Journal of Environmental Management 297 (2021) 113420

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Journal of Environmental Management

journal homepage: www.elsevier.com/locate/jenvman

A step toward reducing air pollution in top Asian economies: The role of
green energy, eco-innovation, and environmental taxes
Fengsheng Chien a, b, Muhammad Sadiq c, Muhammad Atif Nawaz d,
Muhammed Sajjad Hussain e, Tai Duc Tran f, *, Tiep Le Thanh g
a
School of Finance and Accounting, Fuzhou University of International Studies and Trade, China
b
Faculty of Business, City University of Macau, Macau, China
c
School of Accounting and Finance, Faculty of Business and Law, Taylor’s University Malaysia, Malaysia
d
Department of Economics, The Islamia University of Bahawalpur, Pakistan
e
Visiting Faculty Superior University Lahore, Pakistan
f
Faculty of Business Administration, Van Lang University, 45 Nguyen Khac Nhu, Dist.1, Ho Chi Minh City, Viet Nam
g
Thu Dau Mot University, Viet Nam

A R T I C L E I N F O A B S T R A C T

Keywords: Environmental degradation is significantly studied both in the past and the current literature; however, steps
Carbon emissions towards reducing the environmental pollution in carbon emission and haze pollution like PM2.5 are not under
PM2.5 rational attention. This study tries to cover this gap while considering the carbon emission and PM2.5 through
Renewable energy
observing the role of renewable energy, non-renewable energy, environmental taxes, and ecological innovation
Environmental taxes
Asian
for the top Asian economies from 1990 to 2017. For analysis purposes, this research considers cross-sectional
dependence analysis, unit root test with and without structural break (Pesaran, 2007), slope heterogeneity
analysis, Westerlund and Edgerton (2008) panel cointegration analysis, Banerjee and Carrion-i-Silvestre (2017)
cointegration analysis, long-short run CS-ARDL results, as well as AMG and CCEMG for robustness check. The
empirical evidence in both the short- and long-run has confirmed the negative and significant effect of renewable
energy sources, ecological innovation, and environmental taxes on carbon emissions and PM2.5. Whereas, non-
renewable energy sources are causing environmental degradation in the targeted economies. Finally, various
policy implications related to carbon emission and haze pollution like PM2.5 are also provided to control their
harmful effect on the natural environment.

1. Introduction greenhouse gases generated from different kinds of activities (Nawaz,

Hussain, et al., 2021). Various developing economies are focusing on
Since the last couple of decades, the titles like environmental achieving a higher level of economic growth (EG), which in return, put
degradation (ED), environmental pollution, global warming, and more destruction towards the natural environment.
changing climate have received much attention from scholars, re­ The title of renewable energy (REN) is observed as among the clean
searchers, policymakers, and energy economists. The various studies in energy sources that cover biomass, hydropower, solar, wind,
theoretical and empirical perspectives have been conducted to examine geothermal, and wave energy (Buhari et al., 2020). It is believed that
the trends in sustainable practices and a range of solutions (Danielle and REN is the key source of a cleaner and sustainable energy for the coming
Masilela, 2020; Hajko et al., 2018). Meanwhile, the natural environment time. Different economies around the world are focusing on REN
has offered various sources to different industries and economies to meet comparatively to traditional or NEN sources. For example, more than 25
their production requirements and day-to-day needs. However, one of % of European Union (EU) energy production comes from REN sources.
the growing pressures on the natural environment is that it has to digest While in Baltics, the electricity production from wind and hydro is ex­
unhealthy by-products in carbon emissions, haze pollution, and various pected to be more than 48 % and 32 %, respectively (Balsalobre-Lorente

* Corresponding author.
E-mail addresses: jianfengsheng@fzfu.edu.cn (F. Chien), Muhammad.Sadiq@taylors.edu.my (M. Sadiq), atif.nawaz.baloch@gmail.com (M.A. Nawaz), Sajjadgift@
gmail.com (M.S. Hussain), tai.td@vlu.edu.vn (T.D. Tran), lethanhtiep@tdmu.edu.vn (T. Le Thanh).

