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Johnson Matthey Technol. Rev., 64, (1), xx-yy https://doi.org/10.1595/205651319X15643932870488 Page 1 of 42

Assessing the role of Big Data and the Internet of Things on the

transition to Circular Economy

An extension of the ReSOLVE framework proposal through a literature

review By Gustavo Cattelan Nobre *

COPPEAD Graduate Business School - Federal University of Rio de Janeiro, Brazil

Elaine Tavares

COPPEAD Graduate Business School - Federal University of Rio de Janeiro, Brazil

Rua Pascoal Lemme, 355 - Ilha do Fundão - Cidade Universitária, Rio de Janeiro - RJ,

21941-918 – BRAZIL

Phone: +55 21 3938-9898

*Email: [email protected]

ABSTRACT

The debate about Circular Economy (CE) is increasingly present in the strategic agenda of

organizations around the world, being driven by government agencies and general

population pressures, or by their own vision for a sustainable future. This is due in part to

the increasing possibility of turning original theoretical CE proposals into real economically

viable initiatives, now possible with modern technology applications such as Big Data and

the Internet of Things (IoT). IT professionals have been called upon to incorporate

technology projects into their strategic plans to support their organizations’ transition to

CE, but a structured framework with the necessary IT capabilities still lacks. This study

focuses on taking the first step towards this path, by extending the technology attributes

present on the existing Ellen Macarthur Foundation (EMF) ReSOLVE framework. The

research was conducted based on an extensive literature review through 226 articles

retrieved from Scopus and Web of Science (WOS) databases, which were triangulated,

validated, and complemented with content analysis using the “R” statistical tool, grey

literature research, and inputs from specialists. Main findings show there are 39 capabilities

grouped into 6 elementary CE principles and 5 action groups, being Public Administration

the most interested sector, forming the CE IT Capabilities Framework. It is expected the

framework can be used as a diagnostic tool in order to allow organizations to evaluate their

technological gaps and plan their IT investments to support the transition to CE.

Keywords: Circular economy; Big Data; Internet of Things; Literature review; IT

capabilities; Industry 4.0



1. Introduction

Information Technology (IT) plays an important role in enabling disruptive

organizational transformations, despite the known privacy and confidentiality issues, and

security risks (1, 2), constantly being run up against with the use of technology itself (3).

Recent studies show decision-making processes have become much faster and more

precise in companies adopting Big Data technologies (4, 5), for example. Internal and

external communications and knowledge sharing (6, 7), not only in large organizations but

also in small and medium-sized enterprises (SMEs) (8) are faster and better due to social

networks (9) and instant messaging technologies (10), just to mention a few recent

examples. In the Corporate Sustainability (CS) and other environmental fields it is no

different. Many recent studies focus on understanding the role of IT in offering solutions to

reduce the negative impacts organizations and society cause on the environment (11–13),

including those generated by modern technologies, known as Green IT (14–17) and several

other studies can be found in the literature. Large data vulnerability risks are also present

in this context, though (18). One special concept based on the planet’s sustainability issues

is gaining. attention from organizations and government recently – Circular Economy (CE).

Although the concept of CE has existed for decades, it has become more evident

in the past few years as resources are becoming scarcer and more expensive mainly

because world population and resource consumption continue to grow for a limited-

resource earth. Moreover, society is now more concerned about global warming, plastics,

other wastes disposal, etc. (19, 20) and aware that we are in need of stewarding our

planet's natural resources (21). Another relevant factor is the quick development of

automation technologies brought by what has been called the Fourth Industrial Revolution,

also known as Industry 4.0, essentially leveraged by Big Data and the Internet of Things

(IoT), which are making the implementation of CE concepts not only possible and more

economically feasible, but necessary (22, 23).

The role big data and IoT perform in enabling the transition to CE has been subject

of many studies and is attracting the interest of the scientific community.

As organizations and governments are being pushed to take action towards

business and city models being transformed to enable CE, more efforts in technology need

to be taken by IT professionals in order to make it a consistent, a fast time-to-market and

a low-cost transition (24–28). However, the IT path to be followed by organizations and

governments still lacks a structured framework, with some practitioners even questioning

whether technologies such as big data really foster sustainability (29).

Researchers have been putting a lot of efforts on establishing CE theories and

models to provide useful and usable tools to help scientists and practitioners develop their

work. Nevertheless, as such initiatives undergo usually independently and motivated by

different interests, dozens of separate studies arouse in recent years (although CE is known



since the 1970’s, >50% of all published studies are from 2014, as shown in Figure 1).

Recent studies show more than 100 CE definitions have already been documented and

published (30), along with dozens of frameworks, each one approaching CE in a different

perspective. Although all offer significant contributions to science and practice, choosing

one to perform as a baseline for research and business strategies development is

challenging. When the IT component is added, situation becomes even more complex, as

new disruptive technologies arise very fast and accelerates the obsolescence of previous

studies. Moreover, it’s being noticed that current CE frameworks rarely explore the IT

component (or ignore it, as described in section 2.2), given modern and disruptive

technologies a secondary role on the transition to CE. An exception is Ellen Macarthur

Foundation (EMF) ReSOLVE framework (31). It not only acts as a basis for other published

frameworks, but also recognizes the greater role IT performs on the transition to CE, yet

it still lacks theorical deepening, which is a gap to be addressed by this research.

