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Time series analysis of environmental quality in the state of Qatar

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Abstract

This study investigated the impact of economic growth, electricity consumption, energy consumption, and the crop production index on environmental quality in Qatar by considering four different types of GHGs emissions (carbon dioxide, methane, nitrous oxide, and F-GHGs) and using a time-series dataset for the period of 1990-2019. This study investigated the long-and short-term impacts between these variables using ARDL bounds testing, while the stationarity properties of the variables were tested by applying the Zivot-Andrews test. The results indicate that electricity consumption, energy consumption, and the crop production index have a positive and significant relationship with GHGs, while economic growth has a negative and significant relationship in the long term with these gases. The VECM Cranger and Toda-Yamamoto causality tests were used to understand the causal relationship between the variables, and the results suggest a different causality relationship between the variables. Several key policy implications derived from the findings of this research to sustain environmental quality in the state of Qatar are discussed in this paper.
Energy Policy 168 (2022) 113089
0301-4215/© 2022 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Time series analysis of environmental quality in the state of Qatar
Ammar Abulibdeh
Applied Geography and GIS Program, Department of Humanities, College of Arts and Sciences, Qatar University, P.O. Box: 2713, Doha, Qatar
ARTICLE INFO
Keywords:
GHGs emissions
Environmental degradation
ARDL technique
Unit root test with structural breaks
Qatar
ABSTRACT
This study investigated the impact of economic growth, electricity consumption, energy consumption, and the
crop production index on environmental quality in Qatar by considering four different types of GHGs emissions
(carbon dioxide, methane, nitrous oxide, and F-GHGs) and using a time-series dataset for the period of
19902019. This study investigated the long- and short-term impacts between these variables using ARDL
bounds testing, while the stationarity properties of the variables were tested by applying the ZivotAndrews test.
The results indicate that electricity consumption, energy consumption, and the crop production index have a
positive and signicant relationship with GHGs, while economic growth has a negative and signicant rela-
tionship in the long term with these gases. The VECM Cranger and Toda-Yamamoto causality tests were used to
understand the causal relationship between the variables, and the results suggest a different causality rela-
tionship between the variables. Several key policy implications derived from the ndings of this research to
sustain environmental quality in the state of Qatar are discussed in this paper.
1. Introduction
Several environmental issues have received extensive attention since
the Rio de Janeiro Earth Summit held in 1992, which aimed to establish
and implement suitable international and national sustainable devel-
opment policies. During the last few decades, good economic develop-
ment performance has contribute to environmental degradation and in a
signicant rise in GHG emissions. Maintaining sustainable use of the
environment is critical when discussing the implementation of sustain-
able development strategies to boost economic growth and develop-
ment. Environmental issues such as global warming, biodiversity loss,
and deforestation have stimulated signicant quantitative and qualita-
tive studies that have predominantly concentrated on the theoretical
examinations and explanations of the economic growth environmental
degradation nexus, known as the Environmental Kuznets Curve (EKC)
(Grossman and Krueger, 1995) (Zambrano-Monserrate et al., 2018).
Energy use is another cause of GHG emissions (El-Montasser and
Ben-Salha, 2019), (Magazzino and Cerulli, 2019).
Recently, sustainable development has attracted growing attention
by policymakers in many countries. The key pillar in achieving this goal
is decidedly linked to the environment (Zmami and Ben-Salha, 2020).
However, to develop active emission reduction policies, factors that
contribute to the growth of carbon emissions must be identied and
quantied. This study contributes to the existing literature by examining
the determinants of environmental degradation in Qatar between 1990
and 2019. Therefore, the focus of this research is to answer the following
questions: What are the short- and the long-term impacts of economic
growth, electricity consumption, energy consumption, and crop pro-
duction index on carbon dioxide, methane, Nitrous oxide, and other
GHGs emissions? What are the nature of the causal relationship between
these variables? What are their impact on environmental degradation in
Qatar? What are the key policy implications that can be derived to
sustain environmental quality in Qatar? To answer these questions, this
paper utilize various econometric models to identify the short- and
long-term as well the causal relationships between these variables.
Although numerous time-series studies have been conducted to inves-
tigate the causes of environmental degradation, no single study has
considered four types of GHG emissions to measure environmental
quality. This study is a signicant and unique contribution, particularly
for an arid country with abundant energy sources, such as Qatar. Few
studies have investigated Qatar, and these studies have considered only
CO
2
as the indicator of environmental degradation in the country
(Charfeddine et al., 2018)(Salahuddin and Gow, 2019a). Other studies
[see (Magazzino) (Salahuddin and Gow, 2014)] focused on the Gulf
Region, which includes Qatar, where the results are comprehensive and
do not reect a single-country result. To ll this gap in the literature, the
focus of this study is to investigate the causes of environmental degra-
dation in the state of Qatar using four different types of GHG emissions
(carbon dioxide, methane, nitrous oxide, and other GHGs) and four
different indicators (economic growth, energy consumption, electricity
E-mail address: aabulibdeh@qu.edu.qa.
Contents lists available at ScienceDirect
Energy Policy
journal homepage: www.elsevier.com/locate/enpol
https://doi.org/10.1016/j.enpol.2022.113089
Received 5 March 2022; Received in revised form 17 May 2022; Accepted 30 May 2022
Energy Policy 168 (2022) 113089
2
consumption, and the crop production index). The reason for consid-
ering these indicators is the comprehensive measures they provide for
measuring environmental quality. Furthermore, depending only on one
measure, that is, CO
2
, as the indicator of environmental quality may
draw conclusive and comprehensive conclusions on the causes of envi-
ronmental degradation. Qatar is an interesting case because of the high
rate of air pollution and its effect on the quality of life and residential
health. Furthermore, the country has abundant energy wealth (oil and
gas), which is its main source of economic growth. Furthermore, the
electricity sector is subsidized, constituting a burden on the government
budget and motivating residents to consume more, consequently
increasing air pollution.
All the countries worldwide have considered protecting our planet
from environmental crises as a rst priority and have been involved in
many international agreements concerning this issue, including the
Kyoto Protocol (COP3), Kyoto Protocol (MOP 1), United Nations
Framework Convention on Climate Change (UNFCCC), Paris Agreement
(COP21), Doha Amendment to the Kyoto Protocol, and Bali Road Map
(COP13) (Charfeddine, 2017). Intergenerational equity theory suggests
that preserving the environment is a moral and ethical commitment for
future generations (Clayton et al., 2016), (Hunt and Fund, 2016), and
many researchers have emphasized the signicance of formulating and
imposing national, regional, and international laws that guarantee
planetary rights and obligations for all generations (Charfeddine, 2017),
(Demirel et al., 2017), (Foley et al., 2005). Nevertheless, these agree-
ments have not achieved the goal of signicantly reducing different
types of pollutants, including CO
2
emissions. Several factors contribute
to the increase in these pollutants and consequently to environmental
Table 1
Different studies on GHGs emission.
Author(s) Elements Period Methodology Findings
Omri et al.
(2014)
CO
2
emissions, economic growth and foreign
direct investment (FDI)
1990
2011
Dynamic simultaneous-equation panel
data models
Bidirectional causality between the CO
2
and
both the economic growth and FDI
Zhang and Bin
Da (2015)
CO
2
, CO
2
intensity, industrial structure and
energy sources
1996
2010
LMDI method, decoupling index Strong positive correlation between CO
2
and
economic growth
Arvin et al.
(2015)
Urbanization, transportation intensity, CO
2
emissions, and economic growth
1961
2012
Panel vector auto-regressive model The network of causal connections among the
extent of urbanization, economic growth,
transportation intensity, and CO
2
emissions in
the short- and long-run.
Shahbaz et al.
(2015)
CO
2
emissions, economic growth, and energy
intensity
19802012 Panel cointegration, VECM Granger
causality,
Positive correlation between energy use and the
increase of CO
2
.
Alam and
Paramati
(2015)
Oil consumption, economic growth,
internationalization, CO
2
emissions, trade
openness and nancial development
19802012 Panel cointegration tests and a vector error
correction model (VECM) framework
A signicant positive relationship between oil
consumption, economic growth,
internationalization, CO
2
emissions, trade
openness and nancial development.
He et al. (2017) CO
2
emissions, afuence, population,
technology
1995-2013 Stochastic impacts by regression on
population, afuence and technology
(STIRPAT) model
There existed an inverted U link between CO
2
emissions and urbanization in three regions.
(Muhammad) Economic growth, energy consumption and CO
2
emissions
20012017 Seemingly unrelated regression (SUR),
dynamic models estimated through means
of the generalized method of moments
(GMM), System generalized method of
moments (Sys GMM)
CO
2
emissions increase in all countries due to
increase in energy consumption. The CO
2
emissions increased while the energy
consumption decreased in developed and
MENA countries but energy consumption
increased and CO
2
emissions decreased in
emerging countries due to increase in economic
growth.
Saidi and Omri
(2020)
Renewable energy, nuclear energy, CO
2
emissions
19902018 Modied OLS (FMOLS), the vector error
correction model approach (VECM)
estimation methods
Investments in nuclear and renewable energy
reduce CO
2
emissions.
Hu et al. (2020) CO
2
emissions, income, 1991-2016 Tapio decoupling model, Kaya-LMDI
model
CO
2
emissions signicantly rise due to
economic growth. Energy intensity reduces CO
2
emissions to some extent. Energy exports
increase CO
2
emissions to varying degrees.
Dauda et al.