https://doi.org/10.1016/j.jenvman.2021.113420
Received 30 May 2021; Received in revised form 12 July 2021; Accepted 26 July 2021
Available online 29 July 2021
0301-4797/© 2021 Elsevier Ltd. All rights reserved.
F. Chien et al. Journal of Environmental Management 297 (2021) 113420

and Leitão, 2020; Ehsanullah et al., 2021). Meanwhile, there is a observed that there is an asymmetric effect of NEN consumption on both
growing demand for renewable or green energy in recent years to ach­ the EG and CE emissions in all the targeted economies. The research has
ieve future sustainable objectives. Anh Tu et al. (2021) have claimed paid less attention to the innovation aspect to examine environmental
that there is a common assumption in environmental sciences and degradation (Koloba, 2020). The current study would consider ecolog­
among the policy analysts that the development of green energy will ical innovation while evaluating environmental degradation. In addi­
positively contribute towards the decoupling EG from carbon emissions. tion, Sharif et al. (2019) examined the relations of REN and NEN
This would justify the argument that green energy sources in renewable consumption with the CE while taking the global sample into account
and nuclear energy have their counter effect on emissions. However, it is through heterogeneous panel estimation. For the study analysis, they
also observed that REN production does not lead towards the expected have considered 74 nations from 1995 to 2015. The authors found that
environmental outcomes (Chien, Kamran, Albashar and Iqbal, 2021; there is a significant and positive effect of NEN on the ED, while the titles
Ehsanullah et al., 2021). like REN are showing significant and negative impacts on the environ­
In addition, the role of ecological innovation and environmental ment as well. They research has sampled data from various countries
taxes is also observed in recent and past studies to control environmental with different environmental issues that could affect the results. How­
degradation. The environmental degradation in the Asian region has ever, the current study examined only Asian economics where the
taken place on some severe grounds. Various areas in the regions are environmental concerns are at peak and similar kind of environmental
highly polluted in terms of increasing environmental degradation. These issues. Moreover, Banday and Aneja (2020) investigated REN, NEN, and
economies have limited policies related to environmental degradation carbon emissions trends for the BRICS economies through bootstrap
and practices to improve the environmental conditions. Undoubtedly, panel causality during 1990–2017. The study findings found a unidi­
these economies have achieved a higher level of EG but caused more rectional causality from gross domestic product to CO2 for India, Brazil,
pollution in the natural. This phenomenon would lead to more China, and Africa while no causality for the economy of Russia. They
destruction in the environment and some unexpected outcomes in the have overlooked at innovation aspect to examine environmental
ecological system like a loss in biodiversity, global warming, and degradation. Hence, the current study has taken ecological innovation
eventually, air and water pollution. These environmental and policy into consideration to evaluate examining environmental degradation.
issues have motivated the authors to examine renewable energy and Destek and Sinha (2020) examined the Environmental Kuznets Curve
non-renewable energy and new variables such as ecological innovation (EKC) validity for the ecological footprints while taking the renewable,
and environmental taxes that would impact the environmental degra­ NEN, and trade openness as key explanatory variables during
dation in Asian economics. The research questions of the study are as 1980–2014 for 24 OECD economies. Their study findings hold that there
follows: is no EKC hypothesis for the relations among EG and ecological foot­
prints. However, increasing the level of REN consumption reduces the
RQ1. What is the role of renewable energy consumption on environ­
ecological footprints while more consumption of energy from NEN
mental degradation in Asian economies?
consumption leads to a higher level of ED as well. The study has paid less
RQ2. How much is the impact of non-renewable energy consumption attention to the environmental taxes that has also a significant influence
on environmental degradation? on environmental performance and the present study has fulfilled this
gap and added the environmental taxes to examine the environmental
RQ3. What is the influence of ecological innovation on environmental
degradation.
degradation in Asian economies?
In addition, the existing literature also tried to focus on the role of
RQ4. What is the role of environmental taxes on environmental innovation in green context in estimating the ED and air pollution as
degradation in Asian economies? well (Chien, Sadiq, et al., 2021; Chien et al., 2020; Othman et al., 2020;
Sadiq et al., 2020). As provided by Lee and Min (2015), the research
2. Literature review contribution investigated the role of development investment and green
research for eco-innovation, carbon emission, and firm performance in
In recent years, increasing energy consumption, specifically from the economy of Japan during 2001–2010. Their study findings showed
some non-renewable sources, is observed with a higher level of envi­ that there is an adverse association between green research and devel­
ronmental pollution (Chien, Anwar, et al., 2021; Huang et al., 2020). For opment and carbon emission. At the same time, there is a positive as­
this reason, various researchers have shown their reasonable interest in sociation between the NEN sources and environmental degradation.
dealing with such ED (Chien, Chau, et al., 2021; Chien, Pantamee, et al., Nguyen, Pham, and Tram (2020) also considered green technology
2021; Li, Chien, Kamran, et al., 2021; C.-H. Nguyen, Ngo, Pham, Nguyen innovation and information and communication technologies in dealing
and Huynh, 2021). In addition, Shen et al. (2020) have expressed their with the carbon emission and EG for the G20 countries. It was found that
view that excessive energy consumption from traditional sources like factors like energy prices, innovation and technology, foreign invest­
fossil fuels has led to global severe air pollution challenges for both ment, and trade openness impede carbon emissions in the targeted
developed and developing economies. Their study has examined the economies. However, their results rejected the existence of the EKC
trends in REN innovation, fossil fuel consumption, and air pollution for hypothesis between the EG and environmental degradation. They have
China’s economy from 2011 to 2017 with the help of a spatial mea­ ignored the renewable and non-renewable energy consumption aspects
surement approach. Their findings revealed that the spatial correlation to examine environmental degradation. The current study has consid­
with fossil fuel consumption is weakening, whereas the global trends for ered renewable and non-renewable energy consumption while exam­
the REN and green innovation are increasing over the period. It is ining environmental degradation. Khan, Ali, Umar, Kirikkaleli, and Jiao
observed that the consumption of fossil fuel energy has increased the air (2020) have also examined the role of REN and ERT in determining the
pollution in the local environment of China. The study has ignored the carbon emissions and international trade for the G7 economies. The
role of environmental taxes in the framework that significantly in­ study findings confirmed that the factors such as ecological innovation
fluences environmental performance. The present study has fulfilled this and REN are playing a positive role in improving environmental
gap and added environmental taxes along with renewable and sustainability.
non-renewable energy consumption and ecological innovation to check The role of ERT in dealing with environmental degradation like
environmental degradation. carbon emission and haze pollution is also under observation by several
Awodumi and Adewuyi (2020) examined the role of NREN con­ researchers (Chien, Ajaz, et al., 2021; Chien, Ngo, Hsu, Chau and Iram,
sumption in dealing with the growth of the economy and CE for the 2021; Hsu et al., 2021; Li, Chien, Ngo, et al., 2021). In addition, Niu et al.
various oil-producing states in the region of the African region. It is (2018) have examined the linkage between ERT shocks and carbon

