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RESEARCH ARTICLE
Impact of globalization, foreign direct investment, and energy
consumption on CO
2
emissions in Bangladesh: Does institutional
quality matter?
Md. Monirul Islam
1
&Muhammad Kamran Khan
2
&Mohammad Tareque
1
&Noor Jehan
3
&Vishal Dagar
4
Received: 5 January 2021 /Accepted: 9 March 2021
#The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Abstract
Bangladesh’s recent doorway to the spectacular growth trajectory is largely associated with the shared contributions of global-
ization, FDI, trade, economic growth, urbanization, energy consumption, innovation, and institutional quality that affect its
natural environment. Earlier studies hardly incorporated these dynamics together especially innovation and institutional quality
to examine their impacts on environmental degradation in Bangladesh. This study attempts to scrutinize the effect of globaliza-
tion, foreign direct investment, economic growth, trade, innovation, urbanization, and energy consumption on CO
2
emissions in
the presenceof institutional quality in Bangladesh over the period 1972–2016 by utilizing dynamic ARDL simulations’model by
Jordan and Philips (2018). The investigated results depict that globalization; foreign direct investment, and innovation have a
negative effect on CO
2
emissions in improving environmental quality while economic growth, trade, energy consumption, and
urbanization positively impact CO
2
emissions and hence stimulate environmental degradation both in the long and short run.
Besides, institutional quality measured by the political terror scale (PTS) affects CO
2
emissions positively and thereby degrades
the quality of the environment in both the long and short run. Therefore, policy implication should go toward encouraging
globalization, foreign direct investment and innovation; and the sensible utilization of income growth, trade potentials, energy
consumption, urbanization and institution is required for the sake of environmental quality in Bangladesh.
Keywords Globalization .Economic factors .CO
2
emissions .Environmental degrada tion .Dynamic ARDL simulations model .
Bangladesh
Highlights
•Globalization helps to reduce the CO
2
emissions.
•Economic growth, trade, energy consumption, and urbanization
positively affect CO
2
emissions.
•The effect of foreign direct investment and innovation on CO
2
emissions
is negative.
•Institutional quality leads to increase environmental degradation.
Responsible Editor: Ilhan Ozturk
*Muhammad Kamran Khan
mkkhan.buic@bahria.edu.pk; kamrankhanaup@gmail.com
Md. Monirul Islam
monirul.islam@bigm.edu.bd; monirdu1981@gmail.com
Mohammad Tareque
director@bigm.edu.bd
Noor Jehan
noorjehan@awkum.edu.pk
Vishal Dagar
dagarvishal99@gmail.com
1
Bangladesh Institute of Governance and Management (BIGM),
University of Dhaka (Affiliated), Dhaka-1207, Bangladesh
2
Management Studies Department, Bahria Business School, Bahria
University, Islamabad, Pakistan
3
Department of Economics, Adbul Wali Khan University,
Mardan, Pakistan
4
Amity School of Economics, Amity University, Noida 201301, Uttar
Pradesh, India
Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-021-13441-4
Introduction
Bangladesh has recently graduated to the status of developing
country from the least developed one by maintaining more
than 6% growth rate over the last decade (Islam et al. 2018).
Bangladesh’s achieved benchmark of developing economy is
mostly influenced by the mutual contributions of external fac-
tors (e.g., globalization, foreign direct investment (FDI)) and
internal dynamics (e.g., energy consumption, urbanization,
trade, innovation, institutional quality), which increases
greenhouse gas (GHG) emissions in affecting environmental
quality. The growth strategy of Bangladesh is similar to the
global practice in which countries are highly adamant to con-
tinue the pace and aspiration of economic growth. Thus, eco-
nomic policies dictate the environmental policies of different
economies (Khan et al. 2021; Shrinkhal 2019). For growth
purpose, industrialized and unindustrialized countries are
emitting a higher quantity of greenhouse gases (GHGs),
which harm the total environment of the globe (Zhang et al.
2021; Chishti et al. 2021;Muhammadetal.2021;Khanetal.
2020a,b;Khanetal.2019a,b).
The emergence of Bangladesh in 1971 with a higher aspi-
ration were somewhat constrained with the uncertainty due to
internal clash among the political leaders. After passing about
two decades of volatility, the country started walking along
the road to democracy, which helped this economy adopting a
substantial reform in its policy-affair by following the path of
deregulation or liberalization apart from the restricted or
inward-looking way (Majumder and Rana 2016). The liberal-
ization policy broadened external trade relations and other
types of socio-economic and political connections with glob-
alization. More specifically, this country brought a significant
transformation in its economic structure through proliferating
industries, trade bases, export and import growths, remittance
earnings via manpower export abroad, and FDI attraction and
escalation. Caused by the escalating flows of globalization,
the export earnings of Bangladesh contributed more than
10% to the total GDP, which is more than 84% of the foreign
exchange earnings in FY-2018-19 (Islam 2020). Similarly,
globalization enabled the economy receiving a significant
amount of remittances, which is on average 46.04% of the
country’s total export earnings and 7.1% of GDP over the
period 1990–2018 (Bangladesh Bank 2019). Bangladesh has
thus access to the array of economic globalization.
This country has become a member of myriad international
forums, made collaboration with globally oriented NGOs,
maintained diplomatic relations, and signed different bi- and
multi-lateral accords with countries and global companies.
These efforts of the country contribute to accessing to the
realm of political globalization (UNCTAD 2017). Besides,
Bangladesh attempts to subscribe as well as use telephones
and cellular phones and provide conducive visa facility for
external visitors that settle in the country to strongly be
connected to social globalization (World Bank 2020).
Moreover, globalization (e.g., economic, political, and social
dimensions) has become stimuli making the people of
Bangladesh well-equipped with technology and bringing the
economy to the juncture of industrialization that requires an
increasing level of energy consumption, leading to GHGs
emissions and hence environmental degradation as supported
by Gunatilake and Roland-Holst (2013).
The multifarious activities and investment operations of
transnational and multinational corporations become work-
able in external countries through foreign direct investment
(FDI) that accelerates recipient countries’trade
(Acheampong et al. 2019; Kim et al. 2019;Mishkin2009;
Shahbaz et al. 2015; Shahbaz et al. 2017a,2017b) and trade-
related activities stimulate carbon emissions (Liu et al. 2020).
The trade liberalization policy of Bangladesh since the 1980s
has brought enormous scope for both the inner and outer in-
vestors (Bashar and Khan 2007). As part of the policy imple-
mentation, the country has established different economic
zones, e.g., export processing zones (EPZs) and special eco-
nomic zones (SEZs). Besides, it has been witnessing a signif-
icant increase in FDI inflows due to lumpy investments in
power generation and labor-intensive industries, such as
readymade garments. As a result, Bangladesh has recorded
substantial FDI inflows in 2018, securing top position in
South Asia, and the country reached the maximum level of
FDI reception at $3.61 billion (UNCTAD 2018). This rising
growth of inbound FDI and its utilization in power and export-
based industries are related to CO
2
emissions in Bangladesh
(Banerjee and Rahman 2012).
The urbanization pattern of Bangladesh is as like as global
trend in which the agricultural labor force moves to industry
and service sector-based urban areas from rural areas due to
migration. This type of economic transformation causes vari-
ous changes in natural resources and energy consumption
(Salim and Shafiei 2014). People’s migration to the urban
areas in Bangladesh is very significant. As per the latest sta-
tistics, 37.41% of the total population has been living in urban
parts and cities in Bangladesh. The people of the country
caused by the poverty shift to the urban areas and engage in
the urban industrial production process as workers that result
in GHG emissions in particular CO
2
emissions (Teng et al.
2021;Hassan2016).
In recent years, energy technology innovation contributes
to more progress in energy efficiency and clean technology
having a significant role to reduce CO
2
emissions (Chuzhi and
Xianjin 2008; Fisher-Vanden et al. 2006; Sagar and Holdren
2002; Sun et al. 2008). In some cases, clean technology inno-
vation adversely impacts CO
2
emissions and environmental
degradation (Murshed 2020; Wei and Yang 2010;Wuetal.
2005). Bangladesh actively started introducing technology in-
novation in 1997 when the infrastructure development com-
pany limited (IDCOL) began to finance the medium and
Environ Sci Pollut Res
large-scale infrastructure and renewable energy projects. The
IDCOL’s solar home system (SHS) has become the most
dominant technology innovation initiative that aimed to “ful-
fill basic electricity requirement of the off-grid rural people of
Bangladesh”(Murshed and Dao 2020; Chowdhury et al.
2011). More importantly, the social innovation cluster at a2i
Project under Prime Minister Office (PMO) has been working
in its “ilab”on 250 ideas on innovation technology since 2016
and 27 of these ideas are currently implementing on a com-
mercial basis. Thus, innovation technology helps Bangladesh
to be attached with a cleaner production process and free from
emissions as well as environmental degradation.
