2. quantify major macro-level social, economic, and environmental in-
dicators, and to establish strategies based on these quantified indicators
to determine optimal solutions is crucial to solving transportation sus-
tainability problems [10,11].
1.1. Integrated life cycle sustainability assessment framework
The traditional LCA method has been transformed into the Life
Cycle Sustainability Assessment (LCSA) where socio-economic aspects
are considered in addition to the environment. In this method, en-
vironmental life cycle assessment (LCA), life cycle cost assessment
(LCC), and social life cycle assessment (S-LCA) are used separately to
assess the three dimensions of sustainability [12]. However, there is a
great effort has been put into improving the methodological aspects of
LCSA to integrate the three pillars of sustainability into a single di-
mensioned-LCSA framework [13]. However, the concept of LCSA is still
relatively new, and the majority of studies addressed only methodolo-
gical or conceptual aspects of LCSA, while the use of LCSA in trans-
portation applications or real case studies is highly limited [14].
LCSA is an integrative framework for sustainability assessment, and
thereby the opportunity of integrating complementary tools and ap-
proaches with the LCSA framework for improving the LCSA applic-
ability is truly huge [15,16]. Some of these approaches that can be
integrated into LCSA to enhance the practical applications of LCSA are
input–output analysis, multi-region input–output analysis (MRIO),
multi-criteria decision- making (MCDM) tools, and system dynamics
modeling [14]. MRIO models are globally-extended version of the
single region input–output modeling approach and hence are eligible
for capturing the entire global supply chain-related impacts [17]. MRIO
models are highly used to estimate the regional and global environ-
mental impacts, however, a combination of MRIO with LCSA is still
rare.
There are several studies found in the literature that studied the
sustainability assessment of electric vehicle technologies. For example,
Aboushaqrah et al. [18] and Onat et al. [19,20] built a hybrid MRIO
model for LCSA of sport utility and sedan cars and assessed the regional
and global life cycle environmental, economic and social impacts of
electric mobility for the State of Qatar. Onat et al. [21] built a com-
prehensive LCSA model to calculate the impacts of 19 macro-level in-
dicators from the environmental and socio-economic perspectives for
the alternative passenger vehicle technologies in the United States.
Various studies applied single region input–output models for alter-
native personal vehicles in the United States to calculate a set of impact
indicators of environmental, social, and economic sustainability [19,22-
25]. In other work, the researchers [24] used an integrated dynamic
LCSA model to quantify environmental, economic and social sustain-
ability impacts (termed as the triple bottom line, TBL) of alternative
vehicles in the U.S. Onat et al. [24] have studied the dynamic re-
lationships between the sustainability indicators involved through
using system dynamics modeling for the case of electric vehicles in the
US. In another study, an uncertainty-embedded dynamic LCSA was
developed by Onat et al. [23]. The researchers addressed some meth-
odological challenges and uncertainties associated with the life cycle
sustainability assessment of electric vehicle technologies. Gemechu
et al. [26] and Hawkins et al. [27] applied a process-based LCA method
for an electric vehicle, which provides a current, transparent, and de-
tailed life cycle inventory data for battery electric vehicles.
1.2. Multi-criteria decision making
MCDM is a well-known branch of operations research in decision
making that concerned with solving decision problems involving the
selection of best alternatives from a set of potential candidates, subject
to several, usually conflicting decision criteria [28]. In literature,
MCDM is typically categorized into two types: Multi-Attribute Decision
Making (MADM) and Multi-Objective Decision Making (MODM). The
primary goal in these approaches is to select the best alternative(s)
across multiple criteria by considering a predefined set of preference
priorities. In general, MADM models are intended to solve problems
involving selection from a predetermined limited number of alter-
natives [28]. Whereas, the focus of MODM models is on design rather
than selection, and these models are employed to solve problems with
an infinitive number of alternatives, by considering a set of pre-
determined design constraints and preferences [28].
There are several MCDM techniques used in the literature such as
Multi-Attribute Utility Theory, Fuzzy Set Theory, Simple Additive
Weighting, Data Envelopment Analysis, ELECTRE, Analytic Hierarchy
Process, PROMETHEE, Case-based Reasoning, Simple Multi-Attribute
Rating Technique, Goal Programming, Compromise Programming, and
Technique for Order of Preference by Similarity to Ideal Solution. These
MCDM techniques have been employed in many fields including, fi-
nance [29], supplier selection [30,31], infrastructures [32], sustainable
energy planning [33], and environmental decision making [34].
Some papers found in the literature presented MCDM models for
vehicles. For example [35], developed an MCDM model to rank mul-
tiple fuel-based vehicles (both renewable and non-renewable) con-
sidering numerous environmental, social, and economic factors. Do-
nateo et al. [36] applied a two-step multi-objective optimization
method for the initial design of a hybrid electric vehicle. Furthermore,
Traut et al. [37] constructed a hybrid LCA model and developed an
optimization model to investigate the optimum designs for alternative
electric vehicles that yield the least life cycle cost and minimum
greenhouse gas emissions (GHG), selected as conflicting objectives. On
the other hand, none of these studies did consider the TBL impacts of
vehicles from the LCSA perspective. In other words, joint applications
of MODM and LCSA for the assessment and selection of electric vehicles
are very rare in the literature. For instance [25], used a combination of
LCSA with multi-objective optimization to determine the optimal mix of
alternative vehicles in the U.S, and Onat et al. [38] applied in-
tuitionistic fuzzy set approaches to LCSA to rank the alternative vehicle
technologies.
1.3. Novelty statement
This paper presents an integrated LCSA framework to cover the
three dimensions of sustainability. The proposed multi-region in-
put–output analysis-based LCSA method has several novel elements as
(1) quantifying macro-level social, economic and environmental im-
pacts concerning the regional and global supply chains by applying a
hybrid multi-region input–output analysis method, (2) incorporating
not only environmental impacts but also several macro-level social
(human health impacts, employment, tax, and compensation) and
economic dimensions (life cycle cost, GDP, and operating surplus) of
sustainability into sustainability assessment of alternative electric ve-
hicle technologies, and (3) designing an integrated sustainability deci-
sion-making framework in which MODM model is developed to de-
termine the optimum vehicle distribution for countries, by taking into
consideration conflicting sustainability objectives and changing
weights of selected indicators. This optimization model is built based
upon the MRIO-LCSA results obtained for each of the studied vehicle
alternatives for the two analyzed charging scenarios. Various optimal
distributions are presented based on varied weights allocated to each
impact category. Furthermore, a set of weighting cases is considered to
reflect the priorities of sustainability indicators involved from the de-
cision-makers point of view.
LCSA analysis is an important method for sustainable transportation
research and therefore the integration of LCSA of alternative vehicle
technologies with MCDM would be a significant contribution to the
literature by optimizing the mix of these vehicles based on a different
set of environmental and socio-economic priorities. To this end, this
paper presented an integrated sustainability assessment and modeling
framework to evaluate multiple and conflicting environmental and
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
2
3. socio-economic objectives of each of the quantified impact categories to
optimize the distribution of alternative vehicles.
2. Case study
According to the Global Footprint Network’s Database, which
compares the ecological footprint of countries, Qatar has the highest
carbon and ecological footprints per capita which is followed by other
wealthy nations such as Luxembourg, United Arab Emirates, Bahrain,
and Kuwait [41]. This is mainly because Qatar has a growing oil and
gas sector and is the largest Liquefied Natural Gas (LNG) producer
worldwide [42]. In addition, the total energy consumption of Qatar is
generated almost entirely from the natural gas, as per the World Bank
collection of development indicators [43], and thus considerably con-
tributing to CO2 emissions. According to the World Health Organiza-
tion, Doha is among the most air-polluted cities in the world, ranked in
12th place in terms of the highest average level of particulate matter
formation (PM2.5). Moreover, personal vehicles are the most important
and commonly used mode of transport for Qataris and the total con-
tribution of the transport sector to the air pollution in Qatar is ex-
tremely high [44].
In this research, Qatar is selected as a case study to implement the
proposed method. Qatar is selected since the country has launched a
“Green Car” initiative which sets an official goal of a 10% market pe-
netration for electric vehicles by 2030 [39]. The Qatar General Elec-
tricity and Water Corporation, has announced a tender to create a large-
scale solar photovoltaic power plant with 350 MW as a first stage [45],
in conjunction with launching a 10% green vehicle initiative. The aim
behind such initiatives is to reduce the carbon footprint of Qatar, en-
courage the use of eco-friendly cars, diversify the sources of electricity
production, and to create a balance between economic growth and
environmental protection to achieve a cleaner, healthier, and more
sustainable environment. The initiative came as part of Qatar’s National
Vision 2030, which aims to strike a balance in the country’s accom-
plishments in terms of environmental protection, social development,
and economic growth [40].
3. Method
A combination of MCDM with the LCSA framework is used to ad-
vance the current literature for the sustainability impact assessment of
alternative passenger vehicles. The step-by-step research method is
presented in Fig. 1 and explained as follows:
1. First, the vehicle alternatives and indicators are selected. The scope
of analysis and the system boundary is also defined in the first step.
