Analysis of Energy Conservation Factors in Buildings Using Interpretive Structural Modeling Methodology: An Indian Perspective

Abstract

Increasing daily energy demand is a cause of concern globally; buildings consume most of the energy generated, so energy conservation in buildings should be a prime concern to save energy worldwide. In the Indian context, among the previous studies to figure out the driving factors responsible for conservation of energy in buildings, they focused mainly on a sector of buildings or highly localized, lacking a holistic approach. The attempt to find building conservation factors is meager and is in scarcity. This research fills the gap by providing a holistic approach to driving factors responsible for energy conservation in buildings. The present study seeks to explore the interrelationships between the twenty factors identified in this research using interpretive structural modeling (ISM) methodology. It arrives at the dominant factors that are highly influential in driving conservation principles in built environments in India. Among the twenty factors identified , the results indicate that the six most highly impactful factors are industry orientation, comfort, change in lifestyle , saving the environment, promoting sustainability, and testing new theories in energy conservation. The managerial applications of the derived results of this research are also discussed.

Full text

ORIGINAL CONTRIBUTION
Analysis of Energy Conservation Factors in Buildings Using
Interpretive Structural Modeling Methodology: An Indian
Perspective
Syed Shuibul Qarnain
1
•S. Muthuvel
1
•Bathrinath Sankaranarayanan
1
Received: 7 February 2020 / Accepted: 21 October 2020
The Institution of Engineers (India) 2020
Abstract Increasing daily energy demand is a cause of
concern globally; buildings consume most of the energy
generated, so energy conservation in buildings should be a
prime concern to save energy worldwide. In the Indian
context, among the previous studies to figure out the
driving factors responsible for conservation of energy in
buildings, they focused mainly on a sector of buildings or
highly localized, lacking a holistic approach. The attempt
to find building conservation factors is meager and is in
scarcity. This research fills the gap by providing a holistic
approach to driving factors responsible for energy conser-
vation in buildings. The present study seeks to explore the
interrelationships between the twenty factors identified in
this research using interpretive structural modeling (ISM)
methodology. It arrives at the dominant factors that are
highly influential in driving conservation principles in built
environments in India. Among the twenty factors identi-
fied, the results indicate that the six most highly impactful
factors are industry orientation, comfort, change in life-
style, saving the environment, promoting sustainability,
and testing new theories in energy conservation. The
managerial applications of the derived results of this
research are also discussed.
Keywords Building energy efficiency Building
energy conservation Energy conservation factors 
Interpretive structural modeling
Introduction
India’s economic and population growth demands new
ways of sustainable construction. The concept of sus-
tainable construction endorses certain factors that help to
achieve sustainability in building construction sector.
Energy consumption problems require a well-defined set
of indicators or factors for analysis to achieve energy
efficiency in buildings [1]. India has doubled its energy
use from 2000 to 2017, and India’s energy use is expected
to exceed by double in 2040 [2]. India’s energy con-
sumption by buildings was 14% of the total delivered
energy in 2015. India will increase its share of delivered
electrical energy to residential buildings from 46% in
2015 to 68% in 2040. EIA projects the electricity share of
India’s total commercial energy consumption to continue
increasing, from 59% in 2015 to 65% in 2040, displacing
some coal consumption, signifying that the rate of energy
consumption in the Indian building sector will have the
fastest growth rate compared to other countries in the
world [3].
Figure 1shows that the domestic sector’s energy con-
sumption was 24% and by industry was 42%, signifying
that industry and domestics sectors together accounted for
most of the energy consumption in 2018–2019.
As a developing nation, India has witnessed huge energy
demand while sustaining the economic development of the
country [4]. India has many programs in place to achieve
energy efficiency in building sectors. Since 2000, energy
efficiency improvements in India prevented 6% of
&Bathrinath Sankaranarayanan
bathri@gmail.com
Syed Shuibul Qarnain
drqarnain@gmail.com
S. Muthuvel
muthuvel.s@gmail.com
1
Department of Mechanical Engineering, Kalasalingam
Academy of Research and Education, Krishnankoil,
Tamilnadu 626126, India
123
J. Inst. Eng. India Ser. A
https://doi.org/10.1007/s40030-020-00483-z

