Green energy investment,
renewable energy consumption,
and carbon neutrality in China
Ying Li
1
*, Haoning Li
1
*, Manru Chang
1
*, Shijuan Qiu
1
*,
Yifan Fan
1
*, Hafiz Kashif Razzaq
2
* and Yunpeng Sun
1
*
1
School of Economics, Tianjin University of Commerce, Tianjin, China,
2
Department of Environmental
Physics, The University of Lahore, Lahore, Pakistan
This study investigates the dynamic impact of green energy investment and
energy consumption on carbon emissions in China from 1995 to 2020. It
employed the Bootstrap Autoregressive Distributed Lag method to examine
the short and long-run relationship. The long-run findings indicate that green
energy investment and renewable energy consumption decrease carbon
emissions, whereas non-renewable energy consumption and economic
growth increase carbon emissions in shorter and longer periods. The long-
term reduction in carbon emissions may imply the transition toward carbon
neutrality. However, the marginal contribution of renewable energy towards
carbon neutrality is significantly higher than green energy investment due to
investment lag effects. Moreover, the Error Correction Term (ECT) is
significantly negative, authorizing the convergence towards steady-state
equilibrium in case of any deviation with a 25% adjustment rate. The
empirical results suggest that China should encourage green energy
investment and increase the share of renewable energy sources to ensure
carbon neutrality in the long run.
KEYWORDS
green energy investment, energy consumption, carbon emissions, carbon neutrality,
China
1 Introduction
In the modern period, achieving economic growth with environmental sustainability
is the goal of both developed and developing countries. However, stimulating economic
growth leads to rapid productivity of goods and services, increasing energy demand. It
implies that the energy sector is considered a backbone of an economy because it links
with economic prosperity. Thus, in this regard, energy is divided into two kinds, the first is
non-renewable, and the second is renewable energy. Non-renewable energy sources are
limited in supply, and once they are used, they cannot be replaced. They include fossil
fuels such as oil, coal, and natural gas and most developing countries depend on non-
renewable energy sources for their energy requirements (Hanif et al., 2019). In
comparison, renewable energy is a form of energy that can be used on a recurring
basis and cannot be depleted. It is also called clean energy and includes different forms
OPEN ACCESS
EDITED BY
Asif Razzaq,
Ilma University, Pakistan
REVIEWED BY
Yu Teng,
University of Portsmouth,
United Kingdom
Muhammad Irfan,
Yibin University, China
*CORRESPONDENCE
Ying Li,
Haoning Li,
Manru Chang,
Shijuan Qiu,
Yifan Fan,
Hafiz Kashif Razzaq,
Yunpeng Sun,
SPECIALTY SECTION
This article was submitted to
Environmental Economics and
Management,
a section of the journal
Frontiers in Environmental Science
RECEIVED 03 June 2022
ACCEPTED 13 July 2022
PUBLISHED 25 August 2022
CITATION
Li Y, Li H, Chang M, Qiu S, Fan Y,
Razzaq HK and Sun Y (2022), Green
energy investment, renewable energy
consumption, and carbon neutrality
in China.
Front. Environ. Sci. 10:960795.
doi: 10.3389/fenvs.2022.960795
COPYRIGHT
© 2022 Li, Li, Chang, Qiu, Fan, Razzaq
and Sun. This is an open-access article
distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or
reproduction in other forums is
permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does
not comply with these terms.
Frontiers in Environm ental Science frontiersin.org01
TYPE Brief Research Report
PUBLISHED 25 August 2022
DOI 10.3389/fenvs.2022.960795
such as solar, wind, hydro, tidal and geothermal. However, many
past studies found that using conventional energy degrades the
environment (Chunyu et al., 2021; Fatima et al., 2021). In order
to achieve rapid economic growth and compete in the foreign
market with more productivity and low cost, developing
countries frequently use non-renewable energy sources. Thus,
due to easy availability and low cost, non-renewable energy
contributes to economic development, but simultaneously
increasing the trend of traditional energy has become a
danger to the environment. Therefore, developing countries
are encouraged to use renewable energy to meet their energy
requirements in various sectors of the economy.