This study intends not only to reinforce the key role technology performs on the

transition do CE – specially Big Data and IoT, both the foundation of the called Industry

4.0, as already presented in some novel published studies (32, 33), but also proposes a

preliminary framework for IT capabilities, built on EMF’s ReSOLVE framework, to be used

by IT professionals in order to understand and assess their organization’s gaps for the

transition to CE. The framework was conceived based on a literature review composed by

four separate sources: traditional review of 226 big data or IoT applications on CE scientific

articles, all retrieved from Scopus and Web of Sciences databases; content analysis (simple

bibliometric review) of the retrieved articles; industry, corporate world and governments

initiatives (grey literature); and industry experts review. This triangulation was a necessary

step not only for validity and reliability issues, but also because of the known gap between

the academy and industry and private sector initiatives for the research subject (19), so

each source provided complementary data. Therefore, this study aims to answer the

following research question: What are the big data and IoT capabilities that IT professionals

need to address in order to support their organizations in the transition to the Circular

Economy?

The remainder of the paper is organized into four sections, starting with the

literature review, including an analysis of the current CE available frameworks, followed by

the methodologies applied to the research a results and discussion section including the

proposed IT capabilities framework. The final section presents the study’s conclusions, its

limitations, and future research recommendations.



2. Literature Review

2.1. Defining Circular Economy

For the past few years, the current production and consumption model essentially

based on a linear flow (take-make-dispose), which generates an increasing natural

resources throughput, brought back to attention a concept originated during the 70s and

closely related to environmental concerns: Circular Economy (CE). It is based on a circular

system where both organic and technical waste are minimized and returned as feedstock,

leading to a zero waste generation model (20, 34–36), being restorative and regenerative

by design and resting on the following principles: preserving and enhancing natural capital,

optimizing resource yields, and fostering system effectiveness (37). The efficient use of

energy (and its transition from fossil to clean and renewable sources) and the promotion

of product reuse and lifetime extension actions also contribute to CE.

Currently only 9.1% of the world economy can be considered as circular (38),

meaning that around 90% of everything that is produced and consumed on the planet still

follows the take-make-dispose flow. Furthermore, the world population continues to grow

for a limited-resource earth (19, 20) with scarce commodities becoming more expensive

(39). For instance, if we look at Brazil alone, only 1% of all organic waste is treated and

benefited in a country where 50% of all wastes are organic, producing every year the

amount of greenhouse gases equivalent to 7 million cars (40).

Recently the United Nations Environment Programme (UNEP) developed a study

focused on the issue regarding the disposal of plastics in the ocean that led to the Global

lessons and research to inspire action and guide policy change report. (41). Some

estimates from the report indicate that the “visible” part of marine debris (what is floating

on the sea’s surface) represents only 15% of all marine debris while those on water

columns account for another 15% and 70% of all marine debris is simply resting on the

seabed. Moreover, most of this plastic breaks up into microplastic over time, representing

a hazard to wildlife, fish, and to people.

Those and many other documented facts demand actions from governments,

organizations and from society, and CE is performing a critical role in this necessary

transformation. For example, in 2018 the European Commission – the executive branch of

the European Union – launched the 2018 Circular Economy Package, which is a set of

measures aiming to transform Europe into a more sustainable continent (42), including

challenging goals such as making all plastics packaging recyclable by 2030. It consists of

several documents focused in legislations about plastics, waste, chemicals, etc. and proper

communication to citizens. This was an outcome of the EU Circular Economy Action Plan

established two years before. China is also making considerable progress transitioning to

CE, being one of the first countries to have laws for CE development (after Germany and



Japan) since 2009 and further detailed in 2013 with its Circular Economy Development

Strategies and Action document, stablishing directives for companies, industrial parks and

even cities and regions (43). In addition, not only can CE address sustainability social and

environmental issues, but it can also help develop the economy. In Europe alone, benefits

of around EUR 1.8 trillion may be achieved by 2030 (44).

Those and other initiatives led to a number of organizations, including public

administration and academia, to put more effort into developing or applying solutions and

researches to promote the transition from the linear to the circular economy model. Science

has recently made significant progress in CE researches as well. This can be noticed by

observing the academic research evolution presented in Figure 1, which shows that most

CE research is relatively new (26% in 2017 and 2018 alone).

Nevertheless, as a consequence, a number of different theories and principles or

constructs are still emerging, making the process of defining CE formally difficult.

Therefore, one of the challenges faced by scholars is agreeing on a common theoretical

scientific definition of CE and its various ramifications, as most of the successful initiatives

have been almost exclusively led by practitioners (Korhonen et al., 2017).

Discussions about the concept's incipience have already been the object of recent

studies. For example, recent research put together 114 different CE definitions and coded

them on 17 dimensions (30). Other studies propose taxonomies based on different

methods (19, 45), while others try to establish a consensus for the adequate use of the

term CE (46) and compare it to the concept of sustainable development (35). There is also

some justified criticism regarding the correct use of the term CE (47) and even questions

whether CE captures the environmental value propositions (48).