additional energy use in 2017 [2]. Some of the diverse
initiatives undertaken to control the energy demand and
make the building’s energy-efficient are: introduction of
Energy Conservation Building Code (ECBC) in 2007 by
Bureau of Energy Efficiency (BEE), India, Establishment
of a green rating system called GRIHA developed by TERI
[5]. National Building Code of India 2016 by Bureau of
Indian Standards (BIS) with a chapter on approach to
sustainability included Association for Development and
Research of Sustainable Habitats (ADaRSH) for small
stand-alone buildings, Leadership in Energy and Environ-
ment Design (LEED India) by the Indian Green Building
Council (IGBC), Eco-Housing rating system developed for
Pune, Energy efficiency labeling programs and the enact-
ment of the Energy Conservation Act 2001. Indian
Government has also introduced the National Smart Cities
Mission Programme to build more than 100 smart cities in
India, including sustainability features for buildings.
Despite many energy efficiency regulations and codes
existent in India, the implementation has been an enormous
challenge. For example, the energy codes draw less atten-
tion toward implementation, particularly in new and
existing buildings. Simultaneously, it is a key factor for
achieving energy efficiency in developed nations [5]. Even
though the Indian Government has introduced the Energy
Conservation Building Code (ECBC), its implementation
has not been reached extensively. Because ECBC
enforcement is not strictly followed. The enforcement
scope is left to the local and state governments. The
equipment side of buildings used for manufacturing is
exempted from the Code [6].
A study by the Indian planning commission estimates
the huge potential for capital investments in the energy
efficiency field in the existing built environment sector. An
investment cost accompanies most of the buildings’ energy
conservation features, but the cost is paid back in terms of
savings. The Indian Government had conducted many
studies in various Indian cities at the national level in
energy efficiency and energy conservation sector. The
studies revealed that most financial investments are paid
back in terms of energy savings, and the rate of return is
high for such energy efficiency retrofits [7]. The cost of
constructing a building with energy efficiency features and
constructing a conventional building remain the same [8].
For a building with energy efficiency features, the capital
investment on energy efficiency features pays back not
only in saving in energy and money but also in savings in
GHG emissions, carbon emissions, reduced carbon foot-
print, and the entire life cycle of the buildings [9]. There-
fore, the investment in energy conservation features in the
building is worthwhile in rational and irrational benefits.
The cost of a building’s energy-efficient design varies as
per the climatic zone and different Indian climatic condi-
tions. But energy conservation building codes and other
regulatory energy efficiency standards define India as five
climatic zones. Therefore, when designed in compliance
with these codes and standards, the energy-efficient design
automatically takes into effect the climatic variation’s
implication to provide an efficient output [6,10].
Classification of energy efficiency variables is vital to
analyze their impact on a category [2]. The 20 factors
selected herein were determined with a literature survey,
interviews, and consultation with energy experts. As this
research perceives buildings as a holistic approach, the
selection of factors was comprehensive. Therefore, current
factors from the contemporary research works have been
shortlisted from a whole group of factors collected. Only
twenty factors were finalized based on energy experts’
input using the Delphi technique; many rounds of short-
listing sessions were conducted until the factor elimination
point reached saturation. They are categorized into three
areas: economic factors, consumer behavioral factors, and
sustainability factors, as shown in Fig. 2.
Classification of Factors
Economic Factors
Economic factors can potentially impact a region’s econ-
omy through energy conservation projects, either through
capital investment or market orientation. Basu et al., in his
case studies conducted in India, have highlighted the
advantages and benefits of unlocking the potential of
conservation through investment mode [7]. The factors
provided by Arukala et al. in his research works show that
investment in energy conservation projects has led to
bringing good outcomes resulting in saving in energy [1].
The works by Basu et al. and Chandel et al. are good
examples of incentives and policies provided by the gov-
ernment of India in supporting investment in energy
Fig. 1 Electricity consumption in India sectorial 2018–2019 [4]
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J. Inst. Eng. India Ser. A

conservation projects [7,11]. The studies by Rupal et al. on
Indian educational institutions are an excellent example of
the payback period for energy conservation projects [12].
Sahoo et al., in his works, have substantiated the orienta-
tion of the Indian industry in achieving conservation and
energy efficiency in Indian building sectors [12]. Vaid and
Kar research works confirm the demand for energy-effi-
cient buildings in the market due to its economic savings
[13]. The factors by Kamal and Barpanda [14] and Vaid
and Kar [13] serve as examples of redundancy in energy
conservation in buildings in India. The above research
works are strong examples of energy factors affecting the
economy of India. It is evident that projects in energy
conservation while bringing in energy efficiency in the
building sector can strongly elevate the region’s economy.
Consumer Behavioral Factors
Consumer behavioral factors depend on end-users behavior
and daily routine; consumer behavior can affect energy
conservation through routine activities. In his studies
conducted in India, Sharma have shown the possession of
conservation knowledge by stakeholders and its benefits on
the buildings [8]. The field experiments conducted in India
by Chen et al. have proven that people who received
information on health factors have shown ‘‘will and self-
motivation’’ toward conserving energy in buildings [15].
Kumar have reviewed major management planning and
management problems in the Indian energy sector [16].
The research works of Mehndi and Chakraborty [17] pro-
vide a case for retrofit analysis by consumers in Indian
buildings, by which energy can be saved. Gandhe et al.
offer a psycho-socio analysis of human behavior toward an
energy-saving attitude that can impact human lifestyle and
comfort [18]. His field experiments in Bhopal have shown
a strong consumer-related effect on middle-class and
middle-income people’s energy-saving culture. Kamal and
Barpanda have provided consumer behavioral patterns in
Indian energy consumers [14]. His research is focused on
higher educational institutes and students. All the above
research studies from India are focused either on one par-
ticular or are very localized to one specific sector, but show
strong influences on consumer behavior and the effect on
energy conservation.
Sustainability Factors
Energy conservation is one of the key components for
attaining sustainability. Shukla and Zia’s works provide
necessary mechanisms, policies, and tools in India to
achieve energy conservation [19]. The research works by
Sharma [8], and Basu et al. [7] consider the effects of
environment and climatic conditions in attaining energy
conservation that leads to sustainability. Shukla and Zia,
[19], Vyas et al., and Sharma [8] in their research work
provide a descriptive account of the depletion of natural
resources and their effects on the environment. Their
results propose solution methodologies for mitigating the
impact of the exploitation of resources. Chaudhary et al.
[20] and Vyas and Jha [21] also stress the importance of
innovations in the energy conservation field to reap the
benefits of new ideas never attempted before. Chaudhary
et al. recognize change as an essential component of sus-
tainability; it also analyzes the scope and provides rec-
ommendations of innovations used in the Indian energy
conservation sector [20]. Table 1lists factors correspond-
ing to a factor number, description, and reference.
Research Problem, Objectives and Scope
The excessive energy buildings consume resulted in a
massive challenge to development over the last couple of
decades. It has caused an economical energy burden and
social energy scare in a region’s growth and progress.
Moreover, urban lifestyle (consumer behavior) in building
communities has added to this problem’s woes. Although
contemporaries declare that ‘‘energy demand and energy
supply’’ have been fulfilled, there still exists a ubiquitous
rise in demand for energy every year. Building communi-
ties and built structures are the major cause of this problem.
The solution lies not in satisfying the ever-increasing
supply and demand corridor, but in analyzing and influ-
encing factors responsible for the significant rise in energy
demand.
While conducting the energy audit of multi-story
buildings that were a representative sample of buildings of
a city, we discovered substantial electricity bills which
Fig. 2 Classification of factors for energy conservation
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J. Inst. Eng. India Ser. A