The institutional quality of a state is determined by the
political and administrative performance of state machinery.
Political performance includes the protection of human rights
such as torture, killing, disappearance, and terrorist attacks. In
the case of Bangladesh, human rights violations are evident
though there appears a significant policy intervention making
a stable state environment. This antagonized state-affair is not
favorable for natural resources conservation and environmen-
tal safety. On the other hand, administrative performance re-
lates to the proper management and implementation of tech-
nologies (non-renewable and renewable) used in the safety of
the environment. In the administrative aspect, Bangladesh’s
attempt is not up-to-the-mark to apply full-fledged cleaner
production technologies to curb emissions and save the envi-
ronment. But external determinants in Bangladesh are proper-
ly dealt with by the quality of the institution (Murshed et al.
2021a,b;Murshed2021; Islam and Islam 2021).
Bangladesh as a home of 165 million people primarily
began a green revolution and then held a path of industriali-
zation providing a congenial life for its inhabitants. This
changing pattern of economy necessitates adopting the up-
grade mechanization of productions that results in increased
energy demand. Thus, the production mechanism of the coun-
try has gradually been energy-dependent (Islam and Islam
2021). The total energy consumption of Bangladesh is 53.65
billion kWh of electric energy each year and the per capita
average is 329 kWh. The country can produce 61 bn kWh by
utilizing fossil fuel energy sources, such as natural gas and
crude oil, and the remaining self-produced energy is exported
into external countries (WorldData 2021). This level of energy
consumption is highly responsible for the fossil fuel-based
CO
2
emissions in Bangladesh, which is accounted for
74,476,230 tons in 2016. The per capita CO
2
emissions are
0.47 tons that show 3.4% per capita change compared to the
previous year 2015. This growing rate of CO
2
emissions has
been the major cause of environmental degradation in
Bangladesh (Worldometer 2021).
Over the last decade, Bangladesh is maintaining more than
6% growth (Islam et al. 2018) and it intends to surpass this
growth trap by utilizing both external and internal macroeco-
nomic forces and determinants for promoting
industrialization. Industrialization requires more use of energy
and an increase in energy consumption contributes to enhanc-
ing GHGs emissions, which is detrimental to environmental
quality. In 2016, fossil fuel-based CO
2
emissions were
74,476,230 tons and it increased by 4.50% compared to the
previous year. The per capita CO
2
emissions accounted for
0.47 tons, which increased by 0.02 over the period of 2015.
This also shows a 3.4% per capita change in CO
2
emissions in
Bangladesh (Worldometer 2021). Rahman and Kashem
(2017) argued that emission-induced industrial as well as eco-
nomic development is attained by Bangladesh that damages
its environmental quality. Against this backdrop, this study
aims at analyzing the impact of globalization (social, econom-
ic and political dimensions), foreign direct investment, trade,
economic growth, urbanization, innovation, and energy con-
sumption on CO
2
emissions within the purview of institution-
al quality in Bangladesh.
Previous studies such as Sharma (2011) on 69 income-
classified countries (low, middle, and high), Saboori et al.
(2012) on Malaysia, Lau et al. (2014a,2014b) on Malaysia,
Shahbaz et al. (2015) on India, You and Lv (2018)on83
countries, Acheampong et al. (2019) on Africa, Chen et al.
(2019) on Central and Eastern European economies, Akadiri
et al. (2019)onTurkey,Shahbazetal.(2019a,2019b)on87
income-classified countries (high, middle, and low), Zaidi
et al. (2019) on APEC economies, and Liu et al. (2020)on
G-7 countries inspected the influence of different indicators
separately on CO
2
emissions. But in the context of Pakistan,
the study of Khan et al. (2019a,2019b) has aggregately used
all these variables except the institutional quality measured by
the political terror scale (PTS). This variable (institutional
quality) is distinctively used in our study to be differentiated
from the study of Khan et al. (2019a,2019b). In Bangladesh,
major empirical investigations such as Shahbaz et al. (2014),
Alam (2014), and Sharmin and Tareque (2018)utilizedsome
economic dynamics and their effects on CO
2
emissions, and
Sharker et al. (2000), Faridul and Shahbaz (2012), and Miah
et al. (2010) checked the EKC hypothesis in the case of CO
2
emissions. Moreover, the study of Husain (2016)analyzedthe
linkage between economic growth and carbon emissions in
Bangladesh. The major gap of these studies includes the lack
of the aggregate uses of different variables and the use of
innovationand institutional quality variables to check for their
impact on CO
2
emissions in Bangladesh. However, the core
contribution of this research encompasses the inclusion of the
institutional quality variable measured by the political terror
scale (PTS) (Gibney et al. 2019) with globalization and other
economic indicators in Bangladesh for the first time. In terms
of methodology, this study contributes to employ the ARDL-
based dynamic simulations method coined by Jordan and
Philips (2018). As a newly developed estimation technique,
the ARDL-based dynamic simulations technique is capable to
examine the tangible changes arising in the independent
Environ Sci Pollut Res
variables and their impacts on dependent variable both in the
short and long run. Moreover, it is very handy to predict the
positive and negative shocks occurring in independent vari-
ables and their influences on the dependent variable (Jordan
and Philips 2018).
Literature review
In recent years, most of the empirical investigations intend to
utilize CO
2
emissions as the proxy of environmental degrada-
tion, which is stimulated by both the external factors (e.g.,
globalization, foreign direct investment (FDI), and trade)
and internal dynamics (e.g., economic growth, urbanization,
energy consumption, and innovation). The relationship be-
tween these dynamics (external and internal) and CO
2
emis-
sions is largely dependent on the quality of institutions. For
our study, we can divide the existing literature into three seg-
ments: (a) external determinants and CO
2
emissions, (b) inter-
nal dynamics and CO
2
emissions, and (c) institutional quality
and CO
2
emissions by highlighting both the global and
Bangladesh-based works of literature.
External determinants (globalization, FDI, and trade)
and CO
2
emissions
The dimensions of globalization (economic, political, and so-
cial), FDI, and trade as the key external factors have a signif-
icant influence on CO
2
emissions as well as environmental
degradation. Theoretically, globalization steers the wheel of
international trade and investment that causes environmental
damage. Globalization depletes the renewable energy
sources—particularly deforestation and fisheries
extinctions—which can be connected to trade. On the other
hand, massive tree plantations, environment-friendly com-
modities, and technologies (e.g., renewables, hybrid cars)
are provided by the globalization forces at a lower cost and
lower rent and hence adopted by the consumers more quickly
(Copeland 2013). The countries with a comparatively weak
environmental policy are affected by the liberalization of trade
and investment that causes pollution-intensive industries. This
is called the pollution haven hypothesis (Copeland 2008).
Besides, the pollution halo hypothesis indicates foreign com-
panies’use of cleaner or greener technologies in the produc-
tion process for reducing emissions in the foreign investment
recipient economies that normally use the traditional mood of
production (Mert and Caglar 2020). The empirical investiga-
tion mainly covers both of these hypotheses (pollution haven
and halo) in the context of developed and developing
countries.
Globalization is mainlydivided into three segments: social,
economic, and political globalization, which are transformed
into an index as prepared by the KOF Swiss Economic
Institute (Dreher 2006). KOF index was utilized to scrutinize
the role of globalization in CO
2
emissions in many recent past
studies, such as Chen et al. (2019), Khan et al. (2019a,b),
Akadiri et al. (2019), You and Lv (2018), and Zaidi et al.
(2019). Bu et al. (2016) utilized the KOF globalization index
in the context of 166 economies during 1990–2009 and found
that overall CO
2
emissions increase with the high-level ex-
ploitation of social, economic, and political globalization
though the impact differs in the case of OECD and non-
OECD countries. Utilizing KOF globalization index, the
study of Liu et al. (2020) scrutinized the influence of global-
ization on CO
2
emissions in G-7 economies and the study
findings indicated a reversed U-patterned curve existed be-
tween globalization and CO
2
emissions, implying the strong
existence of the EKC proposition. This finding is supported
by the “world polity theory”in facilitating the homogenization
of social, economic, political, and cultural aspects of
globalization.
Khan and Ullah (2019) tested the association between
globalization and CO
2
emissions in Pakistan using yearly data
during 1975–2014. The study explored that CO
2
emissions
are impacted by globalization positively and significantly.
Shahbaz (2019) examined the connectivity of globalization
with CO
2
emissions in the case of Next-11 (N-11) economies.
