sustainability

Article
Assessment of Clean Energy Transition Potential in Major
Power-Producing States of India Using Multi-Criteria
Decision Analysis
Venkatraman Indrajayanthan * and Nalin Kant Mohanty

Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering,

Chennai 602117, India; nkmohanty@svce.ac.in
* Correspondence: venkat.ram33@gmail.com

Abstract: India has an ambitious target to promote clean energy penetration, but as of 2021, the
electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy
potential and the ability of the individual states to entrench energy transition in the upcoming
years will be supportive for policymakers. This study is propounded to assess the clean energy
transition potential with a focused analysis on seven major power-producing states of India. These
states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and
Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-
criteria decision analysis methodologies such as the Technique for Order Preference by Similarity
to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA).
Further, the analysis is performed against four major criteria that include high carbon energy resource
dependency, low carbon energy resource dependency, clean energy potential, and policy support.
Altogether, the assessment criteria include four primary level criteria and fourteen secondary level



parameters. In order to reflect the significance of each parameter and criterion to the characteristics

 of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic
Citation: Indrajayanthan, V.; Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition
Mohanty, N.K. Assessment of Clean potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh
Energy Transition Potential in Major
exhibited the least performance, and a complete energy transition to clean energy resources is less
Power-Producing States of India
likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis
Using Multi-Criteria Decision
between the two methods was also accomplished. This study suggests that India should focus on the
Analysis. Sustainability 2022, 14, 1166.
clean energy policy with vigorous efforts on top-performing states which will effectively accelerate
https://doi.org/10.3390/su14031166
the power sector decarbonization.
Academic Editor: Farooq Sher

Received: 17 December 2021

Keywords: multi-criteria decision analysis; TOPSIS; MOORA; Fuzzy AHP; clean energy transition
Accepted: 13 January 2022 potential; India
Published: 20 January 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in
1. Introduction
published maps and institutional affil-
iations. The energy sector dependency should be revamped from fossil fuel resources to
clean energy resources, and optimized energy configuration is required to achieve energy
sustainability [1]. A specific focus on the power sector is desideratum since it provides a
firm foundation to decarbonize the energy sector completely. Given the population growth,
Copyright: © 2022 by the authors. urbanization, and other development factors that are favored in the upcoming years, the
Licensee MDPI, Basel, Switzerland. utilization of electricity would increase [2,3]. Thus, policies and investments should be
This article is an open access article focused on decarbonizing the power sector in the upcoming years. On investigating the
distributed under the terms and electricity consumption of various countries across the world, it can be observed that China,
conditions of the Creative Commons the United States, and India are the countries having the highest power consumption in the
Attribution (CC BY) license (https://
world. The amount of electricity consumed by the top 10 countries is presented in Figure 1.
creativecommons.org/licenses/by/
4.0/).

Sustainability 2022, 14, 1166. https://doi.org/10.3390/su14031166 https://www.mdpi.com/journal/sustainability

Sustainability 2022, 14, x FOR PEERin
consumption REVIEW
the world. The amount of electricity consumed by the top 10 countries is 2 of 28
presented in Figure 1.

Sustainability 2022, 14, 1166 consumption in the world. The amount of electricity consumed by the top 10 countries
2 of 27 is
presented in Figure 1.

Figure 1. Top 10 highest electricity consuming countries. Data source [4].

India1.1.isTop
Figure
As of 2018,Figure Topthe10 highest electricity
10third-largest
consuming countries.
greenhouse
highest electricity consuminggas (GHG)
countries.
Dataemitter
source [4].
Data sourceaccounting
[4]. for
about 3346.63 MtCO2 equivalents [5]. Besides, India’s future trajectory has the potential
As of 2018, India is the third-largest greenhouse gas (GHG) emitter accounting for
to emit more emissions
aboutAs ofin2018,
3346.63 the upcoming
MtCO2 is theyears
Indiaequivalents such
third-largest
[5]. that
Besides, the increase
greenhouse
India’s in
gas (GHG)
future emissions
trajectory emitter can
has the accounting
potential for
surpass the UnitedaboutStates
to emit 3346.63
more byemissions
about
MtCO22035 [6].upcoming
equivalents
in the This[5].
canBesides,
be
yearsattributed thatto
India’s
such anincrease
future
the increase inin
trajectory perthe potential
has
emissions can
capita emissionsto and
emit
surpass access
more
the toemissions
Unitedenergy.
StatesTherefore,
in
bythe decarbonizing
upcoming
about 2035 [6]. years
This can the
such various
be that sectors
the increase
attributed ofinIn-
to an increaseemissions
in per can
surpass
dia would contribute tothe United
reducing
capita emissions States
andglobal
access tobyenergy.
about Therefore,
emissions 2035 [6]. This
significantly. can
India
decarbonizingbe attributed
witnessed
the various to an increase
a sectors
surge in per
of India
capita emissions
would
in electricity generation contribute
in the past and access toand
to reducing
decade, energy.
global Therefore,
inemissions
2020, decarbonizing
the significantly.
electricity India the
generated various
witnessed
in India asectors
surge in of In-
electricity
dia would generation
contribute in the
to past
reducing decade,
globaland in 2020,
emissions
was about 1342 TWh which is 62.8% higher when compared to 2010 [7]. The reason for the electricity
significantly. generated
India in India
witnessed was
a surge
about
in 1342generation
electricity
the increase in electricity TWh whichisisin
generation 62.8% higher
the past
linked to whenand
decade,
population compared tothe
2010
in 2020,increased
factors, [7].electrifica-
Thegenerated
electricity reason for in theIndia
increase
was about in electricity generation is linked to population factors, increased electrification,
tion, and urbanization [8]. 1342
Figure TWh which the
2 shows is 62.8% highermix
electricity when compared
of India fromto 2010to[7].
2010 2020. The reason for
andincrease
the urbanization in [8]. Figure
electricity 2 shows is
generation thelinked
electricity
to mix of India
population fromincreased
factors, 2010 to 2020. In
electrifica-
In 2020, it can be2020,
observed
it can that
be India’sthat
observed electricity
India’s primarilyprimarily
electricity depends on coalonwhich
depends coal consti-
which constitutes
tutes around 71%. tion,Among
and urbanization [8]. Figure 2 shows the electricitythe mix of India from 2010 to 2020.
around 71%.the Amongrenewables, hydropower
the renewables, hydropower contributes
contributes highest
the highest (12.2%),
(12.2%), while
In 2020, it can be observed that India’s electricity primarily depends on coal which consti-
while solar and solar
windand energy
windconstitutes 4.4% and
energy constitutes 4.4%4.5%, respectively.
and 4.5%, respectively.
tutes around 71%. Among the renewables, hydropower contributes the highest (12.2%),
while solar and wind energy constitutes 4.4% and 4.5%, respectively.

Figure 2. Indian electricity mix from 2010 to 2020. Data source: [7].
Figure 2. Indian electricity mix from 2010 to 2020. Data source: [7].
Figure 2. Indian electricity mix from 2010 to 2020. Data source: [7].
Sustainability 2022, 14, 1166 3 of 27

For achieving decarbonization and the Paris agreement, focused and strategical in-
vestment is of the utmost importance, and most of these investments are required in the
developing countries [9]. Further, deep decarbonization is an extremely convoluted and
systematic process that requires blended contributions from policies, technologies, people,
companies, and markets [10–13]. The power sector is the most significant sector contribut-
ing to India’s total GHG emissions [14], and decarbonizing the power sector is closely
interlinked with digitalization and decentralization [15]. The major challenges accompa-
nying the decarbonization of the energy sector rely on energy security, environmental
sustainability, social factors, and economic stability [16]. Further, the studies suggest that
a global carbon tax is an effective policy instrument that can accelerate the decarboniza-
tion process [16]. As a contribution to decarbonizing the Indian power sector, this study
is proposed to assess the clean energy transition potential of the country by selectively
focusing on major power-producing states. By doing so, a bigger picture on a decentralized
state-wise decarbonization vision can be accomplished. On the other hand, the clean energy
transition potential is the key to decarbonization, and the analysis performed will be of
crucial importance for the policymakers and energy companies to tap the untapped clean
energy potential.
The section-wise content description is as follows. Section 2 presents the literature
review, knowledge gap, and contributions of this study. Section 3 deals with the selection
of major power-producing states of India. Section 4 elaborates the methodology in terms
of the assessment criteria description as well as utilized MCDA methods’ elucidation.
Section 5 presents the results of obtained weightage through Fuzzy AHP, MCDA results
of TOPSIS, and MOORA, as well as a comparative analysis. Section 6 presents the policy
implications, while Section 7 summarizes and concludes this study.