presented a concern. An energy audit of a building will
provide the necessary information on the scope of energy
conservation in any building. The scope of energy-saving
opportunities can be assessed through the energy audit
study of representative buildings of any city. This finding
motivated us to pursue this research and craft a solution
that might address not just one building. Still, it may also
influence an entire ‘‘community of buildings’’ and influ-
ence a region’s current trends and usage. A survey was
conducted face-to-face and by telephone, with twenty dif-
ferent energy experts from the Indian subcontinent. The
respondents included experts from India, with experience
in design, construction, industry, and academia. This
research attempts to bring forth a solution to the above
problem by investigating factors that can influence and
reduce energy consumption.
Because buildings consume energy, the demand for
energy in cities naturally increases, posing a critical social
energy problem and a burden on the country’s regional
economic growth. The energy produced to supply this
demand creates various environmental disruptions such as
climate change, environmental imbalances, depletion, and
natural resources pollution. Therefore, to nullify the effect
of buildings’ energy use, it is necessary to build and
maintain buildings that are energy efficient so that factors
central to energy conservation are given prime importance.
This research study attempts to analyze these factors and
their impact on energy conservation. Energy experts in the
field of buildings are consulted to arrive at twenty factors
mentioned in this paper. Some attributes affect building
communities to follow the conservation path. This research
study presents a critical analysis of the most influential
factors on energy conservation about buildings.
This research study’s main objectives are
(a) To identify and classify the factors into different
levels based on interpretive structural modeling
methodology.
(b) Analyze the most influential factors that help to save
energy in buildings and their interrelation with others.
The scope of this research covers only permanent,
immovable buildings on the land and the factors that
effects conservation of energy in these buildings. This
research’s scope does not include temporary structures,
moveable or mobile buildings, building structures on water
such as ships, and built environment in the atmosphere
such as airplanes.
This research work attempts to answer the following
research questions:
1. What factors can be controlled to achieve energy
conservation in buildings?
2. Which are the factors that are extremely important and
more impactful contributing to energy conservation in
buildings?
This research is significant because the results of this
research represent the real-life mainstream current factors
trending in building the conservation industry. The inputs
are obtained from real-time energy practitioners engaged in
extensive energy conservation work with building energy
experience. The second important contribution of this
research work is that the results are specific to India and are
narrow, so the application is more impactful, accurate, and
result-oriented.
Methodology
This research follows the MCDM-based ISM methodology.
ISM is a structural modeling process that is interactive and
intuitive; through ISM, diverse and directly related ele-
ments are modeled into a comprehensive systematic
structure [35]. The structure implicates the complexity of a
specific problem in an area and expresses the problem
pattern in visible models. ISM converts experts’ inputs into
a multilevel structural model, and it decomposes the
problem into different sub-elements. ISM is also used to
identify relationships among specific items and define a
problem or an issue [36,37]. In short, ISM is a kind of
group learning process that can also be used for individual
problem-solving methodologies. According to existing lit-
erature, no researcher has used this methodology to
determine the factors’ levels in buildings’ energy conser-
vation. The steps involved in the ISM methodology are
presented in Fig. 3.
Application of the Model to the Case Illustration
The proposed ISM model is applied as an Indian approach
for energy conservation cases in building communities.
The proposed case illustrates variables with high influence
in driving the principle of conservation of energy in a large
south Indian urban neighborhood. The ISM model is for-
mulated with inputs from the Indian expert panel of
building designers, energy experts, building construction
contractors, consultants, and academia. In this research, the
case of trailing energy in a built environment of South
India is illustrated, and a robust solution to overcome the
case is presented in the outcome. The following subsec-
tions are pertinent: Structural Self-Interaction Matrix
(SSIM), reachability matrix and level partitions, formula-
tion of ISM structural model, and MICMAC analysis.
The opinions of the experts were collected through a
survey. The demography of the expert panel and
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questionnaire are provided in Appendix (Table 8). A
questionnaire was framed and circulated among 20 highly
experienced building energy efficiency professionals, and
their responses were collected. Among these 20 profes-
sionals, five were doctoral degree holders in energy-related
fields, seven were master degree holders in energy-related
fields, and eight were graduates in fields related to building
services. Furthermore, about their domain of working, five
were university professors from academics, six were spe-
cialists from energy systems execution and installation, six
were design engineers in various consultancies, and three
were on-site energy engineers from mainstream energy
design contractors. All experts possess more than ten years
of experience in building energy systems. The responses
were collected through telephonic calls and face-to-face
interviews. The average time taken by an expert to com-
plete the questionnaire was about 90 min.
Structural Self-Interaction Matrix (SSIM)
Structural Self-Interaction Matrix (SSIM) represents the
contextual pair-wise relationship among all factors
(Table 2). The symbols i and j are used to denote the
relationship (the factors in this research study). Symbol ‘V’
states that ihelps to achieve j; Symbol ‘A’ states that
jhelps to achieve i; symbol ‘X’ represents that iand
jmutually help each other, and symbol ‘O’ means factors
iand jdo not help each other.
For instance,
•Capital investment for energy conservation projects
(F1) will help test and achieve new theories/innovations
in energy conservation (F20). The interrelationship of
‘V’ is achieved for SSIM.
•Demand for energy conservation in buildings (F13) will
help achieve more capital investment in energy con-
servation projects (F1); the interrelationship ‘A’is
achieved for SSIM.
•Capital investment for energy conservation projects
(F1) and keeping abreast with technology (15) help
each other; the interrelationship ‘X’ is achieved for
SSIM.
Capital investment for energy conservation projects (F1)
and change in lifestyle (F16) are not interrelated; hence,
‘O’ is achieved for SSIM.
Reachability Matrix and Level Partitions
Reachability matrices are developed with the help of SSIM
from Table 2. The initial reachability matrix in Table 3is a
binary matrix with zeros and ones. If there is an interre-
lation between building energy efficiency factors, then the
value is considered 1 and if there is no interrelation
between the factors, the binary value 0 is considered. The
V,A,X, and Ovalues are filled according to Rule 1 to Rule
4 given below [36]:
Table 1 List of factors with factor number, description, and references
Factor No. Factor description References
1 Capital investment for energy conservation projects [1,20,22,23]
2 Possessing knowledge in energy conservation [1,5,24]
3 Attractive incentive schemes from government [11,23,25]
4 Will and self-motivation toward energy conservation [15]
5 Availability of energy conservation resources [16,19]
6 Favorable climatic environment for energy conservation projects [7,19,21]
7 Payback period for conservation investment [1,12,23]
8 Depletion of energy resources [19,26]
9 Mismanagement of energy [16,24]
10 Health factors/diseases [15,27,28]
11 Industry orientation [29,30]
12 SHIFT-retrofitting and makeover of energy conservation measures (ECM) [7,17]
13 Demand in the market for energy conservation structures [8,13]
14 Comfort and luxury [18,31]
15 Abreast with technology [8,14,31]
16 Change in lifestyle [18,31]
17 Redundancy of conventional source [14,21]
18 Save environment [21,24,27]
19 Promote sustainability/educate/advertise & create awareness in the community [18,32,33]
20 Test new theories/innovations in energy conservation [20,34]
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J. Inst. Eng. India Ser. A

