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Analyzing the enablers to overcome the challenges in the adoption of Electric
Vehicles in Delhi NCR
Sreeraj Ramesan, Pravin Kumar, Suresh Kumar Garg
PII: S2213-624X(22)00127-4
DOI: https://doi.org/10.1016/j.cstp.2022.06.003
Reference: CSTP 846
To appear in: Case Studies on Transport Policy
Received Date: 13 February 2022
Revised Date: 27 May 2022
Accepted Date: 12 June 2022
Please cite this article as: S. Ramesan, P. Kumar, S. Kumar Garg, Analyzing the enablers to overcome the
challenges in the adoption of Electric Vehicles in Delhi NCR, Case Studies on Transport Policy (2022), doi:
https://doi.org/10.1016/j.cstp.2022.06.003
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Analyzing the enablers to overcome the challenges in the adoption of Electric Vehicles in
Delhi NCR
Sreeraj Ramesan
Department of Mechanical Engineering
Delhi Technological University,
Bawana Road, Delhi-110042
India
Email: sreeraj.ramesan468@gmail.com
Pravin Kumar*
Department of Mechanical Engineering
Delhi Technological University,
Bawana Road, Delhi-110042
India
Email: Pravin.dce@gmail.com
Suresh Kumar Garg
Department of Mechanical Engineering
Delhi Technological University,
Bawana Road, Delhi-110042
India
Email: skgarg@dce.ac.in
*corresponding author
Analyzing the enablers to overcome the challenges in the adoption of Electric Vehicles
in Delhi NCR
Highlights
1. Exploring the challenges in the adoption of electric vehicles in Delhi NCR and the
concerned policy of the state and central governments.
2. Finding the opinion of the experts and users through focus-group discussion and
incorporate them in the TISM analysis.
3. The economic and health issues, technological advancement in EVs, and Government
policies influence all the factors associated with the adoption of EVs.
4. The government’s support in terms of favorable transport policy, tax rebates,
subsidies, provision of better infrastructure, public outreach program, etc. may play
an important role in adoption of EVs.
Analyzing the enablers to overcome the challenges in the adoption of Electric Vehicles in
Delhi NCR
Abstract
The major contributor to air pollution in the cities like Delhi is emissions from vehicles running
on fossil fuels. Delhi has been listed as one of the highest air polluted cities. World Health
Organization has also produced a similar report. The Indian economy is highly dependent on
the import of fossil fuels. One of the best ways to address these issues may be the adoption of
all types of Electric Vehicles (EVs). The benefits of electric mobility are numerous. Some of
these advantages include better air quality, less reliance on fuel imports, lower greenhouse gas
(GHG) emissions, the enhanced plant load factor for the electrical grid, and the possibility to
be a leader in a booming worldwide market. The government of India and the state governments
have taken several initiatives to promote the use of EVs but the adoption of EVs is still very
low. Thus, this study aims to identify the key enablers that the state government should build
to encourage the adoption of EVs in Delhi. In this context, a focus group discussion composed
of industry experts, practitioners, and policymakers has been used to get the opinion regarding
the challenges in the adoption of EVs and enablers to overcome these challenges. Some major
challenges in the adoption of EVs have been explored through the literature review and focus
group discussion (FGD). To overcome these challenges, thirteen enablers for adopting EVs
were identified through a literature review and FGD. Then, using the Total Interpretive
Structural Modelling methodology (TISM), the mutual relationships amongst these identified
enablers were evaluated and enablers with high driving power were determined. This study
may help the governments and policymakers for the effective adoption of EVs in Delhi and
other Metro cities.
Keywords: Electric Vehicles, Government policy, Focus Group Discussion, Interpretive
Structural modeling, MICMAC Analysis.
1. Introduction
The number of private cars registered in Delhi has been increasing at an exponential rate. In
the year 1988, it was 0.28 million and increased to 3.31 million in 2020 (Statista, 2022). In
addition to these, lakhs of vehicles enter Delhi from its neighboring states daily. The rapid
growth of vehicle users in Delhi NCR leads to an increase in air pollution in the form of
particulate matter (PM2.5 and PM1.0) and other harmful gases. Air Quality Index (AQI) in
Delhi remains above the danger level for several months in a year. India has the highest number
of pollution-related mortality in the world. Approximately, 6,00,000 premature deaths per year
have been recorded in India due to poor air condition (Lelieveld et al. 2015; Ghude et al. 2016;
WHO 2016). Delhi NCR has lost the average life expectancy by 6.4 years (Kumar and Singh,
2021); however, it is recorded as 3.4 years for the rest of the country due to exposure to PM2.5
(Ghude et al., 2016). The growth in passenger vehicles not only adversely affects the
environment; it’s a big drain on the foreign exchange reserves of the country for the import of
crude oil. India’s economy is highly dependent on fossil fuel imports. This leads to further
economic issues caused as a result of instability in crude oil prices (Madras I.I.T., 2019). To
solve these issues, India needs to reduce its dependency on crude oil. The dependency on crude
can be reduced by adopting EVs in large and populated metro cities. Research in the area of
EVs is being pursued globally for the last 2-3 decades but now in the last decade, an
economically viable and technologically feasible solution has been made available for
commercial use.
