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Energy Reports 8 (2022) 0–13
Contents lists available at ScienceDirect
Energy Reports
journal homepage: www.elsevier.com/locate/egyr
Research paper
Estimating the multiple impacts of technical progress on Bangladesh’s
manufacturing and industrial sector’s CO2emissions: A quantile
regression approach
Muhammad Yousaf Raza a, Mohammad Maruf Hasan b,∗
aSchool of Economics, Shandong Technology and Business University, Yantai, Shandong, 255000, China
bSchool of International studies, Sichuan University, Chengdu, Sichuan, 610065, China
article info
Article history:
Received 21 December 2020
Received in revised form 16 September 2021
Accepted 2 January 2022
Available online xxxx
Keywords:
CO2emissions
Quantile regression
Industrial and manufacturing sectors
Bangladesh
abstract
Technological progress has a positive footprint on the global economy, and this can curb carbon
emissions. This study aims to estimates the technical progress and CO2emissions from Bangladesh’s
manufacturing and industrial sector covering the period 1980 to 2018. We carried a quantile regression
model to analyze the impact of technological progress on CO2emissions and to establish the association
among the variables. The study’s empirical outcomes are: First, the model’s R-square reaches 0.9,
which suggests that these models clarify the driving factor of carbon emissions more than 90%. Second,
the manufacturing sectors’ technological progress has a positive impact while the industrial sectors’
technological progress has mix impact on CO2emissions. Third, the estimated CO2emissions growth
is rising with lower environmental effects from 2019 to 2040. Fourth, the estimations and policies
might help Bangladesh contribute to improving management capability, technical development, public
awareness, energy-saving technologies, energy efficiency measures, and supporting green technology
in both sectors. Further policies are given below, which will assist Bangladesh’s policymakers in
acknowledging appropriately.
©2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc- nd/4.0/).
1. Introduction
In the last few decades’ global societies have grown more
concerned with global warming because it has a direct negative
impact on the environment, Today it poses major challenges to
both the environment and human life, such as causing glacial
retreats, floods, low-level water flows in summer, and sea-level
rise. The United Nations (UN) secretary for agriculture is also
worried about climate change and global warming. The scholarly
consensus is that climate change is caused by the anthropogenic
release of greenhouse gas (GHGs), the effects of which generate
environmentally unfriendly social and economic consequences
(Rafique and Rehman,2017;Nawaz et al.,2020;Sadatshojaie and
Rahimpour,2020). The most prevalent effects of GHGs are caused
by CO2emissions (Anderson et al.,2016;Wang et al.,2020a). The
quantity of CO2emissions is unprecedented today (Pearson and
Palmer,2000) in the last 20 million years, and the quantity of
carbon emissions has arrived at dangerous levels.
Further, the IPCC (Intergovernmental Panel on Climate
Change) has determined that from 1880–2012, the average global
∗Corresponding author.
E-mail addresses: yousaf.raza@ymail.com (M.Y. Raza), marufpc@yahoo.com
(M.M. Hasan).
surface temperature has increased by about 0.85 ◦C. Arctic sea
ice is curving at an aerial rate of 3.5–4.1% every ten years. Also,
the global seawater level has increased by 0.19 m from 1901
to 2010 (Pachauri et al.,2014) In spite of these grim signs, and
climate change has not received adequate attention. In develop-
ing countries, malignancy has also dramatically increased CO2
emissions. It is thus prudent, here and now, to address climate
change and reduce this emission without inhibiting sustainable
socio-economic development, and which is an important issue for
further investigation (Shan et al.,2018). Thus, in recent decades,
scientists, economists, and others have shifted their focus and
research to environmental and safety protection through tech-
nology (Nawaz et al.,2020;Wang et al.,2020a). Ren et al.,
2021 used the dynamic spatial panel model for the European
Union to analyze the impact of economic, energy transition on
various countries’ carbon emission variations from 1990–2015.
They found that economy has a positive impact on rising CO2
emissions. Dong et al. (2020) examined the influence of China’s
natural gas infrastructure on CO2emissions using the quantile
regression approach covering the period of 2004–2017 and found
that gas consumption relatively reduces the CO2emissions. Ren
et al. (2019) employed a similar analysis for the European Union
countries (EU) and found similar results, while Yan et al. (2020)
used a similar method for 273 cities of China from 2010 to
https://doi.org/10.1016/j.egyr.2022.01.005
2352-4847/©2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
2016 and found that the association among PM2.5 attention
with economic development, urbanization, industries, and fiscal
deficit are heterogeneous. However, various scholars have found
different results for different countries at different times. Our
literature shows that technological change can boost economic
development, simplify the economy’s transition to carbon emis-
sion reduction, and enable the rapid growth of new energy
sources. Although many scholars have found a clear link between
technological development and carbon emission, the effect of
technological progress on the various sectors of the economy has
not been exhaustively examined (Nawaz et al.,2020;Wang et al.,
2020b;Shan et al.,2018). It is important to build a framework
for technological progress that will reduce carbon emissions in
varying economic sectors.
The focus of this study is on Bangladesh. Because Bangladesh
is the 2nd fastest economy growing country in South Asia and
the 5th fastest growing economy in the world (World Bank,
2019). Carbon emissions are increasing; the country’s carbon
emissions have increased by no less than 0.52%. The government
of Bangladesh has aimed to fight against climate change by an-
nouncing new adaptation methods and redacting CO2emissions
(Karmaker et al.,2020). The agriculture, energy, transport, man-
ufacture, industry, town planning, and other related sectors goal
to redact climate change adversity. Accompanying the rapid eco-
nomic growth of the last decade, Bangladesh’s industry and tech-
nology have also experienced a significant balance. Bangladesh is
a South Asian country with a population of 165 million, and the
nation’s labor cost is lower than many other developing countries.
Bangladesh is the 2nd largest readymade garment manufacturing
country in the world. The manufacturing sector contributes 15%
of GDP. As a developing country, Bangladesh lacks many crucial
forms of technology, which is helpful in economic development
and needs to improve the manufacturing sector to reduce huge
environmental pollution (Shahbaz et al.,2015).
The Bangladeshi energy sector depends on a variety of energy
sources. According to Bangladesh Power Development Board An-
nual Report (2018), natural gas (61%), furnace oil (22%), power
import from India (4%), hydropower (1%), coal (3%), and solar
PV3 MW (0%) are contributing to its 15,953 MW power sup-
ply in during 2017 to 2018. The energy consumption by sec-
tors, such as Agriculture 1393 MW, transport 3010 MW, com-
mercial 1250 MW, industry consumes 50% of total energy by
12,485 MW, and residence consumes 30% of energy (7974 MW)
in 2015 (Resources and Agency,2015). For the last three decades,
Bangladesh’s electricity has mainly been generated by natural
gas. To support economic development and industrialization, For
long-term power demand issue, Bangladesh’s government has
established thermal power plant policies (International Atomic
Energy Agency,2018), to meet the power demand for the do-
mestic market (Khondaker et al.,2019). However, these policies
of energy have seriously impacted energy security, the balance of
payment, environmental sustainability, and the primary issue of
the nation’s CO2emissions. Bangladesh has attempted to boost
its economy by attracting foreign direct investment (FDI), as it
is believed that greater FDI will bring new technology. As such,
Bangladesh has turned into a model place for investigating the
relationship between technological progress and CO2emissions in
different industrial and economic sectors. Research on the impact
of technological and industrial development in Bangladesh can
offer innovative suggestions and understandings to help other de-
veloping and less developing countries reduce carbon discharges
without compromising economic growth.