The following rules are adopted to formulate reacha-
bility matrices:
Rule 1:if(i,j) entry in the SSIM is V, then (i,j) entry in
the reachability matrix is set to 1 and (j,i) entry is set to 0
Rule 2:if(i,j) entry in the SSIM is A, then (i,j) entry in
the reachability matrix is set to 0 and (j,i) entry is set to 1
Rule 3:if(i,j) entry in the SSIM is X, then (i,j) entry in
the reachability matrix is set to 1 and (j,i) entry is set to 1
Rule 4: if (i,j) entry in the SSIM is O, then (i,j) entry in
the reachability matrix is set to 0 and (j,i) entry is set to 0
Table 4represents the final reachability matrix con-
structed from the antecedent initial reachability matrix
Fig. 3 Flow diagram for preparing the ISM
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J. Inst. Eng. India Ser. A

using the transitivity rule, which states that if A=B and
B=C, then A=C. This transitivity rule is the basic
assumption, laying the foundation of ISM to construct final
reachability matrix in Table 4[36].
Table 5represents the level partitions which are graded
into nine levels. The reachability set and antecedent set are
derived from the final reachability matrix. For a single
driver, the reachability set contains the driver and other
drivers, which might help it. The antecedent set contains
drivers that help to achieve and also the driver. The
intersection of both the antecedent and reachability set is
provided in Table 5. If both sets (reachability and inter-
section) are identical, then the factor is graded as in level 1
and represented in the top hierarchy. After the above
grading, level 1 factors are removed, and an iteration is
performed. Similarly, iterations are done until the last
factor is graded into a level. All factors graded in an ISM
structural model are depicted in Fig. 4.
MICMAC Analysis
MICMAC stands for ‘‘Matriced Impacts Croises-Multipli-
cation Applique and Classement’’ (cross-impact matrix
multiplication applied to classification). MICMAC analy-
sis’s basic objective is to examine the factors’ driver power
and dependence power [37]. According to this research, the
factors are grouped into autonomous factors, dependent
factors, linkage factors, and independent factors.
The characteristics of all four categories of factors that
are classified are evident in Fig. 5. The first category rep-
resents autonomous factors; Category I has weak driving
power and weak dependence power. Category II represents
dependent factors, with weak driving power but strong
dependence power. Category III represents linkage factors
with strong dependence power and strong driving power.
Category IV represents independent factors, with strong
driving power but weak dependence power. Table 6shows
the numerical values of driving and dependence powers for
all factors. Policymakers and decision-makers can select
factors with various combinations of these values to suc-
cessfully apply energy conservation projects to get the
intended result.
In Fig. 6, Category III has a maximum number of fac-
tors; Category III factors are also called linkage factors, as
shown in Fig. 5. This category of linkage factors is con-
sidered the strongest category among all four categories in
the graph; Category III factors are characterized by high
driving and dependent values. In Fig. 6, the absence of
factors in other categories except for Category III and II
implies that the proposed research factors are highly
influential. Any small change in these factors can highly
influence other factors in the system. In Figs. 5and 6,itis
observed that the Category III factors are characterized by
Table 2 Structural self-interpretive matrix (SSIM)
Factor
number
20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
1VOVOOXOAVAOOOOAOOAO–
2VXV OOXOOV OOOOOOOAA –
3VVVOOVOVVVOOOOOVV–
4VVVOVVOAVAOOXAAA–
5VOVVVVOAVOOOOOA–
6VOOOVOOOV XOOOO–
7VXAAOVOVVVOOO–
8OXVVOOOOVVOA–
9OVVOVVOVOVV–
10 VVXOXOOVVV–
11 XXVVXOXVO–
12 OAVXXXXA–
13 VAOOXVO–
14 OOOAXV–
15 VVVOO–
16 OOXO–
17 OOV–
18 XV–
19 O–
20 –
123
J. Inst. Eng. India Ser. A

Table 3 Initial reachability matrix
1000000000
0100000000
1111100000
0101000100
0001100000
1001110000
0001001000
0001000100
0000000111
0000000001
1001010000
0000000000
1001100000
0000000000
1100000000
0000000001