2. Second, the hybrid MRIO-LCSA model is built to quantify and ana-
lyze fourteen sustainability indicators representing three pillars of
sustainability as environment, economy, and society.
3. Third, the life cycle inventory with corresponding data sources re-
lated to alternative vehicle technologies for the operation life cycle
phase is given.
4. Fourth, the sustainability impacts of alternative vehicles are calcu-
lated and compared for two different scenarios. The multi-objective
problem involving a set of conflicting environmental and socio-
economic objectives is formulated as the MODM model, namely,
compromise programming to optimize the distribution of the alter-
native SUV technologies.
5. Fifth, the model is solved using the LINGO optimization software.
The optimal vehicle distribution is then estimated based on the
quantified life-cycle impacts of each vehicle type, taking into con-
sideration the conflicting objectives of each impact category and the
weights (relative importance, ranging between 0 and 1) assigned to
each objective.
6. Finally, the findings are analyzed, and vehicle technologies are
compared.
Two charging scenarios are considered in this analysis. Scenario 1 is
where electricity to power electric vehicles (PHEVs and BEVs) gener-
ated from natural gas, and Scenario 2 is where electricity to operate
PHEVs and BEVs generated exclusively from solar energy. Fourteen
sustainability indicators are quantified for four different SUV type ve-
hicle options (ICV, HEV, PHEV, and BEV) based on two charging sce-
narios; 1) average electricity generation mix and 2) 100% solar char-
ging. In each scenario, the results are obtained for two different analysis
boundaries/scopes to show that the selection of boundary affects the
results at different degrees, depending on the system of interest. The
importance of boundary selection is emphasized in the literature [17].
Usually, traditional process-based LCA methods cannot track to impact
throughout the entire supply chain, which introduces truncation error
[46,47]. Hence, in this study, we considered both of the cases: Case A)
what-if our analysis boundary encompasses all supply-chain related
impacts (total impacts), and Case B) What-if our analysis boundary is
only limited to regional boundaries of Qatar.
3.1. Scope of analysis
This analysis covers the vehicle's operation phase due to its dom-
inance in terms of environmental impacts in comparison with other life
cycle phases, while phases such as vehicle manufacturing and disposal
are beyond the scope of this paper due to the insufficiency of data and
their relatively lower impacts [27,48]. Considering the fact that all
vehicles imported to Qatar are manufactured outside, determining the
impacts generated from the vehicle’s manufacturing phase is challen-
ging. Besides, no data is available on vehicles' disposal-related impacts
for the country, as vehicles at their end of life are directly sent to
landfills without recycling. On the other hand, all life-cycle-phases are
covered in the analysis of LCC. Fig. 2 presents the system boundary of
the analysis. The vehicle's operation phase encompasses the impacts
related to petroleum extraction and production, electricity generation,
transmission & distribution, and repair & maintenance. Two sub-phases
are typically addressed when quantifying the vehicles’ operation phase;
Well-to-Tank (WTT) and Tank-to-Well (TTW). WTT impacts encompass
the impacts of fuel power stations and its supply chains. In this study,
WTT sub-phase is calculated through using MRIO modeling and is di-
vided into three main components, 1) inside Qatar fuel supply (impacts
of petroleum extraction & production and electricity generation at
power plant inside Qatar), 2) inside Qatar sectors (all impacts inside the
regional boundaries of Qatar excluding fuel supply), and 3) outside
Qatar sectors (all impacts take place in the global supply chains). While
TTW refers to the direct impacts (tailpipe emissions) which occur
during vehicle travel due to petroleum combustion. Four SUVs of dif-
ferent types and technologies are analyzed: Toyota Land Cruiser (ICV),
Lexus (HEV), BMW (PHEV), and Tesla (BEV). SUVs are used heavily in
Qatar as passenger vehicles; therefore, the analysis focuses on evalu-
ating the impacts and optimizing the distribution of SUVs technologies.
Further technical information on these vehicle technologies is provided
in Table S1 in the Supplementary Information (SI) File. 14 macro-level
indicators of environmental, social, and economic impacts are selected
for this sustainability assessment. Table S2 in the SI outlines the no-
minated indicators with a clear and concise description of each. The
functional unit of the assessment is defined as one kilometer (km) of
vehicle travel. As shown in Fig. 1, each vehicle technology is re-
presented by different colors, and the arrows denote the relationship
between the vehicle technologies and the corresponding life cycle
processes. For example, the electricity supply is calculated for electric
vehicles only; BEV and PHEV, while the gasoline supply process is re-
lated to ICV, HEV, and PHEV.
3.2. Hybrid MRIO Modeling/Life- cycle inventory
The direct and indirect impacts for gasoline production and elec-
tricity generation for the vehicle's operation phase are calculated using
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
3
4. the MRIO-LCSA model, due to its eligibility to quantify the life cycle
sustainability impacts associated with the supply chains of these sectors
at a global scale.
As discussed before, the vehicle's operation phase encompasses two
main sub-phases, WTT and TTW. In WTT analysis, the impacts asso-
ciated with the gasoline and electricity supplies are covered. The im-
pacts of WTT are analyzed in terms of the following three components,
1) inside Qatar fuel power stations (petroleum and electricity
Fig. 1. The research flow chart.
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
4
5. generation), 2) other sectors inside Qatar, and 3) all other sectors
outside Qatar.
The upstream impacts related to gasoline production is calculated
for ICVs, HEVs, and the gasoline mode of PHEVs through using the
impact factors obtained from the MRIO model, using a global MRIO
database, EXIOBASE 3.4. Using this database, we calculated the up-
stream impacts of gasoline production, and electricity generation from
power plants, and their supply chains both inside and outside Qatar.
A symmetric industry-by-industry input–output table at basic prices
and associate economic transactions for world economies including the
Middle East as a region is obtained from the EXIOBASE 3.4 database.
The database includes 43 countries, 5 rest-of-the-world regions, and
163 industries, covering almost the entire world’s economy [49]. EXI-
OBASE’s MRIO datasets are constructed using the Supply and Use Ta-
bles at current prices with a fixed product sales assumption and consist
of national and global IO tables and obtain as raw data from the UN's
System of National Accounts, Comtrade, and Eurostat databases [50].
Social, economic and environmental impacts are estimated by multi-
plying the output of each sector by its impact category per economic
output [51]. For further details about developing a symmetric industry-
by-industry MRIO model, please refer to [50]. In this paper, a global
MRIO model has been used to quantify the upstream life cycle sus-
tainability impacts of electric vehicles in Qatar. In the presented model,
input–output tables show monetary flows in between industries, in
other words, inputs, and outputs, within an economy, using the Leon-
tief's inverse formula:
=
x I A y
( ) 1
(1)
In Eq. (1), an output vector, x is defined as a function of I, A, and y,
where y is the column vector of total demand (in M.Eur). I is the
identity matrix and × is the column vector of total output (in M.Eur)
and A is the input–output coefficient matrix (in M.Eur/M.Eur). The
expression I A
( ) 1
is defined as the Leontief inverse, also denoted as
capital L indicating the total requirements matrix.
Using the total requirement matrix and sector and country-specific
environmental satellite accounts obtained from the EXIOBASE 3.4 (such
as energy use, water consumption, carbon emissions, resource use, etc.)
and socioeconomic accounts (such as tax, employment, income, value-
added, etc.), a unit of output of a particular sector as well as indirect
impacts stemming from the international supply chains of the industry
are calculated. In Eq. (2), a vector of environmental, economic and
social impacts generated by each industry (sector and country-specific
environmental satellite accounts) per unit of economic output (M. Euro)
is represented by B as follows:
=
B E diag x
( ( )) 1
(2)
where we can denote the totals with x (in M. Euro) and the satellite
accounts with the letter E. Therefore, B is the matrix of intensities in
terms of per M. Euro. With diagonal, I indicate that the vector × has to
be diagonalized. By multiplying B, L, and y, Eq. (3) can be constructed
as follows;
=
r BLy (3)
In this equation, r vector is calculated by multiplying L by B (in-
tensity matrix per unit of output), and further multiplying by y which
represents the total output of each sector (final output vector). By using
Eq. (3), vector quantifies the direct plus indirect social, economic and
environmental impacts sectors. To this end, the Eq. (3) allows us to
keep tracking the sustainability impacts through the regional and in-
ternational supply chains. To perform all matrix operations, a Python
programming language is used to compute and process big matrix data
and obtain sectorial multiplier for sectors such as petroleum produc-
tion, electricity production from natural gas. The model later is hy-
bridized to calculate sustainability impacts from both upstream and
tailpipe emissions. For instance, upstream (regional and global supply
chain-related) carbon footprints and air pollutants such as PM10 from
Fig. 2. System boundary of the analysis.
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
5
6. electricity production in Qatar are calculated using the developed MRIO
model and then tailpipe emissions from internal combustions vehicle
are added to these sectoral emissions to calculate both upstream and
direct tailpipe emissions from vehicles. For a more detailed methodo-
logical explanation for developing a hybrid life cycle inventories for
electric vehicles, please refer to [10].