2
F. Chien et al. Journal of Environmental Management 297 (2021) 113420

emissions for China’s economy. The study findings confirmed that ERT Table 1
shocks can improve the structure of energy by promoting the usage of Results of CSD analysis.
clean energy in China’s economy. Nevertheless, they study has only Variable Test Statistics (p-values)
investigated the Chinese economy and ignored the other economics.
CE 18.012*** (0.000)
Thus, limit the scope of their study. Contradictorily, the present study PM2.5 21.102*** (0.000)
has examined the Asian countries where the environmental issues are at REN 17.231*** (0.000)
their peak and have similar kind of environmental issues. Wolde-Rufael NEN 20.100*** (0.000)
and Mulat-Weldemeskel (2021) investigated ERT and stringency pol­ ECO 22.010*** (0.000)
ERT 19.006*** (0.000)
icies in reducing carbon emissions among the seven emerging econo­
mies. For this purpose, they have applied the heterogeneous panel Note: ***, ** & * explain the level of significance at 1 %, 5 %,
modeling and the augmented mean group, which is unbiased and pro­ and 10 % respectively, CE means carbon emission, PM2.5
duces some consistent outcomes. At the same time, the factors like ERT means haze pollution, REN means renewable energy, NEN
means non-renewable energy, ECO means ecological inno­
have shown their good role in reducing the issues like ED in the form of a
vation, and ERT means environmental taxes.
higher level of carbon emission. Aydin and Esen (2018) have tested the
impact of ERT and their association with CO2 emissions. The study
findings confirmed an asymmetrical association between the ERT, en­ examined the role of renewable and non-renewable energy and
ergy taxes, transport taxes, and taxes on pollution, which reduces carbon ecological innovation, and environmental taxes on environmental
emissions in the natural environment. Yet, they have neglected the degradation. These variables are mentioned in Equation (1) as follows.
innovation aspect to examine environmental degradation. Therefore, CEit = δ1it + δ2itRENit + δ3itNENit + δ4itECOit + δ5itIMPit + αiϕit
the present study has taken ecological innovation into consideration (Equation 1)
while examining the environmental degradation.
Although a range of studies have discussed the association between ∑
Pw ∑
Pz
environmental degradation and its positive and negative determinants Wi, t = γI, i Wi, t − 1 + βI, iZi, t − 1 + εi, t (Equation 2)
(Chien, Kamran, Nawaz, et al., 2021; Li, Chien, Hsu, et al., 2021; Mohsin I=0 I=0

