Waste Management 130 (2021) 48–60

Contents lists available at ScienceDirect

Waste Management
journal homepage: www.elsevier.com/locate/wasman

A hybrid Pythagorean fuzzy AHP – CoCoSo framework to rank the

performance outcomes of circular supply chain due to adoption of its
enablers
Swapnil Lahane ⇑, Ravi Kant
Mechanical Engineering Department, Sardar Vallabhbhai National Institute of Technology, Ichchhanath, Surat 395007, Gujarat, India

a r t i c l e i n f o a b s t r a c t

Article history: In the last few years, the Circular Supply Chain (CSC) has gained considerable attention among
Received 5 February 2021 researchers, practitioners, and policymakers. It offers immense opportunities to embrace supply chain
Revised 30 April 2021 operations in three dimensions of sustainability. This study aims to identify and rank the performance
Accepted 11 May 2021
outcomes (POs) realized due to CSC enablers (CSCEs) adoption. The study proposes a hybrid framework
of the Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy combined
compromised solution (PF-CoCoSo) to achieve the objectives of this research. PF-AHP is used to obtain
Keywords:
the CSCEs relative weights while PF-CoCoSo is used to ranks the POs concerning the CSCEs. An empirical
Circular supply chain
Circular economy
case study is conducted for an Indian manufacturing organization to demonstrate the proposed
Enablers framework’s applicability. The result reveals that ‘global climate pressure and ecological scarcity of
Performance outcomes resources’ is the most significant CSCE to achieve the sustainability in the supply chain, followed by
Pythagorean fuzzy AHP ‘government rules, legislations and directives for CSC adoption’, ‘environment management certifications
Pythagorean fuzzy CoCoSo and systems’, whereas, ‘reduces waste and promotes green development’ is the most critical PO realized
due to adoption of CSCEs in CSC implementation process. The proposed framework is a systematic, more
comprehensive, accurate, and structured approach to the business organization to improve its POs in a
step-wise manner by implementing CSCEs. Sensitivity analysis is performed to check the effectiveness
of the proposed framework. This research provides substantial contributions to sustainable development
in the society as well as in the industry, and it will help researchers, practitioners, and policymakers
working in the domain of CSC.
Ó 2021 Elsevier Ltd. All rights reserved.

1. Introduction (Farooque et al., 2019). CSC is introduced as a novel concept that

incorporates Circular Economy (CE) thinking into the organiza-
The rising population, rapid economic growth, fast tional supply chains (Lahane et al., 2020). It acts as an alternative
urbanization, and varying living standards have significantly solution to the dominant linear (i.e., take, make and dispose of)
declined the earth’s available natural resources (Hasanuzzaman, supply chain model (Batista et al., 2018). Further, it boosts the CE
2021; Lieder and Rashid, 2016). Consequently, it increases environ- value creation and value proposition aspects in the business orga-
mental pollution, waste generation, causes ecological degradation, nization (Cainelli et al., 2020; Mangla et al., 2018). CSC interlinked
and resource scarceness (Tauqeer et al., 2021; Goyal et al., 2018). the various subjects of industrial ecology, such as regenerative
Ecological degradation and pollution-related issues are also caused design (Rosa et al., 2019), process integration (Lahane et al.,
due to the presence of different organic and inorganic pollutants in 2020), extended producer responsibility (Ansari et al., 2019), eco-
the ecosystem generated from various industrial activities across industrial park (Batista et al., 2018), industrial symbiosis (Lahane
the firms (Rasool et al., 2021; Iftikhar et al., 2021; Zubair et al., et al., 2020), reverse logistics (Guarnieri et al., 2020), environmen-
2021). Hence, to attain sustainability in various business organiza- tal stewardship (Govindan and Hasanagic, 2018), eco-innovation
tions, there is a need to implement sustainable initiatives such as (Goyal et al., 2018), industrial economy (Geissdoerfer et al.,
the Circular Supply Chain (CSC) in manufacturing organizations 2018), etc.
CSC enhances the ecological aspect and improves the socio-
economic issues of a business organization (Kurita and Managi,
⇑ Corresponding author. 2021; Guarnieri et al., 2020; Korhonen et al., 2018). Developed
E-mail address: swapnillahane03@gmail.com (S. Lahane).

https://doi.org/10.1016/j.wasman.2021.05.013
0956-053X/Ó 2021 Elsevier Ltd. All rights reserved.
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

nations had already implemented CE strategies in their business The paper’s remainder is structured as follows: Section 2
from the last three decades, which form an integral part of their reviews the literature on CSCEs, POs and finds the research gaps.
supply chains (Batista et al., 2018). They have well-developed laws, Section 3 explains the solution methods adopted in this research.
stringent policies, regulations, and modern infrastructure for CSC Section 4 describes the proposed research framework. The empiri-
adoption (Garrido-Hidalgo et al., 2020; Sharma et al., 2020). cal application of the proposed research framework is presented in
Emerging economies still lag in adopting such practices in their Section 5. Section 6 presents the results and discussion of the
supply chain operations, especially in India (Lahane et al., 2021). study. Section 7 presents the managerial implications of this study
A country like India is the second most populated nation globally, and performs the sensitivity analysis. Section 8 presents the con-
having 17% of the global population and fastest-growing economy clusions, limitations, and future research directions.
globally, and generating a tremendous amount of waste day-by-
day causes the problems of ecological unease and resource scarce-
ness (Mangla et al., 2018). India alone produces 62 million tons of 2. Literature review
solid waste daily by volume, reaching 436 million tons by 2050
(Goyal et al., 2018). Existing waste management practices are not The literature review is the backbone of any research work
enough to effectively manage these wastes (Geissdoerfer et al., (Yadav et al., 2020). Hence, the present study adopts a systematic
2018). Therefore, there is a need to manage these wastes through literature review (SLR) approach for reviewing the literature on
efficient utilization of CE 6R principles, namely reusing, recycling, enablers / critical success factors / drivers of CSC implementation.
reducing, remanufacturing, redesigning, and repairing in the man- The Scopus database is searched for articles on CSCEs, and POs due
ufacturing industry’s supply chain operations (Bertanza et al., to the adoption of CSC. This research uses the forward snowball
2021; Goyal et al., 2018). However, current waste management and backward snowball technique for scrutinizing the literature
strategies in saline wastewaters (brine) promote the CE principles (Yadav et al., 2020). This step helps to extract the articles which
in supply chain operation of several industries (e.g., desalination, are more relevant to the CSC field. Further, a review of shortlisted
energy, and oil production) and helps to improves the material literature is carried out in the following sub-sections to explore the
and resource efficiency in a sustainable way (Panagopoulos and CSC domain.
Haralambous, 2020a; Panagopoulos and Haralambous, 2020b).
Mangla et al. (2018) proposed that most Indian manufacturing 2.1. CSC related enablers
organizations require a competent approach to adopt CSC strategy
into their business significantly. CSC enablers (CSCEs) can help in Every manufacturing organization desires to have its supply
the efficient adoption of CSC practice in an organization. In India, chain more sustainable (Lahane et al., 2020). The variables that
industries are unaware of the CSC concept and its key benefits help in successful CSC adoption in the organizations are termed
(Tura et al., 2019). It is important to have a deep understanding as CSCEs (Gusmerotti et al., 2019). The organizations must identify
of key performance outcomes (POs) that the organizations can enablers that enhance their CSC adoption capability (Agyemang
realize by implementing CSCEs. Thus, the objective of the present et al., 2019). Agyemang et al. (2019) identified and examined the
study is to extensively explore the CSCEs for circularity adoption several critical drivers to CSC adoption in Pakistan automobile
in the supply chain and rank the POs realized due to its adoption manufacturing industries. Moktadir et al. (2018) evaluated various
in the context of Indian manufacturing industries. The POs are sustainability drivers based on CE in Bangladesh’s leather indus-
derived due to the adoption of CSCEs are subjective and may be tries using the graph theory matrix approach. Prieto-Sandoval
assumed as multidimensional. Hence, to handle the relative impor- et al. (2018) identified and examined significant enablers using
tance amongst CSCEs and POs, an appropriate Multi-criteria the Delphi method.
decision-making (MCDM) method is required. A hybrid framework Nascimento et al. (2019) explored CSCEs linked to Industry 4.0
of PF-AHP and PF-CoCoSo is proposed to fulfill the research implementation in a manufacturing context based on a conceptual
objective. framework. Kiefer et al. (2019) recommended the enablers of eco-
Pythagorean fuzzy sets (PFS) are an extension of intuitionistic innovation based on Spain’s Small and medium-sized enterprises
fuzzy sets (IFS). PFS provides more freedom to experts in express- (SMEs). Salim et al. (2019) review the drivers and enablers of
ing their opinions regarding the vagueness and uncertainty of the end-of-life (EOL) management of solar photovoltaic and battery
considered MCDM problem. Experts do not have to assign mem- energy storage systems. Levering and Vos (2019) investigated
bership and non-membership degrees, whose sum is at most 1. CSC operational and organizational drivers. Sharma et al. (2020)
However, the sum of squares of these degrees must be at most 1. evaluated e-waste management enablers using the decision mak-
Therefore, this research employs an analytical hierarchy process ing trial and evaluation laboratory (DEMATEL) method. Caldera
(AHP) and a combined compromise solution (CoCoSo) technique et al. (2019) recognized various enablers to CE in SMEs. Hussain
with extensions to PFS. The previous studies on CSC have explored and Malik (2020) discussed several organizational enablers in sus-
the conceptual framework of CSC implementation (Kristensen and tainable supply chain management practices. Govindan and
Remmen, 2019; Rosa et al., 2019; Kazancoglu et al., 2018), but they Hasanagic (2018) reviewed the drivers, barriers, and practices
fail to identify the impact of CSCEs on its implementation and asso- towards CSC adoption in the mobile phone industry. Gusmerotti
ciated POs derived due to CSCEs adoption. Indian manufacturing et al. (2019) illustrate the list of key drivers that help CSC adoption
industries can achieve several key benefits by adopting the pro- in the selected case organization. Vence and Pereira (2019) pro-
posed framework in actual practice. These benefits are like reduced posed the eco-innovation and circular business models as impor-
industrial/domestic waste (Yadav et al., 2020), reduced the carbon tant drivers for CSC adoption. Tura et al. (2019) presented the
footprint from supply chain operations (Ansari et al., 2019), various CSC adoption drivers / critical success factors. Mishra
improved cost-effectiveness (Masi et al., 2018), improved resource et al. (2019) identified, scrutinized, and finalized the CSC drivers
and material efficiency (Sharma et al., 2020), and improved the using the political, economic, social, technological, legal, and envi-
employment rate (Goyal et al., 2018), etc. Therefore, this research ronmental - strengths, weaknesses, opportunities, and threats
decouples the socio-economic and environmental growth of man- (PESTLE–SWOT) elimination algorithm and used AHP to rank the
ufacturing industries. selected drivers.

