is how financial development affects environmen-
tal sustainability and China’s economic growth.Finan-
cial development incorporates a pivotal role in promoting
economic development. It encourages economic growth
through technological advancement and capital accumula-
tion by pooling and mobilizing savings, promoting invest-
ment, encouraging and facilitating foreign direct investment
inflows, and optimizing capital allocation. Countries with
developed financial sectors incline to develop faster over
long periods. Financial development is not merely an out-
come of economic growth; it contributes to it and generates
higher income (World Bank2021). Financial development
persuades growth and development, generally because it
invites foreign direct investment. The rise in investment
and economic accomplishments raises energy consumption
and, subsequently, environmental pollutants. An increase
in credit could enhance the consumption and purchase of
energy-intense products, consequently growing ecological
concerns (Ntow-Gyamfi etal.2020).
Another grave concern in this study discussed is how
manufacturing value-added affects environmental sustain-
ability and China’s economic growth. Manufacturing fac-
tories and plants are major contributors to water and air
pollutionworldwide. The proscribed discarding of gases,
contaminated water, heavy metals, radioactive chemicals,
and materials into the waterways destroys marine life and
environmental degradation (Field2018). However, pros-
perous industrialized manufacturing is crucial to stimu-
late the production process and thus economic growth. A
strong manufacturing sector encourages economies to grow
for low, lower-middle, and upper-middle-income countries
(Yong2021). Industrialization promotes urbanization; the
water cycle changes as cities have more precipitation than
surrounding areas. Outbreaks of pandemics lead to the
dumping of sewerage from factories in water bodies, and
water pollution arises. However, urbanization encourages
economic growth andensures a higher level ofeconomic
development (Chen etal.2014).
A thorough empirically studying the case of Chian is cru-
cial as China has the world’s highest clean energy invest-
ment. China invested 83.4 billion US$ into the research
and development of clean energy in 2019. In 2018, China’s
Environmental Science and Pollution Research
1 3
cumulative wind power capacity amounted to 209.5 giga-
watts which is the highest worldwide. As an industrial and
economic powerhouse, China is hampered with massive
daily clean energy demand and greater awareness for renew-
able energy sources. China’s Solar PV demand is also com-
mon. In 2019, China’s solar power installed capacity was
204.7 gigawatts (Statista2019).Its huge prospective endures
up China’s pledge in investing renewable clean energy for
further consumption and production. As reported by the
International Energy Agency, about 36 to 40% of the world’s
wind and solar energy growth will come from China in the
next 5years. Deployment of renewable energy is also a part
of tremendous efforts from China in developing a cross-
industrial strategy to reduce pollution and nonrenewable use
and ecological civilization, improve energy efficiency, and
mitigate climate change. China’s National Development and
Reform Commission (NDRC) and National Energy Admin-
istration planned to spend about 360 billion US$ in the
development of renewable energy and creating 13 million
jobs in this sector by 2020 (Chiu2017). The literature has
ignored the investigation of China’s clean energy investment
perspective for environmental sustainability and economic
growth. The purpose of this study is to examine the impact
of clean energy investment and financial development on
environmental sustainability and China’s economic growth,
using manufacturing value-added and urbanization as mod-
erator variables from 1970 to 2016.
The remainder of this paper is outlined as follows. Sec-
tion2 presents a literature review to help position the paper.
Section3 provides a methodology. Section4 presents the
result and discussion, while the last section provides the
conclusion and policy propositions.
Literature review
The extant literature has focused on exploring the complex
intersectional relationship between environmental sustain-
ability andeconomic growth. Clean energy investment is a
core subject globally concerning climate change and eco-
nomic growth trade-off perspectives. Moner-Girona etal.
(2021) studied a high-resolution multidimensional strategy
in boosting decentralized energy investment in sub-Saharan
Africa. They concluded that investment in electricity genera-
tion might have the most incredible social benefits. Meng
etal. (2021) analyzed creative problem-solving priorities
for investment in renewable energy storage. They expressed
that renewable energy storage investment is essential for
environmental concerns. Zhou etal. (2021) examined the
risk priorities for renewable energy investment using fuzzy
decision-making modeling with alpha cuts. They demon-
strated that cost efficiency and organizational effectiveness
are crucial components of renewable energy investment. Xie
etal. (2021) expressed that environmental regulations highly
affect an enterprise’s investment behavior and that structural
investment optimism is vital to green development (Khan
and Hou2021b).