This research attempts to make a significant contribution to
the literature. Whereas past studies have focused attention only
upon the overall impact of technological development on carbon
emissions, this study comprehensively analyzes the relationship
between technological progress and CO2emission by sector on
the effect of technological progress on CO2emissions (Munir and
Ameer,2018). This study also differs from other previous studies
that were conducted only at the city or at a provincial level in
different countries. Wang et al. (2019), Santra (2017), Ma et al.
(2019) and Khan et al. (2020a) by forecasting carbon emissions
from 2018 to 2040 for various sectors. This study also examines
Bangladesh’s carbon emission reductions from the outlook of
multiple sectors and industries.
To regulate the effect of various carbon emissions factors, this
analysis smears the quantile regression model (QR) by Koenker
and Bassett (1978). This method is an extension of linear re-
gression adoption when the linear regression conditions are not
met, and this model is more robust against linear regression. QR
model gives compressive picture dependence with the nonlinear
relationship. We adopt the QR model to analyze the relationship
between technological development and carbon emission at 25%,
50% and 75%. Due to the impact of technological progress on
carbon emission here, we also applied the STRIPAT model and
IPAT. In contrast, the IPAT has been used for environmental
analysis (Woodwell,1970). The IPAT identity examining the role
of economic activity in carbon emission (Liu et al.,2014). We
also use the STIRPAT model for estimates every coefficient as a
parameter to identify the problem of the IPAT model. In addition,
CUSUM and CUSUM of Squares are employed and summarized
each problem that covers the specific scenarios. This study at-
tempts to address the gap in the literature by answering the
following questions: (1) what are the main significant factors
that determine the energy-related changes in CO2emissions? (2)
How much CO2emissions does each sector generate? (3) What
will be the CO2emission level for the next 20 years (2019–
2040)? And (4) what will be the policy implication of reducing
carbon emissions from these sectors? This study will be an orig-
inal study that provides an in-depth analysis of past, present,
and future carbon emission trends. This work seeks to be of
utility to Bangladesh’s government in effectively forming a carbon
policy that will accelerate Bangladesh’s economic development.
The current predicament of CO2emissions in Bangladesh are as
follows:
1.1. CO2emissions in period and spatial distribution
The increases in carbon emission from various fossil fuels are
described in Fig. 1. Emissions from fuel consumption have been
started increasing since 1990. One can see that carbon pollution
grew gradually during the planning period of 1999. However, the
smart and soft growth of carbon emission has been depressed
by faster-growing economic growth in 2001 is the turning point
period. Carbon emission increased with an annual growth rate of
about 15% from 2001 to 2007. And the growth has been slowed
since 2006 with the framework of national energy sustainability
and the new emission reduction policy. This study estimated
carbon emissions based on three main fossil fuels (i.e., oil, coal,
and gas).
Moreover, in the 21st century, the fast growth of the petro-
chemical industry pushed up the oil and energy production de-
mand. Since 1985, countrywide carbon pollution has experienced
a jump forward, rising from the overview of total carbon pollution
in Bangladesh. However, major variations have been found be-
tween carbon emission in the main cities, such as Dhaka and Chit-
tagong, due to Bangladesh’s large area and sufficient resources.
The average carbon emissions in Bangladesh from 1990 to 2016
are seen in Fig. 1.
The large volumes of CO2emission zones are mainly con-
centrated in the main riverside commercial city, such as Dhaka,
Narayanganj, Khulna, and Rajshahi (Hoornweg et al.,2011). And,
the sector-wise carbon emission contribution is power industry
(41.7%), industrial (17.5%), transport (14%), building (14.4%), and
non-combustion (12.4%) during 2016 in Bangladesh.
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Fig. 1. CO2emission (Kt) from various fuel types in 1990–2016.
Source: World development indicators.
1.2. Technological progress relationship with CO2emission
In the perspective of developing countries, capital and tech-
nological deficiency have been found. To increase the domestic
capital and economic development, the foreign and local firms
have established their firms without any environmental quality.
The global open-door economic policies are adopted and consid-
ered lower ecological standards. The companies’ pressure paid
competitive efforts to find cheap price production techniques to
save the environment (Demena and Afesorgbor,2020). Hence,
developing countries encouraging all kinds of firms (domestic and
international) to move their pollution base production in their
country (Golub et al.,2011). This could have a positive impact
on the economy and increase the emission. Cole et al. (2017) and
Dilip (2016) found, the USA has a positive effect on past income
is the cause of the increase in CO2emissions. For CO2emissions
reduction, clean technology can solve the problem. There are
several studies on technological progress and emission reduction.
For example, (Harrison and G.S.E.,2003), in their research, found
the US outward foreign direct investment (OFDI) clean technology
is energy-efficient and has a significantly positive impact on
local counterparts. Similar results by Wang et al. (2013) studies
in China (Guangdong province) and find technology progress
positively impact GDP per capita, industrialization, and energy
consumption on CO2emission reduction. Also, (Lin et al.,2017a)
focus on China’s energy-intensive industry and found technolog-
ical progress can rebound effect with 83.02% on a downward
trend. Another study by Gu et al. (2019),the energy technology
progress on CO2emission in China’s 30 provinces and found that
the technological progress and CO2emission have a U-shaped
relation with the energy sector and have a direct technical effect
on the country’s technological progress energy.
Section 2provides literature, Section 3is based on data and
method, Section 4includes the results, Section 5discusses the
results, and Section 6includes the conclusion and policy sugges-
tion.
2. Literature review
Given the truthful statistical time-series data, past studies
have generally used sectorial technological progress, CO2emis-
sions data (Nawaz et al.,2020), and Du et al. (2012) used energy
intensity just as a classic inverse indicator of energy capacity
and technical advances. Furthermore, many scholars have ac-
knowledged that technological progress negatively impacts CO2
emissions, which means that technological advances can decrease
carbon emissions. Tian et al. (2016) examine energy efficiency
and carbon emission intensity of commercial trucks in China, and
they find a 15%–25% variation in the energy efficiency and CO2
emissions. Another study by Poumanyvong and Kaneko (2010)
examined ‘99’ cross-country energy intensity and CO2emission
from 1975–2005 and found energy intensity positively linked
with carbon emission. Some similar studies Ahyahudin Sodri
(2016) for Indonesia; Sha and Salim (2014) studies for OECD
countries; (Khurram et al.,2020) for Kuwait; Muhammad and
Khan (2019) for Asia countries; Yang et al. (2020) for China;
Pakrooh et al. (2020) for Iran’s agriculture sector; Bayram and
Koc (2019) for Turkey (Istanbul) urban transportation found a
positive growth between energy consumption and CO2emission.