For sustainable development, renewable energy is considered
the best strategy. The importance of efficient energy has gained
the focus of scholars; therefore, prevailing studies explored the
influence of renewable energy consumption on the environment.
However, previous research studies explored that renewable
energy positively relates to carbon neutrality (Sharif et al.,
2020a; Shan et al., 2021). Renewable energy is also called
green energy because it reduces energy consumption through
energy efficiency. Moreover, it enhances economic growth as well
as environmental quality. Thus, renewable energy consumption
leads the economy to low carbon emissions. In comparison, a few
studies highlight that renewable energy resources have an
insignificant or negative impact on mitigating environmental
pollution (Apergis et al., 2010; Marques and Fuinhas, 2012)
because, in the early stages of development, the limited size of
the economy has not increased renewable energy. In addition, the
share of renewable energy in total energy is not much increased
due to a lack of investment because renewable energy is more
expensive than non-renewable energy, and there is a need for
investing more in the promotion of renewable energy sources.
Hence, the association between these two variables is still
inconclusive (Sun et al., 2022b). Recently, the policymakers
and government have given their attention to green energy
investment to improve the quality of the environment. Green
investment is a type of investment that improves production
efficiency, saves the environment from hazards, and conserves
energy (Shen et al., 2021). Thus, green investment is a broader
term which does not limit to renewable energy, but it also
incorporates multiple techniques such as water recycling,
waste processing and recycling, carbon-capture technology,
electric motor cars, green buildings, and energy-saving
products (Razzaq et al., 2021a; Sun et al., 2022a).
There are two strands of literature about the nexus between
green investment and the environment in the current literature.
One group of studies has discussed the positive contribution of
green energy investment in increasing the quality of the
environment (Bekun et al., 2019; Xiong and Sun, 2022).
Investing in green energy increases economic growth, ensures
environmental sustainability through renewable energy
consumption, and further leads to green technology through
research and development. Therefore, investing in green projects
and providing a sustainable environment is imperative to
promoting green policies. The second group of studies stated
that green energy investment has a negative or no influence on
the environment (Nehler and Rasmussen, 2016; Stucki, 2019).
Moreover, countries are often interested in profit and energy
cost-saving benefits. They do not consider the non-energy and
indirect gains from green investments, such as more productivity,
fewer emissions, and better product quality. As a result, the
advantages of green energy investment have decreased. Hence,
the findings of studies related to green energy investment and the
environment are inconclusive.
Although, former empirical studies have separately explored the
relationship between energy consumption, green energy investment,
and carbon neutrality (Sun and Razzaq, 2022). However, there is a
dearth of empirical evidence that considers all these essential factors
in a multivariate framework. In addition, few prior studies related to
the nexus between green energy investment and environmental
damage. The prevailing studies regarding the nexus between
energy consumption and carbon emissions mainly consist of
panel data analysis and less attention to the time series analysis.
Thus, the present research study investigates the dynamic influence
of green energy investment and energy consumption on carbon
neutrality in China. The reason for the selection of China is that it
contributes to 30% of total carbon emissions globally, making the
country the world’s largest carbon emitter because it is highly
dependent on coal for energy consumption. Further, China is the
fastest-growing economy with a high population, and increasing
energy demand cannot satisfy from use of coal; thus, it leads to
environmental degradation. China has taken essential steps in this
crucial situation, including transforming non-renewable energy into
renewable energy (Park et al., 2017). However, the industrial sector is
the main contributor to economic growth and CO
2
emissions. Thus,
China made a substantial green investment in 2017 in this sector,
whose share is 0.106% of total investment to increase carbon
neutrality (Chen et al., 2021).
The literature, as mentioned earlier, shows that relatively
little attention has been given to the dynamic role of green energy
investment and energy consumption on carbon neutrality. Thus,
the current study contributes to the literature. The study’s
objective is to examine the short and long-run dynamic
relationship between green energy investment, energy
consumption, and carbon emissions in China by using the
monthly data from 1995 to 2020. In addition, Bootstrap
Autoregressive Distributed Lag (BARDL) modeling is applied
for an empirical estimation which is superior to the conventional
ARDL approach in time series analysis because it addresses the
issues of low power and small size of data. Thus, the bootstrap
ARDL test provides consistent and robust outcomes in
estimating the short and long-run relationship. Finally, we
used the stability test on the model estimations to check the
reliability of the results.