The study results describe that Bangladesh, South Korea, and
Iran witness the U-shaped relationship; South Korea and
Pakistan experience an inverted U-shaped association accord-
ing to the traditional approach; and the Philippines and
Vietnam belong to the U-patterned linkage between
globalization and CO
2
emissions. Akadiri et al. (2019)inves-
tigated the data of globalization, tourism, real income, energy
use, and CO
2
emissions from 1995 to 2014 for 15 developed
economies and explored that the linkage between
globalization and CO
2
emissions are significantly positive.
Wang et al. (2020a,2020b) inspected the influences of eco-
nomic globalization on carbon emissions in G-7 economies
for the time of 1996–2017 and discovered that economic glob-
alization contributes to increasing carbon emissions.
Sharmin and Tareque (2018) and Ahad and Khan
(2016) scrutinized the impact of globalization on environ-
mental degradation and these researches found a positive
effect of globalization on CO
2
emissions in degrading the
environmental quality of Bangladesh. Apart from the
abovementioned studies, the environmental Kuznets curve
(EKC) proposition was examined by Shahbaz et al.
(2017a,2017b) in the Chinese economy that revealed that
globalization index and sub-indices reduce CO
2
emissions
and hence improve the quality of the environment. Wang
et al. (2019) analyzed the effect of globalization (social/
cultural, economic, and political dimensions) on CO
2
emissions in 137 developed and less developed econo-
mies during 1970–2014 and found these dimensions’role
in decreasing CO
2
emissions in developed countries.
Environ Sci Pollut Res
Many studies considering panel dataset (Al-mulali 2012;
Baek 2016; Behera and Dash 2017;Bokpin2017; Chandran
and Tang 2013; Kivyiro and Arminen 2014; Pao and Tsai
2011; Zhu et al. 2016) and single country-based time series
data properties (Abbasi and Riaz 2016;Bakhshetal.2017;
Lau et al. 2014a,2014b;Sbiaetal.2014; Seker et al. 2015)
were conducted to scrutinize the role of FDI in CO
2
emissions
as well as environmental degradation. Recently, to include
biomass energy use as a supplementary indicator of carbon
emissions, Shahbaz et al. (2019a,2019b) attempted to inves-
tigate how FDI and carbon emissions are related in the context
of MENA economies from 1990 to 2025. The study results
confirmed the positive as well as N-shaped link between FDI
and carbon emissions. To check for the EKC hypothesis, To
et al. (2019) studied the level of environmental degradation
influenced by FDI in the case of Asian emerging markets over
the period 1980–2016. The study found that the effect of FDI
on carbon dioxide emissions is strongly positive, which im-
plies the evidence of the “pollution heaven hypothesis”and
EKC curve for these countries. Owusu-Brown (2017) ana-
lyzed the linkages between FDI and environmental degrada-
tion including per capita GDP and capital-labor ratio as the
key determinants of carbon emissions for 16 West African
countries during 1980–2010. The findings divulged a positive
relationship between FDI and carbon emissions.
In the Bangladesh context, Sarker et al. (2016) attempted to
analyze the influence of FDI, economic growth, natural gas
use, energy consumption, and CO
2
emissions in Bangladesh
from 1978 to 2010. The study results found a positive impact
of FDI in stimulating CO
2
emissions as well as environmental
degradation. All these studies establish the “pollution haven”
hypothesis as caused by the effect of FDI. On the other hand,
Zhu et al. (2016) examined how carbon dioxide emissions are
influenced by the impacts of FDI, economic growth, and en-
ergy use in the 5-ASEAN countries. The study results con-
firmed that FDI negatively impacts carbon emissions,
confirming the presence of the “pollution halo effect”in these
countries. This also implies that FDI is favorable for improv-
ing the environmental quality of these countries.
Most of the studies on trade–CO
2
emissions nexus consider
trade as the “trade openness”measured by the ratio of exports
plus imports to GDP and these empirical investigations are
mainly territory-based panel studies in their kinds (Hasanov
et al. 2018). The mixed findings were captured by these panel
studies (Al-mulali and Sheau-Ting 2014; Al Mamun et al.
2014; Farhani et al. 2014;Omri2013; Shahbaz et al. 2017a,
2017b; Tamazian and Rao 2010) while examining the rela-
tionship between trade openness and CO
2
emissions along
with other variables in the context of different regions. In
particular, many panels and country-specific researches con-
ducted in the immediate past that explores the influence of
trade/trade openness on CO
2
emissions or environmental deg-
radation. Kalayci (2019) scrutinized the impact of trade
openness and globalization on carbon emissions for NAFTA
countries over the period 1990–2015. This study confirmed a
significantly positive role of trade openness in CO
2
emissions.
Managi and Kumar (2019) found that carbon dioxide emis-
sions are positively influenced by trade openness and it (trade
openness) leads to decreasing the cost of production. That is
why producers are concentrated to produce more by avoiding
environmental rules and regulations, which adversely affect
the environment.
Le et al. (2016) investigated the impact of trade on the
change of carbon dioxide emissions with the earning level of
different economies. The study findings revealed that the trade
openness of higher-income countries is more influential on the
environment than the lower- and middle-income economies.
In the case of 10 recently developed economies, Zhang et al.
(2017b) and Zhang et al. (2017a) explored a significantly ad-
verse influence of trade deepening on carbon dioxide emis-
sion. The study of Oh and Bhuyan (2018) examined the rela-
tionship between trade openness, income growth, energy use,
and population density on CO
2
emissions in Bangladesh cov-
ering the time 1975–2013. Apart from other variables, trade
openness impacts CO
2
emissions negatively in both the short
run and long run and hence improves environmental quality in
Bangladesh. In contrast, Nguyen and Le (2020) aimed to scru-
tinize how trade openness and globalization impact carbon
emissions in Vietnam covering data from 1990 to 2016. The
outcomes of the study indicated that as a component of trade,
exports decrease carbon emissions in both the long and short
run.
Internal dynamics (economic growth, urbanization,
energy consumption, and innovation) and CO
2
emissions
From the theoretical viewpoint, economic growth and the en-
vironment are related to three aspects. First of all, the emis-
sions level for regulated pollutants fall and the air quality
improves in the cities. Secondly, the measures of pollution
control become successful and cheap amid any type of eco-
nomic development and finally, the environmental quality de-
grades at first at lower income level, but it improves at higher
income level, which is called the environmental Kuznets
Curve (EKC) hypothesis (Brock and Taylor 2005). Major
empirical studies such as Jalil and Mahmud (2009), Apergis
and Ozturk (2015), Li et al. (2016), Lau et al. (2019), and
Sarkodie and Strezov (2019) concentrated on the last aspect,
i.e., EKC hypothesis to examine the nexus between economic
growth and CO
2
emissions as well as environmental pollution.
Khan et al. (2020a) examined the linkage among economic
growth, energy use, and carbon emissions in Pakistan cover-
ing annual data properties during 1965–2015. The investigat-
ed findings depict that economic growth per capita leads to
enhancing CO
2
emissions in Pakistan both in the long and
Environ Sci Pollut Res
short run. Therefore, economic growth does not ensure the
safety of the environment in Pakistan.
By adding the variables namely economic growth per
capita, globalization, and tourism and their impacts on
GHGs and CO
2
emissions, and the ecological footprint, the
study of Sharif et al. (2020) was conducted in the Chinese
context over the period 1978Q1–2017Q4. The study findings
divulge that economic growth contributes to environmental
deterioration, invalidating the EKC hypothesis in China. The
novel research carried out by Godil et al. (2020) attempted to
look into the effect of economic growth, financial openness,
ICT, and institutional quality on carbon dioxide emissions in
the context of Pakistan from 1995Q1 to 2018Q4. The study
outcomes revealed that economic growth positively affects
carbon dioxide emissions in the long run while this emission
is still high. This situation implies that the rise in economic
growth contributes to increasing carbon emissions in Pakistan.
On the other hand, Aye and Edoja (2017) attempted to
analyze the dynamic relationship between economic growth
and CO
2
emissions in the case of 31 developing economies
from 1971 to 2013. The obtained results depict an adverse
effect of economic growth on CO
2
emissions in the low
growth regime but a positive impact in the high growth
regime. Demissew Beyene and Kotosz (2020) investigated
the EKC hypothesis by measuring the nexus between eco-
nomic growth and environmental degradation for 12 East
African countries during1 1990–2013. This study found an
inverted U-shaped curve, implying that economic growth
leads to the safety of the environment in these economies.
