Sustainable Production and Consumption 26 (2021) 455–468
Contents lists available at ScienceDirect
Sustainable Production and Consumption
journal homepage: www.elsevier.com/locate/spc
Review article
Nano and micro level circular economy indicators: Assisting
decision-makers in circularity assessments
Carla Tognato de Oliveira∗, Thales Eduardo Tavares Dantas, Sebastião Roberto Soares
Federal University of Santa Catarina (UFSC), Department of Sanitary and Environmental Engineering, Life Cycle Assessment Research Group (CICLOG),
Florianopolis, 88040-970, Brazil
a r t i c l e
i n f o
Article history:
Received 17 September 2020
Revised 29 November 2020
Accepted 30 November 2020
Available online 2 December 2020
Editor: Prof. Konstantinos Tsagarakis
Keywords:
Sustainability
Circular economy
Circularity
Indicators
Nano level
Micro level
a b s t r a c t
The growing interest for Circular Economy (CE) urged experts to develop assessment metrics regarding
the shift from a linear to a circular rationale. As a response, the recent literature has been populated
with a plethora of circularity indicators addressing different CE scales: nano (products), micro (companies), meso (industrial symbiosis), and macro (governments). However, simply shifting to circular systems
does not necessarily result in favorable alternatives, as trade-offs may occur concerning environmental,
economic, or social impacts. In order to assist decision-makers in the processes of choosing the bestsuiting indicator for circularity assessments, this study presents a systematic literature review aiming at
nano- and micro-level indicators, which were evaluated according to their relation to the sustainability
pillars and life cycle stages (take, make, use, recover). Sixty-one publications were analyzed (44 peerreviewed, 16 from gray literature, 1 dissertation). Fifty-eight indicators were explored (38 nano, 14 micro,
6 directed to both levels). Findings show that the majority of metrics are nano-level environmentallydriven indicators that focus on material and resource recovery strategies. A second expressive group is
simultaneously focused on the environmental and economic pillars. Social repercussions are rarely addressed. We argue that the analyzed indicators mainly focus on material and resource recirculation and
lack robustness to assess the sustainability performance of circular systems. Future research could analyze
the integration of the investigated indicators with consolidated methodologies to overcome the barrier of
combining circularity and sustainability performance.
© 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
1. Introduction
Abbreviations: APL, assessment of circular economy strategies at the product
level; BWPE, bim-based whole-life performance estimator; BCI, building circularity indicators; BPI, end of life best practice indicators; CAM, circularity assessment model; CBA, circular building assessment prototype; CBM-IS, circular business
model set of indicators based on sustainability; CC, circularity calculator; CE, circular economy; CEBI, circular economy benefit indicators; CEI, circular economy index;
CEIP, circular economy indicator prototype; CEMS, circular economy measurement
scale; CE-PCF, evaluation index system of circular economy for pcfs; CET, circular
economy toolkit; CEV, circular economic value; CG, circular gap; Check, circularity
check; CI, circularity index; CLC, Closed Loop Calculator; CMT, Circularity Measurement Toolkit; CP, Circular Pathfinder; CPI, Circular Economy Performance Indicator;
CTI, Circularity Transition Indicators; CYT, Circulytics; eDiM, Ease of Disassembly
Metric; EEI, Economic-Environmental Indicators; EER, Economic-Environmental Remanufacturing; EMF, Ellen MacArthur Foundation; EoL, End of Life; EoLi, End of
Life Indices; FPI, Environmental Sustainability for Food Packaging Indicators; GRI,
Global Resource Indicator; IOBS, Input-Output Balance Sheet; LCA, Life Cycle Assessment; LCSA, Life Cycle Sustainability Assessment; L&C, Longevity and Circularity;
MCEM-PLCS, Multi-Criteria Evaluation Method of Product-Level Circularity Strategies; MCI, Material Circularity Indicator; MECI, Material and Energy Circularity Indicator; MESCS, Material Efficiency in Supply Chain Spreadsheets; MI, Mine Site MFA
Indicator; MIPS, Material Input Per Service Delivered; MIS, Multidimensional Indicator Set; PCIP, Product Circularity Improvement Program; PCM, Product-Level Circularity Metric; PRISMA, Preferred Reporting Items for Systematic Reviews and meta-
The transition from the current linear economic model to a circular economy (CE) is a prominent topic in academic literature,
public governance, and the corporate domain (Lindgreen et al.,
2020). A CE requires decoupling economic growth from finite resource consumption and extraction (Ghisellini et al., 2016), as well
as designing out pollution and seeking better strategies to extract
the maximum value from energy and materials inserted into sys-
Analyses; PR-MCDT, Product Recovery Multi-Criteria Decision Tool; PRP, PRP Circular e-Procurement Tool; PRDI, Product Recycling Desirability Index; QC, Circularity
of Material Quality; RDI, Resource Duration Indicator; REAPro, Resource Efficiency
Assessment of Products; RI for CE, Recycling Indices for the CE; RPI, Reuse Potential Indicator; SCI, Sustainable Circular Index; SDGs, Sustainable Development Goals;
SIAS, Set of Indicators to Assess Sustainability; SICED, Systems Indicators for Circular Economy Dashboard; S-LCA, Social Life Cycle Assessment; SPI, Sustainability Performance Indicators; WBCSD, World Business Council for Sustainable Development;
WCI, Improved Water Circularity Index.
∗
Corresponding author.
E-mail
addresses:
carla.tognato@posgrad.ufsc.br
(C.T.
de
Oliveira),
sr.soares@ufsc.br (S.R. Soares).
https://doi.org/10.1016/j.spc.2020.11.024
2352-5509/© 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
C.T. de Oliveira, T.E.T. Dantas and S.R. Soares
Sustainable Production and Consumption 26 (2021) 455–468
tems (EMF and Granta, 2015). As interest in CE grows, companies
need to prepare transition strategies based on information regarding their circular performance and associated risks and opportunities (WBCSD, 2018). To do so, following the emblematic corporative
phrase “what gets measured, gets done” (Nuñez-Cacho et al., 2018),
businesses need a consistent way to assess their circularity.
Companies are using circular metrics to communicate with
their customers (Howard et al., 2019; Vanegas et al., 2018). Thus,
there is a need for credible and coherent disclosure or reporting
of circular initiatives to relevant stakeholders, including customers,
investors, regulators, media, suppliers, and non-governmental organizations (WBCSD, 2018). As the resource-centered rationale proposed by CE is rapidly being incorporated by governmental and
private strategies (Geissdoerfer et al., 2017), new projects must
have clear circular intentions and be accompanied by an assessment of their actual impact to draw the attention of investors and
the civil society. Hence, in the context of this ongoing economic
model shift, decision-makers are faced with the need to address
effectively and measure the outcomes of the transition from linear to circular models (Jabbour et al., 2019), stressing the need for
comprehensive indicators at the product and company levels of CE.
MACRO
Cities and regions
MESO
Industrial symbiosis
MICRO
Companies
NANO
Products
Fig. 1. CE levels.