0000001000
0000001001
0100001100
0000000000
0100100101
0100100111
1110100111
0100110111
0100111101
1100010001
1110100011
1100001110
1010110110
1110010111
1011011111
0101111100
0110110001
1101110000
0100100111
1111010100
0101001100
0000010111
1110000010
1000000101
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Table 4 Final reachability matrix
1100001001
1100001101
1111111101
1101001101
1101101101
1101110101
1101111100
1101011101
1101111111
1101111101
1101111101
1100001001
1101100101
1101010001
1100001101
0001111001
0001001001
1101001101
1101111100
1001011001
1101111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111111111
1111110111
1111111111
1011011111
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J. Inst. Eng. India Ser. A

high driving power and high dependent power, called
linkage factors. They signify that a small change to these
factors can have a huge impact on the entire system
because of high driving and dependent values. These fac-
tors can influence all other factors in the system, creating
links with other factors; they are also called linkage factors.
These factors are considered as among the significant
factors from all other factors in the system.
Results and Discussion
Figure 6shows a graph of driving power at the x-axis and
dependent power at the y-axis. Factors are represented on
the graph. The graph is divided into four sectors. The first
sector, toward the lower-left corner, represents autonomous
factors characterized by low driving power and low
dependent power; the second sector on the upper left rep-
resents dependent factors characterized by low driving
power but high dependent power. The third sector at the
extreme right upper corner represents linkage factors
characterized by high dependent power and high driving
power. Most factors are characterized by high driving
power and high dependent power. Finally, the fourth sector
in the lower extreme right corner represents independent
factors characterized by high driving power but low
dependent power.
Apart from the MICMAC analysis, it is observed from
the initial reachability set, antecedent set, and intersection
set that there are nine structural hierarchy levels repre-
sented by twenty factors. Six factors in Table 7are at the
top level. One should be careful not to ignore the impor-
tance of other levels of the hierarchy because it is due to
the interaction and participation of those factors that have
pushed the six factors to the topmost level. Every factor in
the SSIM matrix has contributed to the other factor and
toward itself. Therefore, managers and decision-makers
should be aware of this before implementing any factor in
their domain. Table 6represents the levels of the factors,
while Table 7represents first-level factors.
The six factors at Level I in Table 7are the key variables
that affect energy conservation on a larger scale. The ori-
entation of industry and the market toward energy-efficient
devices and technologies could reduce energy
Table 5 Level partitions
Factor
No.
Reachability set Antecedent set Intersection set Level
11 1245678101112131415161718
19 20
123456789101112131415161718
19 20
12456781011121314151617
18 19 20
I
14 1 2 4 6 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20
124610111213141516171819
20
I
16 1456710111213141516171819
20
123456789101112131415161718
19 20
14567101112131415161718
19 20
I
18 2 4 7 8 10 11 12 13 14 15 16 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20
247810111213141516181920 I
19 1245678111213141516171819
20
123456789101112131415161718
19 20
12456781112131415161718
19 20
I
20 1 4 6 7 10 11 13 14 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19 20
1 4 6 7 10 11 13 14 16 17 18 19 20 I
12 1271012131517 1234567891012131517 1271012131517 II
15 12781012131517 1234567891012131517 12781012131517 II
17 4 7 10 12 13 15 17 1 2 3 4 5 6 7 8 9 10 12 13 15 17 4 7 10 12 13 15 II
1 12710 1234567891013 12710 III
2 1 2 7 8 10 13 1 2 3 4 5 6 7 8 9 10 13 1 2 7 8 10 13 III
4 4781013 34567891013 4781013 IV
8 4 6 7 8 10 13 3 4 5 6 7 8 9 10 13 4 6 7 8 10 13 IV
13 4581013 34567891013 4581013 IV
5 5 7 10 3 5 6 7 9 10 5 7 10 V
6 610 367910 610 VI
7 7 37910 7 VII
10 10 3 9 10 10 VIII
33 3 3 IX
99 9 9 IX
123
J. Inst. Eng. India Ser. A