2. Literature Review
Pradhan et al. utilized the Computable General Equilibrium (CGE) model to investi-
gate coal tax and renewable energy technologies for an effective energy transition [17]. The
study highlighted that high carbon pricing can be conducive to an energy transition but has
the potential to degrade the income distribution. Besides, the endogenous development in
renewable energy technologies can bolster economic as well as income equality. A study
argues that engaging with the energy sector workforce and citizens, implementing low-cost
financing and carbon pricing, and emphasizing co-benefits in development are the key to
achieving a net-zero carbon energy sector in India [18]. Azad and Chakraborty proposed
the Energy Policy with Equity for India to accelerate the energy transition [19]. This policy
mobilizes the taxed money to invest in renewables and provide free energy to the household
up to a certain limit. To implement such policy efforts, the authors put forward that the
required tax rate would be $ 60.4 per metric ton of carbon dioxide, while the free entailment
of fuel and electricity contributes around 2268 kWh per household per annum.
Godil et al. used the Quantile Autoregressive Distributed Lag (QARDL) method to
analyze the factors influencing energy utilization in India [20]. The results suggest that
globalization and financial performance have a positive influence on energy utilization,
whereas R&D and institutional quality have a negative influence on energy utilization.
On analyzing the clean energy consumption pattern of urban households in India, it is
observed that those households with low income and a lack of education are the significant
influencing factor for the reliance on dirty fuels [21]. Therefore, the study suggests that
framing the policies inclusive to enhance education and income will assist in the clean
energy transition.
Madurai Elavarasan et al. reviewed the development of renewable energy, challenges,
and policies in Indian states [22]. Saraswat and Digalwar evaluated the energy alternatives
such as thermal, gas, nuclear, solar, wind, biomass, and hydro energy for sustainable
development of the Indian energy sector by using an integrated Shannon’s entropy fuzzy
multi-criteria decision approach [23]. The energy alternatives were assessed with technical,
economic, environmental, social, political, and flexible criteria. The results demonstrated
Sustainability 2022, 14, 1166 4 of 27

that solar energy is the best energy alternative that can promote energy sustainability in
India followed by wind and hydro energy resources. In another study, the same group of
scholars performed an empirical investigation and validation of indicators for investigating
the energy sources in India through the sustainability importance index [24]. A study
analyzed the potential of solar and wind energy resources in India [25]. The results
highlight that the estimated availability of solar energy falls within the Levelized Cost of
Energy (LCOE) range of 51.6 $/MWh to 89 $/MWh. Concerning wind energy resources,
a total of 3102 GW of wind capacity is estimated that can be below 115 $/MWh of LCOE.
Pathak et al. analyzed the barriers to the development of renewable energy technologies in
India through an integrated modified Delphi and AHP methodology [26]. The results show
that political barriers tend to be the major barrier to renewable energy penetration in India.
To perform a robust analysis to assess the clean energy transition potential quan-
titatively, several criteria and parameters need to be selected as well as an appropriate
methodology being pivotal. A multi-criteria decision analysis (MCDA) methodology is
more suitable because the method can handle ambiguity, a multitude of perspectives, and
conflicting criteria, and it produces an aggregated result based on which solid conclusions
can be obtained [27]. Some unique advantages that MCDA offers to policymakers include
(i) building an evidence-based system to capture the economic, environmental, social, tech-
nological, and other metrics via quantitative as well as qualitative attributes; (ii) offering
a flexible view since the decision can be obtained in a finite set of objectives from a large
set of actors; (iii) analyzing the synergic and trade-off effect induced on the objective by
numerous factors [28].
The MCDA technique is utilized in a number of studies and applications such as
the evaluation of challenges in reliable solar panel selection [29], measurement technique
selection for particulate matter emission [30], sustainable material selection for construc-
tion projects [31], utility-scale solar photovoltaic siting with social considerations [32],
performance assessment of alternative jet fuels [33], sustainability evaluation of the energy
sector [34], analysis of waste-to-energy management strategies [35], sustainability analysis
of the second generation of biofuels [36], and assessment of energy storage systems for grid
applications [37]. Further, studies focusing on a country or location-specific assessment
were also found to utilize MCDA methodology. Some of the examples include sustainable
energy consumption evaluation in Europe [38], sustainability assessment of alternatives
for waste plastics in Norway [39], optimal location selection for solar energy plants in
Indonesia [40], investigation of the potential of renewable energy sources for electricity
generation in Serbia [41], and assessment of the success factors for the sustainable energy
sector in China for prioritization [42].
In the studies using MCDA, the widely employed methods include the Best-Worst
Method (BWM) [43–45], Analytic Hierarchy Process (AHP) [46–48], Preference Ranking
Organization Method for Enrichment Evaluation (PROMETHEE)—I and II [49–52], VlseKri-
terijumska Optimizacija I KOmpromisno Resenje (VIKOR) [53–55], Elimination and Choice
Translating Reality (ELECTRE) [56,57], Decision making trial and evaluation laboratory
(DEMATEL) [58–60], and Technique for Order Preference by Similarity to Ideal Solution
(TOPSIS) [61–63]. Further, the studies that deal with qualitative attributes utilize fuzzified
MCDA approaches such as Fuzzy AHP [64,65], Fuzzy TOPSIS [66,67], and many others. All
these MCDA methods can be typically categorized as value measurement models; goal, as-
piration, and reference level models; and outranking models [68]. For several applications,
studies are found to use single MCDA methods. The application of MCDA is gaining atten-
tion in the energy domain, and often two or more MCDA methods are commonly utilized
in energy sector applications. Boumaiza et al. used AHP, TOPSIS, SAW, and ELECTREII
MCDA methods for analyzing the residential PV adoption. Such analyses aid in comparing
the ranking of outcomes and covariation of ranking in alternative scenarios [69]. Ribeiro
et al. evaluated future energy scenarios for the Portuguese power generation sector using
MCDA [70]. The study utilized 13 criteria in the themes of economic, technical, quality
of life of local populations, job market, and environmental issues. Zhao et al. applied
Sustainability 2022, 14, 1166 5 of 27

MCDA methods such as Data Envelopment Analysis (DEA) and Fuzzy AHP to determine
the potential solutions to the production of hydrogen energy in Pakistan using various
renewable energy resources [71]. The study performed the analysis with four primary
criteria including social acceptance, economic, commercialization, and environmental. The
results highlight that the production of hydrogen from biomass energy resources would
be economically beneficial to Pakistan and is also a socially accepted energy resource.
However, the production of hydrogen from wind energy resources is found to be the most
efficient way. Browne et al. employed MCDA based on the NAIADE (Novel Approach to
Imprecise Assessment and Decision Environments) software to analyze six policy measures
for residential heating energy and domestic electricity consumption in an Irish city [72].
From the literature review, it can be observed that several studies are proposed for
framing strategies, instituting policies, and simulating scenarios to decarbonize the energy
sector effectively or to promote clean energy penetration. However, there are relatively
few studies that provide the importance for specific geographical locations and investigate
the energy transition ability of the country or location. Furthermore, the studies do not
analyze the potential of the states or countries to undergo energy transition considering the
various factors that can influence the clean energy transition. To address this knowledge
gap, the authors aim to assess the clean energy transition potential of India by specifically
focusing on seven major power-producing Indian states. This is achieved by using the
MCDA methods such as TOPSIS and Multi-Objective Optimization Method by Ratio
Analysis (MOORA) since these are simple and robust methods but are based on a different
framework. Therefore, the results achieved through these methods might vary which will
aid in understanding the influencing factors and criteria. Thus, a comparative analysis
is performed to summarize the results obtained by each method. On the other hand, to
direct the current power sector scenario towards a clean energy transition, allocating more
weightage to the criteria that favor such a transition is vital. Therefore, the authors also
utilized the Fuzzy AHP methodology to obtain a quantified weightage for each parameter
considered in the analysis. The novelty of this study is as follows:
• Assessing the clean energy transition potential for seven significant power-producing
states of India against 14 parameters.
• The analysis is performed from the dimensions of high carbon energy resource
dependency, low carbon energy resource dependency, clean energy potential, and
policy support.
• A progress-based analysis is accomplished in the policy support criterion.
• A comparative result analysis of TOPSIS and MOORA is performed.

3. Major Power-Producing Indian States

To obtain an unambiguous insight on the clean energy potential analysis in the Indian
electricity sector, a state-specific focus is required. Further, analyzing the installed power
generation capacity in each state will aid in identifying the major power-producing states.
Figure 3 represents the state-wise cumulative installed power generation capacity in 2020.
It can be inferred that seven major power-producing states cover 60% of the total installed
power generation capacity of India. These states include Maharashtra, Gujarat, Tamil Nadu,
Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. Therefore, assessing
the clean energy transition potential in these states will contribute to shifting the electricity
dependency from high carbon to low carbon energy resources strategically. A detailed
investigation of the energy characteristics of these seven states is performed in this section.