The axiomatic theory of urbanization has three groundbreak-
ing perspectives: the process of ideas and practices ranged from
urban centers to nearby hinterlands; the process of an increasing
pattern of considered urban-centric practices and problems; and
process of population attention where urban population ratio to
the total population rises (Schwirian and Prehn 1962). Major
pieces of literature (Dietz and Rosa 1997; Poumanyvong and
Kaneko 2010; Martínez-Zarzoso and Maruotti 2011;Zhang
and Lin 2012;LiandLin2015;Wangetal.2016a; Wang
et al. 2016b;Xuetal.2016;Heetal.2017; Lin et al. 2017;
Ouyang and Lin 2017) emphasize the last aspect of urbanization,
i.e., population increase issue to investigate the effect of urbani-
zationonCO
2
emissions. Salahuddin et al. (2019)checkedfor
the emission level of carbon dioxide which is influenced by
urbanization and globalization in South Africa utilizing annual
data during 1980–2017. The study findings confirm
urbanization-induced carbon emissions in both the short run
and long run. Anwar et al. (2020) examined the linkages of
urbanization, GDP growth, and trade openness with CO
2
emis-
sions for Japan, China, South Korea, the Philippines, Malaysia,
Singapore, Hong Kong, Thailand, and Macau during 1980–
2017. This research also showed the positive role of urbanization
in producing CO
2
emissions. Liu and Bae (2018) also found a
positive and significant effect of urbanization on CO
2
emissions
in the Chinese economy, and Ali et al. (2019a,2019b) found the
same findings in the case of emerging economies.
In contrast, Ali et al. (2017) found a negatively significant
effect of urbanization on CO
2
emissions in Singapore over the
period 1970–2015. Salim et al. (2017), within the non-liner
relationship, found a negative role of urbanization in pollutant
emissions in some Asian economies from 1980 to 2010. This
implies that the urbanization process is not detrimental for
environmental safety in these Asian economies.
Worldwide environmental agencies and major environ-
mentalists denoted energy consumption as the fundamental
source for environmental pollution in which it is very hard
to explore the environmental Kuznets curve (EKC). Despite
this, developed countries continue consuming energy and
hence degrading the environment, but they are disposed to
pay a carbon tax and promote renewable energy consumption
(Munir and Riaz 2020). The energy consumption-driven
emissions, as well as environmental degradation, are support-
ed by many studies (Akin 2014; Chandran and Tang 2013;
Chun-sheng et al. 2012;Desteketal.2016; Dogan and
Turkekul 2016; Kais and Sami 2016; Kasman and Duman
2015;T.Lietal.2016;MunirandAmeer2018;Omri2013;
Saidi and Hammami 2015; Shahbaz et al. 2016). Recently,
Khan et al. (2020a) examined the linkage among energy con-
sumption, economic growth, and carbon emissions in Pakistan
covering annual data properties during 1965–2015. The inves-
tigated findings depicted that energy consumption leads to
enhancing CO
2
emissions in Pakistan both in the long and
short runs. Besides, as the traditional sources of energy con-
sumption, coal and oil consumptions help increase the CO
2
emissions, but natural gas consumption leads to declining
CO
2
emissions and thus ensuring the safety of the environ-
ment in Pakistan. Utilizing annual time series data of
Australia, China, and the USA during 1975–2018, Munir
and Riaz (2020) established that increase in oil, gas, coal,
and electricity contributes to augmenting CO
2
emissions and
vice versa, but all these dynamics of emissions are not
similarly workable in these three economies. Kim (2020)
adopted a panel data property for OECD countries from
1990 to 2014 to delve into the effect of energy consumption
and economic growth on CO
2
emissions. This author found
out a detrimental effect of energy consumption on CO
2
emis-
sions in degrading the quality of the environment.
Contrarily, Ozcan et al. (2020) investigated the dynamic
association between energy consumption, income growth, and
environmental degradation by considering the sample of 35
OECD economies during 2000–2014. The study findings ex-
plored the enhancement of these economies’level of environ-
mental performance caused by the energy consumption pattern
and economic growth. Therefore, the growth strategies and
energy consumption patterns have begun to be parallel with
the countries’environmental policies. But the study of
Arminen and Menegaki (2019) did not find any nexus between
Environ Sci Pollut Res
energy consumption and CO
2
emissions in high-income
countries.
In Bangladesh perspective, Islam and Shahbaz (2012),
Sarker et al. (2016), and Sarkar et al. (2018) found a signifi-
cant contribution of energy consumption to emit carbon
dioxide and Shahbaz et al. (2014)confirmedtheroleofelec-
tricity consumption in increasing CO
2
emissions.
Innovation has become a focal issue for maintaining the trade-
mark of firms and international economies. The major compo-
nent of innovation—both patent and trademarks—can vitally
curb environmental degradation (Mendonça et al. 2004). The
escalating flows of globalization increase emissions in different
countries. An increase in global emissions motivates to scrutinize
the role of innovations that help curb emissions. Mensah et al.
(2019) delved into the role of innovations in CO
2
emissions in
OECD economies over the period 1990–2015. The study find-
ings depict that eco-patents and trademarks as the major compo-
nents of innovation lead to mitigating CO
2
emissions. In recent
years, energy technology innovation contributes to more prog-
ress in energy efficiency and clean technology having a signifi-
cant role to reduce CO
2
emissions (Chuzhi and Xianjin 2008;
Fisher-Vanden et al. 2006; Sagar and Holdren 2002; Sun et al.
2008). The opposite figure is also drawn in some empirical in-
vestigations (Wei and Yang 2010;Wuetal.2005), establishing
that clean technology innovation positively impacts CO
2
emis-
sions and hence environmental degradation.
Institutional quality and CO
2
emissions
Resource mobilization and their utilization are key to the success
of environmental safety initiative. The process of resources’man-
agement is dependent on the performance of the authorized in-
stitutions, i.e., institutional quality that is associated with the local
institutional implemented policies set under the legal and cultural
structures (Canh et al. 2019). Political instability and confronta-
tion among the people significantly weaken the institutional per-
formance that restricts the ability of an economy in developing
productive components—human and physical capitals; constrain
the new technology adoption, adaptation and innovation; and
encourage confiscated functions and jurisdictional abuse. This
ultimately worsens the safety of the environment by ignoring
ecological externalities and economic growth-laden conse-
quences (Slesman et al. 2015). The study of Salman et al.
(2019) aimed to analyze how institutional quality affects the
nexus between growth and emissions by incorporating two other
variables—energy consumption and trade openness in three East
Asian economies during 1990–2016. The study explored that
institutional quality helps influence CO
2
emissions positively.
The novel research of Godil et al. (2020) attempted to look into
the effect of institutional quality, financial openness, economic
growth, and ICT on carbon dioxide emissions in the context of
Pakistan from 1995Q1 to 2018Q4. The study outcomes revealed
that institutional quality and economic growth positively affect
carbon dioxide emissions in the long run while this emission is
still high. This situation implies that the rise in institutional qual-
ity and economic growth contributes to increasing carbon
emissions.
On the other hand, Lau et al. (2014a,2014b) supported the
adverse effect of institutional quality on CO
2
emissions to
improve the environmental quality in the context of
Malaysia. Ibrahim and Law (2016) delved into the marginal
effect of institutional quality and trade on CO
2
emissions for
40 African economies and they discovered that the influence
of trade on the environment is institutional quality dependent.
This implies that the adverse effect of trade openness on en-
vironmental quality is spurred by poor institutional quality.
Our study is deviated from the abovementioned pieces of
literature by aggregating both the inner and outer macroeconom-
ic determinants and energy consumption along with innovation
and institutional quality. The use of the novel dynamic ARDL
simulations approach is also scarce in the Bangladesh-based lit-
erature that examined environmental degradation issue within the
purview of different macroeconomic dynamics.
Methodology
The study utilizes the annual time series data for analysis purpose
in the context of Bangladesh over the period 1972–2016. Table 1
depicts the detail descriptions of each of the variable. The data
namely CO
2
emissions (LnCO
2
), economic growth per capita
(LnGDP), Trade % of GDP (LnTRD), foreign direct investment
(LnFDI), innovations (LnINV), urbanization (LnURB), and en-
ergy use (LnENC) are extracted from WDI (World Bank 2020);
the data of globalization index is taken from KOF Globalization
Index (Dreher 2006); and the data of institutional quality
(LnIQV) is sourced from the website of political terror scale
(PTS) developed by (Gibney et al. 2019).
To choose the variables, this study follows the previous
empirical studies of Bu et al. (2016), Kalayci (2019), Wang
et al. (2019), Liu et al. (2020), Nguyen and Le (2020), Sharif
et al. (2020), and Wang et al. (2020a,2020b) that delved into
the influence of globalization with other economic factors on
CO
2
emissions; Haseeb et al. (2018), Shahbaz et al. (2018),
Salman et al. (2019), Khan et al. (2019a,2019b), Godil et al.
(2020), and Khan et al. (2020a) investigated the contribution
of GDP growth with other dynamics to CO
2
emissions;
Haseeb et al. (2018), Shahbaz et al. (2018), Khan et al.
(2019a,2019b), Salman et al. (2019),andKhanetal.