et al., 2021; Nawaz, Seshadri, et al., 2021; Sadiq et al., 2021). However, Under Equation (2), we specified the autoregressive distributed lags
none of the above studies were observed in the Asian region specifically model. However, Equation (3) was extended to Equation (4) to consider
for those economies who are entitled as highly polluted (Shair et al., the cross-section averages for each study regressor (Chudik and Pesaran,
2021; Xueying et al., 2021; Kikulwe and Asindu, 2020; Sun et al., 2020; 2015).
Zhuang et al., 2021). Additionally, the trends in the present literature
∑
Pw ∑
Pz ∑
Px
confirmed that much attention is paid towards carbon emission and Wi, t = γI, i Wi, t − 1 + βI, iZi, t − 1 + αi, IXt − 1+εi, t
similar other greenhouse gas emissions, yet haze pollution like PM2.5 is I=0 I=0 I=0
not widely observed through some advanced methodological perspec­ (Equation 3)
tive. For this reason, the present study tries to cover this gap while
Where in the above Equation (3), the titles like X indicate the average
exploring the trends in carbon emission and haze pollution like PM2.5
values for both of the dependent variables and set of independent var­
through renewable, NREN, ecological innovation, and ERT.
iables under observation. Additionally, Pw, Pz, and Px show the lag
values for each of the variables, while Wi,t specifies the dependent
3. Research methodology
variables like carbon emission, whereas Zi,t indicates all the explanatory
variables which were entitled such as REN, NREN, ERT, and ecological
Initially, our study focused on the cross-sectional dependence (CSD)
innovation. Finally, the long-run estimation for the study coefficients
among the different units. It was observed that while going for the unit
through the mean group estimator was observed for the CS-ARDL with
root process, testing for the ERT was very much useful. Furthermore,
the help of the following Equation (4).
various factors were associated with CSD. This would also specify that if
/
the CSD is ignored, it may lead to some spurious outcomes along with ∑Pz ∑
the bias stationarity (Flores, 2019; Westerlund and Edgerton, 2007). In πĈS − ARDL, i = B1, i Pw 1 − . (Equation 4)
our study, we employed the Pesaran Cross-section Dependence (CD)
I=0 I=0

test. After getting the findings through the Pesaran CD test, the next step Meanwhile, the mean group of the study was presented with the help
was to examine the stationarity for the study data. Concurrently, the of following Equation (5).
study’s data problem of non-stationarity was dealt both in the recent and
past literature (Cheung et al., 2019; Fotheringham et al., 1997; Jiang 1 ∑N
πMG = πi (Equation 5)
et al., 2020). Additionally, as provided by Bai and Carrion-I-Silvestre N i=1
(2009), the research contribution was dealing with the issue of multi­
However, the short-run coefficients in the present study were esti­
ple structural breaks as well. In the presence of CSD, the title of het­
mated with the help of following Equation (6).
erogeneity and issues like non-stationarity in our study applied the
heterogeneous estimation like Westerlund and Edgerton (2008), as such P∑
w− 1 ∑
Pz

methods help to find the structural breaks along with the existence of ΔWi, t = ϑi[Wi, t − 1 − πiZi, t] − γI, i, ΔIWi, t − 1 + t − 1βI, iΔIZi, t
cointegration. Furthermore, unlike the first- and second-generation
I=1 I=0

∑
Px
tests, Westerlund and Edgerton (2008) deal with the CSD and the + αi, IXt + εi, t
possible structural breaks at different cross-sections. I=0
In addition, whenever there is an issue of unobserved common fac­ (Equation 6)
tors correlated with the regressors in the model, the CS-ARDL approach
is quite feasible, which has several dynamics. In addition, the present 4. Results and discussion
study developed the research framework by using energy security the­
ory. This theory exposed that the rapid technological variations in the The findings in Table 1 reports the CSD of the study variables. It was
energy sector and innovation could radically change the future energy observed that without examining the title of CSD, there would be a bias
outlook while switching to renewables has some positive climate and in the cointegration, and unit root analysis as expressed by Churchill
environmental effects. The present study also followed this theory and