Guo etal. (2021) studied green investment and innova-
tion in energy for environmental quality. They concluded
that investment in renewable energy and the energy indus-
try is convincing contributors in explaining carbon emis-
sions. Ahmad and Zhao (2018) investigated the causal links
between energy investment and economic growth. They
explained that energy investment has multiple impacts on
growth and development. M. Zhang etal. (2021) studied
the impact of renewable energy investment on China’s car-
bon emissions. Their findings elucidated the existence of
the EKC hypothesis for renewable energy investment and
environmental sustainability. They claimed that at the initial
stage, renewable energy investment may deteriorate the envi-
ronment; in the middle phase, renewable energy started play-
ing a role in reducing emissions. Abban and Hasan (2021)
revisited the determinants of renewable energy investment
for the panel of 60 countries. They claimed that renewable
energy investment is essential for environmental protection.
A vast body of existing literature examined the impact
of financial development on environmental sustainability
and economic growth. Khan and Ozturk (2021) examined
the direct and indirect effects of financial development on
88 developing country’s CO2 emissions. They concluded
that indirect channels of financial development reduce
carbon emissions. Moreover, they indicated that the pollu-
tion heaven hypothesis exists when financial development
traverses certain limits. Ibrahim and Vo (2021) studied
financial development and environmental pollution in the
selected industrialized economies from 1991 to 2014. They
claimed that financial development promotes environmental
deterioration. Aluko and Obalade (2020) investigated the
financial development and environmental quality nexus in
sub-Saharan Africa from 1985 to 2014. They demonstrated
that financial development is a positive driver, and it has
unfavorable technology effects on environmental quality.
Zhao etal. (2021) analyzed the impact of financial devel-
opment on China’s environment from 1953 to 2006. Their
findings indicated that financial development has improved
environmental sustainability in China and that financial
development has not occurred at the expense of environ-
mental pollution. Xu etal. (2021) examined the influenced
pathways of financial development on environmental qual-
ity using smooth transition regressions from 2001 to 2017.
They claimed that financial development has indirect posi-
tive effects on the environment through several pathways.
The impact of such paths is different in different regions with
high or low levels of financial development.
Evidence suggested that financial development is crucial
for economic growth and substantially enhances growth and
Environmental Science and Pollution Research
1 3
development (Khan etal.2021a). Li and Wei (2021) studied
the impact of financial development on economic growth
in 30 Chinese provinces from 1987 to 2017. They claimed
that both liner and nonliner relationships do exist between
financial development and economic growth. Ahmed etal.
(2021) examined the impact of financial development on the
sustainable economic growth of South Asian countries from
2000 to 2018. They demonstrated financial development as a
driving factor in promoting green economic growth.
Manufacturing industries are contributing towater pollu-
tionacross the globe. The prohibited discharge of harmful
gases, contaminated water, heavy metals, chemicals, and
radioactive materials into waterways damaged the environ-
ment (Field2018). However, very little literature is available
to analyze the environmental impact of the manufacturing
sector. Sustainable manufacturing is one of the essential
goals of advanced manufacturing companies to decrease
their manufacturing-related emissions (Khan etal.2021c).
Harun etal. (2013) expressed that the environmental impact
of manufacturing depends upon the design of the product
and material selection. Moreover, the manufacturing sec-
tor has involved massive energy and resources, resulting in
an enormous impact on economic growth. There is a clear
indication that a prospering manufacturing sector is funda-
mental to amplified production in an economy and economic
growth.
Methodology
The purpose of this study is to examine the impact of clean
energy investment and financial development on environ-
mental sustainability and China’s economic growth, using
manufacturing value-added and urbanization as moderator
variables from 1970 to 2016:
where
𝛽0
in all three equations is an intercept and
𝛽1to𝛽4
are
coefficients of independent variables clean energy invest-
ment (CNI), financial development (FND), manufacturing
value-added (MVA), and urbanization (UR).