3.1. Maharashtra
The state of Maharashtra has the highest installed power generation capacity of about
42.1 GW. The electricity mix of Maharashtra is dominated by coal, while the installed power
generation capacity of wind is the highest among the renewable energy resources. Solar
power generation is the least deployed among renewables and is growing rapidly [74].
Maharashtra is blessed with rich water bodies, and the total hydropower based installed
Sustainability 2022, 14, 1166 6 of 27

capacity is 34.27 GW. Altogether, the high carbon energy resource (HCER) installed capacity
is 27.1 GW, while the low carbon energy resource (LCER) installed capacity is 15 GW which
constitutes about 35.6% of the total installed power generation capacity in Maharashtra. The
state aims to install an additional 17.385 GW of renewables within 2025. Of the 17.385 GW
target, a major focus is on solar energy projects accounting for a target of 12.93 GW, followed
by wind, cogeneration, small hydro, and solid-waste projects [75]. Further, the policy also
proposed to invest $ 11.55 billion to 13.58 billion on the projects as well as to create job
opportunities [75]. On the other hand, the state has a cumulative clean energy potential
of 110 GW (constituting from solar, wind, and small hydropower) [76,77]. Tapping the
potential of solar energy alone can shift the state’s electricity mix completely depending on
LCER since the solar energy potential is 1.5 times higher than the current installed power
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 28
generation capacity of Maharashtra.

Figure3.3.State-wise
Figure State-wiseinstalled
installedpower
powergeneration
generationcapacity
capacityininIndia
Indiaup
uptoto2020.
2020.Data
Datasource:
source:[73].
[73].

3.1. Maharashtra
The state of Maharashtra has the highest installed power generation capacity of about
42.1 GW. The electricity mix of Maharashtra is dominated by coal, while the installed
power generation capacity of wind is the highest among the renewable energy resources.
Sustainability 2022, 14, 1166 7 of 27

3.2. Gujarat
Gujarat ranks second in having the most installed power generation capacity with
a total installed capacity of 41.3 GW. In this state, the HCER installed capacity is 6 GW
more than the LCER installed capacity, and LCER contributes 42.75% of the total installed
capacity of Gujarat. Once again, the highest installed capacity is based on coal energy
resources, but there exists 7 GW of difference between the coal dependence of Maharashtra
and Gujarat with Maharashtra depending more on coal. The rapid deployment of wind
and solar energy-based projects in recent years is the reason for a higher contribution of
LCER when compared to Maharashtra [78]. As per the estimates of the state government,
Gujarat’s renewable power generation capacity will increase to 38 GW by 2025 from 17 GW
in 2021 [79]. The government also plans to reach a target of over 61 GW by 2030 [79]. These
ambitious targets are backed up by mega green energy projects supported by policies’ initia-
tives and firm investments. Gujarat is rich in wind energy resources which has the potential
to support 84 GW, while the solar energy potential is estimated to be 35.7 GW [76,77].

3.3. Tamil Nadu

Tamil Nadu is one of the crucial states that is blessed with various renewable energy
resources. In this state, the LCER installed capacity is about 20.3 GW which constitutes
58.5% of the total installed capacity. Moreover, Tamil Nadu has the highest LCER installed
capacity when compared to the considered seven states. Although coal-based installed
capacity is the highest in Tamil Nadu, it is only 3.3 GW more than wind energy based
installed capacity, positioning Tami Nadu as the top power producer from wind energy
resources in India despite its wind energy potential being less than Gujarat, Maharashtra,
Karnataka, and Andhra Pradesh. Further, the total wind and solar installed capacity
surpasses the HCER installed capacity in Tamil Nadu. In recent years, the Government of
Tamil Nadu is emphasizing solar energy projects, and the drafted policy (Tamil Nadu Solar
Energy Policy 2019) aims to reach 9 GW of installed capacity from solar energy resources
by 2023 from the current value of 4.7 GW [80]. The roadmap to this target also focuses on
the consumer category of solar energy systems [80]. The estimated solar and wind energy
potential in Tamil Nadu is 17.7 GW and 33.8 GW, respectively. A study also suggests that
among the Southern states of India, Karnataka and Tamil Nadu have a better opportunity
for tapping the solar energy potential for various applications [81].

3.4. Uttar Pradesh

Uttar Pradesh has the fourth-highest installed power generation capacity. This is the
state where coal dominates other energy resources in the electricity mix with a significant
difference. The HCER installation capacity is about 25.2 GW, while the LCER installation
capacity is 5.2 GW, marking a contribution of only 17.1% in the total installed capacity.
Uttar Pradesh has the least LCER installation capacity among the considered seven states
of India. Among the renewables, bioenergy installation capacity (2.2 GW) is higher than
the rest. Wind energy potential is negligible in this state, and thus, wind energy projects are
not preferred in Uttar Pradesh. Solar energy constitutes 2 GW and is expected to witness a
surge in the upcoming years. The hydro energy potential is also low in this state. Altogether,
the cumulative clean energy potential in Uttar Pradesh constitutes about 24.5 GW which is
lesser than the total installed capacity. In other words, even if the state taps all the clean
energy resources, it is impossible to shift the power sector dependency on a high carbon
energy resource completely. Therefore, in this scenario, complete decarbonization of the
energy sector can be accomplished only by importing energy from neighboring states
provided it can supply sufficient electricity generated from clean energy resources. The
state aims to reach a solar installation capacity of 10.7 GW by 2022 [82] which is less likely
to reach given the current contribution of solar energy projects.
Sustainability 2022, 14, 1166 8 of 27

3.5. Karnataka
The total installed power generation capacity of Karnataka is 29.8 GW, out of which
68.1% is constituted from LCER. In terms of the energy mix, Karnataka has the highest
LCER penetration among the considered countries. However, coal is the major source
that Karnataka depends on. Among the renewables, the solar energy resource has the
highest installation capacity with almost 2 GW behind the coal-based installed power
generation capacity. The hydropower, wind energy, and bioenergy based installed capacities
in Karnataka are 4.9 GW, 5 GW, and 1.9 GW, respectively. The state envisions adding 10 GW
of installed capacity based on renewable energy resources in the span of 5 years from 2021
to 2026 [83]. A study highlights that optimally handled Feed-in-tariff and Renewable
Energy Certification policies will aid the energy companies to be benefited from the energy
transition [84]. Further, to ensure economic growth during the energy transition, priority
should be given to reductions in cross-subsidization, universal metering, the transmission of
a reliable power supply to all the sectors, and optimization of the energy mix to substantiate
efficient management of electricity production and consumption [85].

3.6. Madhya Pradesh

Madhya Pradesh is the state having the second least LCER based installed power
generation capacity. The total installed capacity is about 29.6 GW, and coal constitutes
around 21.9 GW of the installed capacity. Overall, the LCER installed capacity is 25% of the
total installed capacity. Among the renewables, solar, wind, and hydro constitute almost
equal contributions with an installed capacity of 2.6 GW, 2.5 GW, and 2.3 GW, respectively.
The government of Madhya Pradesh fixed a target of reaching 12 GW of renewables by
2022 [86] which needs channelized policy support and investment. Madhya Pradesh has
about 61.6 GW potential for solar energy, and therefore, the state should take steps to tap
the solar energy potential. An analysis performed by Rout et al. indicates that the off-grid
solar polygeneration system is feasible in Madhya Pradesh and Andhra Pradesh with the
Benefit-Cost ratio of 1.25 and 1.32, respectively, while the corresponding annual levelized
cost of energy is about 9.6 and 9.8 INR per kWh [87]. On the other hand, wind energy
potential is comparatively low which constitutes about 10.4 GW.

3.7. Andhra Pradesh

The total installed power generation capacity of Andhra Pradesh constitutes about 7%
of India’s total installed capacity. With coal being the highest contributor to the installed
capacity, the dependency on other HCERs such as gas is also higher in this state. Overall,
the HCER installed capacity is 16.5 GW, while the LCER installed capacity is 10.7 GW.
Among the renewables, solar and wind energy resources have the highest installed capacity
constituting about 4.3 GW and 4.1 GW, respectively. The government aims to install an
additional 5 GW within the 5 years from 2019 to 2024 [88]. On investigating the clean
energy potential in Andhra Pradesh, it is found that the state has abundant solar and wind
energy potential of around 38.4 GW and 44.2 GW, respectively. The state has a mature
renewable energy market, and policy schemes such as Feed-in-tariff is more effective in
increasing the renewable energy penetration [89].
The installation power generation capacity of seven states of India is represented in
Figure 4.
Sustainability 2022, 14, x FOR PEER REVIEW 9 of 28
Sustainability 2022, 14, 1166 9 of 27

Figure
Figure 4. Installed
4. Installed powerpower generation
generation capacitycapacity
in major in major power-producing
power-producing states
states of India. ofsource:
Data India.[73].
Data
source: [73].
4. 4.
Methodology
Methodology
The methodology
The methodologyto to
analyze thethe
analyze clean
cleanenergy transition
energy potential
transition can
potential bebe
can perceived
perceived
from the enumeration of assessment criteria and the elaboration of MCDA models.
from the enumeration of assessment criteria and the elaboration of MCDA models.