Recently, China has been the highest carbon emission nation;
here, some studies have found many similar results, such as Xia
et al. (2020) examined Xinjiang (China) industrial sector’s CO2
emission, energy conservation, and carbon emission reduction as
well as found a positive pursuit between the industrial sector and
energy conservation. Furthermore, Shahbaz et al. (2020) investi-
gate an interesting issue: public–private energy sector investment
and considering the major role of technological innovations in
CO2emission future in China. And the results show that the
energy sector’s public–private partnerships investment positively
impacts increasing carbon emission.
However, some researchers have originated a dissimilar result
association between technological progress and carbon emission.
For proof, Wang et al. (2012) study’s found energy-related tech-
nology patent inventory measurements represent technological
advancements, and the result is not similar to another study.
Furthermore, other studies have supported that technological
progress promotes carbon emission, such as Wang et al. (2013)
focus on the passion of CO2emission to reflect advancements
of the technology on cutting down carbon emission in China
(Guangdong province), and found that there are two determi-
nants that are negatively related. Another study by Duarte et al.
(2013) examines 11 developed countries, and the result exhibited
technology performs positively with carbon emission, particularly
in the United Nations and the United Kingdom. Nawaz et al.
(2020) investigate the impact of technological progress on car-
bon emissions in Pakistan using the STRIPAT model and rustle
find that the service and agriculture sectors have a negative
and manufacturing and transportation sectors with a positive
relationship with CO2emission. Another study by Wang et al.
(2017a) investigates China’s four major cities (Beijing, Shanghai,
Guangzhou, and Tianjin) using STRIPAT model to estimate the
impact of the selected variables on carbon emission and result
found economic growth, urbanization as well industrialization
will edge to increased carbon emission.
On the other hand, service and technology levels can do-
nate to the reduction of carbon emission, also found a negative
association between energy intensity and CO2emission. Sor-
rell and Dimitropoulos (2007) found the rebound enforcement
refers to the economic development caused by improving energy
sufficiency with technological advances. The demand increasing
always meets the increase in efficiency or the product price
and services associated with the energy. In this consideration
Roy (2000) and Chai et al. (2016) said technological progress
perhaps compensates by saving energy rebound effects also in
some cases and boost the carbon emissions. The technological
advance’s impact is similarly expected to change significantly
based on sectorial nature. The effective carbon reduction from
various sectors and industrial cities has been examined by Shan
et al. (2018). Generally, this paper found that mega manufacturing
is higher significant than light manufacturing’s CO2emission. Lin
et al. (2017a) studies China’s nonferrous metals industry rebound
effect indicates energy savings are allayed, so the Chinese gov-
ernment cannot think that only technology progress can save
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
energy and decrease emissions. However, researchers have no
clear picture of technological progress and CO2emission despite
these progress effects.
All countries have focused on their economic development and
little care about environmental changes. Several scholars focused
on this issue of economic growth and CO2emission. A study by
Ahmad et al. (2019) focused evaluation of 30 China provinces
aggregated construction sector, economic growth, urbanization,
energy consumption, and CO2emission from 2000–2016 and
found that energy consumption growth, economic growth and
construction sector growth have significant positive impacts on
CO2emission. Hao et al. (2019) used LMDI decomposition method
examined the aggregated growth of Malaysia’s carbon emission
from 1978–2014 and found that population, GDP per capita,
and energy intensity are the major sweeping factors that are
increasing energy-related carbon emission. Another study by Lise
(2006) examines decomposition analysis on turkey’s GDP growth,
energy, and CO2emission from 1980–2003 by using Kaya identity.
The result shows that the spreading economy is the biggest
influencing matter of carbon emission, the economy’s energy
intensity, and CO2emissions. Therefore, no empirical study can
find evidence to accept the decoupling of economic growth and
CO2emission.
Industrialization growth has a major contribution to economic
growth and also the causes of CO2emission. Therefore, scholars
have taken an interest in investigating the impact of CO2emis-
sion in the industrial sector. Abokyi et al. (2019) investigated
developing countries on Ghana’s industries and applied the ARDL
model and EKC hypothesis, focusing on fossil fuel impact on car-
bon emission. They find out the relationship between industrial
growth and CO2emission. Results found industrial growth and
carbon emission have U-shaped with the short and long-run rela-
tionship. Another study by Montes-hernandez et al. (2020) found
advanced technology can reduce industrial carbon emissions. Lin
et al. (2020) investigated China’s six major provinces industrial
subsectors and results found power and electric industry posi-
tively related to CO2emission. Another study by Ma and Stern
(2008) show that a positive relationship with pollution decreases
over time.
The quantile regression model used to regulate the relation-
ship between different technological advances and carbon emis-
sion in various sectors led many researchers to conduct effec-
tive studies on multiple sectors’ carbon emissions. Haseeb et al.
(2021) used quantile regression model and investigated natu-
ral resources and economic growth in top Asian countries, and
Haseeb et al. (2020) used the same method to analyzed manufac-
turing and textile’s energy intensity impact in 10 Asian countries.
Also, the different research employed different methods, econo-
metric models, IDM method, OLS method, input–output method
to examine the sectorial CO2emission effects and the impact
of motive forces on carbon emission. There are two significant
advantages of quantile regression. First, the random error term
of least squares regression is applied to the independently and
equally distributed condition, and it is normally spaced out. For
these circumstances, the best estimation can be accessed from
least squares.
Moreover, these implementations are hardly fulfilling the ac-
tual applications. Quantile regression is not making distributional
assumptions. For that reason, QR is more robust than the OLS
model. Second, the regression model will be more fit when the
main conditional distribution is beside the covariates. Despite
that carbon emissions significantly alter across the different sec-
tors, this model can disclose the different variable’s effects on the
delivery of carbon emission (Nawaz et al.,2020). Future more,
to understand the limitation of technological advances that have
buffered the group with CO2emissions and energy efficiency
(Hussain et al.,2020), such as funded globalization and financial
development has a significant and positive impact on energy
intensity level in the top Association of Southeast Asian Nations
(ASEAN) countries. And find the limitation of technological ap-
proach transmitters and influence different sectors. This study
results can be used in future policymaking and the designed
scenarios of Bangladesh’s carbon emission reductions between
2018 to 2040 time periods.
A couple of research has studied the economic sector of
Bangladesh from ecological protection. For example, Karmaker
et al. (2020) applied Hybrid Optimization of Multiple Energy
Resources (HOMER) software to analyzed the power plants’ fossil
fuel greenhouse gas emission and found that coal and natural gas
could reduce carbon emission. In another study by Sarkar et al.
(2015), energy consumption has been increased CO2emission.
Furthermore, Sakamoto et al. (2019) focus on economic devel-
opment and environmental changes, apply the EKC hypothesis,
and find that CO2remained the same straight line in max cases.