Tables S3–S5 in the SI present the upstream impact factors for the
production of a liter of gasoline, generating electricity from natural gas,
and for generating electricity from solar energy, respectively. Ad-
ditionally, the fuel efficiency (FE) values for each vehicle are presented
in Table 1. These values represent the fuel requirement, i.e., the amount
of gasoline or kWh of electricity required to travel one km.
The total impact (/km) related to gasoline production is the result of
the multiplication of the FE of each vehicle (L/km) and the corre-
sponding upstream impact factor. The electricity generation sector is
the second major part of the analysis of WTT related impacts. BEVs and
the electric mode of PHEVs consume electricity. The environmental
impacts of electric vehicles depend to a large extent on the source(s) of
electric power generation. It is important to note that natural gas is the
only energy source available to generate electricity in Qatar [52]. The
total impact associated with the electricity generation sector results
from the multiplication of the FE and the associated impact factors per
kWh of electricity generated by natural gas obtained from the MRIO
model, given in the SI in Table S4. The equation used for calculating the
total WTT impact per km is presented in the SI (Eq. (S1)).
The total WTT impact for ICVs, HEVs, and BEVs can be calculated
using Eq. (S1), given in the SI, however, the impacts associated with
PHEVs are calculated differently, as they operate in two different
modes: gasoline mode and electric mode. Since PHEVs are capable to
operate in two different modes, identifying the portions of total vehicle
km traveled in each mode is essential for evaluating their impacts. The
daily rate of traveled distance in electric mode is defined as Utility
Factor (UF). The UF for the analyzed PHEV-22 km in Qatar is found to
be 35%. The PHEVs related to WTT impacts are determined using Eq.
(S2), presented in the SI.
There are two scenarios in which electricity to operate electric ve-
hicles is generated from only solar energy, the upstream impacts oc-
curring as a result of generating one kWh of electricity by solar energy
is estimated through using the upstream impact factors to generate per
kWh of electricity from solar energy gained from the MRIO model,
introduced in Table S5 in the SI. On the other hand, in TTW analysis,
the impacts that are directly associated with the quantity of gasoline
combusted during a km of a travel (tailpipe emissions) are calculated
for ICVs, HEVs, and the gasoline mode of PHEVs. It should be noted that
BEVs and PHEVs in electricity mode do not release tailpipe emissions.
The tailpipe emissions occurring due to the burning of a liter of gasoline
within the vehicle’s operation are obtained from the Greenhouse Gases,
Regulated Emissions, and Energy Use in Transportation (GREET)
model. Table S3 in the SI also presents the tailpipe emission factors,
which are later multiplied by the FE of each vehicle type to calculate
their respective TTW impacts. The calculations of TTW impacts gen-
erated from alternative vehicles are performed using Eqs. (S3) and (S4),
shown in the SI.
3.3. Life cycle costing
Life cycle cost (LCC) is an economic tool directed at all costs related
to purchasing, operating, maintaining as well as the end of life costs of a
product, project or, service over a defined period. LCC is a method used
for estimating the total cost of ownership over the life span of an asset,
and it is widely used to identify the most cost-effective option among
different alternatives. LCC technique can help make decisions for var-
ious purposes such as evaluating and comparing among competing al-
ternatives, trading off between options, as well as estimating total costs
in the early stage of a project to assess its economic feasibility. In LCC
analysis, four cost elements are considered: initial costs, annual fuel
costs, maintenance costs, and insurance costs. The main assumptions
for this analysis are explained in the SI. LCC analysis is performed as
follows: First, the vehicle useful life and the costs of each cost element
are estimated for each vehicle type, in addition, basic economic factors
are incorporated into the analysis. Finally, the analysis results are
evaluated and compared, and the vehicle alternative with the least LCC
is selected. The analysis aims to identify the most economical vehicle
option over the estimated life span based on a present worth analysis.
Table 2 summarizes the cost elements considered in this LCC analysis.
3.4. Multi-objective optimization model
After quantifying the sustainability indicators, the conflicting en-
vironmental and socio-economic objectives are identified. MODM is
crucial to finding the optimal mix of alternatives that gives the best
feasible values for all objectives. Compromise programming approach is
commonly used for formulating multi-objective linear, nonlinear or
integer programming problems. Hence, a compromise-programming
model is developed in this study to optimize the selected multiple
sustainability objectives based on a set of environmental and socio-
economic priorities.
The basic concept of compromise programming technique was in-
itiated by Zeleny (1973) and is aimed to find out a set of solutions
closest to the ideal solution by some measure of distance. Compromise
solution set is a subset chosen from the non-dominated solution set by
employing a distance function to measure the degree of closeness of
these solutions to the ideal point.
The distance-based function is given in Eq. (4), where La is a dis-
tance metric defines the distance between two points, Zk* and Zk(x),
indicating the extent to which solutions are close to the ideal solution
[53]
Table 1
Fuel efficiency of alternative vehicle technologies [19].
Fuel Efficiencies ICV HEV PHEV-AER 22 km BEV
Electricity
mode
Gasoline
mode
Liter Per Km (L/
100 km)
14.5 7.84 37.4* 9.8 22.5*
Miles per gallon (MPG) 16.24 30 56** 24 93**
*kWh/100 km, **MPG equivalent.
Table 2
Summary of the cost elements for each vehicle technology for LCC Analysis.
Life cycle costs ICV HEV PHEV BEV
Initial/Purchase Price (QR and $USD) 259,000 QR ($69,930) 199,344 QR
($53,823)
232,145 QR
($62,679)
289,499 QR
($78,165)
Fuel Usage (L/100 km) 14.5 Lt 7.84 Lt 37.4 kwh electricity
9.8 L gasoline
22.5 kwh
Annual Fuel Costs (QR/Year) ($USD/year) 6,376.8 QR ($1721) 3448.3 QR ($931) 3048.9 QR ($823) 421.2 QR ($113.7)
Average Maintenance Costs (QR/Year) ($USD/year) 283.1 QR ($76) 374.4 QR ($101) 728.2 QR ($749) 758.9 QR ($203)
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
6
7. =
=
L Z X Z X
( ( ) ( ))
a
k
P
k
a
k k
a
1
a
1
(4)
where “a” is a distance parameter ranges between 1 and ∞, “п” denotes
the weight assigned to each objective function, “P” is the number of
objective functions k, “Z*k” is the ideal solution for objective k, and “Zk
(x)” is a function of objective k.
Before optimizing the use of alternative vehicles, normalization is
required as each objective function has its specific unit that is different
from the other. After normalization, the values will range between 0
and 1. The normalization function is shown in Eq. (5) as below [53]:
=
Z
Z X Z X
Z X
( ) ( )
( )
k k
k (5)
Below is the compromise programming equation obtained after
normalization:
Table 3
TBL impacts of vehicle types per km.