Some major advantages, such as improvement in local air quality and reduction in the pollution
level (Soman et al., 2019) and greenhouse gas (GHG) emissions, adoption of renewable sources
of energy, and minimum dependency on the import of crude oil, the high energy density of
EVs compared to internal combustion engines (ICEVSs) (Morton et al., 2017), etc. have been
observed by promoting the EVs. With these advantages, some limitations in the adoption of
EVs have also been observed. These limitations are high upfront costs of EVs, scarce battery
raw materials, lower driving range between two consecutive charging of EVs, high electricity
tariffs, long charging duration, poor charging infrastructure, use of hazardous raw materials in
battery manufacturing, etc. (SIAM, 2017; Goel et al. 2021).
From the above discussion, it is very clear that all major cities which are suffering from adverse
air pollution have to switch from ICEVSs to EVs. For a city like Delhi, it is important to
promote EVs by providing the necessary support system. The government of NCT of Delhi has
already taken some initiatives but this is not sufficient. The purpose of this study is to explore
the enablers to overcome the challenges in the adoption of EVs. To explore the enablers to
overcome the challenges in the adoption of EVs, we have gone through the past literature as
well as experts’ opinions through focus group discussion (FGD). Then, using the Interpretive
Structural Modelling methodology, we identified the mutual relationships amongst the enablers
and determined the enablers with high driving power. To achieve the objective FGD and Total
Interpretive Structural Modelling (TISM) integrated technique has been used. This study is
focused on the following objectives:
(i) To explore the challenges in adopting the EVs in Delhi NCR
(ii) To propose the enablers to be created by the government to overcome the challenges
in adopting the EVs in Delhi NCR
(iii) To propose a framework to find the interrelationship between the enablers
The rest part of the paper has been organized as: the second section represents the literature
review, the third section represents the research methodology and case illustration, the fourth
section leads to result discussion, and the fifth section concludes the study with limitations and
future scope of the research.
2. Literature Review
The early adoption of EVs in India is very difficult due to the high initial cost, supply chain
complexities, lack of charging and service infrastructures, poor availability of EVs, lack of
reliability and need to change the mindset of the people. The general users are not ready to
come out from the existing comfort level of an internal combustion engine vehicle. In this
section, the challenges in the adoption of EVs and the government initiatives to be taken to
promote the EVs are reviewed.
2.1 Challenges in the adoption of EVs
The higher upfront cost of EVs is one of the major challenges in adopting the EVs. Dua et al.
(2021) observed that 63% of the respondents show the higher upfront cost of EVs as a major
concern. The lack of charging infrastructure is the second most important concern for EV users.
Dua et al. (2021) have stressed the financial incentives to be given by the government to
promote EVs. Approximately, 80% of respondents supported the need for financial incentives.
The financial barriers that customers face in adopting EVs include higher upfront costs (Noel
et al., 2020) and difficulty in availing of low-interest loans from banks (Dua et al., 2021). It is
very crucial to reduce the difference that exists between the cost of EVs and ICEVSs. To
overcome this challenge, the government, banks, and other financial institutions need to
support innovative schemes. Studies made on financial incentives conclude that they have a
positive impact on the adoption of EVs and make ownership simpler and cheaper for the
customer. Studies made in other countries like Norway show that EVs receive financial
purchase incentives in addition to free parking, toll fee waivers, bus lane access, and no annual
tax (Soman et al., 2019). Tax credits have reduced affectivity, making them the least effective
incentives for changing purchasing decisions. However, they still have an effect and should not
be abolished without the introduction of an equivalent subsidy. Public awareness campaigns
should also be focused to promote the incentives for customers (Hardman et al., 2017).
Rental taxies and public transport should also be provided incentives to use the EVs so that it
may be like a backseat test drive for passengers. Potential buyers may shift to EVs. Incentives
should be provided to delivery service providers like to switch to electric delivery vehicles. An
example of this is seen in the Delhi EV policy 2020 which states that all delivery service
providers are expected to transition 50% of their fleet to electric by March 31, 2023, and the
rest 50% by March 31, 2025, all of their fleets should be electric (GNCTD, 2020). State
governments should encourage the purchase of EVs by exempting/reducing the road tax and
vehicle registration fees, which are payable at the time of purchase of the vehicle. The parking
fees and tolls may be reduced or eliminated for EV users. To encourage people to switch from
traditional automobiles to EVs, the government might provide vehicle replacement incentives
from ICEVSs to EVs (Soman et al., 2019).
Enhancing the charging infrastructure facilities with fast charging stations will boost the appeal
of owning an electric vehicle and allow electric vehicle users to take longer trips (Hardman,
2019). Fast charging stations are a very essential proposition for EV users to replicate the
convenience of conventional fuel pumps and provide a better business model to reduce the
waiting time for vehicles for charging. The developing charging infrastructure and swappable
batteries are other options for small vehicles such as two-wheeler and three-wheeler vehicles
(Soman et al., 2019). The Central and state governments must evaluate the strategy in terms of
regulation, incentives, standards, metering, and invoicing for establishing the private charging
facilities so that acceptance of EVs can be increased rapidly. Parking areas need to be electric
vehicle ready i.e., they should have all the power supply infrastructure in place for EV chargers
(GNCTD, 2020).
In addition to private charging facilities, public charging infrastructure also plays a very crucial
role in enabling EV adoption. Public charging stations (PCS) can address the issue of range
anxiety while traveling in an EV. In the regions, where parking spaces are scarce, PCS is the
only solution for EV charging (TERI, 2019). Fast charging stations even though they have a
larger cost can serve a large number of users. One fast-charging station can replace the need
for approximately 20 slow charging stations (Schroeder et al., 2012). Therefore, establishing
and supporting projects of charging stations in collaboration with multiple third parties may
enhance EVs adoption in the city (Menon et al. 2019).
