Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis

Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis Soumava Boral, Leon Black, Costas Velis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5844499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Circular economy (CE) quantification features intrinsic complexity, mandating the application of systems thinking and associated methodologies to navigate multifaceted and dynamic intricacies; posing challenges for science-policy interfacing. Well-established approaches such as System Dynamics (SD) and emergent Agent-Based Modeling and Simulation (ABMS) are adept at interrogating such complexities within intricate systems. While SD employs a macroscopic, top-down lens, ABMS delves into a microscopic, bottom-up perspective. However, to date there are no comprehensive reviews quantifying circularity through systems thinking and its associated complexity modelling. Here, we analyse this topic through a systematic scoping review using PRISMA-ScR. Our analysis has identified core limitations in existing approaches, regarding the extent to which CE complexity has been captured holistically. Although both SD and ABMS can address circularity’s dynamic interactions and feedback loops, they are predominantly applied in isolation due to the absence of standardised platforms that can integrate both approaches, and to reduce computational costs. Exploration of the potential synergies from combining these two approaches and coupling them with traditional decision-support tools such as life-cycle and multi-criteria ones are minimal. Such a fragmented approach limits their ability to model internal dynamics; in turn restricting their utility to inform system-wide decision-support. The review also accentuates the lack of standardised metrics and the need for a more holistic evaluation framework for CE incorporating economic, environmental, social, and technical value metrics. A more unified approach to support sustainable, informed decisions in the pursuit of circularity is imperative for improving evidence-based policymaking and empowering industrial adoption of circularity. Environmental Engineering Environmental Policy Circular economy Systems thinking Complexity modelling Agent-based modelling and simulation System dynamics Circularity Metrics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction The concept and practice of circular economy (CE)/circularity intend to go beyond the established practices of waste management and resource recovery, towards an even more sustainable management of material resources. Numerous circular economy/circularity definitions have been proposed, as summarised in detail by Kircherr and co-workers (Kirchherr et al., 2023 , 2017 ). For example, the United Nations Environment Programme (UNEP) described CE as “an economy that reduces consumption of resources and the generation of waste, and reuses and recycles waste throughout the production, distribution and consumption processes” (United Nations Environment Programme (UNEP), 2011 ). An alternative definition proposes that “[… circular economy] is restorative and regenerative by design and aims to keep products, components, and materials at their highest utility and value at all times, distinguishing between technical and biological cycles” (Ellen MacArthur Foundation, 2013 ) encourages a shift from ‘cradle-to-grave’ thinking to a ‘cradle-to-cradle’ philosophy, thus promoting the idea of maximising the positive ‘value’ associated with material, components and products (MCPs). This wording emphasises the concept of MCPs ‘ value ’ regeneration and redistribution through their entire lifecycle, and multiple iterations thereof. Nonetheless, this approach does not define the inherently multi-dimensional and therefore complex meaning of ‘ value ’ , which usually goes beyond the monetary loss/benefits, to incorporate environmental benefits, social equity and prosperity, and minimal technical performance loss (e.g., design for reuse) (Iacovidou et al., 2017b , 2017a ; Millward-Hopkins et al., 2018 ). Such a value transforms: is created/destroyed/transferred between places and owned by organisations as MCPs are physically moved and transformed in the extraction of resources, materials, components, semi-finished goods and goods are manufactured, retailed, used and becoming after-use/waste and entering the waste and resource recovery/disposal part of the cycle. Measuring this value as an attempt to quantify circularity is therefore feasible only in a comparative way, within a system at which the value carried by MCPs takes specific ‘states’/‘levels’ at different points. For example, simplified typologies on the mode of circularity have been historically narrated via ‘R’-type based frameworks (Table 1 ). They offer a simple conceptual hierarchy of circularity modalities applicable at various MCPs lifecycle stages, focusing on slowing, closing, or narrowing resource flows. Similarly, quantifying the nature and degree of circularity achieved by these R-type modalities, requires as a bare minimum definition of ratios between parts of a material flows system. From a whole system’s perspective, the implementation of each ‘R’ philosophy is highly time-dependent, short- and long-term impacts (i.e., time dynamicity and delays) with potential benefits (e.g., diversion from landfill) or drawbacks (e.g., insufficient recycling capabilities driving increased disposal), and it depends on stakeholder activity along value chain (Walzberg et al., 2022b ), i.e. in material flow systems. Table 1 A variety of ‘R’ concept-based frameworks proposed. Framework ‘R’- type content Reference 3-Rs-based Reduce, Reuse, and Recycle Brennan et al. ( 2015 ) 4-Rs-based Reduce, Reuse, Recycle, Recover Yang et al. ( 2017 ) 6-Rs-based Reduce, Redesign, Recover, Reuse, Remanufacture, Recycle Jawahir and Bradley ( 2016 ) 9-Rs-based Refuse, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, and Recover energy Van Buren et al. ( 2016 ) 10-Rs-based Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, Recover and Refuse Kirchherr et al. ( 2017 ) In such material flow systems, multiple variables may cause system dynamicity: i.e., qualitative and quantitative, endogenous and exogeneous, linear or non-linear. Understanding stocks and flows of material, energy, finance, etc. is key to sufficiently describe such a CE system, where multiple stakeholders are involved along the entire value-chain, sometimes in closed loops introducing feedback in the system, each aiming to maximise its own benefits, therefore transforming the monetised or wider perceived ‘value’ of MCPs at each system point over time. As a result, such systems feature numerous complexities, and feature trade-offs among aspects of ‘value’ which would be very difficult, if at all possible, to capture/summarise solely through traditional analytical, equations-based, approaches (Sun et al., 2016 ). Similarly, system boundaries (time, geographical, administrative, scale) may play a crucial role in systems thinking, and while the actions of system agents may not reveal impacts at a ‘micro’ scale, they may do so at ‘meso’ or ‘macro’ -scales. For example, even where systems tend towards a balanced state, external agents all along the value chain, such as early adopters of new technologies, can disrupt this state until a new balanced state develops. A series of system-level failures have been proposed as preventing the creation and operation of genuine circular economy (Velis, 2018 ): description, verification and quantification of such phenomena, however, would necessitate a systems approach. Quantifiable metrics of circularity could be exhibiting highly non-linear behaviours, as a system emerges through the adaptive nature of agents. Therefore, we argue that ultimately, effective qualification and quantification of circularity by default can be only achieved through a so-called ‘systems thinking’ approach. Simply put, systems thinking adopts holistic and interconnected approaches to understanding and addressing inherent complexities of an entire system, rather than its isolated components (Arnold and Wade, 2015 ; Bassi et al., 2021 ; Demartini et al., 2023 ). Richmond ( 1994 ) defined systems thinking as the art and science of making trustworthy decisions about behaviour by developing deep understanding of underlying structures. Meanwhile, Sterman ( 2000 ) defined systems thinking as an approach and mindset for understanding and analysing complex systems as interconnected and dynamic entities, and thus asserting the principle of dynamic complexity for solving systemic challenges. It involves recognizing that a system is more than the sum of its parts; and that its behaviours and properties emerge from interactions and interdependencies among its components. In this context, such a methodologically analytical approach has the potential to identify leverage points in the system under a multiplicity of conditionalities. Systems thinking and its pertinence to CE are discussed in detail S.2.1 and S.3 of the Supplementary Material (denoted as S for tables, figures, and sections). Two prominent approaches (schools of thought) in this regard are system dynamics modelling and agent-based modelling and simulation (ABMS). System dynamics and ABMS approaches can to a variable degree address challenges around static and liner descriptions of circularity: Sections S.2.2 and S.2.3 offer more details of system dynamics and ABMS theorising. To date, applied research on CE and systems thinking has often focused on complexity modelling of the ‘micro’ scale. For example, addressing the meaning of systems thinking in a CE context, but not the inherent complexities and dynamicity of the whole system in ‘macro’ scale (Iacovidou et al., 2021 ). Recent research has advanced CE, but often still omitting systems thinking and even more so its corresponding quantification (Table 1 ), which, as we argue, could provide a sound base for more holistic, meaningful, and reliable circularity analytics. Table 2 Reviews on systems thinking and its quantification from a CE perspective. Summary Scope limitations Reference Integrated review of CE concerning construction and the built environment. Omitted systems thinking and dynamic quantification of CE. Çimen ( 2021 ) Focused on common modelling approaches to analyse industrial symbiosis. Did not focus on dynamics of circularity metrics and parameter quantification. Demartini et al. ( 2022 ) Reviewed life cycle assessment studies of a biorefinery system. Omitted quantification of systems thinking by complexity modelling. Vance et