1.2. Nano and micro-level circularity indicators
1.1. Circular economy and circularity indicators
This study’s understating of CE levels is primarily based on
the division among the macro, meso, and micro circularity levels commonly applied in CE research (Kirchherr et al., 2017). The
macro level is the CE development in cities, provinces, or regions.
It involves redesigning infrastructural systems, such as clean energy, transportation, the cultural framework, and the social system
(Ghisellini et al., 2016). The meso level presents CE strategies to industrial eco-parks or inter-enterprise associations known as industrial symbiosis (Balanay and Halog, 2016). In turn, the micro level
is related to the CE progress to consumers, a single company, or a
product and its components (Franklin-Johnson et al., 2016).
As the micro level has a broad scope, many metrics referred
to as micro-level indicators do not cover the complexity of a CE
and may lead to different interpretations of what this specific CE
level is targeting during circularity assessments (Lindgreen et al.,
2020). Therefore, Saidani et al. (2017) introduced a new productcentered term to the CE context, the nano level, which describes
“the circularity of products, components, and materials, included in
three wider systemic levels, all along the value chain and throughout their entire lifecycle” (Saidani et al., 2017).
In parallel, the systemic CE view provided by Huamao and
Fengqi (2007) shows that CE levels influence and interact with one
another, i.e., the upper levels are based on the lower levels, which,
in turn, orient their development. This point of view is graphically
exemplified in Fig. 1.
Thereby, we adopt the views of Saidani et al. (2017) to deepen
the scientific understanding regarding the nano- and micro-level
circularity indicators, as these levels offer greater specificity concerning the context in which strategies are applied (Blomsma et al.,
2019). As pointed out by Lindgreen et al. (2020), grouping all corporate operations under the same category to assess companylevel circularity may be overly general and extensive. The further
division into four circularity levels aims to dissolve the common
confusion derived from a far too broad view of the micro level.
In this context, the implementation of circularity indicators
to the nano level is a way to strictly distinguish the influence
of specific products and design options from the overall company circularity. As supported by Huamao and Fengqi (2007) and
Saidani et al. (2017), the nano level is intrinsically present in the
upper levels. However, by highlighting the circularity degree of
products, decision-makers may implement strategies to better attend to specific parts of their production process, which would also
benefit the upper circularity levels.
As the topic challenged the scientific community, the early
phase of CE-related research developed a plethora of concepts trying to define best such a broad idea (Korhonen et al., 2018). This
study employs the definition brought by Kirchherr et al. (2017) as
a conceptual basis for CE: “an economic system that replaces the
‘end of life’ (EoL) concept with reducing, alternatively reusing, recycling, and recovering materials in production/distribution and
consumption processes. It operates at the micro level (products, companies, consumers), meso level (eco-industrial parks), and
macro level (city, region, nation) to accomplish sustainable development, thus simultaneously creating environmental quality, economic prosperity, and social equity to the benefit of current and
future generations”. This study, however, further distinguishes the
CE levels of operation by adding a fourth degree, the nano level,
further explored in Section 1.2.
Due to the nature of this research, it becomes necessary to define the term indicator. As pointed out by Saidani et al. (2019), indicators are valuable analytical tools applied to simplify information.
The main goals of indicator use are related to tracking, monitoring,
and measuring the progress and performance of specific systems or
processes. Moreover, it is important to stress that these tools may
aid the understanding of complex phenomena through both quantitative and qualitative assessments. Parallel terms, such as metric,
index, and indices, are hereby understood as synonyms to the term
“indicator” and applied within the same context.
As this paper addresses assessment methods within the CE context, a clear definition of circularity is essential for a comprehensive
understanding of the evaluated metrics. Linder et al. (2017) defined
circularity at the product level as “the fraction of a product that
comes from a used product”. However, circularity assessments are
imperative to the monitoring and improvement of a CE at different
scales. Therefore, this study presents a broader definition of the
beforementioned concept.
We define circularity as the alignment of a material or energy
flow, product, processes, or system to a set of CE strategies (redesign, product disassembly, recycling, use of renewable energy,
etc.) that meet the general CE goals. Therefore, circularity indicators may be understood as analytical tools focused on measuring
the degree of association of a system (or part of one) to practices
and strategies applied to develop a CE further. In that sense, higher
circularity means that a specific item or system is closer to achieving the goals set by the guiding standards of a CE.
456
C.T. de Oliveira, T.E.T. Dantas and S.R. Soares
Sustainable Production and Consumption 26 (2021) 455–468
1.3. Goal and scope of this work
Science database, two of the most extensive repositories in the related scientific field.
Subsequently, a variation of a semi-structured snowballing approach (Wohlin, 2014) was performed to capture additional and
emerging methodologies and indicators presented in reports and
gray literature (Lindgreen et al., 2020). Although gray literature
may present preliminary and even simplistic findings (Costa et al.,
2019), the inclusion of such publications is vital to the objective of
this paper since many of the circularity assessment tools are developed as initiatives from business organizations and non-academic
institutions (Geissdoerfer et al., 2017; Lindgreen et al., 2020).
The literature search and snowballing were carried out until no
new interactions (publications showcasing metrics different from
those already present in the portfolio) were found. The search
led to the identification of 53 peer-reviewed documents, and the
snowballing completed the set with the inclusion of 61 publications. Duplicates accounted for 16 excluded publications, with
97 documents remaining. Eight additional papers were excluded
based on the information presented in titles, abstracts, and keywords.
While recognizing that studies have already been developed
to review metrics used to measure circularity assessments, to the
best of our knowledge, no recent review has focused on identifying CE indicators considering the division between the nano
and micro levels. Therefore, this article builds on previous reviews (Kristensen and Mosgaard, 2020; Lindgreen et al., 2020;
Parchomenko et al., 2019; Saidani et al., 2019) and aims to update the list of critically analyzed indicators while still contributing to the indicator selection processes by separating nano- and
micro-level circularity indicators. Additionally, to better support
sustainability-oriented decision-makers, we innovate by identifying the pillars of sustainability (environmental, economic, and social) and life cycle stages (take, make, use, recover) that each indicator covers. To fill these scientific gaps and achieve the goal of
this article, three research questions were drawn up and addressed
throughout this study:
1 Why divide circular economy levels further into nano and micro
levels?
2 Can the three sustainability pillars be assessed through circularity indicators?
3 What are the life cycle stages covered by the nano- and microlevel indicators analyzed?
2.2. Eligibility
The selection of eligible publications was based on a full reading of the remaining articles and their adherence to the search criteria presented in Table 1.
A total of 28 publications were ruled out upon full-text reading
as they did not fit the purpose and goals of this study. This leads
to an initial portfolio of 14 articles remaining from the database
search and 47 publications from the semi-structured snowball
technique (30 papers are present in peer-reviewed literature, 16
are part of gray literature publications, 1 dissertation). Thus, the final portfolio included 61 publications that addressed nano- and/or
micro-level circularity indicators through reviews, case studies, and
methodological approaches.