consumption. For example, the non-availability of incan-
descent bulbs in the market led end-users to rely instead on
energy-efficient LED lights, which resulted in high energy
savings [38]. Similarly, a change in lifestyle, comfort, and
luxury has great potential to conserve energy. For example,
switching over to smart homes and altering our behavior
toward an energy consciousness routine, efficient man-
agement of our home appliances, and switching off lights
and air conditioners during non-occupancy times are some
behavioral changes that can contribute to energy
Fig. 4 ISM structural model
J. Inst. Eng. India Ser. A
123

conservation [39]. The versatility in factors-pairing with
each other to produce a positive manifestation and syn-
chronizing to achieve one another without a trade-off is one
reason for the six factors in Table 7to be placed in the level
1 hierarchy. These factors have contributed to themselves
and also to other factors to reach high driving and depen-
dence power.
Level II factors (F12, F15, and F17) affirm the link
between economic and behavioral factors. Energy systems
in built environments associated with either electrical or
mechanical systems work efficiently in their initial years of
commissioning. For example, as per ASHRAE, the life
expectancy of a window air conditioner is ten years. After
that, the efficiency starts to deteriorate. After a certain
period, the energy systems become inefficient, increasing
the facility’s utility bills and its operational and mainte-
nance costs. Level II factors are highly instrumental in
overcoming these institutional barriers toward energy
conservation.
Factors F13, F15, and F17 of Level II are applied as
remedial measures to overcome the energy inefficiency
when energy conserving measure (ECM) measures are
adopted. Such actions may be in the form of retrofitting or
a technology upgrade. Factor F15 calls to be abreast of the
latest technology to maintain energy conservation. Some
examples include building information modeling in design,
the concept of smart cities, and the use of blockchains in
energy conservation sectors.
Table 6 Dependence power and driving power
Factors 1 2 3 4 567891011121314151617181920Driving
power
1 1100001001110111111113
2 1100001101111111111115
3 1111111101111111111119
4 1101001101111111111116
5 1101101101111111111117
6 1101110101111111111117
7 1101111100111111111117
8 1101011101111111111117
9 1101111111111111111119
10 1101111101111111111118
11 1101111101111111111118
12 1100001001111111111114
13 1101100101111111111116
14 1101010001111111111115
15 1100001101111111111115
16 0001111001111111111116
17 0001001001111111111113
18 1101001101111111011114
19 1101111100111111111117
20 1001011001101101111113
Dependence
power
18 17 1 16 10 11 17 14 1 18 20 19 19 20 19 20 19 20 20 20
Table 7 First level factors
Factor No. Name of the factor Level
11 Industry orientation I
14 Comfort and luxury I
16 Change in lifestyle I
18 Save environment I
19 Promoting sustainability/educating/creating awareness I
20 Test new theories/ innovation in energy conservation I
J. Inst. Eng. India Ser. A
123