al. ( 2022 ) Comprehensive review on adoption of system dynamics and agent-based models in construction waste management. Did not focus on the necessity of systems thinking, its complexity, and thereafter dynamic quantification of circularity metrics. Ding et al. ( 2018 ) To the best of our knowledge, there are no reviews and analyses with a comprehensive scope on the quantification of circularity through systems thinking and its associated complexity modelling. Therefore, here we adopt a systematic scoping review approach according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews), concerning the application of systems thinking through system dynamics and ABMS. Out approach and scope can offer novel insights on: (a) considering how complexity arises in CE systems, highlights the relevance of systems thinking applied to CE, and particularly its quantification; (b) by the way of a case study, it applies a magnifying lens on applications of system dynamics and agent-based modelling in quantifying different aspects of CE in the built environment, specifically also considering circularity metrics and wider parameters; and, (c) by describing and analysing how other decision-support tools have been integrated with system dynamics and ABMS aiming at more refined analytics. Additionally, we also consider case study geography, along with the significance of policy and governance aspects. The paper is structured as follows: Section 2 highlights the methodology (research questions and literature review approach); Section 3 describes applications of system dynamics and ABMS to CE in series of main sectors/categories, including bio-based, construction, electrical and electronics equipment, single materials, industrial symbiosis, along with miscellaneous; Section 4 provides critical observations and quantified insights; Section 5 identifies key future research avenues; and Section 6 offers overarching conclusions. 2 Methodological Approach to Systematic Literature Review We conducted a systematic scoping review following the PRISMA-ScR guidelines (Peters et al., 2020 ) and the associated preferred reporting items checklist (Tricco et al., 2018 ) ( Table S.3 ). The review explored three specific questions: RQ1: What were the contexts/necessities of applying system dynamics modelling and ABMS approaches in CE? RQ2: Which types of decisions were provided through system dynamics and ABMS approaches? RQ3: Were any other tools/methods applied in conjunction with system dynamics, and ABMS? If so, why? The literature search terms are listed in Section S.5 . Searching was confined to articles featured in the SCOPUS bibliographic database ( www.scopus.com ), given that it is multidisciplinary and futures comprehensive main research outputs coverage (peer-reviewed journal articles). Conference articles and book chapters were excluded here, to ensure peer-reviewed content and avoid duplication of ideas. The search was practically restricted to recently published articles, arbitrarily defined as in the last 8 years at the point of the start of the review: 2016–2023. As a matter of testing for previous period outputs, indeed only 3 relevant articles were retrieved from 2003–2015, and these were not useful to answer the research questions. The detailed inclusion-exclusion criteria are shown in Section S.6 , with the selection of sources and data charting presented in Section S.7 . 3 Sector-wise Applications of SD and ABMS in Quantitative Circular Economy 3.1 Bio-based Sector 3.1.1 System dynamics modelling in bio-based sector Articles cover a diverse range of contexts, such as; food waste reduction in the food, energy, water, and climate (FEWC) nexus (Parsa et al., 2024 , 2023 ) or through an online food sharing application (Ranjbari et al., 2024 ), the flow of chicken and associated waste products in the economy (Abbasi et al., 2024 ), addressing reactive nitrogen’s societal contribution (Xing et al., 2023 ), utilising waste cooking oil as second-generation fuel (de Carvalho Freitas et al., 2022 ), examining urban sustainability transition in food, energy, water, and waste (FEWW) sectors (Valencia et al., 2022 ), waste oil flow in the motor industry and its circularity (Viruega Sevilla et al., 2022 ), and scrutinising phosphorus recycling process (El Wali et al., 2021 ). The initial step of system dynamics modelling involves the creation of causal loop diagrams, offering a qualitative understanding of intricate relationships between different parts. Afterwards, stocks and flows within the system are identified, and quantitative analysis is executed by stocks and flows diagrams. Some authors exclusively presented the causal loop diagrams before developing the stocks and flows diagrams (Abbasi et al., 2024 ; de Carvalho Freitas et al., 2022 ; Ranjbari et al., 2024 ; Xing et al., 2023 ), whereas some incorporated the causality of different variables within the stocks and flows diagram (Valencia et al., 2022 ). Some did not report the stocks and flows diagram directly, but presented the flows of materials, energy, or finance through Sankey diagrams (Parsa et al., 2024 , 2023 ). Stocks and flows diagrams were used to illustrate the dynamic interactions of flows of food, energy, water and climate changes (FEWC) between different sectors in an agri-food supply chain (Parsa et al., 2023 ), to highlight socio-economic interactions coupled with material and finance flows (Parsa et al., 2024 ), and to integrate the forward and reverse value-chain mapping in chicken farming (Abbasi et al., 2024 ). Furthermore, they aided the understanding and mapping of flows within various scenarios: for example reactive nitrogen flows from food waste and its associated impact on the nitrogen cycle (Xing et al., 2023 ); waste cooking oil generation pattern by households, and its impact on water pollution (de Carvalho Freitas et al., 2022 ); food, energy, water and waste flow (Valencia et al., 2022 ); and, flow of waste oil in the motor industry (Viruega Sevilla et al., 2022 ). Some authors even modelled material flows (viz., surplus food) with the knowledge of the system entities (viz., population) (Ranjbari et al., 2024 ) and coupled materials flow with social metrics (El Wali et al., 2021 ). System dynamics model is in principle able to identify key leverage points of systems, and in scenario generation and metrics forecasting, which, in turn, could help policy recommendations. Key leverage points may not be obvious in baseline scenarios but emerge through examining various alternative scenarios. For instance, crop planting and aquaculture were identified through scenario generation as the main sectors for emitting reactive nitrogen (Xing et al., 2023 ). In de Carvalho Freitas et al. ( 2022 ), household cooking oil consumption patterns were identified as the key influence on water pollution. Meanwhile, food waste and environmental footprints could be minimised by reducing waste generation at consumer and redistribution levels (Parsa et al., 2023 ). Children’s knowledge of a food-sharing app was identified as a key lever to reducing food waste (Ranjbari et al., 2024 ). In Parsa et al. ( 2024 ) the key sectors for food waste minimisation were identified which, in turn, benefitted the socio-economic metrics. Some studies demonstrated self-sustainability through landfill gas and stormwater reuse (Valencia et al., 2022 ). Holistic linkages between small and medium-scale farms have been emphasised for sustainable development (Abbasi et al., 2024 ). Additionally, the practice of CE enhanced global phosphorus security and social prosperity in low and middle-income countries as found in El Wali et al. ( 2021 ). Additionally, all these studies mapped stocks and flows over extended time periods, across various geographical scales (El Wali et al., 2021 ) and coupled them with various CE metrics (see Table 3 ), not only to map the current situation, but also to forecast future scenarios. The variables considered in the models were often non-linear. For details refer to Table S6-S7 . Table 3 Case-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in system dynamics applications within the bio-based sector. Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? Use of other tools Reference Food waste reduction Intermediate cost, total GVA, and sectoral GVA, severe food insecurity rate, food waste generation, redistribution cost, redistribution benefit Reduce, reuse, recycle, recover n.a. Parsa et al. ( 2024 ) Food waste reduction in food, energy, water, and climate (FEWC) nexus Food footprint, food waste generation, energy footprint, water footprint, and carbon footprint. Reduce, reuse, recycle, recover Group model building to create, refine, and restructure the system dynamics model with stakeholders’ interventions. Parsa et al. ( 2023 ) Food-waste mitigation strategy by food-sharing platform Total amount of food waste prevented, total CO 2 emissions prevented, and food-sharing platform performance. Rethink, reduce, and reuse Bass diffusion model to account the adoption of food-sharing app by the population. Ranjbari et al. ( 2024 ) Chicken farming Behaviour of egg laying chicken, egg, hatchery, and day-old chick stocks, poultry litter and poultry litter to banana farm stock, chicken flow to slaughterhouse, waste flow to fish farm, total income, profit, and total cost of the stakeholders Reuse, repurpose, recycle, recover n.a. Abbasi et al. ( 2024 ) Food and agricultural system Total reactive nitrogen output, air nitrogen emission, soil reactive nitrogen emission, water reactive nitrogen emission Reuse and recycle n.a. Xing et al. ( 2023 ) Fuel: waste cooking oil to second generation fuel Household consumption and industrial consumption of vegetable oil, waste oil not recycled, water pollution, and oil to recycle Recycle n.a. de Carvalho Freitas et al. ( 2022 ) Food, energy, water, waste nexus Reclaimed water utilization, food wate reuse, waste to energy generation, food production, carbon footprint, water footprint, water resilience index, food supply index and different types of wastes produced. Reduce, recycle, reuse, recovery TOPSIS (Technique for order of preference by similarity to ideal solution), a MCDM tool was used to identify the most suitable scenario based on sustainability and resilience indicators. Valencia et al. ( 2022 ) Motor industry residues (waste oil, spent solvent, battery waste, and dirty wipes) Outflows of waste oil, emissions (CO2), human toxicity, energy cost Recycle, recovery n.a. Viruega Sevilla et al. ( 2022 ) Phosphorus (P) recycling Worker safety, employment equality, employment rate, poverty rate, child