(2020a) studied the influence of energy use with other dynam-
ics on CO
2
emissions; Liu and Bae (2018), Salahuddin et al.
(2019), Ali et al. (2019a,2019b), Khan et al. (2019a,2019b),
and Anwar et al. (2020) scrutinized the role of urbanization in
CO
2
emissions; Owusu-Brown (2017), Shahbaz (2019), Khan
et al. (2019a,2019b), and To et al. (2019) analyzed the influ-
ence of FDI with other indicators on CO
2
emissions; Managi
Environ Sci Pollut Res
and Kumar (2009), Le et al. (2016), Kalayci (2019), Zhang
et al. (2017a), Zhang et al. (2017b), Khan et al. (2019a,
2019b), and Nguyen and Le (2020) investigated the impact
of trade with other economic factors on CO
2
emissions; Dauda
et al. (2019), Khan et al. (2019a,2019b), and Mensah et al.
(2019) inspected the role of innovation with other components
in CO
2
emissions; and Khan et al. (2019a,2019b), Salman
et al. (2019), and Godil et al. (2020) studied the effect of
institutional quality with other variables on CO
2
emissions.
Supported by the aforementioned works of literature to select
variables, this study attempts to delve into the effect of glob-
alization and foreign direct investment (external factors), eco-
nomic growth, trade, urbanization, innovation, energy con-
sumption and institutional quality (internal indicators) on
CO
2
emissions (environmental degradation) in Bangladesh
covering the period of 1972–2016. For the time series data
of the study, the following regression model can be written:
LnCO2t¼β0þβ1LnGLBtþβ2LnGDPtþβ3LnTRDt
þβ4LnFDItþβ5LnINVtþβ6LnURBt
þβ7LnENCtþβ8LnIQVtþεtð1Þ
where CO
2
denotes carbon dioxide emissions; GLB indicates
globalization that is the PCA (principal component analysis)
of social, economic, and political globalization index; GDP is
the annual economic growth per capita; TRD shows trade %
of GDP; FDI represents foreign direct investment; INV elab-
orates innovations; URB illustrates urbanization; ENC de-
scribes energy consumption; IQV shows institutional quality,
and ε
t
is error term. Ln represents natural logarithm.
Bounds testing approach
The bounds test is executed to analyze the co-integrating link-
age among the variables. The calculated F-statistic value at
5% significance level confirms the long-term association
Table 1 Variables used and their descriptions
Variable Description Measurement units Source of data
CO
2
Carbon dioxide emissions Per capita metric tons World Bank, (2020)
GLB PCA index of social, economic,
and political globalization index
Social globalization is measured by the flows of personal
interaction, information, and cultural proximity
KOF Index (Dreher 2006)
Economic globalization is measured by the flows of trade
with other economies, inbound and outbound FDI flows,
and portfolio investment, and restraints on their flows
Political globalization is measured by the no. of embassies
set in external countries, membership of international
organizations, complying with the missions of Security
Council, UN and signing the no. of pacts with external states
GDP GDP per capita Per capita real GDP (2010 US $) World Bank, (2020)
TRD Trade Trade percentage of GDP
FDI Foreign direct investment Net inbound FDI flows (BoP, Constant US $)
INV Innovations Total applications of trademark
URB Urban population growth Annual percentage
ENC Fossil fuel energy use Energy use (total percentage)
IQV Institutional quality Measured by Political Terror Scale (PTS) Gibney et al. (2019)
Table 2 Results of unit root tests
Variables Constant and trend Constant and trend
ADF test (At level) PP test (At level)
CO
2
0.8848 2.6126
GLB −0.1438 −2.5802
GDP 0.7723 1.9352
TRD −2.7502 −2.7466
FDI −2.0812 −2.7751
INV −2.7078 −2.7078
URB −2.1045 −2.5401
ENC −1.0109 −2.8717
IQV −4.4364*** −4.6215***
ADF test (At first difference) PP test (At first difference)
CO
2
−7.7106*** −7.9422***
GLB −6.3453*** −6.2645***
GDP −9.6735*** −9.8426***
TRD −3.6223** −7.0656***
FDI −5.5978*** −7.0816***
INV −6.8815*** −7.0893***
URB −3.9956** −4.0118**
ENC −5.9460*** −8.1019***
IQV −6.8624*** −14.6206***
Note: ***, **, and * denote 1%, 5%, and 10% significance levels
respectively
Environ Sci Pollut Res
between the variables. The estimated value of F-statistic is
determined by comparing the upper and lower critical bounds
values (Pesaran et al. 2001). The null hypothesis of no
cointegration among the variables is nullified if the calculated
F-statistic value remains above the upper bounds critical val-
ue. Besides, if the computed value stays underneath the lower
bound critical value, the result accepts the null hypothesis of
no cointegration. Then again, the inference becomes inconclu-
sive if the estimated F-statistics value lies between the value of
the upper and lower critical bound. For the estimation of the
long-run association among the variables, two hypotheses are
drawn below:
H0¼σ1¼σ2¼σ3¼σ4¼σ5¼σ6¼σ7¼σ8¼0
H1¼σ1≠σ2≠σ3≠σ4≠σ5≠σ6≠σ7≠σ8¼0
The bounds testing equation for the model is written as
follows:
ΔLnCO2t¼α0þσ1LnCO2t−iþσ2LnGLBt−i
þσ3LnGDPt−iþσ4LnTRDt−i
þσ5LnFDIt−iþσ6LnINVt−i
þσ7LnURBt−iþσ8LnENCt−i
þσ9LnIQVt−iþ∑
r
i¼1
β1LnGLBt−i
þ∑
r
i¼1
β2LnGDPt−iþ∑
r
i¼1
β3LnTRDt−i
þ∑
r
i¼1
β4LnFDIt−iþ∑
r
i¼1
β5LnINVt−i
þ∑
r
i¼1
β6LnURBt−iþþ ∑
r
i¼1
β7LnIQVt−i
þ∑
r
i¼1
β8LnIQVt−iþεtð2Þ
where Δdenotes the change operator and t−i show the
AIC- and SIC-based appropriate lag length. The compo-
nents of σ
1
−σ
9
and β
1
−β
8
will be calculated. If the esti-
mated bounds testing outcome confirms the co-integrating
relationship among the variables, this condition directs to
perform the ARDL approach to the measurement of short-
and long-run coefficients.
Autoregressive distributed lag (ARDL) model
The study employs the autoregressive distributive lag (ARDL)
approach to cointegration. The ARDL model is mainly applied to
look into the short- and long-run linkages among the considered
variables. This procedure has some advantages over the existing
techniques of cointegration analysis. According to Pesaran et al.
(2001), ARDL model suitably functions in terms of small sample
size with mixed integration orders, such as I(0) and I(1). Besides,
this cointegration technique is utilized with different lag lengths
in the case of dependent and independent variables, and the long-
run estimations become unbiased though there are some endog-
enous regressors included in the model (Odhiambo 2009). The
investigated bounds test results for ARDL long-run equation can
be specified as follows:
LnCO2t¼φ0þ∑
h
i¼1
δ1LnGLBt−iþ∑
h
i¼1
δ2LnGDPt−i
þ∑
h
i¼1
δ3LnTRDt−iþ∑
h
i¼1
δ4LnFDIt−i
þ∑
h
i¼1
δ5LnINVt−iþ∑
h
i¼1
δ6LnURBt−i
þ∑
h
i¼1
δ7LnENCt−iþ∑
h
i¼1
δ8LnIQVt−i
þεtð3Þ
The δ
1
−δ
8
in the aforesaid equation underlie the long-term
linkage among the variables used in the study. The error cor-
rection model can be shown as follows:
LnCO2t¼φ0þ∑
h
i¼1
σ1LnCO2t−iþ∑
h
i¼1
σ2LnGLBt−i
þ∑
h
i¼1
σ3LnGDPt−iþ∑
h
i¼1
σ4LnTRDt−i
þ∑
h
i¼1
σ5LnFDIt−iþ∑
h
i¼1
σ6LnINVt−i
þ∑
h
i¼1
σ7LnURBt−iþ∑
h
i¼1
σ8LnENCt−i
þ∑
h
i¼1
σ9LnIQVt−iþγECTt−iþεtð4Þ
Table 3 Lag length selection criteria
Lag LogL LR FPE AIC SC HQ
0 281.2916 NA 2.64e−20 −13.86453 −13.40009 −13.69660
1 641.3346 148.3403* 2.06e−25 −24.8266* −20.2733* −24.81155
Note: * shows the criteria-based lag order selection; LR, sequentially modified LR test statistic (all tests at 5% level); FPE, final prediction error; AIC,
Akaike information criterion; SC, Schwarz information criterion; HQ, Hannan-Quinn information criterion
Environ Sci Pollut Res
In the abovementioned equation, σ
1
−σ
9
illustrate the
short-run relationship between the variables used in the study.