The rest of the paper is organized as follows: Section 2
describes the literature review, Section 3 represents data and
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Li et al. 10.3389/fenvs.2022.960795
methodology, Section 4 shows the empirical findings and
discussion, and Section 5 indicates the conclusion and policy
recommendations.
2 Literature review
In this section, the literature review is divided into two
dimensions. First shows the nexus between green energy
investment and the environment. The second indicates the
nexus between energy consumption and environmental
degradation; however, this part divides energy consumption
into two segments, i.e., renewable energy consumption and
non-renewable energy consumption.
2.1 Green energy investment and
environmental degradation
Green investment is defined as the investment that protects
the environment from degradation and saves energy. However,
various prior studies have examined the nexus between green
technology and the environment, but the literature regarding
green energy investment is not extensive. There are two schools
of thought regarding the nexus between green energy investment
and carbon neutrality.
One school of thought states that green energy investment
substantially stimulates economic growth without damaging the
environment (Sachs et al., 2019). Sachs et al. (2019) investigated
the contribution of green investment and projects to achieve
sustainable development goals (SDGs). Thus, green bonds, green
funding, and carbon market instruments help promote
sustainable growth and get SDGs. Saeed Meo and Karim
(2022) examined the impact of natural resources rent, green
investment, financial development, and energy use on pollution
in 30 provinces of China from 1995 to 2017. The results
confirmed the positive impact of green investment in reducing
CO
2
emissions while financial development, natural resources
rent and energy consumption lead to air pollution. Lee and Min
(2015) found the role of research and development (R&D)
investment in green technology on environmental quality in
Japan’s manufacturing firms from 2001 to 2010. The study
explored the negative association between (R&D) expenditures
with CO
2
emissions. International Renewable Energy Agency
determined the influential positive impact of green investment in
controlling environmental pollution in 34 provinces of China
from 2003 to 2017. Thus, green investment is essential for low
carbon emissions in the environment. Wang et al. (2021)
explored the link between renewable energy, green finance,
and CO
2
pollutants with other controlling variables in BRICS
countries from 2000 to 2018. The results showed that green
finance and renewable energy increase carbon neutrality while
trade openness, economic growth, and foreign direct investment
stimulate CO
2
emissions. Therefore, green finance is the best
policy for mitigating pollutant emissions in these countries.
In contrast, the other school argues that green energy
investment increase carbon emissions (Nehler and Rasmussen,
2016; Stucki, 2019). Stucki (2019) explored the impact of energy
investment at the firm level in Austria, Germany and Switzerland
and found that if any country’s energy cost is low, corporations
are unwilling to invest in green energy. They conclude that
energy cost greatly impacts a firm’s decision to invest in green
energy. Firms with higher energy costs invest more in green
energy than firms with lower energy costs. Similarly (Nehler and
Rasmussen, 2016) determined that Industrial enterprises
prioritize profitability from green investments over energy cost
savings and also observed that most green investment decisions
do not consider advantages other than energy, such as increased
productivity, reduced emissions, improved product quality,
optimum material utilization, and lower repairing and
cleaning costs, which, if considered, would enhance green
investment. Therefore, the literature regarding green energy
investment and environmental damage has mixed results.
2.2 Energy and environmental degradation
Many past studies have extensively discussed the influence of
energy consumption on environmental pollution. However,
literature regarding the nexus between energy usage and the
environment disaggregated energy into non-renewable and
renewable energy sources.
2.2.1 Non-renewable energy consumption and
environment
Former studies argued that conventional energy
consumption increases carbon emissions through the excessive
use of fossil fuels in industrialization, urbanization,
transportation, and other economic activities. Thus, a positive
relationship exists between non-renewable energy consumption
and environmental pollution (Khan et al., 2020; Chunyu et al.,
2021). Saboori and Sulaiman (2013) empirically analyzed the
influence of energy consumption on environmental pollution in
Malaysia and found that high consumption of non-renewable
energy sources leads to CO
2
emissions. Similarly, Khan et al.