labour, water use, nutrition supply, P efficiency and security Recycle and recovery n.a. El Wali et al. ( 2021 ) n.a.: not applicable 3.1.2 ABMS in bio-based sectors ABMS is a bottom-up system modelling approach. In Farahbakhsh et al. ( 2023 ) it was utilised to identify the barriers to adopting emerging technologies in waste treatment plants, exploring potential contribution of chemical recycling of carbon-containing waste of municipal solid waste (MSW). It has also been used for dynamic life-cycle sustainability assessment to contribute to UN sustainable development goals (UN SDGs) (Voss et al., 2022 ), and to investigate households’ intentions regarding recycling (Tong et al., 2018 ). In general, we sense that ABMS is preferred over systems dynamics when the heterogeneities of the stochastic agents are essential to modelling. In Farahbakhsh et al. ( 2023 ) it was induced by agents’ (viz., waste treatment plants) environmental awareness, investment required to adopt emerging technologies, and expected returns, etc. In Voss et al. ( 2022 ) agents were characterised based on their geographic locations, annual residual MSW (rMSW) production volume, treatment capacity, etc. In Tong et al. ( 2018 ), households’ intentions towards recycling and/or landfilling were considered as heterogeneities. These heterogeneous agents sustain by dynamically adapting to their environment. For example, in the case of valorising organic waste into high-end products, the agents adapted within a global market in response to economic, social and environmental pressures (Farahbakhsh et al., 2023 ). Strict sustainability regulations led agents (viz., administrative areas, and waste treatment plants) to adapt in Voss et al. ( 2022 ) regarding circular utilisation of organic residual MSW as a chemical feedstock via gasification. While in Tong et al. ( 2018 ) waste separation and recycling were agents triggered by changes in consumer behaviour. Interactions between agents and with the environment can lead to the emergence of new, not easily foreseeable, behaviours. This can result from social pressure provided by other agents to adopt CE technologies (Farahbakhsh et al., 2023 ), or be reward-driven (Tong et al., 2018 ). Temporal (Farahbakhsh et al., 2023 ; Tong et al., 2018 ; Voss et al., 2022 ) and spatial considerations (Tong et al., 2018 ; Voss et al., 2022 ) offer a nuanced understanding of dynamic processes. This consideration over temporal scales led to scenarios analysis and metrics quantification as outlined in Table 4 (for further details see Table S20-S22 ). Table 4 Case-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications within the bio-based sector. Case-study domain Metrics and/or parameters quantified Which ‘R’s were considered? Use of other tools Reference European organic waste treatment facilities Volatile fatty acid platform (VFAP) adoption rate by changing the: (a) subsidies (both investment and operational), (b) market growth in polyhydroxyalkanoates (PHA) segment, (c) improvement in technological efficiencies, (d) social pressure. Recycle n.a. Farahbakhsh et al. ( 2023 ) Carbon containing waste of MSW Climate change, terrestrial acidification, fossil resource scarcity, system cost, and impact on local environment for different defined scenarios. Recycle n.a. Voss et al. ( 2022 ) Household recyclable wastes Participation rate of households regarding three choices: landfilling, placing waste at a central container, or being collected, every day for a period. Recycle, recovery Theory of planned behaviour (TPB) was used to identify key influencing factors affecting the residents’ inclination to recycling. Tong et al. ( 2018 ) n.a.: not applicable 3.2 Construction Sector 3.2.1 System dynamics modelling in the construction sector System dynamics has been used for a range of applications in the construction sector. It was used to examine the circularity potential of recycled paver blocks (RPB) (Gandhi et al., 2024 ), and the decarbonisation potential of US commercial buildings (Eissa and El-adaway, 2024 ). Ghufran et al. ( 2022 ) explored enablers for the CE transition in the construction sector, focusing on causal interdependencies, while Kliem et al. ( 2021 ) specifically concentrated on policy. Mostert et al. ( 2022 ) meanwhile estimated and forecast construction and demolition materials flows in the German construction sector. Causal loop diagrams have been constructed to depict the dynamic interdependencies between cost components when manufacturing recycled paver blocks (Gandhi et al., 2024 ), to examine causal links between CE enablers in the construction industry (Ghufran et al., 2022 ), and public policy instruments’ impacts on CE business model (Kliem et al., 2021 ). Stocks and flows diagrams meanwhile aided quantification of the temporal evolution of manufacturing cost of recycled paver blocks (Gandhi et al., 2024 ), flows of newly built floorspace, demolished floorspace, total carbon emissions associated with concrete and emissions per unit floorspace (Eissa and El-adaway, 2024 ). They have also been used to examine organisational incentive schemes, policy supports, and sustainable development (Ghufran et al., 2022 ), flows of demolished materials in the economy (Mostert et al., 2022 ), and in modelling sand and gravel quarry and disposal volumes (Kliem et al., 2021 ). Thus, stocks and flows diagram prove versatile, not only capturing material flows, but also evolving policy variables (Ghufran et al., 2022 ). All these studies considered the temporal evolution of dynamic systems, and subsequent scenarios analysis led to recommendations based on reported metrics (see Table 5 ) and identifying the key leverage points. For instance, Gandhi et al. ( 2024 ) identified contractor profit, overhead expenses, and labour costs as the main barriers to using RPBs in India. Eissa and El-Adaway ( 2024 ) suggested that a comprehensive CE policy in the US could deliver a 52% decarbonisation potential by 2050, while policy support and incentive schemes have been recognised as key factors for CE transition in Ghufran et al. ( 2022 ). Delay modelling, another key feature of system dynamics, as adopted by Kliem et al. ( 2021 ) and Mostert et al. ( 2022 ). The CE transition in the construction sector not only requires adequate recycling, but also a constant supply of secondary materials. While Ghufran et al. ( 2022 ) considered global participants, all other studies were conducted at the micro or meso-scale. For further information see Table S8-S9 . Table 5 Quantified metrics, consideration of 'R' principles in system dynamics applications within the construction sector Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? Reference Recycled paver block circularity Manufacturing cost of recycled paver block Recycle Gandhi et al. ( 2024 ) Decarbonisation potential of US commercial buildings Annual commercial floorspace, embodied emissions Reuse, reduce, Eissa and El-adaway ( 2024 ) Policy impacts on CE transition within the construction industry Policy supports, organizational incentive scheme, and sustainable development Not applicable Ghufran et al. ( 2022 ) Circularity potential of recycled building materials to replace virgin materials Demand for aggregates for concrete, demand for recycled aggregates for concrete, savings in sand and gravels, unused recycled aggregates Reduce, recycle Mostert et al. ( 2022 ) Barriers in adopting CE transition in the construction industry Recycling and recovering quota of CDW and excavation materials, primary gravel price, disposal price Reduce, recycle, recovery Kliem et al. ( 2021 ) 3.3 Electrical and Electronics Products Sector 3.3.1 System dynamics modelling in the electrical and electronics sectors Lähdesmäki et al. ( 2023 ) modelled the CE potential of lithium in a global context, while Guzzo et al. ( 2022b , 2022a , 2021 ) showed how CE policy interventions facilitated nationwide electrical and electronics waste collection and treatment. Llerena-Riascos et al. ( 2021 ) explored the operative-strategic interdependency in improving the representation and performance of waste electrical and electronics equipment (WEEE) collection and processing steps. Salim et al. ( 2021 ) delved into socio-technical transition pathways for end-of-life management of rooftop photovoltaic solar panels. In both works Chaudhary and Vrat ( 2020 , 2019 ) and Sinha et al. ( 2016 ) used systems dynamics to investigate circularity regarding mobile phones. Chaudhary and Vrat ( 2020 , 2019 ) highlighted the drivers for proper circular flow of gold in mobile phones, while Sinha et al. ( 2016 ) drew attention to the potential of closing material flow loops in global mobile phone product systems, addressing sustainability challenges of material recovery. Causal loop diagrams have been key in offering an explicit representation of dynamic interdependencies (Alamerew and Brissaud, 2020 ; Chaudhary and Vrat, 2020 , 2019 ; Salim et al., 2021 ), while others presented causality passively (Guzzo et al., 2022a , 2021 ; Sinha et al., 2016 ). Facilitation of CE was found to be not only dependent on cost and revenues, but also on strategic regulatory decisions, and proper policy implementation (Alamerew and Brissaud, 2020 ; Chaudhary and Vrat, 2019 ). Whereas, circular value-chain mapping of gold, e-waste, and mobile phones with their sustainability benefits were evident in Chaudhary and Vrat ( 2020 , 2019 ) and Sinha et al. ( 2016 ), where sustainability indicators were causally linked. Additionally, inappropriate disposal of hazardous waste was addressed in Salim et al. ( 2021 ), urging proper recovery and recycling. Stocks and flows diagrams were prevalent throughout the literature, effectively representing material flows inside system boundaries. However, Guzzo et al. ( 2021 ) used them to represent the flow of technology adoption processes, and Salim et al. ( 2021 ) for recycling fund flow. Delay and lifetime distribution modelling through different distributions were shown in Guzzo et al. ( 2022a , 2022b , 2021 ). Notably, Guzzo et al. ( 2022a ) presented an inductive-deductive system dynamics model, where the empirical theory was constructed in the inductive stage, and then deductive theory was tested in another case-study. All the studies reviewed in this sub-section explored the common capabilities of system dynamics modelling - temporal dynamics, scenario analysis, metrics quantification, and thereby recommendations (see Table 6 ). The work of Sinha et al. ( 2016 ) was a global-scale study. In scenario analysis, multiple previous scenarios can be superimposed (Guzzo et al., 2021 ; Lähdesmäki et al., 2023 ). Specifically, Lähdesmäki et al. ( 2023 ) recommended to implement a combined CE policy, informed by such multiple scenarios. Guzzo et al. ( 2021 ) suggested the systemic change for CE implementation. While, Salim et al. ( 2021 ) found that there were high uncertainties in waste collection, recovery performance, and landfill disposal. System dynamics can also handle uncertainties of the parameters, and can provide decisions accounting, as shown by Salim et al. ( 2021 ). Chaudhary and Vrat ( 2020 ) underscored the value of consumer awareness and stakeholder incentives for better CE implementation, while their earlier work (Chaudhary and Vrat, 2019 ) emphasised the policy interventions for addressing challenges associated with the informal handling of e-waste without environmental and health protection. Meanwhile Sinha et al. ( 2016 ) recommended closed-loop recycling through improved collection systems, longer mobile phone retention time, improved recycling in developing countries, and shorter phone hibernation times. This work proposed reducing informal recycling of e-waste, as they claim that it leads to lower resource recovery along with higher pollution. Further details are in Table S10 - S11 . Table 6 Case-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in system dynamics applications within the electrical and electronics products sectors. Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? Use of other tools References Waste lithium Lithium demand by application, lithium content in waste stream, and lithium accumulation Reduce, refuse, repurpose, remanufacture, recycle, and reuse n.a. Lähdesmäki et al. ( 2023 ) Waste from Electrical and Electronics equipment Official EEE collection, inadequate disposal of EEE, ratio of material treated in last years Recycle, reuse, recovery n.a. Guzzo et al. ( 2022a ) Waste from Electrical and Electronics equipment Material extraction, official EEE collection, inadequate disposal of EEE, ratio of material treated, and ratio of material lost Reuse, recycle n.a. Guzzo et al. ( 2022b ) Waste from Electrical and electronic equipment Availability of raw material, material extraction, EEE commissioning, total EEE in use, disposal of EEE as WEEE, WEEE recycled Reuse, remanufacture, repair, recycle, recovery Bass diffusion model to model the diffusion of technology in the system. Guzzo et al. ( 2021 ) Waste from Electrical and Electronics equipment Avoided environmental burden (AEB), repurchase price, WEEE generated, profits Repair, recycle, remanufacture, recovery Mixed-integer non-linear programming approach was used to fine tune the system dynamics parameters. Llerena-Riascos et al. ( 2021 ) EOL management of photovoltaic solar cells Collection fraction, collection rate, total recovered materials, landfill amount, payback period and number of dwellings with PV Reuse, repair, recycling, recovery Quantitative-qualitative triangulation method was used to capture the mental models of the stakeholders. Salim et al. ( 2021 ) Gold recovery from cell phone recycling Depletion/savings of gold reserve, gold from E waste, gold from discarded cell phones, economic benefits, environmental benefits, social benefits (job creations), collection efficiency Reuse, refurbish, recycle, recovery n.a. Chaudhary and Vrat ( 2020 ) E-waste reverse supply chain Depletion of material reserve, material conversion rate, extracted material, economic, environmental, and social benefits, increase of collection coverage, and pre-processing capacity, demand of recycled materials, change in recycled products with change in refurbish and reuse rate, etc. Reuse, refurbish, recycle, recovery n.a. Chaudhary and Vrat ( 2019 ) Electric vehicle batteries Gross benefit of remanufacturing, remanufacturing margin, and price for remanufactured and new electric vehicle batteries Reuse, remanufacture, repurpose, recycle, recovery n.a. Alamerew and Brissaud ( 2020 ) Mobile phone recycling Gold use by phone manufacturers and gold recovery at the end of life of phone, loop leakage, closed loop efficiency of global mobile phone product systems Reuse, refurbish, recycle, recovery Opt Quest optimiser to optimise high and low sensitive parameters. Sinha et al. ( 2016 ) n.a.: not applicable 3.3.2 ABMS in the electrical and electronics sectors ABMS has been employed to assess the influence of techno-economic and social factors on the circularity of hard-disk drives (HDDs) (Walzberg et al., 2022a ). The importance of considering social factors in empirical CE analysis has been underscored, alongside techno-economic considerations (Walzberg et al., 2021 ). ABMS was also adopted to model waste household appliance recovery (Luo et al., 2019 ). Mashhadi et al. ( 2019 ) stressed that product-service-systems (PSS), such as mobile phone leasing, can overcome CE implementation barriers. ABMS was instrumental in quantifying demand behaviour for circular business models (leasing or functional sales) (Lieder et al., 2017a ). Another study by the same authors employed multi-method simulation approach combining ABMS and discrete event simulation (DES) to quantify design efforts for circular options (reuse, remanufacture, and recycle) and supply chain settings (buy-back, pay-per-use, and leasing) (Lieder et al., 2017b ). The details of each study and the types of agents can be found in Table S23-S25 . The ability of ABMS to capture the agents’ distinct characteristics, attributes, behaviours, and decision-making rules, i.e. heterogeneity, has been useful in tracking agents’ specific CE pathways (Walzberg et al., 2021 ). By integrating the theory of planned behaviour (TPB) and its associated factors, Walzberg et al. ( 2022a ) determined how the agents’ decisions at the individual level led to changes at the systemic level, specifically in their adoption of circular economy (CE) pathways. The heterogeneity of residents, governments, and recycling agents was considered by Luo et al. ( 2019 ), who included factors such as waste appliance generation, industrial standards development, tax incentives, distances from residents, etc.. The ‘memorising’ capability of agents was demonstrated by Mashhadi et al. ( 2019 ) and Lieder et al. ( 2017a ) where previous positive experiences influenced the subsequent decisions regarding adopting CE options (e.g., buying/leasing/pay-per-use, etc.). Meanwhile, heterogeneity demonstrated a single component’s four distinct stages (Lieder et al., 2017b ): manufactured, assembled in product, disassemble after use, and material recovered. With more knowledge of the positive impacts of CE, agents can be self-encouraged, i.e. show adaptation. While agents always try to choose a position where their benefits are maximised, the rewards are dependent on the path taken (Luo et al., 2019 ). Adaptation has been demonstrated due to socio-economic and social status (Walzberg et al., 2022a , 2021 ), environment-friendly awareness, and peer-pressure (Mashhadi et al., 2019 ), socio-demographics, social networks, and product utility (Lieder et al., 2017a ), and proper marketing and pricing strategy (Lieder et al., 2017b ). Complex interactions between agents, and their stochastic natures, were consistently emphasised in each work. ABMS has been employed to represent the flow of materials (Lieder et al., 2017b ; Luo et al., 2019 ; Walzberg et al., 2021 ), and also spatial considerations (Luo et al., 2019 ; Walzberg et al., 2021 ). Meanwhile, agent heterogeneity was obtained from survey data by Mashhadi et al. ( 2019 ) and used within an ABMS framework. Scenarios analysis led to recommendations, as evidenced by metric quantification, for achieving increased circularity (see Table 7 ). For instance, the reuse of hard disk drives was found to be more environmentally friendly (Walzberg et al., 2021 ). Additionally, CE practice was found to be facilitated by proper education on recycling and recovery options, effective regulatory frameworks, technological innovations, improving product eco-design, and establishment of waste appliance recovery networks (Luo et al., 2019 ). Scenarios analysis also revealed agents’ previous positive experience on buying/leasing affects further buying/leasing decisions (Mashhadi et al., 2019 ). The ‘pay-per-use’ approach supported by advertisements provided environmental friendliness and service-orientation. This was further aligned with a ‘buy-back’ scenario for more returns after a certain period in Lieder et al. ( 2017a ). Table 7 Case-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications within the electrical and electronics products sectors. Case-study domain Metrics and/or parameters quantified Which ‘R’s were considered? Use of other tools References Photo-voltaic cell circularity Recycling costs, material recycling rate, material recovery rate, initial recycling costs, recyclers’ net income, reuse rate, landfill costs, societal costs Recycle, reuse, recovery Theory of planned behaviour (TPB) was used to model decision-making for PV cell purchase/reuse. Walzberg et al. ( 2021 ) End of management of magnets of hard-disk drives Recovered mass of rare earth material (REE), material value, and avoided greenhouse gas (GHG) emission by recycling, hard disk drives (HDD) reuse, and magnet reuse, enhancement of trust towards data wiping. Reuse, recycle, recovery Theory of planned behaviour (TPB) was used to model customers’ intention to reuse/recycle the hard-disk drives. Data uncertainties and qualities were modelled through data pedigree matrix. Walzberg et al. ( 2022a ) Waste electrical appliance recovery industry Waste appliance generated per capita, extent of environmental pollution perceived by residents, service level of each recycler perceived by the residents, residents’ recycling tendency with respect to each recycler, recovery rate, policy costs, profits of the agents. Recycle, recovery TPB was used to model residents’ recycling behaviour. Luo et al. ( 2019 ) Cell phone leasing Total buy/lease decision count, tendency to lease next cell phone if the lease term is increased, percentage of leases over time for different new product prices. Reuse, recycle, recovery Discrete choice analysis to predict future market demand considering social influences of new product adoption. Mashhadi et al. ( 2019 ) Washing machine Market share, customer satisfaction, price, environment friendliness, service. Refurbish, reuse, recycle, recovery n.a. Lieder et al. ( 2017a ) Washing machine Number of customers served, aggregated lifecycle cost, aggregated lifecycle impact, and material savings. Reuse, remanufacture, recycle Discrete event simulation was