TBL indicators Scenario ICV HEV PHEV BEV
GWP Scenario 1 A 3.551E+02 1.920E+02 1.962E+02 6.918E+01
Scenario 1B 3.527E+02 1.907E+02 1.951E+02 6.896E+01
Scenario 2 A 3.551E+02 1.920E+02 1.564E+02 7.701E−01
Scenario 2B 3.527E+02 1.907E+02 1.551E+02 1.991E−01
PMF Scenario 1 A 9.509E−02 5.142E−02 5.616E−02 2.472E−02
Scenario 1B 9.102E−02 4.921E−02 5.413E−02 2.432E−02
Scenario 2 A 9.509E−02 5.142E−02 4.255E−02 1.325E−03
Scenario 2B 9.102E−02 4.921E−02 4.008E−02 1.661E−04
POF Scenario 1 A 3.408E−01 1.843E−01 2.075E−01 9.924E−02
Scenario 1B 3.352E−01 1.812E−01 2.046E−01 9.861E−02
Scenario 2 A 3.408E−01 1.843E−01 1.511E−01 2.300E−03
Scenario 2B 3.352E−01 1.812E−01 1.476E−01 5.441E−04
Human Health Scenario 1 A 3.904E−07 2.111E−07 2.181E−07 8.000E−08
Scenario 1B 3.856E−07 2.085E−07 2.157E−07 7.954E−08
Scenario 2 A 3.904E−07 2.111E−07 1.724E−07 1.553E−09
Scenario 2B 3.856E−07 2.085E−07 1.696E−07 2.903E−10
GDP Scenario 1 A 2.538E−01 1.372E−01 1.308E−01 3.320E−02
Scenario 1B 2.435E−01 1.317E−01 1.256E−01 3.202E−02
Scenario 2 A 2.538E−01 1.372E−01 1.308E−01 3.320E−02
Scenario 2B 2.435E−01 1.317E−01 1.213E−01 2.462E−02
Total tax Scenario 1 A 5.882E−02 3.180E−02 3.143E−02 9.603E−03
Scenario 1B 5.768E−02 3.119E−02 3.085E−02 9.471E−03
Scenario 2 A 5.882E−02 3.180E−02 2.728E−02 2.478E−03
Scenario 2B 5.768E−02 3.119E−02 2.640E−02 1.816E−03
Compensation Scenario 1 A 4.244E−02 2.295E−02 2.523E−02 1.132E−02
Scenario 1B 3.749E−02 2.027E−02 2.274E−02 1.077E−02
Scenario 2 A 4.244E−02 2.295E−02 2.401E−02 9.219E−03
Scenario 2B 3.749E−02 2.027E−02 1.898E−02 4.318E−03
Operating surplus Scenario 1 A 1.526E−01 8.249E−02 7.417E−02 1.228E−02
Scenario 1B 1.484E−01 8.023E−02 7.204E−02 1.178E−02
Scenario 2 A 1.526E−01 8.249E−02 7.953E−02 2.150E−02
Scenario 2B 1.484E−01 8.023E−02 7.594E−02 1.848E−02
Employment Scenario 1 A 5.139E−10 2.779E−10 2.941E−10 1.174E−10
Scenario 1B 3.147E−10 1.701E−10 1.911E−10 9.089E−11
Scenario 2 A 5.139E−10 2.779E−10 3.045E−10 1.353E−10
Scenario 2B 3.147E−10 1.701E−10 1.593E−10 3.620E−11
Land use Scenario 1 A 5.333E−09 2.884E−09 3.139E−09 1.368E−09
Scenario 1B 6.695E−11 3.620E−11 5.651E−11 4.657E−11
Scenario 2 A 5.333E−09 2.884E−09 2.838E−09 8.506E−10
Scenario 2B 6.695E−11 3.620E−11 3.235E−11 5.043E−12
Energy inputs from nature Scenario 1 A 7.524E−06 4.068E−06 3.844E−06 9.255E−07
Scenario 1B 7.473E−06 4.040E−06 3.816E−06 9.166E−07
Scenario 2 A 7.524E−06 4.068E−06 3.422E−06 1.998E−07
Scenario 2B 7.473E−06 4.040E−06 3.390E−06 1.849E−07
Water consumption Scenario 1 A 8.705E−10 4.707E−10 6.441E−10 4.498E−10
Scenario 1B 2.437E−11 1.317E−11 8.167E−11 1.220E−10
Scenario 2 A 8.705E−10 4.707E−10 4.544E−10 1.238E−10
Scenario 2B 2.437E−11 1.317E−11 1.160E−11 1.544E−12
Water withdrawal Scenario 1 A 8.598E−10 4.649E−10 7.265E−09 1.184E−08
Scenario 1B 7.849E−10 4.244E−10 7.227E−09 1.183E−08
Scenario 2 A 8.598E−10 4.649E−10 3.867E−10 1.540E−11
Scenario 2B 7.849E−10 4.244E−10 3.458E−10 1.589E−12
LCC Scenario 1 A 1.437E+00 1.042E+00 1.145E+00 1.302E+00
Scenario 1B 1.437E+00 1.042E+00 1.145E+00 1.302E+00
Scenario 2 A 1.437E+00 1.042E+00 1.145E+00 1.302E+00
Scenario 2B 1.437E+00 1.042E+00 1.145E+00 1.302E+00
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
7
8. =
=
MinL Min
Z X Z X
Z X
( ) ( )
( )
a
k
P
k
a k k
k
a
1
a
1
(6)
Subject to
=
=
1, 0
k
P
k k
1 (7)
a
1 (8)
In Eq. (3), Z*k values represent the ideal solution for objective k
which can be found by independently optimizing each objective func-
tion. Also, the parameter p indicates the number of objectives, and πk
denotes the weights allocated for each objective function, representing
the priorities from the decision maker’s point of view. The proposed
model is formulated based on the distance-based compromise pro-
gramming function introduced in Eq. (6). The following model has been
proposed to optimize the use of alternative vehicles based on different
environmental and socio-economic priorities.
Indices:
• s: Sustainability indicator
• g: Type of vehicle technology
• v: Total number of vehicle technologies
• q: Total number of sustainability indicators
Parameters:
• Tsg: The environmental and socio-economic impact of vehicle g for
the sustainability indicator s
• Rs: The relative importance allocated to each sustainability indicator
s
Decision variables:
• Xsg: The allocation percentage of vehicle g for sustainability in-
dicator s
Objective function:
=
= =
MinZ X W T X
( )
s
s
q
g
v
s sg sg
1 1 (9)
Subject to
= =
=
X fors q
1 1, 2, 3,
g
v
sg
1 (10)
= =
X fors qandforg v
0 1, 2, 3, , 1, 2, 3, ,
sg (11)
As shown in the model, Eq. (9) represents the environmental and
socio-economic objective function, in which a total of 14 objective
functions are used. Zs(x) indicates the environmental and socio-eco-
nomic objective function. Tsg represents the environmental and socio-
economic potential impacts for sustainability indicator s for alternative
vehicle technology g. The TBL impacts associated with each vehicle
technology during the operation phase are presented in Table 3. Ws
indicates the weight of each environmental and socio-economic impact
indicator, and the total of Ws is 1. Furthermore, the total of Xsg is 1 (Eq.
(10)). The evaluation procedure is carried out as follows: the positive
socio-economic impact categories, total tax, operating surplus, GDP,
employment, and compensation are transformed into minimization
form, after that, all (fourteen) objective functions are minimized. Then,
every single objective function is optimized individually, and a pay-off
table is created to develop the distance function that is shown in Eq. (3).
This distance-based function gives solutions that are as close as possible
to the ideal solution in terms of weighted geometric distance, and is
specifically called the “Euclidean distance.”
The environmental objectives weights are taken from version 4.0 of
the BEES (Building for Economic and Environmental Sustainability)
software which was introduced by the National Institute of Standards
and Technology (NIST) [54]. The NIST developed this set of weights
through soliciting data from an assembled panel of volunteer stake-
holders. The environmental impacts weights and their associated cal-
culations are well presented and explained by Gloria et al. [55]. On the
other hand, there is no widely accepted methodology or “a set of
weights” for the relative importance of socio-economic indicators both
within socio-economic indicators (e.g. GPD versus employment) and as
opposed to the environmental indicators (socioeconomic indicators
versus environmental indicators) [56]. In this paper, we used a set of
weights obtained from Gloria et al. [55], which presented a compre-
hensive weighting schema based on the survey with a large and diverse
panel group. The National Institute of Standards and Technology (NIST)
introduced one of the most widely used expert judgment-based en-
vironmental weights in the BEES software, which is more appropriate
for selecting environmentally preferable and cost-effective building
products. In contrast to the weight sets currently presented by the NIST,
a comprehensive weighting study developed by Gloria et al. [55] used
the Analytic Hierarchy Process (AHP) as a pairwise comparison and
multi-criteria decision making method. In their study, the weight set
was created by a large and multi-stakeholder panel consisting of users,
producers and LCA experts. The weight set draws on each panelist’s
personal and professional understanding of each impact category. Since
there is no widely accepted “a set of standard weights” to determine the
relative importance of socio-economic indicators, in this study, human
health indicator (one of the socio-economic indicator) is assumed to
have relative importance of 60%, while the rest of socio-economic in-
dicators; total tax, GDP, operating surplus, employment, compensation,
and life cycle coast are assumed to have equal importance. Similar
weighting approach was also proposed in the literature [57,58].
Afterward, the compromise-programming model is combined with
the MRIO-LCSA results to optimize the multiple and conflicting en-
vironmental and socio-economic objective functions. Then the com-
promise-programming model is coded and solved by LINGO optimiza-
tion software. Finally, the optimal distribution of the alternative
vehicles is analyzed as presented in the following subsections.
4. Results and discussions
The following sub-sections present the analysis results of the sus-
tainability impact assessment and the compromise-programming
model.
4.1. Sustainability impact analysis
The impacts are classified into two categories: inside Qatar impacts
and outside Qatar impacts. Inside Qatar's impacts, refer to the impacts
that occur inside the regional boundaries of Qatar and are divided into
three main components; tailpipe emissions, inside Qatar fuel power
plants (petroleum and electricity supply), and other sectors inside Qatar
excluding the fuel power plants. While outside Qatar impacts refer to
the impacts that take place in the global supply chains of petroleum and
electricity supply sectors, i.e. impacts arising from these sectors outside
the regional boundaries of Qatar.
The resulting impacts of each environmental impact category for
each vehicle type are presented in Fig. 3. ICV has the largest environ-
mental impact in all selected environmental impact categories except
for the Water Withdrawal indicator. In Scenario 1, PHEV has the
second-largest environmental impact after ICV except for the energy
category. Besides, powering BEVs and PHEVs through electricity gen-
erated by solar energy significantly reduces their environmental im-
pacts. In Scenario 2, HEV has the second-largest contribution after ICV,
as opposed to Scenario 1. The environmental impacts of BEVs are
considerably lower than that of PHEVs in both scenarios except for
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
8
9. water withdrawal in scenario1, where BEVs have a higher contribution
to that impact category than PHEVs do have.