CO2
is CO2
emissions,
EFP
is ecological footprint, and
GDP
economic
growth. These three variables are the dependent variables
of this study.
t
is study time 1970–2016, and
𝜀
is the error.
We collect the ecological footprint data from Global Foot-
print Network (GFN). The data for CO2 emissions, economic
growth, clean energy investment, financial development,
(1)
CO2t=𝛽0+𝛽1CNIt+𝛽2FNDt+𝛽3M VAt+𝛽4URt+𝜀t
(2)
EFPt=𝛽0+𝛽1CNIt+𝛽2FNDt+𝛽3M VAt+𝛽4URt+𝜀t
(3)
GDPt=𝛽0+𝛽1CNIt+𝛽2FNDt+𝛽3M VAt+𝛽4URt+𝜀t
manufacturing value-added, and urbanization is collected
from World Development Indicators (WDI). To ensure
precise estimations, we transform the data for all variables
into the logarithm form before the empirical assessments
(Li2021; Su et al.2021; Weimin etal.2021). The data
for ecological footprint measured in consumption gha per
capita. CO2 emissions are measured in per capita metric
tons. Economic growth measured in GDP per capita US$.
Clean energy investment is estimated as an investment in
the energy sector with the participation of the private sector
in the current US$. Financial development is taken as the
monetary sector’s credit to the private sector measured in
percentage of GDP. Manufacturing value-added is measured
in percentage of GDP, and urbanization is calculated as a
percentage of the total population.
In statistics and probability theories, Khan and Hou
(2021a, b) stated that a unit root is a piece of particular sto-
chastic procedures that causes problems in multiple statis-
tical inferences, including time series models. A linear or
nonlinear stochastic approach has a unit root if one is a root
of the procedure’s characteristic equation. Aunit rootis a
unit of capacity in determining how stationarity a time series
models have. This study applies augmented dickey fuller
(ADF) Dicky-Fuller (1997) and structural break unit-root
tests to confirm our data’s static properties. The bare ADF
unit root formed as:
where
Δyt
is time variations, c, a, and B are postulated coef-
ficients, and
Δyt
−
j
spectacles first-order differencing rudi-
ments, controlled by the random error, perceived from the
autocorrelation.
In this study, we engaged the co-integration test suggested
by Johansen (1991, 1995) due to its numerous auspicious
characteristics. Predominantly, it shelters variables endog-
enously, and it approves to produce more than one cointe-
grating equations Johansen (1991, 1995). Johansen’s (1991,
1995) basic equation molded as:
where
yt
is nonstationary k-vector,
𝛽xt
is the deterministic
trend of d-vector, and
𝜀t
is vector error.
For the long-run estimates, we employed Phillips and
Hansen (1990) fully modified least squares (FMOLS),
Stock and Watson (1993) dynamic ordinary least squares
(DOLS), and (Yohai1987) robust least squares regressions.
Phillips and Hansen (1990) demonstrated that FMOLS is
a thoroughly efficient mixture of normal asymptotic and
an asymptotically unbiased estimator, allowing general
Wald estimates involving asymptotic chi-square statistic
(4)
Δ
yt=c+ayt−1+𝛽t+
k
∑
j
=1
djΔyt−j+𝜀t
.
(5)
yt=A1yt
−
1+
⋯
+Apyt
−
p+𝛽xt+𝜀t.
Environmental Science and Pollution Research
1 3
interpretations. The FMOLS regressions use preliminary
estimators of symmetric and one-sided long-run covariance
residual’s matrices. The FMOLS estimator formed as:
where
Zt
=
(
X�
t
,D�
t)
�
.
The DOLS approach incorporates augmenting co-integra-
tion regressions, including leads and lags; this resulting co-
integration equation’s error term is orthogonal to the whole
history of stochastic estimator’s innovation (Stock and Wat-
son 1993). The DOLS orthogonal regression for the entire
history of stochastic regressor innovations formed as:
under the assumption that the addition of
q
lags and
r
leads
to differenced regressors.