The government needs to formulate policies to create a conducive environment for EV
adoption. Government policies will play a key role in the deployment of the environment for
EVs (Melton et al., 2020). To generate the fund for subsidies and rebates to promote the EVs
adoption in Delhi NCR, the government can implement some repressive policies for polluting
the air using ICEVSs (Lieven, 2015). One of the earliest adopters of this method was seen in
California with the introduction of the California zero-emission vehicle mandate which has
further been extended by nine other US states (Collantes and Sperling, 2008; Axsen et al.,
2020). To implement the zero-emission policy, the government has to see the fulfillment of
auto manufacturing companies so that they can easily shift to the manufacturing of EVs. Apart
from regulations, the government should also work toward motivating the industry to increase
its R&D efforts (Lieven, 2015). To boost the local EV supply chain, the government and
policymakers should come up with new tax reduction policies and grants for research and
development purposes (Soman et al., 2019).
Reducing the upfront cost of EVs is a very powerful enabler in EV adoption. Battery price is a
major component of the total EVs prices, hence, a significant investment in research,
development, and demonstration is required (Catenacci et al., 2013). Reuse/repurposing of
batteries can help reduce the cost of recycling batteries. Electric vehicle batteries are made out
of costly and harmful elements (Dua et al., 2021). End-of-life (EOL) batteries need to be
recycled to recover valuable materials while minimizing the disposal of hazardous materials in
open or landfilling. Therefore, policies that encourage the reuse of EV batteries that have
reached the end of their useful lives, as well as the establishment of recycling businesses in
partnership with battery and EV makers can help reduce EV costs in India. Manufacturers may
be provided with subsidies based on the remaining capacity of batteries bought for recycling
(Hardman, 2019). Used EV batteries can also be used as power storage devices in a power grid
to meet the power demand during peak hours.
EV adoption will also lead to the creation of a large number of new jobs such as charging
service operators, EV mechanics, e-cab drivers, etc. (GNCTD, 2020). Education and skills
development programs in EV technology must be initiated by the government (Hardman,
2019). In collaboration with EV manufacturers and energy operators, vocational courses can
be started to educate EV drivers, mechanics, and charging center workers. The GNCTD
(Government of National Capital Territory of Delhi) announced in its Electric Vehicle Policy
2020 that these courses will be taught through GNCTD's world-class skill centers (GNCTD,
2020). The GNCTD is already running a skill university to develop cross-functional skilled
workers. To build technology that suits the Indian road and climate conditions, research and
development into EV design, usage, boosting EV efficiency, and charging equipment may be
carried out.
A major issue in developing the charging infrastructure is the high cost of commercial
electricity (Soman et al., 2019). One of the ways to overcome this issue would be to provide
concessions on their electricity bills and to promote the use of renewable sources of energy for
power generation. It will reduce the electricity cost as well as the carbon footprint. This will
attract more companies to enter this business. To enable the widespread adoption of EVs, it is
very important to provide all relevant information to potential buyers regarding electric vehicle
technology, incentives being offered, and government policies related to EVs.
The areas can be identified for public electric vehicle demonstrations, such as charging
infrastructure rollout, fleet conversions, electric ride-sharing programs, ride-and-drive events,
electric car operation and repair centers, and public displays (Hardman, 2019). This will help
in adopting EVs. The replacement of ICEVSs with EVs may reduce premature deaths per year
in India (Gai et al., 2020).
Developing profitable and sustainable business models for EVs is an important enabler (Dua
et al., 2021). Globally, several business models are being adopted to overcome the challenges
such as range anxiety, high upfront costs, battery reliability, and charging costs that are hurdles
in EVs’ adoption (Menon et al., 2019). These business models include vehicle
subscription/leasing where the EV is sold on a monthly rental basis with no upfront costs,
battery Subscription/Leasing where the vehicle is sold without the battery, and battery
swapping model where the drained battery is swapped with a charged battery for a given price,
charging-as-a-service (TERI, 2019), providing companies with incentives like subsidies, loans
with low-interest rates, exemptions from registration duties, and power tariff subsidies (Menon
et al., 2019). Based on the literature review and the FGD some of the major challenges in the
adoption of EVs have been observed. These challenges are summarized in Table 1.
Table 1: Challenges in adopting EVs
Challenges in adopting
EVs
Descriptions
References
High cost of EVs and
batteries
The cost of EVs and batteries are
high due to the poor availability of
raw materials (Lithium-based
batteries) used in the
manufacturing of batteries.
Zhuge et al. (2020),
Shashank et al. (2020),
Goel et al. (2021), Patyal
et al. (2021), Kumar et al.
(2021), Capuder et al.
(2020)
Insufficient charging
infrastructure
The main reason for the
unpopularity of the EVs is
insufficient charging stations in
Delhi NCR and poor mileage
between two consecutive charging.
Shashank et al. (2020),
Goel et al. (2021), Prakash
et al. (2018), Patyal et al.
(2021), Kumar et al.
(2021), Capuder et al.
(2020), Morrissey et al.
(2016).
Lack of Government
support to promote EVs
There is poor motivation from the
government to promote EVs by
providing the proper facilities and
tax relaxation.
Patyal et al. (2021)
Lack of recycling facilities
for EV batteries
There is a lack of recycling
facilities for the recycling of EV
batteries. The materials used in the
batteries are very harmful to the
environment.