The overarching objective of this article is to develop a list
of circularity indicators at the nano and micro levels to assist
decision-makers in indicator selection processes based on the
three dimensions of sustainability and life cycle stages. To do so,
this article is organized as follows: Section 2 outlines the research method for the literature review; Section 3 presents the
bibliometric and indicator-related results; Section 4 explores the
results concerning the three aforementioned research questions;
Section 5 closes the study by drawing the research conclusions and
future research proposals.
2.3. Content analysis
2. Methodology
R
The final portfolio was organized through Endnote
software
and moved to a spreadsheet for further analysis (for the final portfolio, please refer to the Supplementary Material). The portfolio
was organized based on bibliometric information (authors, year
of publication, title, journal, type of publication) and indicatorrelevant data. The latter occurred through the differentiation of
the level of application of the indicators (nano or micro) based on
Saidani et al. (2017), the sustainability pillars addressed (environmental, economic, social), and the life cycle stages (take, make, use,
recover).
The selection of the four life cycle stages applied in the study
followed the common division applied in life cycle thinking. The
only exception is the lack of a “disposal” stage, since it is outside
the scope of the assessed indicators. The stage “Take” refers to the
extraction of natural resources from the environment (energy and
materials) (Lèbre et al., 2017). Circularity indicators directed toward the “Make” stage focus on the manufacturing process under
the CE rationale (Zhang et al., 2020). The “Use” stage hints at the
acceptance and behavioral shift of consumers to the circular context (Sadhukhan et al., 2020). Lastly, the life cycle stage “Recover”
refers to CE practices promoting waste, materials, and energy recovery after their first use (Prieto-Sandoval et al., 2018).
For the analysis regarding the connection of the indicators to
the sustainability pillars, all those that strictly involved material
and energy flows were considered to address the environmental
dimension of sustainability. For an indicator to be considered economic, it had to clearly show an economic nature and purpose (i.e.,
monetary flows, material costs, etc.). Social indicators were those
This study proposes a comprehensive analysis of circularity indicators directed toward resources, products, and companies. Nevertheless, it is important to state that strictly environmental, economic, social, or sustainability indicators are not within the scope
of this study. Therefrom, we conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines (Moher et al., 2009),
aiming at both peer-reviewed articles and gray literature (i.e., reports, commercial publications, tools present in websites, etc.). The
literature review was carried out following a four-step procedure
illustrated in Fig. 2.
2.1. Identification and screening
Guided by the research questions presented in Section 1.5, a
set of keywords encompassing both business- and CE-related terms
were selected. The search was conducted through the combination
of such keywords using Boolean operators (AND/OR). The search
string applied to the scientific databases was:
(("compan∗ ") OR ("business∗ ") OR ("entreprise∗ ") OR
("organi?ation∗ ") OR ("micro level")) AND (("circular
indicator∗ ") OR ("circular index∗ ") OR ("circular metric∗ ")
OR ("circular tool∗ ") OR ("circular economy measur∗ ") OR
("circular economy assess∗ "))
The keyword combination was applied to “title, abstract, and
keywords” in the Scopus database and to “topic” in the Web of
457
C.T. de Oliveira, T.E.T. Dantas and S.R. Soares
Sustainable Production and Consumption 26 (2021) 455–468
Fig. 2. Literature review procedure as per the PRISMA guidelines (Moher et al., 2009).
Table 1
Literature search criteria.
Inclusion criteria
Exclusion criteria
Circular economy-related
Nano or micro level
Peer-reviewed or gray literature (reports)
Tools present in websites
Papers that did not present circularity indicators
Papers focused on meso and macro levels
directed toward common social issues regarding the system operations (i.e., employment, safety, etc.)
Once all nano- and micro-level circularity indicators were organized and screed based on the aforementioned criteria, the content
was analyzed, and results were qualitatively and quantitatively extracted. The communication of the results follows in the next section.
dicators over the years, as well as their geographical distribution
(based on the first-author affiliation), type of publication (peerreviewed journals, gray literature, and dissertations), and the distribution of publications per journal.
Circularity assessment is not only a matter of interest to
academia but also a deep concern of companies and governments
(Stahel, 2016). Therefore, it is natural that the development of circularity assessment tools is carried out by non-academic institutions as well. This fact becomes evident in this analysis, as 26%
of all publications assessed were issued in reports, online tools, or
on the websites of specific institutions (16 publications). However,
the majority of the nano- and micro-level indicators gathered were
published in peer-reviewed articles (44 publications). Additionally,
one dissertation was assessed (Verbene, 2016), accounting for 2%
of the total portfolio.
The first publication evaluated dates from 2002 (Ritthoff et al.,
2002), years before CE became a hot topic within the academic literature (Ghisellini et al., 2016). This publication was accepted by
this study’s search criteria because many CE practices and strategies (i.e., recycling, composting, waste-to-energy, etc.) were already
developed and vastly explored before being discussed under the CE
3. Results
At the end of the literature review procedure illustrated in
Fig. 2, the final portfolio was analyzed following the steps explained in Section 2. The results are shown and discussed in this
section. The bibliometric results are presented next, followed by
the analysis of the several circularity indicators found through the
review procedure.
3.1. Bibliometric results
The bibliometric analysis was carried out based on the distribution of papers related to nano- and micro-level circularity in458
C.T. de Oliveira, T.E.T. Dantas and S.R. Soares
Sustainable Production and Consumption 26 (2021) 455–468
Fig. 3. – Publication distribution over the years.
Table 2
Geographical distribution of publications.
banner (Korhonen et al., 2018). In contrast, all other publications
reviewed in this study present direct links to CE in their texts. The
three highest-ranked years according to the number of CE-related
articles issued were: 2017 (16 publications), 2018 (12 publications),
and 2019 (9 publication). Although only eight papers were released
in 2020 so far, it is expected that more papers follow throughout
the year, as this analysis only accounts for articles published by the
search date (August 2020). To illustrate the emerging development
of the topic, the 61 publications with direct links to CE were published according to the chronological distribution shown in Fig. 3.
Fig. 3 illustrates the scientific effort to better measure and understand circularity, a concept that has rapidly evolved in the last
decade. As mentioned by Saidani et al. (2019), the development
of circularity indicators has been strongly addressed by many researchers as an urgent scientific gap, with a plethora of indicators
emerging swiftly ranging from the nano to the macro level. As previously stated, the product- and company-level circularity indicators released within this timeframe are assessed in this study and
discussed in the further topics.
Table 2 shows the geographical distribution of publications according to the first author affiliation.
Although intercountry scientific collaboration is a reality in
the current interconnected world, this analysis hints at the global
trends in CE research. As one may observe in Table 2, most publications addressing nano- and micro-level circularity indicators may
be traced to European countries. All gray literature publications
have European origin, amplifying the expressive contribution of
this region. The five highest-ranked origins of papers analyzed in
this review are the United Kingdom, the Netherlands, Germany,
France, and Italy. Moreover, the circularity assessment methods
seem to be an emerging topic in the Latin American context, represented by publications from Brazil, Ecuador, and Colombia. Other
regions (e.g., Asia) are less expressive in terms of the number of
articles assessed. Four publications were marked as “n/a” since
they represent indicators developed by European Union projects
and cannot be traced to a specific country. These findings show
the concern and interest of (mainly European) institutions and
research bodies from different nations to develop comprehensive
circularity indicators to assess and leverage this economic model
shift.