ECT shows the coefficient of the error correction model that
determines the correction speed from short-run imbalance to
long-run equilibrium.
In the study, stationary of the variables are examined by
applying ADF and PP tests with constant, constant and trend.
In addition, Breusch-Godfrey LM test is executed to verify the
serial correlation; Ramsey RESET test is performed to analyze
the functional form and the Breusch–Pagan–Godfrey: the
ARCH test is carried out to investigate the heteroskedasticity
and the model stability is confirmed by checking the CUSUM
and CUSUM square tests.
Dynamic ARDL simulations model
Jordan and Philips (2018) developed a new model, i.e., dynamic
ARDL simulations model for the cointegration analysis of time
series variables due to problems experienced in the traditional
ARDL method. The ARDL-based dynamic simulations proce-
dure has prominence to measure, simulate, and draw the figures
mechanically for delineating the exact positive and negative
shocks occurred in the selected independent variables and their
impacts on the dependent variable while the residual variables in
the equation stay same. To apply this simulations model, the mix
order of integration e.g. I(0) and I(1) is a must to check for the co-
integrating association among the variables. For estimating the
dynamic ARDL error correction mechanism, the study applied
5000 simulations for predictions (Khan et al. 2019a,2019b).
Following Jordan and Philips (2018), the forms of ECT for
ARDL bounds test can be written as follows:
ΔLnCO2ðÞ
t¼φ0þθ0LnCO2t−1þβ1ΔLnGLBt
þθ1LnGLBt−1þβ2ΔLnGDPt
þθ2LnGDPt−1þβ3ΔLnTRDt
þθ3LnTRDt−1þβ4ΔLnFDIt
þθ4LnFDIt−1þβ5ΔLnINVt
þθ5LnINVt−1þβ6ΔLnURBt
þθ6LnURBt−1þβ7ΔLnENCt
þθ7LnENCt−1þβ8ΔLnIQVt
þθ9LnIQVt−1þγECTt−i
þεt
ð5Þ
Empirical results and discussions
The time series data can have stationarity problems. To check
this issue, the study utilizes ADF and PP unit root tests. The
examined results show that series used in the study are of mix
integrating order—I(0) and I(1), and the series are not station-
ary at I(2) as portrayed in Table 2. This situation corroborates
to perform the ARDL bounds testing approach to
cointegration.
Table 4 ARDL bounds test
Test statistic Value k
F-statistic 4.6438 8
Critical value bounds
Significance I(0) Bound I(1) Bound
10% 1.8300 2.9400
5% 2.0600 3.2400
2.5% 2.2800 3.5000
1% 2.5400 3.8600
Table 5 The findings of dynamic ARDL simulations technique
Variables Coefficient Std. error t-
Statistic
Prob.
Cons −2.7916 1.0098 −2.7600 0.0180
GLB −0.8024 0.4041 −1.9856 0.0539
ΔGLB
t−1
−0.6102 0.3231 −1.8885 0.0662
GDP 0.1528 0.3620 0.4200 0.6810
ΔGDP
t−1
0.5777 0.1528 3.7800 0.0030
TRD 0.0014 0.0010 1.3800 0.1960
ΔTRD
t−1
0.0002 0.0010 0.2500 0.8090
FDI −0.0071 0.0031 −2.3000 0.0420
ΔFDI
t−1
−0.0063 0.0039 −1.6300 0.1320
INV −0.0243 0.0407 −0.6000 0.5620
ΔINV
t−1
−0.0533 0.0630 −0.8500 0.4150
URB 0.0023 0.0171 0.1300 0.8960
ΔURB
t−1
0.0046 0.0125 0.3700 0.7200
ENC 0.0019 0.0018 1.0700 0.3060
ΔENC
t−1
0.0041 0.0028 1.4800 0.1670
IQV −0.0243 0.0407 −0.6000 0.5620
ΔIQV
t−1
−0.0533 0.0630 −0.8500 0.4150
ECT (−1) −1.2463 0.2726 −4.5700 0.0010
R
2
0.8721
N45
Simulations 5000
Pro (F-statistic value) 0.0168
DW statistic 2.1891
Note: Dependent variable is CO
2
emissions. Independent variables in-
clude GLB, globalization; GDP, economic growth; TRD, trade; FDI,
foreign direct investment; INV, innovations; URB, urbanization; and
IQV, institutional quality
Environ Sci Pollut Res
Table 3reveals the findings of the lag length selection
criteria of AIC, SC, and HQ together with other tests per-
formed under VAR framework. All the criteria for lag selec-
tion confirm 1 (one) lags, which is optimal for the chosen
model.
Table 4indicates the ARDL bounds testing results per-
formed to delve into the long-term connection between the
considered variables in the study. The investigated outcomes
depict that there exists a long-run co-integrating association
among the variables at 1% significance level as the estimated
F-statistic value is greater than upper bound critical values at
1% and 5% respectively.
The results of dynamic ARDL simulations model are
depicted in Table 5. The investigated outcomes indicate that
globalization has significantly and negative impact on the car-
bon dioxide emissions in Bangladesh in both long and short
runs. It implies that globalization as an external determinant is
not harmful to the environmental quality in Bangladesh. With
the blessing of globalization, Bangladesh has been linked up
with developed economies interdependently in terms of so-
cial, economic, and political aspects. Country’sadoptionof
trade liberalization policy in the 1980s has given huge oppor-
tunities for developed countries and their firms to set up their
business-base in Bangladesh mostly by establishing different
industries. Therefore, developed economies have gained the
scope of shifting their industries to Bangladesh. For industrial
production, foreign countries and their firms utilize energy-
efficient techniques and clean technologies to increase their
cleaner production (Demena and Afesorgbor 2020).
Moreover, global companies are concentrated to the better
management of sophisticated technologies comparing to host
countries’firms for the sake of environmental safety. Thus,
globalization plays a significant role in shrinking CO
2
emis-
sions and hence improves the quality of the environment in
Bangladesh. The investigated outcomes are compatible with
the previous empirical investigations of Bu et al. (2016),
Shahbaz et al. (2017a,2017b), Zhang et al. (2017a), Zhang
et al. (2017b), Haseeb et al. (2018), Wang et al. (2019), and
Sharif et al. (2020) that delved into the influence of globaliza-
tion and other economic variables on CO
2
emissions in differ-
ent developing and developed economies. Besides, the exam-
ined findings are incompatible with Shahbaz et al. (2018),
Kalayci (2019), Khan et al. (2019a,2019b), Khan and Ullah
(2019), Salahuddin et al. (2019), Akadiri et al. (2019), Nguyen
and Le (2020), and Wang et al. (2020a,2020b) who looked at
the linkage between globalization and CO
2
emissions.
Table 5demonstrates the result of dynamic ARDL simula-
tions model. The scrutinized findings of economic growth
depict a positively insignificant impact on CO
2
emissions in
Bangladesh in the long run and positively significant impact
in the short run. The results indicate that 1% rise in economic
growth causes to raise CO
2
emissions by 15% in the long run,
whereas in the short run, 1% growth in economic growth
causes CO
2
emissions by 57% in Bangladesh. Graduated from
the less developed to developing economy, Bangladesh is
much ahead of materializing its vision to be a developed econ-
omy by 2041 through strengthening industrialization process.
Generally, industrialization is hugely dependent on energy
consumption in Bangladesh and massive energy consumption
contributes to rising CO
2
emissions. It is argued that
Bangladesh has reached the trajectory of industrial as well as
economic growth by sacrificing the environmental quality
(Rahman and Kashem 2017). Thus, growth-related intention
and activities of Bangladesh leads to increasing CO
2
Table 6 Diagnostic statistics Test pvalue pvalue-based decision
Normality test: Jarque-Bera 0.8685 Residuals of the model are normally dealt out.
Serial correlation: LM test 0.1638 No serial correlation issue exists.
Breusch–Pagan–Godfrey: ARCH test 0.5326 There is no heteroskedasticity problem
Ramsey reset test 0.4005 Model is identified properly.
-15
-10
-5
0
5
10
15
94 96 98 00 02 04 06 08 10 12 14 16
CUSUM 5% Significanc e
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
94 96 98 00 02 04 06 08 10 12 14 16
CUSUM of S quares 5% Significanc e
Fig. 1 CUSUM and CUSUM
square test
Environ Sci Pollut Res
emissions and hence degrading the environmental quality.
The examined results are similar to the study of Cederborg
and Snöbohm (2016), Alshubiri and Elheddad (2019),
Muhammad (2019), Anwar et al. (2020), and Khan et al.
(2020a) that highlighted different production-related activities
of the people including the use of natural gas, oil, and other
chemicals, which are the fundamental sources of electricity
and energy used in industries, transportations, and services.