49
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

2.2. Performance outcomes due to adoption of CSCEs the ambiguity present in the decision-making problems, Zadeh
(1965) proposed the fuzzy sets described by a grade of member-
To sustain in the competitive globalized market, most ship function assigned to each member ranging between 0 and 1.
manufacturing organizations are looking forward to adopting Later, Atanassov (1986) proposed the Intuitionistic fuzzy sets
novel CSC strategies into their business unit (Govindan and (IFS) expressed in three different forms, i.e., membership function,
Hasanagic, 2018). CSC has a high potentiality of enhancing organi- non-membership function, and hesitancy degree. It can communi-
zational performance in various sustainability dimensions cate more precise information than fuzzy sets. However, IFS can’t
(Farooque et al., 2019; Kirchherr et al., 2017). Thus, it is crucial fulfill the conditions for the degree of membership and non-
to have a deep understanding of key POs that the organizations membership. Therefore, its few extensions of IFS were developed,
can realize by implementing CSCEs. The POs may be defined as such as the Neu-trosophic set (Smarandache, 1995), Pythagorean
the metrics which quantify how much an organization goals are fuzzy set (Yager, 2013), and Orthopair fuzzy set (Yager and
met by utilizing available resources based on CE aspects. Bibby Alajlan, 2017). These sets were capable of addressing such issues.
and Dehe (2018) discussed various critical factors of CSC, which This research employs the PFS and which has been developed by
strongly influence POs related to manufacturing industries activi- Yager in 2013. Fig. 1 depicts the comparison among spaces of PFS
ties. Geissdoerfer et al. (2018) developed a framework and dis- and IFS.
cussed the circular business models sustainability performance. Let us consider lP and mP are the Pythagorean membership
Ansari et al. (2019) examined the various key POs due to the adop- grade, whereas, lI and mI are the Intuitionistic membership grade.
tion of supply chain remanufacturing enablers and ranked these In Intuitionistic membership grade all the points are beneath the
POs using a hybrid decision-making approach. Kazancoglu et al. line lI + mI = 1, whereas, in the Pythagorean membership grade
(2018) proposed the holistic conceptual green supply chain man- all the points are with the line l2 P + m2 P = 1. Therefore, it is clear
agement framework based on CE. Zhu et al. (2010) discussed per- that the set of Pythagorean membership grades is greater than
formance improvement opportunities due to CSC adoption based the set of Intuitionistic membership grades. Hence, PFS provide
on environmental and economic aspects. Jiao and Boons (2017) greater flexibility to the decision-makers to figure out their opin-
mentioned key POs due to CE adoption policy in Chinese manufac- ions about uncertainty (IIbahar et al., 2018). The mathematical rep-
turing industries. resentation / preliminaries of PFS are given in Appendix A of this
paper. In recent times, PFS have been used in many research areas
2.3. Research gaps such as hydropower plant selection (Yucesan et al., 2019); CSC
implementation risks evaluation (Lahane and Kant, 2021); sustain-
The following research gaps are observed based on literature able supply chain innovation enablers evaluation (Shete et al.,
survey: 2020); landfill site selection (Karasan et al., 2019); occupational
health and safety (IIbahar et al., 2018); Information security risk
i. Several research studies are available in the previous litera- analysis (Ak and Gul, 2019), etc.
ture that deal with enablers / critical success factors / drivers
for CSC adoption (Tura et al., 2019; De Jesus and Mendonça, 3.2. Pythagorean fuzzy analytical hierarchy process
2018; Govindan and Hasanagic, 2018). However, very few
articles could manage to compute the impacts of identified AHP is considered the most effective and powerful MCDM tech-
factors on CSC implementation success using any decision- nique to solve complex problems where multiple conflicting crite-
making techniques. ria exist (Gandhi et al., 2016). It assesses all criteria related to
ii. Various circular business models and frameworks exist in decision-making to organize the complicated issues in hierarchical
previous literature. However, fewer numbers articles could order (Sedghiyan et al., 2021). AHP method is associated with the
direct the linkage between the CSCEs and their POs. number of benefits compared to other similar techniques such as
iii. Most of the articles on circular business models and frame- ANP, entropy, and SWARA to calculate the weight of criteria. AHP
works are non-verified or confirmed which will raise a ques- is equally useable for quantitative as well as qualitative data. It uti-
tion on their relevance in manufacturing industries. lizes a hierarchical framework for developing complex decision
iv. Few of the circular business models and frameworks used issues. AHP enables the decision-makers to calculate the consis-
case studies and survey approaches. At the same time, none tency of the evaluation procedure. Hence, this research selects
of them employed the MCDM techniques to intensify its the AHP method for the evaluation of CSCEs. Further, the AHP
practical applicability. method is integrated into the PFS theory to dispose of vagueness
v. Very few articles report the POs derived due to the adoption and imprecision in MCDM problems. Therefore this research
of CSCEs. But, these articles fall short to measures their employs a PF-AHP method to identify the weights of CSCEs. The
intensity using decision-making methods. steps involved in the PF-AHP method are as follows:

Thus, the above research gaps justify the objective of the pre-
sent study.

3. Methodology

This section presents the detail about research methods i.e. PF-
AHP and PF-CoCoSo which is being utilized to achieve the objec-
tives of this research.

3.1. Pythagorean fuzzy sets and their preliminaries

The input information required for solving any decision-making

problems contains incomplete or uncertain information. To handle Fig. 1. Comparison of spaces of PFNs and IFNs (Source: Lahane and Kant, 2021).

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S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Step 1: Construct a pairwise comparison matrix A ¼ ðaik Þmn in Step 3: Calculate the score function R = (r ij ) mn of each PFN pij =
accordance to responses taken from decision-making panel with (lij ,mij ) using Eq. (9).
the help of linguistic variables provided in Appendix B (See
Table B1). rij ¼ l2 ij  m2 ij  lnð1 þ p2 ij Þ ð9Þ
Step 2: Compute the differences matrix D ¼ ðdik Þmn between
Step 4: Convert the score function matrix R = (r ij ) mn into an
the lower and upper values of the membership and non- 0 0

membership functions using Eqs. (1) and (2): orthonormal Pythagorean fuzzy matrix R = (r ij ) mn using Eq. (10).
8 rij r j
dikL ¼ l2ikL  v 2ikU ð1Þ 0
< rþ r ; ifjB;
j j
r ij ¼ rþ j rij
ð10Þ
: ; ifjC;
v
r þ j r  j
dikU ¼ l 2
ikU
2
ikL ð2Þ
where,
Step 3: Compute the Interval multiplicative matrix S ¼ ðsik Þmn
r  j ¼ min r ij , and r þ j ¼ max r ij
using Eqs. (3) and (4): i i
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Step 5: Determine the total of the weighted comparability
SikL ¼ 1000dikL ð3Þ sequence for each alternative using Eq. (11).
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X
n
0
SikU ¼ 1000dikL ð4Þ Si ¼ wj  r ij ð11Þ
j¼1
Step 4: Calculate determinacy value s ¼ ðsik Þmn of the aik using
Eq. (5): Step 6: Calculate the whole of the power weight of comparabil-
  ity sequences for each alternatives using Eq. (12).
sik ¼ 1  l2ikU  l2ikL  ðv 2ikU  v 2ikL Þ ð5Þ X
n
0 wj
Pi ¼ ðr ij Þ ð12Þ
Step 5: Compute the matrix of weights, T ¼ ðtik Þmm before nor- j¼1
malization by multiplying the determinacy degrees with
S ¼ ðsik Þmm matrix using Eq. (6): Step 7: Determine the relative weight of the alternatives using
aggregation score strategies with the help of Eqs. (13)–(15).
SikL þ SikU
t ik ¼ ð Þsik ð6Þ P i þ Si
2 K ia ¼ Pm ð13Þ
i¼1 ðP i þ Si Þ
Step 6: Compute the normalized priority weight, wi using Eq.
(7):
Si Pi
Pm K ib ¼ þ ð14Þ
tik min Si min P i
wi ¼ Pm k¼1
Pm ð7Þ i i
i¼1 k¼1 t ik

kSi þ ð1  kÞPi
K ic ¼ 0  k  1; ð15Þ
3.3. Pythagorean fuzzy combined compromised solution k max Si þ ð1  kÞ max Pi
i i