Robust least-square M-estimation estimates address
the dependent variable’s outliner where the values of the
dependent variable vary evidently from the regression
(6)
̂
𝜃
=
[
𝛽
̂𝛾 1
]
=
(
T
∑
t
=2
ZtZ�
t
)−1(
T
∑
t
=2
Zty+
t−T
[
𝜆+
12�
0
])
(7)
y
t=X�
t𝛽+D1t�𝛾1
+
r
∑
j
=−
q
ΔXt+�j𝛿+v1t
model’s large residuals. Robust least-square S-estimation is
a computationally rigorous process that emphasizes outli-
ers in the high leverages estimator. However, robust least
square’s MM-estimation is the grouping of S-estimation and
M-estimation. Overall, robust least square addresses outlin-
ers in both independent and dependent variables.
Results anddiscussion
Table1 describes the results of descriptive statistics and
pairwise correlations for CO2 emissions, ecological foot-
print, economic growth, clean energy investment, financial
development, manufacturing value-added, and urbaniza-
tion. There is no significant difference between mean and
maximum observations. This result reflects that the present
time series no normally distributed. The higher difference
of standard deviation value relative to its mean signifies the
higher level of dispersion in the economic growth dataset.
However, clean energy investment’s data set have the lowest
level of dispersion. Pairwise correlation results show a posi-
tive pairwise correlation among ecological footprint, CO2
emissions, economic growth, and clean energy investment.
Table 1 Descriptive statistics
Methods CO2 Emissions EFP GDP CNI FND MVA UR
Mean 0.954654 0.608292 6.969350 21.33689 4.453576 4.274776 3.382692
Median 0.893145 0.518792 6.906159 21.51036 4.481446 4.349584 3.370841
Maximum 2.022502 1.313807 8.840430 22.50604 5.051329 4.430864 4.038409
Minimum − 0.058758 0.053792 5.431582 19.39716 3.906826 3.928735 2.843979
Std. Dev 0.624069 0.394853 1.098409 0.700303 0.367097 0.155067 0.398807
Skewness 0.334847 0.524954 0.170596 − 0.704347 − 0.142847 − 1.058088 0.096989
Kurtosis 2.027075 2.020123 1.719364 3.038253 1.705814 2.744041 1.664311
Jarque–Bera 2.732019 4.038996 3.439696 3.889014 3.439889 8.898109 3.567479
Probability 0.255123 0.132722 0.179093 0.143058 0.179076 0.011690 0.168009
Sum 44.86875 28.58970 327.5594 1002.834 209.3181 200.9145 158.9865
Sum Sq. Dev 17.91525 7.171809 55.49908 22.55954 6.198958 1.106107 7.316164
CO2 Emissions 0.381175
1.000000
EFP 0.239776 0.152592
0.994208 1.000000
GDP 0.661006 0.417042 0.180831
0.985256 0.982471 1.000000
CNI 0.014960 0.010611 0.005024 0.479990
0.034975 0.039208 0.006673 1.000000
FND 0.208448 0.129892 0.382286 − 0.008414 0.131893
0.929660 0.915599 0.968688 − 0.033440 1.000000
MVA − 0.032349 − 0.021543 − 0.050235 0.001813 − 0.013567 0.023534
− 0.341541 − 0.359493 − 0.301344 0.017058 − 0.243518 1.000000
UR 0.238548 0.150312 0.428189 − 0.000573 0.139416 − 0.017634 0.155663
0.979311 0.975293 0.998732 − 0.002097 0.972995 − 0.291352 1.000000
Environmental Science and Pollution Research
1 3
However, manufacturing value-added negatively correlated
with CO2 emissions, ecological footprint, economic growth,
and financial development. Urbanization is also negatively
correlated with manufacturing value-added while positively
correlated with CO2 emissions, ecological footprint, and
economic growth.
Table2 describes the results of the ADF unit root test.