Goel et al. (2021), Kumar
et al. (2021), Capuder et al.
(2020), Gaines (2014)
Unavailability of skilled
workers for EV
ecosystems
There is no skill center oriented to
EVs and battery technologies.
There is a lack of skilled employees
for helping and providing the
services for EVs.
Experts’ Opinion
High electricity tariff
Due to the increase in peak hour
load on the grid, the supply of
electricity is a big challenge,
including the cost of electricity
generation.
Experts’ Opinion
Lack of taxation support
for adopting the EVs
There is a requirement of taxation
support from the government in
terms of relaxation in GST and
other taxes.
Experts’ Opinion
Lack of public awareness
The general people are not much
aware of a clean and healthy
environment, which leads to more
inclination towards the internal
consumption vehicles. They do not
know the benefits of using
advanced technologies, including
the use of EVs.
Patyal et al. (2021), Goel et
al. (2021),
Lack of business model to
overcome the challenges in
the adoption of EVs
There are many challenges related
to EV business and supply chain
management. There is a lack of
Experts’ Opinion
business models to overcome these
challenges.
Reluctance towards
enhancing a clean and
healthy environment
There is a lack of effort in
controlling the factors responsible
for the pollution. The contribution
of road transport in increasing the
pollution level in the city is very
high.
Experts’ Opinion
Poor efforts to reduce the
pollution from
vehicle/transport
There is no effective plan with the
government to control the emission
from vehicles.
Experts’ Opinion
Lack of public willingness
and public resistance to
adopting new EV
technologies
The public is resisting the use of
EVs due to several reasons, such as
high cost, poor charging
infrastructure, and safety-related
issues.
Goel et al. (2021),
Sovacool and Hirsh
(2009), Patyal et al. (2021)
Immature technological
advancement in EVs and
battery technologies
The battery technologies for EVs
are evolving. The present
technology related to lithium-ion
batteries is costly as well as unsafe.
Shashank et al. (2020),
2.2 Governments' initiatives to promote EVs
The central and state governments have continuously been working to improve the
environmental conditions of Delhi NCR. Delhi is one of the most polluted cities in the world.
According to the Center for Science and Environment (2021), vehicles have emerged as the
biggest contributor to Particulate matter in Delhi. The vehicles’ contribution has increased up
to 50% of the total particulate matter in the air. Both central and state governments are
continuously making efforts to popularize and adopt EVs in Delhi including other cities.
The first electric car ‘Reva’ was launched by Mahindra in 2001 (Goel et al., 2021). In 2010,
the Prius hybrid car was launched by Toyota (Goel et al., 2021). The initial growth of EVs was
very low. Later, the Government of India (GOI) curved its vision through the National Electric
Mobility Mission Plan (NEMMP) started by the Department of Heavy Industries (DHI) in
2013. This plan leads to the manufacturing and usage of EVs and Hybrid Vehicles (HVs). In
2015, GOI initiated an incentive scheme through Faster Adoption and Manufacturing of EVs
(FAME). FAME I backed 2.8 Lakh electric and hybrid cars with demand incentives of $130
million. FAME II started in April 2019 with a budget of $ 1.4 billion. Further, at the central
level, some more initiatives have been taken, such as a reduction in Goods and Service Tax
(GST) from 12% to 5%, states are directed to reduce the road tax for EVs, and tax reduction
on the interest charged on loans of EVs (Patyal et al., 2021).
The GNCTD has planned to use 50% public transportation fleet as EVs by 2023 (GNCTD,
2020). All these afforest applied by the central and state governments are not sufficient. Several
challenges in the adoption of EVs have been observed through the literature review and
experts’ opinions as shown in Table 1. These challenges can be overcome by some enablers
as explored through the literature review and FGD is summarized in Table 2.
Table 2: Enablers to overcome the challenges in the adoption of EVs
Enablers to Overcome the
Challenges in adopting
the EVs
Remarks
References
Financial Incentives to
purchase the EVs (E1)
The cost of EVs and batteries are
very high and the public cannot
afford it without subsidies and
incentives from the government
Shashank et al. (2020),
Prakash et al. (2018),
Patyal et al. (2021), Dua
et al. (2021)
Establishing charging
infrastructure (E2)
The government is required to
establish a sufficient number of
charging stations with quick
charging technology.
Patyal et al. (2021), Dua
et al. (2021)
Government Policies to
promote EVs (E3)
Government should promote EVs
with the provision of tax
relaxation and infrastructural
support to the people of the city.
Dua et al. (2021)
Setting up a recycling
Ecosystem for batteries
(E4)
The disposal of lithium-ion
batteries is a major issue. Also,
the recycling of lithium-ion
batteries is hazardous to health. A
safe technology is required for the
recycling of batteries.
Patyal et al. (2021)
Setting up skill centers for
EVs ecosystems (E5)
The government has started a
skilled university in Delhi. A
skilled center devoted to
enhancing the EVs ecosystem is
required.
Patyal et al. (2021)
Relaxation in electricity
tariff (E6)
To attract people to use EVs, the
government will have to reduce
the electricity tariff.
Patyal et al. (2021),
Relaxation in Road tax (E7)
Relaxation in road tax may also
be an attractive factor for the
people towards the adoption of
EVs.
Experts’ Opinion
Intensive public outreach
programs (E8)
To make the awareness about the
environment, health, and the use
of environmentally-friendly
vehicles such as EVs, an intensive
Shashank et al. (2020),
public outreach program is
required.