Level III factors (F1 and F2) are interlinked between
capital investment for energy conservation projects and the
right place for investing to achieve the intended goal.
Adequacy of knowledge in energy conservation is essential
to achieve it, and these are considered the primary barriers
in the adoption of energy conservation practices [40]. The
impact of knowledge or the lack of knowledge in investing
in energy conservation projects could be large. Therefore,
the knowledge gap should be filled before an investment in
energy is attempted.
Level IV factors (F4, F8, and F13) are a combination of
economic, behavioral, and sustainability factors. These
findings emphasize that energy conservation principles are
behavioral practices among the system; secondly, there
should be a felt social responsibility for a collective sus-
tainable future and a will generated. It has been observed
that owners will pay more for buildings with energy effi-
ciency features [41]. Energy conservation should be a day-
to-day practice and should be part of an individual’s culture
to accomplish. Suppose this sense of responsibility is not
nurtured as a culture. In that case, the ultimate result will
be an exploitation of energy, thereby depleting resources
that produce it and prompting an energy-saving culture in
coercion. Moreover, if the principles of energy conserva-
tion are applied in a building, its value is increased. In turn,
it creates a demand for such structures due to the potential
it has for saving energy and the corresponding financial
savings for the owner of the building.
Level V factors (F5) emphasize the importance of
resources for energy conservation and utilization. Any
energy conservation project’s success depends on resources
such as sustainable materials, skilled, knowledgeable
workers, and ease of implementation of technology without
legislative barriers. These resources should exist for energy
conservation to prevail. Factor F5 emphasizes the essential
need to fulfill this conservation principle.
In Level VI, factor F6 emphasizes favorable climatic
and environmental conditions with respective geographical
areas needed for energy conservation projects to existing.
Consider an example of renewable energy (either solar or
wind). Renewable energy is an alternative for electricity,
but this resource helps in energy conservation only if
sunlight or wind is available throughout the year. Without a
dependable resource, its production is not viable, as the
return on investment and payback period will suffer, and
the project will suffer a huge loss in capital. Therefore,
favorable environment conditions should exist for any
respective energy conservation principle to be applicable.
No investment will be lucrative if the payback period is
not viable. Factor F7 emphasizes capital investment for
energy conservation in Level VII and denotes the invest-
ment projects’ payback period. Recent studies have shown
that capital investments in energy conservation projects
have tangible benefits, including saving energy with a good
return on investments. It also enhances the facility’s real
estate value and contributes to reducing carbon emissions
[42]. If health benefits using energy conservation practices
are communicated to households, there will be a relative
impact on cost savings. The ill-effects of electricity gen-
eration by a thermal power plant can cause childhood
asthma and cancer and, additionally, it may release tons of
pollutants into the atmosphere [43]. If these health effects
are communicated to users, they may prefer to use
renewable energy generation practices. Factor F10
emphasizes and calls for conserving energy to prevent its
ill effects on human health. Factors F3 and F9 interlink
incentive schemes and governmental policies in energy
management. Programs like demand response to discour-
age energy use at peak hours and avoid load shedding
should be encouraged. Energy legislation promoting
incentive schemes helps distributors and energy suppliers
to manage energy sustainably. The hierarchical structural-
level diagram provides interlinking between factors and
their interdependence on one another to attain goals.
Policymakers and energy managers often find difficulty
in overcoming the barriers in implementing energy man-
agement. This research study presents various factors
responsible for overcoming the barriers to energy conser-
vation. Decision-makers should realize that before pro-
moting any factor, it may interact with other factors
resulting in either a pessimistic or optimistic situation for
energy conservation. This is because each factor is inter-
linked with another, and each interacts with others based
on real-time situations. Therefore, the resultant situation is
the output of interaction of all factors combined. This paper
has attempted to find the outputs. From the interpretive
structural matrix (ISM), it is evident that most factors listed
in Table 7have a high potential toward influencing a built
structure and an urban building community to adopt energy
conservation measures. Factors 16, 17, and 19 (changes in
lifestyle, redundancy of energy source, and saving the
environment) seem to have little effect. In contrast, capital
investment, which possesses knowledge of energy conser-
vation and attractive incentive schemes from the respective
legislative authority, seems to have maximum impact.
Establishments should prioritize those factors with depen-
dent power and driving power, which have the power to
influence energy conservation barriers.
There are no factors in sector 1 of the graph. In other
words, there are no factors with weak driving power and
weak, dependent power. Similarly, in sector 2 of the graph,
there are no factors with high driving power and low
dependent power. By diagnosing dominant factors, the
industry can orient itself toward energy conservation. In
contrast, if dominant factors are found for any specific
industry, it will be easy to eliminate non-dominant or
J. Inst. Eng. India Ser. A
123

submissive factors. Therefore, to drive the building
industry toward adopting energy conservation measures,
sector 3 of the graph is highly influential. This paper aims
to find the most influential factors to drive managers of
buildings to adopt energy conservation measures to benefit
society; hence, promoting highly impactive factors cat-
alyzes conservative energy measures in the community and
drives the economy toward stable energy management.
Managerial Implications
All the above factors will affect only if the leadership of a
particular area of the given factors acts responsibly,
implements, and makes decisions for the growth and pro-
motion of energy conservation. For a careful analysis of the
existing situation in various countries, some of the sug-
gestions are:
(a) For Factor 1, capital investment, banks should provide
loans and security at concessional interest rates for
energy conservation projects. The beneficiary of this
investment would be the entire region in which the
investment is made, and it will benefit the people and
the environment of that geographical area.
(b) For Factor 2, possessing knowledge and dissipation of
energy conservation knowledge should be intuitive
ways targeting end-users of all ages, ranging from
school children to adults. This can be accomplished
by indoctrination of energy conservation. In schools
and colleges, workshops and campaigns in offices,
and advertisements in mass media and social media
emphasize energy conservation.
(c) For Factor 3, attractive incentive schemes from the
government, various schemes, and policies should be
framed by the government to attract energy conser-
vation investment. For example, an industrial tax levy
for about 5 years on new energy conservation
equipment for industries is a good option.
Fig. 6 Dependence power vs driving power [36]
Dependence Power (Y)
Category II
(Dependent Factors)
Category III
(Linkage factors)
Category I
(Autonomous Factors)
Category IV
(Independent Factors)
Driving Power (X)
Fig. 5 Driving power and dependence power diagram [36]
J. Inst. Eng. India Ser. A
123