used to model the closed loop-supply chain movements. Lieder et al. ( 2017b ) n.a.: not applicable 3.4 Circularity of Single Materials 3.4.1 System dynamics modelling for circularity of single materials In a 'learning-by-doing'-based system dynamics model, Ghosh et al. ( 2023 ) explored end-of-life pathways for PET bottles, calculating circularity indicators considering technological, economic, and policy constraints. Meanwhile Saidani et al. ( 2021 ) employed a multi-method simulation approach to identify, classify and assess key parameters and action levers to close the material cycle loop of platinum in catalytic converters. System dynamics also modelled key endogenous policy impacts on the development of a circular model for zinc manufacturing growth (Ojha and Vrat, 2020 ), considering exploration, demand, collection, recycling, repair and reuse. Finally, Pfaff et al. ( 2018 ) applied system dynamics to examine sectoral primary and secondary copper flows and stocks in Germany. Causal loop diagrams were presented by Ojha and Vrat ( 2020 ) and Saidani et al. ( 2021 ) to illustrate the dynamic connections between collection, recovery, circularity rate, and influence of linear and circular economy factors affecting manufacturing output growth. Ghosh et al. ( 2023 ) didn’t show a stock-and-flow diagram, but conveyed it implicitly to perform the lifecycle and techno-economic analyses, which considered the flow of plastics. Stocks and flows diagrams have been presented to illustrate the flow of platinum (Saidani et al., 2021 ), zinc (Ojha and Vrat, 2020 ), and copper (Pfaff et al., 2018 ). Temporal evolution of material flows was intrinsic in each study. However, spatial aspects were considered less frequently (Ghosh et al., 2023 ; Pfaff et al., 2018 ). With the exception of Saidani et al. ( 2021 ), all studies conducted scenarios analysis, leading to recommendations through metrics quantification (see Table 8 ). For instance, optimal PET circularity was when ‘recyclate glycolysis’ was adopted with improved collection access, due to increased replacement of virgin materials with recycled resin (Ghosh et al., 2023 ). Meanwhile, increased geological exploration, improved collection facilities, cost-effective remanufacturing technologies, and better incentive schemes were recommended for improving zinc circularity (Ojha and Vrat, 2020 ). Finally, Pfaff et al. ( 2018 ) concluded that imported primary copper had detrimental environmental impacts. However, this work was contingent on extensive data requirements. See Table S12-S13 for more details. Table 8 Case-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications concerning circularity of single materials. Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? Use of other tools Reference Plastics (PET bottles) Circularity (closed loop circularity, open loop circularity, upcycling circularity, incineration circularity, average inflow outflow circularity, landfill diversion circularity) environmental impact (GWP in kg CO 2 eq.) and life cycle costs Recycle, recover n.a. Ghosh et al. ( 2023 ) Platinum in vehicular catalytic converters Platinum circularity, potential collection, collection of catalytic converters, recovery of platinum Recycle and recovery Fuzzy cognitive mapping to identify action levers, drivers, and parameters responsible for loop-closure. Functional analysis system technique (FAST) and matrix impact cross-reference multiplication applied to a classification (MICMAC) to list the potential action levers and to select and cluster the key variables, respectively. Saidani et al. ( 2021 ) Zinc recycling Behavioural trends of four variables: green reserve, extraction material, manufacturing output and total refeed for different developed scenarios Repair, reuse, remanufacture, recycle, recovery n.a. Ojha and Vrat ( 2020 ) Copper production and recycling sector End of life (EOL) collection rate and recycling rate, total copper input to domestic production of finished goods, total collected domestic EOL copper scrap, primary copper input to domestic production of finished goods. Reuse, recycle, recovery ASTRA based macroeconomic model was combined with system dynamics to evaluate the economic aspects of the sector. Pfaff et al. ( 2018 ) n.a.: not applicable 3.5 Manufacturing Sector 3.5.1 System dynamics modelling applications in the manufacturing sector A multi-method simulation approach was proposed for a circular manufacturing system (CMS) of washing machines (Roci et al., 2022 ), integrating ABMS, discrete event simulation (DES), and system dynamics modelling. Franco ( 2019 ) demonstrated the impacts of product design (i.e., slowing resource flows through design for longevity, ease of maintenance and repair; and by design for disassembly and recycling), business models (i.e., slowing resource flow by product-service-system and maintenance, and reuse), and post-use strategies on facilitating CE. In a theoretical study on data-driven circular economy Charnley et al. ( 2019 ) developed the concept of certainty of product quality (CPQ) and applied it to electric motor circularity through remanufacturing, by integrating DES and system dynamics modelling. Neither Charnley et al. ( 2019 ) nor Roci et al. ( 2022 ) developed causal loop diagrams, but they have been presented to highlight the causality between different indices (e.g., disassembly index, recyclability index, functional risk, etc.). Stock-and-flow diagrams accounted material flows (Charnley et al., 2019 ; Franco, 2019 ), as well as dynamic evolutions of finance, and emission flows (Roci et al., 2022 ). With Austria as their system boundary, scenarios analysis and recommendations driven by metrics quantification revealed the benefits of circular economy for washing machines (Roci et al., 2022 ) (see Table 9 ). The work also found that manufacturing cost dominated lifecycle costs, followed by installation, refurbishment, and deinstallation costs. However, environmental impact was dominated by the use phase, possibly due to the long lifespan of white goods. Franco ( 2019 ) observed that when perceived functional risks of recycled or reused products were low, it positively impacted circularity. Additionally, increased CPQ was helpful in promoting the CE as shown in Charnley et al. ( 2019 ). See Table S14-15 for more information. Table 9 Case-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications within the manufacturing sector. Domain of case-study Metrics quantified through system dynamics model Which ‘R’s were considered? Use of other tools Reference White goods manufacturing Lifecycle costs, environmental performance (kg CO 2 eq.), revenue streams and profit over time. Reuse, refurbish, recycle, recovery ABMS was adopted to describe the heterogeneity of the agents (i.e., manufacturers, customers, etc.). DES was utilised to describe the processes flow (e.g., manufacturing processes and logistic activities). Roci et al. ( 2022 ) Hypothetical manufacturing firm Products collected for recycling, recycled products inventory, recycling waste rate, recycling rate, recycled products sales rate, purchase rate, products in use, landfill products, products collected for recycling, etc. Repair, reuse, refurbish, recycle Bass diffusion model was adopted to model the shape of the distribution of the short and long-life product. Franco ( 2019 ) Remanufacturing process of electric motors (rotor and shaft) Time spent for remanufacturing based on the certainty of product quality (CPQ) value, effect of CPQ on reusable products. Reuse, repair, refurbish, recovery DES for enumerating the sequence of operations in a remanufacturing process. Charnley et al. ( 2019 ) 3.6 Industrial Symbiosis Industrial symbiotic networks (ISNs) represent clusters of firms engaged in exchanging residual materials, energy, and information, with the overarching goals of fostering economic prosperity, social advancements, and mitigating environmental impacts. They have been considered as ‘complex adaptive systems’ for compelling reasons: actors emerge into coherent forms over time, their adaptive nature reflecting their ability to change over time, self-organisation because of their ability to find new partners and collaborations based on mutual utility, path-dependency and non-linearity (Fraccascia and Yazan, 2018 ). 3.6.1 System dynamics modelling in industrial symbiosis While ABMS is considered as more adept at capturing symbiotic patterns with heterogeneous agents in ISNs, some authors tried to model it through system dynamics models. A dynamic empirical evaluation model for capturing sustainable development of eco-industrial parks was introduced by Zhao et al. ( 2023 ), using an emergy index. Meanwhile, Morales et al. ( 2022 ) modelled a meso-scale CE implementation for bio-based industrial symbiosis (BBIS) of the sugar-beet value chain, emphasising the concept of viable value chain (VVC) during disruptive events (e.g., COVID-19, climate change). In a different context, Dong et al. ( 2017 ) proposed a system dynamics model to understand the long-term materials flow inside a coal power plant and a cement production plant for integrated circularity. The inclusion of a causal loop diagram in Zhao et al. ( 2023 ) facilitated capture of the dynamics of different industrial sectors. Causality of economy and ecology by sharing of the (waste) materials was presented in Dong et al. ( 2017 ). The concept of emergy consists of money, material, and energy flows, and either or both were captured through stocks and flows diagrams in all studies. Table 10 Case-study domains, quantified metrics, consideration of 'R' principles in system dynamics applications for the manufacturing sector. Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? References Eco-industrial park Economic development (EDR 1 , EYR 2 ), environmental compatibility (EWR 3 , ELR 4 ), social acceptability (ED 5 , and CP 6 ) Not mentioned. Zhao et al. ( 2023 ) Sugar-beet value chain Sugar production, CO 2 emission, stillage, and bioethanol production Recycle Morales et al. ( 2022 ) Cement and coal production plant Natural resource saving (e.g., gypsum, clay, limestone, coal consumption, water consumption), reduction of pollutants (SO 2 , smoke dust emission), carbon emission from calcination, costs savings, sales revenue, increase of costs of water, supply demand difference (SDD) of fly ash, de-sulphurised gypsum and slag generation, electricity power yield, cement yield Reduce, recycle, repurpose, recover Dong et al. ( 2017 ) EDR is the ratio of total emergy use and industrial added value of the park in one year. EYR is the ratio of the total emergy output to the emergy purchased from society (e.g., fuels, goods and services). EWR is the ratio of the sum of emergy, with three wastes (viz., waste gas, wastewater, and solid waste) to the total emergy, which was used to measure the pressure of wastes on the ecosystem. ELR is the ratio of purchased and non-renewable local emergy to the free/renewable resources emergy. ED is the ratio of emergy created by production processes to the area of the eco-industrial park. CP is the ratio of available and per capita emergy usage. All of these studies considered system boundaries at the meso-scale and included temporal dynamics. Through scenarios analysis, and metrics quantification (see Table 9 ) these studies provided insights for decision-making. Zhao et al. ( 2023 ) adopted a science and technology-driven scenario to deliver optimal sustainable development. Systematic consideration of the value chain in industrial symbiosis to prevent supply chain collapse during shocks was underscored in Morales et al. ( 2022 ), with their forecasts unveiling complexities overlooked by linear economic models. Meanwhile, efficient supply chain eco-design emerged as critical for circularity of two mutually dependent sectors (Dong et al., 2017 ). See Table S16 – S17 for details. 3.6.2 ABMS in industrial symbiosis ABMS modelled actors’ behavioural patterns (viz., waste suppliers, waste processor), and their intricate relationships towards ISN robustness (in terms of network survival for a particular time period, and cash flows per tonne of waste) (Lange et al., 2021a ). While the viability of ISN survivability was examined for two business models (Lange et al., 2021b ), namely circular waste management and waste as by-product. The dynamics of the construction supply chain, focusing on recycled concrete aggregate in an ISN was presented by Yu et al. ( 2021 ). Geographically oriented symbiotic relationships were examined in Raimbault et al. ( 2020 ), and material flows in a complex stakeholders' involvement network in Fernandez-Mena et al. ( 2020a , b ). Several aspects of ISN survival through information sharing (Fraccascia and Yazan, 2018 ), cost-sharing negotiations between companies (Yazan and Fraccascia, 2020 ), redundancy strategy (Fraccascia et al., 2020 ), and the impact of online platforms (Fraccascia, 2020 ) have been shown. Agent heterogeneity ( Table S27 ) has been captured in terms of changing supply chain quantity, physical degradation of waste and chemical composition (Lange et al., 2021a , 2021b ). Agents were also heterogeneous in terms of their quantity, location, storage capacity, material flow rate (destination agent), and various attributes for ‘vehicle’ agent (Yu et al., 2021 ), and through their demand-offer functions (Raimbault et al., 2020 ). Finally, agents were heterogeneous due to industry type plus waste processor (Fernandez-Mena et al., 2020a , b ) or input-output characteristics (Fraccascia, 2020 ; Fraccascia et al., 2020 ; Fraccascia and Yazan, 2018 ; Yazan and Fraccascia, 2020 ). Emergent patterns due to agents’ adaptation capability ranged from leaving an ISN network (Raimbault et al., 2020 ), choosing new partners (Lange et al., 2021b ), and navigating uncertain encounters with the environment (Fraccascia et al., 2020 ). Materials flow (Fernandez-Mena et al., 2020a , b ), and cash flow (Lange et al., 2021b ) between agents have also been quantified. In Raimbault et al. ( 2020 ), and Yu et al. ( 2021 ) material flows were modelled stochastically. The spatial dispersions of the agents (Fernandez-Mena et al., 2020a , 2020b ; Yu et al., 2021 ) were also contemplated through ABMS, proving beneficial over system dynamics. The memorising capability of agents in ABMS was showcased through ‘fitness to process the waste’, ‘leaving threshold’ in Lange et al. ( 2021a ). A similar ‘fitness function’, based on economic benefits, encouraged agents to stay in ISNs (Yazan and Fraccascia, 2020 ). How the agents’ activities were triggered by realising the material transfer from the vehicle partner was presented in Yu et al. ( 2021 ). Note that, unlike with system dynamics, all the agents in the ABMS are not required to function during the entire simulation period. For instance, allocation of materials to agents only when they have the capacity to accommodate it (Fernandez-Mena et al., 2020a ). Scenarios analysis, leading to recommendations through metrics quantification (see Table 11 ) was conducted across most studies, apart from (Lange et al., 2021a ; Raimbault et al., 2020 ). Notably, changes in scenarios in ABMS allowed observations at both micro and system levels, presenting its unique advantage over system dynamics. Additionally, during scenario analysis in ABMS, new emergent patterns could develop, which is not the case in system dynamics modelling. For instance, economic behaviour preceded behavioural patterns, such as ISN partners leaving networks due to insufficient waste supply (Lange et al., 2021b ). Yu et al. ( 2021 ) suggested that increasing upcycling efficiency and subsiding stakeholders plays a pivotal role in RCA circularity. Distances between agents were key for ISN survival (Fernandez-Mena et al., 2020a ; Raimbault et al., 2020 ). A circularity scenario has been reported to mitigate GHG emissions, but at a substantial trade-off between food production and livestock number (Fernandez-Mena et al., 2020b ). Sensitive information sharing through gradual trust-building was emphasised in Fraccascia and Yazan ( 2018 ). Minimising the differences between input-output quantities of waste facilitated the ISN survivability (Yazan and Fraccascia, 2020 ). Transaction cost played a pivotal role in economic and environmental benefit provided by ISN (Fraccascia et al., 2020 ). While Fraccascia ( 2020 ) found that when over 60% of ISN members engaged with online platform sharing, partners who did not participate in the ISN were disadvantaged, and adversely impacted the environmental performance. For more information, see Table S26 – S28 . Table 11 Case-study domains, quantified metrics, consideration of 'R' principles and use of other tools in agent-based modelling applications for industrial symbiosis. Case-study domain Metrics and/or parameters quantified Which ‘R’s were considered? Use of other tools References ISN partners: (a) large scale agricultural area, (b) small-scale urban agricultural area focused on sustainable food production and recreation, (c) a business park Cash flow per each industrial symbiotic network actor, and failure or success of network (robustness). Recycle, recovery Theory of planned behaviour (TPB) was used to model agents’ negotiation and self-evaluation process. Lange et al. ( 2021a ) Bio-based material suitable for anaerobic digestion for processing local waste and energy production from biogas Circular business model survival rate percentage, value captured or lost per actor for each of the scenario. Reuse, recovery TPB was used for bilateral negotiations between waste processor and suppliers. Lange et al. ( 2021b ) Recycled concrete aggregate ISN Delivered recycled concrete aggregates, reduced CO 2 emissions, and space of cooperation/industrial symbiosis probability between firms involved in the network for different scenarios. Reuse, recycle GIS was adopted to articulate the complex spatial relationships of industrial actors. Yu et al. ( 2021 ) Hypothetical symbiotic network Total waste flow and relative cost. n.a. Multi-objective optimization was adopted to minimize the cost and waste products given that agents were distributed in a different geographical location. Raimbault et al. ( 2020 ) Agro-food network Nitrogen flow, CO 2 emissions, number of local flows, CO 2 eq. emitted per gigagram of protein and tera-calorie of metabolizable energy in food production, crop production, meat and milk production, animal feeding district balance, biogas, and electricity production. Reuse, recycle and recovery GIS was adopted to highlight the distances between the agents. Fernandez-Mena et al. ( 2020a ) Agro-food network Different local material flows within the network (e.g., local fertilization flow, animal requirements flow, and energy flows), the average distance in exchanges of manure and grass, and the CO 2 emission from material transport. Reuse, recycle and recovery Multi-criteria assessment to compare the performances of different scenarios. Fernandez-Mena et al. ( 2020b ) Hypothetical industrial symbiosis case studies comprising marble waste and concrete production, and alcohol slops used for fertilizer production Economic performance indicator = ( \(\:\frac{economic\:benefits\:created\:by\:IS\:}{production\:costs\:of\:firms}\) ), and environmental performance measure = ( \(\:\frac{total\:waste\:diverted\:from\:landfill\:}{\begin{array}{c}primary\:inputs\:saved+total\:waste\:\\\:produced\\\:+required\:primary\:inputs\end{array}}\) Reuse, recycle, recovery Physical and monetary flows were modelled through enterprise input-output analysis (EIOA). Fraccascia and Yazan ( 2018 ) A hypothetical marble-concrete industrial symbiosis case-study Economic benefits, probability of implementation of industrial symbiosis. Reuse, recycle and recovery n.a. Yazan and Fraccascia ( 2020 ) A hypothetical marble-concrete industrial symbiosis case-study Economic performance indicator ( \(\:\frac{economic\:benefits\:created\:by\:IS\:}{production\:costs\:of\:firms}\) ), and environmental performance measure ( \(\:\frac{total\:waste\:diverted\:from\:landfill\:}{\begin{array}{c}primary\:inputs\:saved+total\:waste\:\\\:produced\\\:+required\:primary\:inputs\end{array}}\) ), Reuse, recycle and recovery n.a. Fraccascia et al. ( 2020 ) Hypothetical industrial symbiosis case studies comprising marble waste and concrete production, and alcohol slops used for fertilizer production Percentage of waste exchange by each company involved in the IS, platform usage rate, and amount of saved residuals. Reuse, recycle and recovery n.a. Fraccascia ( 2020 ) n.a.: not applicable 3.7 Miscellaneous Sectors 3.7.1 System dynamics modelling applications in miscellaneous sectors Territorial competitiveness index (TCI) has emerged as a significant metric for gauging sustainable growth. Sezer et al. ( 2024 ) projected the development trajectory of Izmir in Turkey through TCI. Meanwhile, recognising the role of extended producer responsibility towards promoting CE, Kuo et al. ( 2021 ) developed a system dynamics model to determine the optimal subsidy (i.e., minimisation of cost and maximisation of recycling rate) between stakeholders pertaining to aseptic paper packaging. A parallel approach has