Nakaten et al. (2014) study Bangladesh energy and fertilizer
supply coal gasification with CO2emission resulted from sup-
port producing combining gas for these two sectors and brings
technology.
Sakamoto et al. (2019) assessed the textile industry’s environ-
mental compliance based on primary data focused on Bangladesh,
installed an effluent treatment plant (ETP) and investigated the
textile industry’s actual situation. They found that government
is not highly willing to protect the environment. Furthermore,
Shahbaz et al. (2014) investigated the relationship between in-
dustrialization CO2emission in Bangladesh’s perspective from
1975–2010 using the ARDL model and found that financial devel-
opment increases pollutions and electricity contribute to carbon
emission. Mondal et al. (2010) examines future technology se-
lection and impacts of carbon reduction for Bangladesh using
MARKAL framework period based on 2005 and making a scenario
to 2035. The results found that fossil base technologies can redact
emission, and renewable energy-based technologies can redact 10
to 30%. Again study by Hossain et al. (2011) focuses on the power
sector’s carbon reduction and a carbon tax on future technologies.
This study used the MARKAL framework during 2005–2035 and
showed high and low fossil-based carbon reduction technologies
that can fulfill the carbon reduction target from 10 to 20%. On the
other side, the carbon tax will be 2500 taka per ton.
Based on the above discussion, we can find several research
gaps from the existing literature. Firstly, most of the current
study focused only on the impact of technological progress and
carbon emission, but very little researches about the sectorial
technological progress impact on CO2emissions, especially for
the case of Bangladesh. Secondly, most of the studies are based
on China, EU, the USA, Pakistan, and some other Asian countries
(Lin et al.,2017a;Chai et al.,2016) and Chinese provinces, but
few scholars focused on Pakistan (Nawaz et al.,2020;Lin and
Raza,2021), but no one studied sectorial technological progress
and its impact on CO2emission in Bangladesh. Therefore, for the
best of our understanding, only a few studies have systematically
investigated this impact for various countries, but no one on
Bangladesh, which has been left behind, as it is the developing
economy.
2.1. The drivers of carbon emissions
Along with global warming and GHG emission, the assessment
of carbon emission becomes a hot issue. The quantitative analysis
of carbon pollution is the basis for assessing the impact of sec-
torial technology progress and pollution reduction effects (Wang
et al.,2020a). Past research work simply pointed out that carbon
pollution is closely linked to economic growth and the devel-
opment of technology (Nawaz et al.,2020;Khan et al.,2020b).
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
To determine the main driving forces of CO2emission and lay
groundwork for our model, we reviewed several previous studies.
All the studies related to our quantile regression model and rele-
vant studies on the impact factors of carbon pollution are shown
in Table 1. Moreover, in respect of single and group of countries’
studies, outcomes have supported the entire hypothesis. Com-
plete literature and their reviews based on energy use, economic
growth and CO2emissions through the different industries are
presented since the period 1980 in Table 1. To some extent, a
huge number of studies are concentrating on multiple countries,
including United States, Asia and Europe, who suggested the
directional hypothesis running from energy consumption to eco-
nomic development (growth hypothesis). These researches also
point to the significance of energy for development and progress,
proposing that energy preservation strategies aimed at lessening
the carbon emissions level would oblige economic growth. On
the other side, the contribution on Bangladesh finds that causality
runs from economic development to energy consumption and not
the other way around.
3. Methods and data collection
3.1. Data source
This study used secondary annual time series data from 1980
to 2018 to analyze the variables, including CO2emission, Popula-
tion, GDP per capita, industrial value-added, manufacture value-
added, and energy intensity (given in Table 2). Data sources
and information have been collected from world development
indicators.
3.2. Model description
The STRIPAT model was applied to check the relationship
between technological progress and CO2emission, and this model
lengthened from the original IPAT identity. This IPAT model was
proposed by Ehrlich and Holdren (1971). After that, many re-
search papers have applied the STRIPAT model to examine China’s
carbon emission (Liu and Xiao,2018b), population, technology,
and affluence (Ghazali and Ali,2019). There is another framework
called Kaya identity; many scholars used this model because of its
unit elasticity (Nawaz et al.,2020), for the determinants of en-
vironment, used STIRPAT model and IPAT individuality has been
an application to explain those issues that provoke ecological
changes (Chontanawat,2018). Also, many scholars have tested
the IPAT identity for analysis for environmental pressure (Zhao
et al.,2018;Wen and Li,2019;Yue et al.,2020). This frame-
work measures ecological research and measures the impact of
financial growth (Xu and Lin,2015).
I=P·A·T·(1)
In 1971 (Ehrlich and Holdren,1971) put forward this IPAT
model. The IPAT identity (I = P ·A·T). In this model, envi-
ronmental impact denotes (I) the impact of the population (P)
with the other variables like per capita GDP and energy intensity,
respectively. But it did not complete from the interpretation style.
To explain more fully, especially, we added CO2emission for the
environment. For sector-wise, this study explains the industrial
value-added, manufacture value-added, services value-added, en-
ergy intensity, and emissions as an important supplement in
this model. The sectorial indicators are using to estimate the
data. Aside from the limitations, this model has been declining
to decently express those factors that generate environmental
changes. This study integrates most carbon emissions influencing
variables in this framework of the IPAT model to explore the
sectorial impact on CO2emission in Bangladesh.
Dietz and Rosa (1994) extended the IPAT identity and establish
the STIRPAT model; Eq. (2) can accord the standard STIRPAT
model idea.
I=δ·Pα·Aβ·Tγ(2)
To analyze the relationship between technology and carbon
emission, we brought in the stochastic regression impacts on
population, technology, and Affluence the STIRPAT model. This
model was widely and strongly used to determine the effects of
main driving forces on environmental impacts. STIRPAT model
was specified as follows:
I=αPα
iAb
iTc
iεi(3)
For environmental changes, The STIRPAT model has been
widely used in various studies (Ghazali and Ali,2019;Zhang
and Zhao,2019;Liu and Xiao,2018a). All of the variables are
taking the natural logarithm in Eq. (3). The STIRPAT model can
be presented as:
ln I=ln α+aln P+bln T+ln ε, (4)
Where I,P,A,and Tare given descriptions alike. In the IPAT
identity, a,b, and csignify the elasticity of I,P,A, and T, ε sym-
bolize the enduring error. And year represents I. STRIPAT model
many studies used to calculate the proper decomposition of every
individual factor (Dietz and Rosa,1994). Also, this model is used
for the analysis of the various effects of technological progress.
This method also has another important side to help recognize
the technological progress improve ‘2ct in sectorial heterogeneity
by decaying impact on manufacturing and industrial including
economic sectors. Xu and Lin (2015) investigated industrial and
urbanization; Liu and Xiao (2018a) and Wang et al. (2017b)
examined the energy-related variables using the STRIPAT model.
