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Impact of energy efficiency, technology innovation, institutional quality, and trade openness on greenhouse gas emissions in ten Asian economies

April 2022
Environmental Science and Pollution Research 30(4):3

April 2022
30(4):3

DOI:10.1007/s11356-022-20079-3

Authors:

Hoang-Tien Nguyen

WSB MERITO University

Amena Sibghatullah

Daru Asih

Universitas Mercu Buana

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Despite the fact that Asian economies have experienced robust economic growth in recent decades, rising pollution emissions have raised worries among policymakers about the long-term stability of this output growth. Knowing this fact, the present study attempts to empirically analyze the impact of some important factors, e.g., energy efficiency, technology innovations, trade openness, and institutional quality, on environment in 10 Asian economies over the period 1995-2018. Taking into account the slope heterogeneity and cross-sectional dependence present in the data, Westerlund and Edgerton (2008) and Banerjee and Carrion-i-Silvestre (2017) cointegration techniques and cross-sectionally augmented autoregressive distributed lag model (CS-ARDL) estimation are applied. For robust analysis, augmented mean group (AMG) and common correlated effects mean group (CCEMG) are also employed in the study. The empirical findings provided by selected variables reveal that both trade openness and institutional quality have detrimental impact, whereas energy efficiency and technology innovations have favorable impact on environmental quality in the selected economies. Empirical findings are robust to various policy recommendations. To create a sustainable future environment, Asian economies should focus on the improvement of their institutions quality and increase investments in technology innovations. The Asian countries must encourage trade-related environmental regulations and energy efficiency policies for better and sustainable environmental quality.

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Vol.:(0123456789)

1 3

Environmental Science and Pollution Research

https://doi.org/10.1007/s11356-022-20079-3

APPLIED ECONOMICS OFENERGY ANDENVIRONMENT INSUSTAINABILITY

Impact ofenergy eﬃciency, technology innovation, institutional

quality, andtrade openness ongreenhouse gas emissions inten Asian

economies

ZhengWenlong1· NguyenHoangTien2· AmenaSibghatullah3· DaruAsih4· MochamadSoelton4· YantoRamli4

Received: 23 October 2021 / Accepted: 31 March 2022

Abstract

Despite the fact that Asian economies have experienced robust economic growth in recent decades, rising pollution emissions

have raised worries among policymakers about the long-term stability of this output growth. Knowing this fact, the present

study attempts to empirically analyze the impact of some important factors, e.g., energy eﬃciency, technology innovations,

trade openness, and institutional quality, on environment in 10 Asian economies over the period 1995–2018. Taking into

account the slope heterogeneity and cross-sectional dependence present in the data, Westerlund and Edgerton (2008) and

Banerjee and Carrion-i-Silvestre (2017) cointegration techniques and cross-sectionally augmented autoregressive distributed

lag model (CS-ARDL) estimation are applied. For robust analysis, augmented mean group (AMG) and common correlated

eﬀects mean group (CCEMG) are also employed in the study. The empirical ﬁndings provided by selected variables reveal

that both trade openness and institutional quality have detrimental impact, whereas energy eﬃciency and technology inno-

vations have favorable impact on environmental quality in the selected economies. Empirical ﬁndings are robust to various

policy recommendations. To create a sustainable future environment, Asian economies should focus on the improvement

of their institutions quality and increase investments in technology innovations. The Asian countries must encourage trade-

related environmental regulations and energy eﬃciency policies for better and sustainable environmental quality.

Keywords Energy eﬃciency· Technological innovations· Trade openness· Institutional quality· GHG emission

Introduction

The most prominent environmental concern faced by

the international community has been greenhouse gas

emission (GHG emission) which is a key determinant of

environmental hazards such as climate change and global

warming (Alola, 2019). Carbon dioxide gas which is mostly

derived from fossil resources causes 76% of total GHG emis-

sions and is widely acknowledged as the most signiﬁcant

environmental pollutant. In this context, GHG emissions

continue to pose a serious risk to the human lives and the

environment. Because of the global warming induced by

Responsible Editor: Roula Inglesi-Lotz

* Nguyen Hoang Tien

nguyenhoangtien@siu.edu.vn

Zheng Wenlong

1556236510@qq.com

Amena Sibghatullah

amena@kiet.edu.pk

Daru Asih

daru_asih@mercubuana.ac.id

Mochamad Soelton

soelton@mercubuana.ac.id

Yanto Ramli

yanto.ramli@mercubuana.ac.id

1 School ofEconomics andManagement, Chang’an

University, Middle-section of Nan’er Huan Road Xi’an,

Xi’an710064, ShaanXiProvince, China

2 Saigon International University, HoCHiMinhCity, Vietnam

3 College ofManagement Sciences, KIET University, Karachi,

Pakistan

4 Universitas Mercu Buana, Jalan Meruya Selatan No. 1,

Meruya Selatan, Kembangan, Jakarta, Indonesia

Environmental Science and Pollution Research

1 3

GHG emissions, countries have taken actions to minimize

these emissions by addressing energy eﬃciency regulations

in recent decades (Akdag & Yıldırım, 2020; Zhuang etal.,

2021). Countries all over the world have continued to look

for environmentally friendly sources of energy like “renewa-

ble energy” and alternate sources. The importance of energy

eﬃciency and technological innovation has grown in recent

years as a result of their potential to reduce environmen-

tal damage and cost saving (Akdag & Yıldırım, 2020; Sun

etal., 2020). Energy eﬃciency means using lesser energy

to produce the same amount of goods and services. Numer-

ous advantages might be gained by increasing investment in

energy eﬃciency. Energy eﬃciency measures, for example,

can help to improve ecosystem sustainability, reduce envi-

ronmental impact by lowering CO2 emissions, reduce fossil

fuel dependence, and stimulate industrial rivalry by lowering

operational costs (Akram etal., 2020b). The global climate

change can be combated by energy eﬃciency in two ways.

First, “The lesser the energy utilized, the lower the amount

of emissions released.” It suggests that eﬃciency in energy

has a signiﬁcant contribution in energy policies for future

programs and climate change. Secondly, energy eﬃciency

that is cost-effective accomplishes these environmental

advantages at a low cost lowering the economic costs of

attaining climate policy goals (Javid & Khan, 2020). Fur-

thermore, energy eﬃciency is a key component of the green

development plans of the countries which aim to minimize

emissions by improving energy eﬃciency (Akram etal.,

2020b; Shair etal., 2021; Zhao etal., 2021). Furthermore,

meeting the Sustainable Development Goals necessitate the

increased use of energy eﬃciency. Regardless of its impact

on GHG emissions, energy eﬃciency has numerous public

advantages too. It lowers energy costs for businesses, boosts

productivity and economic growth, improves energy secu-

rity, reduces prices of energy, and mitigates air pollution

emissions (Nawaz, etal., 2021a, b).

Several earlier studies (e.g., Goulder & Mathai, 2000;

Hasanbeigi etal., 2012; Santra, 2017) identiﬁed that adop-

tion of modern technology has the ability to minimize GHG

emissions as it improves energy eﬃciency without aﬀecting

economic development. Environmental technology measures

may have two eﬀects on GHG emissions. At one side, they

inﬂuence carbon-based fuel pricing by implementing taxes

that eﬀectively minimizes consumption of energy and pollu-

tion emissions, and on the other side, these policies incentiv-

ize companies to acquire or develop new technologies to get

alternative fuels with lower carbon emissions. However, it is

argued that the technological advancements add to resource

depletion and environmental damage through the rebound

eﬀect. Industrial sector technology tends to enhance produc-

tion activities which necessitate additional raw materials and

resources of energy and compromises environmental quality

(Greening etal. 2000; Khan etal. 2017). However, there is

limited empirical work available indicating the link between

technology innovations and GHG emissions, and this study

is an addition to the existing literature by estimating whether

or not innovations help to mitigate GHG emissions (Amin

etal. 2020; Mohsin etal. 2021).

The environmental impacts of trade openness have also

been a hot topic of research in recent years. Empirical stud-

ies suggest that free trade policies may result in shifting

pollution-intensive items or “dirty goods” production from

industrialized to underdeveloped countries (Ansari etal.,

2019; Salahuddin etal., 2018; Shahbaz etal., 2013). The

expansion of liberated trade and the relocation of polluting

item industries from one region to others are causing envi-

ronmental issues. Despite the fact that poor countries have

increased their emissions in recent years, rich countries have

historically been the source of a major percentage of world

emissions. In general, it is unclear whether cross-national

free trade causes environmental degradation or not. Trade

aﬀects the environment depending upon its three eﬀects on

an economy, namely, composition eﬀect, scale eﬀect, and

technological eﬀect. According to scale eﬀect, the produc-

tion scale and economic growth level are increased by the

trade resulting in higher GHG emissions due to increased

output and energy consumption. According to the prin-

ciple of comparative advantage when the trade composi-

tion matters, especially when demand for dirty items rises

output rises at the same time. Finally, the technique eﬀect

suggests that trade increases competitiveness, eﬃciency,

and environmentally beneﬁcial technology, all of which

reduce GHG emissions. Hence, trade can have a favorable

or harmful impact on GHG emissions depending on the rela-

tive magnitude of these three eﬀects (Ansari etal., 2020).

Moreover, with the increased acknowledgment of the institu-

tions, institutional quality indicators have just begun to be

integrated into environmental sustainability studies. In this

context, institutional factors, e.g., voice and accountability,

government eﬀectiveness, democracy, law, political stabil-

ity, corruption, bureaucratic quality, and regulatory quality,

have been extensively studied as the impact of institutional

quality on issues related to environment particularly carbon

and other pollutant emission. It is evidenced that market fail-

ures can be avoided, eﬀective regulations can be generated,

and sound environmental policies can be planned with well-

performing institutions, all of which can have a signiﬁcant

impact on environmental stability (Li etal., 2021; Riti, Shu,

& Kamah, 2021).

Taking into consideration the above-mentioned argu-

ments, the study aims to estimate the impact of technol-

ogy innovation, institutional quality, energy eﬃciency,

and trade openness on GHG emissions in a panel of ten

Asian economies (India, Indonesia, China, Malaysia,

Philippines, Pakistan, Singapore, Sri-Lanka, Thailand,

and Vietnam) over 1995–2018 period. To the author’s

Environmental Science and Pollution Research

1 3

knowledge, no attempt has been made to evaluate the rela-

tionship between these variables in a single framework

across Asian countries. Asia is a well-known region of the

largest economies of the world, accounting for more than

half of the global population. The decision to study Asia is

also inﬂuenced by the fact that it accounts for 28% of the

global energy demand and 53% of the total coal consump-

tion of coal, both of which are signiﬁcant sources of GHG

emissions (Amin etal., 2020). This region contributes to

half of the global CO2 emission. The Republic of Korea,

China, India, and Singapore are among the top greenhouse

gas–emitting countries in the world. South Korea, Japan,

Malaysia, and China are among the top trading economies

in the world. India, Pakistan, China, and Japan are among

the most populous countries in the world. China, as one

of the world’s fastest-growing economies, has seen a mas-

sive increase in energy consumption, as well as a massive

increase in greenhouse gas emissions over the last three

decades (Chien etal., 2021a, 2021b; Saleem etal., 2020).

Thus, our study focuses on the relation between technol-

ogy innovation, energy eﬃciency, institutional quality,

trade openness, and GHG emissions in such a highly emit-

ting and fast-growing region and helps policymakers to

employ eﬃcient policies for environmental sustainability

in these countries. Hence, the foremost objective of the

study is to check to what extent the technology innova-

tion control GHG emissions. The objectives also include

examining energy eﬃciency role in reducing GHG emis-

sions. In addition, the investigation of the institutional

quality impact on GHG emission is also part of the study

objectives. Finally, the objective of the study includes the

investigation of trade openness impact in reducing GHG

emission.

The remaining sections of the study are structured as

follows: the “Literature review” section gives the existing

literature review. The “Econometric methodology and data

sources” section provides the description of the variables

and the methodology employed for empirical analysis. The

“Empirical results” section outlines the empirical ﬁndings

of the analysis. Last, conclusion of the study and some

policy recommendations are given in the “Discussions”

section.