The 44 peer-reviewed articles analyzed in the study were issued over 15 different journals, the highest-ranked of which were
the Journal of Cleaner Production (15 publications), Resources, Con-
Country (first author affiliation)
Number of publications
United Kingdom
Netherlands
France
Germany
Italy
Belgium
Slovenia
Spain
Sweden
United States of America
Australia
Austria
Brazil
China
Colombia
Denmark
Ecuador
Hungary
Poland
Portugal
Singapore
Switzerland
n/a
9
7
6
6
5
4
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
4
servation and Recycling (8 publications), digital journal Sustainability (5 publications), and the Journal of Industrial Ecology (3 publications). The International Journal of Production Research and the
International Journal of Sustainable Engineering showcased 2 publications each. The nine remaining journals accounted for a single
publication analyzed per each (for more information, please refer
to the Supplementary Material). It can be noticed that the articles
are spread over a variety of engineering and sustainability-related
journals. These findings show how circularity indicators have been
developed mostly by the scientific niches engaged in the resource
efficiency and sustainability agenda, contributing both to general
discussions around the quantification of CE-related processes and
as an argument in sector-specific topics.
3.2. Circularity indicator analysis
A total of 58 nano- and micro-level circularity indicators were
assembled through the literature review. Table 3 shows the full list
459
C.T. de Oliveira, T.E.T. Dantas and S.R. Soares
Sustainable Production and Consumption 26 (2021) 455–468
Table 3
Nano- and micro-level circularity indicators.
#
Indicator
Circularity
level
1
2
Global Resource Indicator (GRI)
BIM-based Whole-life Performance Estimator (BWPE)
Nano
Nano
3
Nano
5
Product Recovery Multi-Criteria Decision Tool
(PR-MCDT)
Multi-Criteria Evaluation Method of Product-Level
Circularity Strategies (MCEM-PLCS)
Resource Efficiency Assessment of Products (REAPro)
Nano
6
Sustainable Circular Index (SCI)
Micro
7
Circular Building Assessment Prototype (CBA)
Nano
8
Micro
9
10
Material Efficiency in Supply Chains Spreadsheets
(MESCS)
Circular Economy Indicator Prototype (CEIP)
Circular Gap (CG)
11
Product Circularity Improvement Program (PCIP)
Nano
Nano and
Micro
Nano
12
13
C2C Indicators
Circularity Index (CI)
Nano
Nano
14
15
Circular Economy Index (CEI)
Circularity Check (Check)
16
Material Circularity Indicator (MCI)
17
Circulytics (CYT)
Nano
Nano and
Micro
Nano and
Micro
Micro
18
Circular Economy Toolkit (CET)
Nano
19
EoL indices (EoLi)
Nano
20
21
22
Longevity and Circularity (L&C)
Circular Economic Value (CEV)
Resource Duration Indicator (RDI)
Nano
Nano
Nano
23
Economic-environmental Indicators (EEI)
Nano
24
Circularity Measurement Toolkit (CMT)
Micro
25
Circularity Assessment Model (CAM)
Micro
26
Set of Indicators to Assess Sustainability (SIAS)
Micro
27
28
29
Circular Economy Performance Indicator (CPI)
Circular Economy Benefit Indicators (CEBI)
End of Life Best Practice Indicators (BPI)
Nano
Nano
Nano
30
Closed Loop Calculator (CLC)
Nano
31
Mine site MFA Indicator (MI)
Micro
32
End-of-Life Index
Nano
33
34
35
Evaluation Index System of CE for PCFs (CE-PCF)
Product-Level Circularity Metric (PCM)
Input-Output Balance Sheet (IOBS)
Micro
Nano
Nano
36
Sustainability Performance Indicators (SPI)
Nano
51
Product Recycling Desirability Index (PRDI)
Nano
37
38
Multidimensional Indicator Set (MIS)
Assessment of Circular Economy Strategies at the
Product Level (APL)
Circular Economy Measurement Scale (CEMS)
Reuse Potential Indicator (RPI)
Nano
Nano
4
39
40
41
42
Environmental Sustainability of Food Packaging
indicators (FPI)
Circ(T)
Sustainability pillars
Environmental and social
Environmental and
Economic
Environmental, Economic,
and Social
Environmental, Economic,
and Social
Environmental
Nano
Environmental, Economic,
and Social
Environmental and
Economic
Environmental
Life Cycle
Stages
Authors
Full life cycle
Full life cycle
Adibi et al. (2017)
Akanbi et al. (2018)
Recover
Alamerew and
Brissaud (2019)
Alamerew et al. (2020)
Recover
Recover
Make, Use,
Recover
Full life cycle
Ardente and
Mathieux (2014)
Azevedo et al. (2017)
BAMB (2020)
Make
Braun et al. (2018)
Full life cycle
Full life cycle
Cayzer et al. (2017)
Circle Economy (2018)
Make, Use,
Recover
Full life cycle
Make, Use,
Recover
Recover
Full life cycle
Circularity IQ and
KPMG (2020)
C2C (2014)
Cullen (2017)
Full life cycle
EMF and Granta (2015)
Full life cycle
EMF (2020)
Full life cycle
Evans and Bocken (2013)
Recover
Favi et al. (2017)
Use, Recover
Make, Use
Use, Recover
Full life cycle
Figge et al. (2018)
Fogarassy et al. (2017)
FranklinJohnson et al. (2016)
Fregonara et al. (2017)
Full life cycle
Garza-Reyes et al. (2019)
Full life cycle
Giacomelli et al. (2018)
Recover
Golinska et al. (2015)
Recover
Recover
Recover
Full life cycle
Huysman et al. (2017)
Huysveld et al. (2019)
Jiménez-Rivero and
García-Navarro (2016)
Kingfisher (2014)
Full life cycle
Lèbre et al. (2017)
Make, Recover
Lee et al. (2014)
Full life cycle
Recover
Full life cycle
Liang et al. (2018)
Linder et al. (2017)
Capellini (2017)
Recover
Mesa et al. (2018)
Recover
Recover
Full life cycle
Mohamed Sultan
et al. (2017)
Nelen et al. (2014)
Niero and Kalbar (2019)
Make, Recover
Recover
Nuñez-Cacho et al. (2018)
Park & Chertow (2014)
Nano
Environmental
Environmental and
Economic
Environmental
Full life cycle
Pauer et al. (2019)
Nano
Environmental
Full life cycle
Pauliuk et al. (2017)
Environmental
Environmental and
Economic
Environmental and
Economic
Environmental and Social
Environmental
Environmental
Environmental and
Economic
Environmental
Environmental and
Economic
Environmental and
Economic
Environmental and
Economic
Environmental
Environmental
Environmental
Environmental and
Economic
Environmental and
Economic
Environmental and
Economic
Environmental, Economic,
and Social
Environmental
Environmental
Environmental, Economic,
and Social
Environmental and
Economic
Environmental and
Economic
Environmental and
Economic
Environmental
Economic
Environmental and
Economic
Environmental, Economic,
and Social
Environmental and
Economic
Environmental
Environmental
Micro
Nano
Di Maio and Rem (2015)
Ecopreneur (2019)
(continued on next page)
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Sustainable Production and Consumption 26 (2021) 455–468
Table 3 (continued)
#
Indicator
Circularity
level
Sustainability pillars
Life Cycle
Stages
Authors
43
Nano and
Micro
Nano
Environmental, Economic,
and Social
Environmental
Full life cycle
Pauliuk (2018)
Full life cycle
Rendemint (2016)
45
Systems Indicators for Circular Economy Dashboard
(SICED)
PRP Circular e-Procurement Tool (PRP) and The
R
ReNtry
-module
Circular Pathfinder (CP)
Micro
Make
ResCoM (2017)
46
Circularity Calculator (CC)
Nano
Make, Recover
ResCoM (2017)
47