The use of fossil fuels and chemicals yield major emissions in
particular CO
2
emissions that are directly connected with eco-
nomic growth. Besides, the study findings are contradicted
with Narayan et al. (2016), Aye and Edoja (2017), and
Jardón et al. (2017).
The results obtained from dynamic ARDL simulations
model show a positively insignificant influence of trade on
CO
2
emissions in Bangladesh in both long run and short
run. The findings imply that 1% increment in trade exerts a
positive influence on CO
2
emissions and thus the environmen-
tal dilapidation increases about .0015% and .0025% in both
the long and short run respectively. The economic develop-
ment of Bangladesh is largely trade (exports and imports)
dependent. Trade liberalization policy of the country has
brought a substantial development in export-oriented policy
and administration. This policy enhanced logistics and raw
materials for export-related industries, which contributed to
consumer and non-production-type imports (Rahman 2001).
Moreover, local firms’use of technologies for the prolifera-
tion of both exports and imports are of lack of better manage-
ment and these technological operations mostly depend on the
fossil fuel (oil, gas, etc.) usages. The consumption of fossil
fuel is the key source of CO
2
emissions that damage the qual-
ity of the environment. This concludes that Bangladesh has
adopted trade policies without regard for the environment.
This study finding conforms to Managi and Kumar (2009),
Shahbaz et al. (2014), Le et al. (2016), Mirza and Kanwal
(2017), Shahzad et al. (2017), Chandia et al. (2018), Kalayci
(2019), and Anwar et al. (2020) who indicated that different
economies’pollution-generating technological function for
trade proliferation causes the increasing CO
2
emissions and
thereby the environmental degradation. The investigated re-
sult contradicts the studies done by Zhang et al. (2017b),
Zhang et al. (2017a), and Nguyen and Le (2020).
The examined results reveal that foreign direct investment
(FDI) shows a negatively significant influence on CO
2
emis-
sions in Bangladesh in the long run and a negatively insignif-
icant impact in the short run. The finding divulges that 1% rise
in FDI adversely affects CO
2
emissions in Bangladesh and
thus environmental decay is reduced by .007% and .006% in
the long and short runs respectively. The study outcomes es-
tablish the “pollution halo hypothesis”in the case of
Bangladesh in which foreign firms follow energy-efficient
strategy and utilize clean technologies to boost cleaner pro-
duction process (Demena and Afesorgbor 2020). The findings
are similar to Eskeland and Harrison (2003), Zeng and Eastin
(2012), Zhu et al. (2016), and Demena and Afesorgbor (2020)
who stated that foreign firms shift green technologies to host
economies’firms, which contribute to the overall decrease in
emissions. Moreover, external companies have better
Fig. 2 Social globalization and
CO
2
emissions. The figure
describes ±10% changes in social
globalization and its impact on
CO
2
emissions. Drawn dots
depict the average forecast value
whereas spotted lines (from dark
to light) denote the confidence
intervals at 75%, 90%, and 95%
respectively
Fig. 3 Economic growth and
CO
2
emissions. The figure
describes ±10% changes in
economic growth and its impact
on CO
2
emissions. Drawn dots
depict the average forecast value
whereas spotted lines (from dark
to light) denote the confidence
intervals at 75%, 90%, and 95%
respectively
Environ Sci Pollut Res
management practice of sophisticatedtechnologies comparing
to host countries’firms in case of environmental safety. It
indicates that foreign technology spillovers lead to reducing
emissions and thereby environmental degradation. The inves-
tigated findings of FDI are inconsistent with Owusu-Brown
(2017), Khan et al. (2019a,2019b), Shahbaz et al. (2019a,
2019b), and To et al. (2019).
The examined results indicated that innovation has an in-
significantly adverse influence on the environmental deterio-
ration in Bangladesh in both long and short runs. The findings
reveal that 1% growth in innovation leads to decreasing CO
2
emissions by .02% and .05% in both long run and short run
respectively. Generally, research and development innova-
tions help improve the technologies which are utilized in the
firm-level production. Technology spillovers hence affect the
emissions and environmental degradations also. The findings
related to innovations are coherent with Sagar and Holdren
(2002), Wu et al. (2005), Sun et al. (2008), Wei and Yang
(2010), Jordaan et al. (2017), Khan et al. (2019a,2019b),
Mensah et al. (2019), and Khan et al. (2019b) who empha-
sized the technology innovation in particular clean and green
technology innovation that contributes to developing the
quality of the environment by lessening CO
2
emissions.
Then again, the study outcomes are dissimilar with the
previous research of Xu et al. (2006) and Chuzhi and
Xianjin (2008).
The investigated findings of urbanization divulge an insig-
nificantly positive impact to exacerbate the environmental
quality in Bangladesh both in the long and short runs. The
findings establish that 1% rise in urbanization impacts to in-
crease CO
2
emissions by .002% and .004% in the short and
long run respectively. Urbanization urges people or labor
force to be involved in industry-based society through migra-
tion. This transformation in the economy creates pressure in
natural environment and energy consumption (Salim and
Shafiei 2014). Therefore, the rapid explosion in urbanization
debases the natural character of economic development as it
influences to set up industries, houses, and other types of
infrastructure, which stimulates the environmental degrada-
tion in Bangladesh. The examined results of urbanization are
consistent with the previous studies of Liu and Bae (2018), Ali
et al. (2019a,2019b), Khoshnevis Yazdi and Dariani (2019),
Salahuddin et al. (2019), and Anwar et al. (2020) and incoher-
ent with Martínez-Zarzoso and Maruotti (2011) and Haseeb
et al. (2018). These studies mainly investigated the influence
of urbanization that makes worse the quality of the environ-
ment in upper-, upper-middle-, and lower-income countries.
The scrutinized findings of energy consumption point to a
positively insignificant effect on the degradation of the envi-
ronment in Bangladesh in both long and short runs. The find-
ings also reveal that 1% rise in energy consumption causes
CO
2
emissions by .001% and .004% in the long and short runs
correspondingly. In Bangladesh, annual per capita energy
consumption is approximately 200 KgOE, which captures
the major share of environmental degradation (Siddiqui
2016). As a newly emerged developing country, Bangladesh
still uses fossil fuels like coal, natural gas, and oil for gener-
ating power. For electricity generation, the contribution of
Fig. 4 Trade and CO
2
emissions.
The figure describes ±10%
changes in trade and its impact on
CO
2
emissions. Drawn dots
depict the average forecast value
whereas spotted lines (from dark
to light) denote the confidence
intervals at 75%, 90%, and 95%
respectively
Fig. 5 Foreign direct investment
and CO
2
emissions. The figure
describes ±10% changes in
foreign direct investment (FDI)
and its impact on CO
2
emissions.
Drawn dots depict the average
forecast value whereas spotted
lines (from dark to light) denote
the confidence intervals at 75%,
90%, and 95% respectively
Environ Sci Pollut Res
natural gas, diesel, coal, heavy oil, and other renewable
sources includes 62.9%, 10%, 5%, 3%, and 3.3% respectively
(Taheruzzaman and Janik 2016). This situation of fossil fuel
use for energy production does not stimulate environmental
safety in Bangladesh. The investigated study results are con-
sistent with Sasana (2017), Haseeb et al. (2018), Shahbaz et al.
(2018), Rehman et al. (2019), Akadiri et al. (2019), Khan et al.
(2020a), and Sahoo and Sahoo (2020), who highlighted that
customary energy sources including coal, natural gas, and oil
desperately used by different countries for their economic
growth, which leads to increased CO
2
emissions and hence
environmental degradations. Besides, the investigated results
are inconsistent with the study of Odugbesan and Rjoub
(2020) and Zheng et al. (2020) that established the slower
growth in CO
2
emissions caused by the impacts of energy
intensity and carbon intensity.
The estimated results of institutional quality depict nega-
tive and insignificant impact on CO
2
emissions in Bangladesh
both in the long and short runs. The findings of institutional
quality indicate that 1% growth ininstitutional quality leads to
decreasing environmental degradation by .014% and .002% in
the long and short runs respectively. The quality of the insti-
tution increases with the decrease of political terror scale
(PTC), implying that risk issues do not hinder the administra-
tive function of the different institutions and this has led to
improving the quality of the institution in Bangladesh. As
such, Salman et al. (2019) stated that efficient and unbiased
local institutions have an important role to stimulate economic
growth and shrink CO
2
emissions or environmental
degradation. The study findings of institutional quality are in
line with Lau et al. (2014a,2014b), Farhani and Ozturk
(2015), Ibrahim and Law (2016), Ali et al. (2019a,2019b),
Hunjra et al. (2020), and Wawrzyniak and Doryń(2020),
which highlighted that environment is damaged and
improved by the weak and strong institutions respectively.