Literature review

For the past few decades, extensive research has been car-

ried out on the relationship between technology innovations,

trade openness, quality of institutions, energy eﬃciency, and

environmental quality both for emerging and developed

countries. We will go through a few key researches from

the current literature in this section. Apart from that, we also

discuss the inﬂuence of our selected variables on a country's

environmental quality.

Technological innovations–environment nexus

Technological innovations have been introduced as a new

determinant in the environmental studies recently because

it is acknowledged as a signiﬁcant mitigating factor of pol-

lutant emissions globally (Chien, etal., 2021a, b, c, d, e;

Nawaz, etal., 2021a, b). In existing literature, many studies

have found an association between quality of environment

and technological innovations. For example, Long etal.

(2018) analyzed how innovations aﬀected carbon emis-

sions in China over the 1997–2014 period. The researcher

concluded that innovations had an adverse impact on CO2

emissions and a contribution to improve the environmental

quality (Long etal., 2018). In a study of Malaysia, Yii and

Geetha (2017) tried to study the link between carbon dioxide

emission and technology innovation. Using the data from

1971 to 2013, authors concluded that technology innova-

tion had negative inﬂuence on carbon dioxide emission in

Malaysia (Yii & Geetha, 2017). The study of Dauda etal.

(2021) in the context of nine African economies concluded

that innovations increased the CO2 emissions, but square

of innovations reduced it for the whole panel. However, the

inverted U-shaped relation of innovation and CO2 emissions

was found only in Egypt, South Africa, Morocco, and Mau-

ritius (Dauda etal., 2021). In the same vein, Hodson etal.

(2018) studied the energy division of the USA and estab-

lished that innovations declined carbon dioxide emissions

by providing eﬃcient energy usage and cost-eﬃcient way

of producing lesser carbon (Hodson etal., 2018).

The purpose of the study of Cheng etal. (2021) was to

see how energy technology innovation aﬀected CO2 emis-

sion in China over 2001 to 2016 period. It was found that

energy technology innovations contributed to intensity of

carbon emission in lower quantile areas but reduced it in

higher quantile areas (Cheng etal. 2021). Using STRIPAT

and EKC theoretical frameworks, Hao etal. (2021) analyzed

the eﬀect of economic growth and technology innovations

on the environment in 25 underdeveloped Asian economies

for 1998–2019 period, and the results of their study indi-

cated that technological innovations and economic growth

reduced carbon emission and protected environment. Moreo-

ver, it was also conﬁrmed that EKC hypothesis existed in

the Asian economies under study (Hao etal. 2021). Omri

and Hadj (2020) researched the eﬀect of governance, for-

eign investment, and technology innovation on CO2 emis-

sions in twenty-three emerging economies over 1996–2014

period. The ﬁndings of GMM estimation also revealed that

technology innovations had a signiﬁcantly negative impact

on emissions (Omri & Hadj 2020). Similar researches were

Environmental Science and Pollution Research

1 3

conducted by Shahbaz etal. (2020) and Khan etal., 2020a,

2020b).

Technological innovations, on the other hand, were

found to accelerate the process of CO2 emissions also in

some studies. For instance, Ganda (2019) estimated the

eﬀect of innovations on CO2 emission in OECD nations

for 2000–2014 period, and a positive eﬀect of technology

innovations on CO2 emissions was found (Ganda, 2019). Yu

and Du (2019) reached the same conclusion and suggested

that technological innovations had promoting impacts on

emission of carbon in the group with higher growth as com-

pared to the group with lower growth in China (Yu & Du,

2019). Erdoğan etal. (2020) analyzed the relation between

technology innovation and carbon emission in fourteen

countries over 1991 to 2017 period. Their ﬁndings showed

that technology innovation contributed to CO2 emissions in

construction sector (Baloch etal., 2021; Chien etal., 2021c;

Huang etal., 2021a; Erdoğan etal., 2020). Thus, the current

study has developed the following hypothesis:

H1: Technology innovation has a positive and signiﬁcant

impact on GHG emission in developing countries.

Trade openness–environment nexus

Many countries have been able to trade across borders as a

result of globalization. Numerous prior studies have exam-

ined the eﬀect of trade liberalization on the environment

over time. The empirical researches, to be more exact, pro-

duce contentious results. The major conclusions of most

of the studies were that increased trade between coun-

tries pollutes the environment (Nasir & Rehman, 2011;

Ozturk & Al-Mulali, 2015). Some studies, on the other

hand, showed that increased trade between countries help

to reduce pollution levels (Dogan & Seker, 2016; Hos-

sain, 2011). For instance, Atici (2012) estimated how trade

aﬀected carbon dioxide emissions for a sample of South

East Asian economies. It was indicated from the ﬁndings

that increase in exports increased CO2 emissions in the

selected economies (Atici, 2012). Shahbaz etal. (2014)

found that a relationship was existing between income,

trade liberalization, consumption of energy, and carbon

dioxide emissions by applying the ARDL technique and

consumption of energy and trade liberalization had a sig-

niﬁcantly positive impact on carbon emission in the long

run. The results also indicated the validation of the EKC

hypothesis in Tunisia (Shahbaz etal., 2014). Similarly,

Al-mulali and Sheau-Ting (2014) researched the linkage

between consumption of energy, trade income, and carbon

emissions for 1990–2011 period. The ﬁndings showed a

signiﬁcant positive relation between trade, energy usage,

and emission in the long run (Al-mulali & Sheau-Ting,

2014). Ertugrul etal. (2016) analyzed how trade open-

ness, real income, and consumption of energy aﬀected

carbon emissions in the top 10 carbon-emitting develop-

ing economies over the period 1971–2011. Applying Zivot

Andrews unit root test and the VECM Granger causality

method, ﬁndings indicated that consumption of energy,

real income, and trade liberalization were the signiﬁcant

determining factors of carbon emissions, and there existed

many fundamental relationships among the variables of

the study (Chien etal., 2021e; Ertugrul etal., 2016).

In the similar line, Zameer etal. (2020) and Huang

etal. (2021c) investigated the impact of technology inno-

vation, trade openness, FDI, economic growth, and energy

usage on carbon dioxide emissions in India over the period

1985–2017. The ﬁndings of VECM and ARDL bound test-

ing revealed that trade liberalization, usage of energy, and

growth aﬀected CO2 emissions positively, whereas tech-

nology innovations and FDI aﬀected CO2 emission nega-

tively. Moreover a bi-directional relationship was found

among innovation, energy usage, and trade openness in

the long run (Chien etal., 2021d; Huang etal., 2021b;

Tan etal., 2021; Zameer etal., 2020). In a recent work,

Tachie etal. (2020) explored the impact of trade open-

ness in EU 18 developed economies. Their ﬁndings indi-

cated that trade openness accelerated CO2 emission in EU

developed economies (Tachie etal., 2020). Likewise, Bal-

salobre-Lorente etal. (2018) analyzed the linkage between

CO2 emissions and growth in 5 EU economies (Germany,

France, Italy, Spain, and UK) over 1985–2016 period. The

ﬁndings showed that trade liberalization, consumption of

renewable electricity, and economic growth had a positive

eﬀect on CO2 emission (Balsalobre-Lorente etal., 2018;

Sadiq etal., 2021a, b, c; Xiang etal., 2021; Xueying etal.,

2021). The study of Zhang etal. (2017) also indicated that

there was a signiﬁcant negative impact of trade on carbon

emissions in ten developed economies (Zhang etal., 2017).

In contrast, Lv and Xu (2019) analyzed the relationship

between trade liberalization and environment using data

for ﬁfty-ﬁve economies. Their ﬁndings revealed that trade

openness improved the quality of the environment in the

short run. But in the long run, trade openness had harmful

eﬀects on environment (Lv & Xu, 2019). Using ARDL

approach, Ling etal. (2015) found that trade liberalization

improved the environmental sustainability for Malaysian

economy (Ling etal., 2015). Hasanov etal. (2018) and

Destek etal. (2018) revealed insigniﬁcant impact of trade

on carbon emission reduction (Destek etal., 2018; Hasa-

nov etal., 2018). Hence, the present article has established

the following hypothesis:

H2: Trade openness has a signiﬁcant impact on GHG

emission in developing countries.

Environmental Science and Pollution Research

1 3

Energy eﬃciency–environment nexus

There appears to be dearth of literature about the nexus

between energy efficiency and GHG emissions so far.

According to the ﬁndings of Brookes (1990), energy eﬃ-

ciency had a signiﬁcant impact on economic growth. The

study concluded that energy eﬃciency improvements had a

favorable inﬂuence on growth but had no signiﬁcant impact

on GHG emissions (Brookes, 1990). Wang etal. (2017) con-

ducted a study on the linkage between energy eﬃciency and

reduction of carbon emission in China and suggested that

carbon dioxide emission reduction methods as a means of

improving energy eﬃciency should be adopted. The studies

of Wang etal. (2017) and Emir and Bekun (2019) suggested

that energy eﬃciency was an important way to lessen con-

sumption of energy and global warming (Liu etal., 2021a;

Chien etal., 2021b; Ehsanullah etal., 2021; Emir & Bekun,

2019; Wang etal., 2017). Akdag and Yıldırım (2020) stud-

ied the linkage between eﬃciency in energy and GHG emis-

sions for 29 European countries and suggested the negative

link between energy eﬃciency and GHG emission as GHG

emission was found to be reduced by increase in energy eﬃ-

ciency (Akdag & Yıldırım, 2020).

Chien etal. (2021b) analyzed how renewable energy and

energy eﬃciency aﬀected carbon emissions in a panel of

Asian economies and found that a rise in renewable energy

and energy eﬃciency reduced carbon emissions in long

run, but in the short run, both strategies were eﬀective

only for Nepal and India as indicated through heterogene-

ous coeﬃcient estimates (Irfan etal., 2021). Akram etal.

(2020a, b) incorporated the eﬀect of energy eﬃciency in

the construction of EKC hypothesis for a panel of 66 devel-

oping economies, and panel ordinary least squares and

ﬁxed eﬀect panel quantile regression revealed that energy

eﬃciency had an adverse impact on CO2 emission in all

quantiles but with varying degrees (Akram etal., 2020a).

Another study by Akram etal. (2020a, b) concluded that

positive shocks in renewable energy and energy eﬃciency

could signiﬁcantly reduce CO2 emission in BRICS nations

(Akram, etal., 2020a, b; Chien etal., 2021e; Hsu etal.,

2021).

The study of Sathaye and Gupta (2010) was about the

function of energy eﬃciency in CO2 emissions reduction

for India by applying a scenario-based analysis. The results

concluded that in the year 2020, energy eﬃciency improve-

ments would likely to mitigate the CO2 emissions by sixty-

ﬁve million tons in India (Sathaye & Gupta, 2010). Another

scenario-based analysis by Chaturvedi and Shukla (2014)

also evidenced that energy eﬃciency improvements could

be a mitigating factor for the reduction of CO2 emissions in

India (Chaturvedi & Shukla, 2014). Lin and Ahmad (2017)

concluded that energy eﬃciency had massive potential to

mitigate carbon emissions in Pakistan (Lin & Ahmad, 2017).

Another study for Pakistan was conducted by Mahmood and

Marpaung (2014) to analyze the CO2 emissions reduction

impact of energy eﬃciency. The study found that energy

eﬃciency and the carbon tax had a remarkable impact on

CO2 emissions reduction in Pakistan (Mahmood & Mar-

paung, 2014). Thus, the current research has established the

following hypothesis:

H3: Energy efficiency has a positive and significant

impact on GHG emission in developing countries.

Institutional quality–environment nexus

In general, the views are divergent about the nexus

between institutional quality and environment. Some

of the researchers have observed a positive institutional

quality–environment relationship, while some studies

found that institutional quality accelerates environmental

degradation. For instance, Godil etal. (2020) estimated

the effect of institutional quality, financial development,

and ICT on carbon emission in Pakistan and concluded

that institutional quality contributed positively to CO2

emission (Godil etal., 2020). Azamet al. (2021) stud-

ied the effect of institutional quality on different envi-

ronment measures (CO2 and CH4 emission, area under

forest, water pollutants (organic), and energy consump-

tion) for 66 underdeveloped economies. The findings

of system GMM estimation indicated that institutional

quality had positive effect on CO2 emission, CH4 emis-

sion, and forest area (Azam etal., 2021). Huang etal.