CoCoSo is a novel and resultful MCDM method proposed by where,

Yazdani et al. (2019). CoCoSo method integrates the simple addi-
tive weighting and exponentially weighted product decision- (i) K ia = Arithmetic mean of sums of weighted sum method
making algorithm with aggregation strategies to obtain a multi- (WSM) and weighted product model (WPM) scores.
faceted compromise solution, and the solution obtained by this (ii) K ib = Denote a sum of relative scores of WSM and WPM com-
method is consistent with respect to change of weight distribution pared to the best.
criteria. Therefore, the CoCoSo method has advantages in reliability (iii) K ic = Balanced compromise of WSM and WPM models
and stability of decision-making results compared to other MCDM scores.
techniques (Yazdani and Chatterjee, 2018). Hence, recently,
CoCoSo method has received much attention among researchers Step 8: Determine the assessment value K i using Eq. (16).
for solving complex decision-making problems such as risk evalu- p ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi K ia þ K ib þ K ic
ation (Peng and Huang, 2020); evaluation of electric vehicles Ki ¼ 3
K ia K ib K ic þ ð16Þ
3
(Biswas et al., 2019), and manufacturing technology assessment
(Yazdani and Chatterjee, 2018). Step 9: Rank the alternative based on the decreasing value of
Yazdani et al. (2019) integrate the PFS theory in the CoCoSo Ki (i = 1, 2. . . m).
method. The PF-CoCoSo is a decision support tool dealing with
uncertain issues that exist in decision-making problems. It has a 4. Proposed research framework
great power to differentiate the best alternatives compared to
other existing MCDM techniques due to the presence of PFS This research proposes a hybrid PF-AHP and PF-CoCoSo frame-
(Peng and Selvachandran, 2019). The computational procedure work for evaluating and ranking the POs derived due to the imple-
involved in PF-CoCoSo is as follows: (Source: Peng and Huang, mentation of CSCEs. This hybrid framework consists of three
2020). phases.
Step 1: Construct the decision matrix D = (Dij ) mn (i = 1, 2 . . . m; The flow diagram of the proposed framework is shown in Fig. 2.
j = 1, 2 . . . n) with the help of experts opinion by assigning linguistic Phase I: Identification and finalization of the most common
scale of PF-CoCoSo is given in Appendix C (See Table C1). CSCEs and POs derived due to the adoption of CSCEs.
Step 2: Convert the linguistic decision matrix into the Pythagor- Phase II: Calculate the major criteria and sub-criteria weight of
ean fuzzy decision matrix using Eq. (8). CSCEs using the PF-AHP method.
Phase III: Ranking the POs derived due to adoption of CSCEs
P ¼ ðPij Þm  nði ¼ 1; 2:::m; j ¼ 1; 2:::nÞ ð8Þ
using the PF-CoCoSo method.
51
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Phase 1:
Establish a decision making panel (Experts from the case
organization)

Identify and finalized the major criteria and sub criteria

(Enablers for CSC implementation)
Literature
analysis
Identify and finalized the alternatives (POs) derived due to
CSCEs adoption

Construct decision hierarchy

Approve No
decision
hierarchy
Yes
Phase 2:
PF-AHP
Calculation of major criteria and sub criteria importance
weights using PF-AHP

No Approve
criteria
weights

Phase 3: Yes
PF-CoCoSo Calculation of weights of alternatives Weight
(performance outcomes) using PF-CoCoSo) calculated
using PH-
AHP used

Determine the final rank

Ranking the POs realize due to adoption of

CSCEs

Fig. 2. Framework on research methodology.

5. An empirical case example of Indian manufacturing of waste is generated, leading to polluting the environment
organization enormously and causes the problem of resource scarceness. Case
organization does not have a recycling facility in their unit for
5.1. The case analysis and the problem description capturing the value recovery of waste components. Hence, from
the environmental sustainability viewpoint, reusability / recy-
Empirical validation of the proposed hybrid PF-AHP and PF- cling of the production waste are crucial according to the coun-
CoCoSo framework is performed for an Indian manufacturing try’s law. The case organization adheres to various standards
organization. Established in 1996, the XYZ organization has var- such as ISO 14001, ISO / TS 16949, and ISO 9001 certification.
ious units spread over 20 different locations in India. The orga- But they fail to make the balance between the three dimensions
nization has more than 15,000 employees with an annual of sustainability.
turnover of about 45.7 US Dollars. XYZ is a manufacturer and Therefore, the XYZ executives are very interested in adopting
supplier of various automotive exhaust systems, chassis and sustainable development strategies in their supply chain opera-
aggregates, truck and bus components, and suspension products. tions. Implementing the CSC strategy is considered a novel sustain-
This case analysis was done at XYZ organization’s manufacturing able approach that will help the case organization boost its circular
unit located in the Maharashtra state of India. During the man- practices of supply chain operation. The executives of XYZ organi-
ufacturing of plastic molded components, a considerable amount zation agreed to contribute to this research.

52
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

5.2. Phase I: Identification and finalization of the most common CSCEs manufacturing organization. ERE2 forces the company to adopt
and POs derived due to adoption of CSCEs CSC practices in their business unit. Consequently, it helps them
overcome the pressure of global climate change. ERE1 is the second
From the literature, 55 CSCEs and 24 POs were listed. Afterward, most significant CSCEs.
the list of CSCEs and POs was prepared in terms of questionnaire India doesn’t have stringent laws, rules, regulations, and direc-
form and presented to the decision-making (DM) panel of XYZ tives to manage end-of-use products or components. Hence, effec-
for validation. The DM panel consists of fifteen experts, Head (Pro- tive laws and regulations for managing waste based on CE
duction), Head (Environmental), Head (Quality and Maintenance), principles must be developed by the Indian government to support
Head (Waste management), Head (Logistics), and other senior CSC adoption (Mangla et al., 2018). Strategic enablers (SEs) has
executives. These experts are highly qualified, knowledgeable, been ranked second among major enablers and their sub-factors
and possess industrial experience of more than 15 years. Upon sev- based on their relative importance are SE1 > SE3 > SE4 > SE5 >
eral rounds of discussion among experts of the DM panel, 37 CSCEs SE2 > SE7 > SE6. Top management support, commitment, and clear
were finally selected. vision are the significant factors among all strategic enablers. Oper-
Further, they added 5 more enablers extending the total to 42 ational enablers (OPEs) ranked third based on their calculated
enablers, and categorized it into seven major criteria. The experts importance weight. Their sub-factors are ranked according to pri-
selected 15 POs from the presented list. Tables 1 and 2 provide a ority weight obtained are OPE1 > OPE5 > OPE4 > OPE2 >
detailed list of selected 42 CSCEs and 15 POs. OPE3 > OPE6. Social enabler (SOCEs) occupies the fourth rank in
the major criteria ranking and its sub-criteria ranking are
5.3. Phase II: Calculate the major criteria and sub-criteria weight of SOCE5 > SOCE6 > SOCE3 > SOCE1 > SOCE4 > SOCE2. Organizational
CSCEs using the PF-AHP method enabler (ORGEs) comes at the fifth rank in the list of major criteria
and its sub-criteria ranking are as ORGE3 > ORGE1 > ORGE4 >
In this phase, the relative weight of enablers and their sub- ORGE2 > ORGE5 > ORGE6.
enablers are computed using the PF-AHP method. The pairwise Technological and Infrastructural enablers (TIEs) and Economic
comparison matrix of major enablers and sub enablers is given enablers (EEs) are at the 6th and 7th spots, respectively. The rank-
by the selected DM panel using the linguistic scale and it is pro- ing of sub-criteria of TIE are TIE2 > TIE1 > TIE3 > TIE5 > TIE4 > TIE6.
vided in Appendix B (See Table B1). Further, the decision matrix The ranking of the sub-criteria of EE is EE2 > EE1 > EE3 > EE5 > EE4.
mode is calculated to obtain a single decision matrix before pro- POs derived due to CSCEs adoption are ranked based on assessment
ceeding for further calculations. Following steps in section 3.2, value Ki . The assessment value Ki for reduces waste and promotes
the calculations were made. The sample calculation for data green development (PO11) is highest, while the Ki for PO1 is the
received from expert 1 for PF-AHP is given in Appendix B. Table 3 lowest. The other POs ranked in descending order are as
presents the final calculated global weights for each CSCE. Environ- PO11>PO7> PO12>PO9>PO6>PO8>PO4>PO2>PO10>PO5>PO3>P
mental and regulatory enablers (ERE) got the highest weight O15 > PO14 > PO13 > PO1. The ranking of POs assists the decision-
among all CSCEs. maker of organizations in understanding the major issues that
exist while implementing CSC and developing the appropriate pol-
icy standards to improve their performance in various dimensions
5.4. Phase III: Ranking the POs derived due to adoption of CSCEs using
of sustainability.
PF-CoCoSo method
CSC implementation upscale the CE 6R’s principles and pro-
duces the recovered products of similar quality like new ones at
In the third phase, the PF-CoCoSo method is used for ranking
a cheaper cost. As a result, CSC implementation generates huge
the POs derived due to CSCEs implementation. The weight calcu-
employment opportunities for people and creates the secondary
lated in PF-AHP is utilized in the PF-CoCoSo technique. A set of a
market for end-of-use products while it increases the market
questionnaire in the form of a decision matrix is provided to the
share. The CSC implementation enormously reduces the waste
same DM panel. The decision matrix mode is calculated to obtain
and carbon footprint throughout the whole lifecycle of products
a single decision matrix before proceeding with further calcula-
and solves resource scarceness issues, thus, the corporate image
tions. Following steps in section 3.3, the calculations were made.
of the organization is greatly improved.
The sample calculation for data received from expert 1 for PF-
CoCoSo is given in Appendix C. The final ranking of POs based on
7. Managerial implications
Ki value is shown in Table 4.

This research work possesses strong theoretical and practical

6. Results and discussion contribution towards the domain of CE and CSC management.
The implication of this study for researchers and practitioners
The adoption of CSCEs aids the organization in implementing along with advantage of the proposed model to the society is dis-
CSC effectively and efficiently. The study makes an effort to prior- cussed in the following sub-sections. Furthermore, a recommenda-
itize the POs through the effective adoption of CSCEs. 15 POs were tion to the policy makers and sensitivity analysis is also explained
ranked against the 42 CSCEs. From the results, environmental and in the sub-sequent sub-section.
regulatory enablers (EREs) are the most significant enablers
amongst all major enablers. It is followed by strategic enablers 7.1. Implications for researchers and practitioners
(SEs), operational enablers (OPEs), social enablers (SOCEs), organi-
zational enablers (ORGEs), technological and infrastructural This research made valuable contributions to the CSC domain
enablers (TIEs), and economic enablers (EEs). Almost 50% of the for researchers and industrial practitioners in the following ways:
enablers from the category of EREs, SEs, and OPEs have a substan-
tial impact on the effective adoption of CSCEs. The ranking of sub- i. The continuous environmental degradation and resource
factors based on their priority is scarceness has appealed to researchers and industrial practi-
ERE2 > ERE1 > ERE6 > ERE4 > ERE3 > ERE5. Global climate pressure tioners to identify and implement the key enablers that can
and ecological scarcity of resources (ERE2) are the most important help to implement CSC in an industry.
factor amongst all the EREs for adopting CSC practice in a
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S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Table 1
List of selected CSCEs.

