How to select the best approach for circular economy assessment? 3D positioning framework, decision support tool and critical analysis for bio-based systems

Environmental Impact Assessment Review 106 (2024) 107493 Available online 23 March 2024 0195-9255/© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). How to select the best approach for circular economy assessment? 3D positioning framework, decision support tool and critical analysis for bio-based systems Marta Macias Aragon´ es a , b , * , F´ atima Arroyo Torralvo b a Technological Corporation of Andalusia (CTA), C Albert Einstein S/N, INSUR Building, 4 th Floor, 41092 Seville, Spain b Departamento de Ingeniería Química y Ambiental, Escuela T´ ecnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain ARTICLE INFO Keywords: Circular economy Circular bioeconomy Assessment Sustainability Decision support tool ABSTRACT Although the transition from linear to circular economy is being carried out by many different actors, there is still not a well-established methodology for its assessment. In this paper, a review of available approaches for evaluating circular economy (technical and biological cycles) has been conducted. In total, 2.113 information sources have been revised and 105 of them were included in the review. They have been categorised per level (macro, meso, micro and nano), cycle (technical, biological or both) and methodology used, together with the sector according to NACE codes. Using this information, a positioning framework for classifying the different appraisal options has been developed and depicted in a 3D cube where each category is represented in one of the axes (level, cycle, and methodology). Moreover, a decision support tool has been created to help triple helix stakeholders selecting the most suitable approach according to the information they are looking for, i.e. the initial question they pose regarding how to assess circular economy. Since the main gap concerning available approaches has been identified for the biological cycle at micro and nano levels, a critical analysis of existing choices has been conducted. Finally, open areas for future work towards promoting circular bioeconomy assessment are identified. 1. Introduction Circular economy (CE) and sustainable development are two concepts that have arisen during the last decades, concerning not only academia or policy makers, but also for other stakeholders such as private companies that try to make their processes and products more sustainable. Sch¨ oggl et al. (2020) investigated about how both terms are related and how they have been addressed in literature over the last two decades. Sustainable development can be considered compatible and consistent with the CE as it is linked to social, environmental, and economic aspects. In fact, it actively pursues both monetary returns (such as value creation and cost savings from using less basic raw materials) and environmental and social positive effects (such as impact reduction and new jobs) (Saidani et al., 2017). Another term that has gained momentum during the last decade is bioeconomy, usually appearing next to sustainability and CE concepts in policies and publications (European Commission, Directorate General for Research and Innovation, 2018). This has led to the term circular bioeconomy (CBE), which arises from the bioeconomy and CE concepts being entwined around 2015 and being progressively used since 2016 (Hetem¨ aki et al., 2017). Before 2015, only some references to the CBE idea were made by the Ellen MacArthur Foundation, affirming that bioeconomy could be considered an inherent part of CE by incorporating the biological cycle into their CE infographic (Pauli, 2010). Later, and regarding biological cycles, Navare et al. (2021) studied how CE could be monitored for them, concluding that the complete evaluation of biological cycle qualities by the CE monitoring standards is lacking. As pointed out by Lindberg et al. (2015), industrial systems Abbreviations: (CE), Circular Economy; (CBE), Circular Bioeconomy; (KPI), Key Performance Indicator; (B2B), Business-to-business; (LCA), Life Cycle Assessment; (LCI), Life cycle Inventory; (LCIA), Life Cycle Impact Assessment; (MCDM), Multi-criteria Decision Methodology; (DfX), Design for X; (DEA), Data Envelopment Analysis; (I/O), Input-Output models; (MFA), Material Flow Analysis; (DES), Discrete Event Simulation; (GDP), Gross Domestic Product. * Corresponding author at: Technological Corporation of Andalusia (CTA), C Albert Einstein S/N, INSUR Building, 4 th Floor, 41092 Seville, Spain. E-mail addresses: [email protected] (M. Macias Aragon´ es), [email protected] (F. Arroyo Torralvo). Contents lists available at ScienceDirect Environmental Impact Assessment Review journal homepage: www.elsevier.com/locate/eiar https://doi.org/10.1016/j.eiar.2024.107493 Received 21 March 2023; Received in revised form 24 February 2024; Accepted 19 March 2024 Environmental Impact Assessment Review 106 (2024) 107493 2 nowadays use widely Key Performance Indicators (KPIs) for improving industrial performance. It is clear that when it comes to circularity, it is important to define the corresponding indicators and methodologies that can allow evaluating the progress of the circular transition. More specifically and on circularity indicators, an extensive review was provided by Saidani et al. (2019) and a lot of discussions that can be found in literature about how these indicators actually capture the different facets of the CE. Sassanelli et al. (2019) pointed out that there is a dearth of literature on CE performance evaluation and a lack of approaches capable of simultaneously measuring and gauging all the variables present in a circular