Innovative business models
to overcome challenges to
EV adoption (E9)
The governments’ supportive
trade policy and new business
model may overcome some
challenges in the business of EVs.
Experts’ Opinion
Achieving the state’s
objectives of a clean and
healthy environment (E10)
Governments’ initiatives are
required to achieve a clean and
healthy environment by making
an effective green energy policy.
Experts’ Opinion
The high contribution of
road transport in the
pollution level in the city
(E11)
Highlighting the contribution of
road transport in the high
pollution level of the city may
force the public to think about the
adoption of EVs in place of
ICEVs (Internal Combustion
Engine Vehicles).
Experts’ Opinion
Motivating the people
towards the use of EVs
(E12)
To motivate the people towards
the use of EVs, the government
will have to run the awareness
programs and supports provided
for the government to reduce the
pollution level due to road
transport.
Shashank et al. (2020),
Support for technological
advancement in EVs and
battery technologies (E13)
For the advanced technologies
and use of alternative materials
for the batteries, the government
should support the research and
development activities through
various educational institutes and
automobile manufacturing
industries.
Experts’ Opinion
3 Research Methodology and Case Illustration
In this study, many challenges and enablers in the adoption of EVs have been explored through
the literature review. Finally, some major challenges and enablers were short-listed considering
the opinion of FGD. The experts in FGD also suggested some challenges and enablers beyond
the literature review that are also included in the analysis. The framework used in this study is
shown in Figure 1.
Figure 1: A framework for research Methodology
3.1 Focus Group Discussion (FGD)
Focus Group Discussion (FGD) has been proved to be a good tool for qualitative research
(Nyumba et al., 2018). The origin of this tool was in sociology (Mishra, 2016). But it is also
frequently used in the medical and other areas of the research (Wong 2008). In this method, a
small group of usually 10 to 12 people is coordinated by a moderator in a loosely structured
discussion of various topics of interest. Here, we requested more than 15 experts from the auto
manufacturing industry, policymakers, and users. Only 11 experts agreed to discuss the matter
proposed. Six experts were from the auto industry, two experts are concerned with the
policymaking, and three experts are the user of EVs. They were asked for their opinion
regarding the challenges and enablers of the adoption of EVs. Some of the challenges and
enablers explored through literature are also proposed in the discussion. Few challenges and
enablers were removed and some were added by the experts. Also, the interrelationship
between the enablers was established through this group discussion, which has been used in
the development of Total Interpretive Structural Modelling (TISM).
3.2 Development of Total Interpretive Structural Modelling (TISM)
Framework
Interpretive Structural Modelling (ISM) was first introduced by J.N. Warfield in 1973 as a
computer-aided method and further developed with structure using a graphical representation
in the form of digraphs (Chauhan et al.,2018). The main concept is to divide a complex system
into subsystems and develop a multilayer structural model using the experts’ opinions. In this
study, after identifying the enablers, causal/contextual relationships between the enables are
established through FGD. The interpretation of the major relationships is also indicated on the
linkage of digraph using the concept of Total Interpretive Structural Modelling (TISM).
In ISM, the linkages are only interpreted in terms of their contextual relationship and the
direction of their association in a pair-wise comparison. There is no interpretation of the causal
relationship. To address this problem, TISM creates an interpretive matrix that provides the
relationships' interpretation (Jena et al., 2017). In TISM, literature and expert opinions are used
to determine which elements are related, and how and why they are connected. Some of the
recent applications of ISM and TISM are summarized in Table 3.
Table 3. Recent applications of ISM and TIMS
SI.
No.
Areas of applications
References
1.
Reverse logistics of lithium-ion batteries
Azadnia et al. (2021)
2.
Establishing the relationship among the Sustainable
development goals
Kumar et al. (2018)
3.
Establishing the relationship between the enablers of
mass customization in the Indian Manufacturing
Industry.
Jain et al. (2021)
4.
Causal relationships between the factors influencing the
performance of service organizations.
Sharma and Kumar (2021)
5.
Barriers in the adoption of blockchain technology in
supply chains
Mathivathanan et al. (2021)
6.
Barriers in repurposing the existing manufacturing plant
Poduval et al. (2021)
The main difference in the processes of ISM and TISM is that in TISM, the relationship
between the enablers is identified and interpreted whereas in ISM the relationship is only
indicated by the directed graph (Yadav and Sushil, 2014). The understanding of a relationship
is clarified by stating how one factor will impact or enhance the other. After that, for each pair-
wise comparison, an interpretive logic-knowledge base is formed. The relationship is
interpreted as how one aspect influences or enhances another. For each pair-wise comparison,
the experts' judgment is indicated by the entry symbol "Y" for yes and "N" for no. If it is Y for
a paired comparison, the experts were asked to mention the reason for the influence.
3.1.1 VAXO Analysis
The relationship between the enablers finalized is established through the FGD. The
relationship between each pair of enablers was investigated in the discussion. This relationship
may exist and may not exist. If a relationship exists between the two enablers it may be
unidirectional or both directional. The enablers' pairwise interactions were then formed into a
matrix known as the VAXO matrix or SSIM (Structural Self-Interaction Matrix) as shown in
Table 4.