(2021)
Innovation, CO
2
emissions 1990-2016 Cross Sectional Augmented Dickey Fuller
(CADF), Westerlund and Johansen
cointegration tests, xed effect model and
generalized method of moments, ordinary
least square
Inverted U-shape relationship between
innovation and CO
2
emission at panel level.
Renewable energy use lessens CO
2
emissions.
Human capital decreases CO
2
emissions.
Ko ç ak et al.
(2020)
CO
2
emissions, tourism developments 1995-2014 Continuously updated fully modied
(CUP-FM), the continuously updated bias-
corrected (CUP-BC) estimators
Tourism arrivals have an increasing effect on
CO
2
emissions, while tourism receipts have a
reducing effect on CO
2
emissions.
Danish and
Zhang (2019)
Natural resourcesabundance, carbon dioxide
(CO
2
) emissions
1990-2015 The augmented mean group (AMG) panel
algorithm
Abundance of natural resources mitigates CO
2
emission.
Khan et al.
(2019)
Globalization, economic factors, energy
consumption, CO
2
emissions
1971-2016 Dynamic ARDL simulations model Energy consumption, nancial development,
trade, foreign direct investment, economic
globalization, social globalization and political
globalization have positive effect on CO
2
emissions. Urbanization, economic growth and
innovation have negative effect on CO
2
emissions.
Chen et al.
(2018)
CO
2
emission intensity of fossil energy, energy
consumption structure, energy intensity, per
capita Gross Domestic Product (GDP),
population distribution, population size, CO
2
emissions
2001-2015 Logarithmic Mean Divisia Index (LMDI).
Tapio decoupling analysis, the LMDI
decomposition formula
Energy intensity and per capita GDP are the
main factors affecting CO
2
emissions. The
impact of population distribution on CO
2
emissions is negligible.
Ali et al. (2019) Urbanization, carbon dioxide emissions 19722014 Auto Regressive Distributed Lag (ARDL),
VECM model
Urbanization was found to enhance CO
2
emissions both in the long- and short- terms.
A. Abulibdeh
Energy Policy 168 (2022) 113089
3
degradation, including human activities, urbanization, energy use, and
population growth (Shahbaz et al., 2013), (Sadorsky, 2014).
Various studies have explored the link between environmental
degradation and other determinants, such as economic growth, inter-
nationalization, foreign direct investment (FDI), transportation in-
tensity, population, industrial structure, and energy use and
consumption at different time periods (see Table 1). These studies have
found a positive correlation between CO
2
emissions and these de-
terminants. For example, economic growth was found to be the main
driver of increased CO
2
emissions in China (Zhang and Bin Da, 2015).
From the perspective of industrial development, innovative global
achievements in reducing CO
2
emissions are a major concern with
globalization. One of the strategies adopted to control global warming is
to lower the percentage of CO
2
emissions. Ma et al. (2016) investigate
the relationship between economic growth and Chinese household CO
2
emissions during 19942012 based on a decoupling indicator. The re-
sults of their study revealed that Chinas household CO
2
emissions
increased rapidly between 1994 and 2012, owing to an increase in en-
ergy consumption due to economic growth. Furthermore, the study
demonstrated weak expansive decoupling and a decoupling state during
the change in CO
2
emissions resulting from economic and population
growth. Muhammad (Muhammad) investigated the unidirectional ef-
fects of energy consumption, economic growth, and CO
2
emissions for
68 countries between 2001 and 2017. The results of the study revealed a
positive relationship between economic growth and energy consump-
tion in developed and emerging countries but a negative relationship in
MENA countries. The study also found that when energy consumption
decreased, CO
2
emissions increased in developed and Middle East and
North Africa (MENA) countries. However, the study found that energy
consumption increased and CO
2
emissions decreased in emerging
countries because of economic growth.
Qatar is considered one of the highest per capita carbon dioxide
(CO
2
) emitters worldwide (Salahuddin and Gow, 2019b). CO
2
emissions
are one of the primary causes of climate change that has severely
affected Qatars economy, such as the supply of desalinated water,
public health, and food security (Zhang et al., 2017), (Al-Maamary et al.,
2017). Over the past 50 years, Qatar has experienced one of the fastest
growth rates in energy consumption worldwide due to population and
economic growth, and recently, because of the preparation for the 2022
FIFA World Cup and the objectives of the Qatar National Vision 2030
(Abulibdeh, 2021a). However, the country depends exclusively on hy-
drocarbons for its energy supply (Al-Awadhi et al., 2022) (Ghofrani
et al., 2021). Qatar is ranked third in terms of having the largest natural
gas (LNG) reserves in the world (Salahuddin and Gow, 2019a); hence,
the economy of the country heavily depends on natural sources that
contribute signicantly to government revenues (Abulibdeh et al.,
2019).
Over the past three decades, the world has witnessed an increasing
concentration of GHGs, particularly CO
2
, emitted to the atmosphere.
GHG emissions are considered the main contributors to global climate
change (Pachauri et al., 2014), (Wu et al., 2020). Countries worldwide
must collaborate to face this common challenge by reducing CO
2
emissions. Therefore, Qatar has increased its commitment nationally
and internationally toward reducing climate-changing carbon emis-
sions. Qatar has ratied several international agreements, implemented
stronger policies and initiatives, and developed strong clean projects.
For instance, Qatar was among the rst countries to accede to the United
Nations Framework Convention on Climate Change in 1996, to ratify the
Kyoto Protocol in 2005, and the recent Paris Agreement in 2016
(Charfeddine et al., 2018). Moreover, policymakers are developing
compressed natural gas (CNG) as an alternative fuel in Qatars public
transport sector. The second project is the Jetty Boil-Off Gas (JBOG)
Recovery Facility, designed to reduce CO
2
emissions by approximately
2.5 million tonnes per year. Other initiatives include the gigantic target
of manufacturing the rst Qatari electric cars by 2023, aiming to
commercialize approximately 500,000 electric cars by 2024. Qatar also
plans for 10% of the transport energy mix to be produced from renew-
able energy, and 2% and 20% of electricity generation to be from
renewable power by 2020 and 2030, respectively (Charfeddine et al.,
2018).
The structure of the paper is organized as follows: section 2 gives
insight into the GHG emissions prole in the State of Qatar and dives in
details into the different types of GHGs emissions, their sources, and
their annual growth. Section 3 describes the data and variables used in
the study. Section 4 details the empirical methodology used and its
implementation in the study. Section 5 presents the detailed empirical
ndings and discussion. Finally, section 6 provides the conclusion,
policy implications and recommendations.
2. GHG emissions prole in the state of Qatar
2.1. GHGs emission in Qatar
Population and economic growth was accelerated in the State of
Qatar from 1970s due to the massive reserves and trade of the natural
resources oil and gas (Abulibdeh, 2021a). During the past two decades,
Qatar witnessed a rapid and massive urban development driven lately by
winning the right to host the 2022 FIFA World Cup (Zaidan and Abu-
libdeh, 2018). The country witnessed as well a rise in government
expenditure and large-scale investment projects resulting in increased
population and economic growth rate. For example, the population and
economic growth rates in the country between 2004 and 2016 were 10%
and 15%, respectively (Abulibdeh, 2021a). The main nancial source
and the keystones for this development was the revenues from the hy-
drocarbon resources (Zaidan and Abulibdeh, 2020), (Abulibdeh,
2021b). The production and usage of oil and gas are main contributors
to the deterioration of the environment. This gradually increased the
atmospheric GHGs emissions concentrations in the country (Charfed-
dine, 2017). The GHGs emissions plays a major role in increasing land
and air surface temperature contributing in the global warming phe-
nomenon. This section presents GHG emissions prole in Qatar.
Figure (1a) shows the total GHG emissions, including the land use
changes. The gure indicates that GHG emissions in Qatar have
increased rapidly during the past three decades. Figure (1b) illustrates
how much GHGs the average person emits, calculated as the total
emissions owing to human activities in the country divided by the total
population. The gure shows an increase in the per capita GHG emis-
sions, reaching a peak in 2005, and decreasing thereafter. This might be
due to the global economic recession and the increase in population after
2010, when the country began preparing for the 2022 FIFA World Cup.
The electricity and heat sector has the highest GHG emissions, followed
by the manufacturing, construction, and transport sectors, as illustrated
in Figure (1c). The emissions increased in all sectors during the last two
decades owing to the development that took place in the country.
However, agricultural and fugitive emissions from the energy produc-
tion sectors produced the highest GHG emissions between 2000 and
2010, as shown in Figure (1d).
Methane (CH
4
) is a key GHG emitted in Qatar in different sectors.
These strong GHGs are measured in tonnes of carbon dioxide equivalents
(CO
2
e) based on a 100-year global warming potential value. Figure (2a)
shows that this type of GHG has been increasing in the country over
time; however, the per capita CH
4
emissions have started to decrease
over the last two decades, as indicated in Figure (2b). The main sources
of CH
4
in Qatar are fugitive emissions (leakages from oil and gas pro-
duction) and other fuel combustion, as shown in Figure (2c).
Nitrous oxide (N
2
O) is another GHG that has increased substantially
in Qatar over the last two decades, as demonstrated in Figure (3a). N
2
O
emissions are measured in tonnes of carbon dioxide equivalents (CO
2
e)
based on a 100-year global warming potential value. Figure (3b) shows
that the per capita N
2
O emissions uctuated during the last two decades.
Agriculture (e.g., from the use of synthetic and organic fertilizers) and
other fuel combustion are the major sectors that contribute to the
A. Abulibdeh
Energy Policy 168 (2022) 113089
4
emissions of this gas, as shown in Figure (3c).