The sectorial flow-chart is presented in (Fig. 1). Our final STIRPAT
model is presented as Eq. (5):
ln CO2 =α+bln PDit +cln pgdpit +d1ln EIit +d2ln MVAit
+d3ln IVAit +d4ln MVAit ∗ln EIit +d5ln IVAit ∗ln EIit
(5)
Where ln indicates the natural logarithm and all variables
value take natural logarithm ln CO2 indicates carbon emission,
ln GDP indicates per capita GDP; ln PD indicates population den-
sity; ln EID indicates energy intensity; lnMVA manufacturing sec-
tor value-added, ln IVA indicate industrial sector value-added.
To find every parameter problem of the IPAT model, the STI-
RAT model can estimate each coefficient, and every factor is ad-
mitting to being completely decomposed (Dietz and Rosa,1994).
Eqs. (1)–(4) are helping to analyze the different aspects of trigger
environmental pollution (Nawaz et al.,2020). Various countries
have applied the STIRPAT model, such as China, Pakistan, South
Korea, and OECD countries (Xu and Lin,2015;Nawaz et al.,2020;
Hashmi and Alam,2019;Lin et al.,2017a,b). This study applied
the STIRPAT model to find the relationship between technologi-
cal progress and CO2emissions for Bangladesh. This framework
has been frequently used in analyzing used for determinants of
ecological changes, such as energy use, technological awareness,
and economic development. Also, the different economic sectors,
particularly the manufacturing and industrial sector, are listed in
Table 3.
3.3. Quantile regression
Several researchers have introduced QR approaches to assess
the issues that influence carbon emissions. This QR is an enhanced
form of OLS presented by Koenker and Bassett (1978). There are
a number of advantages of OLS are provided above. One of the
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Table 1
A quick review of earlier studies on extended quantile regression model including dependent variable as CO2emission.
Authors Regions Period Variables Methods Main results Hypothesis
Khan et al. (2020a) 192 countries 1980–2018 FD, RE consumption,
CO2emission
Panel quantile
regression model
RE consumption has a negative
effect & FD has a positive
influence on CO2emission.
Growth
hypothesis
Sharif et al. (2020) Top-10 polluted
countries
1990–2017 RE, CO2emission Quantile-on-
Quantile
regression
RE consumption has a negative
relation with CO2emission
Feedback
hypothesis
Hussain et al. (2020) Top 5 ASEAN
countries
1990–2018 Globalization, EI, FD Adaptive
Neuro-Fuzzy
Inference System
Globalization and FD boost the
level of EI, and the developed
countries are in the top ASEAN
countries.
Growth
hypothesis
Benjamin and Lin (2020) China 1991–2014 Metallurgy industry,
CO2emission
Quantile estimates Carbon intensity has a
significant positive impact on
CO2emission.
Growth
hypothesis
Nawaz et al. (2020) Pakistan 1991–2017 Energy efficiency, TP,
carbon emission
Quantile estimates Manufacturing, transport,
construction and sector have a
significant positive besides
agriculture and have negative
impact found from service
sector on carbon emission.
Feedback
hypothesis,
Growth
hypothesis
Dogan et al. (2020) African countries 1980–2014 EC, energy economy,
energy policy,
environmental
pollution
Nonparametric
quantile causality
approach
Angola, Benin, Kenya, Egypt,
Nigeria, cote d’Ivoire, and
Tunisia have a significant
positive relation with EC and
carbon emissions.
Feedback
hypothesis,
Growth
hypothesis
Haseeb et al. (2020) 10 Asian
countries
1990–2018 EI, textile
manufacturing, Asia
Quantile-on-
Quantile
regression
Textile and clothing production
has a positive and significant
impact on EI.
Feedback
hypothesis
Ike et al. (2020) 15 oil-producing
countries
1980–2010 Oil production,
environmental
sustainability, CO2
emission, EKC
Moments Quantile
Regression
Oil production, electricity
production, tread condenses
has a significant positive
relation with carbon emission.
Growth
hypothesis
Haseeb et al. (2021) 5 Asian countries 1970–2018 Natural resources, EG,
Asia
Quantile-on-
Quantile
regression
Natural resources have a
significant and positive impact
on this Asian country’s EG.
Growth
hypothesis
Wang et al. (2019) China 2001–2013 Energy efficiency, TP,
carbon pollution
panel quantile
regression
approach
TP of (heavy and light
industry) has positive while
service industries have a
negative impact on carbon
pollution.
Feedback
hypothesis
Lin and Benjamin (2019) Shanghai (China) 1990–2015 Industrial sector, CO2
emission
Quantile
framework
EG, urbanization had an
extreme influence on carbon
pollution
Growth
hypothesis
Wang et al. (2018) G-20 countries 2000–2014 Democracy,
urbanization, political
globalization, PM2.5
concentration
panel quantile
regression
approach
Democracy, political
globalization has a significant
positive impact on PM2.5 (low
and high-emission countries)
and EKC existence on
urbanization and PM2.5
concentrations
Growth
hypothesis
Hübler (2017) 149 countries 1985,2012 Inequality, EI, tread,
FD, EKC
Simultaneous
Quantile
regression
Tread, FDI has positive relation
and inequality; energy
intensities have a negative
relationship with emission.
Feedback
hypothesis
Xu and Lin (2016) China 32
Provinces
1990–2014 Population, GDP, EI,
EC, carbon emission
Quantile
regression model
All variables have a positive
impact on carbon emission
Growth
hypothesis
Zhang et al. (2016) 19 APEC
countries
1992–2012 EC, GDP, urban
population, CO2
emission, EKC
Corruption has a significant
negative effect with low
carbon emission countries but
is insignificant with high
carbon pollution countries.
U-shaped EKC between
emissions besides corruption
has a direct negative effect on
pollution and positively affects
GDP per capita.
Equal
hypothesis
Hammoudeh et al.
(2014)
USA 2006–2013 The carbon tax,
energy price
Quantile
regression model
Oil prices increased the carbon
tax, changing gas and coal
price has a small negative
effect on the carbon price.
Feedback
hypothesis
Note: FD, Financial development; RE, renewable energy; EI, energy intensity; TP, technological progress; EC, Energy consumption; EG, economic growth; EKC,
environmental Kuznets curve.
most important advantages is that this model can make bounds
in the different points and deliver a complete picture of the de-
pendent variable and the symmetrical and nonlinear relationships
between the explanatory variables over the dependent variable
values (Nawaz et al.,2020). Another advantage, this model can
change the distribution of each regression coefficient. If this QR
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Fig. 2. All the individual variables varied during 1980–2018.
Table 2
Description of variables.
Variables Unit Sources Mean Standard deviation
CO2emission Kt WDI 3.237242 0.809361
PD km2people WDI 6.830829 0.214978
GDP per capita US$ WDI 24.72223 0.802959
EI PPP GDP WDI 22.34466 0.164431
MS Current US$ WDI 22.82630 0.866453
IS Current US$ WDI 23.24718 0.903573
Note: WDI, World development indicators; PD, population density; EI, energy
intensity; MS, manufacturing sector; IS, industrial sector; PPP, purchasing power
parity; km2, kilometer square.
model compares with the OLS, QR is more robust, skewness,
outliers, and non-normal errors with the dependent variables
(Nawaz et al.,2020). The quantile regression was implemented
before lots of researchers to assess the impact on increasing
carbon pollutions. For example, Xu and Lin (2018), Wang et al.