The results explain that CO2 emissions, ecological footprint,
economic growth, financial development, and urbanization
are not significant at level. These variables are not station-
ary at the level. Clean energy investment and manufactur-
ing value-added are significant at 1% and 10% levels, thus
stationary at the level. However, when we transformed these
variables into the first difference, they became stationary at
different significance levels. Economic growth and urbani-
zation are significant at 10%, CO2 emission at 5%, and eco-
logical footprint, clean energy investment, financial devel-
opment, and manufacturing value-added at 1% significance
levels. Moreover, clean energy investment and manufactur-
ing value-added are stationary at both level and first differ-
ence. Overall, these results represent a mixture of content-
ment in which some variables are I (0), some are I (1), and
none is I (2).
Table3 presents the outcome of the structural break
unit root test. Here result explains that clean energy invest-
ment, manufacturing value-added, and urbanization are 1%
stationary at level. While CO2 emissions, ecological foot-
print, financial development, and economic growth are not
significant, thus nonstationary at the level. However, at the
first difference, all these variables are various levels signifi-
cant and stationary at first difference. Furthermore, struc-
ture break dates prevail in 2001, 2003, 2004, 1991, 1976,
2012, 1977, 1982, 2007, 1985, 1978, and 1977 presenting
the years of economic policy fluctuations. Table4 presents
the outcome of the Johnson co-integration test for the three
study models separately. Model 1 for CO2 emissions and
model III for economic growth presents that at most, one
equation is cointegrated. However, model II for ecological
footprint shows that at most, two equations are cointegrated.
This result demonstrates a long-run relationship among the
variables and co-movement over the study period.
Similarly, in Table5, we presented the outcome of long-
run estimates of FMOLS, DOLS, and robust least squares
separately for all three study models. Overall, the results
determine that clean energy investment is negatively asso-
ciated with CO2 emissions and ecological footprint while
positively associated with China’s economic growth. Moreo-
ver, financial development, manufacturing value-added, and
urbanization are positively associated with CO2 emissions,
ecological footprint, and China’s economic growth.
Clean energy investment is significant at a 10% level of
significance. Its negative values of coefficients for CO2 emis-
sions and ecological footprint implies that a 1% increase
in clean energy investment decrease CO2 emissions and
ecological footprint (improve environmental sustainabil-
ity) by 0.342100% and 0.008141% (FMOLS), 0.384760%
and 0.153590% (DOLS), and 0.223430% and 0.129080%
(robustness). However, its positive coefficient’s value in the
case of economic growth implies that a 1% increase in clean
energy investment increases China’s economic growth by
0.032155% (FMOLS), 0.031649 (DOLS), and 0.009208%
(robustness). These findings imply that clean energy invest-
ment improves environmental sustainability and stimulates
China’s economic growth. This finding suggests that clean
energy investment encourages environmental sustainability,
not at the expense of economic growth. Moreover, focus-
ing on renewable energy sources instead of nonrenewable
Table 2 Augmented Dickey-Fuller unit root
*** = 1%,** = 5% and * = 10% significance level
Variables/meth-
ods
Level First difference
t-Statistic Prob t-Statistic Prob
CO2 emissions − 0.276634 0.9202 − 4.301460** 0.0013
EFP 0.178507 0.9682 − 4.140367*** 0.0021
GDP 0.596013 0.9881 − 3.211762* 0.0259
CNI − 7.968395*** 0.0000 − 7.226850*** 0.0000
FND − 0.252502 0.9238 − 5.984499*** 0.0000
MVA − 2.847965* 0.0595 − 7.255355*** 0.0000
UR − 1.298013 0.6225 − 3.017373* 0.0408
Table 3 Breakpoint unit root
*** = 1%, and * = 10% significance level
Level Break date First difference Break date
CO2 Emissions − 2.996321 (0.6881) 2001 − 4.652665* (0.0282) 2003
EFP − 2.297318 (0.9470) 2001 − 4.631109* (0.0299) 2004
GDP − 1.172942 (0.9999) 1991 − 5.468374*** (0.0000) 1976
CNI − 8.317674* (0.0100) 2012 − 14.19021*** (0.0000) 1977
FND − 1.779175 (0.9999) 1982 − 6.201338* (0.01000) 1976
MVA − 5.057525* (0.0100) 2007 − 8.245154*** (0.0000) 1985
UR − 4.746539* (0.0209) 1978 − 5.767504* (0.0100) 1977
Environmental Science and Pollution Research
1 3
fossil fuels might be helpful to avoid harmful environmental
impacts, particularly from greenhouse gases and air pollu-
tion (Khan etal.2021d; Khan and Hou2020).