Major criteria Code Sub-Criteria References

Organizational Enablers ORGE1 Employee involvement / motivation for environmental consciousness Agyemang et al., 2019; Piyathanavong et al., 2019;
(ORGEs) Jabbour et al., 2019; Moktadir et al., 2018
ORGE2 Training and education towards awareness and development programs Bag et al., 2020; Schroeder et al., 2019
for circular business model
ORGE3 CSC supportive organizational culture Bassi and Dias, 2019; Mangla et al., 2018; Sousa-
Zomer et al., 2018
ORGE4 Development of skills and capabilities Li et al., 2020; Rosa et al., 2019; Mangla et al., 2018
ORGE5 Providing proper incentives to end customers for products return Experts opinion
ORGE6 Strong coordination and collaboration among supply chain members Levering and Vos, 2019; Mangla et al., 2018; Corsini
et al., 2017
Operational Enablers OPE1 Product recovery mechanism for second hand / end of use products Jedelhauser and Binder, 2018; Salminen et al., 2017
(OPEs) OPE2 Implement and monitor the product returns mechanism Machado et al., 2019; Korhonen et al., 2018;
Kalmykova et al., 2018
OPE3 Monitoring and controlling of operational activities Gusmerotti et al., 2019; Tura et al., 2019; De Jesus
and Mendonça, 2018
OPE4 Circular supply and demand network design Gusmerotti et al., 2019; Machado et al., 2019;
Korhonen et al., 2018
OPE5 Design for circularity (i.e. part based design) aspects Tura et al., 2019; Ansari et al., 2019; Prieto-Sandoval
et al., 2018
OPE6 Lean tools as a continuous improvement philosophy and effective gate- Ansari et al., 2019; Kurilova-Palisaitiene et al., 2018
keeping for recovery of second hand / end of use products
Strategic Enablers (SEs) SE1 Top management support, commitment and clear vision Machado et al., 2019; Casper and Sundin, 2018;
Dentchev et al., 2018
SE2 Benchmarking and redefining the firm business model Veleva et al., 2017; Mangla et al., 2016
SE3 Supplier, consumer and organization strategic alliance Experts opinion
SE4 Value creation / proposition strategy across the full lifecycle of product Machado et al., 2019; Gusmerotti et al., 2019;
Geissdoerfer et al., 2018
SE5 Cradle to cradle paradigm and circular public procurement Sonnichsen and Clement, 2020; Diani et al., 2019;
De Jesus and Mendonça, 2018
SE6 Standardization and warranties for recycled / remanufactured products Gusmerotti et al., 2019; Garza-Reyes et al., 2018
SE7 Industrial symbiosis enabled supply chain network Liu et al.,2019; Patricio et al., 2018
Environmental and ERE1 Government rules, legislation and directives for CSC adoption Cheng et al., 2019; Govindan and Hasanagic, 2018;
Regulatory Enablers Huybrechts et al., 2018
(EREs) ERE2 Global climate pressure and ecological scarcity of resources Tunn et al., 2019; Pla-Julián and Guevara, 2019;
Moktadir et al., 2018
ERE3 Mandatory take-back policies for hazardous materials and products Halkos and Petrou, 2019; Masi et al., 2018; Moktadir
et al., 2018
ERE4 Government rules and regulation towards end of life management of Salim et al., 2019; Batista et al., 2018; Wong et al.,
products and components 2018
ERE5 Laws and regulations prohibiting informal waste handling sector Giannakitsidou et al., 2020; Garza-Reyes et al.,
2019; Batista et al., 2018
ERE6 Environment management certifications and systems Kiefer et al., 2019; Korhonen et al., 2018
Economic Enablers (EEs) EE1 Government preferential tax policies and subsidies for circular business Gusmerotti et al., 2019; Tura et al., 2019
model
EE2 Separate fund allocation to develop a circular business model Expert’s opinion
EE3 Investment in remanufacturing/ recycling related research and Ansari et al., 2019; Moktadir et al., 2018
development
EE4 Fund for the acquisition of additional machinery equipment and tools Expert’s opinion
EE5 Understanding of organizational profitability from customer’s return Kurita and Managi, 2021; Ansari et al., 2019;
Kalmykova et al., 2018
Social Enablers (SOCEs) SOCE1 Opportunities for employment generation Ansari et al., 2019; Tura et al., 2019; Salim et al.,
2018
SOCE2 Employee health schemes programs for environmental consciousness Gusmerotti et al., 2019; Korhonen et al., 2018
SOCE3 Consumer attitude and ecological awareness towards eco-friendly Mazahir et al., 2019; Ansari et al., 2019
products
SOCE4 Green image building and potential to increase workplaces and vitality Tura et al., 2019; Mao and Wang, 2019
SOCE5 Stakeholder’s support and involvement Ansari et al., 2019; Korhonen et al., 2018
SOCE6 Corporate social responsibility and ethical standards Experts opinion
Technological and TIE1 Up-gradation of existing information and communication technologies Ansari et al., 2019; Tura et al., 2019; Agyemang
Infrastructural Enablers et al., 2019
(TIEs) TIE2 Process integration technology for cleaner production Ansari et al., 2019; Van Fan et al., 2019
TIE3 Information and communication technology infrastructure development Mishra et al., 2019; Moktadir et al., 2018
TIE4 Technology enabled integration of forward and reverse supply chain Jabbour et al., 2019;Govindan and Hasanagic, 2018
TIE5 Digital /Artificial intelligence transformation across supply chain Moreno et al., 2019; Nascimento et al., 2019
network
TIE6 Management information system enabled supply chain network design Mazahir et al., 2019; Kalmykova et al., 2018

ii. A structural framework concerning to CSCEs with its iii. The present study investigates the 42 CSCEs, categorized in
impact on POs using any decision-making approach is seven major criteria. It is a comprehensive study on CSCEs
rarely observed in the literature. Hence, a proposed frame- and a kind study integrating enablers and POs in CSC litera-
work will help the managers of companies to adopt CSC ture. The deep understanding and outcome of every criterion
effectively. would aid the industrial practitioners to adopt CSC
successfully.

54
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Table 2
POs derived due adoption of CSCEs.

Code POs realized due to adoption of CSCEs References

PO1 Generates new market for secondary products and improves the market share Tura et al., 2019; Ansari et al., 2019
PO2 Improves corporate image and environmental sustainability Pla-Julián and Guevara, 2019
PO3 Encourages social duty among suppliers and improves stakeholders involvement Patricio et al., 2018; Mesa et al., 2018
PO4 Improves economic and operational performance Wong et al., 2018; Zhou et al., 2017
PO5 Improves industrial symbiosis/ eco-industrial park network Mishra et al., 2019; Genovese et al., 2017
PO6 Avoids waste limitation penalties and savings in capital investment Kristensen and Remmen, 2019; Unay-Gailhard and Bojnec, 2019
PO7 Reduces carbon footprint Giannakitsidou et al., 2020; Ansari et al., 2019
PO8 Minimizing products life cycle environmental damages Agyemang et al., 2019; Moktadir et al., 2018
PO9 Minimize high disposal cost concern to land fillings Ansari et al., 2019; Mazahir et al., 2019
PO10 Increases employment rate Ansari et al., 2019; Moktadir et al., 2018
PO11 Reduces waste and promotes green development Giannakitsidou et al., 2020; Mazahir et al., 2019; Vimal et al., 2019
PO12 Improves resource and material efficiency Giannakitsidou et al., 2020; Jain et al., 2018; Nasir et al., 2017
PO13 Attracts environmentally conscious customers Mazahir et al., 2019; Ansari et al., 2019
PO14 Quality ensured product at low cost Korhonen et al., 2018; Moktadir et al., 2018
PO15 Rise in sales and enhances after sales service Korhonen et al., 2018; Govindan and Hasanagic, 2018

iv. It is challenging to implement all the CSCEs at the same time v. The ranking of the POs derived due to the adoption of CSCEs
in an organization. Hence, the ranking of CSCEs obtained using PF-CoCoSo helps the practitioners to develop the inno-
through the application of PF-AHP facilitates the practition- vative action plan in the beginning phase itself. It will min-
ers to focus on high weightage CSCEs for efficient adoption imize the failure possibility and enhances the success
of CSC. possibility of CSC adoption.

Table 3
Final ranking of sub-enablers (Sub-CSCEs).