system. So far, measuring circularity is usually conducted by external consulting companies, as the role of in-house circularity and/or sustainability expert/manager is still emerging. These companies depend on their appropriate commercial and marketing experience. Sometimes, even though these methodologies and instruments provide a first evaluation of product circularity performance, the complexity of the CE paradigm as a whole is seldom taken into account (Saidani et al., 2017). Also, evaluation of CE at value chain, company, or product level is barely considered in indicators reviewed, e. g. Ghisellini et al. (2016) only focussed on country level and technology or industrial parks level where several firms are interconnected. Furthermore, Saidani et al. (2017) conclude that, at the micro level, the CE is only partially examined in terms of ecologically friendly consumption and greener manufacturing. Micro level measures for the CE do not consider all its complexity or all practical end-of-life options to stop the loop. Thus, although some recent reviews focus on the biological cycle (e.g. Zhang et al. (2022)), there is still a lack of a comprehensive revision of CBE assessment approaches for the micro level. Finally, there is a quick growth of papers about CE and CBE implementation and assessment (the use of the topics “circular economy”, “circular bioeconomy”, “assessment” and “evaluation” in title, abstract or key words over time is explained in Fig. S1, included in the Supplementary materials). For example, 126 and 159 new papers were produced only during 2021 and 2022, respectively, for CE assessment. This would make making necessary to update the past reviews on this topic, including now the CBE dimension. As for positioning frameworks built on top of reviews, Moraga et al. (2019) concluded that there was not one positioning framework comprehensive for all the indicators and aspects included in the CE. As stated by the authors, the review and framework provided only analysed output and result indicators. Also, this positioning framework requires extensive knowledge about CE key aspects, making difficult for newcomers and/or industry practitioners to use it. Within this context, industry practitioners often ask themselves how the circularity potential can be evaluated during the product development phase and across its lifetime, and at the value chain level, especially for bio-based processes. Through this journey, private companies experience challenges and barriers when implementing and appraising CE as summarised by Rinc´ on-Moreno et al. (2021). These authors concluded that companies face hard and human-based barriers and that hard barriers may be remedied by financial stimulus and technical modernisation since they are related to a lack of financial resources, technology, poor information systems, etc. Bressanelli et al. (2018) conducted a systematic review of supply chain redesign for CE, identifying “measures, metrics, indicators” as one of the standards and regulation challenges when it comes to CE implementation. Moreover, Garc´ es-Ayerbe et al. (2019) explored the challenges and opportunities for European Small and Medium-Sized Enterprise companies, pointing out that: (1) those that have not transitioned to CE would be probably due to lack of expertise to carry out such activity; and (2) companies already implementing some CE practices found difficult to find the expertise needed to measure CE. Thus, it can be drafted that private companies might need support when it comes to CE in terms of assessing its progress and identifying best technologies and spots to be implemented. To overcome this, there have been efforts from regional actors that have performed a survey of CE tools and platforms that support CE application on a business-to-business (B2B) level, e.g. the one from Circular Flanders (Vlaanderen Circulair, 2024). However, to the best of this research authors knowledge, there is no decision support tool that can guide the private stakeholders in selecting an approach for CE appraisal according to information needed, their commercial sector and the measurement scope. This way, the following specific research questions arise: •Which approaches are available for CE assessment (both technical and biological cycles) and how could they be classified in a proper positioning framework? •How to develop a decision support tool that could help and guide stakeholders, from different knowledge levels and linked to different systems, on their evaluation of CE? •Which appraisal approaches can be found for CBE, specially at micro and nano level as this are the most relevant for companies? •Which are the open areas that can be identified for future research? The research work carried out is presented herein as follows. Section 2 summarises the methodology that has been used for the conducted literature review and for the development of the positioning framework and the decision support tool. Section 3 presents main results and introduces the new positioning framework and decision support tool. Discussions about the relevance of the findings presented are gathered in Section 4. Finally, Section 5 goes over the main points and conclusions drafted, together with details of current limitations of the study and suggestions for future research. 