Financial development is 1% significant for CO2 emis-
sions and ecological footprint while 10% significant for eco-
nomic growth. The positive values of its coefficients indi-
cates that a 1% increase in financial development increases
CO2 emissions, ecological footprint, and economic growth
by 1.00090%, 0.865628%, and 0.348851% (FMOLS);
0.911140%, 0.731631%, and 0.317777% (DOLS); and
0.642979%, 0.617591%, and 0.007745% (robustness).
These findings suggest that financial development deterio-
rates environmental sustainability and encourages economic
growth simultaneously.
Manufacturing value-added is significant at 5% and 10%
levels of significance. The positive values of its coefficients
implies that a 1% increase in manufacturing value-added
increases CO2 emissions, ecological footprint, and eco-
nomic growth by 0.425773%, 0.263719% and 0.239403%
(FMOLS); 0.432184%, 0.266697%, and 0.244440%
(DOLS); and 0.243615%, 0.154945%, and 0.138081%
(robustness). These findings suggest that manufacturing
value-added degrade environment and stimulates China’s
economic growth (Khan etal.2021b, 2021d).
Similarly, urbanization is significant at a 1% level of
significance. The positive values of its coefficients imply
that a 1% increase in urbanization increases CO2 emissions,
ecological footprint, and economic growth by 2.351375%,
1.704173%, and 3.025273% (FMOLS); 2.271045%,
1.577164%, and 2.987485% (DOLS); and 2.066896%,
1.501690%, and 2.823120% (robustness). These results
signify that urbanization increases economic growth at the
expense of environmental sustainability (Ahmad2020a,
2020b; Ahmad et al. 2019). Moreover, the positive
implications of urbanization imply that it produces employ-
ment opportunities, improves technological infrastructure,
encourages communication and transportation, and improves
medical, educational, and standard of living.
Table6 describes the findings of pairwise Granger causal-
ity tests for CO2 emissions, ecological footprint, economic
growth, clean energy investment, financial development,
manufacturing value-added, and urbanization. The results
show that pairwise bidirectional Granger causality runs from
ecological footprint to CO2 emissions and from financial
development to CO2 emissions. However, the unidirectional
Granger causality relationship runs between CO2 emissions
to manufacturing value-added, urbanization to CO2 emis-
sions, ecological footprint to manufacturing value-added,
clean energy investment to urbanization, and from urbaniza-
tion to financial development.
Conclusions andpolicy propositions
The importance of environmental sustainability can have
a significant impact on the fight against the climate crisis.