Major enablers Relative weights Sub-enablers Weight Globalized weight Global rank
Organizational enablers (ORGEs) 0.1259 ORGE1 0.2117 0.0266 16
ORGE2 0.1402 0.0176 29
ORGE3 0.2952 0.0372 8
ORGE4 0.1952 0.0246 20
ORGE5 0.0885 0.0111 34
ORGE6 0.0693 0.0087 37
Operational enablers (OPEs) 0.1443 OPE1 0.2248 0.0324 12
OPE2 0.1756 0.0253 18
OPE3 0.1729 0.0249 19
OPE4 0.1810 0.0261 17
OPE5 0.2015 0.0291 14
OPE6 0.0443 0.0064 38
Strategic enablers (SEs) 0.1664 SE1 0.2660 0.0443 4
SE2 0.0984 0.0164 30
SE3 0.2412 0.0401 7
SE4 0.1762 0.0293 13
SE5 0.1212 0.0202 25
SE6 0.0315 0.0052 39
SE7 0.0654 0.0109 35
Environmental and Regulatory enablers (EREs) 0.1810 ERE1 0.2606 0.0472 2
ERE2 0.2792 0.0505 1
ERE3 0.0645 0.0117 33
ERE4 0.1306 0.0236 22
ERE5 0.0126 0.0023 41
ERE6 0.2525 0.0457 3
Economic enablers (EEs) 0.1213 EE1 0.2020 0.0245 21
EE2 0.3638 0.0441 5
EE3 0.1804 0.0219 23
EE4 0.0974 0.0118 32
EE5 0.1563 0.0190 26
Social enablers (SOCEs) 0.1375 SOCE1 0.1322 0.0182 27
SOCE2 0.0278 0.0038 40
SOCE3 0.1485 0.0204 24
SOCE4 0.1306 0.0180 28
SOCE5 0.3035 0.0417 6
SOCE6 0.2574 0.0354 11
Technological and Infrastructural enablers (TIEs) 0.1237 TIE1 0.2871 0.0355 10
TIE2 0.2928 0.0362 9
TIE3 0.2235 0.0277 15
TIE4 0.0800 0.0099 36
TIE5 0.1064 0.0132 31
TIE6 0.0101 0.0013 42

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S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Table 4
Final ranking of POs based on assessment valueK i .

Criteria code Performance outcomes K ia K ib kic Ki Rank

PO1 Generates new market for secondary products and improves the market share 0.0110 2.0000 0.1474 0.8703 15
PO2 Improves corporate image and environmental sustainability 0.0742 9.9561 0.9946 4.5783 8
PO3 Encourages social duty among suppliers and improves stakeholders involvement 0.0674 9.4966 0.9033 4.3238 11
PO4 Improves economic and operational performance 0.0713 10.1688 0.9547 4.6171 7
PO5 Improves industrial symbiosis/ eco-industrial park network 0.0690 9.5779 0.9249 4.3742 10
PO6 Avoids waste limitation penalties and savings in capital investment 0.0698 10.6537 0.9356 4.7737 5
PO7 Reduce the carbon footprint 0.0735 11.3489 0.9851 5.0732 2
PO8 Minimizing products life cycle environmental damages 0.0696 10.3737 0.9327 4.6697 6
PO9 Minimize high disposal cost concern to land fillings 0.0697 10.6606 0.9341 4.7747 4
PO10 Increases employment rate 0.0724 9.7836 0.9704 4.4925 9
PO11 Reduces waste and promotes green development 0.0720 11.4214 0.9644 5.0789 1
PO12 Improves resource and material efficiency 0.0712 10.6145 0.9545 4.7780 3
PO13 Attracts environmentally conscious customers 0.0568 8.2595 0.7612 3.7378 14
PO14 Quality ensured product at low cost 0.0604 8.6440 0.8097 3.9242 13
PO15 Rise in sales and enhances after sales service 0.0655 9.2454 0.8781 4.2084 12

vi. The CSC implementation is at the initial stage in developing 7.3. Sensitivity analysis
nations, such as India. The empirical relevance of the pro-
posed framework is conducted in the Indian manufacturing It is always better to conduct the sensitivity analysis test to
industry. The proposed framework with some modification check the proposed framework’s robustness (Ansari et al., 2019).
will help the academicians and industrialists of other geo- Sensitivity analysis ranks the POs (alternatives) concerning the
graphical locations improve their organizational changes in the importance weight of identified CSCEs. This study
performance. performs twenty experiments (details in Appendix D). It is
vii. The proposed framework reduces the social, economic, and observed that in the first 18 experiments, the importance weight
environmental risks to the society that would have arisen of each CSCEs are set higher one by one, while the weight of other
due to the unplanned adoption of CSCEs in an industry. Fur- CSCEs is set to low and assigned as equal values. Based on the sen-
ther, this research outcome helps to change the attitude of sitivity analysis findings, the weight of enabler ORGE1 is taken as
consumers regarding the use of second-hand products. 0.6, and the weight of the remaining 41 enablers (WORGE2-
viii. The proposed framework outcomes help the case company WTIE6) are assumed to be of equal importance and set values equal
as well as Indian industries to adopt CSC efficiently as a long to 0.00975. The ranking of POs (alternatives) is determined. Simi-
time strategy that leads to economic benefits, environmental larly, weights of other factors were modified in the subsequent cal-
sustainability, and high returns to society. culations, and results are obtained. Fig. 3 (a), 3 (b), and 3 (c)
ix. The CSC adoption provides the necessary boost to govern- illustrates the changes in the final ranking of the POs (alternatives)
ment of India missions such as ‘‘Swacch Bharat” (Clean when the weights of the CSCEs are changed. Out of 20 experiments,
India) and ‘‘Green India”. The Swacch Bharat / Clean India PO11 has obtained the highest assessment valueKi in 8 experi-
mission is an integral part of CSC network that focuses on ments (i.e., experiments number 6, 7, 8, 9, 14, 18, 19, 20) and
the activities related to the management of waste from its reported as the best outcome.
inception to its final disposal. It includes collecting, trans-
portation, treating, and disposing of waste and examining
and regulating the waste management process. 8. Conclusions, limitations and future research directions
x. The Green India mission aims to protect, reestablish, and
improve India’s diminishing forest cover and react to envi- Changes in global climate, rising population, and increased
ronmental change by consolidating CE transformation and industrial activities have forced the manufacturing organization
mitigation measures in the production network. Thus, this to adopt a sustainable strategy such as CE in its supply chain. CSC’s
research provides a crucial roadmap to those willing to primary aim is to focus on value creation and proposition aspects
adopt CSC practices into their business organization. of CE in which used products after its end of life again reuse or
recycled and remain in the closed-loop further until maximum
7.2. Recommendations for policy makers value can be obtained from it. Consequently, it minimizes the
use of resource inputs required and the creation of waste and pro-
CSC is an emerging concept now a day and acts as a sustainable motes green development to achieve sustainability in the manu-
solutions approach for business organizations. It minimizes the facturing organization. The present study is intended to identify
negative influence of their operational activities on the environ- and analyze the CSCEs, and the POs derived due to their implemen-
ment. Therefore, it is expected that the government should formu- tation. 42 CSCEs and 15 POs were finalized through literature
late stringent laws and policies supporting CSC adoption. Further, review and input received from experts.
the government should also develop preferential tax policies and This study developed the structural framework using the hybrid
subsidies for circular business model adoption. First, the govern- PF-AHP and PF-CoCoSo method for ranking the POs derived from
ment institutions should enforce the laws for controlling the infor- the implementation of CSCEs. Initially, the PF-AHP method was
mal waste products and ensure that they go through the proper used for the calculation of relative importance weight of CSCEs,
EOL management treatment for value recovery purposes. Such and based on the obtained result, CSCEs were ranked. The result
initiatives boost other industries’ interest in adopting circular reveals that, environmental and regulatory enablers are the most
practices. The government officials can utilize the final ranking of significant enablers amongst all major enablers. It is followed by
POs obtained in this study to formulate useful strategies favoring strategic enablers, operational enablers, social enablers, organiza-
the organizations and consumers that could help improve the tional enablers, technological and infrastructural enablers, and
nation’s sustainable economy. economic enablers. Almost 50% of the enablers from the category
56
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

8 PO1
PO4

Ki score for POs

6
PO10

4 PO11
PO3
2

0
Expt.1
Expt.2
Expt.3
Expt.4
Expt.5
Expt.6
Expt.7
Expt.8
Expt.9
Expt.10
Expt.11
Expt.12
Expt.13
Expt.14
Expt.15
Expt.16
Expt.17
Expt.18
Expt.19
Expt.20
(a) Result of sensitivity analysis ( scores)
9
PO13
Ki score for POs

PO5
7
PO8
PO14
5
PO12

3
Expt.1
Expt.2
Expt.3
Expt.4
Expt.5
Expt.6
Expt.7
Expt.8
Expt.9
Expt.10
Expt.11
Expt.12
Expt.13
Expt.14
Expt.15
Expt.16
Expt.17
Expt.18
Expt.19
Expt.20
(b) Result of sensitivity analysis ( scores)

9
PO2
Ki score for POs

PO6
7
PO7

PO9
5
PO15

3
Expt.1
Expt.2
Expt.3
Expt.4
Expt.5
Expt.6
Expt.7
Expt.8
Expt.9
Expt.10
Expt.11
Expt.12
Expt.13
Expt.14
Expt.15
Expt.16
Expt.17
Expt.18
Expt.19
Expt.20

(c) Result of sensitivity analysis ( scores)

Fig. 3. (a) Result of sensitivity analysis (Ki scores). (b) Result of sensitivity analysis (Ki scores). (c) Result of sensitivity analysis (Ki scores).

of environmental and regulatory enablers, strategic enablers, and CSCEs. The POs, namely, reduce carbon footprint, improve resource
operational enablers substantially impact the effective adoption and material efficiency, minimize high disposal cost concern to
of CSCEs in the CSC implementation process. land fillings, avoid waste limitation penalties, and save capital
Furthermore, the weight obtained in PF-AHP for CSCEs were uti- investment in the first five ranks. Moreover, the POs, namely, rise
lized in PF-CoCoSo method for the ranking of POs (alternatives). in sales and enhance after-sales service, quality ensured product
POs derived due to CSCEs adoption are ranked based on assessment at low cost, and attracts environmentally conscious customers
valueKi . From result it has been clear that, the assessment value Ki are among the low-rank POs in priority. Although low-rank POs
for PO11 (i.e., reduces waste and promotes green development) is are also equally important to the organization, the ranking of POs
highest amongst all POs. whereas, the assessment value for PO1 facilitates the managers of case organization to systematically
(i.e., generates a new market for secondary products and improves implement the CSCEs based on their priority to derive the POs.
the market share) is the lowest-ranked PO due to the adoption of Therefore, the circular performance of the organization gets