The GWP, PMF and POF emission impacts are shown in Fig. 3(a–c),
respectively for each vehicle type. Tailpipe impacts are found to be the
largest contributor to GWP, PMF, and POF emissions, for ICVs, HEVs,
and PHEVs in both scenarios, where ICVs produce the largest tailpipe
impacts and PHEVs emit the least. Most of the total impacts for these
vehicle types occur inside Qatar, and more specifically during driving
through tailpipe emissions. On the other hand, BEVs have zero tailpipe
impact, and as per scenario 1, the majority of BEV emissions are as-
sociated with the electricity generation power plant and very low
emissions occur in the other sectors inside and outside Qatar. On the
contrary to Scenario 1, in Scenario 2, when BEV is powered with solar
charging stations, outside Qatar sector dominates the total impacts,
while the fuel supply chain and inside Qatar sectors are found to have
an insignificant contribution. In scenario 1, BEVs have the highest
amount of GWP, PMF, and POF emissions related to the fuel supply, and
HEVs produce the least, whereas considerable low impacts are
generated from the fuel power plant for all vehicles in scenario 2.
Overall, BEVs charged by solar stations are better options than BEVs
powered with electricity generated from natural gas as the total impacts
of GWP, PMF and POF produced by the earlier option constitute around
1–5% of the total impacts produced by the latter option. According to
the results, inside Qatar boundaries related impacts, including fuel
supply chain impacts, tailpipe impacts, and impacts occurring in other
sectors inside Qatar, account for around 94–99% of the total impacts of
GWP, PMF and POF for all vehicle types including PHEV solar, while
when BEVs are powered with solar, global supply chain-related impacts
become the highest contributor to GWP, PMF, and POF, with around
74–87% of the total impact. This indicates that the majority of GWP,
PMF, and POF takes place inside the regional boundaries of Qatar for all
vehicle types, however, this is not the case when BEV is powered with
solar, as outside Qatar boundaries related impacts are found to be the
main contributor to these impacts. In scenario 1, in GWP, HEV, PHEV,
and BEV have an emission reduction potential of 46%, 45%, and 81%,
respectively. For the same scenario, the potential PMF reduction for
Fig. 3. Environmental impacts of alternative vehicle technologies: (a) GWP (gCO2-eqv. per km); (b) PMF (gPMF-eqv. per km); (c) POF (gPOF-eqv. per km); (d) Water
withdrawal (liter per km); (e) Water Consumption (liter per km); (f) Energy Inputs from Nature (TJ Per km.); (g) Land Use (km2 per km.).
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
9
10. HEV, PHEV, and BEV is 46%, 41%, and 74%, respectively. Similarly, in
terms of POF, HEV, PHEV, and BEV has a reduction potential of 46%,
39%, and 71%, respectively. However, in Scenario 2, when electric
vehicles are charged through electricity generated from solar energy,
PHEV and BEV can reduce GWP, PMF and POF emissions up to around
56% and 100% respectively. In other words, solar-powered BEVs have
the largest potential in terms of GWP, PMF, and POF emission reduction
and they can reduce up to 100% (the maximum reduction potential that
can be achieved) of these emissions resulting from the use of ICVs.
Overall, ICV emits the largest GWP, PMF, and POF in comparison to
other alternatives, while BEV produces the least and it is found to be the
best option when charged with solar stations.
The impact of water footprint including water withdrawal and
water consumption are illustrated in Fig. 3(d, e), respectively. The
findings show that the impacts resulting from the fuel power plants
dominate the total contribution of water withdrawal for PHEVs and
BEVs, whereas the impacts of suppliers within Qatar for the petroleum
and electricity generation sectors have the highest contribution to
water withdrawal for ICVs, HEVs, and solar-powered PHEVs. The re-
sults in Scenario 1 indicate that BEV is the most water-intensive vehicle,
mainly due to the water-intensive processes that occur in the electricity
generation by natural gas power plants, while HEV is the best option in
this impact category. On the other hand, solar-powered BEV in Scenario
2, has the best performance in terms of water withdrawal as opposed to
Scenario 1, due to lower water withdrawal from the solar power plant
for electricity generation and its supply chains. It's found that the
adoption of solar-powered BEV can reduce the water withdrawal im-
pact by up to 98%. Overall, Qatar's regional boundaries related impacts
for water withdrawal range between 89% and 99% for all vehicle types,
which implies that a great majority of this impact category occurs in-
side Qatar, however when BEV is powered with solar, the contribution
of the impacts inside Qatar becomes insignificant with roughly 10% of
the total water withdrawal impact. On the other hand, the contribution
to water consumption occurs mainly in the global supply chain for all
vehicle types accounting roughly for 97% for ICV, HEV, and 87% and
73% for PHEV and BEV respectively. While, in Scenario 2, more than
97% of the total impact for water consumption takes place in the global
supply chain for PHEV and BEV. This indicates that the vast majority of
the water consumption occurs in the global supply chains of the pet-
roleum and electricity generation sectors in Qatar, and not inside the
country. In both scenarios, BEVs are the best options in the water
consumption impact category, while ICVs are the worst. The maximum
reduction of water consumption (86%) can be achieved when solar-
powered BEVs are adopted.
Fig. 3(f), presents the results of energy inputs from nature impact,
and it can be seen that the petroleum production and electricity gen-
eration sectors in Qatar have the highest contribution to energy inputs
for all vehicle types except for BEVs powered with electricity by natural
gas, as the majority of this impact generated from this vehicle type
takes place in the regional supply chains of Qatar. Overall, more than
90% of the total energy inputs in all vehicle types takes place inside
Qatar. For the energy category, BEVs are the best options in both sce-
narios, while ICVs have the worst performance compared to other ve-
hicle types. Solar-powered BEVs are superior to other alternatives as
they can reduce energy consumption by 97%. Fig. 3(g) illustrates the
land use impact findings, and as shown Over 95% of total impact takes
place outside the regional boundaries of Qatar for all vehicle types.
ICVs have the largest land use impact, while BEVs in both scenarios
cause the least. As found, BEVs can reduce the impact by 74% in sce-
nario 1 and 84% in Scenario 2.
Fig. 4(a–d) presents the results of four social impact categories for
each vehicle type; the total tax, compensation, employment, and human
health impact, respectively. In total tax generation and compensation
categories, more than 88% of the total tax and compensation benefits
occur inside Qatar for all vehicle types in scenario 1, while in scenario
2, the contribution of inside Qatar benefits to total tax decreases to 73%
for BEVs and reduces to 79% and 47% of total compensation for PHEVs
and BEVs respectively. In the employment category, in Scenario 1,
approximately over 60% of the employment takes place within Qatar,
while in Scenario 2, roughly more than 70% of employment takes place
outside the regional boundaries of Qatar for BEVs. As can be seen, ICVs
account for the largest benefit in terms of taxes, compensation, and
employment which indicates that gasoline production and its supply
chains generate more taxes, compensation, and employment. On the
other hand, the benefits decrease sharply when electric vehicles operate
on electricity generated by solar energy and specifically the BEVs by
roughly 96% of the total tax, and 78% of compensation. For the em-
ployment category, it is found that electric vehicles in Scenario 2 are
performing better than electric vehicles charged with electricity from
natural gas. In contrast, in the human health impact indicator, tailpipe
emissions that occur while driving are the highest contributor for all
vehicle types. Approximately more than 98% of the human health im-
pacts occur inside the regional boundaries of Qatar. This is because BEV
has zero tailpipe emissions, most of the emissions of BEV in scenario 1
results from the electricity generation power plant within Qatar with at
least 90% of total human health contribution, while in Scenario 2 most
of BEV related emissions occur in the global supply chains accounting
for roughly 81%of its total human health impact contribution. ICVs are
the worst options in terms of human health impact, while BEVs have the
best performance especially solar-powered BEVs. According to the re-
sults, BEVs achieve 80% and 100% reduction of human health impact in
scenario 1 and scenario 2 respectively. Overall, results show that the
adoption of electric vehicles favors the human health impact indicator
while it does not favor the other selected social indicators.
Fig. 5(a, b) presents the economic impacts of alternative vehicles,
represented by operating surplus and GDP respectively. The fuel supply
is the highest contributor to operating surplus and GDP for all vehicle
options. For BEVs in Scenario 1, the supply chains inside Qatar for the
electricity generation sector is the dominant contributor to operating
surplus with roughly 69%. Furthermore, the contribution of BEVs in
Scenario 1 to GDP takes place mostly in the electricity generation sector
and its supply chain within Qatar, constituting approximately 48% and
49% of total GDP respectively. Overall, in scenario 1, beyond 90% of
the economic benefits take place inside Qatar, however for BEVs in
Scenario 2, the economic benefits within Qatar account for around 86%
and 74% of total operating surplus and GDP respectively. According to
the results, ICVs are more profitable as their contribution to GDP and
the operating surplus is higher than other vehicle alternatives. The
operating surplus and GDP benefits can reduce up to 92% by increasing
the adoption of BEVs. Overall, the results show that electrification does
not favor the selected economic indicators.