(2019), Khan et al. (2020a), Cai et al. (2018) and Rong et al.
(2018) used the QR model to determine the increasing variable of
carbon emission. QR also assistance in resolving such issues; this
could affect the performance of estimations, such as unobserved
heteroscedasticity, heteroscedasticity, and outliers (Nawaz et al.,
2020;Wang et al.,2020a). However, to the relationship between
carbon emission and technological innovation, this investigation
pursues the QR model at various concentrations, such as 25%, 50%,
and 75%. As per Eq. (5), the model’s variables are presented in
Fig. 2 and schematic diagram in Fig. 3.
3.4. Various sectorial technological progress on carbon emission re-
lated framework
(See Fig. 3).
4. Results
This unit discusses the study’s main discoveries and conducts a
few basic statistical tests. It also conducts a correlation test. The
tests’ results for the belongings of sectorial factors are assessed
by conducting QR between carbon pollution and their driving
forces in second. Scenario analysis is conducted for emission
and reduction in Bangladesh’s various economic sectors in third.
Finally, variations in the entire variable are individual from 1980
to 2018, plotted in Fig. 2.
4.1. Stationary test
To prevent spurious regression, that each of the variables
needs to check for the unit root test to measure the stationarity.
This is an important experiment in the case of time-series data
that are stationary or non-stationary (Plosser,1982). This test
is conducted using Fisher-ADF and Fisher-PP, which identify the
common units of cross-sections Dickey and Fuller (1979) and
Phillips and Perron (1988) introduced an independent unit root
test. Where H0posits the null hypothesis of a unit root and Ha
posits an alternative hypothesis when the variable is stationary.
Testing is performed at the level and the first difference, while the
variables are log-format and consist of a unit root at the level, but
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Table 3
Economic sectors division.
Sector ISIC code Subsectors
Manufacturing sector 15–37 Food production, textile and wearing,
pharmaceuticals and chemicals, machinery and
metal products, transport and vehicle equipment,
leather, fertilizer, cement, and leather products,
Mfg (others)
Industrial sector 10–45, 15–37 Manufacturing, construction, electricity, Mining,
water, gas, natural resource
Note: According to the international standard of industrial classification 2017, the ISIC code donates
as an industry code.
Fig. 3. Flowchart general view of our study.
after the first deference, variables turn stationary at the signifi-
cance level of 1%. The normality without a natural log (ln ) by the
Jarque–Bera measuring all variables is 1.8108 with a P-value of
0.4044 when the P-value is 0.7304 and normality is 0.628. The P-
value surpasses 5%, and then the null hypothesis is accepted, thus
proving that data have normality. Table 4 described the outcomes
of all factors.
4.2. QR and correlation tests
The correlation test establishes relationships with all selected
variables. Below 0.9 coefficients of all sectors variables seem
tremendously correlated. These tests are presented in Table 5.
There may also be a multi-collinearity issue due to the objective
data nature, which may negatively affect the integrity of the
regression variables. Very few variables exhibit a strong correla-
tion. This result may be incompatible with the present situation,
and also it influences the model. In this situation, CUSUM1and
CUSUMQ2are applied to confirm the model where the structural
stability lies within a defined range. The Figs. 4 and 5results
suggest that the model parameters are significant. CUSUM and
CUSUMQ are applied in various studies to examine the economic
and various sectors of different countries. Such as, (Bekhet and
1Cumulative sum test.
2Cumulative sum of squares test.
Fig. 4. CUSUM test for stability. (For interpretation of the references to color in
this figure legend, the reader is referred to the web version of this article.)
Fig. 5. CUSUM of squares test for stability. (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of this
article.)
Matar,2013) used this method in Jordan for the stock market,
(Bahmani-oskooee and Bohl,2000) applied these methods in the
M3 money demand function of Germany, (Brown et al.,1974)
applied this method in their study for the constancy of regression,
Raza and Shah (2020), Raza and Sultan (2019) applied these
methods to examine the energy consumption of Pakistan, and
Shahbaz et al. (2019) used this method in the USA to examine the
energy demand. Suppose the CUSUM and CUSUMSQ plot within
the significance level range (5%) means a blue line within the
two flat-out lines. In that case, the null hypothesis flat-out all
are coefficients in the error correction model and this flat-out
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Table 4
Unit root test.
Unit root
method
Fisher-ADF Fisher-PP
ln CO2
∆ln CO2
−4.27446 (0.0087),
−6886060 (0.0000)***
−4.279443 (0.0086),
−8.720465 (0.0000)***
ln PD
∆ln PD
−2.969956 (0.1567),
−2033570 (0.09179)***
0.060139 (0.9957),
−2.077293 (0.0541)***
ln GDP
∆ln GDP
−0.815486 (0.9551),
−5.595835 (0.003)***
−0.781354 (0.9586),
−5.588767 (0.0003)***
ln EI
∆ln EI
−0.703814 (0.9655),
−7.839673 (0.0000)***
−0.205268 (0.9906),
−8.682104 (0.0000)***
ln MS
∆ln MS
−0.131608 (0.9924),
−5.653054 (0.0002)***
−0.177052 (0.9913),
−5.671772 (0.0002)***
ln IS
∆ln IS
−0.115780 (0.9927),
−5.548199(0.0003)***
−0.190093(0.9910),
−5.577171 (0.0003)***
ln MS*ln EI
∆ln MS*ln EI
−0.138535 (0.9922),
−6.681625 (0.0000)***
0.088958 (0.9960),
−6.658355 (0.0000)***
ln IS*ln EI
∆ln IS*ln EI
−0.187686 (0.9911),
−6.657843 (0.0000)***
0.084793 (0.9960),
−6.610903 (0.0000)***
Ho:∆donate the first difference.
***Specifies 1% level significance. All the variables’ p-value is <5% at the first difference, so all
variables are stationary. For that, we can reject the null hypothesis.
Table 5
Correlation test.
Covariance
correlation
CO2GDP PD EI MS MS*EI IS IS*EI
CO20.638269
1.000000
GDP 0.623139
0.984080
0.628211
1.000000
PD 0.167265
0.986622
0.160241
0.952728
0.045030
1.000000
EI 0.037958
0.292726
0.058013
0.450948
0.005682
0.164968
0.026344
1.00000
MS 0.669191
0.979363
0.676650
0.998175
0.171554
0.945245
0.064829
0.467006
0.731491
1.000000
MS*EI 15.91326
0.937612
16.55138
0.982986
3.985860
0.884169
2.070306
0.600423
17.94236
0.987508
451.3029
1.000000
IS 0.700792
0.983478
0.706416
0.99275
0.180268
0.952451
0.065078
0.449541
0.762466
0.999524
18.64428
0.983985
0.795511
1.000000
IS*EI 16.63854
0.945011
17.24463
0.987245
4.183736
0.894613
2.087690
0.583642
18.66588
0.990302
467.9685
0.999554
19.41427
0.987693
485.6825
1.000000
cannot be rejected (Bekhet and Matar,2013). If any of the lines
are tried to cross, then the constancy of the coefficient level
of 5% significance can be rejected of the null hypothesis. Figs.