(2019) explored the positive contribution of non-renewable
energy consumption in deteriorating the environment in the
case of Pakistan. Raza and Shah (2018) determined the effect of
financial development, gross domestic product, and energy
consumption on CO
2
emissions in Pakistan from 1972 to
2014. The estimated results revealed that financial
development, economic growth, and energy consumption
significantly increase environmental degradation. Rehman and
Rashid (2017) determined the role of energy usage on CO
2
emissions in SAARC countries. The examined results
indicated that energy consumption signi ficantly contributes to
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Li et al. 10.3389/fenvs.2022.960795
environmental degradation. Hanif (2018) examined the positive
effect of non-renewable energy consumption on CO
2
emissions
in 34 emerging economies from 1995 to 2015. Their findings
suggest that using non-renewable energy sources mitigates the
environmental pollution in these countries. Bhat (2018) found
the relationship between energy consumption, economic growth,
and CO
2
emissions in BRICS countries from 1992 to 2016. The
results revealed that traditional energy, economic growth, and
population increase the environmental degradation in BRICS
countries. Kim and Perron, 2009 analyzed the positive
contribution of non-renewable energy towards economic
development and carbon emissions in high emitting countries
from 1980 to 2014. Leng Chunyu et al. (2021) examined the effect
of energy consumption and financial development on CO
2
emissions in European and Central Asian developing
countries from 2010 to 2019. The results show that fossil fuel
consumption enhances while renewable energy consumption
decreases CO
2
emissions. Financial development raises
environmental deterioration in the short-run and reduces it in
the long run in concerned countries.
2.2.2 Renewable energy consumption and
environment
Renewable or efficient energy sources are alternatives to
conventional energy sources that enhance the environmental
quality and substantially increase economic growth through
energy efficiency.
In the current literature, two strands of studies exist about the
interdependence between renewable energy utilization and
environmental sustainability. First-strand of studies has
analyzed the positive linkage between renewable energy
consumption and environmental degradation (Shan et al.,
2021). Kalmaz and Kirikkaleli (2019) determined the influence
of energy consumption, trade, GDP growth, and urbanization on
CO
2
emissions in Turkey from 1960 to 2015. The results
highlight the long-run co-integration relationship among the
variables, and trade, energy consumption, and economic growth
increase CO
2
emissions. Alharthi et al. (2021) analyzed the
association between urbanization, economic growth, energy
use, and CO
2
emissions in the (MENA) countries from
1990 to 2015. The study found that renewable energy
consumption decreases, and conventional energy usage
increases CO
2
emissions, respectively. Anwar et al. (2021)
examined the role of clean and fossil fuel energy consumption
on the CO
2
emissions in ASEAN countries from 1990 to 2018.
The results found the mitigating role of renewable energy sources
in improving the quality of the environment in concerned
countries. Sharif et al. (2020b) investigated the nexus between
renewable energy consumption and environmental pollution in
the top 10 carbon-emitting countries from 1990 to 2017. The
findings show that renewable energy sources significantly reduce
CO
2
emissions in these countries, and bi-directional causality
exists between these two variables.
However, the second strand of studies shows renewable
energy consumption’s insignificant or inverse effect in
decreasing CO
2
emissions. Pata (2018) determined the
relationship between clean energy usage, urbanization,
financial development, and GDP on the environment in
Turkey from 1974 to 2014. The estimated results show that
urbanization, economic growth, and financial development raise
the environmental damage while renewable energy consumption
has no influential role in diminishing ecological degradation.
Similarly, Apergis et al. (2010) examined the significant positive
effect of renewable energy consumption on CO
2
emissions in
19 developed and developing countries from 1984 to 2007.
Therefore, ambiguity exists in the association between
renewable energy use and pollution.