Table 4: Structural self-interaction matrix
Enablers
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10
E11
E12
E13
E1
X
V
A
O
O
O
V
V
O
O
O
V
O
E2
X
O
X
A
A
A
O
O
O
O
V
O
E3
X
O
V
V
V
V
O
V
A
V
A
E4
X
A
O
O
O
O
O
O
V
O
E5
X
O
O
V
V
O
O
O
O
E6
X
O
V
V
O
O
V
O
E7
X
V
V
V
O
V
O
E8
X
V
O
O
V
O
E9
X
O
O
V
A
E10
X
O
A
O
E11
X
O
O
E12
X
O
E13
X
The codes assigned as E1, E2… E13 for the enablers is shown in Table 2. The type (direction)
of the relationships identified between the enablers (i, j) are denoted using the following four
symbols:
V: Enabler ‘i’ leads to or helps to achieve ‘j’
A: Enabler ‘j’ leads to or helps to achieve ‘i’
X: Enabler ‘j’ and ‘i’ lead to or help achieve each other (i.e. they are interdependent)
O: No relationship exists between enabler ‘i’ and enabler ‘j’.
3.1.2 Initial Reachability Matrix
After forming the SSIM, Table 4 is converted into the initial reachability matrix which resulted
in Table 5. Here, the pair-wise relationships are mentioned in the binary format of 0 and 1. The
following rules are used to translate the symbols V, A, X, and O into binary representation:
If the symbol in a cell (i, j) was V, then that particular cell was replaced with 1, while
the cell (j, i) was replaced with 0.
If the symbol in a cell (i, j) was A, then that particular cell was replaced with 0, while
the cell (j, i) was replaced with 1.
If the symbol in a cell (i, j) was X, then that particular cell and the cell (j, i) were
replaced with 1.
If the symbol in a cell (i, j) was O, then that particular cell and the cell (j, i) were
replaced with 0.
Table 5: Initial Reachability Matrix
Enablers
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10
E11
E12
E13
E1
1
1
0
0
0
0
1
1
0
0
0
1
0
E2
0
1
0
1
0
0
0
0
0
0
0
1
0
E3
1
0
1
0
1
1
1
1
0
1
0
1
0
E4
0
1
0
1
0
0
0
0
0
0
0
1
0
E5
0
1
0
1
1
0
0
1
1
0
0
0
0
E6
0
1
0
0
0
1
0
1
1
0
0
1
0
E7
0
1
0
0
0
0
1
1
1
1
0
1
0
E8
0
0
0
0
0
0
0
1
1
0
0
1
0
E9
0
0
0
0
0
0
0
0
1
0
0
1
0
E10
0
0
0
0
0
0
0
0
0
1
0
0
0
E11
0
0
1
0
0
0
0
0
0
0
1
0
0
E12
0
0
0
0
0
0
0
0
0
1
0
1
0
E13
0
0
1
0
0
0
0
0
1
0
0
0
1
3.1.3 Final Reachability Matrix
In the next step, transitivity was incorporated into the initial reachability matrix which resulted
in the final reachability matrix. Euclid's definition of transitivity states that if enabler ‘A’ leads
to enabler ‘B’ and enabler ‘B’ leads to enabler ‘C’, then enabler ‘A’ must also lead to enabler
‘C’. This property was applied to all the enablers. The cells with ‘0’ entry having a transitive
relation are replaced with 1*. After completing all the transitive relations, the final reachability
matrix is obtained as shown in Table 6.
Table 6: Final Reachability Matrix
Enablers
E
1
E
2
E
3
E
4
E
5
E
6
E
7
E
8
E
9
E1
0
E1
1
E1
2
E1
3
Drivin
g
Power
E1
1
1
0
1*
0
0
1
1
1*
1*
0
1
0
8
E2
0
1
0
1
0
0
0
0
0
1*
0
1
0
4
E3
1
1*
1
1*
1
1
1
1
1*
1
0
1
0
11
E4
0
1
0
1
0
0
0
0
0
1*
0
1
0
4
E5
0
1
0
1
1
0
0
1
1
1*
0
1*
0
7
E6
0
1
0
1*
0
1
0
1
1
1*
0
1
0
7
E7
0
1
0
1*
0
0
1
1
1
1
0
1
0
7
E8
0
0
0
0
0
0
0
1
1
1*
0
1
0
4
E9
0
0
0
0
0
0
0
0
1
1*
0
1
0
3
E10
0
0
0
0
0
0
0
0
0
1
0
0
0
1
E11
1*
1*
1
1*
1*
1*
1*
1*
1*
1*
1
1*
0
12
E12
0
0
0
0
0
0
0
0
0
1
0
1
0
2
E13
1*
1*
1
1*
1*
1*
1*
1*
1
1*
0
1*
1
12
Dependenc
e
4
9
3
9
4
4
5
8
9
13
1
12
2
*This indicates the transitivity in the contextual relationship.
3.1.4 Establishing Reachability, Antecedent, and Intersection Sets
The enablers mentioned in the reachability set have the values ‘1’ in each row and the enablers
mentioned in the antecedent set have the value ‘1’ in each column. The intersection set contains
enablers that are common to both the reachability and antecedent sets as shown in Table 7.