been adopted to quantify and evaluate the implementation and effectiveness of regional CE (Gao et al., 2020 ). Tracking material and energy flows between industries considering the impact of environmental fragility-economic poverty vicious cycles (FPVC) were shown in Cheng et al. ( 2019 ). Additionally, Asif et al. ( 2016 ) developed an integrated ABMS and systems dynamics approach to understand the dynamic relationships between business approaches, supply chains and product-design, along their influences on economic and environmental performance. Causal loop diagrams outlined the causal interplay between sustainability indicators and TCI in Sezer et al. ( 2024 ). A similar diagram was utilised in Kuo et al. ( 2021 ) to represent the dynamics of waste generation, incorporating interventions related to production, costs, and recycling. When the multiple metrics for a particular value domain in CE is considered, there are always some cross-domain interconnectedness as presented in Asif et al. ( 2016 ) and Gao et al. ( 2020 ), where resource consumption subsystem, environmental impact subsystem, etc. were causally linked. With the exception of Sezer et al. ( 2024 ), all studies in this sub-section developed stocks and flows diagrams to represent material flows. Sezer et al. ( 2024 ) used it for showing the accumulation of GDP growth, alongside material flows (i.e., waste generation). Temporal consideration was integral in all studies, leading to scenarios analysis and recommendations through metrics quantification (see Table 12 ). For instance, Sezer et al. ( 2024 ) projected a peak of TCI in 2020, followed by a moderate decline to 2022, then a subsequent rise to 2027, due to development of sustainable policies, efficient resource utilisation, GDP increase, etc. While, Kuo et al. ( 2021 ) identified collection cost and recycler capacity were the most sensitive parameters in the system, highlighting the decreased collection costs increased EPR fund generation, and decreased recycler capacity led to increased landfilled waste. Gao et al. ( 2020 ) identified that, along with growth of CE, GDP also increased, contingent to slight decrease in birth rate and development of tertiary industries. Cheng et al. ( 2019 ) showed that CE improved the ecological and economic benefits in terms of improved livelihoods and reduced pollution of the considered system. For more information, see Table S18-S19 . Table 12 Case-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications for miscellaneous sectors. Case-study domain Metrics quantified through system dynamics model Which ‘R’s were considered? Use of other tools References Sustainable development of a city in Turkey Annual GDP growth rate, social well-being, sustainable land use, CO 2 emission, renewable energy production, technology innovation index, waste generation, and territorial competitiveness index. Reuse, reduce, recycle, recover. n.a. Sezer et al. ( 2024 ) Aseptic paper packaging waste Collection rate, recycling rate, extended producer responsibility fund, collection flow, recycling flow and waste in landfill Recycle, repurpose n.a. Kuo et al. ( 2021 ) Regional CE of Guangdong province in China Mass of total and direct material input and output components, Resource consumption and waste emission (biological substance consumption, fossil fuel consumption, solid waste consumption), intensity efficiency index (total material input of 10,000 RMB of GDP, and total material output of 10,000 RMB of GDP), building material consumption, and industrial exhaust emission NA n.a. Gao et al. ( 2020 ) A meso-scale implementation of a FPVC area in China Benefits of livestock faeces recycling (e.g., biogas production rate, recycling amount of faeces, conversion amount of organic fertilizer, pollution free fruits and vegetables output), effects of water savings (e.g., water saving amount, annual recycling of wastewater, terrace area, total number of water cellar), effects of waste recycling (e.g., utilization amount of potato residue, annual straw utilization and burning, mulching fil remained, feeding beef cattle), effects of energy savings (e.g., fossil energy decreases, CO 2 emissions reduction). Reduce, reuse, recycle, repurpose, recovery n.a. Cheng et al. ( 2019 ) Circular product systems Environmental performance, cost-based economic performance, and profit-based economic performance. Remanufacturing, reuse, recycle ABMS was coupled with system dynamics to capture the market information (population, income of the population, etc) and offer attributes (price to offer, convenience of the offer, etc.) Asif et al. ( 2016 ) n.a.: not applicable 3.7.2 ABMS applications for miscellaneous sectors ABMS has been adopted to examine circularity of wind power generation, considering end-of-life options for turbine blades (Walzberg et al., 2022b ). Whereas, circularity via fashion renting, i.e., a product-service system has been examined using ABMS to represent customers’ behaviour and interactions (Fani et al., 2022 ). Both studies showed the heterogeneity of agents in terms of behaviour. This led to different paths for material circularity. With theory of planned behaviour (TPB) used to represent the behaviours of the agents’, ABMS was crucial in representing how micro level changes of the TPB parameters impacted the whole dynamics of the system. In each study, all of the agents were interconnected, and connected with the environment. Changes in any of them led to the emergence of new behavioural patterns to adapt them in the system. Spatial and temporal considerations were inherent, along with the stochastic natures of the agents. The quantified metrics are shown in Table 13 . Furthermore, Walzberg et al. ( 2022b ) found that regulatory pressure and attitudes positively impacted recycling, and agents were more prone to recycle when the recycling facilities were located close-by. For more information, see Table S29-S31 . Table 13 Domains of case-studies, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications for miscellaneous sector. Domain of case-study Metrics and/or parameters quantified Which ‘R’s were considered? Use of other tools References Onshore wind turbine blades circularity Regional cumulative mean landfill rate according to different transportation costs, landfill behaviour under logistic barriers, adoption of thermoplastic blade design and the dissolution recycling pathway. Reduce, reuse, repurpose, recycle TPB was adopted to represent agents’ behaviour and its effects on the neighbouring agents. Walzberg et al. ( 2022b ) Fashion renting process Customers’ attitude towards fashion renting, performance of the service store, and experience of the customer. Reuse, refurbish, recovery DES was used to model the fashion renting process. Fani et al. ( 2022 ) 4 Critical Observations 4.1 Major Themes of System Dynamics and ABMS Approaches 4.1.1 Why is system dynamics modelling favoured in CE quantification? The preferences of system dynamics modelling in quantifying CE can be attributed to several key themes identified in the earlier discussions. These themes encompass causal loop diagrams, stock and flow diagrams, consideration of non-linearity or unpredictable variable evolution, temporal scale, spatial considerations, delay modelling, scenario generation and recommendations achieved through quantification of metrics. A circular economy system is naturally complex, with lots of uncertainty and variability due to its constantly changing patterns. Thus, organisations seek an approach that can model systems and analyse their behaviour before actual implementation. System dynamics has both qualitative and quantitative analysis capabilities (Sumari et al., 2013 ). The qualitative capability is exemplified through causal loop diagrams (e.g., reinforcing and balancing) to account for the causal relationships between system variables. Quantification involves the transformation of causal loop diagrams into stocks and flows diagrams. The usual workflow for developing a system dynamics model is shown in Figure S1 . It shows that initial models often cannot capture reality, and system dynamics modelling is therefore an iterative approach, where the model is continually updated based on inputs from the system thinker. This was clearly stated by Sterman ( 2000 ). The stock and flow diagrams in the literature are not only being used to represent the flow of materials, but also to model flow of money, energy, policy variables, etc. The diagrams represent the aggregation and disaggregation of stocks and flows in a continuous time domain through various differential equations, accounting for the causality of other system variables. Thus, compared to dynamic MFA by system dynamics models, static MFA and Bayesian MFA do not allow extrapolation and exploration of future scenarios, but they rather provide snapshots of systems at a given time, and do not consider non-deterministic causality and/or interdependency of other system variables. However, this aggregation in a continuous time domain leads to loss of individual properties, and a perfect mixing condition becomes prevalent in terms of dynamic MFA (Guerrero et al., 2016 ). The (commercially) established/ standardised dynamic MFA performing platforms, often associated with life-cycle assessment approaches (e.g., STAN, Umberto, and OpenLCA, SimaPro) do not consider the internal dynamics within the system. Additionally, stocks and flows within any circular economy system (or complete value-chain) may be a stop-start process. For example, products may stay in a process, e.g., use phase, for some period, causing delays. This delay modelling is a unique characteristic of system dynamics modelling and has been incorporated either stochastically or deterministically (Guzzo et al., 2021 ). This delay modelling can give rise to non-linearity, as can causal loops, where one parameter may have a non-linear relationship with other parameters, in turn affecting stocks and flows. Each of the subsections have shown examples of system dynamics being used to demonstrate changes over time. While both ABMS and system dynamics can perform spatial analysis, the former requires more, finer system details than the latter. Conversely, the aggregation property of the latter provides decisions at a system level and may not provide finer details at the local level. While finer details may be possible, this comes at the cost of increased computational complexity. 