2. Material and methods The methodology followed for the literature review is based in the PRISMA statement (Page et al., 2021). Main steps have been deciding on the target journals and information sources, establishing the exclusion and inclusion criteria, doing the review (identification and selection of relevant publications according to the defined criteria), systematisation of the main findings and drafting the discussions and conclusions. Due to the novelty of the topics, not only scientific peer reviewed journals have been considered, but also non-peer-reviewed publications, following the recommendations from Geissdoerfer et al. (2017). They delved into the relationship between CE and sustainability and concluded that the primary constraints of their study stem from conducting a bibliometric analysis that relied on the assumption that researchers disseminate their most significant findings in journals and build upon previously published articles. Hence, they stated that it is essential to acknowledge that valuable contributions may come from unpublished documents, reports, and other non-academic sources not featured in scholarly journals. Accordingly, sources of grey literature from business organisations, project reports, etc. as well as peer reviewed publications included in Science Direct, Web of Science, SAGE, Springer, Taylor & Francis and Google Scholar, together with European Commission documents have all been looked at as potential data sources. Concerning the strings of words used, the ones from Sassanelli et al. (2019) were used as starting point. Since they did not put special focus on the biological cycle, the term “bio” has been added to the key words used to capture the biological cycle dimension. It is important to note as well that, when looked for CBE, its synonyms were also looked in titles, abstracts, and keywords (string: “circular bioeconomy” OR “circular bio-based economy” OR “circular bio-economy”). Strings used are depicted in Table 1 next to the results of the review work that has been carried out for database sources. Regarding the classification strategy, the inclusion criteria were defined by the authors at the beginning of the investigation, these have been: (i) information published up to 2022; (ii) peer reviewed publications as well as grey literature; (iii) content actually dealing with CE (technical or biological cycle); (iv) when quite similar papers from the same research group were identified, only the most recent one (as it has been considered as the most complete and updated) has been included. Regarding the third criteria, it is important to notice that several articles M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 3 included CE in the title, key words or abstract but the topic was not found in the research work (being sometimes related to e.g. environmental impact assessment, ecology, sustainability, etc.). After the revision and selection made following these criteria, a final categorisation step was conducted. To do so, the following categories were identified for each selected piece of information: level, cycle, methodology, sector, subsector, year, and country. As for the level, these are related to the scope of the analysis exercise, I.e., system boundaries. These are nano, micro, meso, macro or a combination of them as in some cases, the studied publication authors state that their method can be applied to several levels. One example is the publication from Ahmed et al. (2022) that developed a multilevel approach for CE assessment, addressing macro, meso, micro and nano levels. Concerning the cycle, this is related to the type of material (technical or biological) as defined by Braungart et al. (2006) and the Ellen Macarthur (2015), and later discussed by Navare et al. (2021). It is worth mentioning that when the paper did not mention explicitly the biological cycle but tackled biobased materials such as food, organic waste, etc. the category tech-bio has been allocated. Such category has also been allocated when the information source explicitly mentioned that both technical and biological cycles would be applicable. Pertaining to the methodology, this is about the theoretical approach used for the analysis, including those listed by Sassanelli et al. (2019) and other categories added to better accommodate the new identified approaches: •Life Cycle Assessment/Life cycle Inventory/Life Cycle Impact Assessment (LCA, LCI, LCIA) •Multi-criteria approaches and fuzzy logic (MCDM & fuzzy) •Design for X and guidelines (DfX & guidelines) •Data Envelopment Analysis and Input-Output models (DEA & I/O) •Material Flow Analysis (MFA) •Emergy- and exergy-based approaches (Emergy & exergy) •Discrete Event Simulation/simulation (DES & simulation) •Other methodologies (Other) •Mixed (this option is used when the approach uses a combination of at least two methodologies) The sector (and subsector) have been identified following the classification from the NACE codes (List of NACE Codes, 2024). It is worth mentioning that some approaches are devoted to the manufacturing sector from a general point of view, i.e. they do not specify the subsector of application. For those cases, the whole set of manufacturing subsectors from the NACE code list has been considered (noted as C10-C33). Finally, the year is linked to the time of the publication and the country to the geographical location of the main author. For a careful analysis of the systematized information, dynamic charts were used in order to better represent and examine the main data and findings. Website links to the dynamic charts and the decision support tool have been produced and can be found in the Supplementary materials section. So far, a total of 2113 papers, documents and reports have been revised with a total of 105 selected for inclusion in the review. A dynamic representation of this table can be found in the Supplementary materials (DC1). Finally, a summary of the process that has been implemented in presented in Fig. 1. 3. Results For the included information, an analysis of the year and corresponding author location has been conducted. Main results are presented in the Fig. 2. When the included information was related to a webpage for a circularity assessment approach from a private company, the term “N/A" has been stated. 