Table 7: Reachability, Antecedent, and Intersection Sets
3.1.5 Level Partitioning
Enablers
Reachability Set
Antecedent Set
Intersection Set
E1
E1,E2,E4,E7,E8,E10,E12
E1,E3,E11,E13
E1
E2
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,E11,E13
E2,E4
E3
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12
E3,E11
E3
E4
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,7,E11,E13
E2,E4
E5
E2,E4,E5,E10,E12
E3,E5,E11,E13
E5
E6
E2,E4,E6,E9,E10,E12
E3,E6,E11,E13
E6
E7
E2,E4,E7,E10,E12
E1,E3,E7,E11,E13
E7
E8
E8,E10,E12
E1,E3,E8,E11,E13
E8
E9
E9,E10,E12
E3,E6,E9,E11,E13
E9
E10
E10
E1,E2,E3,E4,E5,E6,E7,E8,E9,E10,
E11,E12,E13
E10
E11
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E11,1E2
E11
E11
E12
E10,E12
E1,E2,E3,E4,E5,E6,E7,E8,E9,E11,
E12,E13
E12
E13
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12,E13
E11,E13
E13
The leveling of the enablers in the ISM hierarchy is determined by the iterations of finding the
exact similarity between the enablers in the antecedent set and intersection set. The enablers
having the same value in the antecedent set and intersection set are leveled first and for the
second iteration, they are removed from both the sets (reachability and antecedent stets). This
is iterated again and again till all the enablers are leveled. Here, Level I iteration was identified
by analyzing Table 7 and identifying the enabler for which the reachability and antecedent set
are the same. In this case, it was found to be enabler 10 (Achieving the state’s clean air
objectives) as seen in iteration 1 is the same in antecedent and intersection sets. Thus, it is
placed at the top of the TISM hierarchy. It means it is highly dependent on all the other enablers
and has no driving power except itself.
Iteration 1: It is used to find the enablers at the top position of the TISM
hierarchy.
Table 8: Level I Partition
Variables
Reachability Set
Antecedent Set
Intersection
Set
Level
E1
E1,E2,E4,E7,E8,E10,E12
E1,E3,E11,E13
E1
E2
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,E11,E13
E2,E4
E3
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12
E3,E11
E3
E4
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,7,E11,E13
E2,E4
E5
E2,E4,E5,E10,E12
E3,E5,E11,E13
E5
E6
E2,E4,E6,E9,E10,E12
E3,E6,E11,E13
E6
E7
E2,E4,E7,E10,E12
E1,E3,E7,E11,E13
E7
E8
E8,E10,E12
E1,E3,E8,E11,E13
E8
E9
E9,E10,E12
E3,E6,E9,E11,E13
E9
E10
E10
E1,E2,E3,E4,E5,E6,E7,E8,E9,E10,E11,E12,E13
E10
I
E11
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E11,1E2
E11
E11
E12
E10,E12
E1,E2,E3,E4,E5,E6,E7,E8,E9,E11,E12,E13
E12
E13
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12,E13
E11,E13
E13
Final Level Partition: After completing all the iterations and finding the levels for all the
enablers the results are summarized in Table 9.
Table 9: Final Partition Level
Variables
Reachability Set
Antecedent Set
Intersection
Set
Level
E1
E1,E2,E4,E7,E8,E10,E12
E1,E3,E11,E13
E1
VI
E2
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,E11,E13
E2,E4
III
E3
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12
E3,E11
E3
VII
E4
E2,E4,E10,E12
E1,E2,E3,E4,E5,E6,7,E11,E13
E2,E4
III
E5
E2,E4,E5,E10,E12
E3,E5,E11,E13
E5
V
E6
E2,E4,E6,E9,E10,E12
E3,E6,E11,E13
E6
V
E7
E2,E4,E7,E10,E12
E1,E3,E7,E11,E13
E7
V
E8
E8,E10,E12
E1,E3,E8,E11,E13
E8
IV
E9
E9,E10,E12
E3,E6,E9,E11,E13
E9
III
E10
E10
E1,E2,E3,E4,E5,E6,E7,E8,E9,E10,E11,E12,E13
E10
I
E11
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E11,1E2
E11
E11
VIII
E12
E10,E12
E1,E2,E3,E4,E5,E6,E7,E8,E9,E11,E12,E13
E12
II
E13
E1,E2,E3,E4,E5,E6,E7,E8,E9,
E10,E12,E13
E11,E13
E13
VIII
3.1.6 Developing the Hierarchy of Total Interpretive Structural Model
A digraph is produced based on the levels of each element and the relationships indicated in
the final reachability matrix. Transitive links are shown by the dotted lines but all the transitive
links are not shown here to give a more clear view of the digraph. The top level of the Digraph
is made up of Level 1 partition enablers (Enabler 10), followed by the second level, which is
made up of Level 2 partition enablers, and so on. The sort of relationship is indicated by the
direction of the arrows. Some of the major links are interpreted in the diagram (the
interpretation is written close to the directed link) as shown in Figure 2.
Figure 2: Total Interpretive Structural Model
3.1.7 Classification of Power of Variables (MICMAC Analysis)
The MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis reveals
the relative relevance of the enablers in terms of dependencies and driving power. It assists in
determining the extent to which an enabler drives the others or is dependent on others. The
driving power and dependence power in the final reachability matrix were determined by
adding the 1s and 0s in each enabler's row and column. The enablers were then divided into
four groups based on their driving and dependence power, which was plotted on a graph termed
the driving power-dependence power diagram or MICMAC graph (Figure 3).