Production-based emissions represent the CO
2
emitted within the
boundaries of Qatar. However, this fails to capture the emissions from
traded goods (e.g., the CO
2
emitted in the production of goods elsewhere
and imported to the country or the emissions from exported goods).
Consumption-based CO
2
emissions are domestic emissions corrected for
trade. The production and consumption of CO
2
emissions are presented
in Figure (4a). The footprint of the average person (per capita emissions)
in Qatar is provided in Figure (4b). The yearly growth in annual CO
2
emissions is shown in Figure (4c), where positive values indicate that
CO
2
emissions in a given year were higher than in the previous year,
while a negative value indicates that emissions were lower than in the
previous year. The countrys share of CO
2
emissions is measured by the
countrys CO
2
emissions, international aviation and shipping, plus the
‘statistical differencesin the carbon accounts in a given year divided by
the sum of the global emissions in the given year. Figure (4e) presents
the annual emissions as a percentage of the global total for a given year.
Figure (4f) shows the share of CO
2
emissions embedded in trade,
measured as the emissions exported or imported, as the percentage of
domestic production emissions. The gure shows negative values of the
share of CO
2
emissions, which indicates net exporters of CO
2
(i.e. 30%
would mean Qatar exports emissions equivalent to 30% of its domestic
emissions). CO
2
emissions in Qatar are from different sources and are
dominated by the industrial production of materials such as cement and
burning fossil fuels for energy production, as indicated in Figure (4 g).
The gure shows that gas was the predominant source of CO
2
emissions
in the country, followed by oil. Qatar has the third largest reserves of gas
in the world and is ranked rst in exporting liqueed gas. The CO
2
emissions are strongly associated with the energy mix available in the
country. However, CO
2
emissions from these sources have decreased
over the past two decades, as demonstrated in Figure (4h). Fig. 5 illus-
trates the relationship between the per capita consumption CO
2
and
GDP, indicating an inverse relationship between the two.
2.2. Energy consumption and CO
2
emissions
The rapid economic growth that Qatar witnessed and in fact con-
tinues to witness poses a number of challenges including increasing
energy demand (Abulibdeh, 2019; Al-Marri et al., 2018; Zaidan et al.,
2022). Several studies examined the correlation between energy con-
sumption and GHGs emissions (Muhammad), (Wu et al., 2020), (Saidi
and Omri, 2020), (Salahuddin et al., 2018). Understanding this corre-
lation enable governments to develop energy saving strategies as well
emission reduction policies for mitigating the impacts on climate and
slow down climate change. This section give more insight on the energy
consumption effect on CO
2
emissions in Qatar.
Fig. 6a indicates that energy is a key contributor of the CO
2
emissions
in the country. Qatar is characterized by excessively high rates of energy
Fig. 1. GHGs emission in Qatar.
A. Abulibdeh
Energy Policy 168 (2022) 113089
5
use due to the population and economic growth. Furthermore, Qatar is
located in arid region with a high level of water scarcity (Abulibdeh,
2021c). The country depends on non-renewable water sources to meet
the needs of the population and economic activities. Therefore, seawater
desalination has been adopted as a solution to produce fresh water and
overcome this water scarcity (Abulibdeh, 2019). Seawater desalination
is energy-intensive process that adds to the countrys energy demand
and subsequently high emissions. Therefore, reducing energy con-
sumption can help to reduce emissions. Energy intensity is a useful
metric to monitor, calculated as the amount of primary energy con-
sumption per unit of gross domestic product in kilowatt-hours per 2011$
(PPP). Energy intensity can be used to effectively measure how ef-
ciently Qatar uses energy to produce a given amount of economic
output. A lower energy intensity means that the country needs less en-
ergy per unit of GDP. The carbon intensity of energy production (i.e., the
amount of CO
2
emitted per unit of energy) is another valuable metric to
monitor whether countries are making progress in reducing emissions. It
is measured as the quantity of carbon dioxide emitted per unit of energy
production in kilograms of CO per kilowatt-hour. Using lower-carbon
energy and transitioning the energy mix toward lower-carbon sources
helps to reduce carbon emissions emitted per unit energy. Fig. (6b)
presents the annual CO
2
emissions per unit energy in the State of Qatar.
The gure clearly demonstrates that the annual CO
2
emissions per unit
energy was reduced during the last two decades.
3. Data and variables description
In this study, per capita GHG emissions, per capita methane emis-
sions, per capita nitrous oxide emissions, and per capita CO
2
emissions
were used as proxies for the environmental degradation in the State of
Qatar. The GHG data emissions were obtained from different resources,
including the World Bank, Worldometer, Climate Analysis Indicators
Tool, and Our World in Data. These GHG emissions are summed and
measured in tonnes of carbon dioxide equivalents (CO
2
e) based on the
100-year global warming potential factors for non-CO gases, meaning
that gases have the same warming effect as CO
2
over a period of 100
years. Therefore, the emissions of each gas were multiplied by its ‘global
warming potential (GWP) value. The GWP measures the amount of
warming that one-ton of that gas would create relative to one-ton of
CO
2
. The estimates of CO
2
emissions include fossil fuel combustion from
different activities and functionalities (e.g., industry, transport, natural
gas aring, heating and cooling, and fossil industry use), production of
cement, production of chemicals and fertilizers, and CO
2
uptake during
the cement carbonation process. Furthermore, the estimation of CO
2
emissions relied primarily on the energy consumption data. The
explanatory variables used to assess environmental degradation include
economic growth, represented by GDP, electricity consumption, energy
consumption, and CPI. The dependent and independent variables are
expressed as per capita or GDP. Annual data for 19902019 were used in
Fig. 2. Methane emissions in Qatar.
A. Abulibdeh
Energy Policy 168 (2022) 113089
6
this study for the State of Qatar. Table 2 lists the dependent variables
used in this study.
4. Methodology
This study examined the long-term and short-term relationships
between four indicators of environmental quality (per capita GHG
emissions, per capita methane emissions (ME), per capita nitrous oxide
(NO) emissions, and per capita CO
2
emissions) as dependent variables to
develop four models to measure the environmental quality in Qatar.
Four determinants (independent variables) were used to examine their
effects on environmental quality, including economic growth (GDP),
energy consumption (ENER), electricity consumption (ELEC), and the
crop production index (CPI), from 1990 to 2019 in the state of Qatar.
Accordingly, four models were constructed to examine the impact of the
independent variables on environmental quality. The rst model
investigated the impacts of these independent variables on environ-
mental quality using the per capita CO
2
emissions as the dependent
variable (GHGCO
2
), while models 2 (GHGF), 3 (GHGME), and 4
(GHGNO) were constructed using the F-GHGs, per capita ME, and per
capita NO emissions as the dependent variables. A linear form was
constructed to examine the long-term, short-term, and causality re-
lationships between the dependent and independent variables, as
specied in Equation (1).
GHGn=β0+β1GDPt+β2ELECt+β3ENERt+β4CPIt+
ε
t1
where GHGn is GHGCO
2
, GHGF, GHGME, and GHGNO; β
0
is a constant
form; β
1
β
4
are coefcients of the model; and
ε
t
is an error term. To
ensure the mobilization of stationarity in the variancecovariance ma-
trix, all the variables used in this study were transformed into natural
logarithms (ln) (Chang et al., 2001). The log-linear model specication
avoids heteroscedasticity problems and generates more efcient and
symmetric results (Lau et al., 2014). The proposed methodology to
assess the environmental degradation in the State of Qatar is shown in
Fig. 7.
4.1. Unit root test
Different methodological steps were considered to examine the long-
and short-term relationships between the dependent variables and their
determinants. Stationary tests were conducted to determine the unit
root of the time-series data. To detect the stationarity at I (0), I (1), or I
(d), the augmented Dickey-Fuller test statistic using generalized least
squares (DF-GLS), PhillipsPerron (PP), and Kwiatkowski, Phillips,
Schmidt, and Shin (KPSS) were employed (Bekhet et al., 2017). How-
ever, unlike the other tests, the KPSS unit root test considers the series in
Fig. 3. Nitrous oxide emissions in Qatar.
A. Abulibdeh
Energy Policy 168 (2022) 113089
7
the null hypothesis to be level-stationary. A stationarity test is necessary
before conducting the regression analysis, because if the time series is
non-stationary, the regression results will become spurious. Further-
more, a regression analysis would not be true if the time-series data were
not stationary. In this case, it is a spurious regression (Bekhet et al.,
2017). It is necessary to ensure that the variables are not at the I (2)
stationary level prior to processing a bounds-testing approach to avoid
spurious results (Pesaran et al., 2001). Furthermore, bounds testing is
based on the assumption that the variables are stationary at I (0), I (1), or
both. Therefore, the F-statistics are not valid if the variables are sta-
tionary at I (2). To ensure that none of the variables are stationary at the
I (2) level, the implementation of a bounds test procedure may still be
necessary.
4.2. Unit root test assuming a single break point in data
The ZivotAndrews (Zivot and Andrews, 2012) unit root test was
employed to identify the presence of a single structural break point in
the data. Structural tests can take the following form, considering the
series as X (Salahuddin and Gow, 2019b):
ΔXt=Φ+ΦXt1+ct +dDt+dDTt
k
j=1
djΔXtj+
ε
t2
ΔXt=+Xt1+bT +cDt+
k
j=1
djΔXtj+
ε
t3
ΔXt=¥+¥Xt1+ct +bDTt+
k
j=1
djΔXtj+
ε
t4
ΔXt=γ+γXt1+ct +dDTt+
k
j=1
djΔXtj+
ε
t5
where:
D: is a dummy variable shows the mean shift at each point.