For this research, we identified a straight-forward classification based on a

comprehensive literature review article covering 20 years of CE studies, that groups it into

six basic principles (20): (a) Design, (b) Reduction, (c) Reuse, (d) Recycle, (e) Materials

Reclassification into Technical or Nutrients, and (f) Renewable Energy. The study had more

than 300 citations in three years since its publication in the Journal of Cleaner Production

(ISSN 0959-6526), which is a very high impact factor journal. Details on each principle

obtained from the literature can be found on the original publication.



Fig. 1 Publication profile on CE for the past 20 years (Source: Scopus)

2.2. CE Frameworks

There are several CE frameworks available on the literature. A query in Scopus

with the expressions “Circular Economy” and “framework” in title, keyword and abstract

retrieved 21 different models, the oldest published in 2016. Of those, we compared the 9

most relevant ones (i.e. from journals with scimago >20 and with at least 5 citations),

which are presented in detail in Appendix 9. Here we describe the Top 3: the most popular,

“A comprehensive CE framework” (49) was proposed through an extensive literature

review and is based on economic benefits, environmental impact and resource scarcity and

is focused in the manufacture industry. The second is called “The 9R Framework” (30) and

extends the classical concept of 3R’s to nine definitions, and suggests an increase of

circularity for each one: Recovery of Energy (less circular: incineration), Recycle,

Repurpose, Remanufacture, Refurbish, Repair, Reuse, Reduce, Rethink and Refuse (more

circular: make product redundant). The third, “circular economy product and business

model strategy framework” (50), proposes the need of design and business model

strategies to be implemented in conjunction in order to better drive circularity. Although

all are unique and proved to be valuable given their popularity, along with authors and

publications relevance, two common characteristics were observed: they do not consider

the “technology” aspect; and all reference directly, as a main source of information, the

Ellen Macarthur Foundation (EMF), known to lead and foster both theorical and practical

initiatives regarding CE since 2010. EMF created a framework called ReSOLVE, which is

used as a basis by some of top frameworks mapped (e.g. the BECE framework (51)) and

is the most popular one in Google (see Appendix 9), besides it is considered as part of the

grey literature rather than a scientific document. It offers organizations a tool for



generating circular strategies and growth initiatives, composed by the levers: (a)

Regenerate, (b) Share, (c) Optimise, (d) Loop, (e) Virtualize and (f) Exchange. Moreover,

technology is key: transformation of products into services, leveraging big data and

automation, and new technologies adoption incentives (e.g. 3D printing) are all aspects

considered by the ReSOLVE framework. Therefore, rather than proposing a new framework

in this study, authors decided to build the model on ReSOLVE.

2.3. Big Data and IoT

The big data concept represents the ability of gathering, processing and analysing

massive amounts of structured and non-structured data continuously (52, 53),

transforming it into useful information mainly for decision-making activities. Researchers

have reduced the definition into a basic 4V’s definition (54, 55), that stand for (a) Volume,

(b) Variety, (c) Velocity, and (d) Veracity, representing its main characteristics. Scholars

have improved the definitions and extended it with (e) Value (56, 57) and (f) Validity, (g)

Visualization, (h) Vulnerability, (i) Volatility and (j) Variability (58) . Big data has already

proved its importance for organizations, as for example in the health industry (59), general

management (60) and government (24).

Internet of Things (IoT) is an emerging technology that enables data acquisition,

transmission, and exchange among electronic devices and targets enabling integration with

every object through embedded systems (61). It has three main components: asset

digitization, asset data gathering, and computational algorithms to control the system

formed by the assets interconnected (62). One relevant data source may be considered

for big data. Not only can it support applications such as providing better disease

diagnostics and prevention, monitor stocks in real time (63) or the transportation of goods,

but it also applies to basically any activity involving data monitoring and control, and

information sharing and collaboration. (64). This emerging term is being considered key

to enable technological solutions and receiving industry extensions such as in mining

(Metallurgical Internet of Things – m-IoT) (65), industry (Industrial Internet of Things –

IIoT) (66) and for environmental causes (Environmental Internet of Things - EIoT) (24,

67).

There are other concepts related to CE being leveraged by Big Data or IoT. They

are described in Table 1. In the context of CE for this research, Servitization relates to

Reuse principle. It improves assets usage rates to their highest utility and value as the

product ownership remains with the manufacturer, who is responsible not only for the

proper product collection and disposal, but also for extending its lifetime and recapturing

value through refurbishment and reuse. Sharing Economy also explores the Reuse and

Reduce principles as product owners can collaborate with each other in order to maximize

the use of their own assets during their idle periods. For example, studies show cars stand



idle for about 95% of the time (68). Smart Cities relates essentially to the Design principle

as it consists basically in planning and reorganizing urban areas.