The purpose of this study is to examine the impact of clean
energy investment and financial development on environ-
mental sustainability and China’s economic growth, using
manufacturing value-added and urbanization as moderator
variables from 1970 to 2016. In this study, we used CO2
emissions and ecological footprint as a measure for envi-
ronmental sustainability. We collect the ecological footprint
data from GFN. The data for CO2 emissions, economic
growth, clean energy investment, financial development,
manufacturing value-added, and urbanization is collected
from WDI. To ensure precise estimations, we transform
Table 4 Johansen co-integration
Note:**5%, and* = 10% significance level
Model I
CO2 emissions = CNI,FND,MVA,UR
Model II
EFP = CNI,FND,MVA,UR
Model III
GDP = CNI,FND,MVA,UR
No. of
co-integration
equations
Trace values Max eigenvalues Trace values Max eigenvalues Trace values Max eigenvalues
None 77.99361**
(0.0096)
31.85270*
(0.0855)
85.12684**
(0.0019)
28.94139
(0.1734)
84.43727**
(0.0022)
33.16782*
(0.0606)
At most 1 46.14091*
(0.0718)
27.07598*
(0.0580)
56.18546**
(0.0068)
26.41799
(0.699)
51.26945*
(0.0231)
25.51359*
(0.0899)
At most 2 19.06493
(0.4883)
13.31171
(0.4240)
29.76747*
(0.0504)
23.88283*
(0.0200)
25.75586
(0.1362)
18.12234
(0.1253)
At most 3 5.753223
(0.7245)
5.179280
(0.7190)
5.884645
(0.7091)
5.483329
(0.6800)
7.633518
(0.5054)
7.588491
(0.4221)
At most 4 0.573943
(0.4487)
0.573943
(0.4487)
0.401316
(0.5264)
0.401316
(0.5264)
0.045027
(0.8319)
0.045027
(0.8319)
Environmental Science and Pollution Research
1 3
Table 5 Long-run analysis
*** = 1%,** = 5% and * = 10% significance level
Variable Model I, CO2 emissions = CNI,FND,MVA,UR
Methods FMOLS DOLS Robustness least squares
Coefficient t-Statistic Probability Coefficient t-Statistic Probability Coefficient z-Statistic Probability
CNI − 0.342100* − 1.311736 0.0967 − 0.384760* − 1.216890 0.0303 − 0.223430* − 0.923108 0.0356
FND 1.000903*** 3.578211 0.0009 0.911140* 2.671570 0.0106 0.642979** 3.161752 0.0016
MVA 0.425773** 3.124632 0.0032 0.432184* 2.600726 0.0127 0.243615* 2.116494 0.0343
UR 2.351375*** 8.750931 0.0000 2.271045*** 6.930586 0.0000 2.066896*** 10.89534 0.0000
Model II, EFP = CNI,FND,MVA,UR
Methods FMOLS DOLS Robustness Least Squares
Coefficient t-Statistic Probability Coefficient t-Statistic Probability Coefficient z-Statistic Probability
CNI − 0.008141 − 0.572292 0.5702 − 0.153590* − 0.909475 0.0682 − 0.129080* − 0.867069 0.0385
FND 0.865628*** 5.673691 0.0000 0.731631*** 4.016507 0.0002 0.617591*** 4.937479 0.0000
MVA 0.263719** 3.548326 0.0010 0.266697** 3.004829 0.0044 0.154945* 2.188578 0.0286
UR 1.704173*** 11.62804 0.0000 1.577164*** 9.011493 0.0000 1.501690*** 12.86989 0.0000
Model III, GDP = CNI,FND,MVA,UR
Methods FMOLS DOLS Robustness Least Squares
Coefficient t-Statistic Probability Coefficient t-Statistic Probability Coefficient z-Statistic Probability
CNI 0.032155* 2.415481 0.0201 0.031649* 1.929800 0.0602 0.009208 0.831130 0.4059
FND 0.348851* 2.443312 0.0188 0.317777* 1.796345 0.0795 0.007745 0.083207 0.9337
MVA 0.239403** 3.442046 0.0013 0.244440** 2.835854 0.0069 0.138081* 2.620927 0.0088
UR 3.025273*** 22.05779 0.0000 2.987485*** 17.57666 0.0000 2.823120*** 32.51312 0.0000
Environmental Science and Pollution Research
1 3
the data for all variables into the logarithm form before the
empirical assessments.
We used advanced econometric methodologies for
empirical estimations, used structural break unit root tests,
FMOLS, DOLS, and robust least square approach for long-
run estimates. Overall, the results determine that clean
energy investment is negatively associated with CO2 emis-
sions and ecological footprint while positively associated
with China’s economic growth. Moreover, financial devel-
opment, manufacturing value-added, and urbanization are
positively associated with CO2 emissions, ecological foot-
print, and China’s economic growth. Moreover, clean energy
investment improves environmental sustainability at the
expense of economic growth. Financial development, man-
ufacturing value-added, and urbanization encourage eco-
nomic growth at the expense of environmental sustainability.