DTt: is a trend shift variable.
In the ZivotAndrews test, the null hypothesis (C =0) states that the
presence of a unit root in the data is without a structural break, against
the alternative that the series trend is stationary with an unknown time
break. Therefore, the ZivotAndrews unit root test selects the time break
Fig. 4. CO
2
emissions in Qatar.
A. Abulibdeh
Energy Policy 168 (2022) 113089
8
that reduces the one-sided t-statistics to test c (=c1) =1 (Salahuddin
et al., 2018).
4.3. ARDL bounds testing approach to cointegration
Conventional cointegration techniques do not provide reliable re-
sults when data are plagued with structural breaks (Uddin et al., 2013).
Therefore, autoregressive distributed lag (ARDL) bounds testing, pro-
posed by (Shahbaz et al., 2012), was used in this study to estimate the
cointegrating or long-term and short-term relationships between the
variables. The ARDL technique has been used in different studies and has
been proven to be efcient in cases of small sample sizes (Pesaran et al.,
2001). It also removes the problems of omission bias and autocorrelation
(Salahuddin et al., 2018). The ARDL bounds testing approach has
various advantages compared to other co-integration models; therefore,
it is considered superior and preferable, particularly for small samples
(Shahbaz et al., 2012). The ARDL model uses more appropriate con-
siderations than the JohansenJuselius (J-J) (Johamen and Jtiselius,
1990) and Engle and Granger (2015) models to test the co-integration
among variables in a small sample (Ghatak and Siddiki, 2010) unlike
the J-J co-integration model, which requires a large data sample for
validity (Bekhet et al., 2017). Furthermore, the ARDL model can be
applied if the underlying variables are purely I (0), purely I (1), or
mixed, whereas other models require that all the underlying variables
are integrated in the same order (Pesaran et al., 2001). Another
advantage of the ARDL model is that it allows the variables to have
different optimal lags that are not available when using conventional
cointegration procedures (Ozturk and Acaravci, 2011). Therefore, ARDL
bounds testing uses a proper lag order to capture the data-generating
procedure and is considered sufcient to simultaneously correct for re-
sidual correlation and endogeneity problems. Furthermore, the ARDL
model provides unbiased estimates of long-term and short-term models
and valid t-statistics, even in the presence of endogeneity problems
Fig. 4. (continued).
Fig. 5. Relationship between per capita consumption CO
2
and GDP.
A. Abulibdeh
Energy Policy 168 (2022) 113089
9
(Harris and Sollis, 2003). This method enables the convenient use of a
single reduced-form equation, long-term equilibrium, and estimation of
short-term dynamics simultaneously within a dynamic unrestricted
error correction model (UECM) (Shahbaz et al., 2012). Therefore, this
study employs the ARDL bounds model to investigate the equilibrium
relationships among variables. The empirical formulation of the ARDL
equations for the models is as follows:
ΔlnCO2Qnt =β0+
α
1lnCO2Qnt1+
α
2lnGDPt1+
α
3lnELECt1
+
α
4lnENERt1+
α
5CPIt1+
n
i=1
β1iΔlnCO2Q(n)ti+
n
i=0
β2iΔlnGDPti
+
n
i=0
β3iΔlnELECti+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
μ
t
6
ΔlnMEQnt =β0+
α
1lnMEQnt1+
α
2lnGDPt1+
α
3lnELECt1
+
α
4lnENERt1+
α
5CPIt1+
n
i=1
β1iΔlnMEQ(n)ti+
n
i=0
β2iΔlnGDPti
+
n
i=0
β3iΔlnELECti+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
μ
t
7
ΔlnNOQnt =β0+
α
1lnNOQnt1+
α
2lnGDPt1+
α
3lnELECt1
+
α
4lnENERt1+
α
5CPIt1+
n
i=1
β1iΔlnNOQ(n)ti+
n
i=0
β2iΔlnGDPti
+
n
i=0
β3iΔlnELECti+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
μ
t
8
ΔlnGHGQnt =β0+
α
1lnGHGQnt1+
α
2lnGDPt1+
α
3lnELECt1
+
α
4lnENERt1+
α
5CPIt1+
n
i=1
β1iΔlnGHGQ(n)ti+
n
i=0
β2iΔlnGDPti
+
n
i=0
β3iΔlnELECti+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
μ
t
9
where β1i - β5i ,
α
1-
α
5 are coefcient, β0 is a constant and,
μ
t is white
noise error term. The error correction models for the above models are
specied as follows:
ΔlnGHGsQnt =β0+
n
i=1
β1iΔlnGHQsQnti+
n
i=0
β2iΔlnGDPti
+
n
i=0
β3iΔlnELECti+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
μ
t10
The cointegrating relationship is examined by conducting a Wald test
Fig. 6. (a) Energy consumption, (b) annual CO
2
emissions per unit energy.
Table 2
Dependent variables used in this study.
Variable Unit Denition
CO
2
emissions (CO
2
) Metrics
tonnes per
capita
CO
2
are the primary driver of global
climate change and is the most
dominant GHG produced by land use
change, industrial production, and
burning of fossil fuels.
Greenhouse gas emissions
(GHG)
metrics
tonnes per
capita
Other GHG includes F-gases
(hydrouorocarbons (HFCs),
peruorocarbons (PFCs), and sulfur
hexauoride (SF6)). These gases are
summed and used in this study as
other GHGs emissions. These gases
contributed signicantly in global
climate change. The sources of these
emissions are mainly from
refrigeration/AC, aerosols, and
semiconductors.
Methane emissions (CH
4
). In
this study, ME is used to
denote Methane.
metrics
tonnes per
capita
The main source of ME is the
agricultural emissions, produced by
aerobic and anaerobic
decomposition processes in crop and
livestock production and
management activities. The
subdomains of the agricultural
emissions that produce Methane
include enteric fermentation,
manure management, and burning-
crop residues.
Nitrous oxide emissions
(N
2
O). In this study, NO
will be used to denote
Nitrous oxide
metrics
tonnes per
capita
The main source of NO is the
agricultural emissions, produced by
aerobic and anaerobic
decomposition processes in crop and
livestock production and
management activities. The
subdomains of the agricultural
emissions that produce NO include
agriculture soils, manure applied to
soils, manure management,
synthetic fertilizers, burning-crop
residues, and crop residues.
A. Abulibdeh
Energy Policy 168 (2022) 113089
10
and an F-test for the joint signicance of the coefcients of the lagged
variables, assuming the null hypothesis, as there is no cointegration
among the variables against the alternative hypothesis of the presence of
cointegration among variables. The short-term, long-term, and ECT1
(error correction term that shows the speed of adjustment of short-term
deviations towards the long-term equilibrium) are estimated using the
ARDL method.
4.4. Causality test
The TodaYamamoto (TY) causality analysis and the vector error
correction model (VECM) short-term Granger causality test were per-
formed to assess the causal direction among the variables to provide a
better understanding of the policy implications of the empirical ndings.
The VECM test is efcient and appropriate for estimating causal link
variables once they are integrated in the same order (Salahuddin et al.,
2018), (Granger, 1969). One of the main advantages of the TY test is that
it is insensitive to the order of integration. In this study, the VECM
short-term Granger causality test is represented according to Equation
(11).
ΔlnGHGt=β0i+
n
i=1
β1iΔGHGti+
n
i=0
β2iΔlnGDPti+
n
i=0
β3iΔlnELECti
+
n
i=0
β4iΔlnENERti+
n
i=0
β5iΔlnCPIti+
ε
t
11
5. Results and discussion
5.1. Descriptive statistics
To determine the nature of the data distribution, Table 3 provides the
basic statistics and pre-estimation diagnostics for all the variables. The
results indicate that Qatars average CO
2
emissions are exceptionally
high compared with the world average of 4.49 (Charfeddine, 2017).
Overall, the mean and median results exhibit no large differences in
their values for any of the variables. The standard deviation values
reect the volatile nature of the variables. Based on the standard devi-
ation values, the data are homogeneous and are nearly normally
distributed within a reasonable range. This is also shown by the kurtosis
values, which indicate that the data are light-tailed to a normal distri-
bution. The values in the table show that CO
2
emissions and CPI are
negatively skewed. Fat tails are present for all the variables, as indicated
by the excess kurtosis and JarqueBera statistics. This indicates that
applying the standard estimation techniques is unlikely to provide
spurious ndings. This allowed us to conduct further statistical analyses
and estimations.
A variance ination factor (VIF) test was performed to examine the
data multicollinearity. The test aims to quantify the extent to which the
variance of the estimated coefcients is inated when multicollinearity
exists. The variance ination factor for the nth predictor is
VIFn=1
1R2
n
12
where R2
n is the R
2
value resulting from the regression of the nth pre-
dictor with the remaining predictors. The results (Table 4) of the
Fig. 7. Proposed methodology owchart to assess the environmental degradation in Qatar.
A. Abulibdeh
Energy Policy 168 (2022) 113089
11
variance ination factor (VIF) test suggest that the data were free from
multicollinearity. The entries in the table show that there is no corre-
lation between the predictors; therefore, the variances of the variables
are not inated.