3
F. Chien et al. Journal of Environmental Management 297 (2021) 113420

Table 2
Unit root test.
Level I(0) First Difference I(1)

Variables CIPS M-CIPS CIPS M-CIPS

CE − 5.025*** − 7.018** – –
PM2.5 − 3.002*** − 8.001** – –
REN − 5.012*** − 9.101** – –
NEN − 4.001*** − 6.021** – –
ECO − 3.011*** − 7.102** – –
ERT − 4.102*** − 8.121** – –

Bai and Carrion-I-Silvestre (2009)

Z Pm P Z Pm P

CE 0.447 0.835 21.149 − 3.036*** 6.215*** 76.154***

PM2.5 0.393 0.933 17.148 − 4.006*** 5.556*** 61.058***
REN 0.499 0.731 24.365 − 3.657*** 6.059*** 68.187***
NEN 0.564 0.672 18.176 − 4.067*** 5.389*** 77.089***
ECO 0.339 0.856 19.765 − 4.561*** 7.005*** 71.058***
ERT 0.429 0.932 20.048 − 3.005*** 6.556*** 62.058***

Note: The significance level is determined by 1 %, 5 %, and 10 % indicated through ***, **, and * respectively. CE means carbon emission, PM2.5 means haze pollution,
REN means renewable energy, NEN means non-renewable energy, ECO means ecological innovation, and ERT means environmental taxes.

Table 3 Table 4
Results of Slope heterogeneity analysis. Results of Westerlund and Edgerton (2008) panel cointegration analysis.
Dependent Variable: CE Test No break Mean shift Regime shift

Statistics Test value (p-value) Dependent Variable: CE

Delta tilde 38.178*** (0.000) Zϕ(N) − 4.234*** − 3.901*** − 5.021***

Delta tilde adjusted 43.246*** (0.000) Pvalue 0.000 0.000 0.000
Zτ(N) − 4.563*** − 3.810*** − 5.010***
Dependent Variable: PM2.5 Pvalue 0.000 0.000 0.000

Statistics Test value (p-value)

Dependent Variable: PM2.5
Delta tilde 30.189*** (0.000)
Delta tilde adjusted 45.156*** (0.000) Zϕ(N) − 4.345*** − 3.387*** − 5.011***
Pvalue 0.000 0.000 0.000
Zτ(N) − 4.789*** − 3.347*** − 5.100***
et al. (2019); Salim and Shi (2019); Westerlund and Edgerton (2007). Pvalue 0.000 0.000 0.000
Specifically, as per the empirical output, the null hypothesis of no ERT Note: ***, **, and * explain the level of significance at 1 %, 5 %, and 10 %
for all the study variables like CE, PM2.5, REN, NEN, ECO, and ERT were respectively.
rejected at 1 % level of significance. This would justify the argument that
there was a presence of ERT in the study data. In addition, the study emission. Similarly, for PM2.5, the findings are observed as highly sig­
findings in Table 2 report the unit root test with and without a structural nificant at 1 % based on the Delta tilde and adjusted Dela tilde.
break as suggested by Pesaran (2007) and Bai and Carrion-I-Silvestre The results in Table 4 show the outcomes for the Westerlund and
(2009) to examine the stationary properties of the study variables Edgerton (2008) panel cointegration analysis. The findings were based
along with the CSD, heterogeneity, and structural breaks as well. To on both null and alternative hypotheses, where the former indicates no
check for the stationary or unit root problem, both null and alternative cointegration among the study variables and the presence of CSD and
hypotheses were under observation where the former indicates statio­ structural breaks. The empirical outcomes have provided evidence that
narity while the latter supports the argument that there is a we have rejected the null hypothesis that no integration exists among
non-stationarity of the study data. The empirical output indicates that the study variables. This would justify the argument that a cointegration
we have rejected the null hypotheses for all the study variables. Addi­ relationship exists between the variables of interest.
tionally, this research has also applied the cointegration analysis sug­ Table 5 shows the cointegration results for the Banerjee and
gested by Banerjee and Carrion-i-Silvestre (2017), which is further based Carrion-i-Silvestre (2017) where we observed that there is an existence
on the common correlated effects mean group or CCEMG approach. One of a cointegrating association between the study variables at 1 % level of
of the key benefits of using this approach is that it helps deal with both significance for the full sample and each of the countries like China,
strong and weak CSD, heterogeneity, non-stationarity panel data, and Japan, South Korea, Russia, Indonesia, Malaysia, Philippine, Singapore,
many other issues. Thailand, and Vietnam. In addition, the cointegration association for
In addition, after analyzing the unit root test and CSD, the next step both Westerlund and Edgerton (2008) and Banerjee and
was to examine the findings for the slop heterogeneity with the help of Carrion-i-Silvestre (2017) for the carbon emission and PM2.5 are
its modified version and the slop homogeneity test as proposed by confirmed. Therefore, we investigated the long-run and a short-run as­
Pesaran and Yamagata (2008). This method will help examine whether sociation between the dependent variables and their core determinants.
there is a presence of homogenous or heterogenous slop coefficients Table 6 shows the outcomes for the CS-ARDL for the study variables
because the homogenous coefficient may lead to some misleading out­ like REN, NEN, ECO, ERT, and both dependent variables like carbon
comes (Alam et al., 2018; Asha and Makalela, 2020Asha & Makalela). emission and PM2.5. The empirical results specify a significant and
Based on the null hypothesis, it is assumed that the slope coefficients are negative impact of REN on the carbon emission with the coefficient of
homogenous, while the alternative hypothesis assumed that they are − 0.211 and t-score of − 6.011, respectively. This would justify the
not. The findings in Table 3 indicate a rejection of the null hypothesis at argument that increasing trends in REN is beneficial for the natural
1 % level of significance where the dependent variable was carbon