(2021a)concluded that institutional quality had a positive

correlation with carbon dioxide emission and indicated

that bad governance, weak bureaucracy, corruption, and

improper execution of environmental laws led towards

environmental deterioration (Chien etal., 2021b; Othman

etal., 2020). For a panel of forty countries of SSA (Sub-

Saharan Africa), Ibrahim and Law (2016) studied what

role trade and institutional quality and the interactions

between both played in carbon emissions and found that

better institutional functioning was favorable to improve

environmental quality (Li etal., 2021; Huang et al.,

2021a, b, c; Liu etal., 2021b; Ibrahim & Law, 2016).

Incorporating other important determinants such as trade

liberalization, urbanization, development of financial

sector, and consumption of energy in underdeveloped

economies, a thorough analysis by Ali etal. (2019) to

estimate the effect of economic growth and quality of

institutions on environmental quality evidenced that the

pollution haven theory is refuted because well-function-

ing institutions could reduce environmental deteriora-

tion by suitably plighting polluted companies’ locations.

Furthermore, institutional action could help to improve

environmental deterioration through correct regulations

Environmental Science and Pollution Research

1 3

(Ali etal., 2019). Lee etal. (2021) analyzed the effect of

institutional quality and urbanization on GHG emissions

in the 48 BRI countries over the years 1984–2017 and

reported that although urbanization reduced the environ-

mental quality, the negative impact of urbanization could

be eliminated by improved institutional quality (Lee etal.,

2021). Similarly Muhammad and Long (2021) elaborated

that rule of law, political stability, and control of corrup-

tion played imperative role in carbon emission mitiga-

tion and improved the quality of environment in the BRI

countries (Muhammad & Long, 2021).

In contrast, there is the study of Chaudhry etal. (2021)

that established that institutional performance had nega-

tive impact on environment quality indicators in countries

of East Asia and Paciﬁc speciﬁcally the high-income East

Asian and Paciﬁc countries (Chaudhry etal., 2021). For

BRICS nations, Hussain and Dogan (2021) investigated the

eﬀect of the quality of institutions and environment-related

technologies on ecological footprints over 1992–2016 period

and found that institutional quality posed negative inﬂuences

on environmental deterioration. Their results also proved the

validation of EKC in BRICS economies (Hussain & Dogan,

2021). In addition, Khan etal. (2020a) investigated what role

governance played in carbon dioxide emission mitigation in

BRICS nations over 1996 to 2017 period. Authors reported

that better quality governance could assist in the CO2 emis-

sion mitigation, thereby reducing the environmental deterio-

ration in these economies (Baloch and Wang, 2019). There-

fore, the article has developed the following hypothesis:

H4: Institutional quality has a signiﬁcant impact on GHG

emission in developing countries.

Literature gap

Review of the existing empirical literature indicated that

many earlier studies examined the eﬀect of technology inno-

vations, trade openness, energy eﬃciency, and institutional

quality on environmental quality regarding individual as

well as panels of diﬀerent countries. Most of these studies

had a herding behavior in measuring environmental quality

in terms of CO2 emissions. Limited to our knowledge, only

Lee etal. (2021) and Akdag and Yıldırım (2020) measured

the eﬀect of institutional quality and energy eﬃciency on

GHG emission, respectively, but their studies include Euro-

pean and BRI countries. Beside these two studies, none of

the preceding studies considered the eﬀect of technological

innovations, trade openness, energy eﬃciency, and institu-

tional quality on GHG emission especially in the context

of Asian economies. Secondly, none of the studies ana-

lyzed the impact of aforementioned variables together in a

model. Therefore, this study intends to ﬁll in this research

gap by examining the impact of technological innovations,

trade openness, energy eﬃciency, and institutional quality

together on GHG emission in ten Asian economies, thereby

contributing to the Asian economies’ literature.

Econometric methodology anddata sources

Model speciﬁcation

This study examines the environmental sustainability in ten

Asian countries, namely, India, Indonesia, Malaysia, China,

Philippines, Pakistan, Singapore, Thailand, Sri-Lanka, and

Vietnam over the period 1995–2018 by analyzing the impact

of technological innovations, trade openness, energy eﬃ-

ciency, and institutional quality on GHG emission. The basic

aim in the selection of the countries is that the research-

ers want to investigate the ﬁve most emerging developing

countries such as China, Malaysia, Singapore, Indonesia,

and Vietnam and ﬁve less emerging developing countries

such as Pakistan, Sri Lanka, India, Thailand, and the Philip-

pines. The general form of the model is presented in Eq.(1).

where GHG is greenhouse gas emissions, EEF is the energy

eﬃciency, TIN is the technology innovations, TOP is the

trade openness, and INSQ is the institutional quality.

The econometric form of the model is presented in

Eq.(2).

where αo shows the intercept and β1, β2, β3, and β4 denote

the estimates for GHG emission from EEF, TIN, TOP, and

INSQ, respectively. The detailed description of the variables

and data sources are given in the Table1.

(1)

GHG = f(EEF,TIN,TOP,INSQ)

(2)

GHGit =𝛼o+𝛽1EEFit +𝛽2TINit +𝛽3TOPit +𝛽4INSQit +𝜖it

Table 1 Description of the variables and data sources

Variables Acronym Measurement Data source

Greenhouse gas

emission (depend-

ent variable)

GHG Kt of CO2 equivalent World

develop-

ment

indicators

(WDI)

Energy eﬃciency EEF

Technology innova-

tions

TIN

Trade openness TOP Imports + exports/

GDP

World

develop-

ment

indicators

(WDI)

Institutional quality INSQ

Environmental Science and Pollution Research

1 3

Data, population, andsampling

The current article has the purpose of investigating develop-

ing countries. Thus, the total population of the article is all

the developing countries. The researchers have selected the

ten developing countries (ﬁve most emerging and ﬁve less

emerging), that is, the selected sample of the study. Thus, the

data were collected from the ten selected countries using a

secondary source like WDI from 1995 to 2018.

Econometric methodology

This study considers advanced and sophisticated economet-

ric techniques when selecting its methodology for empirical

analysis. The procedure of analysis is carried out through six

basic steps as follows:

a. First, Pesarn’ (2004) CSD (cross-sectional dependence)

test is applied to estimate the eﬀects of common shock.

b. Second, the heterogeneity test proposed by Pesaran and

Yamagata (2008) is used to examine the homogeneity/

heterogeneity of slope parameters in data.

c. Third, the stationarity of the selected variables is esti-

mated through the stationarity/unit root tests proposed

by Pesaran (2007) and Bai and Carrion-I-Silvestre

(2009).

d. Fourth, Westerlund and Edgerton (2008) and Banerjee

andCarrion‐i‐Silvestre (2017) panel cointegration tests

are applied to estimate the long-run cointegration among

the variables.

e. Fifth, the short-run and the long-run relationship are

estimated through CS-ARDL (cross-sectionally aug-

mented ARDL) developed by Chudik and Pesaran

(2015).

f. Last, the ﬁndings are reconﬁrmed with the help of the

augmented mean group (AMG) and common correlated

eﬀect mean group (CCEMG). The employed techniques

are discussed in detail below.

Cross‑sectional dependence test

We used the cross-sectional dependence (CSD) test to see

if CSD is present in the data. The CSD examination is

the ﬁrst step towards formally beginning the panel data

study (Pesaran, 2015). A number of factors including

interdependency of residuals, economic interdependence,

unexpected common stocks volatility, a surge in oil prices,

shocks in demand, and globalization, as well as unob-

served and hidden factors, contribute to the CSD (Shao

etal., 2021). Furthermore, ﬁnding the CSD in panel data is

crucial since the Westerlund cointegration and CIPS meth-

ods imply heterogeneity and dependency across distinct

sections. Thus, in order to give the most eﬃcient ﬁndings,

the current study used the Pesaran (2007)’s CSD test. Dur-

ing the estimation of the correlation factors, the values of

the means are removed in this test. The null hypothesis

assumes no CSD, opposite to the alternative hypothesis

which states that the CSD is present in data (Ahmad etal.,

2020). The CSD test is given as:



𝝆

shows the coeﬃcient of residual pairwise correlation

of the least square residuals. In this case, null hypothesis

cannot be rejected if the panel data does not show any cross-

sectional dependency.

Slope heterogeneity test

Pesaran and Yamagata (2008) developed slope heterogene-

ity test originally attributed to Swamy (1970)).1 This test

is applied in the study to reveal the slope heterogeneity

or homogeneity between the cross-sections under study.

Other homogeneity measures, like the seemingly unrelated

regression equation (SURE) technique, are less preferable

to this test due to the fact that it does not allow for the CSD

(Atasoy, 2017). A homogeneous slope is assumed as the null

hypothesis, while a heterogeneous slope is assumed as the

alternative hypothesis in this test (Shao etal., 2021). The

model of the test is given below:

where

∼

= Swamy test statistic and k = number of explana-

tory variables.

∼

ΔSH

denotes the delta tilde,

∼

ΔASH

denotes the adjusted

delta tilde assuming the homogenous slope coeﬃcients as

null hypothesis and heterogeneous slope coeﬃcients as alter-

native hypothesis.

(3)

√

N(N−1)

(∑

N−1

i=1

∑

j=i+1𝜌 ij

)

∼N(0,1)i,

(4)

CD =1, 2, 3, 4 ………10 ………N

(5)

=√2T

N(N−1)(

∑

N−1

i=1

∑

j=i+1𝜌 ij)(T−k)𝜌

ij −E(T−k)𝜌

Var (T−k)𝜌 2

(6)

∼

SH=(N)

2(2K)−1∕2



S−k

)

(7)

∼

ASH=(N)



2k(T−k−1)

T+1



−



∼

S−k



1 See Swamy (1970) and Pesaran and Yamagata (2008) for more

details.

Environmental Science and Pollution Research

1 3

Panel unit root tests

After CSD and slope heterogeneity tests, it is necessary

to test the integration order among the variables selected

for the study. In this context, ﬁrst-generation unit root tests

may give spurious ﬁndings if the CSD exists in the data

(Dogan & Seker, 2016). To prevent this bias in the ﬁnd-

ings, Z. Khanet al. (2020a) proposed employing both para-

metric and non-parametric tests. Therefore, this study used

the cross-sectionally augmented IPS (CIPS) tests proposed

by Pesaran (2007). CIPS has the capability to deal with

the CSD and slope heterogeneity and therefore provides

more reliable and accurate results. The CIPS test equation

is speciﬁed as:

where

shows the average cross-section and is represented

as:

The CIPS test statistics is stated as:

Moreover, second-generation test introduced by Bai and

Carrion-I-Silvestre (2009) is also used in the study. Many

breaks at structure in heterogeneous panels can be handled

by this panel unit root test. Under varying magnitudes of

shift, breaks are permissible at slope, level, or both at dif-

ferent time periods. It is important to compute the structural

breaks as the time spans the era of the global and Asian

economic crisis. Common factors are used to capture the

CSD as described by Bai and Ng (2004). These common

factors can either be stationary or non-stationary as well as

a combination of both. A modiﬁed Sargan-Bhargava (MSB)

test for each series is pooled by this technique after includ-

ing several breaks at structure for every series and common

elements. Three pooling statistics result in three distinct test

statistics: Z, individual statistics standardization and P and

Pm combining their p values. Monte Carlo simulations con-

ﬁrm that these tests perform well in inﬁnite samples (Nas-

reen etal., 2020).

Cointegration tests

It is necessary to conﬁrm whether cointegration exists among

the selected variables before estimation of long-run param-

eters. Thus, in order to deal with the problems of the CSD and

slope heterogeneity in the data, this study applies the panel

cointegration techniques proposed by Westerlund and Edg-

erton (2008) and Banerjee and Carrion-i-Silvestre (2017).