5.2. Unit root tests analysis
The rst step in the empirical analysis of the data is to conduct unit
root tests to determine the order of integration of all the selected vari-
ables using three different standard unit root tests: DF-GLS, PP, and
KPSS. The results for the level and difference in the variables of these
unit root tests for the State of Qatar are presented in Table 5. The results
indicate that the three tests were in harmony. The table shows that most
of the variables are stationary after the rst difference by comparing the
absolute terms of the observed values of the DF-GLS, PP, and KPSS test
statistics, with the critical values of the test statistics at the 1%, 5%, and
10% levels of signicance. These results are strong indicators of statio-
narity at both the level and rst difference. However, there are still unit
roots in some variables based on the results of some tests at various
levels; thus, the null hypothesis is accepted for these variables.
Furthermore, the null hypothesis of non-stationarity is rejected, and it is
safe to conclude that some variables are stationary at I (0), while other
variables are stationary at I (1). This indicates that the variables are
mutually integrated in the order of zero and one (I (0) and I (1)), which
enables us to apply the ARDL test. However, these tests have been
criticized for the lack of any indication or information related to the
presence of structural breaks in the time series. Therefore, these tests
may lead to biased results concerning the stationarity of variables
(Mrabet and Alsamara, 2017).
***, **, * denotes the signicant level of 1% 5% 10% respectively;
Critical values for DF-GLS test are: 2.657(1%), 1.954(5%), 1.609
(10%); Critical values for PP test are: 3.711(1%), 2.981(5%), 2.630
(10%(; Critical values for KPSS test are: 0.739(1%), 0.463(5%), 0.347
(10%). For DF-GLS and PP tests, the null hypothesis H0 is that the series
has a unit root (isnt stationary), while the null hypothesis for the KPSS
test is that the series is stationary.
5.3. Unit root tests with structural break
Because the DF-GLS, PP, and KPSS tests have been criticized for
their poor explanatory power and inability to consider break(s) in the
variables, to overcome this weakness, these variables were further
examined using the ZivotAndrews (Zivot and Andrews, 2012) struc-
tural break unit root test to allow for structural breaks in the series. The
results are detailed in Table 6. The results further indicate that the null
hypothesis cannot be rejected for all the variables. Most of the variables
are stationary at the level and when considering the rst difference
stationary, that is, I (1), in the presence of single structural breaks in the
variables; hence, they meet the pre-condition for cointegration. There-
fore, it is safe to investigate the cointegrating relationships between the
variables. The results also conrm that most of the variables are
rst-difference-stationary. Furthermore, the results indicate that the test
detects numerous break points predominantly around two periods, the
rst half of the 1990s (1993, 1995) and in the 2000s (2003, 2004, 2005,
2006, and 2009), for some variables in level and rst difference, as
shown in the table. The break in 1993 may have been due to the rst
Gulf War in 1990, and the break in 2009 may be attributed to some
effects of the global nancial crisis. This indicates that the pattern of
change in these variables is not characterized by signicant volatility.
5.4. ARDL cointegration analysis
The unit root test demonstrates that most of the variables are sta-
tionary and integrated with the rst order; therefore, the next step is to
Table 3
Summary statistics of the data.
Denition Variables Mean Median Std. D. Kurt. Sk. J-B P-value
CO
2
emissions (metrics tonnes per capita) CO2 51.36 54.82 11.78 2.0799 -0.43 1.70 0.43
Greenhouse gas emissions (metrics tonnes per capita) GHG 39.76 38.28 5.59 2.262 0.427 1.34 0.511
Methane emissions (metrics tonnes per capita) ME 3.38 3.43 0.40 1.576 0.19 2.43 0.297
Nitrous oxide emissions (metrics tonnes per capita) NO 0.46 0.40 0.15 2.70 0.995 4.07 0.131
GDP (metric billion USD) GDP 1.18e+11 0.39e+11 1.36e+11 2.247 0.94 4.6 0.100
Energy consumption (metric kg of oil equivalent per gdp) ENER 4.9634 3.156 3.975 2.416 0.917 4.169 0.124
Electric power consumption (kWh per capita) ELEC 13502.06 14153.71 2163.185 2.3205 -0.732 2.9317 0.230886
Crop Production Index CPI 80.09926 82.65000 17.25923 2.3123 -0.2646 0.8472 0.6547
J-B is the Jarque Bera test statistic for normality hypothesis.
Std. D. Is the standard deviation.
Kurt. Is the Kurtosis.
Sk. Is the skewness.
Table 4
Multicollinearity Testing (among the independent variables): Variance Ination
Factor (VIF) results.
Feature Variance Ination Factor (VIF) 1/VIF
lnGDP 5.843865 0.171120
ENER 5.699187 0.175464
lnELEC 2.859047 0.349767
lnCPI 2.165853 0.461712
Table 5
Standard unit root tests (checking for stationarity).
Variables Level First Difference
DF-GLS PP KPSS DF-GLS PP KPSS
lnGDP -0.9139 -0.5452 0.77*** -2.6781*** -2.9018* 0.1503
ENER -2.8380*** -0.9361 0.6040** -2.6810*** -3.9287*** 0.1439
lnELEC -1.0735 -2.0491 0.5136** -1.9127* -4.9106*** 0.3925*
lnCPI -1.6758* -3.2072** 0.4900** -4.3805*** -5.8707*** 0.1743
lnCO
2
-1.6855* -3.0424** 0.2710 -2.7840** -4.4815*** 0.4307*
lnGHG -1.3252 -2.0537 0.2106 -3.5690*** -4.5110*** 0.5599**
lnME -1.2630 -1.2728 0.2873 -1.9336* -1.9 0.4569*
lnNO -0.9165 -3.0274** 0.6510** -7.1702*** -18.1266*** 0.2250
A. Abulibdeh
Energy Policy 168 (2022) 113089
12
estimate the short- and long-term coefcients of the variables. The ARDL
cointegration approach was used to test for long-term relationships, and
the results are listed in Table 6. The results of the Wald test F-statistics
for the four models are statistically signicant at 10% and 5%.
Compared to the Pesaran et al. (2001) critical values, the calculated
F-statistics indicate that strong cointegration exists among the variables,
which in turn stimulates the ARDL procedures to continue to estimate
the short- and long-term coefcients. Nevertheless, prior to such an
estimation, prior information is necessary for the optimal lag length. The
optimal lag was selected based on the Bayesian information criteria
(BIC) for the four models, as listed in Table 7.
5.5. ARDL short-term analysis
The next step is to investigate the long- and short-term impacts of
GDP, ELECT, ENE, and CPI on different types of GHGs. The short-term
relationship between the dependent and independent variables was
investigated using the ARDL model, and the results are presented in
Tables 811. As indicated in the table, the short-term effects of GDP,
ELECT, ENE, and CPI on all the indicators of environmental quality are
statistically insignicant. This suggests that GDP has a negative but
insignicant effect on CO
2
, GHGs, and NO and a positive but insigni-
cant effect on the other MEs. Electricity consumption was found to have
negative but insignicant effects on CO
2
, NO, and ME but positive and
insignicant effects on the other GHGs. Furthermore, energy consump-
tion has varying effects on GHG emissions. Energy consumption has a
positive but insignicant effect on CO
2
, GHGs, and NO, and a negative
but insignicant effect on ME. CPI has a negative but insignicant effect
on NO and a positive but insignicant effect on the other GHGs.
Furthermore, the estimated coefcients associated with the error
correction coefcients ECT (1) for the four models have their expected
negative sign, which implies that the disequilibrium can be adjusted to
the long term with higher speed. Furthermore, ECT (1) for CO
2
and NO
was signicant at the 10% and 1% levels, respectively. This result in-
dicates that the speed of adjustment in CO
2
and NO from short-term
toward the long-term equilibrium will occur by 0.57% and 1.67%,
respectively, every year. The results afrm that in the short term, there is
no causality in the independent variables for CO
2
, ME, NO, or other GHG
emissions. The R
2
values range between 33% and 68%, which conrms
that the model has a moderately good t.
5.6. ARDL long-term analysis
The ARDL model was used to investigate the long-term impacts of the
independent variables on the dependent variables; the estimation results
are reported in Tables 1215. Table 12 shows the ARDL long-term
analysis for model lnCO
2
as an endogenous variable and provides the
Table 6
ZivotAndrews unit root test assuming a single break point in data.
Variables Level First Difference
T-statistic Time break Decision T-statistic Time break Decision
lnGDP -2.0898 2013 Unit root -5.7043*** 2010 Stationary
ENER -5.5726*** 1995 Stationary -5.3526*** 1992 Stationary
lnELEC -5.5726*** 1995 Stationary -5.3526*** 1992 Stationary
lnCPI -5.246** 2000 Stationary -6.9*** 1996 Stationary
lnCO
2
-7.0175*** 2006 Stationary -5.6744*** 1991 Stationary
lnGHG -3.615 2007 Unit root -6.7597*** 2004 Stationary
lnME -5.363*** 2004 Stationary -3.8196 2009 Unit root
lnNO -5.9697*** 2003 Stationary -8.1301*** 2005 Stationary
Note: ** and *** denote 5% and 1% levels of signicance, respectively; the corresponding critical values: 5.34(1%), 4.8(5%), 4.58(10%).
Table 7
Walt test of the ARDL cointegrations
Models Endogenous variables Function Optimal
Lag lengths
Wald Test
F-statistic
Cointegration Decision
1 lnCO
2
F (lnGDP, lnCPI, lnELEC, ENER) (1,2,2,1,2) 4.816816
a
Cointegrated
2 lnGHG F (lnGDP, lnCPI, lnELEC, ENER) (1,2,2,1,1) 3.482566** Cointegrated
3 lnME F (lnGDP, lnCPI, lnELEC, ENER) (2,2,3,2,1) 4.349594
a
Cointegrated
4 lnNO F (lnGDP, lnCPI, lnELEC, ENER) (1,2,2,2,2) 4.196223
a
Cointegrated
a
, and ** denote statistical signicance at 5%, and 10% levels respectively.