57
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

improved. Sensitivity analysis was performed to check the robust- Cainelli, G., D’Amato, A., Mazzanti, M., 2020. Resource efficient eco-innovations for a
circular economy: Evidence from EU firms. Res. Policy 49,. https://doi.org/
ness of the proposed framework.
10.1016/j.respol.2019.103827 103827.
This study’s proposed research framework has some limitations Caldera, H.T.S., Desha, C., Dawes, L., 2019. Evaluating the enablers and barriers for
and can be accepted as open doors for future researchers. The input successful implementation of sustainable business practice in ‘lean’SMEs. J.
data required for computation in the proposed framework is based Cleaner Prod. 218, 575–590. https://doi.org/10.1016/j.jclepro.2019.01.239.
Casper, R., Sundin, E., 2018. Addressing Today’s challenges in automotive
on DM panel responses, which can be subjective. Any biasing by remanufacturing. J. Remanuf. 8, 93–102. https://doi.org/10.1007/s13243-018-
the experts judging the CSCEs will influence the result. Therefore, 0047-9.
it is anticipated that the result should be calculated with extreme Cheng, H., Dong, S., Li, F., Yang, Y., Li, Y., Li, Z., 2019. A circular economy system for
breaking the development dilemma of ‘ecological Fragility-Economic
care. This study’s proposed framework’s application and findings poverty’vicious circle: A CEEPS-SD analysis. J. Cleaner Prod. 212, 381–392.
are restricted to a single empirical case organization in Indian https://doi.org/10.1016/j.jclepro.2018.12.014.
manufacturing organizations. Therefore, the proposed framework Corsini, F., Rizzi, F., Frey, M., 2017. Extended producer responsibility: The impact of
organizational dimensions on WEEE collection from households. Waste
can also be extended to manufacturing industries of other geo- Manage. 59, 23–29. https://doi.org/10.1016/j.wasman.2016.10.046.
graphical locations with some modifications for generalizations De Jesus, A., Mendonça, S., 2018. Lost in transition? Drivers and barriers in the eco-
of results. Further, the result obtained in this study can be com- innovation road to the circular economy. Ecol. Econ. 145, 75–89. https://doi.org/
10.1016/j.ecolecon.2017.08.001.
pared and evaluates with that of other MCDM methods such as Dentchev, N., Rauter, R., Jóhannsdóttir, L., Snihur, Y., Rosano, M., Baumgartner, R.,
Pythagorean fuzzy preference ranking organization method for Jonker, J., 2018. Embracing the variety of sustainable business models: A prolific
enrichment of evaluations (PF-PROMETHEE), Pythagorean fuzzy field of research and a future research agenda. J. Cleaner Prod. 194, 695–703.
https://doi.org/10.1016/j.jclepro.2018.05.156.
vlsekriterijums kaoptimizacijai kompromisno Resenje (PF-VIKOR),
Diani, M., Pievatolo, A., Colledani, M., Lanzarone, E., 2019. A comminution model
Pythagorean fuzzy technique for order of preference by similarity with homogeneity and multiplication assumptions for the Waste Electrical and
to ideal solution (PF-TOPSIS) and Pythagorean fuzzy elimination Electronic Equipment recycling industry. J. Cleaner Prod. 211, 665–678. https://
et choice translating reality (PF-ELECTRE). doi.org/10.1016/j.jclepro.2018.11.084.
Farooque, M., Zhang, A., Thürer, M., Qu, T., Huisingh, D., 2019. Circular supply chain
management: A definition and structured literature review. J. Cleaner Prod. 228,
882–900. https://doi.org/10.1016/j.jclepro.2019.04.303.
Funding
Gandhi, S., Mangla, S.K., Kumar, P., Kumar, D., 2016. A combined approach using
AHP and DEMATEL for evaluating success factors in implementation of green
This research did not receive any specific grant from funding supply chain management in Indian manufacturing industries. Int. J. Logistics
agencies in the public, commercial, or non-profit sectors. Res. Appl. 19, 537–561. https://doi.org/10.1080/13675567.2016.1164126.
Garrido-Hidalgo, C., Ramirez, F.J., Olivares, T., Roda-Sanchez, L., 2020. The adoption
of internet of things in a circular supply chain framework for the recovery of
Declaration of Competing Interest WEEE: The case of lithium-ion electric vehicle battery packs. Waste Manage.
103, 32–44. https://doi.org/10.1016/j.wasman.2019.09.045.
Garza-Reyes, J.A., Kumar, V., Chaikittisilp, S., Tan, K.H., 2018. The effect of lean
The authors declare that they have no known competing finan- methods and tools on the environmental performance of manufacturing
cial interests or personal relationships that could have appeared organisations. Int. J. Prod. Econ. 200, 170–180. https://doi.org/10.1016/j.
ijpe.2018.03.030.
to influence the work reported in this paper.
Garza-Reyes, J.A., Salomé Valls, A., Peter Nadeem, S., Anosike, A., Kumar, V., 2019. A
circularity measurement toolkit for manufacturing SMEs. Int. J. Prod. Res. 57,
7319–7343. https://doi.org/10.1080/00207543.2018.1559961.
Appendix A. Supplementary material
Geissdoerfer, M., Morioka, S.N., de Carvalho, M.M., Evans, S., 2018. Business models
and supply chains for the circular economy. J. Cleaner Prod. 190, 712–721.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2018.04.159.
https://doi.org/10.1016/j.wasman.2021.05.013. Genovese, A., Acquaye, A.A., Figueroa, A., Koh, S.L., 2017. Sustainable supply chain
management and the transition towards a circular economy: Evidence and
some applications. Omega 66, 344–357. https://doi.org/10.1016/j.
References omega.2015.05.015.
Giannakitsidou, O., Giannikos, I., Chondrou, A., 2020. Ranking European countries on
the basis of their environmental and circular economy performance: A DEA
Agyemang, M., Kusi-Sarpong, S., Khan, S.A., Mani, V., Rehman, S.T., Kusi-Sarpong, H.,
application in MSW. Waste Manage. 109, 181–191. https://doi.org/10.1016/j.
2019. Drivers and barriers to circular economy implementation: An explorative
wasman.2020.04.055.
study in Pakistan’s automobile industry. Manag. Decis. 57, 971–994. https://doi.
Govindan, K., Hasanagic, M., 2018. A systematic review on drivers, barriers, and
org/10.1108/MD-11-2018-1178.
practices towards circular economy: a supply chain perspective. Int. J. Prod. Res.
Ak, M.F., Gul, M., 2019. AHP–TOPSIS integration extended with Pythagorean fuzzy
56, 278–311. https://doi.org/10.1080/00207543.2017.1402141.
sets for information security risk analysis. Complex & Intelligent Syst. 5, 113–
Goyal, S., Esposito, M., Kapoor, A., 2018. Circular economy business models in
126. https://doi.org/10.1007/s40747-018-0087-7.
developing economies: lessons from India on reduce, recycle, and reuse
Ansari, Z.N., Kant, R., Shankar, R., 2019. Prioritizing the performance outcomes due
paradigms. Thunderbird Int. Business Rev. 60, 729–740. https://doi.org/
to adoption of critical success factors of supply chain remanufacturing. J.
10.1002/tie.21883.
Cleaner Prod. 212, 779–799. https://doi.org/10.1016/j.jclepro.2018.12.038.
Guarnieri, P., Cerqueira-Streit, J.A., Batista, L.C., 2020. Reverse logistics and the
Atanassov, K.T., 1986. Intuitionistic fuzzy sets. Fuzzy Sets and System 201, 87–96.
sectoral agreement of packaging industry in Brazil towards a transition to
https://doi.org/10.1007/978-3-7908-1870-3_1.
circular economy. Resour. Conserv. Recycl. 153,. https://doi.org/10.1016/j.
Bag, S., Wood, L.C., Mangla, S.K., Luthra, S., 2020. Procurement 4.0 and its
resconrec.2019.104541 104541.
implications on business process performance in a circular economy. Resour.
Gusmerotti, N.M., Testa, F., Corsini, F., Pretner, G., Iraldo, F., 2019. Drivers and
Conserv. Recycl. 152,. https://doi.org/10.1016/j.resconrec.2019.104502 104502.
approaches to the circular economy in manufacturing firms. J. Cleaner Prod.
Bassi, F., Dias, J.G., 2019. The use of circular economy practices in SMEs across the
230, 314–327. https://doi.org/10.1016/j.jclepro.2019.05.044.
EU. Resour. Conserv. Recycl. 146, 523–533. https://doi.org/10.1016/j.
Halkos, G., Petrou, K.N., 2019. Analysing the energy efficiency of EU member states:
resconrec.2019.03.019.
The potential of energy recovery from waste in the circular economy. Energies
Batista, L., Bourlakis, M., Liu, Y., Smart, P., Sohal, A., 2018. Supply chain operations
12, 3718. https://doi.org/10.3390/en12193718.
for a circular economy. Production Planning & Control 29, 419–424. https://doi.
Hasanuzzaman, M., 2021. Approaches to the Remediation of Inorganic Pollutants.
org/10.1080/09537287.2018.1449267.
https://doi.org/10.1007/978-981-15-6221-1.
Bertanza, G., Mazzotti, S., Gómez, F.H., Nenci, M., Vaccari, M., Zetera, S.F., 2021.
Hussain, M., Malik, M., 2020. Organizational enablers for circular economy in the
Implementation of circular economy in the management of municipal solid
context of sustainable supply chain management. J. Cleaner Prod. 256,. https://
waste in an Italian medium-sized city: A 30-years lasting history. Waste
doi.org/10.1016/j.jclepro.2020.120375 120375.
Manage. 126, 821–831. https://doi.org/10.1016/j.wasman.2021.04.017.
Huybrechts, D., Derden, A., Van den Abeele, L., Vander Aa, S., Smets, T., 2018. Best
Bibby, L., Dehe, B., 2018. Defining and assessing industry 4.0 maturity levels–case of
available techniques and the value chain perspective. J. Cleaner Prod. 174, 847–
the defence sector. Production Planning & Control 29, 1030–1043. https://doi.
856. https://doi.org/10.1016/j.jclepro.2017.10.346.
org/10.1080/09537287.2018.1503355.
Iftikhar, S., Turan, V., Tauqeer, H.M., Rasool, B., Zubair, M., Khan, M.A., Ramzani, P.M.
Biswas, T.K., Stević, Ž., Chatterjee, P., Yazdani, M., 2019. An Integrated Methodology
A., 2021. Phytomanagement of As-contaminated matrix: Physiological and
for Evaluation of Electric Vehicles Under Sustainable Automotive Environment.
molecular basis. In: Handbook of Bioremediation. Academic Press, pp. 61–79.
In Advanced Multi-Criteria Decision Making for Addressing Complex
https://doi.org/10.1016/B978-0-12-819382-2.00005-3.
Sustainability Issues (pp. 41-62). IGI Global. 10.4018/978-1-5225-8579-4.
ch003.