4.1. Assessment
4.1. AssessmentCriteria
Criteria
ToToanalyze
analyzethetheclean
cleanenergy
energytransition
transitionpotential
potentialof of the
the considered
considered seven states, it it is
is of
of utmost
utmostimportance
importancetotoselect
select the appropriate assessment criteria that cover the charac-
the appropriate assessment criteria that cover the characteristics
teristics of the current
of the current clean energy
clean energy scenario,scenario, energy transition
energy transition potential,potential,
and policyand policydrivers.
support sup-
port
In adrivers. In these
nutshell, a nutshell, these characteristics
characteristics can be with
can be measured measured with the
the criteria such criteria such as
as high-carbon
high-carbon energy(HCER)
energy resource resource (HCER) dependency,
dependency, low-carbonlow-carbon
energy resourceenergy(LCER)
resource (LCER)
dependency,
clean energy
dependency, resource
clean energypotential,
resource and policyand
potential, support.
policy In this section,
support. In this the proposed
section, the pro-four
assessment
posed criteria are
four assessment elaborated
criteria in detail. in detail.
are elaborated

4.1.1.
4.1.1. High-Carbon
High-Carbon Energy
Energy Resource
Resource Dependency
Dependency
TheThe clean
clean energy
energy transition
transition potential
potential cannot
cannot be determined
be determined without
without assessing
assessing the de-the
gree of dependency on HCER by each major power-producing state. The greater thethe
degree of dependency on HCER by each major power-producing state. The greater
HCER
HCER installation
installation capacity,
capacity, thethe lesser
lesser is is
thethe clean
clean energy
energy transition
transition potential
potential since
since it will
it will
consume humungous efforts, investment, and targeted plans to reduce
consume humungous efforts, investment, and targeted plans to reduce the dependency the dependency
onon HCER.
HCER. Coal,
Coal, gas,gas,
andand diesel
diesel are three
are the the three
HCER HCER energy
energy resources
resources that arethat are widely
widely uti-
lized in India for power production. To enumerate the HCER dependency criterion, thethe
utilized in India for power production. To enumerate the HCER dependency criterion,
installed
installed capacity
capacity of of coal,
coal, gas,
gas, andand diesel
diesel energy
energy resources
resources can
can bebe summed.
summed. However,
However, thethe
emission potential during energy conversion is higher for coal followed by gas and diesel
emission potential during energy conversion is higher for coal followed by gas and diesel
(as shown in Table 1). In such a case, a weighted summation is required to depict the HCER
(as shown in Table 1). In such a case, a weighted summation is required to depict the
dependency scenario accurately. In this study, the weightage is evaluated using the Fuzzy
HCER dependency scenario accurately. In this study, the weightage is evaluated using
AHP methodology. Moreover, the weighted scenario will be supportive to highlight the
the Fuzzy AHP methodology. Moreover, the weighted scenario will be supportive to
impact of high emission energy resources because the weightage is derived from the degree
highlight the impact of high emission energy resources because the weightage is derived
of greenhouse gas (GHG) emissions that each resource produces in the entire lifecycle.
from the degree of greenhouse gas (GHG) emissions that each resource produces in the
entire lifecycle.
Sustainability 2022, 14, 1166 10 of 27

Table 1. Lifecycle GHG emission of various energy resources. Data source: [90,91].

Commercially Available Power Generation Life Cycle Emissions

Technologies (g CO2 e/kWh)
Coal 820
Gas 490
Diesel 253
Nuclear 12
Hydropower 24
Wind energy 11
Bioenergy 230
Solar energy 48

4.1.2. Low-Carbon Energy Resource Dependency

LCER installation capacity directly portrays the current clean energy scenario in the
given state. This parameter acts as a baseline for policymaking to achieve the effective
energy transition. The higher the LCER installation capacity, the higher is the clean en-
ergy transition potential since lesser progress will be required if LCER installed capacity
dominates the energy mix. The predominantly utilized LCERs in India include solar,
wind, biomass, hydro, and nuclear energy resources. Similar to the evaluation of the
HCER dependency criterion, the LCER dependency criterion is obtained by evaluating
the installed capacity of each LCER. Further, depending on the lifecycle emissions of each
LCER, the weightage is obtained through the Fuzzy AHP methodology. Table 1 shows
the lifecycle emission of various energy resources when utilized for power production. It
can be observed that nuclear and wind energy resources have the least GHG emissions.
Bioenergy has the highest median GHG emission among the LCER resources. Therefore,
higher weightage is provided for nuclear and wind energy resources, while the lowest
weightage is allocated for bioenergy resources. The rest of the LCERs such as hydro and
solar are provided with a weightage according to the closeness of the emission values to
the extreme values (i.e., high and low values).

4.1.3. Clean Energy Resource Potential

This criterion is crucial in evaluating the clean energy transition potential since it
elucidates to what extent the energy transition can be accomplished in a given state. For
instance, as discussed earlier, the state of Uttar Pradesh has a total clean energy resource
potential less than that of cumulative installed power generation capacity (dominated by
HCER) which indicates that it is infeasible to reach a complete energy transition. Thus,
the clean energy resource potential parameter is of prime importance when compared to
the rest of the assessment criteria. On analyzing the recent trends, it can be inferred that
the government of India is focused on the solar and wind energy resources as they can be
rapidly installed with less hindrance [73]. Further, the total bioenergy resource in India
is 28 GW which is insufficient to satisfy the demand of each state [92]. Concerning the
hydropower resource, the construction of large-scale hydropower resources is emphasized
to a far lesser extent when compared to tapping the potential of small hydropower resources.
On the other hand, the nuclear energy resource is difficult to quantify in terms of availability,
and uncertainties in implementing nuclear power plants in India are high since it has
already received social refusal [93]. Therefore, to witness a clean energy transition rapidly,
this study focuses only on solar, wind, and small hydropower energy resources under the
clean energy resource potential criterion. This is because the energy technologies based on
these resources are widely accepted, rapidly emerging in the Indian energy market, and the
Levelized Cost of energy is competitive to fossil fuels [94]. The weightage is also provided
for each of these three clean energy resources while computing clean energy potential
parameters. This weightage is based on the preference that India offers to these energy
resources which can be inferred from the recent progress of installed capacity. It can be
observed that solar and wind have been given pivotal significance, while small hydropower
Sustainability 2022, 14, 1166 11 of 27

is given lesser importance, and the decision matrix in the Fuzzy AHP methodology is
constructed accordingly (Refer to Appendix A, Table A4).

4.1.4. Policy Support

Policy support is a qualitative term, but the intensity of support can be quantified
based on the progress made and target that each state proposes. Policy support serves as a
criterion that establishes certainty in the energy transition. In other words, policy support
can accelerate the clean energy transition, and therefore, clean energy transition potential is
higher in the state where policy support is higher. While the clean energy potential criterion
is built on the availability of energy resources, policy support is designed to emphasize the
implementation ability of each state. The policy support criteria can be evaluated based on
three parameters—the annual HCER installation capacity rate, annual LCER installation
capacity rate, and performance gap.
The annual HCER installation capacity rate is the parameter that is obtained by
finding the average per year increase in the HCER installation capacity from 2018 to the
present. Similarly, the annual LCER installation capacity rate parameter can be calculated
by obtaining the LCER installation capacity data corresponding to 2018 and current data.
The annual HCER installation capacity parameter will aid in assessing the policy support
in installing the dependency on the HCER, while the annual LCER installation capacity
parameter highlights the progressing capacity of the state towards clean energy which, in
turn, maps the observable results of policy support in the energy transition.
The performance gap is the parameter that aims to quantify the performance of the
state with respect to the clean energy policy targets that the state proposes. From the targets
fixed by the seven major power-producing states, it can be inferred that there exist two
types of clean energy target frameworks. One is based on reaching a targeted cumulative
installation capacity, and another type deals with the addition of a certain quantity of clean
energy-based installed power generation capacity within a specific time frame. For instance,
Gujarat aims to reach 38 GW of renewable-based installed capacity from the current value
of 15.2 GW, while Maharashtra drafts a policy target to install an additional 17 GW to the
existing capacity. The performance gap can be determined by utilizing the annual LCER
installation capacity rate and calculating the required installed capacity rate. The required
installed capacity rate is enumerated by finding the required additional installation capacity
from the present year to the target year and dividing it by policy span. Altogether, the
performance gap is considered as the ratio of the required additional installation capacity
to the annual LCER installation capacity. If the performance gap is less than 1, then the
progress of the state is well supported by the policy to achieve the proposed target. If
the performance gap is greater than 1, then the proposed target needs additional policy
support, and a higher performance gap might also indicate an unrealistic target given the
current progress.
The data corresponding to each parameter under each criterion are represented in
Table 2. As a whole, the clean energy transition potential is assessed for seven major
power-producing states of India based on 4 criteria and 14 parameters. The clean energy
transition potential assessment framework is represented in Figure 5.