4
F. Chien et al. Journal of Environmental Management 297 (2021) 113420

Table 5 Table 7
Cointegration analysis. CS-ARDL analysis (Short-run).
Countries No deterministic specification With constant With trend Variables Coefficients t-statistics p-values

Dependent Variable: CE emission Dependent Variable: CE

Full Sample − 5.872*** − 3.761*** − 6.983*** REN − 0.087*** 4.078 0.000

China − 4.285*** − 4.174*** − 4.396*** NEN 0.028** 2.201 0.038
Japan − 6.145*** − 6.034*** − 6.256*** ECO − 0.017*** − 3.013 0.000
South Korea − 5.247*** − 5.136*** − 5.358*** ERT − 0.095** − 2.083 0.046
Russia − 3.276*** − 3.165*** − 3.387*** ECT(-1) − 0.302*** − 4.003 0.000
Indonesia − 4.176*** − 4.065*** − 4.287***
Malaysia − 6.278*** − 6.167*** − 6.389*** Dependent Variable: PM2.5
Philippine − 3.201*** − 3.101*** − 3.301***
REN − 0.086*** − 3.062 0.000
Singapore − 7.145*** − 7.034*** − 7.256***
NEN 0.049 1.510 0.148
Thailand − 5.247*** − 5.136*** − 5.358***
ECO − 0.012*** − 3.033 0.000
Vietnam − 3.201*** − 3.101*** − 3.413***
ERT − 0.086** − 2.673 0.018
Dependent Variable: PM2.5 ECT(-1) − 0.203*** − 4.101 0.000