The LCC of the studied alternatives is presented in Fig. 6. The LCC
model is built based on a present worth analysis. According to data
obtained from FAHES, a local authority doing vehicle inspection on
behalf of the government, SUV vehicles to travel 22,000 km/year and to
have an average useful life of 12-years. The model includes different
cost elements such as initial cost, fuel consumption cost (electricity and
gasoline), maintenance cost, insurance cost, and salvage value. As can
be seen, BEVs have the highest initial cost of 1.097 QAR/km, while
HEVs have the least with 0.755 QAR/km. According to the results, the
maintenance and insurance costs vary between (0.037) for BEVs and
(0.014) for ICVs, and between (0.367) for BEVs and (0.253) for HEVs,
respectively. Furthermore, significant variation is found in the fuel cost
across the alternatives and it ranges between 0.312 QAR/km (ICV) and
0.023 QAR/km (BEV). The reason is that gasoline has higher prices in
comparison to the electricity price. On the other hand, salvage values
vary insignificantly between BEVs (0.222) and HEVs (0.152). The
overall LCC results reveal that HEV has the least cost followed by PHEV,
while BEV has higher LCC compared to the previous alternatives, and
ICV is found to have the highest LCC.
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
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11. In the literature, the sustainability impacts of SUV type vehicles
were not sufficiently studied. According to our literature search, only a
handful of studies adopting the LCA approach investigated the en-
vironmental impacts of alternative-fuel SUV type vehicles [59]. Among
the impact categories investigated, no study found analyzing macro-
level socio-economic indicators for the assessment of SUV class ve-
hicles. Table 4 shows the comparative results of different LCA studies in
the GWP impact category, which is the most common environmental
impact category studied in the literature.
Among the vehicle types, BEV SUVs included only in recent studies,
while earlier studies have PHEV and HEV types as electric SUV options,
of which BEVs are found the best option for the GWP impact category.
The variations in between amounts of “per km GWP” highly depend on
the analysis scope and the source of electricity generation. In Qatar, the
source of electricity generation is entirely natural gas and therefore, has
a greener/more environmentally friendly electricity generation mix
compared to U.S. average electricity generation mix. All of the studies
showed that ICV type SUV has the highest GWP (existing traditional
SUV type on roads).
4.2. Optimum distribution of alternative vehicles
The optimization model analysis is conducted based on two sce-
narios. In Scenario 1, electric vehicles are charged through electricity
that is generated entirely from natural gas. In Scenario 2, solar charging
stations are used exclusively to feed electric vehicles. Each scenario is
analyzed in terms of two types of impacts; a) The total impacts; en-
compassing the impacts occurring inside and outside Qatar, and b), the
impacts of inside Qatar's regional boundaries only. A compromise
programming model is developed in this study to determine the optimal
distribution of alternative vehicle options, considering various prio-
rities of environmental and socio-economic indicators.
Fig. 4. Social impacts of alternative vehicle technologies: (a) Total Tax (QAR per km); (b) Compensation (QAR per km); (c) Employment (1000P per km); (d) Human
Health (Daly per km).
Fig. 5. Economic impacts of alternative vehicle technologies: (a) Operating surplus (QAR per km); (b) GDP (QAR per km).
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
11
12. As can be seen in Fig. 7, the weights assigned to sustainability in-
dicators are ranged between 0 and 1. HEV is found to have the highest
fleet share in Scenario 1 when both types of impacts are considered (A&
B). When environmental indicators have the highest priority, HEV has
the highest distribution rate with over 90% in scenario 1 A & B. The
allocation percentage of HEV is very sensitive to changing weights, as it
declines sharply by increasing the weights of the socio-economic in-
dicators. The reason is that, although HEV has high positive socio-
economic impacts in almost most of the indicators including, total tax,
compensation, operating surplus, GDP, and employment, its contribu-
tion to human health impacts is critical compared to BEV. The dis-
tribution of HEV decreases as socio-economic indicators become im-
portant. In a balanced weighing case, when environmental and socio-
economic indicators are equally important, the optimal fleet share
consists of 81% HEV and 19% BEV in scenario 1 A. Almost the same
behavior is also observed when only inside Qatar impacts are con-
sidered in Scenario 1. Besides, when socio-economic indicators have
more importance compared to environmental indicators, BEV becomes
the most preferred option. Especially, when socio-economic indicators
weights are higher than 0.7, the model tends to favor BEV until it be-
comes the dominant vehicle type with almost 100% share when socio-
economic indicators are set to 100% priority. The same behavior is also
observed for Scenario 1B. Furthermore, it can be seen that ICV and
PHEV are not selected in any tested weighing case in scenario 1 A and
Scenario 1B, and this demonstrates that electricity by natural gas
charging stations do not favor these vehicle types. Hence, the proposed
model favors HEVs when environmental indicators are assigned higher
weights, whereas, if only socio-economic aspects are considered, BEVs
have the largest allocation rate in comparison to other vehicle options
(HEVs).
The optimal vehicle distribution in scenario 1 when only inside
Qatar's impacts is considered is very similar to that when the total
impact is taken into account. The findings of scenario 1B indicate that
HEVs are favored when environmental indicators have more im-
portance, while BEVs become the preferred option when only socio-
economic indicators are taken into account. It can be seen that at dif-
ferent weighting situations, the optimal vehicle distribution when only
inside Qatar's impacts is considered, is almost the same as the optimum
distribution when total impacts of inside and outside the regional
boundaries of Qatar is considered. Scenario 1 A & Scenario 1B have
almost the same vehicle distribution since the majority of impacts occur
inside Qatar for most of the impact categories. Considering the percent
shares of HEVs and BEVs obtained from the proposed model in Scenario
1, it is crucial for the government to initiate effective strategies and
policies and to implement incentives to promote the increased adoption
of HEVs and BEVs.
In Scenario 2 A, when environmental indicators are set as the top
priority, the optimum vehicle distribution consists entirely of BEVs.
This allocation has resulted because BEVs have the lowest environ-
mental impacts among other alternatives, and therefore it is the pre-
ferred vehicle type among others when environmental indicators have
higher importance. In addition, the percent fleet share of BEVs is over
99.9% for the rest of the weighting cases, and the remaining insignif-
icant percentage of the optimal fleet going to PHEVs. In other words, as
socio-economic weights increase, the model starts to select PHEVs
however in a very small percentage that is less than 0.05%. Although
Fig. 6. Life Cycle Cost breakdown for each vehicle type (QAR/km).
Table 4
Comparative GWP results for SUV class vehicle types in literature.
Current Study Karaaslan et al. [59] Nordelöf et al. [60] Duvall [61]
Life cycle analysis (LCA)
method
Hybrid MRIO-LCSA
(multi-region)
Hybrid IO-LCA (single region) Process LCA Process LCA
Scope WTW Full Life Cycle WTW WTW
Country Qatar USA Multiple countries (review study) USA
Vehicle Type ICV, HEV, PHEV, BEV
(all SUV type)
Gasoline ICV, diesel ICV FCEV,
PHEV, and BEV (all SUV type)
gasoline ICV SUV, diesel ICV SUV, LPG SUV,
HEV SUV, and 12 different class vehicles
HEV SUV, PHEV SUV, ICEV SUV,
and other class vehicles
Vehicle with the lowest
GWP
BEV SUV BEV SUV HEV SUV (among SUV class vehicles) PHEV (among SUV class vehicles)
69 gCO2-eq/km 233 gCO2-eq/km 265 gCO2-eq/km 159 gCO2-eq/km
Vehicle with the highest
GWP
Gasoline ICV Gasoline ICV Gasoline ICV (among SUV class vehicles) Gasoline ICV (among SUV class
vehicles)
355 gCO2-eq/km 589 gCO2-eq/km 350 gCO2-eq/km 388 gCO2-eq/km
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
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13. BEV is the worst option in terms of socio-economic impacts based upon
most of the selected socio-economic indicators, it has the lowest human
health impact, and as mentioned earlier, human health indicator is
assigned a weight factor of 0.6, and therefore, its share to the optimum
distribution when socio-economic weights increase remains as high as
when environmental indicators have higher importance.
Similar behavior is observed when only inside Qatar’s impact is
considered in Scenario 2. Specifically, when environmental indicators
weights vary between 1 and 0.1, the optimum vehicle distribution is
composed entirely of BEVs, and it is observed that when socio-economic
indicators are assigned 100% priority, the model selects PHEVs in a
very small percentage that is around 0.005%. Since the number of
impacts generated in scenario 2 A & scenario 2B are very similar, the
vehicle optimum distribution for both is found to be almost the same. It
is found that ICVs and HEVs are not allocated in any of the tested
weighting cases in scenario 2 A & B. Overall, the proposed model in
Scenario 2 when solar charging stations are considered, tends to favor
vehicles that use electric power efficiently, i.e., this highlights how the
characteristics of solar charging stations would prefer electric vehicles
in most cases, among the alternatives, this corresponds to BEV.