The long-run results advocate that clean energy invest-
ment improves environmental sustainability. Based on this
finding, we suggest that the local governments play a criti-
cal role in lifting the outstanding barriers to cleaner energy
investment by enhancing their enabling environment. The
essential challenge is the consistent and coherent signal
across different policy frameworks that do not consider
climate-related concerns. Motivational measures to reas-
sure cleaner energy deployment are not enough to ensure
the investment reaches the required levels. The government
needs to address disincentives, including pricing carbon
dioxide emissions and reform inefficient nonrenewable fos-
sil fuel subsidies (Ahmad2021a, 2018; Xin etal.2021).
Besides, the government should resolve regulatory and mar-
ket rigidities that can undesirably affect the attractiveness
of clean energy investment—creating a level playing field
between independent energy producers of cleaner energy
vis-à-vis nonrenewable fossil fuel-based energy utilities.
The long-run estimates of this study confirm that finan-
cial development deteriorates the environment and stimu-
lates economic growth. Based on the findings, this study
suggests the policymakers unlock the opportunities for
green investment such as renewable energy, energy effi-
ciency, agricultural development, insurance market, and
small and medium enterprises’ (SMEs) productivity are
potentially commercially viable but facing inadequate
owed barriers in supply and demand (Ahmad2021b; Ding
etal.2021; Xiaosan etal.2021). Policymakers are sug-
gested to encourage green finance strategy for the finan-
cial sector to broader sustainable development objectives.
At the heart of green manufacturing, industrialization
policies are needed to integrate diverse intentions, like
inclusive growth, environmental protection, and produc-
tivity through a wider range of economic, social, and
environmental policy frameworks suitable for decoupling
growthfrom economic and environmental unsustainability.
Similarly, the long-run findings of this study eluci-
date that urbanization stimulates economic growth at the
expense of environmental sustainability. This study sug-
gests adopting green urbanism through practicing com-
munities beneficial to humans and the environment. It is
suggested to attempt to shape resilient, sustainable places,
communities, and sustainable lifestyles and use lessor
world’s resources. Moreover, policymakers are suggested
to incorporate sustainable cities integrated approach using
least sustainable urban development methodologies, apply
sustainable land management to urban planning and devel-
opment, and seek ad hoc breakthrough for injecting sus-
tainable principles and actions into the city lifestyle.
Table 6 Pairwise Granger Causality
** = 5% and * = 10% significance level
Variables CO2 Emissions EFP GDP CNI FND MVA UR
CO2 Emissions _____________ 2.70900*
(0.0788)
2.02495
(0.1453)
1.69725
(0.1961)
3.07751*
(0.0571)
0.52921
(0.5931)
2.55914*
(0.0900)
EFP 4.03379*
(0.0254)
_____________ 2.24836
(0.1188)
1.03175
(0.3657)
1.39331
(0.2600)
1.22549
(0.3044)
2.74414*
(0.0764)
GDP 0.03255
(0.9680)
0.53270
(0.5911)
_____________ 0.50684
(0.6062)
1.11120
(0.3391)
0.76822
(0.4706)
6.92726**
(0.0026)
CNI 2.01693
(0.1464)
0.36818
(0.6943)
0.06995
(0.9325)
_____________ 0.22983
(0.7957)
0.22203
(0.8019)
0.65826
(0.5233)
FND 3.48346*
(0.0403)
2.10583
(0.1350)
4.11215*
(0.0238)
0.23516
(0.7915)
_____________ 0.09890
(0.9061)
4.22746*
(0.0216)
MVA 3.31049*
(0.0467)
3.31384*
(0.0466)
4.22365*
(0.0217)
0.08284
(0.9207)
1.07328
(0.3515)
_____________ 2.15596
(0.1291)
UR 0.29667
(0.7449)
0.73534
(0.4857)
2.35910
(0.1075)
4.01739*
(0.0257)
0.06130
(0.9406)
0.77904
(0.4657)
_____________
Environmental Science and Pollution Research
1 3
Author contribution Zahid Zahoor: Original draft preparation, concep-
tualization. Irfan Khan: Methodology, modeling, and software. Fujun
Hou: Supervision and reviewing.
Availability of data and materials Not applicable.
Declarations
Ethics approval Not applicable
Consent to participate Not applicable
Consent for publication Not applicable
Competing interests The authors declare no competing interests.
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