(4)–(5) shows that the CUSUM and CUSUMSQ plot and showing
long-term stability and the coefficient.
In this study, for calculating the QR model, we employed
Eviews 10.0. Xu and Lin (2016) used the QR model and considered
CO2emission as a dependent variable. QR can also compensate for
the limitations of OLS. A paper by Koenker and Bassett (1978) also
examine the quantile method. The independent and dependent
variables are calculated in the conditional quantiles model for all
quantiles. Therefore, the QR model is more robust than the OLS
of variables estimates (Nawaz et al.,2020).
4.3. Correlation test
This study’s dependent variable (Y) is the effect of carbon
emission, which takes as linearly dependent on independent vari-
ables (X). In this quantile function, likely Y is conditional of τth
given as
Qy(τ|x)=inf {b|Fy(b|x)≥τ}=∑
k
βK(τ)χκ=X′β(τ)
Where the distribution function X gave Y is provisional of Fy(b|x)
and vector xis the dependence of β(τ), and Y is the conditional
quantile of τth. The values of τfrom β(τ) are inside of [0,1],
hence, highlighting the dependence structure of Y. The deviation
of Y and X is measured as minimizing coefficients of β(τ) for
given τ.
Table 6 shows the QR regression results. The findings provide
an extensive explanation of two technical factors of the response
dynamics of CO2pollution. Every quantile accurately explains the
distribution of emission at the high (75th), middle (50th), and low
(25th) levels of CO2emission. Furthermore, QR can demonstrate
the marginal effect of independent variables from various quan-
tiles on CO2emission. Every quantile is being used to indicate
the impact of technological progress. Each model’s results are
relevant, and every sector is calculated as the main sector to
increasing the carbon effect in Bangladesh. The listed variables of
industry size do not suggest any significant difference. Therefore,
every factor is measured based on its actual strength. The model
pseudo-R-square reaches 0.9, suggesting that these models clarify
the driving factor of carbon emissions by more than 90%. In Figs. 4
and 5, the CUSUM analysis is presented, and this is a comparative
analysis.
In terms of technological progress, the relation between
ln MVA and CO2emission has a significant negative impact. The
manufacturing sector (ln MVA) coefficients at quantiles of 25th,
50th, and 75th % are 0.022486, 0.630240, and 0.540264, respec-
tively. On the other hand, the manufacturing sector’s techno-
logical progress increase accounts for 0.017%, 0.11%, 0.25%, and
decline mutually in the CO2pollution in the high, medium, and
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Table 6
Quantile regressions estimation results.
Variables Quantiles
25% 50% 75%
ln CO2−84.73328 (0.0351) −68.91998 (0.1534) −121.4315 (0.0053)
ln PD −0.726660 (0.9768) −18.88867 (0.02868) −49.04242(0.1740)
ln GDP 24.67194 (0.3684) 2.565937 (0.9413) 5.246603 (0.7872)
ln EI 23.74756 (0.0000) 23.00018 (0.0000) 22.32741 (0.0000)
ln MVA 0.022486 (0.9586) 0.630240 (0.0021) 0.540264 (0.0633)
ln IVA −0.563122 (0.0730) −0.570077 (0.0404) 0.141580 (0.7704)
ln (IVA*EI) −2.976690 (0.7914) 13.10289 (0.0196) 12.55927 (0.0723)
ln (MVA*EI) −12.04951 (0.06331) −14.03983 (0.0021) −0.500300 (0.9587)
R-Square = 0.90
low emission levels. Turning now to the similar experimental
evidence by Nawaz et al. (2020), found the relationship between
technological development and carbon emission negatively in-
fluences service and construction sectors. Another similar result
by Wang et al. (2019) found the similar result that the manu-
facturing sectors have a positive impact between technological
development and CO2emission. The next sector’s results are
concerned with the industrial sector (ln IVA) coefficients are
−0.563122, −0.570077, and 0.141580 at the 25th, 50th 75th %,
respectively. It implies that each 5% rise in IVA leads to 0.15%,
1.22%, and 1.48% decline of CO2emission at high, medium, and
low of emission, respectively. Although the coefficients are 25,
50th have negative value and 75th % positive value. The ln IVA
may not satisfy the confidence level of 1% at the percentiles
25th and 50th. However, the coefficient is positive at the 25th
%, still significantly at the 1% level. Therefore, technological de-
velopment in the industry sector poorly performs to promoting
energy efficiency. Coefficients at 25 and 50% are negative for the
terms of ln IVA*EI but do not reach the minimum confidence level.
The emission coefficients are significant at 11%, 1%, and 5% at
all levels. In this situation, the technological development of the
industrial sector cannot significantly reduce carbon emissions due
to energy quality issues. If we now turn to the models concerning
the other sectors, such as industrial and manufacturing results
show a similar positive relationship. Overall, the coefficients of EI
are positively increasing the CO2emission in high, medium, and
low levels. For the cooperation term ln IVA*EI and its coefficients
at the 25th, 50th, 75th % are −2.976690, 13.10289, and 12.55927,
respectively, thereby suggesting that in the construction sector,
technological progress increase 12% then emission level con-
tributes efficiently. In sum, the performance of the construction
sector carbon emission is not controlling well. In Table 6, the
coefficients of ln MVA*EI are positive. The manufacturing sector’s
technological development performs not fine for that reason for
CO2emission increases.
Let us now turn into the industrial sector. This sector is the
main sector that uses national energy and is responsible for
increasing pollution also has a massive impact on environmental
systems. Several authors have considered analyzing industrial
production, and energy intensity are the leading contributors to
carbon emissions production and reduction, respectively. Few
notable examples (Yang et al.,2018) focus on China industrial
growth and carbon pollution and find a positive relationship be-
tween the industry sector and carbon pollution, Xia et al. (2020)
examine Xiangjiang (China) industries carbon emission and found
the similar positive relation with CO2emission, and Ouyang and
Lin (2015) focused on China’s industrial sector energy-related car-
bon pollution. The results indicate industrial activity contributes
to increasing the industrial carbon emission, respectively.
4.4. CUSUM and CUSUMQ test
The CUSUM and CUSUM test results are given in Figs. 4 and 5.
In both cases, 5% critical bound at the lines fall, and these lines
Fig. 6. Scenarios based on CO2emissions from 1980–2040.
show that the model’s parameters are stable and fit for policy
implications.