3.1. Positioning and classification frameworks review and critical analysis CE and CBE can be modelled, implemented, and measured for a system with boundaries. A taxonomy for the systems that can be considered in CE has been discussed in literature, and although most of the authors point out macro, meso and micro levels as the main taxonomies (Moraga et al., 2019), the definition of these terms is not used on a consistent manner and might differ slightly between the different authors. Moreover, Saidani et al. (2017) go beyond and propose a fourth term, the nano level, specifically addressing “an operational and product-level including components and materials” that belongs to value chains and could be considered throughout their entire lifecycle. A summary of the different interpretations of the taxonomies is provided in Table 2. It is interesting to pay attention to the meso level, which has been mostly covered in Chinese publications as stated by Geng and Doberstein (2010) and Geng et al. (2012). In fact, all those that fall under the category of meso level have the corresponding author based in China (Han et al., 2017; Li, 2011; Pan et al., 2016). Only when references address mixed levels such as macro-meso or meso-micro, information from other countries can be found. The development of industrial parks is being fostered and supported by the Chinese government with a wide approach since they include not only industrial facilities, but also business and research facilities, residential buildings, and service areas. Another definition for meso scale is provided by Geng et al. (2012) as they consider the natural environment and corresponding regional networks as well. Furthermore, in some studies, the term meso level conflicts with macro scale. For example, Chinese CE law considers regions (area between larger than a city but smaller than a country) as macro scale, while Smol et al. (2017) propose considering regions as the connection between macro and micro levels when measuring CE ecoinnovation, i.e., consider regions at meso level. As a potential solution to this controversy, Moraga et al. (2019) suggested that when a taxonomy is used, it could be helpful to state as well the scope. There have been several attempts to identify, categorise and review Table 1 String words used in the databases review exercise and results for revised and included information sources. Science Direct Scopus N. Search Revised Included Revised Included 1 “circular economy” AND “performance assessment” AND “methodology” 310 17 75 6 2 “circular economy assessment” 35 14 35 8 3 “circular economy performance” 78 12 61 3 4 “end of life” AND “performance assessment” AND “methodology” 667 14 190 2 5 “end of life assessment” 63 1 94 0 6 “end of life performance” 98 6 134 5 7 “circular bioeconomy” AND “performance assessment” AND “methodology” 13 1 4 2 8 “circular bioeconomy assessment” 2 0 0 0 9 “circular bioeconomy performance” 2 0 0 0 10 “end of life” AND “performance assessment” AND “methodology” AND “bio” 157 3 0 0 11 “end of life assessment” AND “bio” 9 1 2 0 12 “end of life performance” AND “bio” 12 0 0 0 13 Random 20 0 41 10 TOTAL 1466 66 636 33 M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 4 CE assessment methods, producing positioning frameworks. Saidani et al. (2017) do not provide a positioning framework itself as they tested and validated three practices related to the assessment of product circularity at micro level and delved into the requirements that an ideal circularity measurement framework might have. Sassanelli et al. (2019) reviewed systematically and categorised different methods according to the approach used. For each of the assessment approaches, triple bottom line aspects were studied (economic, environmental, and/or social aspects), as well as adopted variables and lifecycle stage. In the relevant review from Moraga et al. (2019) a large pool of CE indicators were studied. A categorisation framework was proposed according to a set of CE strategies defined by the authors and to parameters established using the Life Cycle Thinking methodology. Finally, it is worth noticing that these reviews cover the technical cycle of the CE butterfly model from the Ellen MacArthur Foundation. In the case of Moraga et al. (2019) both cycles are used in the framework, but these are addressed equally. As for the biological cycle, it is worth mentioning the review and discussion from Navare et al. (2021) who studied the gaps in CE monitoring for the biological cycle. 3.2. Proposal for a positioning framework for circular economy assessment approaches classification So far, the available positioning frameworks are not able to address all aspects from CE, nor CBE specificities. Therefore, as a result of the research work presented herein, a positioning framework is proposed with the aim of providing a classification able to cover new CBE assessment approaches to be further developed during the next years as well (i.e., not only CE systems linked to the technological cycle). The proposed positioning framework consists of a triple-axis space Fig. 1. PRISMA Flow Diagram for the conducted literature search. Fig. 2. Included circular economy assessment approaches per location of corresponding author and publication date. Table 2 Levels for modelling, implementing, and measuring circular economy according to literature. Level \Reference Saidani et al., 2017 Balanay et al., 2016 Banait˙ e and Romeris, 2016 Moraga et al., 2019 Macro City, province, region, nation Society City, province, region, country National level, global scale as and