(d) For Factor 9, a considerable amount of taxpayer
money and public funding dollars are spent in
building energy infrastructure like power grids,
thermal power plants, and so forth. Punitive measures
like fines should be imposed on the end-user if energy
is wasted to discourage energy wastage and promote
responsible energy use.
(e) Factors 18, 19, and 20, saving the environment,
promoting sustainability, and testing new theories and
innovation in energy conservation, respective legisla-
tures should be in effect. Leaders should establish
research councils and think tanks by the government
for every state and a particular geographic area to
promote energy conservation. ‘‘Annual Energy Con-
servation Day’’ should be conducted, and prizes and
rewards recognizing significant contributions by the
end-user should be established. Residential, indus-
trial, and educational sectors should be clearly defined
because energy conservation efforts and energy
consumption patterns in each sector are different.
Energy conservation Hackathons and festivals pro-
moting new ideas at the institutional level should also
be encouraged.
The above are examples of energy management the
authors intend to pursue further. A full paper on the man-
agerial implications is planned after the ongoing research
study following various stakeholders’ consultation. This
paper could also serve as a roadmap for policymakers and
managers to control and manage factors responsible for the
conservation of energy in urban buildings and
communities.
Conclusion
The conclusions from the outcome of this research are:
(a) This research’s major contribution is that it has
analyzed 20 building energy conservation factors and
has provided a visual model of hierarchical structure
for energy conservation factors with 9 levels.
(b) The six factors at the topmost level in Fig. 4are the
factors with high priority. These factors are consid-
ered top drivers of energy conservation in buildings.
(c) Based on the driving power, it is found that the six
factors according to the level of priority are: Industry
orientation (F11), promote sustainability (F19),
Change in lifestyle (F16), Comfort and Luxury
(F14), Save environment (F18), Testing of new
theories (F20).
(d) Among the twenty factors, only two factors are
independent factors present in Category IV in Fig. 6.
The two independent factors are: Attractive incentive
Schemes from Government and Mismanagement of
Energy.
(e) It is observed from Fig. 6that the absence of energy
conservation factors in Category I and Category II
strongly substantiates the fact that this proposed
research has considered only influential factors and
not weak factors for analysis.
This paper presents the results based on the interpretive
structural modeling methodology for a particular geo-
graphic area with its limitations. For example, the complete
modeling is based on Indian experts’ judgment consulted
and opinions based on their experiences. Experiences may
differ from one expert to another expert based on different
situations. In this paper, ISM is used to collect the diverse
opinions of subject experts in energy conservation. The
future extension of this research could be to repeat the
same methodology for a different geographical area or
country to know the building conservation factors. This
paper used the interpretive structural modeling methodol-
ogy for prioritization. Furthermore, researchers can use
multi-criteria decision-making tools such DEMATEL,
AHP, VIKOR to know the interrelationship and behavior
of factors among one another.
Appendix: Questionnaire
Below is the format of the questionnaire used in the survey.
Demographic profile of the expert:
1. Name: -……………………………………………
2. Educational Qualification: -………………………….
3. Energy conservation Experience (in years): -………
4. Name of Organization: -
………………………………………………
5. Current position in organization: -
……………………………………
6. Telephone number & Email: -
…………………………………………
7. Sector (Industry/Academic/Contractor/Design Consul-
tant/etc.): -……………………
Based on your experience in the industry and your
knowledge, kindly fill up the table with the following
criteria:
J. Inst. Eng. India Ser. A
123

V:ihelps to achieve j
A:jhelps to achieve i
X:iand jhelp each other to achieve mutually
O:iand jdo not help each other
Your responses will be dealt with in complete confi-
dence; it will not be shared on any public platforms or
social networking sites. It will not be shared with your
competitor or any third party. These data are for research
purposes only.
As a subject expert and specialist in energy conserva-
tion, your input is valuable as it will help bring about a
productive outcome in this research.
My heartfelt gratitude for your time and effort.
Factor
number
20j 19j 18j 17j 16j 15j 14j 13j 12j 11j 10j 9j 8j 7j 6j 5j 4j 3j 2j 1j
1i
2i
Table 8 Demographical details of expert panel
Sl
No.
Experts Designation Qualification Experience in years
1 Expert 1 Professor PhD 16 years—academic
2 Expert 2 Professor PhD 10 years—academic
3 Expert 3 Professor PhD 15 years—academic
4 Expert 4 Professor PhD
5 Expert 5 Professor PhD
6 Expert 6 Project Manager—Execution of energy systems for
buildings
PhD 12 years in execution and contracting
7 Expert 7 Energy System Expert-LEED and energy efficiency Master
degree
13 years in execution and contracting
8 Expert 8 Engineer in conservation and energy efficiency Master
Degree
12 years—design consultancy
9 Expert 9 Building Services Engineer Master
Degree
10 years—design consultancy
10 Expert
10
Building Services Engineer Master
Degree
11 Expert
11
Design Engineer Graduates More than 10 years of experience in design consultancy
12 Expert
12
Graduate
13 Expert
13
Design Engineer Graduate
14 Expert
14
Design Engineer Graduate
15 Expert
15
Design Engineer Graduate
16 Expert
16
Design Engineer Graduate
17 Expert
17
Design Engineer Graduate
18 Expert
18
Energy Engineer Graduate
19 Expert
19
Energy Contractor Graduate More than 10 years of experience in execution and
contracting
20 Expert
20
Energy Contractor Graduate
J. Inst. Eng. India Ser. A
123

Appendix continued
Factor
number
20j 19j 18j 17j 16j 15j 14j 13j 12j 11j 10j 9j 8j 7j 6j 5j 4j 3j 2j 1j
3i
4i
5i
6i
7i
8i
9i
10i
11i
12i
13i
14i
15i
16i
17i
18i
19i
20i
The following table describes the factors with factor
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