The above extensive literature review shows that many
previous studies have separately examined the relationship
between energy consumption and the environment, green
investment, and environmental degradation. Therefore, less
attention has been given to the literature about the dynamic
influence of energy consumption and green energy investment
on improving ecological pollution. Thus, the present study fills
the literature gap by examining the dynamic effects of energy
consumption and green energy investment on carbon neutrality
for China in the short and long run.
3 Data and methodology
3.1 Data sources and model specification
For empirical analysis, the study utilizes China’s monthly
1
data from 1995 to 2020. The data include carbon emissions (CE)
measured as consumption-based carbon emissions in metric tons
per capita, green energy investment (GIE) is measured in a
million USD investment in green energy supply, renewable
energy consumption (REC) as % of total energy consumption,
non-renewable energy consumption (NREC) measured as energy
% produced from fossil fuels while economic growth (GDP) is in
constant. Sun et al., 2022c the data of all variables are in different
units; thus, we converted them into logarithm form to receive
more efficient estimates by following (Razzaq et al., 2021b). The
data for carbon emissions and non-renewable energy
consumption is taken from the Statistical Bulletin of China;
data for green energy investment is sourced from the
International Renewable Energy Agency (IRENA), while
economic growth and renewable energy consumption are
sourced from.
1 We follow Shahbaz et al. (2020) to convert the annual data into
monthly data by applying a quadratic match-sum approach. The
advantage of using this method is that it excludes deviations in data
and resolves the problem of seasonal variation (Sharif et al., 2019).
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Li et al. 10.3389/fenvs.2022.960795
The study has adopted the empirical model of Shan et al.
(2021) to examine the dynamic role of green energy investment
and energy consumption on carbon emissions in China. The
model specification is defined as below:
CE
t
α
0
+ β
1
GIE
t
+ β
2
REC
t
+ β
3
NREC
t
+ β
4
GDP
t
+ ε
t
(1)
In the model equation, CE is the dependent variable, and
GIE, REC, NREC, and GDP are independent variables.
Moreover, coefficients are shown as from β
1
to β
4
, α
0
shows
constant term, the error term is represented as ε, and t shows the
time period (1, 2...., T).
3.2 Empirical techniques
In time series analysis, the stationary test is imperative before
using the co-integration test. Thus, the current research has used
both the ADF unit root and structural-based unit root tests to
investigate the properties of time series data. However, past
empirical studies have only applied the ADF unit root test,
which is not suitable when structural breaks and their impact
exist in the data. Therefore, the ZA unit root test permits the
presence of structural breaks without showing the breakpoint
time in time series data (Andrews and Eric, 2002).
After examining the stationary properties of the
concerned variables, the study applied the bootstrap ARDL
co-integration model to determine the co-integration
relationship among the studied variables. The benefitof
using the bootstrap ARDL method over the conventional
ARDL approach is that it addresses the issues of low power
and small size in the time series data. Moreover, this approach
is based on a new co-integration test, thus enhancing the
power of both the t-test and F-test. However, there are two
conditions in the traditional ARDL test. The first condition is
the s ignificant coefficient of e rror correction term, and
another requirement is t he s ignificant coefficients for
lagged explanatory variables. In this technique, upper and
lower b ounds are necessary for the second condition but not
required for the first condition (Pesaran et al., 2001). In
addition, this method only cons iders th ose vari ables wh ich
are integrated with order one to e xamine the first condition
(Go h et al., 2017). Hence, the convent ional A RDL method has
lacked in power and explanatory characte ristics. Due to these
issues, bootstrap ARDL modeling is more appropriate for time
series analysis because of the inclusion of the F test on lagged
coefficients of independent variables. Further, this approach
incorporates the variables integrated of mixed order in the
dynamic model. Therefore, the bootstrap ARDL method
provides more consist ent and robust results than the
traditional ARDL approach (McN own et al., 2018).
Lastly, the stability test is applied in the study to examine the
consistency and reliability of estimates. Generally, significant
issues exist in time series data due to structural variations
over time that cause inconsistent results.