4.2. Multi-Criteria Decision Analysis (MCDA) Models

There exist numerous parameters, and each parameter can be beneficial or non-
beneficial to the assessment of the clean energy transition potential. For instance, HCER
dependency is a non-beneficial criterion since the higher the HCER score, the lower is the
clean energy transition potential. Therefore, for a higher clean energy transition potential, a
higher score in beneficial criteria and a lower score in the non-beneficial criteria are required.
Since the nature of the parameters considered varies, and numerous parameters exist, the
objective of assessing the clean energy transition potential in major power-producing states
can be accomplished by using the Multi-Criteria Decision Analysis (MCDA) methodology.
In this study, MCDA models are utilized for two purposes: (i) for evaluating the weightage
 Sustainability 2022, 14, x FOR PEER REVIEW 12 of 28
Sustainability 2022, 14, 1166 12 of 27

Table 2. Data corresponding to various criteria and parameters (decision matrix).

to the parameters and criteria, (ii) for assessing the clean energy transition potential of
seven Indian states. The former Major Power-Producing
purpose of weightageStates of India is performed using
determination
Criteria Parameters
the Fuzzy AHP model, while the latter Tamilpurpose Uttar Madhya
is accomplished by utilizing Andhra
TOPSIS and
Maharashtra Gujarat Karnataka
MOORA MCDA methods. The flowchart Nadu of the Pradesh Pradesh
MCDA process involving Pradesh
Fuzzy AHP,
High-carbon TOPSIS, and 23.856
Coal MOORA is depicted
16.092 in13.160
Figure 6. 23.729 9.480 21.950 11.590
energy resource Gas 3.207 7.551 1.027 1.493 0.000 0.000 4.899
dependency (GW) Table 2. Data corresponding
Diesel 0.000 to various criteria
0.000 0.212 and parameters
0.000 (decision
0.025 matrix).0.000 0.037
Nuclear 1.400 0.440 2.440
Major 0.440 States0.880
Power-Producing of India 0.000 0.000
Low-carbon
Criteria Hydropower
Parameters 3.428 2.073 2.301
Tamil 0.551
Uttar 4.970 2.335
Madhya 1.772
Andhra
Maharashtra Gujarat Nadu Pradesh Karnataka Pradesh Pradesh
energy resource Solar 2.541 6.093 4.738 2.032 7.512 2.674 4.381
dependency (GW)resource Wind Coal
High-carbon energy 23.856
5.013 16.092
8.953 13.160
9.847 23.729
0.000 9.480
5.039 21.950
2.520 11.590
4.097
Gas 3.207 7.551 1.027 1.493 0.000 0.000 4.899
dependency (GW) Bioenergy Diesel 2.632
0.000 0.100
0.000 1.040
0.212 2.180
0.000 1.902
0.025 0.128
0.000 0.536
0.037
Solar energy
Nuclear 1.400 0.440 2.440 0.440 0.880 0.000 0.000
Hydropower 64.320
Low-carbon energy resourcepotential 3.428 35.770
2.073 17.670
2.301 22.830
0.551 24.700
4.970 61.660
2.335 38.440
1.772
Solar 2.541 6.093 4.738 2.032 7.512 2.674 4.381
dependency (GW) Wind 5.013 8.953 9.847 0.000 5.039 2.520 4.097
Clean energy Wind energy
Bioenergy 2.632 0.100 1.040 2.180 1.902 0.128 0.536
45.390 84.430 33.790 1.260 55.850 10.480 44.220
potential (GW) potential
Solar energy
64.320 35.770 17.670 22.830 24.700 61.660 38.440
Small hydropower
potential
Wind energy 0.786
45.390
0.202
84.430
0.604
33.790
0.461
1.260
3.726
55.850
0.820
10.480
0.409
44.220
Clean energy potential (GW)potential
potential
Small
Annual hydropower
HCER
0.786 0.202 0.604 0.461 3.726 0.820 0.409
Installation
potential −0.457 0.200 0.133 0.660 −0.043 1.208 0.006
capacity rateHCER
Annual
Installation −0.457 0.200 0.133 0.660 −0.043 1.208 0.006
Policy support Annualcapacity
LCER rate
Policy support Annual LCER
Installation 0.427 2.448 1.271 0.463 0.942 0.370 0.631
Installation 0.427 2.448 1.271 0.463 0.942 0.370 0.631
capacity rate rate
capacity
Performance gap 10.167 2.325 1.676 9.370 2.123 17.768 2.640
Performance gap 10.167 2.325 1.676 9.370 2.123 17.768 2.640

Figure 5.
Figure 5. Framework
Framework for
for assessing
assessing clean
clean energy
energytransition
transitionpotential.
potential.
 methodology. In this study, MCDA models are utilized for two purposes: (i) for evaluat-
ing the weightage to the parameters and criteria, (ii) for assessing the clean energy transi-
tion potential of seven Indian states. The former purpose of weightage determination is
performed using the Fuzzy AHP model, while the latter purpose is accomplished by uti-
Sustainability 2022, 14, 1166 lizing TOPSIS and MOORA MCDA methods. The flowchart of the MCDA process involv- 13 of 27
ing Fuzzy AHP, TOPSIS, and MOORA is depicted in Figure 6.

Figure 6. MCDA flowchart for assessing clean energy transition potential.

Figure 6. MCDA flowchart for assessing clean energy transition potential.
4.2.1. Fuzzy AHP
4.2.1.Fuzzy
FuzzyAHP
AHPis a widely used MCDA method because of its capability to handle uncer-
Fuzzy
tainties AHP is a widelyproblems.
in decision-making used MCDA method
In this study, because
Fuzzy AHP of its
is capability
utilized to to handle un-
determine the
certainties
weightage in ofdecision-making problems.
each primary level criteria In
andthis study, Fuzzy
secondary levelAHP is utilized
parameters. to weightage
The determine
the weightage of
determination viaeach primary
Fuzzy levelbecriteria
AHP can and secondary
enumerated level
by utilizing theparameters. The weight-
following steps.
age determination via Fuzzy AHP can be enumerated by utilizing the following steps.
• Step 1: Pairwise comparison matrix
• Step 1: Pairwise comparison matrix
The pairwise comparison matrix consists of comparison scores for n x n parameters.
The comparison score is based on Saaty’s comparison scale [95]. In this matrix, the Aji
element is always the reciprocal of the Aij element where Aij represents the element of the
matrix corresponding to the ith row and jth column. The pairwise comparison matrix is
mathematically represented in Equation (1).

A11 A12 ... A1n

 
−1
 A12 A22 ... A2n 
Aij =  (1)
 
.. .. .. .. 
 . . . . 
A−
n1
1
A−
n2
1
... Ann

• Step 2: Fuzzification
Each element of the pairwise comparison matrix is fuzzified into Triangular Fuzzy
Numbers (TFN) which indicate the triangle corner points represented in the format of
(a, b, c). Equation (2) represents the fuzzification process with constraints.
Sustainability 2022, 14, 1166 14 of 27

!

−1 1 1 1
Aij−1


Aij = lij , mij , uij and = lij , mij , uij = , , , such that lij < mij < uij (2)
uij mij lij

• Step 3: Aggregated fuzzy decision matrix

The fuzzified pairwise comparison matrix is aggregated through the geometric mean
which is represented in Equation (3).
s
n
r i = ( li , m i , u i ) = n
∏ Aij (3)
i =1

• Step 4: Fuzzy weight determination

The ratio of the Aij element of an aggregated fuzzy decision matrix to the summation
of all the elements in the jth column will yield fuzzy weights. This is mathematically shown
in Equation (4).
! −1
n
ri
wi = n = ri × ∑ ri (4)
∑ i =1 r i i =1

where wi is the fuzzy weight of criteria i.

• Step 5: Defuzzification
The obtained fuzzy weights are subjected to defuzzification by using the expression in
Equation (5).
lŵ ⊕ mŵi ⊕ uŵi
Centre of weight ( Mi ) = i (5)
3
where Mi is the weight after defuzzification corresponding to the ith parameter.
• Step 6: Normalization of weights
Normalization is performed if the sum of weights after defuzzification is not equal to
1. It is performed by utilizing Equation (6).
!
Mi
Ni = (6)
∑nN=1 Mi

where Ni is the normalized weightage obtained through Fuzzy AHP analysis for the given
ith parameter.

4.2.2. TOPSIS
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is
a MCDA method originally proposed by Hwang and Yoon to make decisions among
various alternatives [96]. In brief, TOPSIS provides an aggregated result by comparing the
geometric distance between each alternative and the best alternative. TOPSIS is one of the
compensatory methods where a poor performance in one criterion can be compensated
by good performance in other criteria, altogether making a robust and realistic evaluation.
The best alternative ranked by the TOPSIS method should have the least distance from
the positive ideal solution, while it should have the highest distance from the negative
ideal solution [97]. The TOPSIS methodology is performed by following a step-wise
procedure [98].
• Step 1: Decision matrix
Sustainability 2022, 14, 1166 15 of 27

A decision matrix is a m × n matrix where rows and columns correspond to alternatives

and parameters, respectively. aij is the element of the decision matrix representing the ith
alternative’s score against the jth parameter. The decision matrix is shown in Equation (7).
 
a11 a12 ... a1n
 a21 a22 ... a2n 
aij =  (7)
 
.. .. .. .. 
 . . . . 
am1 am2 ... amn

• Step 2: Normalized decision matrix

Since the parameters considered for analysis have incongruous dimensions, nor-
malization is required. Normalization of the decision matrix is accomplished by using
Equation (8).
aij
rij = q (8)
∑im=1 a2ij

The element rij corresponds to the normalized decision matrix element.