Table 1: CE-related concepts leveraged by Big Data or IoT according to the literature review

Concept Description References

Servitization Shift from selling products to providing services

with an emphasis in the use rather than

possession. Providers such as Netflix and

Salesforce.com are examples of businesses born

using the concept. Traditional companies such as

Philips (selling lighting services instead of bulbs),

Michelin (pay-by-the-kilometre services instead of

tyres) and Renault (leasing batteries for electric

cars) are shifting some of their business models to

Servitization.

(69), (70), (71), (72), (73),

(74), (19), (75), (76)

Sharing

Economy

Underused products, services or assets made

available to third parties, paid or not. Businesses

such as TaskRabbit, Thumbtack, Uber, DogVacay,

Airbnb and WeWork are examples.

(77), (78), (79), (80), (81),

(82), (83)

Smart Cities Urban spaces leveraged with the use of

technology focused on improving the living

conditions of citizens or inhabitants.

(84), (85), (86), (87), (88),

(89), (79), (90), (91), (92),

(93), (94), (95), (96), (97),

(98), (99), (100), (101),

(102), (103), (104), (105),

(106), (107), (108), (109)

3. Methodology

In this section, all methods applied in this study are explained to ensure research

replication and allow validity and reliability confirmation (110, 111). Also, in order to

establish an acceptable degree of reliability in the research, the data analyses were

triangulated (111) through different methods and techniques as necessary for social

science literature reviews (112) to provide a consensus regarding the proposed capabilities

list: traditional literature review, basic content analysis, grey literature mapping, and

experts review and confirmation (proposed model presentation and conformity

verification), thus reducing the risks of common biases from inaccurate or selective



observations and overgeneralization (113), as shown in Figure 2. Details for each step are

presented below.

Fig. 2 Research methods applied

3.1. Data Collection – Scientific Papers

Data collection from scientific databases consisted in two basic steps: data source

identification and data extraction criteria definition.

Although some previous published researches used only one database source, for

this study we combined data from two relevant and robust databases. The first one was

Scopus, which is considered to be the largest abstract and citation database of peer-

reviewed literature, while the second independent and unbiased database was Web of



Science (WoS), known as one of the largest citation databases available and the first one

in the market. Both provide significant results for English-language journals according to

comparative studies (114) and are very consistent with each other (115).

The same query logic was applied for both databases, along with the same filters

and constraints, following the recommendations found in a previous published study, thus

using similar expressions and precautions with specific taxonomies (19). Query logic for

both Circular Economy and Big Data and IoT expressions are shown in Figure 3 and were

applied for document title, keywords, or abstract. Coding of key terms and themes to

represent both CE and Big Data or IoT on databases queries were obtained from a robust

previous research (19), in the absence of a comprehensive taxonomy and are reproduced

in Appendix 5. Coding categories criteria are presented in Appendix 6 and the complete

and detailed results in Appendix 7. After running the independent queries individually for

both databases, the results were combined, generating an integrated result of 370 unique

documents for authors analysis. At this point, no restrictions to document types or

relevancy had been applied yet. Step two consisted of applying the authors’ analysis to

eliminate incoherent documents. In order to avoid author biases during this phase,

objective criteria for document elimination were defined: items retrieved from keywords

or abstract but with no direct relation to document contents (e.g. abstract mentioning, but

document not about big data – term appears in abstract but is not related to it); term

appears in document body but as a future research recommendation or indirect implication;

namesake term used (e.g. “blue economy”). A total of 110 documents were removed from

the set after reading. This represented an improvement from a previous research (19) that

focused only on the bibliometrics part without applying authors detailed in-depth

proofreading and review. Then, not applicable items such as some conference reviews,

errata, no content docs were also discarded, representing a total of 29 documents. Finally,

a total of 5 documents not in English were removed. The final set of documents used in

the research consisted of 226 documents. The complete filter process is presented in Figure

4.



Fig. 3. Query logic for Scopus and WoS – adapted from previous published research with the use

of the same lists of terms (19)

Fig. 4. CE and Big Data or IoT docs search summary

Previous literature review researches were consulted in order to try to identify

other criteria to narrow the number of documents to be analysed to the most relevant



ones. Cut-off methods based on scientific recognition were mapped (47, 116, 117), some

of them applying Pareto principles to focus on the most cited articles and author research

relevance. Nevertheless, as shown in Figure 1, most of the papers retrieved are less than

two years old, so relying on scientific recognition on number of citations could have

produced undesirable results. Because of this the authors decided to analyse the entire set

of articles (226 documents) for this research.

3.2. Scientific Literature Review

Documents were classified according to the following criteria: country and region1;

methodology type, in compliance with similar literature review researches (118), composed

of: (a) theoretical and conceptual papers, (b) case studies, (c) surveys, (d) modelling

papers, and (e) literature reviews; industry, according to the Standard Industrial

Classification codes (SIC codes) assigned by the U.S. government to business

establishments to identify its primary business (119); and related CE Principle according

to the classification mapped for this research (20), divided into: (a) design, (b) reduction,

(c) reuse, (d) recycle, (e) reclassification, and (f) renewable energy.

Due to the considerable number of documents used in the review (226 after initial

screening), the complete list with corresponding classifications is available in Appendix 7.