Material Input Per Service Delivered (MIPS)
Full life cycle
Ritthoff et al. (2002)
48
Full life cycle
Rossi et al. (2020)
49
Circular Business Model Set of Indicators based on
Sustainability (CBM-IS)
Improved Water Circularity Index (WCI)
Nano and
Micro
Nano and
Micro
Micro
Environmental and
Economic
Environmental and
Economic
Environmental
Make, Recover
Sartal et al. (2020)
50
Circularity of Material Quality (QC)
Nano
Steinmann et al. (2019)
52
Ease of Disassembly Metric (eDiM)
Nano
Take, Make,
Recover
Recover
53
Economic-Environmental Remanufacturing (EER)
Nano
54
Recycling Indices (RIs) for the CE
Nano
55
Expended Zero Waste Practice Model (ZWP)
Micro
56
Building Circularity Indicators (BCI)
Nano
57
Circularity Transition Indicators (CTI)
Micro
58
Material and Energy Circularity Indicators (MECI)
Micro
44
Environmental, Economic,
and Social
Environmental and
Economic
Environmental
Environmental and
Economic
Environmental and
Economic
Environmental
Environmental, Economic,
and Social
Environmental and
Economic
Environmental and
Economic
Environmental
of indicators compiled, along with their respective circularity level,
connection to the sustainability pillars, and life cycle stages.
As previously stated, we identify circularity indicators as analytical tools focused on measuring the degree of association of a
system (or part of one) to practices and strategies applied to develop a CE further. However, the majority of indicators assessed in
this study combine different metrics to deliver simplified results.
In other terms, they apply a group of methods and calculations
to measure a system’s circularity under their premises. From the
58 indicators discussed in the study, only 9 (16%) rely on a single indicator in their procedure. Most of the indicators shown in
Table 3 (48 indicators, 84%) depend on a set of analytical tools to
deliver their results. For more information on whether a specific
indicator presented in the aforementioned table relies on a single
indicator or a group thereof, please refer to the Supplementary Material.
Recover
Make, Recover
Make, Use,
Recover
Make, Use,
Recover
Full life cycle
Take, Make,
Recover
Vanegas et al. (2018)
van Loon and van
Wassenhove (2018)
Van Schaik and
Reuter (2016)
Veleva et al. (2017)
Verbene (2016)
WBCSD (2020)
Zore et al. (2018)
pillar. The remaining 24 indicators (circa 39%) aim to assess circularity through the lens of a single sustainability dimension.
The largest group of assessed indicators, composed by 23 indicators (40% of the portfolio) provide information solely on the
environmental aspects of circular systems. One indicator, representing less than 2% of the portfolio, delivered circularity assessments strictly linked to the economic pillar. No circularity indicator
showed an exclusive focus on the social impacts of a CE.
A total of 24 indicators (41%) had simultaneous links to the economic and environmental pillars. Only two indicators (3.5%) had
the purpose of assessing the environmental and social aspects connected to circularity at the same time. The remaining eight indicators (14%) aimed to deliver a circularity assessment guided by the
triple bottom line model and covered all the sustainability pillars
in their methodologies.
If summed up, the environmental pillar is the one that attracts
the most attention from scholars and organizations, being present
in 57 of the indicators analyzed (98%). The economic pillar follows
in second, having been evaluated by 33 indicators (57%). The social
dimension, although only considered in combination with other
pillars, is shown in 10 circularity indicators (17%).
3.2.1. Levels of circularity
According to the findings shown in Table 3, most indicators
that fit the search criteria applied in this review were developed
to assess product-level circularity. Of the indicators included in
the portfolio, 66% are directed toward energy and material flows
within the nano level of circularity (38 indicators). Micro-level indicators (i.e., company-level circularity) accounted for 24% of the
analyzed metrics (14 indicators). A third cluster constituted of indicators that may be used to assess both nano- and micro-level
circularity was established and accounts for 6 indicators (10%). For
further information about the indicators analyzed and a summary
that assists in identifying the targeted circularity level of each indicator, please refer to the Supplementary Material.
3.2.3. Life cycle stages
The explored nano- and micro-level indicators were analyzed
according to the life cycle stages they cover. Fig. 5 shows the distribution of indicators per life cycle stage.
Out of the 58 indicators, 25 (43%) comprise all stages in their
scopes (referred to here as “Full life cycle”). The majority of indicators equipped to deliver full life cycle evaluations (13 indicators, 22%) target product-level circularity. Only 6 metrics (10%) focus on companies. The remaining 6 indicators (10%) connected to
this group may be used to examine both the nano and micro levels.
The “Take” life cycle stage is the least assessed by the indicators
included in the investigated portfolio. Only two indicators (3%), one
from each circularity level, partially address this stage. All other
3.2.2. Sustainability pillars
The several indicators examined in this study have diverse relations to the pillars of sustainability. Fig. 4 shows how each indicator is connected to particular pillars. Approximately 59% of the
portfolio (34 indicators) deliver results linked to more than one
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Sustainable Production and Consumption 26 (2021) 455–468
Fig. 4. Distribution of indicators per pillars of sustainability.
indicators that cover the “Take” stage are more holistic methods
grouped under “Full life cycle”.
A group of 12 indicators targets manufacturing processes and
product design (“Make” life cycle stage) along with other life cycle
stages (21%). Only 3% of the total portfolio (2 indicators) are exclusively related to the “Make” stage and focus on company-level circularity assessment. No nano level indicator exclusively addressed
the “Make” stage.
The “Use” stage is directly considered in 8 indicators (14%),
among which only 2 are oriented toward the micro level of circularity. All 6 other indicators were developed to assess the “Use”
stage of a product along with other life cycle stages.
The “Recover” stage, the main characteristic of circular systems
related to closed loops and resource recirculation, was the most
addressed by the reviewed indicators, of which 17 are strictly dedicated to energy or material recovery strategies (15 nano, 2 micro), accounting for 29% of the total. If the indicators that deal with
other life cycle stages combined with recovery strategies are considered, 30 indicators (52%) may be linked to this stage (excluding
the Full life cycle indicators).