These estimations have the advantage of incorporating the

(8)

Wi,t=∅

i+∅

iZi,t−1+∅

iZt−1+

∑p

l=0

∅ilΔWt−1+

∑p

l=0

∅ilΔWi,t−1+𝜇

(9)

i,t

=∅

EEFi,t

+∅

TINi,t

+∅

TOPi,t

+∅

INSQ

(10)



CIPS

=N−1

∑n

i=1

CADF

detection of cointegration with breaks at structure. Westerlund

and Edgerton (2008) technique handles the problems of CSD,

heterogeneous slopes, serially correlated disturbance terms,

and structural at each cross-section in contrast to the ﬁrst-and

second-generation tests. The LM unit root technique of Ahn

(1993), Schmidt and Phillips (1992), and Amsler and Lee

(1995) provide the basis for Westerlund and Edgerton (2008)

test. The test model is presented below with the null hypothesis

of no cointegration (with breaks) and the alternative hypoth-

esis of cointegration (with breaks):

where i = 1 to N and t = 1 to T. xit is speciﬁed as a random

walk, and it contains explanatory variables. Dit represents

a scalar break dummy variable such that Dit equals 1 if t

is greater than Ti and zero otherwise. Intercept speciﬁc for

cross unit and slope coeﬃcient earlier to break are repre-

sented by αi and βi, respectively, while the changes in these

parameters after the break are denoted by δi and γi. The dis-

turbance zit is assumed to allow CSD by using unobservable

common factors Ft.

where

∅

i(L)=1−

∑p

j=1

∅ijL

is a lag operator’s scalar poly-

nomial. L, Ft is a re-dimensional vector of unobservable

common factors (Fit) with j equal to 1—r, and λi is the factor

loading parameters vector. The relationship in Eq. (i) is co-

integrated if ϕi is less than zero and it is biased if ϕi equals

zero.

Two LM-based statistics are deﬁned by Westerlund and

Edgerton (2008):

where



∅𝐢

is the estimate of ϕi ( least square estimate) and



𝝈i

is its estimated standard error,



represents the long-run

variance of Δνit, and SE

(

∅𝐢

)

denotes the standard error of



∅𝐢

(Damette & Seghir, 2013).

The panel cointegration test developed by Banerjee and

Carrion-i-Silvestre (2017) is also applied in this study. It

implies that CSD is linked to common variables as meas-

ured by the averages of each cross-sectional variable. There

is ﬂexibility in using this test of cointegration as it permits

panel data structural breaks (Le & Ozturk, 2020).

(11)

yit

=∝

+𝜇

t+𝛿

+x�

𝛽

(

it)�

𝛾

(12)

xit =xit−1+wit

(13)

zit

=λ

�

(14)

Fit =𝜌jFjt−1+ujt

(15)

∅it(L)Δvit =∅

ivit−1+e

(16)

∅(i)=T



∅iwi

𝜎 iand LMT(i)=



∅i





∅

i

Environmental Science and Pollution Research

1 3

Short‑run andlong‑run estimation (CS‑ARDL)

After performing these preliminary estimations,

we analyzed the short-run and the long-run relation

between the dependent and independent variables. For

the long-run analysis of the panel data, economists

have proposed multiple techniques. Many of the ear-

lier studies have a herding behavior in applying first-

generation techniques (e.g. DOLS, FMOLS, ARDL)

which do not produce reliable findings if slope hetero-

geneity and CSD are present in the data. Therefore, we

applied the CS-ARDL estimation proposed by Chudik

and Pesaran (2015) to deal the slope heterogeneity and

CSD present in data. CS-ARDL technique can easily

be implemented in empirical terms and is helpful to

remove bias in the data. The CS-ARDL approach in its

general form is given below.

CSA stands for cross-sectional averages and is further

represented by

CSA

(

ΔYt,

EXVs,t

)�

variables; i.e., explan-

atory variables are shown by EXV's.

CCEMG (common correlated eﬀects mean group)

The CCEMG (common correlated eﬀects mean group) and

AMG (augmented mean group) are two contemporary panel

data estimation approaches robust to CSD and slope hetero-

geneity. Pesaran (2007) developed CCEMG, and it is further

strengthened by Kapetanios etal. (2011) which is robust to

breaks at structure and non-stationary common factors that

are unobserved. CCE estimator is given as follows:

where βi is the slope of the regressors

xit

, α1i denotes the

ﬁxed eﬀects that captures the heterogeneity among groups

that is time invariant, ft is the common factor (unobserved)

with heterogeneous factor loadings (ϕi), and εit is the dis-

turbance term. Above equation is augmented with depend-

ent and explanatory variables cross-sectional averages as

follows:

In the case of slope heterogeneity, the whole panel inference

is made by estimator of mean group. For the CCE, the mean

group estimator is calculated as follows:



𝜷i

is the coeﬃcient estimates.

(17)

Yit =𝜑i+

∑p

l=1

𝜑itΔYi,t−1+

∑p

l=0

𝜑

�

EXVs,i,t+

∑1

l=0

𝜑

�

CSAi,t−1+𝜀

(18)

yit =a1i+𝛽ixit +𝜑ift+𝜀it

(19)

yit =a1i+𝛽ixit +𝛿iyit +𝜃ixit +𝜑ift+𝜀it

(20)

CCEMG

=N−1

∑N

i=1



𝛽

AMG (augmented mean group)

The second approach used is the AMG (augmented mean

group) estimation proposed by Bond and Eberhardt (2013)

and Eberhardt and Teal (2010). Like CCEMG, AMG is also

robust to heterogeneous slope and CSD. The diﬀerence exists

between CCEMG and AMG estimations only in the computa-

tion of the unobservable common factors ft in (p). The cross-

sectional combinations means the observed common eﬀects

and the dependent and independent variables are used in

CCEMG estimation. After this, every coeﬃcient is estimated

by ordinary least squares. AMG estimation follows a two-step

procedure for the estimation of the unknown common dynamic

eﬀects and permits CSD by adding the parameter of common

dynamic eﬀect. In ﬁrst step, the equation is augmented with

dummy variables for time and is estimated by the ﬁrst diﬀer-

ence ordinary least square method.

Δ shows the diﬀerence operator, and τ shows the time

dummy coeﬃcient known as common dynamic process. In

the 2nd step, it augments the regression model (group spe-

ciﬁc) with an explicit variable or coeﬃcient on each group

member. Estimator of AMG is subtracted from the explained

variable to impose the implementation of the unit coeﬃcient.

Like CCEMG estimator, it also averages the model parameters

across the panel. The intercept is added in each regression that

captures the ﬁxed eﬀects that are time invariant. The AMG

mean group estimator is given as follows.

where,

∼

𝜷i

is the estimates of coeﬃcients in the following

equation:

Empirical results

Before evaluating the stationarity of the variables, the CSD

is checked in the panel. The Pesaran (2007) CSD technique

is used for this estimation and Table2 shows the results.

The estimations show that CSD is present in the data as

null hypothesis cannot be accepted at 1% signiﬁcance level.

After evaluating the CSD, we estimated the homogeneity

of the slope parameters by applying Pesaran and Yamagata

(2008) slope homogeneity test based on delta

∼

(Δ)

and

adjusted delta

(∼

Δadj

)

. The outcomes of the test are given in

Table3.

(21)

yit =a1i+𝛽iΔxit +𝜑ift+

∑T

t=2

𝜏tDUMMYt+𝜀

(22)

AMG

=N−1

∑

i=1

∼

𝛽

(23)

yit =a1i+𝛽iΔxit +𝜑ift+

∑T

t=2

𝜏tDUMMYt+𝜀

Environmental Science and Pollution Research

1 3

The results reveal that slopes are heterogeneous as the

null hypothesis is clearly abandoned at 1% level of signiﬁ-

cance showing that GHG emissions, EEF, TOP, TIN, and

INSQ vary among selected Asian countries.

Since CSD is present and slopes are heterogeneous, the

next step is to test the unit root/stationarity of every series.

For this purpose, Bai and Carrion-I-Silvestre (2009) tests

for unit root/stationarity are applied and their results are

given in the following Table4.

Bai and Carrion-i-Silvestre (2009): for Z and Pm statis-

tics, the critical values 2.326, 1.645, and 1.282 are for 1,

5, and 10% signiﬁcance levels, respectively, whereas for

P, critical values are 56.06, 48.60, and 44.90, separately.

It is indicated from the results of CIPS and M-CIPS that

the null hypothesis cannot be accepted, and it is concluded

that all the series are stationary at level. However, the ﬁnd-

ings of Bai and Carrion-I-Silvestre (2009) indicate that

GHG, EEF, TOP, TIN, and INSQ represent the unit root at

the level. However, there is no unit root problem (station-

ary) at the ﬁrst diﬀerence in these variables.

After stationary tests, the cointegration tests of West-

erlund and Edgerton (2008) and Banerjee and Carrion-i-

Silvestre (2017) allowing structural break are carried out.

The estimates of Westerlund and Edgerton (2008) are given

in Table5.

The test results conﬁrm the cointegration relationship

among GHG, TOP, TIN, EEF, and INSQ without break, at

mean and at regime shift.

In addition, we carried out Banerjee and Carrion-i-Sil-

vestre (2017) cointegration estimation and long-run coin-

tegration and a steady relationship with constant and trend

are conﬁrmed by the results for the entire sample as given

in Table6.

Table 2 CSD test results

*** , **, and * show signiﬁcance at 1, 5, and 10% levels, respectively,

whereas P values are given in parantheses

Variable Test statistics (p values)

GHG 19.102*** (0.000)

EEF 22.001*** (0.000)

TIN 18.101*** (0.000)

TOP 21.100*** (0.000)

INSQ 17.654*** (0.000)

Table 3 Slope heterogeneity test results

*** , **, and * show signiﬁcance at 1, 5, and 10% level, whereas par-

antheses contain P value

Explained variable: GHG

Test statistics Test value and P value

Delta tilde 46.012*** (0.000)

Adjusted Delta tilde 58.075*** (0.000)

Table 4 Unit root results: with

and without structural break

(Pesaran (2007)

* , **, and *** show the signiﬁcance at 10, 5, and 1% levels, respectively

Level I(0) 1st Diﬀerence I(1)

Variables CIPS M-CIPS CIPS M-CIPS

GHG − 3.010*** − 4.123** - -

EEF − 5.020*** − 6.010** - -

TIN − 3.003*** − 4.101** - -

TOP − 4.110*** − 5.321** - -

INSQ − 5.050*** − 6.030** - -

Bai and Carrion-i-Silvestre (2009)

Z PmP Z PmP

GHG 0.312 0.495 20.101 − 3.030*** 4.002*** 45.023***

EEF 0.430 0.525 17.010 − 5.011*** 6.100*** 25.063***

TIN 0.391 0.414 23.201 − 4.202*** 5.030*** 57.056***

TOP 0.591 0.620 19.006 − 3.003*** 4.251*** 44.084***

INSQ 0.333 0.405 16.301 − 5.125*** 6.006*** 36.052***

Table 5 Westerlund and Edgerton (2008) panel cointegration results

10%, 5%, and 1% signiﬁcance levels are denoted by *, **, and ***,

respectively, and parentheses contain P value

Test No break Mean shift Regime shift

Explained variable: GHG

Zφ(N) − 3.130*** − 3.010*** − 4.121***

Pvalue 0.000 0.000 0.000

Zτ(N) − 4.141*** − 4.000*** − 5.050***

Pvalue 0.000 0.000 0.000

Environmental Science and Pollution Research

1 3

Country-based analysis reported in the above table also

veriﬁes co integration and steady connection for all countries

with both constant and trend. On the basis of the critical

values, the results given above are signiﬁcant at 5% level of

signiﬁcance.

After completing preliminary analysis, now we proceed

to the long-run and short-run estimation of the relationship

of the panel variables. As CSD and slope heterogeneity are

present in our data, the cross-sectional autodistributive

lag model (CS-ARDL) is applied to investigate the output

coeﬃcients. The test results are reported in Table7.

The results reveal a negative relationship between

energy eﬃciency and GHG emissions. A one-unit rise in

energy eﬃciency reduces GHG emission by 0.28 units in

the long term and 0.086 units in the short term in Asian

countries. Thus, energy eﬃciency appears to be a potential

technique to reduce GHG emissions in the panel coun-

tries, enhancing environmental quality in turn. Energy eﬃ-

ciency is helpful to reduce emissions of GHG by sustain-

ing energy in the transportation and construction sectors.