58
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

Ilbahar, E., Karasßan, A., Cebi, S., Kahraman, C., 2018. A novel approach to risk Mazahir, S., Verter, V., Boyaci, T., Van Wassenhove, L.N., 2019. Did Europe move in
assessment for occupational health and safety using Pythagorean fuzzy AHP & the right direction on e-waste legislation? Prod. Operations Manage. 28, 121–
fuzzy inference system. Saf. Sci. 103, 124–136. https://doi.org/10.1016/j. 139. https://doi.org/10.1111/poms.12894.
ssci.2017.10.025. Mesa, J., Esparragoza, I., Maury, H., 2018. Developing a set of sustainability
Jabbour, C.J.C., de Sousa Jabbour, A.B.L., Sarkis, J., GodinhoFilho, M., 2019. Unlocking indicators for product families based on the circular economy model. J. Cleaner
the circular economy through new business models based on large-scale data: Prod. 196, 1429–1442. https://doi.org/10.1016/j.jclepro.2018.06.131.
an integrative framework and research agenda. Technol. Forecast. Soc. Chang. Mishra, S., Singh, S.P., Johansen, J., Cheng, Y., Farooq, S., 2019. Evaluating indicators
144, 546–552. https://doi.org/10.1016/j.techfore.2017.09.010. for international manufacturing network under circular economy. Manag.
Jain, S., Jain, N.K., Metri, B., 2018. Strategic framework towards measuring a circular Decis. 57, 811–839. https://doi.org/10.1108/MD-05-2018-0565.
supply chain management. Benchmarking: An Int. J. 25, 3238–3252. https://doi. Moktadir, M.A., Rahman, T., Rahman, M.H., Ali, S.M., Paul, S.K., 2018. Drivers to
org/10.1108/BIJ-11-2017-0304. sustainable manufacturing practices and circular economy: A perspective of
Jedelhauser, M., Binder, C.R., 2018. The spatial impact of socio-technical transitions– leather industries in Bangladesh. J. Cleaner Prod. 174, 1366–1380. https://doi.
The case of phosphorus recycling as a pilot of the circular economy. J. Cleaner org/10.1016/j.jclepro.2017.11.063.
Prod. 197, 856–869. https://doi.org/10.1016/j.jclepro.2018.06.241. Moreno, M., Court, R., Wright, M., Charnley, F., 2019. Opportunities for redistributed
Jiao, W., Boons, F., 2017. Policy durability of Circular Economy in China: A process manufacturing and digital intelligence as enablers of a circular economy. Int. J.
analysis of policy translation. Resour. Conserv. Recycl. 117, 12–24. https://doi. Sustainable Eng. 12, 77–94. https://doi.org/10.1080/19397038.2018.1508316.
org/10.1016/j.resconrec.2015.10.010. Nascimento, D.L.M., Alencastro, V., Quelhas, O.L.G., Caiado, R.G.G., Garza-Reyes, J.A.,
Kalmykova, Y., Sadagopan, M., Rosado, L., 2018. Circular economy–From review of Rocha-Lona, L., Tortorella, G., 2019. Exploring Industry 4.0 technologies to
theories and practices to development of implementation tools. Resour. enable circular economy practices in a manufacturing context. J. Manuf.
Conserv. Recycl. 135, 190–201. https://doi.org/10.1016/j. Technol. Manage. 30, 607–627. https://doi.org/10.1108/JMTM-03-2018-0071.
resconrec.2017.10.034. Nasir, M.H.A., Genovese, A., Acquaye, A.A., Koh, S.C.L., Yamoah, F., 2017. Comparing
Karasan, A., Ilbahar, E., Kahraman, C., 2019. A novel pythagorean fuzzy AHP and its linear and circular supply chains: A case study from the construction industry.
application to landfill site selection problem. Soft. Comput. 23, 10953–10968. Int. J. Prod. Econ. 183, 443–457. https://doi.org/10.1016/j.ijpe.2016.06.008.
https://doi.org/10.1007/s00500-018-3649-0. Panagopoulos, A., Haralambous, K.J., 2020a. Minimal Liquid Discharge (MLD) and
Kazancoglu, Y., Kazancoglu, I., Sagnak, M., 2018. A new holistic conceptual Zero Liquid Discharge (ZLD) strategies for wastewater management and
framework for green supply chain management performance assessment resource recovery–Analysis, challenges and prospects. J. Environ. Chem. Eng.
based on circular economy. J. Cleaner Prod. 195, 1282–1299. https://doi.org/ 8,. https://doi.org/10.1016/j.jece.2020.104418 104418.
10.1016/j.jclepro.2018.06.015. Panagopoulos, A., Haralambous, K.J., 2020b. Environmental impacts of desalination
Kiefer, C.P., Del Rio Gonzalez, P., Carrillo-Hermosilla, J., 2019. Drivers and barriers of and brine treatment-Challenges and mitigation measures. Mar. Pollut. Bull.
eco-innovation types for sustainable transitions: A quantitative perspective. 161,. https://doi.org/10.1016/j.marpolbul.2020.111773 111773.
Business Strategy and the Environ. 28, 155–172. https://doi.org/10.1002/ Patricio, J., Axelsson, L., Blomé, S., Rosado, L., 2018. Enabling industrial symbiosis
bse.2246. collaborations between SMEs from a regional perspective. J. Cleaner Prod. 202,
Kirchherr, J., Reike, D., Hekkert, M., 2017.Conceptualizing the circular economy: An 1120–1130. https://doi.org/10.1016/j.jclepro.2018.07.230.
analysis of 114 definitions.Resources, conservation and recycling 127, 221-232. Peng, X., Huang, H., 2020. Fuzzy decision making method based on CoCoSo with
https://doi.org/10.1016/j.resconrec.2017.09.005 critic for financial risk evaluation. Technol. Econ. Dev. Econ. https://doi.org/
Korhonen, J., Honkasalo, A., Seppälä, J., 2018. Circular economy: the concept and its 10.3846/tede.2020.11920.
limitations. Ecol. Econ. 143, 37–46. https://doi.org/10.1016/j. Peng, X., Selvachandran, G., 2019. Pythagorean fuzzy set: state of the art and future
ecolecon.2017.06.041. directions. Artif. Intell. Rev. 52, 1873–1927. https://doi.org/10.1007/s10462-
Kristensen, H.S., Remmen, A., 2019. A framework for sustainable value propositions 017-9596-9.
in product-service systems. J. Cleaner Prod. 223, 25–35. https://doi.org/ Piyathanavong, V., Garza-Reyes, J.A., Kumar, V., Maldonado-Guzmán, G., Mangla, S.
10.1016/j.jclepro.2019.03.074. K., 2019. The adoption of operational environmental sustainability approaches
Kurilova-Palisaitiene, J., Sundin, E., Poksinska, B., 2018. Remanufacturing challenges in the Thai manufacturing sector. J. Cleaner Prod. 220, 507–528. https://doi.org/
and possible lean improvements. J. Cleaner Prod. 172, 3225–3236. https://doi. 10.1016/j.jclepro.2019.02.093.
org/10.1016/j.jclepro.2017.11.023. Pla-Julián, I., Guevara, S., 2019. Is circular economy the key to transitioning towards
Kurita, K., Managi, S., 2021. Circular economy in cities: An economic theory to sustainable development? Challenges from the perspective of care ethics.
decouple economic development from waste. MPRA Paper, 105533. https:// Futures 105, 67–77. https://doi.org/10.1016/j.futures.2018.09.001.
mpra.ub.uni-muenchen.de/105533/. Prieto-Sandoval, V., Jaca, C., Ormazabal, M., 2018. Towards a consensus on the
Lahane, S., Prajapati, H., Kant, R., 2021. Emergence of circular economy research: a circular economy. J. Cleaner Prod. 179, 605–615. https://doi.org/10.1016/j.
systematic literature review. Manage. Environ. Quality: An Int. J. https://doi.org/ jclepro.2017.12.224.
10.1108/MEQ-05-2020-0087. Rasool, B., Ramzani, P.M.A., Zubair, M., Khan, M.A., Lewińska, K., Turan, V., Iqbal, M.,
Lahane, S., Kant, R., 2021. Evaluation and ranking of solutions to mitigate circular 2021. Impacts of oxalic acid-activated phosphate rock and root-induced
supply chain risks. Sustainable Production and Consumption 27, 753–773. changes on Pb bioavailability in the rhizosphere and its distribution in mung
https://doi.org/10.1016/j.spc.2021.01.034. bean plant. Environ. Pollut. 280,. https://doi.org/10.1016/j.envpol.2021.116903
Lahane, S., Kant, R., Shankar, R., 2020. Circular supply chain management: A state- 116903.
of-art review and future opportunities. J. Cleaner Prod. 120, 85–89. https://doi. Rosa, P., Sassanelli, C., Terzi, S., 2019. Towards circular business models: A
org/10.1016/j.jclepro.2020.120859. systematic literature review on classification frameworks and archetypes. J.
Levering, R., Vos, B., 2019. Organizational drivers and barriers to circular supply Cleaner Prod. 236,. https://doi.org/10.1016/j.jclepro.2019.117696 117696.
chain operations. In: Operations Management and Sustainability. Palgrave Salim, H.K., Stewart, R.A., Sahin, O., Dudley, M., 2019. Drivers, barriers and enablers
Macmillan, Cham, pp. 43–66. https://doi.org/10.1007/978-3-319-93212-5_4. to end-of-life management of solar photovoltaic and battery energy storage
Li, Q., Guan, X., Shi, T., Jiao, W., 2020. Green product design with competition and systems: A systematic literature review. J. Cleaner Prod. 211, 537–554. https://
fairness concerns in the circular economy era. Int. J. Prod. Res. 58, 165–179. doi.org/10.1016/j.jclepro.2018.11.229.
https://doi.org/10.1080/00207543.2019.1657249. Salminen, V., Ruohomaa, H., Kantola, J., 2017. Digitalization and big data supporting
Lieder, M., Rashid, A., 2016. Towards circular economy implementation: a responsible business co-evolution.In Advances in Human Factors. In: Business
comprehensive review in context of manufacturing industry. J. Cleaner Prod. Management, Training and Education. Springer, Cham, pp. 1055–1067. https://
115, 36–51. https://doi.org/10.1016/j.jclepro.2015.12.042. doi.org/10.1007/978-3-319-42070-7_96.
Liu, X., Guo, P., Guo, S., 2019. Assessing the eco-efficiency of a circular economy Schroeder, P., Anggraeni, K., Weber, U., 2019. The relevance of circular economy
system in China’s coal mining areas: Emergy and data envelopment analysis. J. practices to the sustainable development goals. J. Ind. Ecol. 23, 77–95. https://
Cleaner Prod. 206, 1101–1109. https://doi.org/10.1016/j.jclepro.2018.09.218. doi.org/10.1111/jiec.12732.
Machado, M.A.D., de Almeida, S.O., Bollick, L.C., Bragagnolo, G., 2019. Second-hand Sedghiyan, D., Ashouri, A., Maftouni, N., Xiong, Q., Rezaee, E., Sadeghi, S., 2021.
fashion market: consumer role in circular economy. J. Fashion Marketing Prioritization of renewable energy resources in five climate zones in Iran using
Manage.: An Int. J. 23, 382–395. https://doi.org/10.1108/JFMM-07-2018-0099. AHP, hybrid AHP-TOPSIS and AHP-SAW methods. Sustainable Energy Technol.
Mangla, S.K., Govindan, K., Luthra, S., 2016. Critical success factors for reverse Assess. 44,. https://doi.org/10.1016/j.seta.2021.101045 101045.
logistics in Indian industries: a structural model. J. Cleaner Prod. 129, 608–621. Sharma, M., Joshi, S., Kumar, A., 2020. Assessing enablers of e-waste management in
https://doi.org/10.1016/j.jclepro.2016.03.124. circular economy using DEMATEL method: An Indian perspective. Environ. Sci.
Mangla, S.K., Luthra, S., Mishra, N., Singh, A., Rana, N.P., Dora, M., Dwivedi, Y., 2018. Pollut. Res. 1–14. https://doi.org/10.1007/s11356-020-07765-w.
Barriers to effective circular supply chain management in a developing country Shete, P.C., Ansari, Z.N., Kant, R., 2020. A Pythagorean fuzzy AHP approach and its
context. Prod. Planning & Control 29, 551–569. https://doi.org/10.1080/ application to evaluate the enablers of sustainable supply chain innovation.
09537287.2018.1449265. Sustainable Prod. Consumption 23, 77–93. https://doi.org/10.1016/j.
Mao, Y., Wang, J., 2019. Is green manufacturing expensive? Empirical evidence from spc.2020.05.001.
China. Int. J. Prod. Res. 57, 7235–7247. https://doi.org/10.1080/ Smarandache, F., 1995. Neutrosophic logic and set, mss.
00207543.2018.1480842. Sonnichsen, S.D., Clement, J., 2020. Review of green and sustainable public
Masi, D., Kumar, V., Garza-Reyes, J.A., Godsell, J., 2018. Towards a more circular procurement: Towards circular public procurement. J. Cleaner Prod. 245,.
economy: exploring the awareness, practices, and barriers from a focal firm https://doi.org/10.1016/j.jclepro.2019.118901 118901.
perspective. Production Planning & Control 29, 539–550. https://doi.org/ Sousa-Zomer, T.T., Magalhães, L., Zancul, E., Campos, L.M., Cauchick-Miguel, P.A.,
10.1080/09537287.2018.1449246. 2018. Cleaner production as an antecedent for circular economy paradigm shift