4.1. Why divide circular economy levels further into nano and micro
levels?
CE is linked to the efficient use of resources. In this new rationale, systems are designed to circulate resources as much and for
as long as possible. When Saidani et al. (2017) coined the term
“nano level”, they stated that the micro-level indicators did not
encompass the complexity of a CE and the EoL possibilities. Additionally, Rincón-Moreno et al. (2021) point out that the definitions of tools and criteria for product-level circularity measurements are still lacking. This view is also present in the work of
Cayzer et al. (2017) and Ghisellini et al. (2016), who note that many
of the circularity indicators shown in the literature are mainly
directed toward companies, industrial parks, regions, or nations.
In contrast, the results illustrated by Fig. 5 point to the growing
trend in product-level circularity indicators. However, the conceptual background and common view of a CE still does not fully
acknowledge the differences between product-level and companylevel circularity.
The findings shown in Section 3.2.1 suggest that the current
micro-level views are filled with indicators aimed at the detailed
analysis of specific products. Indicators such as PCIP, L&C, and RDI
differ considerably from CEMS, MI, SIAS in terms of the inputs
needed to perform their assessments. That discrepancy is grounded
in the clear distinctions between the scopes investigated. The first
group (nano indicators) rely on material and component specificities (Figge et al., 2018; Franklin-Johnson et al., 2016) and deliver results aimed at improving product quality, design, and resource recovery (Mestre and Cooper, 2017; Mohamed Sultan et al.,
2017; Steinmann et al., 2019). On the other hand, the second
group (company-level circularity indicators) centers on environ-
4. Discussion
This section is divided into three sub-sections and aims to answer the research questions introduced in Section 1.3. First, we
discussed the distinction between nano and micro-level circularity indicators. Following, we analyzed the links between circularity
indicators and the pillars of sustainability. Lastly, we discussed the
results regarding the life cycle stages covered by the indicators of
the examined portfolio.
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Sustainable Production and Consumption 26 (2021) 455–468
Fig. 5. Distribution of indicators per level and life cycle stage.
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Sustainable Production and Consumption 26 (2021) 455–468
mental benefits and value generation by identifying proactive and
preventive waste management strategies (Lèbre et al., 2017) while
allowing direct measurements of the importance of a CE for companies (Nuñez-Cacho et al., 2018). This scope difference may be noticed across this study’s portfolio and directly influences the goals,
intended public, and result formats of each indicator.
Under such circumstances, we argue that the further division between these circularity levels is crucial at the current development pace of CE-related studies. As pointed out by
Lindgreen et al. (2020) while reviewing micro-level circularity indicators, grouping all industrial activities under the same umbrella
to evaluate their circularity may be overly general and extensive. In
this regard, an additional division of the micro level would reduce
confusion concerning a far too broad view of company-level circularity, which, under the current concept, embraces all activities and
outputs of businesses. This approach not only better portraits the
different attention given to products and components (Mestre and
Cooper, 2017; Vanegas et al., 2018) by companies (Azevedo et al.,
2017; Blomsma et al., 2019) but also enables a more detailed analysis of specific processes that are parts of circular business models. Therefore, by putting product-level circularity under the spotlight, decision-makers may identify the specificities of different
goods developed by the same company, which could, for example,
highlight the outcomes from strategical and innovative changes to
product design (Cayzer et al., 2017; Favi et al., 2017). It is important to stress that these conceptual changes would not exert any
changes to micro-level circularity, as the low levels are intrinsically
present in the uppers circularity levels (Huamao and Fengqi, 2007).
Lastly, this division between nano and micro-level circularity
would also assist in an organizational matter by providing stricter
partitioning of the abundant circularity indicators. Although it
entails a more simplistic outcome from this approach, decisionmakers and scholars could benefit from more direct and intuitive compartmentalization during the indicator selection process,
sometimes clouded by the misconceptions regarding the different
approaches aimed at circular products and companies.
2016), translated here as the low recurrence of circularity indicators linked to this pillar.
The following topics offer a deeper analysis of the interface
between circularity indicators and each sustainability pillar. Additionally, the last topic provides insights regarding the indicators
equipped to deliver results according to all three sustainability pillars.
4.2.1. Environmental pillar
The environmental pillar was covered by the majority of the indicators assessed. It should be noted that our analysis was based
on indicators that handle environmental aspects (i.e., materials,
components, energy, water, etc.). Hence, indicators that highlight
the reinsertion of materials or components into the system were
grouped into the environmental pillar. Within these circumstances,
we corroborate Corona et al. (2019) by reaffirming that the majority of circularity indicators (i.e., REAPro, MESCS, CEI, CEV, etc.)
focus on material recirculation and resource efficiency, not being
capable of portraying the complex reality and possible trade-offs
of circular systems. The lack of information regarding the calculation processes of certain indicators is also alarming. For example, according to Kristensen and Mosgaard (2020), EOLi does not
demonstrate how the environmental impact and amount of emissions from recycling and remanufacturing are considered. Only a
few indicators are equipped to demonstrate the actual environmental performance of circular systems (i.e., PR-MCDT, EZWP, APL).
Harris et al. (2021) describe this shortcoming as searching for
“circularity for circularity’s sake”. In alignment with the aforementioned authors and corroborating Corona et al. (2019) and
Linder et al. (2017), we highlight the excessive focus given to resource recirculation in contrast to the assessment of environmental impacts by the circularity indicators. Although material and resource reinsertion may be important inputs for decision-makers
involved in circular business models (Manninen et al., 2018), to
bridge this gap, we support the use of robust environmental accounting methods for the evaluation of circular systems in parallel
with the use of circularity indicators. Examples of advised methodologies are Life Cycle Assessment (LCA) (de Souza Junior et al.,
2020), Material Flow Analysis (Haas et al., 2015), and Emergy Analysis (Santagata et al., 2020).
4.2. Can the three pillars of sustainability be assessed through
circularity indicators?
Indicators are developed to simplify information and provide
performance improvement tools (Saidani et al., 2019). Several authors indicate that circular practices and potential loop-closing
have to be assessed through a multilateral approach to avoid tradeoffs (Geissdoerfer et al., 2017; Inghels et al., 2019) and ultimately
promote the comprehensive understanding of the system repercussions. This claim is also supported by the increasing attention
given to the links between a CE and the Sustainable Development
Goals (SDGs) (Dantas et al., 2021). Different authors have stipulated sets of main SDGs covered by a CE (Fassio and Tecco, 2019;
Rodriguez-Anton et al., 2019; Schroeder et al., 2019). Although the
lists of targeted SDGs vary according to each publication, they all
point to CE being a valuable strategy to contribute to the SDGs.
However, that relation must be supported by quantitative indicators to assist decision-making processes (Elia et al., 2017). Therefore, a deeper understanding of the links between circularity indicators and sustainability pillars is beneficial not only for CE but
also for creating assertive pathways towards the SDGs.