Table 8
ARDL short-term analysis for model (lnCO
2
) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C 0.045259 0.054447 0.831249 0.4174
LNCO
2
(-1) 0.260085 0.298958 0.869970 0.3964
LNGDP (-2) -0.251173 0.303425 -0.827793 0.4193
LNCPI(-2) 0.113246 0.144089 0.785946 0.4427
LNELEC (-1) -0.611206 0.550786 -1.109697 0.2826
ENER(-2) 0.015226 0.015903 0.957424 0.3518
ECT (-1) -0.574821 0.323662 -1.775991 0.0936
R-squared 0.336902 Mean dependent var -0.014485
Adjusted R-squared 0.102868 S.D. dependent var 0.102763
S.E. of regression 0.097334 Akaike info criterion -1.582846
Sum squared resid 0.161056 Schwarz criterion -1.239247
Log likelihood 25.99415 Hannan-Quinn criterion -1.491689
F-statistic 1.439542 Durbin-Watson stat 2.227118
Prob (F-statistic) 0.257086
Table 9
ARDL short-term analysis for model (lnGHG) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C 0.014068 0.026186 0.537218 0.5981
LNGHG (-1) 0.169692 0.231878 0.731818 0.4742
LNGDP (-2) -0.091109 0.140496 -0.648484 0.5253
LNCPI(-2) 0.078639 0.076065 1.033846 0.3157
LNELEC (-1) 0.103305 0.337001 0.306543 0.7629
ENER(-2) 0.002292 0.009007 0.254507 0.8022
ECT (-1) -0.460391 0.269294 -1.709625 0.1055
R-squared 0.445810 Mean dependent var 0.001825
Adjusted R-squared 0.250213 S.D. dependent var 0.056462
S.E. of regression 0.048890 Akaike info criterion -2.959974
Sum squared resid 0.040635 Schwarz criterion -2.616375
Log likelihood 42.51969 Hannan-Quinn criterion -2.868818
F-statistic 2.279231 Durbin-Watson stat 1.969280
Prob (F-statistic) 0.084875
A. Abulibdeh
Energy Policy 168 (2022) 113089
13
long-term coefcients from the ARDL estimates. The results suggest that
ENER, ELECT, and CPI cause a deterioration in environmental quality
and that there is a highly signicant, at the 1% level of signicance,
long-term relationship between GDP, ELEC, and ENER as well as a
highly signicant, at the 5% level of signicance, long-term relationship
between CPI and CO
2
emissions. However, GDP has a signicantly
negative association with CO
2
emissions. A 1% increase in GDP causes a
0.11% decline in CO
2
emissions. In contrast, ENER, ELECT, and CPI have
a signicant positive association with CO
2
emissions. A 1% increase in
ELECT, ENER, and CPI leads to 1.78%, 0.058%, and 0.33% increases in
CO
2
emissions, respectively. Although these coefcients are still small,
their magnitudes and effects must not be undermined, indicating that a
substantial reduction in emissions is still far from reality.
The results illustrated in Table 13 reveal a negative relationship
between GDP and per capita GHG emissions. These results imply that a
0.084% decrease in the per capita GHG emissions is linked to a 1% in-
crease in GDP. This relationship is statistically signicant at the 1%
level. However, the results show a positive and statistically signicant
effect of ELEC on the per capita GHG emissions. This indicates that this
variable plays a substantial role in increasing GHG emissions in the
country in the long term. Keeping the other factors constant, a 1% in-
crease in ELEC increases GHG emissions by 0.97%. ENER and CPI have
no signicant impact on GHG emissions.
Table 14 suggests a negative and statistically signicant relationship
at the 1% level between GDP and per capita ME emissions. These results
imply that a 0.104% decrease in the per capita ME emissions is linked to
a 1% increase in GDP. In contrast, the results suggest a positive rela-
tionship between the other independent variables (ENER, ELEC, and
CPI) and the per capita ME emissions. This indicates that these variables
play a critical role in increasing ME emissions in a country in the long
term. The results identify a positive and statistically signicant effect of
ENER on the per capita ME emissions at the 10% level. Keeping the other
factors constant, a 1% increase in ENER increases the ME emissions by
0.0058%. Moreover, ELEC and CPI have positive and signicant
Table 10
ARDL short-term analysis for model (lnME) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C 0.009796 0.018494 0.529678 0.6036
LNME (-2) 0.467663 0.260469 1.795468 0.0915
LNGDP (-2) -0.072553 0.091329 -0.794414 0.4386
LNCPI(-2) 0.012297 0.047783 0.257357 0.8002
LNELEC (-1) -0.086857 0.176549 -0.491968 0.6294
ENER(-2) -0.000155 0.006532 -0.023774 0.9813
ECT (-1) -0.454848 0.296126 -1.535994 0.1441
R-squared 0.342140 Mean dependent var -0.005616
Adjusted R-squared 0.095442 S.D. dependent var 0.033774
S.E. of regression 0.032122 Akaike info criterion -3.792772
Sum squared resid 0.016509 Schwarz criterion -3.447187
Log likelihood 50.61687 Hannan-Quinn criterion -3.705858
F-statistic 1.386878 Durbin-Watson stat 1.281853
Prob (F-statistic) 0.278937
Table 11
ARDL short-term analysis for model (lnNO) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C 0.017243 0.111567 0.154557 0.9790
LNNO(-1) 0.330236 0.224011 1.474198 0.1587
LNGDP (-2) 0.143997 0.565122 0.254806 0.8019
LNCPI(-2) -0.079449 0.316680 -0.250880 0.8049
LNELEC (-2) -0.757842 0.956003 -0.792719 0.4389
ENER(-2) 0.011847 0.034128 0.347147 0.7327
ECT (-1) -1.670209 0.363623 -4.593241 0.0003
R-squared 0.681230 Mean dependent var 0.020713
Adjusted R-squared 0.568723 S.D. dependent var 0.322425
S.E. of regression 0.211742 Akaike info criterion -0.028407
Sum squared resid 0.762187 Schwarz criterion 0.315192
Log likelihood 7.340879 Hannan-Quinn criterion 0.062750
F-statistic 6.055000 Durbin-Watson stat 2.148547
Prob (F-statistic) 0.001537
Table 12
ARDL Long-term analysis for model (lnCO
2
) as endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C -11.99555 2.140509 -5.604061 0.0000
LNGDP -0.110877 0.036227 -3.060635 0.0057
LNELEC 1.781649 0.217682 8.184625 0.0000
ENER 0.057618 0.013423 4.292550 0.0003
LNCPI 0.327423 0.140958 2.322847 0.0298
R-squared 0.828540 Mean dependent var 3.910081
Adjusted R-squared 0.797366 S.D. dependent var 0.253185
S.E. of regression 0.113971 Akaike info criterion -1.340170
Sum squared resid 0.285767 Schwarz criterion -1.100200
Log likelihood 23.09229 Hannan-Quinn criterion -1.268814
F-statistic 26.57747 Durbin-Watson stat 1.470067
Prob (F-statistic) 0.000000
Table 13
ARDL Long-term analysis for model (lnGHG) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C -3.894478 1.257173 -3.0997806 0.0053
LNGDP -0.083962 0.021277 -3.946175 0.0007
LNELEC 0.969783 0.127850 7.585305 0.0000
ENER -0.002267 0.007884 -0.287567 0.7764
LNCPI 0.099757 0.082788 1.204974 0.2410
R-squared 0.803933 Mean dependent var 3.673526
Adjusted R-squared 0.768285 S.D. dependent var 0.139058
S.E. of regression 0.066938 Akaike info criterion -2.404526
Sum squared resid 0.098575 Schwarz criterion -2.164556
Log likelihood 37.46110 Hannan-Quinn criterion -2.333170
F-statistic 22.55165 Durbin-Watson stat 1.025732
Prob (F-statistic) 0.000000
Table 14
ARDL Long-term analysis for model (lnME) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C -5.002116 0.471047 -10.61914 0.0000
LNGDP -0.104101 0.007972 -13.05800 0.0000
LNELEC 0.869099 0.047904 18.14256 0.0000
ENER 0.005853 0.002954 1.981418 0.0602
LNCPI 0.005853 0.031020 3.638685 0.0014
R-squared 0.962281 Mean dependent var 1.211770
Adjusted R-squared 0.955423 S.D. dependent var 0.118792
S.E. of regression 0.025081 Akaike info criterion -4.367851
Sum squared resid 0.013839 Schwarz criterion -4.127882
Log likelihood 63.96599 Hannan-Quinn criterion -4.296496
F-statistic 140.3162 Durbin-Watson stat 1.696399
Prob (F-statistic) 0.000000
Table 15
ARDL Long-term analysis for model (lnNO) as Endogenous variable.
Variable Coefcient Std. Error t-Statistic Prob.
C -7.943123 3.893762 -2.039961 0.0535
LNGDP 0.243897 0.065899 3.701052 0.0012
LNELEC 0.190638 0.395982 0.481431 0.6350
ENER 0.039957 0.024417 1.636451 0.1160
LNCPI -0.199366 0.256414 -0.777515 0.4451
R-squared 0.596383 Mean dependent var -0.803535
Adjusted R-squared 0.522998 S.D. dependent var 0.300183
S.E. of regression 0.207323 Akaike info criterion -0.143506
Sum squared resid 0.945618 Schwarz criterion 0.096464
Log likelihood 6.937329 Hannan-Quinn criterion -0.072150
F-statistic 8.126764 Durbin-Watson stat 2.475617
Prob (F-statistic) 0.000350
A. Abulibdeh
Energy Policy 168 (2022) 113089
14
relationships with the per capita ME emissions at the 1% signicance
level, implying that 0.869% and 0.0058% of per capita ME emissions
increased by a 1% increase in ELEC and CPI, respectively.