4 Empirical findings and discussion
4.1 Results of unit root test
In an empirical analysis of time series data, it is essential to
investigate the order of integration in the study variables. The
investigation of stationary properties assists further in selecting a
proper co-integration method to determine the co-integration
relationship among the variables. Therefore, the study uses both
ADF unit root and structural break unit root tests to analyze the
stationary level of variables. The estimated results of both ADF
and Zivot unit root tests are presented in Table 1. The findings of
the ADF test represent that all the variables in the model are
stationary at the first difference. However, the results of ADF in
the presence of structural breaks may provide misleading results.
This issue is resolved by the structure-based unit root test. Thus,
the Zivot unit root test results also con firm that all the concerned
variables are integrated at first difference with structural breaks.
It means that all the variables exist in long-run co-integration.
4.2 Results of bootstrap ARDL co-
integration:
To determine the long-run co-integration between carbon
emissions, green energy investment, and energy consumption,
bootstrap ARDL testing is used in the study. Table 2 represents
the findings of the bootstrap ARDL co-integration test. The t-test
and F-test findings reject the null hypothesis of no co-integration,
which means that all the variables are co-integrated in the long
run. The optimal lag length selection is found using Akaike
Information Criteria. Moreover, the explanatory power (R2)
value is 64.8%, which confirms that all the regressor variables
explain the dependent variable carbon emission. The value of JB
also shows that the residuals are normally distributed in the
model. In addition, there is no problem with serial correlation in
the model.
4.3 Results of long run bootstrap ARDL
After finding the co-integration among the concerned
variables, the study investigates the short and long-run results
of the bootstrap ARDL approach. The long-run outcomes of the
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Li et al. 10.3389/fenvs.2022.960795
bootstrap ARDL method are presented in Table 3. In the
findings, green energy investment has a significantly negative
association with carbon emissions at a 5% significance level. It
means that a 1% increase in green energy investment reduces
carbon emissions by 8.5%. Thus, the findings are the same as the
results of (Shahbaz et al., 2017) for 30 provinces of China. It
means that green energy investment has played a vital role in
reducing carbon emissions in China. In contrast, the findings are
against Stucki (2019), who found that green energy investment
increases CO
2
emissions because the firms in China are incurring
more energy costs in green investment; thus, they are reluctant
toward green investment.
The association between renewable energy consumption and
carbon emissions is significantly negative, showing that
renewable energy sources are the best alternative for fulfilling
energy requirements and helping to mitigate carbon emissions in
China. Thus, a one percent increase in renewable energy
decreases carbon emissions by 18.5%, keeping all other factors
constant. The results are identical to Alharthi et al. (2021),in
which clean energy usage is inversely associated with carbon
emissions in MENA countries, while contrary to Pata (2018),in
which renewable energy has no impact on CO
2
emissions for
Turkey.
In addition, the findings highlight that the coefficient of non-
renewable energy consumption is 0.478, which is positive and
significant. Thus, a one percent increase in non-renewable energy
consumption leads to a 47.8 percent increase in carbon emissions.
The estimated results indicated that China mainly uses coal as an
energy source that contributes to carbon pollutants and ultimately
damages the environment. These outcomes are similar to Leng
Chunyu et al. (2021) for European and central Asian countries in
which these countries excessively used fossil fuels for energy
consumption and emitted more pollution.
Economic growth is positively related to carbon pollutants at
a 1% significance level. It represents that a one percent rise in
economic growth contributes to carbon emissions by 52%. Thus,
the results are identical to the findings of Bhat (2018), which
determined that BRICS countries polluted the environment to
achieve fast economic growth. In the study, the Year 2009 has
been taken as a dummy variable for empirical analysis in the
bootstrap ARDL method because, after the Global Financial
Crisis (2007–2008), the government of China announced a
TABLE 1 Results of Unit Root tests.
Variables ADF (level) ADF (Δ) ZA (Level) ZA (Δ) Break year
(Δ)
CE −0.712 −3.950* −0.954 −3.640* 2001 (M-5)
GIE −0.489 −2.897* −0.315 −4.417* 2011 (M-9)
REC −0.947 −3.420* −0.638 −3.540* 2015 (M-6)
NREC −0.629 −3.068* −0.950 −3.557* 2005 (M-11)
GDP −1.205 −4.215* −0.989 −4.632* 2009 (M-10)
*p < 1%.