The findings illustrated by Fig. 4 indicate that the current circularity indicators have a strong relation to environmental aspects,
followed by the economic assessments and the societal traits of
the analyzed systems. Such results corroborate the view that a CE
is driven by markets and governments willing to transition the current economy to a less environmentally impactful yet economically
profitable system (Ghosh and Agamuthu, 2018). However, the social side of a CE still receives insufficient attention (Ghisellini et al.,
4.2.2. Economic pillar
Although mainly considered in combination with the environmental dimension, the economic pillar was the second most covered sustainability pillar. These indicators vastly tackled the resource fluxes in terms of their monetary value. The only indicator
strictly related to the economic dimensions of sustainability was
PCM. Its authors recommend using the recirculated economic value
to total product value ratio as a circularity metric, using value
chain costs as estimators (Linder et al., 2017). This procedure may
initially aid stakeholders in developing and adjusting the strategies
of new circular business models (Ghisellini et al., 2016); however,
it still lacks the comprehensiveness needed to assess circular systems (Geissdoerfer et al., 2017).
The common combination of economic indicators with environmental and social approaches suggests an alternative to such a limitation. For example, indicators such as EER, eDiM, PRDI, and RPI
were developed to translate the economic gains of material recirculation through CE practices like recycling and remanufacturing.
On the other hand, CC, EOLi, MI, and other indicators focus on the
price of products or the CE strategy applied. Either way, the economic evaluation of circular systems lies at the core of the transition to a CE since businesses unchangeably hold profit as their
main goal. Based on our analysis and in alignment with the work
of Lindgreen et al. (2020) and Niero and Kalbar (2019), we argue
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Sustainable Production and Consumption 26 (2021) 455–468
that economic analyses are of imperative importance to a CE. However, they do not cover the sustainability performance of the circular system. A superior analysis could provide results based on the
integration of economic circularity indicators and methods capable
of addressing the environmental and social traits of a system.
indicators only partially cover the three sustainability pillars, we
recommend that decision-makers analyze the vast list of available
circularity indicators carefully and either be mindful of the fragilities of each approach or combine them with other procedures to
reach more substantial and detailed results.
It is important to underline that each indicator is created to translate information regarding specific circumstances
(Patterson et al., 2017). However, since the scope of most of
our portfolio was flexible in terms of the industrial sector they
could be applied to (for more information, please refer to the
Supplementary Material), we build on the views of Niero and
Kalbar (2019) and argue that the use of a robust sustainabilitydriven methodology such as the Life Cycle Sustainability Assessment (LCSA), in parallel with circularity indicators based on material recirculation and resource efficiency, would prove to be a more
comprehensive strategy. This approach would not only enable full
life cycle evaluations but also use LCA to point the environmental
impacts of CE strategies, address the societal issues through a refined S-LCA framework, and measure the prices, costs, and further
economic repercussions through Life Cycle Costing. Albeit timeconsuming and highly dependent on technical expertise, we believe this method, when combined with circularity indicators, may
bring optimized results compared to the procedure followed by the
eight indicators in the center of Fig. 4.
4.2.3. Social pillar
The social dimension is the least addressed by the indicators in our portfolio. As shown in Fig. 4, no indicator was particularly developed aiming at this pillar. The societal repercussions of circular systems were only addressed in combination
with economic and environmental indicators. These results endorse the work by (Lee, 2020), which highlights the scarcity of social performance measurement systems, and the study by PadillaRivera et al. (2020), which states that there is still no consensus on
how to analyze the social issues related to circular systems comprehensively. Furthermore, our review also corroborates these authors regarding the most common social aspects analyzed (i.e., employment, health and safety, wage).
The clear focus on the economic and environmental features
of a CE demonstrates that the developed indicators have not
yet reached a consensus regarding the social analysis of circular systems (Blomsma and Brennan, 2017; Kristensen and Mosgaard, 2020). Padilla-Rivera et al. (2020) point to the use of Social
Life Cycle Assessment (S-LCA) as a way to bridge this gap. Nevertheless, S-LCA is still in its developing phase, with many methodological difficulties still to be solved by scholars and practitioners
(Petti et al., 2018). Thus, while consensus has not yet been found,
the inclusion of social aspects in circularity indicators is a welcoming approach to this scientific field. Even if the social pillar is often not the main target, every product system invariably interacts
with it (Reinales et al., 2020), and, therefore, it should also be accounted for in assessments concerning systemic shifts such as the
ones proposed by a CE.
4.3. What are the life cycle stages covered by the nano- and
micro-level indicators analyzed?
Based on our portfolio, the majority of indicators highlight the
life cycle stage “Recover”, which centers on EoL strategies. Proportionally, nano-level indicators focus more on EoL practices than
micro-level indicators, as they aim to facilitate an effective flow
of materials while ensuring that their highest value is extracted
throughout the extension of product lifetimes (Alamerew and Brissaud, 2019). Against this background, the investigated nano indicators are directed toward recycling, refurbishment, reconditioning,
repurposing, reuse, and other circular strategies. However, their
main focus is on resource recirculation, whereas not many indicators provide further information regarding the impacts and repercussions of such practices. These findings are in alignment with
work by Corona et al. (2019) and Linder et al. (2017), who previously identified the same tendency in groups of circularity indicators.
These results also confirm that the concept of nano circularity indicators is not yet well defined (Rincón-Moreno et al., 2021).
The nano-level denotation introduced by Saidani et al. (2017) describes it as “the circularity of products, components, and materials
[…] throughout their entire lifecycle”, hinting at the novelty of this
concept. We argue that the division between nano- and micro-level
indicators would benefit this scientific area more by further exposing different trends related to products and companies. Nonetheless, our analysis also shows that attention given to product level
circularity does not encompass the entire product life cycle, focusing instead on specific life cycle stages.
Most micro-level indicators focus on the “Make” life cycle stage.
Although companies do not work solely on the production of
goods, our results stress the importance given to this part of the
supply chains in the making of circularity indicators. As microlevel circularity assessment intrinsically focuses on all stages and
procedures of the production processes (Lindgreen et al., 2020),
it also entails phases before the physical manufacturing of goods.
As a product’s design directly influences the rest of its life cycle
(Cayzer et al., 2017; Mestre and Cooper, 2017), we propose that
micro-level circularity indicators may be used to analyze and compare the circularity scores of different design options. Therefore,
the high frequency of the “Make” life cycle stage in our results rep-
4.2.4. Circularity assessments based on the three sustainability pillars
Only eight indicators provided a procedure built over the three
sustainability pillars. Despite the initial similarity, the approaches
differ considerably. Two indicators are developed based on life cycle and multi-criteria decision tools (PR-MCDT and MCEM-PLCS)
and apply indicators related to the impact categories used in LCA.
SCI follows the Global Report Initiative indicators. SPI focuses on
material flows, reusability, reconfiguration, and functional performance. BPI, CBM-IS, SIAS, ZWP are formed by a group of indicators proposed based on literature reviews, assumptions, and onsite monitoring. These differences illustrate the plurality found in
circularity indicators, even in a smaller group of sustainabilityfocused indicators.