• Step 3: Weighted normalized decision matrix
The weighted normalized decision matrix is obtained by multiplying the weightage
corresponding to each parameter to the elements of the normalized decision matrix. This is
expressed in Equation (9).
vij = w j rij (9)
where wj is the weightage obtained by the Fuzzy AHP method in this study; vij is the
element of the weighted normalized decision matrix.
• Step 4: Enumerate positive-ideal solution (PIS) and negative-ideal solution (NIS)
The best performing and worst performing alternatives corresponding to each criterion
are regarded as PIS and NIS, respectively, for each criterion. The determination of PIS
and NIS depends on the nature of criteria, i.e., beneficial or non-beneficial criteria. For
beneficial criteria, PIS is the alternative having a maximum score, and NIS is the alternative
having a minimum score and is vice-versa for non-beneficial criteria. The mathematical
representation for the determination of PIS and NIS is shown in Equations (10) and (11).

∗ maxvij , f or bene f it criteria
A = = {v1∗ , v2∗ , . . . , v∗n } (10)
minvij , f or non − bene f icial criteria

− minvij , f or bene f it criteria
= v1− , v2− , . . . , v−

A = n (11)
maxvij , f or non − bene f icial criteria
where A* and A− are the PIS and NIS, and n represents number of alternatives.
• Step 5: Determine the distance of each alternative from PIS and NIS
The alternative’s distance from PIS and NIS can be computed by using Equations (12)
and (13), respectively. v
um 2
Si∗ = t ∑ vij − v∗j
u
(12)
j =1
v
um 2
Si− = t ∑ vij − v−
u
j (13)
j =1

where m represents the total number of criteria.

• Step 6: Calculate the closeness coefficient
Sustainability 2022, 14, 1166 16 of 27

The closeness coefficient represents the aggregated score which is calculated based
on the distance of each alternative from PIS and NIS. It is mathematically expressed in
Equation (14).
S−
CCi = ∗ i − (14)
Si + Si
where CCi is constructed such that its value varies within the range between 0 and 1.
• Step 7: Ranking of alternatives
Since the closeness coefficient produces an aggregated score, the final ranking is
accomplished based on the CCi score. The higher the CCi score, the better is the performance
of the state. A CCi score near 1 and 0 indicates that it is near to PIS and NIS, respectively.

4.2.3. MOORA
Multi-objective optimization based on ratio analysis (MOORA) is a MCDA method
where the optimum solution is favored by aggregating maximum benefits and minimum
costs. In this study, a ratio system type of the MOORA method is utilized [99]. The
algorithm of MOORA is detailed as follows:
• Step 1: Formulation of decision matrix
A decision matrix is a m × n matrix where rows and columns correspond to alternatives
and parameters, respectively. xij is the element of the decision matrix representing the ith
alternative’s score against the jth parameter. The decision matrix is shown in Equation (15).
 
x11 x12 ... x1n
 x21 x22 ... x2n 
xij =  (15)
 
.. .. .. .. 
 . . . . 
xm1 xm2 ... xmn

• Step 2: Normalized decision matrix

The normalization is performed to each element of the decision matrix by dividing that
element with the root sum square of all the elements in the column in which the element is
present. This is expressed as Equation (16).
xij
xij∗ = q (16)
∑im=1 xij2

where xij∗ is the element of the normalized decision matrix.

• Step 3: Weighted normalized decision matrix

The weighted normalized decision matrix is obtained by multiplying the weightage
corresponding to each parameter to the elements of the normalized decision matrix. This is
expressed in Equation (17).
Wij = w j xij∗ (17)
where wj is the weightage obtained by the Fuzzy AHP method in this study; Wij is the
element of the weighted normalized decision matrix.
• Step 4: Enumeration of optimum score
The optimum score is obtained by summing the weighted normalized scores of all the
beneficial criteria and subtracting the weighted normalized scores of all the non-beneficial
criteria. The mathematical expression for the same is shown in Equation (18).
g n
yi = ∑ Wij − ∑ Wij (18)
j =1 j =1
Sustainability 2022, 14, 1166 17 of 27

where yi is the optimized score; g is the total number of beneficial parameters; and n is
the total number of non-beneficial parameters. In this study, g and n values are 9 and
5, respectively.
• Step 5: Ranking of alternatives
The ranking is accomplished based on the optimized score, yi . The greater the value
of yi , the higher is the performance of the corresponding state.

5. Results
The clean energy transition potential of major power-producing states of India is
determined by utilizing the MCDA method in which the assessment includes 4 criteria and
14 parameters. The results are categorized as weightage results, MCDA results, and the
comparative analysis.

5.1. Fuzzy AHP Weightage

The pairwise comparison decision matrix for each Fuzzy AHP analysis is represented
in Appendix A, Tables A1–A5. The weightage is evaluated in two stages, i.e., primary and
secondary level weightage where the primary level corresponds to weightage for various
criteria, while the secondary level refers to weightage to parameters within each criterion.
The final weightage is calculated by multiplying the weightage values obtained at both
levels. The resulting final weightage for each parameter is shown in Table 3. The highest
weightage is resulted for solar and wind energy potential parameters, while the lowest
weightage value is obtained for the bioenergy based LCER installation capacity. This maps
the Indian clean energy scenario where recent efforts are concentrated on solar and wind
energy. On the other hand, policy support parameters are given priority to improve the
progress that each state makes. Coal is utilized to a higher extent in the Indian power sector,
and thus, the resulting higher weightage to the coal parameter under the HCER installation
capacity criterion will diminish the performance score of each state in the MCDA since
this parameter is treated as a non-beneficial parameter. Therefore, the resulting weightage
depicts the relation between the clean energy transition potential and each criterion. For
instance, clean energy potential is the foundation for assessing the clean energy transition
potential, and thus, higher weighted parameters are found in this criterion. Policy support
fosters progress, and it is given second preference after the clean energy potential criterion.
The HCER and LCER installation capacity mark the current scenario, and it supports only
to assess the state’s present status, and therefore, these criteria are given a lesser weightage.

Table 3. Fuzzy AHP weightage results.

Secondary Level Primary Level Aggregated

Criteria Parameters
Weightage Weightage Weightage
Coal 0.7273 0.0811
High-carbon energy
Gas 0.1896 0.1115 0.0211
resource dependency
Diesel 0.0831 0.0093
Nuclear 0.3208 0.0389
Hydropower 0.2058 0.0249
Low-carbon energy
Solar 0.1229 0.1211 0.0149
resource dependency
Wind 0.3208 0.0389
Bioenergy 0.0297 0.0036
Solar energy potential (GW) 0.4424 0.2004
Clean energy
Wind energy potential (GW) 0.4424 0.4529 0.2004
potential
Small hydropower potential (GW) 0.1151 0.0522
Annual HCER Installation capacity rate 0.3068 0.0965
Policy support Annual LCER Installation capacity rate 0.5202 0.3145 0.1636
Performance gap 0.1730 0.0544
Sustainability 2022, 14, 1166 18 of 27

5.2. TOPSIS Results

Table 2 serves as the decision matrix for the TOPSIS method. The weighted normal-
ized decision matrix, along with PIS and NIS, is shown in Appendix A, Table A6. The
computed distance from PIS and NIS, closeness coefficient value, and alternative’s ranking
are represented in Table 4.

Table 4. Results of TOPSIS.

States Si + Si − CCi Rank

Maharashtra 0.1358 0.1591 0.5395 2
Gujarat 0.0885 0.1944 0.6870 1
Tamil Nadu 0.1454 0.1168 0.4455 5
Uttar Pradesh 0.2107 0.0459 0.1788 7
Karnataka 0.1226 0.1432 0.5388 3
Madhya Pradesh 0.2077 0.0844 0.2890 6
Andhra Pradesh 0.1415 0.1207 0.4603 4

It can be observed that Gujarat has the highest clean energy transition potential
when compared to other major power-producing states of India. This is because Gujarat’s
score for each parameter is the PIS or is near the PIS (i.e., second or third highest) in
most cases. This is evident from the distance from PIS and NIS values corresponding to
Gujarat in Table 4. In other words, the distance from PIS is a minimum, while the distance
from NIS is a maximum in the case of Gujarat when compared to other states considered
for the analysis. The second and third rank is obtained by Maharashtra and Karnataka,
respectively. On comparing these two states, it can be found that the distance from the PIS
value of Maharashtra is more than that of Karnataka. Besides, Maharashtra has a much
larger distance from the NIS value than Karnataka, and thus, Maharashtra obtains the
second rank.
The difference between the scores of Gujarat and Maharashtra is higher since Gujarat
has the highest clean energy potential, especially wind energy potential being the highest
among seven states which is also the highest weighted parameter in the analysis. On the
other hand, the difference between the scores of Maharashtra and Karnataka is less, but
there is no single reason for such a small difference since the results represent an aggregated
score. As an example, Maharashtra has a significantly higher wind and solar energy
potential as well as an annual HCER installation capacity rate. Meanwhile, Karnataka
scores much higher in parameters such as solar energy installed capacity, reduced coal
usage, the performance gap, and the annual LCER installation capacity rate. This induces
conflicting decisions, and thus, MCDA proves useful in such a scenario to provide a big
picture of clean energy transition potential.
The least score is obtained by Uttar Pradesh since it has the highest distance from PIS
and lowest distance from NIS. This is because Uttar Pradesh performs poorly under policy
support criteria, and to add upon it, the total clean energy potential is less than its total
energy installation capacity marking an unfavorable clean energy transition potential.