5. Conclusion
This paper mainly presented a novel MRIO- LCSA as an integrated
decision-making framework for optimizing the vehicle technologies
mix. The findings of the proposed optimization model deliver crucial
policy insights to policymakers when optimizing the allocation of ve-
hicle technologies based on a predefined set of environmental and
socio-economic priorities. This paper presented methodological con-
tributions towards life cycle sustainability assessment of alternative
vehicles, and they are as follows, 1) broadening the system boundary of
LCSA framework through building MRIO-LCSA model that encompasses
numerous social and economic indicators, besides the environmental
indicators, 2) extending the scope of analysis to a macro/national level
assessment through developing an MRIO-LCSA model that is capable to
cover the entire supply chain-related impacts at a global scale, 3)
providing a decision-making platform on the optimal distribution of
alternative vehicles through developing an MCDM-LCSA model that is
built upon obtained LCSA results.
In accordance with the analysis findings, the following points are
highlighted:
Fig. 7. Optimum distributions of alternative vehicles for different weighting cases for a) Scenario 1 A, b) Scenario 1B.
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
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14. • In both scenarios, ICVs are found to have the largest environmental
impact in all selected indicators except for the water withdrawal
impact category in comparison with other alternative vehicles.
• BEVs have the lowest environmental impact than other vehicle types
do have with an exception of the water withdrawal category and are
found to be the best alternative when charged with solar stations.
• Charging BEVs and PHEVs with solar energy significantly reduce
their environmental impacts.
• The analysis revealed that the great majority of the GWP, PMF, POF
emissions, and water withdrawal impact occur inside the country for
all vehicle types, while, in Scenario 2, when BEV is charged with
solar energy, sector outside of the country are found to be the
highest contributor to these impact indicators.
• In both scenarios, the contribution to water consumption and land
use impacts occur mainly in the global supply chains of the petro-
leum and electricity generation sectors for all vehicle types. On the
other hand, the majority of the total energy impact takes place in-
side the country for all vehicle types including solar-powered ve-
hicles.
• In the social indicators, ICVs generate the highest taxes, compen-
sation, and employment, while the benefit of these indicators de-
creases significantly if solar energy is used to power electric ve-
hicles. On the other hand, the total human health impact is found to
be the highest when using ICVs, however, it is the least with BEVs,
especially the solar-powered BEVs.
• The majority of taxes, compensation, and employment benefits, as
well as the human health impact, take place inside the region
boundaries of the country for all vehicle types in scenario 1, while,
in Scenario 2, the global supply chain of petroleum and electricity
generation sectors is the highest contributor to social impacts except
for total taxes for solar-powered BEVs.
• In terms of economic impact results, ICVs perform better than all
alternatives in both scenarios in respect of operating surplus and
GDP indicators, while BEVs have the worst performance in these
impact categories. On the contrary, charging electric vehicles with
solar energy slightly improves the performance of electric vehicles
in the operating surplus indicator.
• The results showed that the global supply chain-related impact is the
highest contributor to economic indicators for all vehicle types in
both scenarios.
• The LCC analysis revealed that the overall ownership cost of BEVs is
lower and thereby better when compared to ICVs.
• The compromise programming results for Scenario 1 A & B revealed
that when environmental indicators are assigned more importance,
HEV is favored. Whereas, BEVs become the dominant vehicle type
when only socio-economic indicators are deemed. The reason is that
BEV is the worst option in terms of most of the socio-economic in-
dicators, and Bev’s performance in the human health impact is the
best, especially is the human health impact assigned a weight of 0.6.
• In a balanced weighting case in Scenario 1 A, HEV is allocated at
around 81% and BEV at 19%. Likewise, the vehicle distribution for
Scenario 1B at the balanced weighing case is comprised of around
85% HEV and 15% BEV.
• In Scenario 2 A, when environmental indicators are given full
priority, the optimal fleet share is entirely composed of BEVs. Also,
when socio-economic weights are considered, BEV is selected with a
percent fleet share of over 99.9%, with the insignificant remaining
share (less than 0.05%) of the optimal fleet going to the PHEV.
• In Scenario 2B, the optimum vehicle distribution consists entirely of
BEVs in all weighing cases except when socio-economic indicators
are assigned a top priority as the model selects PHEVs in a negligible
percentage that is around 0.005%.
• The optimum distribution findings in Scenario 1A & B never suggest
the use of ICVs and PHEVs, while the findings of Scenario 2 A & B do
not select ICVs and HEVs in any tested weighted combinations.
• Overall, the model results in Scenario 1 reveals that the adoption of
HEVs and BEVs that run by electricity generated from natural gas
should be a top priority. On the other hand, the model in Scenario 2
encourages the use of the vehicle alternatives that run by electricity
generated from solar in the most efficient strategy, this corresponds
to BEVs.
This paper revealed the benefits of the integration of MCDM models
with the LCSA framework to demonstrate how quantified sustainability
indicators can be used for policy targets aligning with of compromise
programming model can assist in yielding optimum distribution for
alternative vehicles by various environmental and socio-economic
weights. The results of the proposed compromise programming model
also showed that, as the priorities of environmental and socio-economic
indicators change, the optimal solutions do change accordingly.
Overall, estimating the optimum vehicle distribution is a dynamic
problem, therefore determining the optimal mix of alternative vehicles
requires multistage solutions and futuristic scenarios assessments.
Furthermore, it is critical to note that for a more holistic analysis, the
inclusion of socio-economic perspectives to the environmental aspect of
sustainable transportation can provide vital insights for the creation of
national policies and strategies that encourage the use of electric ve-
hicles in the country. In this regard, this research sought to raise
awareness of the benefits of MRIO based life cycle sustainability mod-
eling and multi-objective decision-making analysis towards sustainable
transportation policies for countries.
It is worth mention that this paper aims to propose a practical
method combining hybrid LCSA with multi-objective optimization
using predetermined weights. The authors also suggest the use of expert
judgment-based MCDM techniques such as fuzzy sets which can enable
decision-makers to develop a case-based and country-specific set of
environmental and socioeconomic weights and rank the sustainability
performance of alternative vehicle options. The applications of different
MCDM techniques, expert judgment-based weighting methods, and
their applications to sustainable transportation are discussed in Onat
et al. [38] and Gumus et al. [62].
CRediT authorship contribution statement
Nuri Cihat Onat: Conceptualization, Methodology, Formal ana-
lysis, Writing - review & editing, Writing - original draft, Project ad-
ministration. Nour N.M. Aboushaqrah: Writing - original draft,
Formal analysis, Investigation. Murat Kucukvar: Writing - original
draft, Writing - review & editing, Visualization, Software. Faris
Tarlochan: Validation, Resources. Abdel Magid Hamouda:
Supervision, Funding acquisition, Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgments
This paper is an output of a project supported by Marubeni, grant
number QUEX-CENG-MJF-EV-18/19. Authors acknowledge and ap-
preciate Marubeni for the generous and continuous support for electric
vehicle research at Qatar University.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.enconman.2020.112937.
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
14
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![Contents lists available at ScienceDirect
Energy Conversion and Management
journal homepage: www.elsevier.com/locate/enconman
From sustainability assessment to sustainability management for policy
development: The case for electric vehicles
Nuri Cihat Onata
, Nour N.M. Aboushaqraha
, Murat Kucukvarb,⁎
, Faris Tarlochana,b
,
Abdel Magid Hamoudab
a
Qatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, Doha, Qatar
b
Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, Qatar
A R T I C L E I N F O
Keywords:
Hybrid life cycle sustainability assessment
Global multiregional input–output analysis
Multi-objective decision making
Electric vehicles
Sustainable transportation
Sustainability management
A B S T R A C T
In this research, a hybrid life cycle sustainability assessment and multi-objective decision making are jointly
applied to highlight how sustainability assessment results can be used for sustainable management and further
country-level policymaking, and Qatar is selected as a case study to implement the proposed method. 14 macro-
level sustainability indicators are quantified for four different technologies of sport utility vehicles (SUV), in-
cluding internal combustion vehicles (ICV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles
(PHEV), and battery electric vehicles (BEV), using a global multiregional input–output analysis to distinguish in
between regional and global supply chain-related impacts. A compromise programming model is developed
based on the sustainability assessment results to determine what should be the optimal distribution of alternative
vehicles based on varying importance of different sustainability indicators and scope of the analysis. The optimal
vehicle distributions are determined for two different battery charging scenarios, through the existing electricity
grid and solar energy. Furthermore, the optimal distributions are also investigated when the scope of the analysis
is limited to regional boundary versus the total impacts encompassing the global supply chains in addition to the
regional impacts. When environmental indicators are assigned the top priority (100%), the results show that
HEVs should compromise over 90% of the vehicle fleet. In a balanced weighting case, the optimal vehicle
distribution consists of around 81% HEV and 19% BEV if charged through the electricity grid. The proposed
method can provide important insights for developing policies to achieve sustainable and efficient policies
considering various aspects including the scope of assessment and relative importance of quantified sustain-
ability indicators.