Besides, the finding is inconsistent with Salman et al. (2019)
and Godil et al. (2020).
The coefficient value of error correction terms (ECT)
shows negative at 5% significance level as required. The com-
puted ECT value implies the speed of correction from short-
term imbalance to long-term equilibrium, which is 1.24% in 1
year. Besides, the investigated R-squared value reveals that
the independent variables of the study explain the variance
of the dependent variable by 87%. The model’sgoodnessof
fit is confirmed with the estimated pvalue of F-statistic at 5%
significance level. Moreover, the Durbin Watson statistic val-
ue is just more than 2, i.e., 2.18, implying that the study model
is devoid of autocorrelation problem.
The outcomes of diagnostic tests are depicted in Table 6.
Jarque-Bera test is performed for checking the normality of
the residuals and the finding shows that the model’sresiduals
are usually allocated. Ramsey reset test is conducted to verify
the proper specification of the ARDL approach. The investi-
gated result of Ramsey reset test depicts that the ARDL tech-
nique is rightly identified.
Table 6indicates the results of different diagnostic statis-
tics. Lagrange multiplier (LM) test is run to inspect the serial
correlation issue. The LM test result delineates that the model
Fig. 6 Innovation and CO
2
emissions.The figure describes
±10% changes in innovation and
its impact on CO
2
emissions.
Drawn dots depict the average
forecast value whereas spotted
lines (from dark to light) denote
the confidence intervals at 75%,
90%, and 95% respectively
Fig. 7 Urbanization and CO
2
emissions. The figure describes
±10% changes in urbanization
and its impact on CO
2
emissions.
Drawn dots depict the average
forecast value whereas spotted
lines (from dark to light) denote
the confidence intervals at 75%,
90%, and 95% respectively
Environ Sci Pollut Res
has no serial correlation problem. Heteroskedasticity problem
were examined with Breusch Pegan Godfrey the examined
results indicate that there is no existence of heteroskedasticity
issue in the model.
The CUSUM and CUSUM square tests are also carried out
to make sure the stability of the model (Fig. 1). The residuals’
values are marked with blue lines whereas confidence levels
are described by the red lines. The estimated figures reveal
that the investigated residuals’values remain within the lines
of confidence at 5% significance level. This situation confirms
the stability of the ARDL model.
Dynamic ARDL simulations graphs
Generally, the figures obtained from the dynamic ARDL sim-
ulation model represent the exact changes arising in the inde-
pendent variables and their impacts on the dependent variable.
Moreover, positive and negative shocks occurring in the inde-
pendent variables at 10% positive and 10% negative levels on
the dependent variable are examined, which are shown in
different graphs as follows:
Figure 2indicates the globalization with 10% positive and
negative shocks and its impact on CO
2
emissions in
Bangladesh. The first figure indicates that 10% increase in
globalization helps to reduce the CO
2
emissions in
Bangladesh in the long run while second figure depicts that
10% decrease in globalization positively affects CO
2
emis-
sions in Bangladesh both in the long and short run. The
examined findings also show that increase in globalization
helps to reduce the CO
2
emissions in Bangladesh the long run.
Figure 3delineates the impact of both positive and negative
changes (at 10% level) in economic growth on CO
2
emissions
in Bangladesh. The initial graph illustrates that 10% increase
in economic growth negatively affects CO
2
emissions in the
short run and positively in the long run. The second figure
shows that economic growth in 10% negative shock negative-
ly impacts CO
2
emissions both in the long and short run, but
the negative effect of economic growth decreases toward neg-
ative in the long run.
Figure 4divulges the impact of trade with 10% positive and
negative changes on CO
2
emissions in the context of
Bangladesh. Both graphs show that positive and negative
shocks of trade contribute to decreasing CO
2
emissions both
in the long and short run.
Figure 5illustrates the foreign direct investment (FDI) with
10% positive and negative changes and its influence on CO
2
emissions in Bangladesh in the short and long run. According
to the graphs, FDI in both positive and negative shocks neg-
atively impact CO
2
emissions both in the long and short run in
Bangladesh.
The influence of innovation with 10% positive and nega-
tive changes on CO
2
emissions are depicted in Fig. 6.Positive
change in innovation delineates the negative effect on CO
2
emissions in Bangladesh in both the long and short run while
10% negative change shows that innovation negatively influ-
ences CO
2
emissions in the short run, but positively in the
long run.
Fig. 8 Energy consumption and
CO
2
emissions. The figure
describes ±10% changes in
energy consumption and its
impact on CO
2
emissions. Drawn
dots depict the average forecast
value whereas spotted lines (from
dark to light) denote the
confidence intervals at 75%, 90%,
and 95% respectively
Fig. 9 Institutional quality and
CO
2
emissions. The figure
describes ±10% changes in
institutional quality and its impact
on CO
2
emissions. Drawn dots
depict the average forecast value
whereas spotted lines (from dark
to light) denote the confidence
intervals at 75%, 90%, and 95%
respectively
Environ Sci Pollut Res
Figure 7indicates that 10% positive and negative changes
in urbanization have a negative impact on CO
2
emissions in
Bangladesh in both the long and short runs. The negative
effect of change in urbanization increases toward positive with
positive shocks in urbanization.
Figure 8illustrates the positive and negative changes in
energy consumption and its impact on CO
2
emissions in
Bangladesh. Two graphs indicate that 10% positive and neg-
ative shocks in energy consumption leads to the adverse effect
on CO
2
emissions in both the long and short runs whereas the
negative changes in energy consumption contribute to
diminishing toward negative in the long run.
Figure 9shows the institutional quality with 10% positive
and negative changes and its impact on CO
2
emissions in
Bangladesh. According to the graphs, 10% positive and neg-
ative changes in institutional quality have a negative influence
on CO
2
emissions in both the long and short runs.
Conclusion and policy recommendations
This study attempts to look into the dynamic effect of global-
ization, economic dynamics, and energy consumption on CO
2
emissions in Bangladesh over the period 1972–2016. The
study utilizes the dynamic ARDL simulations approach to
the study of the long-run and short-run associations among
the variables, the predicted shock of independent variables,
and their effects on the dependent variable. ADF and PP tests
are applied to avoid the stationarity problems of the study
data. The investigated results depict that the series are of
mix integrating order, i.e., I(0) and I(1), and they are not sta-
tionary at I(2). The mix integrating order existed in the time
series properties supports to apply the dynamic ARDL simu-
lations model. In the case of normality, autocorrelation, and
heteroskedasticity problems, the study uses different diagnos-
tic tests that indicate the nonexistence of those issues.
Moreover, the results obtained from the dynamic ARDL
simulations model reveal that globalization (social, economic,
and political dimensions), foreign direct investment, and in-
novation have a negative impact on CO
2
emissions in
Bangladesh in both the long run and short run while economic
growth, trade, urbanization, and energy consumption affect
CO
2
emissions positively both in the long run and short runs.
More importantly, institutional quality has a positive impact
on CO
2
emissions. In sum, the examined results reveal that
globalization, foreign direct investment and innovation con-
tribute to improving environmental degradation whereas eco-
nomic growth, trade, urbanization, and energy consumption
lead to boosting the environmental deterioration. In addition,
institutional quality is not handy for the environmental safety
in Bangladesh.
Based on the investigated results, the government of
Bangladesh should adopt a policy to encourage foreign
countries and their companies to invest more in utilizing their
cleanest production mechanism for sustained economic devel-
opment and the safety of the environment of Bangladesh. In
this case, policymakers should also emphasize to develop a
relationship with external countries and their multinational
corporations (MNCs) in terms of different social, economic
and political aspects. This will facilitate these countries and
their MNCs to utilize renewable energy technologies to save
the environment in Bangladesh. In addition, the government
should take the pragmatic policy to ensure the sensible utili-
zation of the major macroeconomic determinants, e.g., eco-
nomic growth, trade, urbanization, and energy consumption
for the sake of environmental quality. In Bangladesh, institu-
tional quality is somewhat set to stimulate growth by any
mean even sacrificing environmental quality. Moreover, the
growth process might not be cherished at the cost of the envi-
ronment in Bangladesh.
Acknowledgements The authors express their gratitude to the editorial
board and reviewer for the efforts in reviewing the paper and suggesting
key modifications that have enhanced the quality of the article. The au-
thors also appreciate the editor for his cooperation during the review
process.
Author contribution Conceptualization, writing, and dataanalysis: MMI.
Supervision and review: MKK. Proofreading: MT. Referencing and final
format: NJ and VD. All authors have read and approved the manuscript.
Data availability Data is available from the corresponding author upon
request.
Declarations
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication All authors have read and approved the
manuscript.
Competing interests The authors declare that they have no competing
interests.
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