3.3. Triangulation: Content Analysis with Word Cloud

Word cloud is a tool that generates a visualization in which the more frequently

used words in a given text are highlighted. Although it provides good presentation and is

visual appealing, it does not provide useful information when applied alone, but can

perform well as a supplementary tool to help with the confirmation of the findings and

related interpretations (120). So in order to support the research results confirmation, all

226 documents selected were converted into a robust text corpus and went through data

mining with the support of “R” statistical tool (121), so that expressions of more

occurrences were ranked.

In order for the analysis to be accurate, compound expressions (bigrams, trigrams

and 4-grams) were bound together into single words prior to word cloud execution. Despite

the existence of formal methods and patents for automated compound expressions

generation (122), authors decided to create the database manually due to the

heterogenicity of subjects under analysis (i.e. Circular Economy, Big Data, Internet of

1 Scopus and WoS Databases do not retrieve country names. Documents were assigned to countries according to (in this order of priority): author affiliation—main author affiliation—conference location— journal location—source title location, using the same criteria applied in prior research (19)



Things), so automatic conversion risks were avoided. Complete list is available in Appendix

4.

The authors then cleansed the results according to the following steps: (a)

concatenation of expressions (e.g. big data to bigdata); (b) unification of same meaning

of words (e.g. recycling and recycled for recycle); (c) separation of similar word with

different meanings (building not the same as build); (d) removal of punctuation, numbers,

URLs; (e) case conversion; (f) singularization (e.g. feet unified with foot); (g) and removal

of stop words2 based on ISO and snowball sources (123), combined with a customized list

compiled by the authors and also shown in Appendix 4. The word cloud image was also

generated with “R”. The following libraries were used in the analysis: ggplot2 (124),

githubinstall (125), pluralize (126), RWeka (127, 128), SnowballC (123), stopwords (129),

tm (130, 131), wordcloud (132).

3.4. Grey Literature Mapping

There are a number of non-academic institutions, such as government agencies,

private businesses and non-governmental organizations (NGO) developing successful

practical CE initiatives that need to be taken into consideration as since both the subject

matters – of CE and Big Data or IoT – are still emerging and evolving scientifically. Finding

literature and information on this particular area of research required the use of non-

scientific sources (133). Moreover, recent studies indicate that there are benefits for

including grey literature in reviews: overall findings enrichment, bias reduction, and to

address stakeholders concerns (134), which are all relevant for this research. Furthermore,

there is known to be a gap between the academic world and practitioners for this research

subject (19).

The complete list of supplementary grey literature sources used to enrich the

analysis is presented in Appendix 3.

3.5. Triangulation: Experts Review

The resulting preliminary framework was submitted to a group of eight domain

experts who individually analysed the capabilities to assess the content clarity and

representativeness, and to provide insights on items that could be revised or added to the

list so that authors could map additional research sources to be studied. The domain

experts were selected first according to methods presented in the literature: type of

knowledge, type of service, and type of expertise (135). After identifying the experts,

accessibility was considered as a second filter. A few conflicts identified were addressed

2 Stop words: function words such as “which”, “the”, “is”, “in”, verbs, auxiliary words, etc.



with additional grey literature confirmation and were considered positive as they are

common and important in social sciences (136). Expert contributions not verified in the

literature were discarded. The list of domain experts is presented in Appendix 2.

4. Results and Discussion

For this study, a complete set of scientific publications was analysed. Regional and

temporal characteristics are presented in Figure 5.3 and Table 2. Europe and Asia lead the

interest in the subject mostly due to the efforts and regulations established by the EU and

China governments. North America (here including Mexico and other Central America

Countries), despite the high level of development of the geographies, occupies only the 3rd

place in publications, with less than 15% of participation. This number also draws attention

to the fact US is one of the major environment polluter countries according to EPA – the

United States Environmental Protection Agency(137), which reveals a context of significant

research opportunities for the region.

Table 2. Detailed publication profile on CE and Big Data or IoT by region – total of 226 documents Region All Years 2007 2010 2012 2013 2014 2015 2016 2017 2018

Africa 3 0 0 0 0 0 0 1 0 2

Asia 77 0 2 0 4 4 8 7 19 33

Europe 103 1 0 2 5 4 9 14 28 40

North America 33 0 0 0 0 1 4 7 13 8

Oceania 6 0 0 0 0 0 0 2 1 3

South America 4 0 0 0 0 0 0 2 2 0TOTAL 226 1 2 2 9 9 21 33 63 86

3 From first publication to 2018. Total of 226 documents, including Articles, Reviews, Conference Papers, and Proceedings Papers, filtered according to remarks presented on the methodology section.



Fig. 5. Publication profile on CE and Big Data or IoT by region – total of 226 documents

Considering all the publications, 53% came from scientific journals, and 15 sources

presented at least 2 publications on the subject with the Journal of Cleaner Production

(ISSN 0959-6526) and Sustainability (ISSN 2071-1050) leading with 19 and 9 publications

respectively, as shown in Appendix 1. The high number of other sources documents (47%),

along with the publication concentration in the past 3 years may indicate science and

academia are still in their early stages of development for the studied subjects.