The long-term relationship between the per capita NO emissions and
the independent variables was investigated and the results are provided
in Table 15. The results suggest a positive and signicant impact of GDP
at the 1% level on the per capita NO emissions, and a 0.244% increase in
the per capita NO emissions is linked to a 1% increase in GDP. This
implies that economic growth plays a vital role in increasing NO emis-
sions in the country. However, ELEC and ENER have positive but
insignicant impacts on the per capita NO emissions, while CPI has a
negative but insignicant impact.
The negative long-term relationship between GDP and CO
2
, ME, and
GHGs indicates that this relationship is U-shaped, which means that the
Environmental Kuznets Curve (EKC) hypothesis is not valid for Qatar
when using CO
2
, ME, and GHGs as indicators of environmental degra-
dation when considering only GDP. This can be attributed to the fact
that these types of GHG emissions to real GDP per capita ratios were
smaller compared to the same ratio after a certain point of economic
development in Qatar. This reects the importance of the other variables
because Qatar is practicing a long-term transition toward signicantly
increasing industrial activities related to the energy sector to diversify its
economy as well as increase gas and oil production. This result agrees
with the ndings of Mrabet and Alsamara (2017). These results reect
the dramatic rise in the production and demand for energy and elec-
tricity in the country in recent decades. For example, in 2016, the
electricity demand in the country increased by 2.3% compared to 2015,
reaching 7435 MW, and the electricity transmitted in 2016 was 39,667
GWH (Abulibdeh, 2021a), (Khalifa et al., 2019). Furthermore, natural
gas is used for electricity and energy production, and there is no
intention to transition to renewable energy sources in the short term in
the country. This is expected because Qatar has the third largest natural
gas reserve in the world. However, authorities in the State of Qatar
recently launched a strategy to minimize the negative impact of eco-
nomic development, including energy production and consumption, on
the environment in the long term.
5.7. VECM cranger and TodaYamamoto causality testing results
An appropriate assessment of environmental degradation in Qatar
depends on the nature of the causal relationship between the dependent
and independent variables. Therefore, the nal step in investigating the
impact of GDP, ELEC, ENER, and CPI on CO
2
, GHG, NO, and ME is to test
the existence of a causal relationship between these variables using
TodaYamamoto and VECM Cranger causality testing. Because most of
the variables are rst difference stationary, VECM Cranger and Toda-
Yamamoto (TY) causality analyses are suitable tests to assess the causal
direction among the variables. Tables (16 and 17) present the empirical
causality relationships between dependent and independent variables.
The two tests identied different causal relationships between the var-
iables. The TY causality analysis (Table 16) indicates a bidirectional
causal relationship between the independent variables GDP, ELEC,
ENER, CPI, and the dependent variable ME, and between GPD, ELEC,
CPI, and NO. Furthermore, the test suggests a bidirectional causal
relationship between GDP, ELEC, CPI, and NO. There is also unidirec-
tional causality from GDP to CO
2
, GHG to ELEC, and GHG to ENER.
However, the VECM Cranger-causality analysis (Table 17) shows only a
unidirectional causality relationship running from CPI, ELEC, ENER, and
GHGs; ELEC and ME; ENER and NO; and CO
2
and ELEC.
5.8. The cumulative sum (CUSUM) test
The cumulative sum (CUSUM) is a stability analysis test that reveals
the supremacy of long- and short-term parameters. If the graph of this
test crosses the critical bounds (red lines), we may reject the hypothesis
of misspecication of the empirical model (Shahbaz et al., 2012).
Table 16
TodaYamamoto Causality test.
Chi-sq Prob. Chi-sq Prob.
ΔlnGDP–>
ΔlnCO
2
11.21033* 0.0008 ΔlnCO
2
>
ΔlnGDP
0.014844 0.9030
ΔlnCPI–>
ΔlnCO
2
0.104320 0.7467 ΔlnCO
2
>
ΔlnCPI
0.183727 0.6682
ΔlnELEC–>
ΔlnCO
2
0.805477 0.3695 ΔlnCO
2
>
ΔlnELEC
0.233759 0.6288
Δ ENER–>
ΔlnCO
2
1.282560 0.2574 ΔlnCO
2
>Δ
ENER
0.067642 0.795
ΔlnGDP–>
ΔlnGHG
2.558 0.4649 ΔlnGHG–>
ΔlnGDP
5.029 0.170
ΔlnCPI–>
ΔlnGHG
3.821 0.282 ΔlnGHG–>
ΔlnCPI
0.326307 0.5678
ΔlnELEC–>
ΔlnGHG
2.281 0.5161 ΔlnGHG–>
ΔlnELEC
35.777* 0.000
Δ ENER–>
ΔlnGHG
3.457 0.326 ΔlnGHG–>Δ
ENER
139.3862* 0.000
ΔlnGDP–>
ΔlnME
601.553* 0000 ΔlnME–>
ΔlnGDP
17.4511* 0.0006
ΔlnCPI–>
ΔlnME
48.002* 0.000 ΔlnME–>
ΔlnCPI
41.7211* 0.000
ΔlnELEC–>
ΔlnME
192.425* 0.000 ΔlnME–>
ΔlnELEC
1767.678* 0.000
Δ ENER–>
ΔlnME
107.495* 0.000 ΔlnME–>Δ
ENER
7.5274*** 0.0569
ΔlnGDP–>
ΔlnNO
477.053* 0.000 ΔlnNO–>
ΔlnGDP
99.367* 0.000
ΔlnCPI–>
ΔlnNO
519.401* 0.000 ΔlnNO–>
ΔlnCPI
23.4025* 0.000
ΔlnELEC–>
ΔlnNO
182.520* 0.000 ΔlnNO–>
ΔlnELEC
58.355* 0.000
Δ ENER–>
ΔlnNO
497.654* 0.000 ΔlnNO–>Δ
ENER
2.71734 0.3766
Note: * and *** show signicance at 1% and 10% levels respectively.
Table 17
VECM cranger-causality analysis.
Chi-sq Prob. Chi-sq Prob.
ΔlnGDP–>
ΔlnCO
2
0.196447 0.6576 ΔlnCO
2
>
ΔlnGDP
0.171449 0.6788
ΔlnCPI–>
ΔlnCO
2
0.166898 0.6829 ΔlnCO
2
>
ΔlnCPI
0.157851 0.6911
ΔlnELEC–>
ΔlnCO
2
0.235059 0.6278 ΔlnCO
2
>
ΔlnELEC
2.977281*** 0.0844
ΔlnENER–>
ΔlnCO
2
0.848049 0.3571 ΔlnCO
2
>
ΔlnENER
2.274734 0.1315
ΔlnGDP–>
ΔlnGHG
7.538323 0.0054 ΔlnGHG–>
ΔlnGDP
0.193018 0.6604
ΔlnCPI–>
ΔlnGHG
7.754302** 0.0303 ΔlnGHG–>
ΔlnCPI
0.326307 0.5678
ΔlnELEC–>
ΔlnGHG
4.692768** 0.0014 ΔlnGHG–>
ΔlnELEC
0.012261 0.9118
ΔlnENER–>
ΔlnGHG
10.18380* 0.0060 ΔlnGHG–>
ΔlnENER
0.031283 0.8596
ΔlnGDP–>
ΔlnME
0.470113 0.4929 ΔlnME–>
ΔlnGDP
0.503590 0.4779
ΔlnCPI–>
ΔlnME
0.023975 0.8769 ΔlnME–>
ΔlnCPI
0.588699 0.4429
ΔlnELEC–>
ΔlnME
3.784971*** 0.0517 ΔlnME–>
ΔlnELEC
0.013635 0.9070
ΔlnENER–>
ΔlnME
0.243587 0.6216 ΔlnME–>
ΔlnENER
0.002258 0.9621
ΔlnGDP–>
ΔlnNO
0.195136 0.6587 ΔlnNO–>
ΔlnGDP
0.030634 0.8611
ΔlnCPI–>
ΔlnNO
1.999195 0.1574 ΔlnNO–>
ΔlnCPI
0.011271 0.9155
ΔlnELEC–>
ΔlnNO
0.590979 0.4420 ΔlnNO–>
ΔlnELEC
0.063616 0.8009
ΔlnENER–>
ΔlnNO
14.88132* 0.0001 ΔlnNO–>
ΔlnENER
0.781769 0.3766
Note: *, ** and *** show signicance at 1%, 5% and 10% levels respectively.
A. Abulibdeh
Energy Policy 168 (2022) 113089
15
Figs. 811 display the CUSUM plots for the four models. These gures
illustrate that the ARDL parameters in all the models are stable at the 5%
signicance level. The graphical plots of CUSUM examine the stability of
the short- and long-term estimates over time. The gure indicates that
the estimated coefcients lie between the upper and lower critical
bounds at a 5% signicance level.
Comparing the outcomes of this research with the outcomes of other
studies reveals the congruence and similarity with some ndings and
inconsistency with others. On the national scale, few studies investi-
gated the factors that may degrade the environmental quality in Qatar.