4.5. Scenario analysis
The carbon emission of the two key economic sectors of
Bangladesh is projected in Fig. 6 in different scenarios. Several
researchers have done this scenario analysis to their probable
reduction and predict the carbon emissions, such as Zhao and
Luo (2018) and Lin and Raza (2019). And Bangladesh has already
promised the United Nations to reduce its carbon emission by
2030. BAU indicates business as usual, scenario ‘‘a’’ is the bench-
mark scenario, and scenario ‘‘b’’ is the advanced scenario. The
assumptions of the scenario are: (I) 1980–2018 is the sample
growth rate period taken the base of the case 2% and 5% sce-
nario; (II) The moderate scenario is taken as BAU and baseline to
determine the pattern over this period; (III) The highest carbon
emission scenario ‘‘a’’ is taken where the BAU is > 5%, and (IV)
lower carbon emission scenario ‘‘b’’ taken where the growth (g)
of BAU is < 5%.
Through this scenarios analysis, energy utilization and carbon
emission can have compared in Bangladesh. As a developing
economic country, Bangladesh needs to improve technology ac-
cording to the environmental aspect. For the free environment
aspect, the benchmark scenario has been measured. And the
advance scenario measures for policy and recommendations.
The country’s economic contribution depends on its economic
sectors, and this also harmful carbon pollution can be measured
by applying the appropriate technology and policies. The sce-
nario represents the highest emission reduction scenario, and
the government can successfully have implemented in the pol-
icy. Generally, in the future, Bangladesh is likely to reduce its
CO2emission by 2% through economic sectors. For Bangladesh’s
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M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
economic sectors, the reduction of carbon pollution is quite im-
portant to achieve the targets of the country’s carbon reduction.
This tends to forecast the result of the advanced scenario also
illustrates the massive potential for the Bangladesh economic
sectors to reduce their carbon pollution. If Bangladesh takes the
necessary initiatives for foreign, local investment and follows the
advanced economic and environmental scenario, the nation can
achieve sustainability.
5. Discussion
This section will address the key findings of the results rele-
vant to our literature and propose their policy implications.
5.1. Main findings discussion
Technological progress manufacturing sector has a positive
impact on CO2emission. Technology transfer could provide the
knowledge necessary to enhance the capabilities by modifying
the design and developing existing technologies, which could
encourage domestic manufacturing equipment with less emis-
sions and higher efficiency. For example, China and India (Lewis,
2007), EU countries (Diakoulaki and Mandaraka,2007), OECD
countries (Pazienza,2019), Japan (Maolin and Yufei,2020), Ghana
(Abokyi et al.,2021) are prominent examples of nations that
have effectively upgraded the country’s technological progress
through technology transfer. The Bangladeshi manufacturing sec-
tor could also potentially be required to focus on this in achieving
a structural transformation.
In the industrial sector, technological progress has had both
negative as well as positive impacts on carbon pollution. How-
ever, there are very few researchers who have done an empirical
study and examined the industrial sector’s technological progress,
such as Li et al. (2019), Saez (2009), Yang et al. (2017), Pan et al.
(2021) and Erdogan (2021). The industry directly affects energy
intensity, particularly in pollution levels. As for the economic
development of Bangladesh, the country has experienced urban-
ization as a result of a large number of industries, commercial
centers, buildings, and other developments, and this sector de-
pletes excess energy; consumption being at more than 40% to 50%
of global energy, and it is thus increasing emissions (Nawaz et al.,
2020;Lin and Benjamin,2019).
6. Conclusions and policy recommendations
6.1. Conclusions
This study empirically examined carbon pollution in
Bangladesh resulting from technological advancement in the man-
ufacturing and industrial sectors. This study applied the Quantile
Regression model over the period from 1980 to 2018 to deter-
mine its variables. At the end of this research, we estimated
the scenario analysis of the carbon emissions forecasting from
2019 to 2040 on the basis of the current level of CO2emissions.
Furthermore, the key results of the existing study are as follows:
(1) The sectorial approach has led to a better understanding
and awareness of the effect of technological progress on
carbon emissions. It has been estimated that the techno-
logical growth of the industrial sector is unable to lessen
the carbon emissions because of the fossil fuel energy con-
sumption, however, the turning into other sectors, for ex-
ample, the manufacturing sector, the outcomes still show
a significant relationship. This is why; the energy intensity
is rising the CO2emission at all levels (low, medium, and
high).
(2) The growth of CO2emissions was estimated via scenario
analysis. Manufacturing has positively correlated with the
industrial sector’s harming of the environment. Thus, the
manufacturing and industrial sectors are significantly in-
creasing CO2emissions.
(3) The high, middle and low scenarios provided in Table 5
present the outcomes of 2019 and 2040 are the predicted
carbon emissions. The emissions could be controlled us-
ing technology and renewable energy resources under the
governmental pollution reduction agreements.
6.2. Policy recommendations
This paper offers several policy recommendations for the re-
duction of CO2emissions in Bangladesh’s several economic sec-
tors. Concerning Bangladesh’s environmental policies for the in-
dustrial and manufacturing sectors, these sectors are hugely de-
pendent on fossil fuel-based energy. Shifting to renewable en-
ergy sources, such as solar, gas, and coal-based energy as the
primary energy sources is strongly recommended. Second, for
manufacturing and industrial sectors, the government should
improve environmentally friendly, long-term policy frameworks
to instigate like policies in related sectors. These measures and
policy structures may also help contribute to improving manage-
ment capability, fostering public awareness of energy efficiency,
and support green technology in the industrial sector. Third,
Bangladesh has been attempting to mandate an environmentally
friendly industry and means for renewable energy and efficient
energy policy but still has not achieved the target level. Fourth,
the country should reduce CO2emissions through renewable
energy efficiency; for example, a study of neighboring countries,
Raza and Lin (2021) and Raza et al. (2021), analyzed that tech-
nological progress in the various sectors of Pakistan, particularly
for industrial (chemical) and agriculture sectors are feasible for
economic growth and CO2emissions reduction. Thus, the govern-
ment needs to introduce a mandatory energy policy. Economic
growth and other global challenges should be considered in for-
mulating any such policy. The manufacturing sector’s vehicles
should use clean fuels. Finally, local governments should develop
both short and long-term policies commensurate with social and
environmentally sustainable development. On the global scale,
the importance of sustainable development for developing coun-
tries has already been emphasized. This goal can be accomplished
at the local level through the dissemination of information and
advertising regarding poverty reduction, green economic growth,
as well as social and environmental development (Nawaz et al.,
2020). However, one limitation of this research is the data used at
a national level and did not add all sectors because of the unavail-
ability of information. Therefore, it could be made a strong rela-
tionship with other sectors over future. For future perspectives,
the multi-national level analysis could be done by comparing the
developed and developing countries based on multiple sectors.
CRediT authorship contribution statement
Muhammad Yousaf Raza: Conceptualization, Methodology,
Software, Data curation, Writing – original draft preparation. Mo-
hammad Maruf Hasan: Methodology, Software, Data curation,
Writing – original draft preparation.
Declaration of competing interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared
to influence the work reported in this paper.
10
M.Y. Raza and M.M. Hasan Energy Reports 8 (2022) 0–13
Acknowledgements
This paper is supported by the Doctors Start Funding Project
(No. BS202137) of Shandong Technology and Business University,
Yantai, Shandong, 255000, China.
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