additional scale Meso Eco-industrial parks Interenterprise Symbiosis association Eco-industrial parks Micro Single company or consumer Enterprise Single company or Consumer Single product, service, organisation Nano Products, components, and materials – – – M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 5 where each axis corresponds to a characteristic of the CE assessment approach. The X-axis refers to the assessment level. The Y-axis refers to the methodology. Finally, the Z-axis refers to the CE cycle considered. This way the positioning framework can be imagined as a 3D cube where the different CE approaches can be positioned inside. The aim behind this design is that even stakeholders with bare knowledge about CE can select a method that is suitable for their needs just knowing the level of their system to evaluate, the cycle involved and the preferred methodology. Approaches have been represented in the proposed positioning framework as depicted in Fig. 3 while in Table 3 detailed information is provided (ordered alphabetically according to CE cycle, level and methodology respectively). For the sake of simplicity, approaches with the same level, cycle and methodology have been grouped under the same dot. Extended information about each approach (brief description) as well as projections from the 3D cube (level vs cycle, cycle vs. methodology and methodology vs. level) can be found in the Supplementary materials (Table S1, Figs. S2, S3 and S4). Projections make easier getting insights about gaps and more studied approaches. 3.3. Decision support tool for triple helix stakeholders on how to select the most suitable circular economy assessment approach Using the information gathered through the literature review, a decision support tool has been created with the aim to help the end-user in identifying the best approach to address its needs when it comes to CE evaluation. To do so, the tool workflow starts by asking the end-user about the information that it is being looked for. Then, the second step is to select the level, followed by the sector. Last step is to select the cycle. As a result, the publications meeting the selected inputs are shown to the end-user by providing the “Name”, “Aim” and “Reference” from Table 4 fields. The link to the decision support tool, which is available online for free, is provided in the Supplementary materials (DC3). The following table summarises the different kinds of information that might be sought by the user and how this is linked to the different CE assessment methodologies that have been identified herein. Also, it is important to consider what these methods do as their different characteristics can also explain why particular methods are employed at certain levels or within particular sectors. LCA, including LCI and LCIA, employs comprehensive data encompassing the entire life cycle of a product or process, from raw material extraction through production, use, and disposal. LCA, primarily focusing on environmental impacts, evaluates each life cycle stage. The overarching goal is to quantify and assess the environmental performance of a product or process, identifying areas for sustainability improvement (Curran, 2016). MCDM & fuzzy utilize multiple criteria, both qualitative and quantitative, relevant to decision-making. MCDM assesses alternatives based on various criteria, while fuzzy logic addresses uncertainties and imprecise information. The primary objective is to facilitate decisionmaking by considering and balancing multiple criteria, particularly in complex and uncertain situations (Stewart and Durbach, 2016). DfX relies on design specifications, constraints, and requirements and aims to optimize a product or process for specific factors such as reliability, sustainability, and manufacturability. The goal is to enhance overall performance by focusing on specific aspects during the design phase (Jari et al., 2011)(Jari et al., 2011). DEA & I/O use input and output data to analyse the efficiency of decision-making units. DEA measures the relative efficiency of multiple units, while Input-Output models analyse interdependencies between economic sectors. The objective is to assess efficiency and resource allocation in various processes or economic sectors (Cooper et al., 2011). MFA relies on quantitative data regarding material inputs, outputs, and stocks within a defined system, i.e. MFA tracks material flow through a system to understand resource consumption and waste generation. The goal is to assess the sustainability and efficiency of material use within a specific context (Graedel and Allenby, 2009). Emergy- and exergy-based approaches involve the use of emergy, which considers various types of energy, and exergy, which assesses energy quality in a system. Emergy evaluates the energy hierarchy, while exergy evaluates energy efficiency and quality, both aiming to provide insights into the use of energy resources and their efficiency within a given system (Sciubba, 2009). Finally, DES relies on input parameters and conditions for simulation models aiming to simulate and analyse system performance under different conditions, facilitating process optimization and informed decision-making (Collins et al., 2023). As a practical example, a medium size company devoted to waste management would like to compare several CBE approaches that they could implement at process level. Accordingly, they launch the tool and select “Appraisal of options from many viewpoints using numerous competing criteria" when asked about the type of information they are looking for. Internally, the tool filters all MCDM & fuzzy logic methodologies. Then, they select the nano level, the waste management sector (E according to NACE) and