In recent researches, a negative relation between energy

eﬃciency and GHG emissions was also reported by Akdag

and Yıldırım (2020) for China and Akram, Akram etal.

(2020a) for developing and BRICS economies. However,

a positive relationship was found by Irfan etal. (2021) for

South Asian economies.

Next, the environmental impact of technology innova-

tions is signiﬁcant and negative. The ﬁndings reveal that

a one-unit increase in technology innovations decreases

GHG emissions by 0.24 units in the long run and 0.04

units in the short run. It indicates that technology innova-

tions are signiﬁcantly and positively related to environ-

ment quality in Asian countries. One possible explana-

tion is that technology innovations increase a country’s

potential to replace harmful resources with ecologically

benign ones. To deal with environmental concerns, a

country can, for example, change traditional resources of

energy production to renewable energy resources. Con-

tinuous innovation, research and development (R&D), new

technological development, and other forms of innovation

have a direct eﬀect on energy eﬃciency and industrial per-

formance. As a result, innovation can help to reduce GHG

emissions, and promoting technology innovations can help

these countries move toward cleaner economies.

The ﬁndings divulge a signiﬁcantly positive inﬂuence

of trade openness on GHG emissions both in the short and

long run. A one-unit increase in trade liberalization increases

GHG emission by 0.195 in the long run and 0.212 units

in the short run. This result supports the pollution haven

hypothesis, suggesting that increased liberalization in trade

opens the door to environmental degradation because with

an increase in level of income, demand for clean environ-

ment increases and dirty corporations of developed econo-

mies emitting high level of GHG and other environmental

pollutants look for areas having lesser strict environmen-

tal legislation. As the majority of the Asian developing

countries lack comprehensive environmental rules, (e.g.,

China, India, and Indonesia), companies of the developed

Table 6 Banerjee and Carrion-i-Silvestre (2017) results

With constant, CV (critical value) at 10%* and 5%** is − 2.18

and − 2.32, whereas with trend CV is − 2.82. and − 2.92

Countries No deterministic

speciﬁcation

With constant With trend

Explained variable: GHG

Full sample − 4.032*** − 4.010** − 5.110**

China − 3.011*** − 3.001** − 4.041**

India − 6.010*** − 5.100** − 7.010**

Indonesia − 6.123*** − 5.321** − 7.107**

Malaysia − 5.025*** − 4.404** − 6.006**

Pakistan − 5.753*** − 4.321** − 6.420**

Philippines − 3.951*** − 3.151** − 4.114**

Singapore − 7.100*** − 6.060** − 8.001**

Sri-Lanka − 4.150*** − 4.004** − 5.011**

Thailand − 4.424*** − 5.020** − 6.112**

Vietnam − 3.110*** − 3.002** − 4.020**

Table 7 CS-ARDL analysis:

short run and long run results

* , **, and *** represent signiﬁcance at 10%, 5%, and 1% levels, respectively

Explained variable: GHG emission

Long run Short run

Variables Coeﬀ t-stat P-value Coeﬀ t-stat P -value

EEF − 0.286*** − 3.456 0.000 − 0.086*** − 4.100 0.000

TIN − 0.246* − 1.673 0.094 − 0.040*** − 5.125 0.000

TOP 0.195*** 3.002 0.000 0.212*** 3.100 0.000

INSQ 0.300** 2.211 0.041 0.071*** 6.142 0.000

ECT(-1) − 0.312*** − 4.040 0.000

CSD-Statistics - - - 0.055 0.720

Environmental Science and Pollution Research

1 3

economies (having higher environmental legislations) shift

their factories and plants to these countries with lax envi-

ronmental rules and regulations. Thus, with trade openness,

these host economies with lax environmental laws and regu-

lations become dirtier (Ertugrul etal., 2016). Moreover, it is

true that underdeveloped countries are more focused on con-

ventional sources of energy, which result in higher emissions

resulting from more industrialization and human actions as

a result of trade liberalization.

For each unit increase in institutional quality, GHG emis-

sions rise by 0.300 units in the long run and by 0.07 units

in the short run. This increase indicates that in the selected

countries, higher institutional quality may provide more

political freedom and civil liberties to general public. The

ﬁndings also indicate that the political situation in these

countries is less concerned with issues of general public like

pollution caused by GHG emissions. While some countries’

political–institutional systems may have taken steps to prior-

itize environmental quality, changing bureaucratic mindsets

to prioritize environmental issues may still take time. The

results of institutional quality in these economies indicate

that economic progress and industrialization appear to be

top priorities; hence, institutional quality improvement is

becoming a reason of fast industrial progress and contribut-

ing to increased pollution (Azam etal., 2021). Moreover, the

error correction term (ECT) meets all three conditions; i.e.,

it is negative, signiﬁcant, and less than one in magnitude.

Its coeﬃcient is 0.312, which signiﬁes the 31% adjustment

speed towards long-run equilibrium. For the robustness of

the analysis, econometric approaches of AMG and CCEMG

are applied ﬁnally, and their results are given in the Table8.

The current study has run a robust test to check and

support the study results extracted from the CS-ARDL

model. The robust results of AMG and CCEMG support

our short- and long-run ﬁndings. Energy eﬃciency causes

decrease in GHG emission by 0.043 units AMG and 0.237

units CCEMG. Similarly, 0.075 units decrease for AMG and

0.131 units decrease for CCEMG in GHG are caused by

technological innovations. Trade openness causes a rise in

GHG emissions of 0.171 units for AMG and 0.297 units

for CCEMG. And an increase of 0.222 units for AMG and

0.166 units for CCEMG in GHG emission is caused by

institutional quality. The signiﬁcant P value of Wald Test

evidenced that the model is highly signiﬁcant.

Discussions

The current article results exposed that energy eﬃciency

has a negative impact on GHG emission that shows the

energy eﬃciency reduce GHG emissions. These ﬁndings

support the arguments of the previous studiesthat energy

eﬃciency is critical for decreasing emissions and high-

light the signiﬁcance of energy eﬃciency improvements

in reducing GHG emissions in developing economies. In

addition, the results of the present article also exposed the

negative linkage among technology innovation and GHG

emission that show selected developing countries adopted

high-technology innovation that reduces the GHG emission.

Similar results were reported by R. L. Ibrahim and Ajide

(2021), Long etal. (2018), Omri and Hadj (2020), and Hod-

son etal. (2018), but opposite results were found by Ganda

(2019), Yu and Du (2019), and Erdoğan etal. (2020). The

ﬁndings also revealed that trade openness increases GHG

emissions, which is the reason of the positive association

among GHG emissions and trade openness. These outcomes

are in line with Dauda etal. (2021); Ali etal. (2020); and

Zameer etal. (2020), who also observed that trade openness

aﬀected emissions positively. The adverse eﬀect of trade

openness on environmental quality is reported by Tachie

etal. (2020) in 18 European Union countries and Amin

etal. (2020) in Asian economies.

The results of the current article also identiﬁed a sig-

niﬁcant and positive link between institutional quality and

GHG emissions. Similar results are reported by Godil etal.

(2020), Azam etal. (2021), and Chaudhry etal. (2021) in

the literature.

Conclusion andpolicy recommendations

Thus, based on the study results, the current article con-

cluded that the Asian countries do not have quality institu-

tions that do not play a signiﬁcant role in reducing GHG

emission, which is the reason for the positive association

Table 8 AMG and CCEMG

results

* , **, and *** represent signiﬁcance at 10%, 5%, and 1% levels, respectively

Dependent vari-

ables GHG

(AMG) (CCEMC)

Coeﬀ t-stat pvalues Coeﬀ t-stat p values

EEF − 0.043*** − 4.101 0.000 − 0.237*** − 6.003 0.000

TIN − 0.075*** − 3.031 0.000 − 0.131*** − 3.100 0.000

TOP 0.171*** 4.004 0.000 0.297*** 5.654 0.000

INSQ 0.222*** 3.123 0.000 0.166*** 4.001 0.000

Wald test - 44.65 0.000 - 18.088 0.000

Environmental Science and Pollution Research

1 3

among institutional quality and GHG emission. In addi-

tion, the selected developing countries are more focused on

energy eﬃciency in reducing GHG emission, which is the

reason of energy eﬃciency has a negative impact on GHG

emission. Results of the study provide evidence that trade

openness has a favorable eﬀect on GHG emissions both in

the short and in the long run. Finally, the selected developing

countries are implementing eﬀective technology innovation,

which is the reason of technology innovation reduce GHG

emission and current study ﬁndings show a negative linkage

among technology innovation and GHG emission in devel-

oping countries.

On the basis of the results, some policies are recom-

mended for the governments of the selected countries. First,

it is critical to strengthen institutions and empower them to

function eﬀectively in these countries. Eﬀective institutional

operations, especially the private institutions’ operations,

would result in the successful implementation of rules and

regulations, which would aid in the battle against corrup-

tion and, if carefully monitored, would result in a reduction

in GHG emissions in the countries. Second, governments

in Asian nations should keep improving cooperation with

advanced countries that are at the forefront of technology

innovations and should allocate more resources in research

and development activities to generate novel innovations.

This will aid Asian countries in mastering clean-environ-

ment technology, as an excessive level of emissions in devel-

oping countries may have a global detrimental impact on

environmental quality.

Third, countries should develop eﬃcient energy eﬃ-

ciency policies and work together to improve energy eﬃ-

ciency to meet the Paris Agreement’s global climate goals

and sustainable development goals. They should collaborate

on projects to help spread knowledge and promote energy-

eﬃcient technologies. Finally, governments should encour-

age trade-related environmental regulations and use of envi-

ronmentally friendly and clean technologies during trade

goods production. Furthermore, increasing the participation

of tertiary industry in FDI is a viable strategy to aid in the

reduction of pollution as international trade liberalizes. In

addition, the current article also suggested to the regulators

that they should gain regional co-operation while developing

the policies related to the multi-lateral trade.

Limitations andfuture directions

The present article has some limitations that provide the

direction for upcoming researchers. The current study

has taken only four predictors such as institutional qual-

ity, trade openness, energy efficiency, and technology

innovation and ignored many factors and recommended

that upcoming studies should add these factors in the

analysis. In addition, the current research has taken ten

developing countries in the analysis and ignored many

emerging developing countries and developed countries

that narrow down the study scope and suggested that

future studies should add these countries in the studies.

Finally, the current article has also taken limited years

under investigation, such as the data were gathered from

1995 to 2018 that also narrowed down the study scope

and recommended that future studies should add more

years in the analysis.

Author contribution Zheng Wenlong: writing—original draft. Nguyen

Hoang Tien: methodology supervision. Amena Sibghatullah: con-

ceptualization, writing—review and editing. Daru Asih: software.

Mochamad Soelton: data curation. Yanto Ramli: visualization and

formatting.

Funding This research is funded by 2020 national social science fund

major project “Research on the spatial eﬀects of cross regional major

infrastructure in China” (approval No.: 20 & zd099), participated in

the sub-project “integration of cross regional major infrastructure and

Urban Agglomeration.”

Data availability The data that support the ﬁndings of this study are

attached.

Declarations

Ethics approval and consent to participate We declare that we have no

human participants, human data, or human tissues.

Consent for publication Not Applicable.

Competing interests The authors declare no competing interests.

References

Ahmad M, Jiang P, Majeed A, Umar M, Khan Z, Muhammad S (2020)

The dynamic impact of natural resources, technological innova-

tions and economic growth on ecological footprint: an advanced

panel data estimation. Resour Policy 69:101817

Ahn SK (1993) Some tests for unit roots in autoregressive-inte-

grated-moving average models with deterministic trends.