TABLE 2 BARDL cointegration analysis results.
Estimated models Lag length Break year F
PSS
T
DV
T
IV
Model 2, 1, 2, 2, 2 2009 M12 4.319*** −6.272*** −4.820**
R
2
Q-stat LM(2) JB
0.648 4.714 2.823 0.635
Note: The ideal lag time was found using the akaike information criterion (AIC). The bootstrap method creates asymptotic critical bounds for the F-statistic FPSS, ***p < 1%, **p < 5%,
and, *p < 10%.
TABLE 3 Results of BARDL (long run) co-integration analysis.
Dependent variable = CE
t
Variables Coefficient t-value Sig. level
GIE
t
−0.085 −2.421 **
REC
t
−0.185 −3.560 ***
NREC
t
0.478 4.855 ***
GDP
t
0.520 4.631 ***
Constant 0.140 1.970 *
D
2009-M12
0.120 2.530 **
R2 0.648
Adj–R2 0.620
Durbin Watson 1.846
Note: Shows ***p < 1%, **p < 5%, and, *p < 23%.
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Li et al. 10.3389/fenvs.2022.960795
fiscal stimulus package to overcome the negative effects of Crisis.
This has increased China’s economic growth enormously by
8.7% from 2009 onwards. Thus, after 2009 China is continuously
on track with rapid economic growth. Therefore, the result shows
that the coefficient of the dummy variable has a positive and
significant influence on carbon emissions. The explanatory
variable (R
2
) shows the model’s goodness, which shows the
value of 64.8% total variation in carbon emissions. The
statistic value of the Durbin Watson test shows no
autocorrelation in the model.
4.4 Results of short-run bootstrap ARDL
Table 4 indicates the empirical findings of bootstrap ARDL in the
short run. The study explored that green energy investment and
renewable energy usage are negative and significant; thus, carbon
emissions decline by 2.0% and 7.5%, respectively. The findings
highlight the contribution of green energy investment and
renewable energy sources in decreasing pollution that deteriorates
the environment in China. Thus, investing more in green energy and
shifting the energy resources from non-renewable to renewable help
China towards better environmental quality. The results of non-
renewable energy consumption and economic growth increase carbon
emissions by 21.5% and 36.8%, respectively. It implies that China’s
economic growth is mainly based on its industrial and energy sectors
that use fossil fuels, and as a result, carbon emission has increased. The
coefficient of error correction term (ECM
t-1
) is 0.253, which is
statistically significant and negative. It shows that a 25.3% speed of
adjustment will be required for the model to achieve equilibrium in
the long run.
4.5 Results of stability test
After estimating the short and long-run results of the
bootstrap ARDL model, the study applied the stability test to
confirm the stability of the estimates because, in time series
analysis, structural variation causes instability in the data.
Therefore, it is crucial to check the consistency of the
estimated model by applying a stability test. The results are
highlighted in Table 5, which reveals that the model has no
problems with serial correction, heteroscedasticity, model
specification error, and data’s non-normality. Further,
CUSUM and CUSUMsq confirm the stability of the
coefficients in the long run.
5 Conclusion and policy implications
China is the growing economy in the world and confronts the
severe problems of environmental damage. It is imperative for
China to achieve economic growth without emitting pollution.
Thus, in this context, the study explores the dynamic effect of
green energy investment and energy consumption on carbon
emissions by applying the bootstrap ARDL approach. China’s
monthly data is used from 1995 to 2020 in the study. The
traditional ADF unit root test is used with a structural-based
unit root test in the empirical estimations. Thus, both unit root
tests confirm the stationary of variables at first difference. Then
after determining the integration order of variables, the bootstrap
ARDL co-integration testing is employed to check the long-run
relationship among the study variables. The test findings revealed
that carbon emissions, green energy investment, and energy
consumption have a long-run co-integration relationship. The
study then examined the long and short-run results through the
bootstrap ARDL model. The long-run results of the bootstrap
ARDL model represent that non-renewable energy consumption
and economic growth enhance carbon emissions by 47.8% and
52%, respectively. Simultaneously, renewable energy
consumption and green energy investment decline carbon
emissions by 18.5% and 8.5%, respectively. The outcome of
the dummy variable also positively increases carbon emissions
for a longer period.