Salahuddin and Gow (2014) investigated the effects of energy con-
sumption, nancial development, economic growth, and foreign direct
investment on environmental quality in the country. They conclude that
energy consumption have an injurious long-term effects on the in-
dicators of environmental quality, which aligns with the ndings of this
research. Mrabet and Alsamara (2017) examined the effect of energy
use, real gross domestic product, the square of real gross domestic
product, the trade openness, and the nancial development on the CO
2
and ecological footprint. When we use the CO
2
emissions, the found that
there is a long-term relationship among the variables and that the
inverted U-shaped hypothesis is not valid. Charfeddine (2017) also
investigate the factors that contribute to environmental degradation in
Qatar. The study used trade openness, economic growth, urbanization,
and energy consumption as indicators of environmental degradation.
They found that EKC hypothesis holds for the CO
2
emissions under the
condition of controlling for breaks and that the electricity consumption
is negatively correlated to CO
2
emissions and to Ecological Carbon
Footprint. On the regional level, Zmami and Ben-Salha (2020) studied
the environmental degradation in the GCC countries. They investigate
the impact of energy consumption, foreign direct investments, urbani-
zation, per capita GDP, and international trade on CO
2
emissions. They
conclude that energy consumption has a negative impact on environ-
mental degradation and that energy consumption is the most variable
that effect environmental degradation on the short-term. Magazzino
(Magazzino) investigate the impact of energy use and economic growth
on CO
2
emissions in the Middle East countries. The study found a
negative correlation between economic growth and CO
2
emissions and a
positive correlation between energy use and CO
2
emissions. Salahuddin
and Gow (El-Montasser and Ben-Salha, 2019) found a positive and sig-
nicant relation between CO2 emissions and energy consumption as
well as between energy consumption and economic growth in the GCC
countries both in the short- and the long-run. They also found no sig-
nicant relation between CO2 emissions and economic growth in this
region.
6. Conclusion and policy implications and recommendations
The aim of this study was to examine the effects of economic growth,
electricity consumption, energy consumption, and the crop production
index on different types of GHG emissions, including CO
2
, methane,
nitrogen oxide, and other types of GHG gases, using time series data for
the State of Qatar between 1990 and 2019. The results from the ARDL
technique illustrate that electricity consumption, energy consumption,
and the crop production index have a positive and signicant relation-
ship in the long term. The VECM Cranger and TodaYamamoto causality
tests identify different causality relationships between the variables. It is
critical to understand the causal relationship (U-shaped or inverted U-
Fig. 8. The plot of the cumulative sum of recursive residuals of the rst
model (lnCO
2
).
Fig. 9. The plot of the cumulative sum of recursive residuals of the second
model (lnGHG).
Fig. 10. The plot of the cumulative sum of recursive residuals of the third
model (lnME).
Fig. 11. The plot of the cumulative sum of recursive residuals of the fourth
model (lnNO).
A. Abulibdeh
Energy Policy 168 (2022) 113089
16
shaped) between the variables considered in this study to formulate
effective environmental policies and strategies to reduce environmental
degradation in the country. Several key policy implications can be
derived from the ndings of this research to sustain environmental
quality in Qatar. The existence of an inverted U-shape between GDP and
CO
2
, ME, and GHGs indicates that Qatars policy to reduce GHG emis-
sions must continue to consider environmental factors. This negative
relationship implies that the high economic growth achieved thus far in
the country is insufcient to achieve a sustainable reduction in per
capita GHG emissions. This further implies that Qatar may witness a
sustainable increase in GHG emissions in the long term but with a high
cost associated with the negative externalities on the economy. Never-
theless, since the 2000s, Qatar has started a signicant long-term eco-
nomic development diversication plan aimed at diversifying its
economy by involving more sectors, such as the industrial and service
sectors, rather than depending on the oil and natural gas sector alone.
These ndings illustrate the challenges for Qatar in pursuing an
energy conservation policy in the time of enormous growing energy
demand in the country and globally. Qatar depends on fossil fuel sources
to generate its growing needs from electricity; hence, there is a need for
the country to seek alternative sources of electricity generation, such as
renewable energy sources associated with electricity generation ef-
ciency, other potential mitigation measures, and additional resources
and logistics to reduce the GHG emissions in the country.
The ndings of this study are relevant to energy and environmental
experts and policymakers in Qatar. Energy and electricity production
and consumption are the main contributors to GHG emissions. The
electricity in Qatar is highly subsidized, which encourages massive and
sprawling consumption, as well as wasting electricity, and is considered
a substantial handicap to improving energy efciency and reducing
energy use. Therefore, the country needs to increase its efforts to
rationalize its electricity consumption to reduce the social and envi-
ronmental costs of a highly subsidized policy. In this sense, achieving
electricity use efciency is essential for reducing consumption and,
hence, emissions from this sector.
The authorities must promote and rely more on renewable energy
sources as clean and green alternatives to traditional energy sources.
Qatar is exceptionally rich in renewable resources owing to its
geographical location and the abundant natural resources available to
generate electricity, such as solar and wind resources, which can
signicantly help improve the quality of the environment. The country is
characterized by its high average daily irradiation and ambient tem-
peratures and is rated excellent in terms of solar energy. Qatar can also
accelerate the development of a cleaner energy sector to sustain long-
term economic growth and environmental protection, as well as
reduce the amount of GHGs emitted from oil and gas production. Water-
and electricity-subsidized tariff systems should be revised, because they
are a considerable obstacle to promoting renewable energy. Therefore,
the country should follow a comprehensive strategy that encourages
investments in environmentally friendly ecosystems and innovative
planning in a green economy.
Key policies may include encouraging technological innovation and
development as well as further investments in research and development
on developing low-carbon technologies and renewable sources of en-
ergy, which could be useful in reducing GHG emissions without any
detrimental effects on Qatars economic growth. Furthermore, the
transport sector in the country has expanded rapidly over the last
decade, driven by population growth, rapid urbanization, and the
preparation for the FIFA 2022 World Cup; therefore, this sector is
responsible for a substantial proportion of emissions. However, emis-
sions from this sector cannot be eliminated for countries where cars are
essential for commuting, particularly in hot weather. Qatari policy-
makers aimed to spread awareness among the Qatari population
regarding the negative effects of environmental degradation on the
Qatari economy, quality of life, and health. Therefore, there is a need to
conduct additional campaigns to increase awareness.
The heavy reliance on the conventional sources of energy in the
country results in increasing economic and environmental costs (Char-
feddine et al., 2018). Therefore, as part of its vision statement, Qatar
consider the environmental objectives and the promotion of environ-
mental stewardship and alternative sources of energy as one of its top
priorities. The country has the opportunity to reduce carbon emissions
and develop strategies and technologies that can play a major part in
achieving global emissions-reduction targets without a major structural
change to its economy. These opportunities mainly depend on improved
management systems in using renewable energy, adoption new tech-
nologies, and the shift to zero-carbon energy systems. Through the
transition to zero-carbon energy technologies and systems, Qatar has the
ability to apply best practice in energy efciency, reduce its
carbon-emission prole at low net cost, and to serve as a platform for
global development of zero-carbon energy technologies. Despite the
increase on energy demand in Qatar, the country is working on reducing
its carbon emission. This ambition gives the country the desire to adapt
and develop new energy technologies and strategies that will provide a
source of long-term economic growth. Therefore, the development of
policies that encourage the transformation to and integration of
zero-carbon energy systems and technologies enable Qatar to meet its
own economic and environmental objectives and stay at the center of
energy economy. However, the transition toward zero-carbon emissions
is a very challenging issue since it involves the interaction between a
large set of factors affecting both energy demand side and supply side.
The identication and analysis of these factors is particularly important
because the transition to zero-carbon emissions cannot be implemented
through large and centralized zero-carbon energy projects.
Qatar is also testing the idea of sustainable communities The
Msheireb Downtown Doha district is under construction to be tted with
solar panels, solar water heaters and overhangs designed to shade the
surrounding sidewalks. While these pioneering initiatives are encour-
aging, the problem of zero-carbon transition remains very challenging
and requiring transformational changes at larger societal levels. Except
for few, these projects focus on capacity building of clean energy pro-
duction, including community level shared energy assets. The Stock-
holm project seems to be the only which is taking a more comprehensive
and integrated approach, e.g., managing waste, Electric Vehicles (EVs)
and energy all together. There are a number of commonly made as-
sumptions that can potentially distort results and outcomes for typical
large-scale projects. The usual cost and benet models relay on sta-
tionary data on technology and demand without much provisions for
drastic behavioral, demand or other external changes. Furthermore, a
common assumption for lifecycle assessment is that the installed system
will be used at around its optimal operational parameter. For example, a
smart building design will always stay smart irrespective of how its
occupants behave or use it. Furthermore, in many of the existing ini-
tiatives, year 2050 targets are set to 50% or slightly more renewable
energy production at city levels. It is not clear how these projects tend to
deal with the Demand Side Management (DSM) and changing social
landscape over time. Neither it is clear how these projects take into
account carbon footprints from other sectors of the economy.
CRediT authorship contribution statement
Ammar Abulibdeh: Conceptualization, Resources, Formal analysis,
Methodology, Writing original draft, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
A. Abulibdeh
Energy Policy 168 (2022) 113089
17
Acknowledgement
This publication was made possible by an NPRP award [NPRP13S-
0206200272] from the Qatar National Research Fund (a member of
Qatar Foundation). The statements made herein are solely the re-
sponsibility of the authors. The open access publication of this article
was funded by the Qatar National Library (QNL).
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