Biometrika 80(4):855–868

Akdag S, Yıldırım H (2020) Toward a sustainable mitigation

approach of energy eﬃciency to greenhouse gas emissions in

the European countries. Heliyon 6(3):e03396

Akram R, Chen F, Khalid F, Ye Z, Majeed MT (2020a) Heteroge-

neous eﬀects of energy eﬃciency and renewable energy on

carbon emissions: evidence from developing countries. J Clean

Prod 247:119122

Akram R, Majeed MT, Fareed Z, Khalid F, Ye C (2020b) Asym-

metric eﬀects of energy eﬃciency and renewable energy on

carbon emissions of BRICS economies: evidence from non-

linear panel autoregressive distributed lag model. Environ Sci

Pollut Research, 27(15).

Al-mulali U, Sheau-Ting L (2014) Econometric analysis of trade,

exports, imports, energy consumption and CO2 emission in six

regions. Renew Sustain Energy Rev 33:484–498

Environmental Science and Pollution Research

1 3

Ali HS, Zeqiraj V, Lin WL, Law SH, Yusop Z, Bare UAA, Chin L

(2019) Does quality institutions promote environmental qual-

ity? Environ Sci Pollut Res 26(11):10446–10456

Ali S, Yusop Z, Kaliappan SR, Chin L (2020) Dynamic common cor-

related eﬀects of trade openness, FDI, and institutional perfor-

mance on environmental quality: evidence from OIC countries.

Environ Sci Pollut Res 1–12.

Alola AA (2019) The trilemma of trade, monetary and immigration

policies in the United States: accounting for environmental

sustainability. Sci Total Environ 658:260–267

Amin A, Aziz B, Liu X-H (2020) The relationship between urbaniza-

tion, technology innovation, trade openness, and CO 2 emis-

sions: evidence from a panel of Asian countries. Environ Sci

Pollut Res 27(28):35349–35363

Amsler C, Lee J (1995) An LM test for a unit root in the presence of

a structural change. Economet Theor 11(2):359–368

Ansari MA, Haider S, Khan N (2020) Does trade openness aﬀects

global carbon dioxide emissions: evidence from the top CO2

emitters. Manag Environ Q: Int J.

Ansari MA, Khan NA, Ganaie AA (2019) Does foreign direct invest-

ment impede environmental quality in Asian countries? A

panel data analysis. OPEC Energy Rev 43(2):109–135

Atasoy BS (2017) Testing the environmental Kuznets curve hypoth-

esis across the US: Evidence from panel mean group estima-

tors. Renew Sustain Energy Rev 77:731–747

Atici C (2012) Carbon emissions, trade liberalization, and the Japan–

ASEAN interaction: A group-wise examination. J Jpn Int Econ

26(1):167–178

Azam M, Liu L, Ahmad N (2021) Impact of institutional quality on

environment and energy consumption: evidence from develop-

ing world. Environ Dev Sustain 23(2):1646–1667

Bai J, Carrion-I-Silvestre JL (2009) Structural changes, common

stochastic trends, and unit roots in panel data. Rev Econ Stud

76(2):471–501

Bai J, Ng S (2004) A PANIC attack on unit roots and cointegration.

Econometrica 72(4):1127–1177

Baloch MA, Wang B (2019) Analyzing the role of governance in

CO2 emissions mitigation: the BRICS experience. Struct

Chang Econ Dyn 51:119–125

Baloch ZA, Tan Q, Kamran HW, Nawaz MA, Albashar G, Hameed

J (2021) A multi-perspective assessment approach of renew-

able energy production: policy perspective analysis. Environ

Dev Sustain 1-29https:// doi. org/ 10. 1007/ s10668- 021- 01524-8

Balsalobre-Lorente D, Shahbaz M, Roubaud D, Farhani S (2018)

How economic growth, renewable electricity and natu-

ral resources contribute to CO2 emissions? Energy Policy

113:356–367

Banerjee A, Carrion-i-Silvestre JL (2017) Testing for panel cointegra-

tion using common correlated eﬀects estimators. J Time Ser Anal

38(4):610–636

Bond S, Eberhardt M (2013) Accounting for unobserved heterogeneity

in panel time series models. University of Oxford, 1–11.

Brookes L (1990) The greenhouse eﬀect: the fallacies in the energy

eﬃciency solution. Energy Policy 18(2):199–201

Chaturvedi V, Shukla PR (2014) Role of energy eﬃciency in climate

change mitigation policy for India: assessment of co-beneﬁts and

opportunities within an integrated assessment modeling frame-

work. Clim Change 123(3):597–609

Chaudhry IS, Ali S, Bhatti SH, Anser MK, Khan AI, Nazar R (2021)

Dynamic common correlated eﬀects of technological innova-

tions and institutional performance on environmental quality:

Evidence from East-Asia and Paciﬁc countries. Environ Sci

Policy 124:313–323

Cheng S, Meng L, Xing L (2021) Energy technological innovation and

carbon emissions mitigation: evidence from China. Kybernetes.

Chien F, Sadiq M, Nawaz MA, Hussain MS, Tran TD, Le Thanh T

(2021a) A step toward reducing air pollution in top Asian econo-

mies: The role of green energy, eco-innovation, and environmen-

tal taxes. J Environ Manage. https:// doi. org/ 10. 1016/j. jenvm an.

2021. 113420

Chien F, Hsu CC, Ozturk I, Sharif A, Sadiq M (2021b) The role of

renewable energy and urbanization towards greenhouse gas

emission in top Asian countries: Evidence from advance panel

estimations. Renewable Energy. https:// doi. org/ 10. 1016/j. renene.

2021. 12. 118

Chien F, Zhang Y, Sadiq M, Hsu CC (2021c) Financing for energy eﬃ-

ciency solutions to mitigate opportunity cost of coal consump-

tion: An empirical analysis of Chinese industries. Environ Sci

Pollut Res. https:// doi. org/ 10. 1007/ s11356- 021- 15701-9

Chien F, Ananzeh M, Mirza F, Bakar A, Vu HM, Ngo TQ (2021d)

The eﬀects of green growth, environmental-related tax, and eco-

innovation towards carbon neutrality target in the US economy. J

Environ Manage. https:// doi. org/ 10. 1016/j. jen vm an. 2021. 113633

Chien F, Sadiq M, Kamran HW, Nawaz MA, Hussain MS, Raza M

(2021e) Co-movement of energy prices and stock market return:

environmental wavelet nexus of COVID-19 pandemic from the

USA, Europe, and China. Environ Sci Pollut Res. https:// doi. org/

10. 1007/ s11356- 021- 12938-2

Chudik A, Pesaran MH (2015) Common correlated eﬀects estimation

of heterogeneous dynamic panel data models with weakly exog-

enous regressors. J Econom 188(2):393–420

Damette O, Seghir M (2013) Energy as a driver of growth in oil export-

ing countries? Energy Econ 37:193–199

Dauda L, Long X, Mensah CN, Salman M, Boamah KB, Ampon-

Wireko S, Dogbe CSK (2021) Innovation, trade openness and

CO2 emissions in selected countries in Africa. J Clean Prod

281:125143

Destek MA, Ulucak R, Dogan E (2018) Analyzing the environmental

Kuznets curve for the EU countries: the role of ecological foot-

print. Environ Sci Pollut Res 25(29):29387–29396

Dogan E, Seker F (2016) The inﬂuence of real output, renewable and

non-renewable energy, trade and ﬁnancial development on car-

bon emissions in the top renewable energy countries. Renew

Sustain Energy Rev 60:1074–1085

Eberhardt M, Teal F (2010) Ghana and Côte d’Ivoire: Changing Places.

International Development Policy| Revue internationale de poli-

tique de développement(1), 33–49.

Emir F, Bekun FV (2019) Energy intensity, carbon emissions, renew-

able energy, and economic growth nexus: new insights from

Romania. Energy Environ 30(3):427–443

Erdoğan S, Yıldırım S, Yıldırım DÇ, Gedikli A (2020) The eﬀects

of innovation on sectoral carbon emissions: evidence from G20

countries. J Environ Manag 267:110637

Ertugrul HM, Cetin M, Seker F, Dogan E (2016) The impact of trade

openness on global carbon dioxide emissions: Evidence from

the top ten emitters among developing countries. Ecol Ind

67:543–555

Ganda F (2019) The impact of innovation and technology investments

on carbon emissions in selected organisation for economic Co-

operation and development countries. J Clean Prod 217:469–483

Godil DI, Sharif A, Agha H, Jermsittiparsert K (2020) The dynamic

nonlinear inﬂuence of ICT, ﬁnancial development, and institu-

tional quality on CO2 emission in Pakistan: new insights from

QARDL approach. Environ Sci Pollut Res 27(19):24190–24200

Goulder LH, Mathai K (2000) Optimal CO2 abatement in the pres-

ence of induced technological change. J Environ Econ Manag

39(1):1–38

Greening LA, Greene DL, Diﬁglio C (2000) Energy eﬃciency and

consumption—the rebound effect—a survey. Energy Policy

28(6–7):389–401

Environmental Science and Pollution Research

1 3

Hao W, Rasul F, Bhatti Z, Hassan MS, Ahmed I, Asghar N (2021) A

technological innovation and economic progress enhancement:

an assessment of sustainable economic and environmental man-

agement. Environ Sci Pollut Res 28(22):28585–28597

Hasanbeigi A, Price L, Lin E (2012) Emerging Energy-eﬃciency and

CO {sub 2} Emission-reduction Technologies for Cement and

Concrete Production: Lawrence Berkeley National Lab.(LBNL),

Berkeley, CA (United States).

Hasanov FJ, Liddle B, Mikayilov JI (2018) The impact of international

trade on CO2 emissions in oil exporting countries: Territory vs

consumption emissions accounting. Energy Econ 74:343–350

Hodson EL, Brown M, Cohen S, Showalter S, Wise M, Wood F, . . .

Cleary K (2018) US energy sector impacts of technology innova-

tion, fuel price, and electric sector CO2 policy: results from the

EMF 32 model intercomparison study. Energy Econ, 73, 352-370

Hossain MS (2011) Panel estimation for CO2 emissions, energy con-

sumption, economic growth, trade openness and urbanization of

newly industrialized countries. Energy Policy 39(11):6991–6999

Ehsanullah S, Tran QH, Sadiq M, Bashir S, Mohsin M, Iram R (2021)

How energy insecurity leads to energy poverty? Do environmen-

tal consideration and climate change concerns matters. Environ

Sci Pollut Res. https:// doi. org/ 10. 1007/ s11356- 021- 14415-2

Hsu CC, Quang-Thanh N, Chien F, Li L, Mohsin M (2021) Evaluat-

ing green innovation and performance of ﬁnancial development:

mediating concerns of environmental regulation. Environ Sci

Pollut Res. https:// doi. org/ 10. 1007/ s11356- 021- 14499-w

Huang SZ, Sadiq M, Chien F (2021a) The impact of natural resource

rent, financial development, and urbanization on carbon

emission. Environ Sci Pollut Res. https:// doi. org/ 10. 1007/

s11356- 021- 16818-7

Huang SZ, Sadiq M, Chien F (2021b) Dynamic nexus between trans-

portation, urbanization, economic growth and environmental

pollution in ASEAN countries: does environmental regula-

tions matter? Environ Sci Pollut Res. https:// doi. org/ 10. 1007/

s11356- 021- 17533-z

Huang SZ, Chien F, Sadiq M (2021c) A gateway towards a sustainable

environment in emerging countries: the nexus between green

energy and human Capital. Econ Res-Ekon Istraživanja. https://

doi. org/ 10. 1080/ 13316 77X. 2021. 20122 18

Hussain M, Dogan E (2021) The role of institutional quality and envi-

ronment-related technologies in environmental degradation for

BRICS. J Clean Prod 304:127059

Ibrahim MH, Law SH (2016) Institutional Quality and CO2 Emission-

Trade Relations: Evidence from S ub-S aharan A frica. S Afr J

Econ 84(2):323–340

Ibrahim RL, Ajide KB (2021) Disaggregated environmental impacts of

non-renewable energy and trade openness in selected G-20 coun-

tries: the conditioning role of technological innovation. Environ

Sci Pollut Res, 1–15.

Irfan M, Mahapatra B, Ojha RK (2021) Examining the eﬀectiveness

of low-carbon strategies in South Asian countries: the case of

energy eﬃciency and renewable energy. Environ Dev Sustain

1–17.