In the short run, the outcomes are similar to the long-run
analysis. Thus, green energy investment and renewable energy
reduce carbon emissions by 2.0% and 7.5%, respectively. At the
TABLE 4 BARDL co-integration analysis estimations (short-run).
Dependent variable = CE
t
Variables Co-efficient t-value Sig. level
GIE
t
−0.020 −1.980 *
REC
t
−0.075 −2.426 **
NREC
t
0.215 3.659 ***
GDP
t
0.368 4.230 ***
Constant 0.158 2.108 **
D
2009-M12
0.087 1.240 —
ECM
t-1
−0.253 −2.478 **
Note: ***p < 1%, **p < 5%, and, *p < 10%.
TABLE 5 Stability tests.
Stability test F-statistics p-value
Χ2 normal 0.465 0.153
Χ2 serial 0.532 0.216
Χ2 ARCH 0.673 0.265
Χ2 hetero 0.521 0.419
Χ2 RESET 0.945 0.139
CUSUM Stable
CUSUMsq Stable
Frontiers in Environm ental Science frontiersin.org07
Li et al. 10.3389/fenvs.2022.960795
same time, economic growth and non-renewable energy
consumption positively increase carbon emissions by 36.8%
and 21.5%, respectively. Therefore, the short and long-run
results are the same, but the magnitude of long-run estimates
is high compared to the short-run. Moreover, the error
correction term is significantly negative and shows that 25.3%
speed will require in the long-run equilibrium. Finally, the
stability test shows that the estimated results are stable.
The outcomes of the study suggest the followin g
recommendations:
(1) As a result of the positive contribution of green energy
investment in improving environmental pollution thus,
the policymakers of China should formulate restrictive
green policies such as environmental taxes; this will
encourage green growth and promote investment in green
technology for achieving a sustainable and eco-friendly
environment.
(2) The findings of renewable energy sources recommend that
utilization of renewable energy minimizes air pollution
levels. Policymakers should also encourage the share of
renewable energy sources into total energy and increase
energy innovation because clean or efficient energy
consumption improves economic development and
decreases carbon emissions. In addition, additional
research and development funding should be allocated to
renewable energy production.
(3) China is mainly based on coal for energy consumption. As a
result of stronger economic growth, China’s energy
requirements have increased, while the proportion of
renewable energy in the energy mix has decreased. Thus,
China should stimulate a reduction in the contribution of
fossil fuels to total energy consumption; investments in clean
energy sources are required mainly in the industrial and
energy sectors.
(4) Moreover, additional energy from economic development must
be converted into renewable energy sources, requiring a technical
transformation that is an effectivewaytoreducecarbon
emissions in China. Developments in renewable infrastructure
may be facilitated by economic advanc ement and income levels.
The study has a few limitations. First, it only captures China’s
economy and cannot be generalized to other highly polluting
economies. Future studies may explore the proposed association
by comparing countries that are not included in the study, such
as the United States, India, Japan, and European countries.
Second, the other relevant and substantial variables, such as
tourism, industrial structure, globalization, financial
development, information and communication technology,
and urbanization, can also be used. Similarly, the alternative
proxies of disaggregated green energy investment may offer
exciting results.
Data availability statement
The original contributions presented in the study are
included in the article/Supplementary Material, further
inquiries can be directed to the corresponding authors.
Author contributions
YL: Conceptualizing, writing, drafting-original draft. HL:
Data and methodology. SQ: Conceptualizing, writing,
drafting-original draft. MC: Review and editing. YF: Review
and editing. KR: Review and editing. YS: Review and editing.
Acknowledgments
The authors thank the financial support from the National
Statistical Science Research Project (2022LY067).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Frontiers in Environm ental Science frontiersin.org08
Li et al. 10.3389/fenvs.2022.960795
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