the bio cycle. As a result, three approaches are shown, together with the main aim and rerence (Briassoulis et al., 2021; D’Adamo et al., 2020; Padi and Chimphango, 2021). This way they can have a look at those publications and get some inspiration, contact the researchers or, if they have the proper staff, develop its own assessment based in the information provided. Fig. 4 presents a snapshot of the developed decision tool, showing the lay-out of the user interface, for the presented example. Although the main end-user targeted for this tool is the private sector, there are other stakeholders than can also benefit from it. Policy makers interesting in appraising CE or CBE in their region could identify the most relevant approaches for macro level. Regional innovation agents or organisations managing technical/industrial parks could explore the options under meso level. Finally, academia could use it in a benchmarking exercise to explore state-of-the-art approaches. 3.4. Circularity assessment approaches identified In order to get some insights from the analysed approaches, a dynamic chart representing the sectors, levels and cycles is provided in the Supplementary materials (dynamic charts DC4, DC5 and DC6). From the sector perspective, a first insight is that the NACE codes covered are A - Agriculture, forestry and fishing, B - Mining and quarrying, C – Fig. 3. Proposed positioning framework and analysed circular economy assessment approaches. M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 6 Table 3 Approaches identified for circular economy assessment; (*): approaches provided by non-peer-reviewed sources. ID Dot ID Name Level Method Cycle Sector & subsector Reference 1 A r.forcircular Macro MCDM & Fuzzy Bio A02 Sacchelli et al., 2022 2 AS Forest-based CBE assessment Macro - Micro DfX & guidelines Bio A02 Paletto et al., 2021 3 L Biochemicals production indicators Micro LCA, LCI, LCIA Bio C20 ¨ Ogmundarson et al., 2020 4 O CBE value chain selection Micro MCDM & Fuzzy Bio E38 Lokesh et al., 2018 5 AA Dairy supply chain CE assessment Micro Mixed Bio E38 Stanchev et al., 2020 6 X Pig farming chain CE assessment Micro Other Bio C10 Secco et al., 2020 7 AD CalcPEFDairy Nano LCA, LCI, LCIA Bio C10 Egas et al., 2020 8 AD Hybridised sustainability metrics for bio-based products Nano LCA, LCI, LCIA Bio E38 Lokesh et al., 2020 9 AF Recirculation potential assessment of bioplastics Nano MCDM & Fuzzy Bio E38 Briassoulis et al., 2021 10 AF Socio-economic Indicator for EoL Strategies Nano MCDM & Fuzzy Bio E28 D’Adamo et al., 2020 11 AF Percentage sustainability index (PSI) Nano MCDM & Fuzzy Bio E38 Padi and Chimphango, 2021 12 AN Bioplastics CE performance indicator Nano Mixed Bio E38 Spierling et al., 2019 13 D Municipal solid waste recycling efficiency Macro DEA & I/O Tech A31 Exp´ osito and Velasco, 2018 14 D Marine CE performance evaluation Macro DEA & I/O Tech A31 Ding et al., 2020a 15 D Extended Malmquist index Macro DEA & I/O Tech D35 Ding et al., 2020b 16 D CE efficiency at regional level evaluation Macro DEA & I/O Tech E38 Wu et al., 2014 17 G CE efficiency of resource and products Macro Emergy, exergy Tech E38 Geng et al., 2013 18 B Spatial assessment of CE indicators Macro MCDM & Fuzzy Tech E38 Stankovi´ c et al., 2021 19 B Urban CE performance evaluation Macro MCDM & Fuzzy Tech E38 Wang et al., 2021 20 F Ecological network for cities Macro MFA Tech E28 Gao et al., 2021 21 F Monitoring framework on CE Macro MFA Tech E38 Mayer et al., 2019 22 I Key indicators for monitoring the CE - French methodology Macro Other Tech B07 Magnier et al., 2017 23 I OECD Inventory of CE indicators* Macro Other Tech E38 OECD, 2020 24 I CE progress monitoring Macro Other Tech E38 Potting et al., 2018 25 I Hierarchical structure model and evaluation index system Macro Other Tech E38 Yang et al., 2011 26 I Displaced production from recycling or reuse quantification Macro Other Tech E38 Zink et al., 2016 27 AR EVR (Eco-cost value ratio) Macro - Micro LCA, LCI, LCIA Tech F41 Scheepens et al., 2016 28 J Emergy analysis Meso Emergy, exergy Tech C24 Pan et al., 2016 29 K CE evolution assessment in aluminium industrial parks Meso Other Tech B07 Han et al., 2017 30 K CE performance of eco-industrial parks Meso Other Tech C20 Li, 2011 31 AV CE evaluation in local productive arrangements Meso - Micro DES, simulation Tech C31 Oliveira et al., 2018 32 AU Performance evaluation of recoverable End-of-Life products Meso - Micro MCDM & Fuzzy Tech E38 Wibowo and Grandhi, 2017 33 AT CE performance measurement model (CEPMM) Meso - Micro - Nano Other Tech E38 Nandi et al., 2021 34 T SeCUWio model Micro DEA & I/O Tech B07 Liao et al., 2019 35 W Product-related environmental performance indicators Micro DES, simulation Tech E38 Issa et al., 2015 36 W Design based remanufacturability index Micro DES, simulation Tech C30 James et al., 2021 37 R Design for Deconstruction (DfD) Micro DfX & guidelines Tech E28 Akinade et al., 2017 38 N Methodological framework for the eco-efficiency assessment Micro LCA, LCI, LCIA Tech F41 Angelis-Dimakis et al., 2016 39 N CE Indicators dashboard Micro LCA, LCI, LCIA Tech C10-C33 Pauliuk, 2018 40 N Life cycle assessment-cradle-to-cradle-predictive building systemic circularity indicator (LCA-C2C–PBSCI) Micro LCA, LCI, LCIA Tech F41 Antwi-Afari et al., 2022 41 N Sustainable CE framework Micro LCA, LCI, LCIA Tech C13 Thakker and Bakshi, 2021 42 Q SCI (Sustainable Circular Index) Micro MCDM & Fuzzy Tech E38 Azevedo et al., 2017 43 Q Green supply chain management performance assessment Micro MCDM & Fuzzy Tech F41 Kazancoglu et al., 2018 44 Q Circularity and Maturity Firm-Level Assessment tool (CM-FLAT) Micro MCDM & Fuzzy Tech C28 Sacco et al., 2021 (continued on next