Full Sample − 4.512*** − 3.445*** − 5.443*** Note: ***, **, & * explain the level of significance at 1 %, 5 %, and 10 %
China − 5.108*** − 3.954*** − 6.085*** respectively. CE means carbon emission, PM2.5 means haze pollution, REN
Japan − 3.568*** − 3.681*** − 4.697*** means renewable energy, NEN means non-renewable energy, ECO means
South Korea − 6.665*** − 5.664*** − 7.058***
ecological innovation, and ERT means environmental taxes.
Russia − 4.417*** − 3.250*** − 5.669***
Indonesia − 5.390*** − 4.005*** − 6.871***
Malaysia − 7.118*** − 6.568*** − 8.572*** years, various authors have also observed the association between NEN
Philippine − 4.055*** − 3.992*** − 5.331*** and carbon emissions. For example, Sharif et al. (2019) have provided
Singapore 6.689*** 5.664*** 7.050***
empirical justification while claiming that NREN is positively and
− − −
Thailand − 4.744*** − 3.774*** − 6.661***
Vietnam − 3.004*** − 3.058*** − 4.448*** significantly linked to environmental degradation. Bekun, Alola, and
Sarkodie (2019) have also justified that NEN sources like fossil fuels are
Note: Critical value (CV) at 5 %** and 10 %* with constant is − 2.32, − 2.18 and
responsible for exerting more distortion to the natural environment in
with the trend is − 2.92 and − 2.82.
the form of higher carbon emissions. Inglesi-Lotz and Dogan (2018) have
also claimed that increases in NEN raise carbon emission. In addition,
Table 6 the findings in Table 6 also report the role of ECO in determining the
CS-ARDL analysis (Long-run). carbon emissions for the targeted economies. The results show that there
Variables Coefficients t-statistics p-values
is a significant and negative impact of ECO on the CO2 emissions with a
Dependent Variable: CE coefficient of − 0.364. This indicates that the increasing trend in
ecological innovation is a good sign towards lowering the carbon
REN − 0.211*** − 6.011 0.000
NEN 0.295*** 4.107 0.000 emissions in the natural environment. This claim is also justified by
ECO − 0.267** − 2.171 0.041 earlier studies like Töbelmann and Wendler (2020), who indicated that
ERT − 0.275* − 1.912 0.055 ecological innovations play their role in lowering carbon dioxide emis­
CSD-Statistics – 0.044 0.981 sions, specifically for the European economies. White and van Koten
(2016) have also provided justification for socio-ecological innovation
Dependent Variable: PM2.5 in reducing carbon emissions. Furthermore, the findings under long-run
REN − 0.317*** − 4.011 0.000 estimation also confirm a significant and negative role of ERT in
NEN 0.270*** 5.006 0.000 reducing carbon emissions. This means that for every 1 % increase in the
ECO − 0.364*** − 4.506 0.000 value of ERT, there is a decline of 0.275 % in the value of CO2 emissions
ERT − 0.257*** − 3.026 0.000
CSD-Statistics – 0.021 0.810
in the targeted economies. Hao, Umar, Khan, and Ali (2021) have also
observed a similar output and claimed that ERT is a good sign in dealing
Note: ***, **, & * explain the level of significance at 1 %, 5 % and 10 % with environmental degradation like carbon emissions. However, the
respectively. CE means carbon emission, PM2.5 means haze pollution, REN
empirical findings by Shahzad (2020) claimed that the role of ERT in
means renewable energy, NEN means non-renewable energy, ECO means
dealing with carbon emission is still ambiguous, hence, requires more
ecological innovation, and ERT means environmental taxes.
investigation.
In addition, the findings for the second dependent variable, PM2.5,
environment while playing its role in lowering the carbon emission.
are also shown in Table 6 based on the long-run estimation. Similar to
Particularly, these findings claim that for every 1 % increase in the value
carbon emissions, REN was showing its role as a significant determinant
of REN, there is a decline of 0.211 % in carbon emission. These findings
in lowering haze pollution like PM2.5 in the natural environment of
are consistent with the empirical contribution of Sharif et al. (2019),
targeted economies. Specifically, the results showed that 1 % increase in
who believe that REN has a negative impact on the degradation of the
the REN caused a − 0.317 % decrease in the value of PM2.5. Similarly,
natural environment. Hence, it helps to reduce environmental hazards
the findings under long-run estimation for the NREN and PM2.5 indi­
as well. Cheng, Ren, and Wang (2019) have also provided a similar
cated a positive and significant association between both. This means
justification for the negative association between the REN and carbon
that higher energy consumption from some traditional sources leads to
emission in the natural environment. The authors have also focused on
more haze pollution like PM2.5. On the contrary, the impact of
China’s economy for observing the linkage between carbon dioxide
ecological innovation in reducing haze pollution was also significant at
emissions and energy sources like NEN. They found a negative impact of
1 % (Table 6). It showed that for every 1 % increase in ECO, there was a
REN on the CO2 emission in the long-run estimation.
reduction of − 0.267 % in the value of PM2.5 in the study period among
On the other hand, the impact of NEN on carbon emission is observed
the targeted economies. The study findings in Table 6 for the long-run
as positively significant at 1 % level of significance. This would justify
estimation confirmed that ERT is a good source for environmental
the argument that higher NEN leads to a higher level of carbon emission
degradation like PM2.5. This fact is observed with a coefficient of
in the natural environment. More specifically, for every 1 % increase in
− 0.257, significant at 1 %.
NEN, there is an increase of 0.295 % in CE value and vice versa. In recent