1. Introduction
The world is currently seeking to develop cleaner and more efficient
passenger vehicle technologies for the aim of dispensing the internal
combustion engines that consume large amounts of fossil fuels, and
emitting huge quantities of air pollutants that destroy the ecosystem
and cause serious implication on human well-being [1,2]. The trans-
portation sector is responsible for nearly 25% of total-energy related
CO2 emissions worldwide and without aggressive greenhouse gas
emission reduction policies being implemented, mobility-related emis-
sions could increase at a faster rate than emissions from the other en-
ergy end-use sectors and reach around 12 Gt CO2eq/yr by 2050 [3].
Electric vehicles have been gaining popularity in terms of being en-
vironmentally friendly, through the mitigation of global climate
change, air pollution, and its related health effects on the population,
fossil fuel dependency, and energy consumption which represent the
leading challenges towards achieving sustainable transportation [4–6].
France, Britain, Germany, India, and Norway are planning to stop using
fossil fuel-powered vehicles and switch to electric vehicles. For ex-
ample, the Norwegian government has a transportation plan in which
all new passenger cars sold in 2025 should be zero-emission vehicles
[7]. Great Britain also aims to abandon sales of gasoline and diesel-
powered vehicles. By 2050, all cars on the British roads will need to
have zero air emissions [8].
There are significant research efforts found in the literature that
studied the environmental aspects of electric-mobility, however, the
effectiveness of these efforts can be improved significantly when so-
cioeconomic aspects of sustainability assessment are also considered
[9]. Therefore, providing a comprehensive sustainability assessment
method through developing a holistic system-based approach to
https://doi.org/10.1016/j.enconman.2020.112937
Received 13 January 2020; Received in revised form 23 April 2020; Accepted 2 May 2020
⁎
Corresponding author.
E-mail address: mkucukvar@qu.edu.qa (M. Kucukvar).
Energy Conversion and Management 216 (2020) 112937
Available online 22 May 2020
0196-8904/ © 2020 Elsevier Ltd. All rights reserved.
T](https://image.slidesharecdn.com/1-s2-211002122903/85/1-s2-0-s0196890420304751-main-1-320.jpg)
![quantify major macro-level social, economic, and environmental in-
dicators, and to establish strategies based on these quantified indicators
to determine optimal solutions is crucial to solving transportation sus-
tainability problems [10,11].
1.1. Integrated life cycle sustainability assessment framework
The traditional LCA method has been transformed into the Life
Cycle Sustainability Assessment (LCSA) where socio-economic aspects
are considered in addition to the environment. In this method, en-
vironmental life cycle assessment (LCA), life cycle cost assessment
(LCC), and social life cycle assessment (S-LCA) are used separately to
assess the three dimensions of sustainability [12]. However, there is a
great effort has been put into improving the methodological aspects of
LCSA to integrate the three pillars of sustainability into a single di-
mensioned-LCSA framework [13]. However, the concept of LCSA is still
relatively new, and the majority of studies addressed only methodolo-
gical or conceptual aspects of LCSA, while the use of LCSA in trans-
portation applications or real case studies is highly limited [14].
LCSA is an integrative framework for sustainability assessment, and
thereby the opportunity of integrating complementary tools and ap-
proaches with the LCSA framework for improving the LCSA applic-
ability is truly huge [15,16]. Some of these approaches that can be
integrated into LCSA to enhance the practical applications of LCSA are
input–output analysis, multi-region input–output analysis (MRIO),
multi-criteria decision- making (MCDM) tools, and system dynamics
modeling [14]. MRIO models are globally-extended version of the
single region input–output modeling approach and hence are eligible
for capturing the entire global supply chain-related impacts [17]. MRIO
models are highly used to estimate the regional and global environ-
mental impacts, however, a combination of MRIO with LCSA is still
rare.
There are several studies found in the literature that studied the
sustainability assessment of electric vehicle technologies. For example,
Aboushaqrah et al. [18] and Onat et al. [19,20] built a hybrid MRIO
model for LCSA of sport utility and sedan cars and assessed the regional
and global life cycle environmental, economic and social impacts of
electric mobility for the State of Qatar. Onat et al. [21] built a com-
prehensive LCSA model to calculate the impacts of 19 macro-level in-
dicators from the environmental and socio-economic perspectives for
the alternative passenger vehicle technologies in the United States.
Various studies applied single region input–output models for alter-
native personal vehicles in the United States to calculate a set of impact
indicators of environmental, social, and economic sustainability [19,22-
25]. In other work, the researchers [24] used an integrated dynamic
LCSA model to quantify environmental, economic and social sustain-
ability impacts (termed as the triple bottom line, TBL) of alternative
vehicles in the U.S. Onat et al. [24] have studied the dynamic re-
lationships between the sustainability indicators involved through
using system dynamics modeling for the case of electric vehicles in the
US. In another study, an uncertainty-embedded dynamic LCSA was
developed by Onat et al. [23]. The researchers addressed some meth-
odological challenges and uncertainties associated with the life cycle
sustainability assessment of electric vehicle technologies. Gemechu
et al. [26] and Hawkins et al. [27] applied a process-based LCA method
for an electric vehicle, which provides a current, transparent, and de-
tailed life cycle inventory data for battery electric vehicles.
1.2. Multi-criteria decision making
MCDM is a well-known branch of operations research in decision
making that concerned with solving decision problems involving the
selection of best alternatives from a set of potential candidates, subject
to several, usually conflicting decision criteria [28]. In literature,
MCDM is typically categorized into two types: Multi-Attribute Decision
Making (MADM) and Multi-Objective Decision Making (MODM). The
primary goal in these approaches is to select the best alternative(s)
across multiple criteria by considering a predefined set of preference
priorities. In general, MADM models are intended to solve problems
involving selection from a predetermined limited number of alter-
natives [28]. Whereas, the focus of MODM models is on design rather
than selection, and these models are employed to solve problems with
an infinitive number of alternatives, by considering a set of pre-
determined design constraints and preferences [28].
There are several MCDM techniques used in the literature such as
Multi-Attribute Utility Theory, Fuzzy Set Theory, Simple Additive
Weighting, Data Envelopment Analysis, ELECTRE, Analytic Hierarchy
Process, PROMETHEE, Case-based Reasoning, Simple Multi-Attribute
Rating Technique, Goal Programming, Compromise Programming, and
Technique for Order of Preference by Similarity to Ideal Solution. These
MCDM techniques have been employed in many fields including, fi-
nance [29], supplier selection [30,31], infrastructures [32], sustainable
energy planning [33], and environmental decision making [34].
Some papers found in the literature presented MCDM models for
vehicles. For example [35], developed an MCDM model to rank mul-
tiple fuel-based vehicles (both renewable and non-renewable) con-
sidering numerous environmental, social, and economic factors. Do-
nateo et al. [36] applied a two-step multi-objective optimization
method for the initial design of a hybrid electric vehicle. Furthermore,
Traut et al. [37] constructed a hybrid LCA model and developed an
optimization model to investigate the optimum designs for alternative
electric vehicles that yield the least life cycle cost and minimum
greenhouse gas emissions (GHG), selected as conflicting objectives. On
the other hand, none of these studies did consider the TBL impacts of
vehicles from the LCSA perspective. In other words, joint applications
of MODM and LCSA for the assessment and selection of electric vehicles
are very rare in the literature. For instance [25], used a combination of
LCSA with multi-objective optimization to determine the optimal mix of
alternative vehicles in the U.S, and Onat et al. [38] applied in-
tuitionistic fuzzy set approaches to LCSA to rank the alternative vehicle
technologies.
1.3. Novelty statement
This paper presents an integrated LCSA framework to cover the
three dimensions of sustainability. The proposed multi-region in-
put–output analysis-based LCSA method has several novel elements as
(1) quantifying macro-level social, economic and environmental im-
pacts concerning the regional and global supply chains by applying a
hybrid multi-region input–output analysis method, (2) incorporating
not only environmental impacts but also several macro-level social
(human health impacts, employment, tax, and compensation) and
economic dimensions (life cycle cost, GDP, and operating surplus) of
sustainability into sustainability assessment of alternative electric ve-
hicle technologies, and (3) designing an integrated sustainability deci-
sion-making framework in which MODM model is developed to de-
termine the optimum vehicle distribution for countries, by taking into
consideration conflicting sustainability objectives and changing
weights of selected indicators. This optimization model is built based
upon the MRIO-LCSA results obtained for each of the studied vehicle
alternatives for the two analyzed charging scenarios. Various optimal
distributions are presented based on varied weights allocated to each
impact category. Furthermore, a set of weighting cases is considered to
reflect the priorities of sustainability indicators involved from the de-
cision-makers point of view.
LCSA analysis is an important method for sustainable transportation
research and therefore the integration of LCSA of alternative vehicle
technologies with MCDM would be a significant contribution to the
literature by optimizing the mix of these vehicles based on a different
set of environmental and socio-economic priorities. To this end, this
paper presented an integrated sustainability assessment and modeling
framework to evaluate multiple and conflicting environmental and
N. Cihat Onat, et al. Energy Conversion and Management 216 (2020) 112937
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