The research also grouped publications according to the Standard Industry Codes

– SIC (119). The majority of documents apply to Public Administration (32.3%), mostly

because of Smart City initiatives and suggests governments are leading initiatives and

sponsoring researches. A considerable number of publications (30.1%) were not allocated

to a specific SIC code as they could not be related to any specific industry. Results ate

presented in Table 3.

Table 3: Publications by Industry type (SIC Code)

Industry # of publications %

Public Administration 73 32.3%

Cross Industry 68 30.1%

Manufacturing 18 8.0%

Building Construction 14 6.2%

Agriculture, Forestry, Fishing 11 4.9%

Transportation Equipment 8 3.5%

Business Services 7 3.1%

Private Households 5 2.2%

Engineering Services 4 1.8%

Retail Trade 4 1.8%

Electric, Gas and Sanitary Services 3 1.3%

Transportation & Public Utilities 3 1.3%

Educational Services 2 0.9%

Mining 2 0.9%

Chemicals and Allied Products 1 0.4%

Computer and Office Equipment 1 0.4%

Food and Kindred Products 1 0.4%

Health Services 1 0.4%

TOTAL 226

Documents were also grouped by methodology types, which demonstrates more

interest in models development and reviews as shown in Figure 6, indicating researches

have been putting more efforts on standards, definitions, frameworks creation and reviews

(which can be justified by the early stage of stability and maturity of the subjects). Other

analysis was made according to CE Principles (20) as demonstrated in Figure 7. The highest

level of participation on the Reduction principle suggests a major focus on changing

consumers behaviour with the use of new technologies rather than investing on clean

energy sources or extending products lifespan. On the other hand, reclassification principle,

despite its importance, still lacks technology efforts.



Fig. 6. Methodologies applied on 226 mapped documents

Fig. 7. CE Principles identified in 226 mapped documents – some articles with more than one

principle

Supplementary details regarding mapped documents, such as top publishing

institutions, journals and authors are available in Appendix 1.

In Appendix 8 we also present some practical case studies mapped during the

literature review for distinct industries and countries in order to illustrate how CE can be

fostered by Big Data and IoT.



4.1. Content Analysis

Research extracted the 150 most frequent words from the 226-article text corpus

in order to verify and confirm that the resulting capabilities list is addressing the most

relevant topics. The word cloud generated is shown in Figure 8.

Bigram, trigram and 4-gram generation proved to be a valuable insights resource

as some compound expressions not only appeared in the top 150 list, but also performed

as an important validation tool for the capabilities generation (e.g. “cloud computing”,

“energy consumption” and “smart sustainable city”), all key aspects of the validated

capabilities list.

Fig 8. Word cloud containing the 150 most frequent words from all 226 mapped articles

The top 20 expressions mapped are presented in Table 4. Complete list of top 150

expressions is available in Appendix 4.



Table 4: Most Frequent 20 expressions and frequencies

Most Frequent 20 expressions and frequencies

product 5658 design 3352 challenge 1470

energy 4894 system 3287 source 1466

process 4278 power 3187 technical 1463

develop 4241 city 3036 monitor 1450

service 3745 urban 2984 measure 1413

environment 3601 operation 2811 strategy 1395

time 3575 local 1481

Words “product”, “service”, “urban”, “city”, all with high frequency, indicates

initiatives for different industry types can benefit from Big Data and IoT, for example, and

therefore influenced the framework development (i.e. specific treatment for industry type).

The same analysis was made for each expression, performing essentially as a verification

tool to ensure the framework and capabilities consistence.

4.2. Experts Review

The first version of the resulting capabilities framework was submitted to a group

of domain experts who provided useful insights into the study. Table 5 shows the main

contributions accepted from the domain experts. Typos, rephrasing, use of synonymous,

and other small revisions are not listed.



Table 5: Domain experts’ main contributions

CE Principle Contribution Contributing Expert

Design

- Clarification on urban areas relation to Public Administration only

3, 4

- Added ISO 20400 - sustainable procurement (applies to Reduction, Reuse and Recycle

principles as well)

1

Reduction

- Process postponing: inclusion of “no effectiveness loss” condition

5

- Decentralized offices: only if proven to provide more efficient use of available resources

4, 5

- Added emissions monitoring

4

Reuse - Added marketplaces for sourcing, value and managing reusable materials 1

Recycle

- Added disassembling and remanufacturing 4, 6

- Policies application rather than only having the policies documented 1, 4

- Use of electronic tags 1

- Added recyclable resin 1

Renewable Energy - Net Metering added to list 4

- Blockchain transactions added to list 2, 4

The list of domain experts is presented in Appendix 2.



4.3. CE IT Capabilities Framework The final framework resulted in a set of 39 capabilities divided according to the six CE

principles and are presented in Figure 9 and Table 6. It builds on both ReSOLVE framework (31) and

the six CE principles (20). The mapped capabilities were separated into application groups and

industries, as some are considered technological tools, others new processes, some long-term

projects, and others punctual actions.

Fig 9. The CE IT Capabilities Framework

The mapping considering each capability and the corresponding block of the framework is

presented in Figure 10. Capabilities not related to any industry are considered as applicable to any

(cross industry).