page) M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 7 Table 3 (continued) ID Dot ID Name Level Method Cycle Sector & subsector Reference 45 Q CE measurement scale for building industry Micro MCDM & Fuzzy Tech F41 Nu˜ nez-Cacho et al., 2018 46 Q WEEE recycling evaluation Micro MCDM & Fuzzy Tech E38 Xu et al., 2018 47 V Circular business model indicator Micro MFA Tech C10-C33 Rossi et al., 2020 48 AC Performance evaluation in sustainable supply chain networks Micro Mixed Tech E38 Motevali Haghighi et al., 2016 49 Z Circle Economy* Micro Other Tech C10-C33 Knowledge Hub | Circle Lab, 2024 50 Z CE performance indicators Micro Other Tech F41 Rinc´ on-Moreno et al., 2021 51 Z Disassembly and deconstruction analytics system (D-DAS) Micro Other Tech E28 Akanbi et al., 2019 52 Z GRI based CE assessment framework Micro Other Tech D35 Baratsas et al., 2022 53 AL Design for EoL Nano DES, simulation Tech C35 Favi et al., 2016 54 AL End-of-Life Index Nano DES, simulation Tech E38 Lee et al., 2014 55 AL Eco-design potential assessment Nano DES, simulation Tech C30 Santini et al., 2010 56 AI Design for disassembly tool Nano DfX & guidelines Tech E28 Favi et al., 2019 57 AK Sustainability ratios and env. Performance assessment Nano Emergy, exergy Tech E38 Jamali-Zghal et al., 2015 58 AE CE Life Cycle Assessment (CE-LCA) model Nano LCA, LCI, LCIA Tech C35 van Stijn et al., 2021 59 AE GRI (Global Resource Indicator) Nano LCA, LCI, LCIA Tech D35 Adibi et al., 2017 60 AE Unit process model-based method. - Design for manufacturing Nano LCA, LCI, LCIA Tech F41 Eastwood and Haapala, 2015 61 AE Indicator for retained env. Value of circular solutions Nano LCA, LCI, LCIA Tech E28 Haupt and Hellweg, 2019 62 AH Product Recovery Multi-Criteria Decision Tool (PR-MCDT) Nano MCDM & Fuzzy Tech E38 Alamerew and Brissaud, 2019 63 AH Framework for End-of-Life mgmt. of electronic products Nano MCDM & Fuzzy Tech E28 Iakovou et al., 2009 64 AJ CE assessment for building envelope systems Nano MFA Tech E38 Finch et al., 2021 65 AJ Circularity calculator* Nano MFA Tech C10-C33 Circularity Calculator, 2024 66 AJ Circularity indicators for electronic sector Nano MFA Tech F41 Pollard et al., 2022 67 AP Resource longevity indicator Nano Mixed Tech F43 Franklin-Johnson et al., 2016 68 AP Decision making support for building design Nano Mixed Tech C26 Fregonara et al., 2017 69 AP Simulation-based analytical framework Nano Mixed Tech E38 Gbededo et al., 2018 70 AP Decision support methodology based on ETV Standards Nano Mixed Tech E38 Grimaud et al., 2017 71 AP CPI (CE Performance Indicator) Nano Mixed Tech E28 Huysman et al., 2017 72 AM BIM-based Whole-life Performance Estimator (BWPE) Nano Other Tech C26 Akanbi et al., 2018 73 AM Resource effectiveness evaluation of CE strategies Nano Other Tech C38 Parchomenko et al., 2020 74 AM 3R rate calculation - Vehicles Nano Other Tech C30 Delogu et al., 2017 75 AM CEI - Circular Economy Index Nano Other Tech C10-C33 Di Maio and Rem, 2015 76 AM Circular Economy Toolkit (CET)* Nano Other Tech C10-C33 Circular Economy Toolkit, 2024 77 AM Circular Economy Indicator Prototype (CEIP) Nano Other Tech E28 Cayzer et al., 2017 78 AM Life cycle sustainability performance Nano Other Tech E28 Kamali et al., 2018 79 AM CN_Con - Metric for evaluating novelty and circularity Nano Other Tech F41 Ruiz-Pastor et al., 2022 80 AM PLCM (Product-Level Circularity Metric) Nano Other Tech E38 Linder et al., 2017 81 AM CE-conviviality design tool Nano Other Tech E38 Ralph, 2021 82 AM eDiM (ease of Disassembly metric) Nano Other Tech F41 Vanegas et al., 2018 83 AM Total EcoSite Integration for urban and industrial symbiosis Nano Other Tech E38 Van Fan et al., 2021 84 C Resource efficiency and competitiveness potential analysis Macro DEA & I/O Tech - bio C10 Pagotto and Halog, 2016 85 C Supply chain-linked ecologically based Life Cycle Assessment Macro DEA & I/O Tech - bio C10 Park et al., 2016 86 A CE composite index Macro MCDM & Fuzzy Tech - bio E38 Garcia-Bernabeu et al., 2020 87 E Water circularity in cities assessment Macro MFA Tech - bio E36 Arora et al., 2022 88 H Performance of waste management systems Macro Other Tech - bio E28 Campitelli et al., 2022 89 H Indicators for progress in CE at regional level Macro Other Tech - bio E38 Avdiushchenko and Zajaç, 2019 90 AQ Circular City Analysis Framework (CCAF) Macro - Meso Other Tech - bio E38 de Ferreira and Fuso- Nerini, 2019 91 AY Multi-level CE assessment framework All MCDM & Fuzzy Tech - bio C24 Ahmed et al., 2022 (continued on next page) M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 8 Manufacturing, D - Electricity, gas, steam and air conditioning supply, E - Water supply; sewerage; waste management and remediation activities, F – Construction and O - Public administration and defence; compulsory social security. Also, the two sectors that can be pointed out as trends are sector C and E. It is interesting to note that sector A has publications only since 2018, linked to the appearance of CBE devoted approaches, all at macro level. From the level perspective, until 2015, the number of papers per year was similar for macro, micro and nano levels, but from that year onwards, micro and nano levels have experienced a larger growth than macro, e.g. in 2022, there were 3 approaches for macro, 15 for micro and 8 for nano. It is also interesting to point out that mixed approaches concerning the level have emerged during the last years, probably as an effort to create flexible CE assessment methodologies. From the methodology perspective, MFA is the methodology that has increased the most the number of approaches identified. Furthermore, when delving into the sector and the different methodologies that are used, sector A approaches linked