Some of them were developed to assist in specific sectors
(BPI, ZWP), some are holistic enough to support general assessments (PR-MCDT, MCEM-PLCS), and others, as pointed out by
Veleva et al. (2017), provide a starting point for companies to develop their industry/company-specific measurement strategies. The
variety of approaches and “sub-indicators” found for these indicators point to the divergences found in the crossroads between sustainability and a CE (Geissdoerfer et al., 2017; Rossi et al., 2020).
According to the aforementioned arguments, we conclude that
the circularity indicators that form our portfolio lack robustness to
provide sustainability performance assessments comprehensively.
Although a small group of indicators is equipped to analyze all
three pillars, they normally focus on specific pillars or are not
structured around solid scientific methodologies. Two exceptions
may be made: PR-MCDT and MCEM-PLCS (Alamerew et al., 2020;
Alamerew and Brissaud, 2019), which are developed through the
application of combined LCA and multi-criteria tools, providing
strong scientific foundations. Nevertheless, as the vast majority of
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resents a need for more comprehensive indicators to understand
the circularity trade-offs that such changes in the design and production phases might result in.
For micro-level indicators, EoL strategies (“Recover”) appear
in the same number of indicators as life cycle stage “Make”.
The indicators include several EoL approaches such as reuse/resell
(e.g., Veleva et al., 2017), repair (e.g., Nuñez-Cacho et al., 2018),
remanufacture (e.g., Golinska et al., 2015), and recycle (e.g.,
Giacomelli et al., 2018). We also identified that “Recovery” is a result of waste legislation in several countries. Such legal provisions
indicate that companies have shared responsibility for product life
cycles (de Oliveira et al., 2019) or even extended the responsibility
(Alamerew and Brissaud, 2019), rendering companies responsible
for monitoring their post-consumer products. It is also important
to stress that waste management hierarchy directives in Europe
(European Parliament, 2008), for example, are older than CE action
plans (EC, 2015), hinting at the potential use of more indicators in
the life cycle stage “Recover” than in “Take” and “Use”.
Each indicator was established to consider specific aspects
of circular systems or even specific practices, such as eDiM
(Venegas et al., 2018) and RPI (Park and Chertow, 2014). By defining proper indicators and assessing manufacturing processes, companies can aim at developing circular products through CE practices (Niero and Kalbar, 2019), redesign goods to favor EoL and
closed loops (Favi et al., 2017), or even change perspectives and
seek different ways to solve customer needs, such as moving to
“Product as a Service” models or digitalizing services (LüdekeFreund et al., 2019). Therefore, for their successful implementation,
one has to consider the particularities and disadvantages of each
approach.
The nano-level indicators are mainly directed toward material
recovery and full life cycle strategies. Such findings indicate the focus given by corporations and scholars to practices aiming at closing loops and assessing the impacts of a product’s manufacturing
through the life cycle perspective. The micro indicators also show
a predisposition to evaluate the overall circularity of a company
through this same viewpoint. However, the nano- and micro-level
indicators focused on “Take” and “Use” stages are spread throughout other alternatives. For example, only one nano circularity indicator (QC) focuses on the extraction of materials (Take) while still
also tacking the "Make" and "Recover" stages.
Considering all 58 indicators from the portfolio, our findings
show that almost half of them promote the circularity assessment
throughout the life cycle (for further information, please refer to
the Supplementary Material). These results show the concern of
companies and scholars in tracking and evaluating the systemic
repercussions of manufacturing processes, which indicates an attempt to move away from a productive-directed rationale towards
systematic thinking regarding EoL options and resource employment.
This research has valuable implications for companies, policymakers, industry professionals, and academic researchers. We innovate by investigating and discussing nano-level circularity indicators. Therefore, we contribute to corporative circularity assessments by further dividing into nano- and micro-levels, aiming to
i) avoid confusion regarding an overly broad view of the microlevel; ii) separate product-level circularity from the overall scores
of companies; iii) organize a large number of circularity indicators
in a stricter division to aid decision-makers.
Our findings show that the division between nano and microlevel indicators ensures that circularity assessments provide a standardized language to simplify the understanding and exchange of
information, thus facilitating the transition of companies to the
circular rationale. Additionally, we argue the investigated indicators assess sustainability performance superficially, mainly due
to their limited scopes. We recommend the use of consolidated
methodologies such as LCSA in parallel with circularity indicators proven to deliver context-specific results (e.g., recycling or
refurbishment indices, indicators directed to particular sectors)
to deliver robust and comprehensive sustainability assessment in
the CE context. From a general perspective, results show that
most indicators in our portfolio provide “Full life cycle” circularity assessments. Nano-level indicators focus on material and resource recovery strategies such as recycling, refurbishing, reconditioning, repurposing, reusing, etc. In turn, company-level circularity indicators center on manufacturing processes (i.e., design strategies, resource implementation). These findings highlight the general efforts in developing methodologies to quantify the move from a product-oriented perspective to circular
systems.
The main limitations related to this study concern: i) the lack
of a classification of circularity indicator implementation feasibility; ii) the exclusion of structured methodologies (e.g., LCA)
as circularity indicators; iii) the sometimes clouded explanations
regarding the indicator methodologies (especially from gray literature), which hampered the analysis regarding the sustainability pillars and life cycle stages covered by each indicator
assessed.
Future research may further investigate the differences between
nano- and micro-level circularity indicators based on views presented in this study or review meso– and macro-level circularity indicators. Moreover, future works could also analyze the 58
circularity indicators in parallel with consolidated methodologies
(e.g., LCSA, LCA) to overcome barriers regarding combined circularity and sustainability performance assessments. We believe that
more empirical research is necessary to examine and compare the
circularity indicators from our portfolio, classifying them by implementation practicability and integrating them. Thus, future works
could also focus on creating specific frameworks for different sectors with the standardization of indicators and data, through which
companies may develop and compare their results and CE strategies.
5. Conclusion
The understanding of the CE background is vital for creating
a holistic strategy to implement circularity indicators successfully.
However, research shows that they often cover large amounts of
information, which results in superficial circularity assessments
through uncomprehensive indicators. Against this backdrop, the
primary aim of this research was to perform a systematic literature review focused on CE indicators. We intended to develop a list
of nano- and micro-level CE indicators to assist decision-makers
based on the three dimensions of sustainability and the four life
cycle stages. To attain this purpose, we distinguished nano- and
micro-level circularity indicators, the connection that such indicators have with the sustainability pillars, and the life cycle stages
they are inserted in.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgments
The authors would like to acknowledge the financial support of
the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES). We also thank the anonymous reviewers
for their insightful comments.
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Sustainable Production and Consumption 26 (2021) 455–468
Supplementary materials
Ellen Macarthur Foundation (EMF), 2020. Circulytics - measuring circularity. Available at: https://www.ellenmacarthurfoundation.org/resources/apply/
circulytics- measuring- circularity . Acessed in August/2020.
European Commission (EC), 2015. Closing the loop–an EU action plan for the circular economy.
European Parliament, 2008. Directive 2008/98/EC of the European Parliament and
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