4.3.1. Framework Highlights The ReSOLVE Framework itself promotes a direct application of modern technologies on the

elements “Optimize” (leverage big data and automation), “Virtualize” (dematerialization) and

“Exchange” (e.g. 3D printing). With the establishment of the CE IT Capabilities Framework, not only

new applications can be observed to those elements, but also it is now possible to notice that all

elements of ReSOLVE can benefit from cutting-edge technologies. Like for example: the “Regenerate”

element can be leveraged with Net Metering and the use of solar energy allows the use of IoT based

devices in remote areas, like agricultural crops; the “Share” element benefits from smart connected

devices monitoring equipment’s usage and providing predictive maintenance data, and technology

also connects users with similar interests allowing higher usage levels; in “Optimize”, waste reduction

can take many advantages from technology, varying from the use of AI and machine learning on

product design to optimize resource consumption to application of Green TI to increase product

efficiency; “Loop” benefits from the use of AI to allow closing the loop on materials and to optimize



waste collection and reverse logistics with IoT; “Virtualize” links directly with cloud computing, home

office; and “Exchange” may use technology on product design to promote shifting to renewable

materials feedstock.



4.4. Capabilities List

Table 6: Mapped Big Data or IoT capabilities on CE Principles according to literature review

CE Principle Big Data or IoT Capabilities Sample Sources

Design (DS) 1. Parts made with compatible components with the support of modern technology

support based on artificial intelligence (AI), machine learning, Big Data or IoT that can

be mixed after use without contamination for efficient recycling or upcycling or

remanufacturing and designed for new uses, enhancing its after-use value.

2. Use of big data or analytics during product design or conception to provide sustainable

feedstock and optimized resources use to reduce waste generation during

manufacturing processes.

3. Product Lifetime Management (PLM4) concepts supported by Big Data or IoT to improve

product design, such as modular or replaceable components.

4. Design and or use of IT infrastructure designed for reuse or easy recyclability.

5. Use of sustainable design criteria on technology selection processes, such as design for

recycle.

6. For Public Administration sector only: CE-planned urban areas designed and conceived

according to smart city principles to optimize waste collection and value recovery with

the use of IoT.

(37), (44), (138), (96), (99),

(139), (140), (141), (142),

(143), (144), (145)

Reduction (RD) 1. Minimize greenhouse gas and other pollutants emissions with the support of modern

technologies such as analytics for monitoring and decision making.

2. Optimize materials savings through smart connected devices.

(146), (44), (147), (148), (141),

(140), (149), (150), (151),

4 Process of managing the entire production lifecycle from design, through engineering, manufacturing and ultimately service and usage.




3. Use of decentralized IT technologies to provide resources use and consumption (either

energy and components) reduction, such as cloud computing with big data, avoiding

the need of robust local physical infrastructure.

4. Product Lifetime Management (PLM) concepts supported by Big Data or IoT to reduce

waste generation and disposal.

5. Use of smart sensors to monitor energy, water and other resources consumption in

manufacturing processes.

6. Use of smart sensors to monitor energy, water and other resources consumption within

facilities of organizations.

7. Machine behaviour monitoring in order to autonomously optimize energy, water and

other resources consumption, even by postponing processes if necessary, without

prejudice to processes effectiveness.

8. Use of IT devices and infrastructure in a way that offers minimal environment impact

(Green IT) by optimizing energy consumption.

9. Use of technology-enabled decentralized offices and data centres proven to provide

more efficient use of available resources (including human - no need to commute etc).

10. Use of energy savings or minimum waste generation criteria on technology selection

processes.

11. Energy efficiency improvement in data centres.

(152), (153), (154), (155),

(144), (156), (145)

Reuse (RU) 1. Improve asset usage rates by applying CE business models such as leasing and Product

as a Service (PaaS), enabled by IoT and Big Data.

2. Product life-time extension by using connected devices for facilitating predictive

maintenance.

3. Product Lifetime Management (PLM) concepts supported by Big Data or IoT to improve

product and component reusability.

(44), (140), (141), (69), (96),

(157), (153), (81), (143), (144),

(158), (145)




4. Product to Service (possession vs use) transition enabled or leveraged by IT to improve

usability rates.

5. Use of cloud-based marketplaces for sourcing, value and managing reusable materials

6. IoT-enabled waste collection or reverse logistics for materials (such as packaging)

reuse.

7. Monitor component location and quality in order to assess state and allow reuse.

8. Use of IT devices or infrastructure in a way that offers minimal environment impact

(Green IT) by reusing components to their maximum.

9. Use of IoT devices to increase component sharing and reuse rates (such as in industrial

symbiosis).

10. Policies for extending IT infrastructure lifecycle (e.g. donation).

11. Use of product or component lifetime criteria on technology selection processes.

Recycle (RY) 1. Apply AI to support “closing the loop” on products and materials, allowing optimized

product sorting and disassembling, remanufacturing and recycling.

2. Product Lifetime Management (PLM) concepts supported by Big Data or IoT to improve

product recyclability.

3. Use of IoT Technologies in order to optimize waste collection and reverse logistics for

recycling or upcycling, including the use of electronic tags on trash bins.