5.3. MOORA Results

Table 2 serves as the decision matrix for the MOORA method. The normalized decision
matrix is represented in Appendix A, Table A7. The optimized score and the ranking of
alternatives are presented in Table 5.
The results indicate that Gujarat, Karnataka, and Maharashtra obtain the first, second,
and third rank, respectively, in clean energy transition potential. Despite Gujarat having a
higher clean energy transition potential, its dependency on coal and gas should be reduced
to witness the energy transition. The gas dependency of Gujarat is the highest when
compared to other states, while the annual HCER installation capacity rate is 0.2 GW which
still indicates an increasing trend. In MOORA analysis, Karnataka’s score is greater than
Maharashtra since Karnataka leads Maharashtra in many parameters with a considerable
Sustainability 2022, 14, 1166 19 of 27

difference. Tamil Nadu attains the fourth rank, and this is attributed to a comparatively
less clean energy potential which pulls down the performance of Tamil Nadu in clean
energy transition potential. On the other hand, Tamil Nadu is the only state that has the
least performance gap but is still slightly greater than 1, indicating the requirement of
improved performance in installing additional clean power generation capacities to reach
the proposed target of reaching 9 GW of solar energy projects. Further, the nuclear and
wind energy based LCER installation capacity is highest in Tamil Nadu which would give
a boost in the final optimized score. Therefore, the score of policy support and LCER
installation capacity favored Tamil Nadu to reach the fourth rank despite having the second
least clean energy potential among the considered seven states. Andhra Pradesh attains the
fifth rank, and this is favored primarily by a higher clean energy potential. Apart from this
criterion, the performance of Andhra Pradesh is moderate in the rest of the criteria. Similar
to Andhra Pradesh, Madhya Pradesh exhibits moderate performance in HCER and LCER
installation capacity but has lesser clean energy potential than Andhra Pradesh. Further,
Madhya Pradesh has the highest annual HCER installation capacity rate and annual lowest
LCER installation capacity rate as well as the widest performance gap among the seven
states which altogether marks the least score under policy support criteria. Thus, Madhya
Pradesh scores considerably less than Andhra Pradesh. Meanwhile, Uttar Pradesh has the
lowest optimized score, and it is ascribed to the least clean energy potential.

Table 5. Results of MOORA method.

States Optimized Score Rank of States

Maharashtra 0.2286 3
Gujarat 0.3136 1
Tamil Nadu 0.1857 4
Uttar Pradesh −0.0230 7
Karnataka 0.2653 2
Madhya Pradesh 0.0181 6
Andhra Pradesh 0.1632 5

5.4. Comparative Analysis

The TOPSIS method depends on each alternative score corresponding to each criterion
since the closeness coefficient depends on the distance between PIS and NIS which, in
turn, depends on relative scores of alternatives in a given criterion. However, the MOORA
method does not depend on the performance of each alternative in the given criteria,
and this gives a major difference in assessment between the considered MCDA methods.
Therefore, the variations in the rank resulted. The comparison of the rank of seven states
obtained by using TOPSIS and MOORA MCDA methods is presented in Figure 7. In both
cases, Gujarat achieved the top score, Madhya Pradesh obtained the sixth rank, and Uttar
Pradesh attained the least score. On the other hand, the ranks of the rest of the four states
vary only by one rank. This is due to the methodological difference in calculating the
final scores. The TOPSIS method utilizes a relative comparison approach, while MOORA
emphasizes synergic and trade-off effects induced by each criterion on the final objective.
As a whole, it is obvious that Gujarat has the highest clean energy transition potential, and
thus, with the proper policy support and channelized investment, Gujarat can become the
clean energy powerhouse of India.
 rank, and Uttar Pradesh attained the least score. On the other hand, the ranks of the rest
of the four states vary only by one rank. This is due to the methodological difference in
calculating the final scores. The TOPSIS method utilizes a relative comparison approach,
while MOORA emphasizes synergic and trade-off effects induced by each criterion on the
final objective. As a whole, it is obvious that Gujarat has the highest clean energy transi-
Sustainability 2022, 14, 1166 20 of 27
tion potential, and thus, with the proper policy support and channelized investment, Gu-
jarat can become the clean energy powerhouse of India.

Figure7.7.Comparison
Figure Comparisonofofranks
ranksofofstates
statesobtained
obtainedininTOPSIS
TOPSISand
andMOORA
MOORAmethods.
methods.

6.6.Policy
PolicyImplications
Implications
The
Thepolicy
policyimplications
implicationsfor
forseven
sevenmajor
majorproducing
producingstates
statesare
areillustrated
illustratedasasfollows:
follows:
•• Gujarat
Gujaratemerged
emergedto tobe
bethe
thestate
statehaving
havingthethehighest
highestclean
cleanenergy
energytransition
transition potential.
potential.
Although
Althoughthe theannual
annualHCER
HCER installation capacity
installation is less
capacity in Gujarat,
is less it hasitthe
in Gujarat, hashigher coal
the higher
dependency and highest gas dependency in the country. Gujarat excels
coal dependency and highest gas dependency in the country. Gujarat excels in the in the efforts
to promote
efforts renewables
to promote as this as
renewables is this
evident in annual
is evident LCERLCER
in annual installation capacity,
installation but
capacity,
the performance gap is more than 2 which indicates that higher progress
but the performance gap is more than 2 which indicates that higher progress is re- is required
to achieve
quired the target
to achieve fixed by
the target thebystate.
fixed Therefore,
the state. Therefore,thethe
policy should
policy shouldbebefocused
focused
on accelerating the focus on renewables. This can be accomplished by tapping the
abundant onshore and offshore wind energy potentials in Gujarat. The policy draft
should emphasize more on wind energy with a clear roadmap towards the target.
Nevertheless, the focus on solar energy technologies can be prioritized in the industrial
and residential sectors in Gujarat.
• Maharashtra, being the highest energy producer in India, should focus on the energy
transition with transformative energy policies where the energy transformation is
underpinned by renewables to the extent that it is sufficient to decouple the fossil
fuel dependency. A two-phase policy is required to entrench the energy transition
where the first phase should emphasize promoting and accelerating renewable energy
penetration, while the second phase should be focused on phasing out coal and gas
power plants. The state of Maharashtra has abundant clean energy potential both
in terms of solar and wind energy. However, the annual LCER installation capacity
rate is less than 0.5 GW. On the other hand, the annual HCER installation capacity
rate witnesses a negative trend, and the phased-out HCER installation capacity rate
is higher than the LCER installation capacity rate. This indicates a poor focus on
renewable energy, and therefore, policies should attract investors and contracts to
increase the renewable energy penetration rapidly in the upcoming years. Further,
Sustainability 2022, 14, 1166 21 of 27

the performance gap is more than 10 which indicates an unrealistic target set by the
government. Hence, a refined renewable energy target, as well as a roadmap to achieve
the same in the stipulated time frame, should be given importance.
• Karnataka has the highest LCER penetration in the energy mix, and the state has the
highest contributions from hydropower and solar energy resources when compared to
the considered states. The energy mix of the state is dominated by LCER, but efforts
are required to reduce the dependency on coal despite being the only state having its
coal dependency of less than 10 GW. The state’s annual HCER installation capacity
witnesses a negative trend, and the annual LCER is near 1 GW. Thus, the policies
should focus on increasing the installation capacity rate of renewables by tapping
the potential of solar, wind, and small hydropower resources. A specific focus on
residential solar energy systems will be conducive to closing the performance gap
between the current installation rate and the fixed target. On the other hand, the state
can also focus on energy trade with the neighboring Southern and Northern states to
promote the utilization of renewable energy without curtailment.
• Tamil Nadu has the highest LCER installation capacity among the seven states con-
sidered for the analysis. On analyzing the policy parameters of Tamil Nadu, it can be
observed that the state has a low annual HCER installation capacity rate, and the an-
nual LCER installation capacity rate is about 1.2 GW. In addition, the performance gap
is the lowest among the seven states. Altogether, the policy support for Tamil Nadu is
good enough to witness an energy transition, but the policy aspect fails to focus on
reducing the expansion of fossil fuel based energy resources. The performance of Tamil
Nadu is impressive in tapping wind energy despite its overall clean energy potential
being around 52 GW which is lower than five considered states. In the upcoming
years, the state should focus on expanding its solar energy harvesting capacity.
• Andhra Pradesh has a total clean energy potential of 83 GW, but the energy mix is
dominated by HCER, and the annual LCER installation capacity rate is just 0.6 GW.
From the policy target framed by the government, it is observed that the target is
less ambitious when compared to other states. The performance gap is greater than
2 because of its lower annual LCER installation capacity rate. Therefore, the policy
aspect for Andhra Pradesh should emphasize accelerating the wind and solar energy
projects with a revamped target.
• The energy mix of Madhya Pradesh constitutes 25.8% of LCER, and the state heavily
depends on coal. Madhya Pradesh has an enormous solar energy potential of about
61.6 GW, but the total solar installed capacity is just 2.6 GW. The performance gap is
the highest in this state due to lesser LCER installation, marking ineffective policy
planning. The state can traverse in the path of energy transition only if the policy
measures focus on promoting solar energy projects. The state is far from phasing
out fossil fuels due to their very high dependency. Therefore, carbon tax policy
can effectively generate revenue for the state which can be invested in developing
clean energy projects. Further, incentive schemes on the solar energy projects and
attractive feed-in-tariff rates should be inclusive to promote solar energy penetration
in Madhya Pradesh.
• Uttar Pradesh has the least clean energy transition potential, and, indeed, the state’s
capacity would not support a complete energy transition. The only possible way
for the state to achieve 100% dependency on LCER is by energy trading with the
neighboring states. This scenario is less likely due to economic constraints, uncertainty
in energy security, and the limitations that the government will face. Therefore, the
policy aspect should focus on maximizing energy efficiency, utilizing polygeneration
and cogeneration systems. Moreover, carbon capture and storage, as well as carbon
capture and utilization technologies, can be integrated into existing fossil fuel based
power plants to reduce emissions. On the other hand, the state should tap as much
solar energy as possible through numerous green energy projects.
Sustainability 2022, 14, 1166 22 of 27