5
F. Chien et al. Journal of Environmental Management 297 (2021) 113420

Table 8
AMG & CCEMG for robustness check.
Dependent Variables CE Augmented Mean Group (AMG) Common Correlated Effect Mean Group (CCEMC)

Coefficients t-statistics p-values Coefficients t-statistics p-values

REN − 0.264*** − 3.710 0.000 − 0.103*** − 4.201 0.000

NEN 0.235*** 6.105 0.000 0.290*** 5.025 0.000
ECO − 0.306*** − 8.352 0.000 − 0.208*** − 3.280 0.000
ERT − 0.400*** − 4.548 0.000 − 0.141*** − 3.685 0.000
Wald test – 24.310 0.000 – 18.546 0.000

Dependent Variable PM2.5

REN − 0.184*** − 5.540 0.000 − 0.103*** − 6.201 0.000

NEN 0.233*** 4.855 0.000 0.362*** 5.025 0.000
ECO − 0.347*** − 3.621 0.000 − 0.208*** − 4.667 0.000
ERT − 0.102*** − 3.473 0.000 − 0.142*** − 4.442 0.000
Wald test – 14.301 0.000 – 8.050 0.000

Note: ***, **, & * explain the level of significance at 1 %, 5 %, and 10 % respectively. CE means carbon emission, PM2.5 means haze pollution, REN means renewable
energy, NEN means non-renewable energy, ECO means ecological innovation, and ERT means environmental taxes.

The short-run findings are reported in Table 7, where we observed a environmental taxes positively impacted the environment in Asian
significant and negative influence of REN on carbon emission and haze countries. However, the policies related to non-renewable energy are
pollution like PM2.5. The results again justify the argument that energy moderately effective and need to be examined frequently to control the
sources from some renewable titles are a good sign in reducing envi­ non-renewable energy effects on environmental degradation. Addi­
ronmental degradation in terms of carbon emission and PM2.5. In tionally, this study provided the guidelines for regulators to develop
contrast, the influence from NEN was observed as positively significant effective policies related to non-renewable energy that severely affected
for the carbon emission but insignificant for PM2.5. This means that the environment. Besides, these economies should give enough attention
energy from fossil fuels consumption and similar other sources are towards ecological innovation and the techniques like ERT thoroughly
causing environmental degradation in the targeted economies. Howev­ to limit harmful outcomes on the natural environment like high emission
er, like long-run estimation, the study findings under short-run estima­ and haze pollution. Meanwhile, emissions-oriented energy sources
tion also provides evidence for the significant role of ecological should be replaced with renewable ones to control environmental
innovation and ERT in reducing the CE and PM2.5, accordingly. degradation up to a reasonable level. The study also suggests that
Finally, Table 8 reports the robustness of findings obtained through changing the technological patterns to some ecological innovations may
augmented mean group and the common correlated effect mean group also lead towards environmental prosperity and more sustainable
methods. The findings confirmed the negative and significant co­ outcomes.
efficients under AMG and CCEMC with the values of − 0.264 and
− 0.103, respectively. These values were highly significant at 1%t. On Credit author statement
the other hand, there was a significant and positive relationship between
NEN and CE under AMG and CCEMG output with values of 0.235 % and Fengsheng Chien: Conceptualization, Writing – Original Draft,
0.290 %. Additionally, the findings for the ECO are observed as nega­ Muhammad Sadiq: Writing – Original Draft, Supervision and Proof­
tively significant under both of the robust checks, as reported in Table 8. reading. Muhammad Atif Nawaz: Methodology and Analysis.
The outcomes for the ERT were also negatively significant at 1 % Muhammed Sajjad Hussain: Software and Revision. Tai Duc Tran:
observed for the CE and PM2.5 for both AMG and CCEMC. Meanwhile, Writing Literature – Review & Editing. Tiep Le Thanh: Analysis and
the relationship between NEN and PM2.5 was also observed as posi­ interpretation – Formatting.
tively significant under both of the robust check methods.

5. Conclusion and implications Declaration of competing interest

The present study analyzed the impact of REN, NREN, ecological The authors declare that they have no known competing financial
innovation, and ERT on carbon emission and haze pollution like PM2.5 interests or personal relationships that could have appeared to influence
in the case of top Asian economies. The findings confirmed a long-run the work reported in this paper.
association between the REN and carbon emission, NEN and carbon
emission, ecological innovation and carbon emission, and ERT and References
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