Javid M, Khan M (2020) Energy eﬃciency and underlying carbon

emission trends. Environ Sci Pollut Res 27(3):3224–3236

Kapetanios G, Pesaran MH, Yamagata T (2011) Panels with non-sta-

tionary multifactor error structures. J Econom 160(2):326–348

Khan A, Muhammad F, Chenggang Y, Hussain J, Bano S, Khan MA

(2020a) The impression of technological innovations and natural

resources in energy-growth-environment nexus: a new look into

BRICS economies. Sci Total Environ 727:138265

Khan MTI, Yaseen MR, Ali Q (2017) Dynamic relationship between

ﬁnancial development, energy consumption, trade and green-

house gas: comparison of upper middle income countries from

Asia, Europe, Africa and America. J Clean Prod 161:567–580

Khan Z, Ali S, Umar M, Kirikkaleli D, Jiao Z (2020b) Consumption-

based carbon emissions and international trade in G7 countries:

the role of environmental innovation and renewable energy. Sci

Total Environ 730:138945

Le HP, Ozturk I (2020) The impacts of globalization, ﬁnancial develop-

ment, government expenditures, and institutional quality on CO

2 emissions in the presence of environmental Kuznets curve.

Environ Sci Pollut Res 27(18):22680–22697

Lee HS, Arestis P, Chong SC, Yap S, Sia BK (2021) The heterogeneous

eﬀects of urbanisation and institutional quality on greenhouse

gas emissions in Belt and Road Initiative countries. Environ Sci

Pollut Res, 1–19.

Li W, Chien F, Kamran HW, Aldeehani TM, Sadiq M, Nguyen VC,

Taghizadeh-Hesary F (2021) The nexus between COVID-19 fear

and stock market volatility. Econ Res-Ekon Istraživanja. https://

doi. org/ 10. 1080/ 13316 77X. 2021. 19141 25

Liu Z, Tang YM, Chau KY, Chien F, Iqbal W, Sadiq M (2021a) Incor-

porating strategic petroleum reserve and welfare losses: A way

forward for the policy development of crude oil resources in

South Asia. Resour Policy. https:// doi. org/ 10. 1016/j. resou rpol.

2021. 102309

Liu Z, Lan J, Chien F, Sadiq M, Nawaz MA (2021b) Role of tour-

ism development in environmental degradation: A step towards

emission reduction. J Environ Manage. https:// doi. org/ 10. 1016/j.

jenvm an. 2021. 114078

Lin B, Ahmad I (2017) Analysis of energy related carbon dioxide

emission and reduction potential in Pakistan. J Clean Prod

143:278–287

Ling CH, Ahmed K, Muhamad RB, Shahbaz M (2015) Decomposing

the trade-environment nexus for Malaysia: what do the technique,

scale, composition, and comparative advantage eﬀect indicate?

Environ Sci Pollut Res 22(24):20131–20142

Long X, Luo Y, Wu C, Zhang J (2018) The inﬂuencing factors of CO

2 emission intensity of Chinese agriculture from 1997 to 2014.

Environ Sci Pollut Res 25(13):13093–13101

Lv Z, Xu T (2019) Trade openness, urbanization and CO2 emissions:

dynamic panel data analysis of middle-income countries. J Int

Trade Econ Dev 28(3):317–330

Mahmood A, Marpaung CO (2014) Carbon pricing and energy eﬃ-

ciency improvement–why to miss the interaction for developing

economies? An illustrative CGE based application to the Paki-

stan case. Energy Policy 67:87–103

Mohsin M, Kamran HW, Nawaz MA, Hussain MS, Dahri AS (2021)

Assessing the impact of transition from nonrenewable to renew-

able energy consumption on economic growth-environmental

nexus from developing Asian economies. J Environ Manag

284https:// doi. org/ 10. 1016/j. jenvm an. 2021. 111999

Muhammad S, Long X (2021) Rule of law and CO2 emissions: a com-

parative analysis across 65 belt and road initiative (BRI) coun-

tries. J Clean Prod 279:123539

Nasir M, Rehman FU (2011) Environmental Kuznets curve for car-

bon emissions in Pakistan: an empirical investigation. Energy

Policy 39(3):1857–1864

Nasreen S, Mbarek MB, Atiq-ur-Rehman M (2020) Long-run causal

relationship between economic growth, transport energy con-

sumption and environmental quality in Asian countries: Evi-

dence from heterogeneous panel methods. Energy 192:116628

Nawaz MA, Hussain MS, Kamran HW, Ehsanullah S, Maheen R,

Shair F (2021a) Trilemma association of energy consumption,

carbon emission, and economic growth of BRICS and OECD

regions: quantile regression estimation. Environ Sci Pollut Res

28(13):16014–16028

Nawaz MA, Seshadri U, Kumar P, Aqdas R, Patwary AK, Riaz M

(2021b) Nexus between green ﬁnance and climate change mit-

igation in N-11 and BRICS countries: empirical estimation

Environmental Science and Pollution Research

1 3

through diﬀerence in diﬀerences (DID) approach. Environ

Sci Pollut Res 28(6):6504–6519. https:// doi. org/ 10. 1007/

s11356- 020- 10920-y

Omri A, Hadj TB (2020) Foreign investment and air pollution: do

good governance and technological innovation matter? Environ

Res 185:109469

Othman Z, Nordin MFF, Sadiq M (2020) GST fraud prevention to

ensure business sustainability: A Malaysian case study. J Asia

Bus Econ Stud 27(3):245–265

Ozturk I, Al-Mulali U (2015) Investigating the validity of the envi-

ronmental Kuznets curve hypothesis in Cambodia. Ecol Ind

57:324–330

Pesaran MH (2007) A simple panel unit root test in the presence of

cross-section dependence. J Appl Economet 22(2):265–312

Pesaran MH (2015) Testing weak cross-sectional dependence in large

panels. Economet Rev 34(6–10):1089–1117

Pesaran MH, Yamagata T (2008) Testing slope homogeneity in large

panels. J Econom 142(1):50–93

Sadiq M, Hsu CC, Zhang Y, Chien FS (2021a) COVID-19 fear and

volatility index movements: empirical insights from ASEAN

stock markets. Environ Sci Pollut Res. https:// doi. org/ 10. 1007/

s11356- 021- 15064-1

Sadiq M, Nonthapot S, Mohamad, Keong OC, Ehsanullah S, Iqbal N

(2021b) Does Green Finance Matters for Sustainable Entrepre-

neurship and Environmental Corporate Social Responsibility

during Covid-19? China Finance Rev Int. https:// doi. org/ 10.

1108/ CFRI- 02- 2021- 0038

Sadiq M, Alajlani S, Hussain MS, Ahmad R, Bashir F, Chupradit

S (2021c) Impact of credit, liquidity, and systematic risk on

ﬁnancial structure: comparative investigation from sustainable

production. Environ Sci Pollut Res. https:// doi. org/ 10. 1007/

s11356- 021- 17276-x

Salahuddin M, Alam K, Ozturk I, Sohag K (2018) The eﬀects of elec-

tricity consumption, economic growth, ﬁnancial development

and foreign direct investment on CO2 emissions in Kuwait.

Renew Sustain Energy Rev 81:2002–2010

Saleem H, Khan MB, Shabbir MS (2020) The role of ﬁnancial develop-

ment, energy demand, and technological change in environmen-

tal sustainability agenda: evidence from selected Asian countries.

Environ Sci Pollut Res 27(5):5266–5280

Santra S (2017) The eﬀect of technological innovation on production-

based energy and CO2 emission productivity: evidence from

BRICS countries. Afr J Sci Technol Innov Dev 9(5):503–512

Sathaye J, Gupta A (2010) Eliminating electricity deﬁcit through

energy eﬃciency in India: an evaluation of aggregate economic

and carbon beneﬁts: Lawrence Berkeley National Lab.(LBNL),

Berkeley, CA (United States).

Schmidt P, Phillips PC (1992) LM tests for a unit root in the presence

of deterministic trends. Oxford Bull Econ Stat 54(3):257–287

Shahbaz M, Hye QMA, Tiwari AK, Leitão NC (2013) Economic

growth, energy consumption, ﬁnancial development, interna-

tional trade and CO2 emissions in Indonesia. Renew Sustain

Energy Rev 25:109–121

Shahbaz M, Khraief N, Uddin GS, Ozturk I (2014) Environmental

Kuznets curve in an open economy: a bounds testing and causal-

ity analysis for Tunisia. Renew Sustain Energy Rev 34:325–336

Shahbaz M, Raghutla C, Song M, Zameer H, Jiao Z (2020) Public-

private partnerships investment in energy as new determinant of

CO2 emissions: the role of technological innovations in China.

Energy Econ 86:104664

Shair F, Shaorong S, Kamran HW, Hussain MS, Nawaz MA (2021)

Assessing the eﬃciency and total factor productivity growth of

the banking industry: do environmental concerns matters? Envi-

ron Sci Pollut Res 28(16):20822–20838

Shao X, Zhong Y, Liu W, Li RYM (2021) Modeling the eﬀect of green

technology innovation and renewable energy on carbon neutrality

in N-11 countries? Evidence from advance panel estimations. J

Environ Manag 296:113189

Sun H, Awan RU, Nawaz MA, Mohsin M, Rasheed AK, Iqbal N (2020)

Assessing the socio-economic viability of solar commercializa-

tion and electriﬁcation in south Asian countries. Environ Dev

Sustain 1-23https:// doi. org/ 10. 1007/ s10668- 020- 01038-9

Swamy PA (1970) Eﬃcient inference in a random coeﬃcient regression

model. Econom: J Econom Soc, 311–323.

Tachie AK, Xingle L, Dauda L, Mensah CN, Appiah-Twum F, Men-

sah IA (2020) The influence of trade openness on environ-

mental pollution in EU-18 countries. Environ Sci Pollut Res

27(28):35535–35555

Tan LP, Sadiq M, Aldeehani TM, Ehsanullah S, Mutira P, Vu HM

(2021) How COVID-19 induced panic on stock price and green

ﬁnance markets: global economic recovery nexus from volatil-

ity dynamics. Environ Sci Pollut Res. https:// doi. org/ 10. 1007/

s11356- 021- 17774-y

Wang J, Lv K, Bian Y, Cheng Y (2017) Energy eﬃciency and marginal

carbon dioxide emission abatement cost in urban China. Energy

Policy 105:246–255

Westerlund J, Edgerton DL (2008) A simple test for cointegration in

dependent panels with structural breaks. Oxford Bull Econ Stat

70(5):665–704

Xiang H, Ch P, Nawaz MA, Chupradit S, Fatima A, Sadiq M (2021)

Integration and economic viability of fueling the future with

green hydrogen: An integration of its determinants from renew-

able economics. Int J Hydrogen Energy. https:// doi. org/ 10. 1016/j.

ijhyd ene. 2021. 09. 067

Xueying W, Sadiq M, Chien F, Ngo TQ, Nguyen AT (2021) Testing

role of green ﬁnancing on climate change mitigation: Evidences

from G7 and E7 countries. Environ Sci Pollut Res 28(47):66736–

66750. https:// doi. org/ 10. 1007/ s11356- 021- 15023-w

Yii K-J, Geetha C (2017) The nexus between technology innovation

and CO2 emissions in Malaysia: evidence from granger causality

test. Energy Procedia 105:3118–3124

Yu Y, Du Y (2019) Impact of technological innovation on CO2 emis-

sions and emissions trend prediction on ‘New Normal’economy

in China. Atmos Pollut Res 10(1):152–161

Zameer H, Yasmeen H, Zafar MW, Waheed A, Sinha A (2020) Analyz-

ing the association between innovation, economic growth, and

environment: divulging the importance of FDI and trade open-

ness in India. Environ Sci Pollut Res 27:29539–29553

Zhao L, Zhang Y, Sadiq M, Hieu VM, Ngo TQ (2021) Testing green

ﬁscal policies for green investment, innovation and green pro-

ductivity amid the COVID-19 era. Econ Chang Restruct. https://

doi. org/ 10. 1007/ s10644- 021- 09367-z

Zhang S, Liu X, Bae J (2017) Does trade openness aﬀect CO 2 emis-

sions: evidence from ten newly industrialized countries? Environ

Sci Pollut Res 24(21):17616–17625

Zhuang Y, Yang S, Chupradit S, Nawaz MA, Xiong R, Koksal C (2021)

A nexus between macroeconomic dynamics and trade openness:

moderating role of institutional quality. Bus Process Manag J.

https:// doi. org/ 10. 1108/ BPMJ- 12- 2020- 0594

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Accessing the efficacy of green growth, energy efficiency, and green innovation for environmental performance in top manufacturing nations in the framework of sustainable development

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In recent years, the quality of the environment has declined dramatically as a result of human activities, which threaten the sustainability of our ecosystem. In this context, a number of earlier studies have investigated the environmental problems through the lens of different environmental indicators such as ecological footprint and carbon dioxide emissions. However, it is possible that by taking these factors into account, the supply side of environmental quality will be neglected. This research seeks to address this deficiency by investigating the impact of green growth, energy efficiency, green technology innovation, economic growth, trade openness, and human capital on the level of load capacity factor. This indicator places equal emphasis on the supply and demand sides of the ecosystem by taking into account both ecological footprint and bio capacity. In this setting, the present study makes use of CS-ARDL model for the top 10 manufacturing countries from the years 1990 to 2019. The short- and long-run findings of the CS-ARDL model unveil that green growth, energy efficiency, green technology innovation, and human capital all positively influence the load capacity factor, suggesting that these factors help the top 10 manufacturing countries to improve their sustainability limits. In contrast, trade openness and economic expansion in underlying countries increase environmental degradation by lowering the level of LCF. These outcomes are also validated by the AMG and CCEMG approaches and the granger causality test. On the basis of the findings, important policy recommendations are presented with the aim of assisting the top 10 manufacturing economies in enhancing the quality of their environment and fulfilling the United Nations Sustainable Development Goals.