4. Use of IT devices or infrastructure in a way that offers minimal environment impact

(Green IT) by applying recycling policies.

5. Use of IT infrastructure recycled from electronic waste.

6. Applied policies for discarding obsolete IT infrastructure in a sustainable (for recycle)

manner.

(44), (140), (141), (96), (153),

(143), (144), (145), (159)




7. Use of product recyclability (or made from recyclable resin) criteria on technology

selection processes.

Reclassification

(RC)

1. Applying IoT integrated with AI to allow mixed industrial technical (non-organic) waste

automated separation.

(44), (160), (161), (162)

Renewable

Energy (RN)

2. Use of renewable energy sources (including light, motion, temperature), for IT devices

to operate autonomously, mainly in poor accessibility remote areas.

3. Power IT devices or infrastructure with renewable clean energy.

4. Net Metering-based5 renewable energy generation, monitoring, consumption and

selling (leveraged by blockchain when applicable).

(163), (164), (165), (153),

(166), (62), (167), (168)

5 Solution where consumers generate their own power and receive credits for the excess power they produce. Excess power is delivered to the grid, so net metering can be thought of as a energy storage solution that allows consumers to push and pull energy to and from the grid.



4.5. Capabilities in CE IT Capabilities Framework

Fig 10. The CE IT Capabilities Framework with mapped capabilities



5. Conclusions

The scientific interest in modern technologies application such as Big Data or IoT

in the transition to CE is growing. Articles from 2017 and 2018 alone account for 66% of

all the publications on the subject, reflecting what takes place in practice, given the number

of cases and models identified – 60% of all articles mapped. Nevertheless, from the 21

different CE frameworks identified, only 3 mentions IT as a component, and most of them

refer to EMF as a primary CE reference, some built on EMF’s ReSOLVE framework.

Therefore, IT scientists, scholars and practitioners still do not have at their disposal a

framework to be followed that would allow a technological gaps assessment. This

framework development was the article’s main purpose, which identified 39 IT capabilities

necessary for organizations to consider themselves technologically circular.

The main scientific contribution of this study was the extension of the existing

ReSOLVE framework to a level of detail that will allow IT professionals to assess their

current CE gaps and plan their actions to enable an easier transition to CE. Additionally,

the role modern technologies aligned with Industry 4.0 play in the organizational transition

to CE was identified, and the status quo of related research around the world and the most

interested institutions and publications were described.

In addition to the traditional literature review of 226 articles retrieved from Scopus

and WOS databases, the following triangulations were carried out to allow research

confirmation and comprehensiveness: content analysis through statistical tool "R", grey

literature analysis, and expert opinions. The capabilities were then divided according to the

six CE principles presented in the literature: 6 for the Design principle, 11 for Reduction,

11 for Reuse, 7 for Recycling, 1 for Reclassification, and 3 for Renewable Energies. The

findings indicate that there are principles currently more susceptible to IT than others and

that the public administration sector has attracted more research interest in the area

possibly because of current initiatives fostered by government entities and agencies.

The following future research opportunities originate directly from this study: the

conception of a scale with metrics to allow organizations to self-assess and benchmark (i.e.

how many and which capabilities should an organization implement and in what extent

before it can be considered circular); and the confirmation of the framework’s performance

by applying it in a form of a questionnaire or survey against selected organisations of

different ports and industries.



The limitations of the study lie mainly in the volatility of recent modern

technologies that may not have a long lifecycle, making the framework obsolete in the

short term. In addition, since it is an essentially theoretical study based on published

documentation, it still lacks practical confirmation through organizational case studies.

Acknowledgements

We would like to thank the NEC (Núcleo de Economia Circular) Group and

Exchange for Change Brasil (e4cb), among other equally relevant experts during data

gathering and validation for their outstanding contribution and for the constructive criticism

provided throughout all the research activities.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal

de Nível Superior – Brasil (CAPES) – Finance Code 001.



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The Authors

Gustavo Cattelan Nobre is a Bachelor and MSc in Business

Administration. Researcher and PhD Candidate from COPPEAD with

emphasis on Big Data and IoT, he also holds post-graduate degrees

in Marketing and Corporate Finance and is a Systems Analyst.

Professor at UFRJ and postgraduate courses in the areas of

administration, finance, project management. Reviewer for

international congresses and journals, he has also more than 20

years of professional experience in the corporate world, most of

them performing executive and project management functions in

multinational consulting companies for large organisations in the

areas of management consulting and IT. Certified PMP®.



Elaine Tavares is a Post-doctorate at the University of Texas at San

Antonio and at the Centre d’Etudes et de Recherche en Gestion

(CERGAM) of Université Aix-Marseille III (France). She received her

DSc in Administration from EBAPE/FGV and MSc in Corporate

Management from EBAPE/FGV.

Dean at COPPEAD. She was professor at University of Brasília (UnB)

and at EBAPE/FGV. She is the leader of the topic Big Data and

Analytics in the Brazilian Academy of Management (ANPAD). More

than 20 years of working experience in large companies, especially

in the financial and education sectors.