to blue economy have used both DEA & I/O. Sector C usually uses LCA/LCI/LCIA (ca. 27%), followed at a distance by MFA and DES &simulation, while in the case of sector E, the most used one is MCDM & fuzzy (ca. 31%). For the rest of sectors (B, D, F and O) is not possible to identify a tendence. As for the level and the methodologies used, at macro level, the two most used (ca.24% each) are MCDM & fuzzy and DEA &I/O. This could be explained by the need of policy makers of information related to insights about several options to be implemented at regional level and by the question that the implementation of CE poses in terms of economic impact. At meso level no conclusions can be drafted as no single methodology excels as the most used one. On the contrary, in the case of the micro level, ca. 30% of the identified approaches use MCDM & fuzzy, followed by LCA/LCI/LCIA. Aside from the need of the companies in investigating the impact of different CE related options, companies are usually interested as well in environmental impact related info due to regulation aspects and because nowadays claiming a lower environmental impact has become a marketing strategy. Finally, at nano level, ca. 20% of the approaches used mixed methodologies, probably to properly capture the CE dimension of products, materials and services. This is followed by LCA/LCI/LCIA and MCDM & fuzzy with 15% each. Moreover, from the identified information sources, it is important to note that there are not many assessment methods specified for CBE. From a total of 2113 identified sources, only 234 included the “bio” prefix, accounting for a ca.11.5% of the total. An analysis of the number of approaches included in the review at a later stage per level and CE loop can be found in Table 5. From those selected (105 approaches) only 12 are specifically designed for CBE (roughly a 11.4%). If the scope is broadened to approaches that could be used for both technical and biological loops, several additional papers can be identified. Specifically, a total of 22 additional approaches can be pointed out, almost the double as for biological loop only. Only 1 approach has been identified for CBE assessment at macro level. In this paper, Sacchelli et al. (2022) developed a decision support tool that uses geography features in order to measure CBE in the forestbased economy (called r.forcircular), together with financial Table 3 (continued) ID Dot ID Name Level Method Cycle Sector & subsector Reference 92 S Green Performance Map (GPM) Micro DEA & I/O Tech - bio C30 Lindahl et al., 2022 93 M Eco-efficiency methodology based on CE thinking Micro LCA, LCI, LCIA Tech - bio C10 Laso et al., 2018 94 M Sustainability performance of food value chains Micro LCA, LCI, LCIA Tech - bio C10 Petit et al., 2018a 95 P Circonomics index Micro MCDM & Fuzzy Tech - bio E36 Kayal et al., 2019 96 P CE assessment framework for public sector organisations Micro MCDM & Fuzzy Tech - bio E38 Droege et al., 2021 97 P Circulytics* Micro MCDM & Fuzzy Tech - bio E36 Ellen Macarthur, 2015 98 U Water Circularity Indicator Micro MFA Tech - bio E36 Kakwani and Kalbar, 2022 99 Y Assessment tool for the evaluation of CE implementation Micro Other Tech - bio E28 Di´ eguez-Santana et al., 2021 100 AW CE Performance Assessment (CEPA) Micro - Nano LCA, LCI, LCIA Tech - bio C10-C33 Rocca et al., 2021 101 AX Material Circular Indicator (MCI)* Micro - Nano MFA Tech - bio C10-C33 Ellen Macarthur, 2015 102 AG Circularity across product-life cycle stages Nano MCDM & Fuzzy Tech - bio C13 Vimal et al., 2021 103 AO 3E assessment systematic framework Nano Mixed Tech - bio E35 Ng and Martinez Hernandez, 2016 104 AB Triple-C indicator Micro Mixed Tech - bio O84 Wurster and Ladu, 2022 105 AX VRE (Value-based Resource Efficiency) Micro - Nano MFA Tech - bio C10-C33 Di Maio et al., 2017 Table 4 Methodologies considered in the present study and information provided by them. Methodology Information provided by the selected methodology Reference LCA, LCI, LCIA Environmental impact European Environment Agency, 2024 MCDM & fuzzy Appraisal of options from many viewpoints using numerous competing criteria Kaya et al., 2019 DfX & guidelines Rules, guidelines, and procedures used throughout the product life cycle Mesa, 2023 DEA & I/O Process efficiency and economic performance Cooper et al., 2011 MFA Resource streams data Laner et al., 2014 Emergy & exergy Resource and energy flows (origin, quality, utilization, losses) Dewulf et al., 2008 DES & simulation Process performance under different conditions Charnley et al., 2019 Other Other kind of information – Mixed Combination of different insights – M. Macias Aragon´ es and F. Arroyo Torralvo Environmental Impact Assessment Review 106 (2024) 107493 9 Fig. 4. User Interface from the developed decision support tool for a given example (circular bioeconomy, nano level, sector E and MCDM & Fuzzy methodologies). Table 5 Circular bioeconomy assessment methodologies identified (ID as depicted in Table 3). Level Circular economy loop Biological Technical Technical - Biological Number of publications ID Number of publications ID Number of publications ID Macro 1 1 14 13–26 6 84–89 Meso 0 – 3 28–30 0 Micro 4 3–6 19 34–52 9 92–99, 104 Nano 6 7–12 31 53–83 2 102, 103 Macro - Meso 0 – 0 – 1 90 Macro - Micro 1 2 1 27 0 – Meso - Micro - Nano 0 – 1 33 0 – Meso - Micro 0 – 2 31, 32 0 – Micro - Nano 0 – 0 – 3 100, 101, 105 Macro - Meso - Micro - Nano 0 – 0 – 1 91 Total 12 71 22 Fig. 5. Methodologies used for micro and nano levels for biological cycle (left) and technical-biological cycle (right). When no approach was available, the category is not shown, e.g. no approach for Micro – Nano and biological cycle. M. Macias Aragon´ es and F. Arroyo Torralvo