E1
E2
E3
E4
E5
E6
E7
E8
E9
E10
E11
E12
E13
1
2
3
4
5
6
7
8
9
10
11
12
13
1
2
3
4
5
6
7
8
9
10
11
12
13
MICMAC Graph
Dependence Power
Driving power
Linkage
Autonom
Independe
nt
Dependen
t
Figure 3: MICMAC Graph
Based on the MICMAC analysis, the enablers are classified into the following four
categories:
Autonomous: The enablers lying in this quadrant have low driving power and low dependence
power. As a result, they do not have much effect on the system. They are mostly irrelevant. In
this case, no enabler is found to be autonomous.
Dependent: The driving powers of these enablers are low. They are heavily dependent on other
enablers. They are heavily influenced by the system. Any change made in the independent
enablers has an impact on these enablers. The enablers lying in the quadrant are expanding
charging infrastructure (E2), Creating a battery recycling ecosystem (E4), Public outreach
programs (E8), Innovative business models E(9), achieving Delhi’s clean air objectives (E10),
and willingness of people (E12) to adopt EVs. These enablers are found at the top levels of the
TISM hierarchy.
Linkage: These enablers have both high driving power and high dependencies. Due to their
enormous capacity of affecting and being affected by other enablers, these enablers generate
system instability. Any modification or changes in these enablers may have a significant impact
on the remainder of the system. In this case, no enablers are belonging to this category.
Independent: These enablers have high driving power but low dependencies. These enablers
have a strong influence on the system. They are not much influenced by the other enablers.
Any action taken on these enablers will have an impact on other enablers that are dependent on
them. Financial Incentives for EVs (E1), Government policies for EV promotion (E3), Skill
Centres (E5), Favourable electricity tariffs (E6), new road tax policies (E7), high pollution
levels (E11), and advancements in battery technology (E13) lie in this category. They are found
at the bottom of the TISM hierarchy levels. Therefore, these are critical enablers and serve as
the foundation for EV adoption in Delhi NCR. Technological advancements and the issues
caused by high pollution levels help in enabling and accelerating policy measures for easing
EV adoption. Financial incentives for customers and manufacturers are the next most critical
enablers for boosting EV adoption.
4. Discussions
In this study, it is observed that technological advancement in EVs motivated policymakers,
users, and manufacturers in the adoption of EVs. It is also observed that the major contribution
of the vehicles’ emissions in the high level of the air pollution in the metro cities like Delhi has
created the awareness about the environment among the people. They are continuously
searching the alternatives to internal combustion engine vehicles. EVs are more reliable than
all the other options. It has also influenced government policies. Governments are forced to
evolve the policies to promote EVs in terms of cars, three-wheelers, and public transport (bus).
It is required to give financial support to the users, manufacturers, and other service providers
of the EVs to enhance the infrastructure required to promote the EVs. Pollution-related issues
and technological advancements are lying at the bottom of TISM hierarchy, which indicates
that they influence all the other enablers.
The willingness of customers to adopt the EVs depends on the availability of charging
infrastructure, financial support from the government, service facilities, and recycling of EOL
batteries. The people are more concerned with the environment but lack of resources and
insufficient infrastructure are the great challenges in the adoption of EVS. There is a lack of
trained and skilled workers to work on the Charging stations and service centers of EVs. The
government will have to develop the skill development centers to fulfill the need of the
industry.
Implications for Industry and policymakers: This study may help the management of the auto
manufacturing organizations and policymakers to make EVs a more attractive alternative for
ICEVs. The leading organization related the EVs manufacturing may collaborate with the third
parties for the other service centers and infrastructure development which may help in the
promotion of EVs. Some of the major challenges and enablers are highlighted in this study
which may help the policymakers to make supportive policies for EVs.
Implications for Academia: This study may lead to further research to find the solutions to the
problems in the adoption of EVs. Also, it motivates the researchers to find some alternative
materials for high energy density batteries having less impact on the environment during the
use and after the end of life. It can easily be recycled and reused for secondary purposes. This
study also leads the researchers to do more research in rural, urban, and semi-urban areas
including the metro cities to find more effective and sustainable solutions for reducing pollution
by adopting the EVS.
5. Conclusions
In this study, major issues in the adoption of EVs have been explored through the literature
review and shortlisted based on the focus group discussion. Also, the enablers required to
overcome these challenges/issues are explored through the literature review and FGD experts’
opinions. An integrated framework of FGD and TISM is used to find the relationship between
these enablers so that the enablers can be prioritized according to their driving
power/dependencies. EVs can assist the Indian government to achieve its overall aims of
enhancing energy security, and lowering local air pollution. Technological advancements in
EV technology and government policies for accelerating EV adoption are the foundation for
accelerating EV adoption in Delhi NCR. High pollution levels were found to be the most
important factor forcing the users to shift to EVs in the cities like Delhi. These factors combined
with better charging infrastructure, new road tax policies, and financial incentives will boost
EV sales and adoption. Reduced electricity tariffs and provision of skill centers to produce the
skilled workers for EV services, incentives for meeting the high upfront cost of EVs, and public
outreach programs are some important enablers that may promote EVs. Funding support for
setting up of battery recycling system and repurposing the EOL batteries will be able to solve
the environmental issues of battery disposal and also lead to incentives for the EV users in
terms of providing the book values for the EOL batteries. Recycling and repurposing will also
boost the business of reverse logistics. It may provide opportunities for new job creation
beyond environmental control.
Limitations and scope for future research: This study is limited to exploring the challenges
and enablers only related to the adoption of EVs in the cities like Delhi. This study may further
be expanded to rural, urban, and semi-urban areas considering the technological, social and
political, environmental, financial, and behavioral barriers and enablers.
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