7. Conclusions
India is the third-largest electricity consuming country in the world, and the energy
mix in the power sector is hugely dominated by coal (about 71%). To accelerate the
government’s action further, a comprehensive analysis of each state’s clean energy charac-
teristics is to be assessed. In line with this, the proposed study evaluates the clean energy
transition potential of seven major power producing states in India. These seven states
include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh,
and Andhra Pradesh.
To analyze the clean energy transition potential, four criteria are put forward which
include high carbon energy resource dependency, low carbon energy resource dependency,
clean energy potential, and policy support. The weightage is provided in a way that the
state having higher clean energy potential and policy support will possess higher clean
energy transition potential. Altogether, the analysis is substantiated with four criteria and
fourteen parameters where the weightage is provided by using Fuzzy Analytic hierarchy
process methodology.
The alternatives are evaluated for clean energy transition potential by employing
TOPSIS and the MOORA multi-criteria decision analysis methodology. The results indicate
that Gujarat has the highest clean energy transition potential which means that Gujarat
might emerge as the clean energy hub of India, given its clean energy potential, policy
support, and recent progress in the LCER installation capacity rate. The second and third
position is attained by Maharashtra and Karnataka, respectively, in TOPSIS and vice-versa
rank in MOORA. Uttar Pradesh has the least clean energy transition potential such that
the state’s clean energy potential does not support the current installed capacity. The
proposed methodology to analyze the clean energy transition potential can be extended
to any country in the world. The policy implication corresponding to each state is also
summarized in this study.
To conclude, India has a rich clean energy resource potential, and tapping those
resources, especially in the top five states in the performed analysis, can cover about twice
the installation capacity that is currently implemented. Therefore, the central and state
government should emphasize feasible policy to promote solar and wind energy harvesting
technologies in the upcoming years.

Author Contributions: Conceptualization, V.I.; methodology, V.I.; software, V.I.; validation, V.I.
and N.K.M.; formal analysis, V.I. and N.K.M.; investigation, V.I.; resources, V.I.; data curation, V.I.;
writing—original draft preparation, V.I.; writing—review and editing, N.K.M.; visualization, V.I.;
supervision, N.K.M. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. Pairwise comparison decision matrix of four criteria for Fuzzy AHP weightage determination.

HCER LCER Clean Energy Potential Policy Support

HCER 1 1 0.25 0.33
LCER 1 1 0.33 0.33
Clean energy potential 4 3 1 2
Policy support 3 3 0.50 1
Sustainability 2022, 14, 1166 23 of 27

Table A2. Pairwise comparison decision matrix of three parameters of HCER dependency criteria for
Fuzzy AHP weightage determination.

Coal Gas Diesel

Coal 1 5 7
Gas 0.20 1 3
Diesel 0.14 0.33 1

Table A3. Pairwise comparison decision matrix of five parameters of LCER dependency criteria for
Fuzzy AHP weightage determination.

Nuclear Hydropower Solar Wind Bioenergy

Nuclear 1 2 3 1 9
Hydropower 0.50 1 2 0.50 7
Solar 0.33 0.50 1 0.33 5
Wind 1 2 3 1 9
Bioenergy 0.11 0.14 0.20 0.11 1

Table A4. Pairwise comparison decision matrix of three parameters of clean energy potential criteria
for Fuzzy AHP weightage determination.

Solar Energy Wind Energy Small Hydropower

Potential Potential Potential
Solar energy potential 1 1 4
Wind energy
1 1 4
potential
Small hydropower
0.25 0.25 1
potential

Table A5. Pairwise comparison decision matrix of three parameters of policy support criteria for
Fuzzy AHP weightage determination.

Annual HCER Annual LCER

Installation Capacity Installation Capacity Performance Gap
Rate Rate
Annual HCER
1 0.50 2
Installation capacity rate
Annual LCER Installation
2 1 3
capacity rate
Performance gap 0.50 0.33 1

Table A6. Weighted normalized decision matrix, PIS, and NIS values in the TOPSIS method.

Weighted Normalized Performance Value

Tamil Uttar Madhya PIS NIS
Maharashtra Gujarat Karnataka Andhra Pradesh
Nadu Pradesh Pradesh
0.0406 0.0274 0.0224 0.0404 0.0161 0.0373 0.0197 0.0161 0.0406
0.0070 0.0164 0.0022 0.0032 0.0000 0.0000 0.0106 0.0000 0.0164
0.0000 0.0000 0.0091 0.0000 0.0011 0.0000 0.0016 0.0000 0.0091
0.0181 0.0057 0.0315 0.0057 0.0114 0.0000 0.0000 0.0315 0.0000
0.0115 0.0070 0.0077 0.0019 0.0167 0.0079 0.0060 0.0167 0.0019
0.0031 0.0073 0.0057 0.0024 0.0090 0.0032 0.0053 0.0090 0.0024
0.0123 0.0220 0.0242 0.0000 0.0124 0.0062 0.0101 0.0242 0.0000
0.0023 0.0001 0.0009 0.0019 0.0017 0.0001 0.0005 0.0023 0.0001
0.1170 0.0651 0.0321 0.0415 0.0449 0.1121 0.0699 0.1170 0.0321
0.0730 0.1358 0.0544 0.0020 0.0898 0.0169 0.0711 0.1358 0.0020
Sustainability 2022, 14, 1166 24 of 27

Table A6. Cont.

Weighted Normalized Performance Value

Tamil Uttar Madhya PIS NIS
Maharashtra Gujarat Karnataka Andhra Pradesh
Nadu Pradesh Pradesh
0.0103 0.0026 0.0079 0.0060 0.0486 0.0107 0.0053 0.0486 0.0026
−0.0300 0.0131 0.0087 0.0433 −0.0028 0.0793 0.0004 −0.0300 0.0793
0.0228 0.1304 0.0677 0.0246 0.0502 0.0197 0.0336 0.1304 0.0197
0.0241 0.0055 0.0040 0.0222 0.0050 0.0421 0.0063 0.0040 0.0421

Table A7. Normalized decision matrix in MOORA method.

Normalized Decision Matrix

Maharashtra Gujarat Tamil Nadu Uttar Pradesh Karnataka Madhya Pradesh Andhra Pradesh
0.5007 0.3377 0.2762 0.4980 0.1990 0.4607 0.2432
0.3298 0.7764 0.1056 0.1535 0.0000 0.0000 0.5037
0.0000 0.0000 0.9785 0.0000 0.1165 0.0000 0.1701
0.4647 0.1461 0.8100 0.1461 0.2921 0.0000 0.0000
0.4624 0.2796 0.3105 0.0743 0.6705 0.3150 0.2391
0.2054 0.4927 0.3832 0.1644 0.6075 0.2162 0.3542
0.3166 0.5654 0.6218 0.0000 0.3182 0.1591 0.2587
0.6442 0.0244 0.2545 0.5335 0.4656 0.0313 0.1312
0.5838 0.3247 0.1604 0.2072 0.2242 0.5596 0.3489
0.3644 0.6778 0.2713 0.0101 0.4484 0.0841 0.3550
0.1968 0.0505 0.1513 0.1153 0.9327 0.2053 0.1024
−0.3104 0.1360 0.0906 0.4487 −0.0290 0.8214 0.0041
0.1392 0.7973 0.4139 0.1506 0.3068 0.1206 0.2056
0.4431 0.1013 0.0731 0.4083 0.0925 0.7743 0.1151

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