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This paper is the first comprehensive research to examine the effect of circular economy on environment employing two environmental degradation indicators (CO2 emissions, ecological footprint) and one environmental quality indicator (load capacity factor) for 57 Belt and Road Initiative (BRI) countries during 2000–2019. The effect of other variables such as renewable energy, industrialization, and globalization was also controlled. The study applied the cross-sectional autoregressive distributed lag method (CS-ARDL), the augmented mean group (AMG), and common correlated effects mean group (CCEMG) methods as a robustness checks. The empirical findings reveal that circular economy and renewable energy have pro-environmental effects by decreasing carbon emissions and ecological footprint and increasing the load capacity factor in BRI countries. However, industrialization and globalization have detrimental effects on the environment. The result of causality shows a bidirectional causality between renewable energy, circular economy, industrialization, and three environmental indicators, but the relationship of globalization with CO2 emissions and the load capacity factor is unidirectional and with the ecological footprint is bidirectional. All the results are confirmed by the robustness tests. The study suggests policy implications for the BRI government.

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The study examines the significance of urbanization, industrialization, technological innovation, foreign direct investment (FDI), and economic growth for the development of smart cities in Pakistan. Data from the World Development Indicators (WDI) spanning the period of 1975 to 2020 was utilized. Various econometric techniques were employed, including Ordinary Least Squares (OLS) regression analysis to investigate the relationships between dependent and independent variables. Outliers were detected to assess the degree of deviation from the regression line, and Robust Least Squares techniques were applied to address their impact on the dataset. The findings indicate that technology innovation and urbanization have a positive impact on the development of smart cities in Pakistan. However, other factors such as industrialization, FDI, and economic growth may also play significant roles, although their impact was not as pronounced in this study. The study emphasizes the importance of implementing proper policies and encouraging government and private sector investment to facilitate smart cities development in Pakistan. It underscores the need for strategic planning and collaboration between various stakeholders to realize the full potential of smart city initiatives in the country.

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Prior studies on environmental standards have highlighted the significance of urbanization and transportation in affecting environmental sustainability worldwide. As the empirical and theoretical debates are still unresolved and divisive, the argument of whether urbanization, transportation and economic growth in Association of Southeast Asian Nations (ASEAN) countries cause greenhouse gas (GHG) emissions remains unclear. This study aim is to examine dynamic linkage between transportation, urbanization, economic growth and GHG emissions, as well as the impact of environmental regulations on GHG emission reduction in ASEAN countries over the years 1995–2018. On methodological aspects, the study accompanies a few environmental studies that check the cross-sectional dependence and slope heterogeneity issues. Moreover, the new cross-sectionally augmented autoregressive distributed lags (CS-ARDL) methodology is also applied in the study to estimate the short-run and long-run effects of the factors on GHG emissions. Substantial evidence is provided that GHG emissions increase with transportation, urbanization and economic growth but decrease with the imposition of environmental-related taxations. Augmented mean group (AMG) and common correlated effect mean group (CCEMG) also support the findings of CS-ARDL estimates. Finally, the study calls for drastic actions in ASEAN countries to reduce GHG emissions, including environmentally friendly transportation services and environmental regulation taxes. This study also provides the guidelines to the regulators while developing policies related to control the GHG emission in the country.

Testing green fiscal policies for green investment, innovation and green productivity amid the COVID-19 era

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This article measures renewable energy firm-level pure innovation efficiency, green productivity, technical efficiency, scale efficiency and total investment efficiency from micro input-output factors using Banker, Charnes and Cooper's (BCC) data envelopment analysis (DEA) approach. Its main novelty is that it clearly explores the effective impacts of government subsidies and tax rebate policies on renewable energy firms' investment efficiency using China's renewable energy firm-level panel data. Our observational findings indicate that between 2001 and 2018, the aggregate degree of total investment performance from renewable energy firms rose steadily before declining. Renewable energy firms had larger ranges of total investment efficiency and size efficiency, and their levels of pure technological efficiency were both greater than 0.457%. At the 16% trust mark, current government subsidies and taxation rebates had dramatically positive effects on pure technological efficiency and total investment efficiency; additionally, government subsidies have a stronger positive impact on total investment efficiency and pure technical efficiency than taxation rebates. Furthermore, the ownership concentrations of renewable energy companies greatly encourage pure technological efficiency, size efficiency and total investment efficiency, and asset returns will significantly increase their average degree of total investment efficiency and pure technical efficiency.

Impact of credit, liquidity, and systematic risk on financial structure: comparative investigation from sustainable production

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Mar 2022
ENVIRON SCI POLLUT R

The role of risk assessment and capital structure is vital for the sustainable growth of firms and increasing the shareholders’ wealth. This research explores the correlation between firm risk and capital structure using datasets from the sugar and cement sectors of Pakistan as a developing economy. This study is unique as it involved two firms of different nature (sugar firms operate seasonally while cement firms operate yearly) to view the real picture on the impact of risk and structure assessment on firms’ credibility and shareholders’ wealth. For this purpose, 15-year data (2000–2014) containing the financial statements of the target sectors were collected and the ANOVA analysis was applied with credit risk, liquidity risk, systematic risk, and firm size were used as the regressor variables, firm growth and dividend payout ratio as the control variables, and leverage as the regression variable. The findings showed that credit risk and liquidity risk are significantly correlated with leverage. This suggests that decision-makers pertaining to firms’ risk and efficiency must focus more on risk to pursue a stronger and sustainable increase in shareholder wealth.

The impact of natural resource rent, financial development, and urbanization on carbon emission

Article

Full-text available

Oct 2021
ENVIRON SCI POLLUT R

There is a shred of evidence of environmental degradation in the form of carbon emissions to behave differently when tested with different macroeconomic variables. This paper aims to examine the long-run and short-run association between natural resource rent, financial development, and urbanization on carbon emission from the context of the USA during 1995–2015 with the help of a contemporary and innovative approach named quantile autoregressive distributed lagged model (QARDL). The stated approach is applied due to the fact that non-linearity is observed for the study variables. The findings indicated that the higher financial development (0.304), natural resource rent (0.102), and urbanization (0.489) have a positive impact on the environmental degradation in the region of USA during long-run estimation in the stated quantiles of the study. This would indicate that higher financial development, urbanization, and natural resources are putting more environmental pressure on the economy of the USA. Similarly, the findings under short-run estimation confirm that past and lagged values of carbon emission, financial development, natural resource rent, and urbanization are significantly determining the current values of the carbon emission. For this reason, it is suggested that the government requires some immediate steps of the USA to control the harmful effect of such financial development, more urbanization, and higher natural resource rent as well. This would indicate the reflection of some green strategies in all three explanatory variables to generate some fruitful environmental outcomes.

The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations

Article

Dec 2021
RENEW ENERG

The increasing greenhouse gas (GHG) emission and environmental degradation (ED) is a serious concern for the various economies, and a similar issue is observed in Asia. This paper investigated the role of clean energy from renewable sources, urbanization, and economic growth in determining the level of GHG emissions from 1995 to 2018 for ten Asian States through a cross-sectional autoregressive distributed lagged (CS-ARDL) model. Meanwhile, the current research also examined the cross-sectional dependence, unit root properties, and co-integration between the study variables. The study findings confirmed that clean energy and GDP² played their constructive role in reducing GHG emissions in the natural environment or targeted economies. In contrast, urbanization and economic growth caused more GHG emissions both in the long and short run. Furthermore, the robust check through augmented mean group (AMG) and common correlated effect means group (CCEMG) also confirmed that clean energy and GDP² have a good sign for lowering ED compared to GDP and urbanization. The study findings could support policymakers, specifically in the field of energy economics and environmental sustainability. Therefore, it is highly recommended that some strong policy implications are needed to reduce environmental issues through controlling the negative impact of economic growth and urbanization. This study contributes in the literature of GHG emission with respect to economic growth, urbanization and clean energy and guided the regulators while formulating policies related to control the GHG emission.

Role of tourism development in environmental degradation: A step towards emission reduction

Article

Nov 2021
J ENVIRON MANAGE

Globally, the interaction and vulnerability of tourism and climate change have recently been in focus. This study examines how carbon dioxide emissions respond to changes in the tourism development. Panel data from 2000 to 2017 for 70 countries are analyzed using spatial econometric method to investigate the spatial spillover effect of tourism development on environmental pollution. The direct, indirect, and overall impact of tourism on environmental pollution are estimated after the selection of the most appropriate GNS method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1 % level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and carbon emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness and economic growth significantly influence on environmental pollution through spatial spill over. In addition, education expenditure and infrastructure play a significant moderating role in the relationship among tourism development and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and environmental pollution and indicate that while a country's emissions initially rise with the tourism industry's growth, they begin declining after a limit.

Article

Jan 2013

Integration and economic viability of fueling the future with green hydrogen: An integration of its determinants from renewable economics

Article

Oct 2021
INT J HYDROGEN ENERG

The volatility of fossil fuel and their increased consumption have exacerbated the socio-economic dilemma along with electricity expenses in third world countries around the world, Pakistan in particular. In this research, we study the output of renewable hydrogen from natural sources like wind, solar, biomass, and geothermal power. It also provides rules and procedures in an attempt to determine the current situation of Pakistan regarding the workability of upcoming renewable energy plans. To achieve this, four main criteria were assessed and they are economic, commercial, environmental, and social adoption. The method used in this research is the Fuzzy Analytical Hierarchical Process (FAHP), where we used first-order engineering equations, and Levelized cost electricity to produce renewable hydrogen. The value of renewable hydrogen is also evaluated. The results of the study indicate that wind is the best option in Pakistan for manufacturing renewable based on four criteria. Biomass is found to be the most viable raw material for the establishment of the hydrogen supply network in Pakistan, which can generate 6.6 million tons of hydrogen per year, next is photovoltaic solar energy, which has the capability of generating 2.8 million tons. Another significant finding is that solar energy is the second-best candidate for hydrogen production taking into consideration its low-cost installation and production. The study shows that the cost of using hydrogen in Pakistan ranges from $5.30/kg to $5.80/kg, making it a competitive fuel for electric machines. Such projects for producing renewable power must be highlighted and carried out in Pakistan and this will lead to more energy security for Pakistan, less use of fossil fuels, and effective reduction of greenhouse gas emissions.

Impact of energy efficiency, technology innovation, institutional quality, and trade openness on greenhouse gas emissions in ten Asian economies

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