59
S. Lahane and R. Kant Waste Management 130 (2021) 48–60

at the micro-level: evidence from a home appliance manufacturer. J. Cleaner automotive to construction industries in Malaysia. J. Cleaner Prod. 190, 285–
Prod. 185, 740–748. https://doi.org/10.1016/j.jclepro.2018.03.006. 302. https://doi.org/10.1016/j.jclepro.2018.04.145.
Tauqeer, H. M., Karczewska, A., Lewińska, K., Fatima, M., Khan, S. A., Farhad, M., ... Yadav, G., Luthra, S., Jakhar, S.K., Mangla, S.K., Rai, D.P., 2020. A framework to
Iqbal, M., 2021. Environmental concerns associated with explosives (HMX, TNT, overcome sustainable supply chain challenges through solution measures of
and RDX), heavy metals and metalloids from shooting range soils: Prevailing industry 4.0 and circular economy: An automotive case. J. Cleaner Prod. 254,.
issues, leading management practices, and future perspectives. In Handbook of https://doi.org/10.1016/j.jclepro.2020.120112 120112.
Bioremediation (pp. 569-590). Academic Press. https://doi.org/10.1016/B978-0- Yager, R.R., 2013. Pythagorean membership grades in multicriteria decision making.
12-819382-2.00036-3 IEEE Trans. Fuzzy Syst. 22, 958–965. https://doi.org/10.1109/
Tunn, V.S.C., Bocken, N.M.P., van den Hende, E.A., Schoormans, J.P.L., 2019. Business TFUZZ.2013.2278989.
models for sustainable consumption in the circular economy: An expert study. J. Yager, R.R., Alajlan, N., 2017. Approximate reasoning with generalized orthopair
Cleaner Prod. 212, 324–333. https://doi.org/10.1016/j.jclepro.2018.11.290. fuzzy sets. Information Fusion 38, 65–73. https://doi.org/10.1016/j.
Tura, N., Hanski, J., Ahola, T., Ståhle, M., Piiparinen, S., Valkokari, P., 2019. Unlocking inffus.2017.02.005.
circular business: A framework of barriers and drivers. J. Cleaner Prod. 212, 90– Yazdani, M., Chatterjee, P., 2018. Intelligent decision making tools in manufacturing
98. https://doi.org/10.1016/j.jclepro.2018.11.202. technology selection. In: Futuristic composites. Springer, Singapore, pp. 113–
_ Bojnec, Š., 2019. The impact of green economy measures on rural
Unay-Gailhard, I., 126. https://doi.org/10.1007/978-981-13-2417-8_5.
employment: Green jobs in farms. J. Cleaner Prod. 208, 541–551. https://doi. Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z., 2019. A Combined Compromise
org/10.1016/j.jclepro.2018.10.160. Solution (CoCoSo) method for multi-criteria decision-making problems. Manag.
Van Fan, Y., Lee, C.T., Lim, J.S., Klemeš, J.J., Le, P.T.K., 2019. Cross-disciplinary Decis. 57, 2501–2519. https://doi.org/10.1108/MD-05-2017-0458.
approaches towards smart, resilient and sustainable circular economy. J. Yucesan, M., Kahraman, G., 2019. Risk evaluation and prevention in hydropower
Cleaner Prod. 232, 1482–1491. https://doi.org/10.1016/j.jclepro.2019.05.266. plant operations: A model based on Pythagorean fuzzy AHP. Energy Policy 126,
Veleva, V., Bodkin, G., Todorova, S., 2017. The need for better measurement and 343–351. https://doi.org/10.1016/j.enpol.2018.11.039.
employee engagement to advance a circular economy: Lessons from Biogen’s Zadeh, L.A., 1965. Fuzzy sets. Information and control 8, 338–353.
‘‘zero waste” journey. J. Cleaner Prod. 154, 517–529. https://doi.org/10.1016/j. Zhou, Z., Zhao, W., Chen, X., Zeng, H., 2017. MFCA extension from a circular
jclepro.2017.03.177. economy perspective: Model modifications and case study. J. Cleaner Prod. 149,
Vence, X., Pereira, Á., 2019. Eco-innovación y modelos de 110–125. https://doi.org/10.1016/j.jclepro.2017.02.049.
negociocircularescomofacilitadores de unaeconomía circular. Contaduría y Zhu, Q., Geng, Y., Lai, K.H., 2010. Circular economy practices among Chinese
administración. 64(SPE1), 0-0. https://doi.org/10.22201/ manufacturers varying in environmental-oriented supply chain cooperation
fca.24488410e.2019.1806. and the performance implications. J. Environ. Manage. 91, 1324–1331. https://
Vimal, K.E.K., Rajak, S., Kandasamy, J., 2019. Analysis of network design for a circular doi.org/10.1016/j.jenvman.2010.02.013.
production system using multi-objective mixed integer linear programming Zubair, M., Ramzani, P.M.A., Rasool, B., Khan, M.A., Akhtar, I., Turan, V., Iqbal, M.,
model. J. Manuf. Technol. Manage. 30, 628–646. https://doi.org/10.1108/JMTM- 2021. Efficacy of chitosan-coated textile waste biochar applied to Cd-polluted
02-2018-0058. soil for reducing Cd mobility in soil and its distribution in moringa (Moringa
Wong, Y.C., Al-Obaidi, K.M., Mahyuddin, N., 2018. Recycling of end-of-life vehicles oleifera L.). J. Environ. Manage. 284,. https://doi.org/10.1016/
(ELVs) for building products: Concept of processing framework from j.jenvman.2021.112047 112047.

60