4.1.2 Why is ABMS favoured in CE quantification? The ABMS literature revealed major themes: heterogeneity, adaptation, agent-agent and agent-environment interactions (sometimes leading to emergent properties), spatial considerations, flow modelling, stochasticity of agents and scenarios analysis. In terms of individuality modelling, ABMS is superior to system dynamics as it can handle finer details (Borshchev and Filippov, 2004 ). The memorising capability of ABMS leads to adaptation potential through path dependency. In other words, if agents are rewarded for following a particular path, then their future behaviour is more likely to also follow that particular path. This rewarding information is sometimes shared with neighbouring agents. Thus, discrete-time disaggregated agents interact with each other and/or their environment at each time-step, and based on pre-defined state-chart rules, they either move to the next state or stay in the current state. The interaction space between agents is usually user-defined (e.g., circle (Fernandez-Mena et al., 2020b )) and can be adjusted as per the case-study (Guerrero et al., 2016 ). Furthermore, some interesting phenomena in the context of CE systems, such as, technology adoption, word-of-mouth, and residents’ intention lends authors to ABMS. Although these can be modelled with a system dynamics approach, it is at the cost of time and complexity (i.e., by incorporating different variables and thereafter setting their values and causal relationships). Another benefit of ABMS for simulating CE scenarios is its capability to incorporate the spatial scale. This has been attempted with a spatial system dynamics model (Neuwirth and Peck, 2013 ), (in different context) by integration of the GIS system in the model, but its use is still not widespread. As shown in Section 3.6.2 and in Raimbault et al. ( 2020 ), the clustering of industrial symbiotic partners is highly relevant to circular economy systems at the meso- (regional) or macro- (national or global) scales. The delay modelling in system dynamics and triggering of a particular event in ABMS are ostensibly analogous, but quite different in mechanism. While the former is part of the model’s internal process and is governed by mathematical equations, the latter signifies the activation of a particular agent, governed by rules. Furthermore, the former does not produce emergent properties, rather it influences the flows, while the latter may produce emergent properties, but cannot be predicted in advance. Thus, depending on the rules, which are generally stochastic in nature, an event is triggered in the ABMS, while at the system level properties are generated. Conversely, ABMS can deal with qualitative data which may be represented in terms of scale values. For instance, considering the technological yield increase probability and attention to social reasoning as discrete values between [0,1] (Farahbakhsh et al., 2023 ) and incorporating these in simulations and scenario analysis. Thus, when such parameters are not easily quantifiable, ABMS can be considered a suitable approach. Scenario analysis in system dynamics and ABMS yield are both feasible, but from different perspectives. While system dynamics considers aggregate properties at the system level, in ABMS emergent properties are observed, which are in turn dependent on the agents and their dynamic interactions with each other and their environment. System dynamics scenario analysis often yields results from a single simulation run for a given parameter set, while ABMS typically requires multiple iterations to account for stochasticity and derive robust calculations. To elaborate, a single simulation run in system dynamics provide a complete picture of the system’s behaviour for a given set of initial conditions and parameters. However, the scenario analysis through system dynamics requires multiple parameters’ adjustments, which in turns require to re-run the model with the adjusted parameters to get the complete understanding of the system. Thus, each scenario typically involves a deterministic simulation unless uncertainty is explicitly incorporated. However, in ABMS, due to stochasticity of the individual agents, and their associated rules, the system is itself stochastic. To understand the system’s behaviour, multiple iterations or simulation are required to account for the variability and derive statistical properties (e.g. confidence intervals). 4.1.3 Flow consideration through system dynamics and ABMS Flows are critical to revealing circularity, be they materials, energies, money, or policy. However, their modelling in ABMS in system dynamics models differ, as the former retains individuality, while it is lost in system dynamics, as shown in Figs. 1a-1b (considering the example of material flows). To explain further, consider the work of Walzberg et al. ( 2021 ), where agents were triggered in the material exchanges by economics or peer pressure. Additionally, at each time-step the agent decided whether the waste material was landfilled or recycled, based on the cost-constraint. Similarly, in Raimbault et al. ( 2020 ), at each time-step, each agent looked whether their waste outputs could be another agent’s input. They then considered spatial and economic perspectives (because increased inter-agent distances increased associated transportation costs). Only if these requirements were fulfilled, did material exchange take place. Conversely, material exchange in Fernandez-Mena et al. ( 2020b ) was completely stochastic in nature, because the preference coefficient was not given as an input by the modeller, but randomly selected by the simulator. Thus, ABMS modelling, considers material exchange both from the micro-level perspective, but also after meeting various criteria. While this can also be modelled by system dynamics models, it is at the cost of modelling complexity. 4.2 Joint Consideration of Metrics System Dynamics and Agent-Based Models CE has been considered a practical approach to progress towards a sustainable future (Geissdoerfer et al., 2017 ; Kirchherr et al., 2017 ), adopting the three pillars of sustainability, as first proposed by Brundtland – economic gains, cleaner environment, and societal prosperity (Brundtland, 1987 ). Figure 2 shows how various studies in the different sectors considered ‘value’-associated metrics from each of these three pillars. Half of the studies considered more than one metric, yet only around 10% considered value metrics covering all three of environmental, economic and social domains. Overall, most studies focused on environmental and then economic aspects. However, there were differences between sectors. Studies in the bio-based sector had a greater emphasis on social and environmental metrics, while studies in the electrical and electronics sectors had a greater emphasis on economic aspects. Similarly, the metrics studied by ABMS can be grouped into economic, environmental, social value, others, and MCP flows (Fig. 3 ). Focussing on environmental, economic, and social value metrics, economic metrics dominated, followed by environmental aspects. Meanwhile, only one study, in the electrical and electronic products sector, considered social aspects, in this case in conjunction with economic metrics. While about half of the system dynamics studies considered multiple metrics, far fewer ABMS studies considered more than a single metric. Aside from studies in the electrical and electronic products sector only one study, in the bio-based sector, considered more than one metric. The dominance of single value metric studies using ABMS can be understood by recognising that this approach is mostly focused on how the changes in the micro level agents’ behaviours impact the whole system. Possibly, researchers have avoided to jointly quantify the CE value metrics through ABMS due to increased computational complexity and traceability of models’ microstructures. However, describing circular economy requires a holistic approach, where overall economic, environmental and social benefits must be quantified from the systems thinking perspective for informed decision making. This enables targeted policy implementation through identification of key leverage points of the whole system. Furthermore, while analysis of individual systems has proved useful, these systems have still been somewhat isolated from a global perspective, and there have been limited endeavours to couple them and quantify metrics from a global perspective. This is possibly because there is, as yet, no global consensus on quantification of circular economy value metrics. Furthermore, value optimisation at key leverage points in the various systems considered here, is missing. 4.3 Utilisation of External Tools along with System Dynamics and ABMS System dynamics can be considered as both rigid and flexible in its approach. It is rigid in terms of maintaining its fundamental principles as outlined in Table S1 , yet flexible in terms of its integration capability with other analytical tools. The inputs to the model can be optimised or tweaked, but there can be no changes in the basic principles of a system dynamics model. Meanwhile, outputs from system dynamics models can be further analysed using other tools, for example for optimal CE scenario selection, plus cost and benefits of each process, or environmental impacts through LCA. In this context, DES has been used to quantify flows but requires more abstract knowledge of the system, which can be resource intensive. This is possibly the reason why some studies resort to describing their models through hypothetical case-studies. ABMS can also be coupled with external tools. Many studies considered the Theory of Planned Behaviour (TPB) for modelling agents’ thought process/behaviours, which influenced the state transition of the ABMS. It has also been adopted to incorporate regulatory behaviour and logistic constraints. Numerous studies have considered material flows in terms of enterprise input-output analysis (EIOA), with metrics quantified through employing stochastic- and/or indicator-based ABMS. DES has been employed, but as mentioned earlier, requires extensive abstract data, which is difficult to obtain. Additionally, data quality, uncertainty and selection of pertinent parameters have been considered in ABMS models. Thus, both SD and ABMS are flexible in modelling the complexities of system thinking, by integrating various tools and techniques to quantify different aspects of circular economy, which in turn leads to more informed decision-making. However, this integration requires judicious thinking and case-specific challenges, which solely depends upon the modeller’s expert judgment. 4.4 Where we are in CE tree – ‘R’-based value retention? In the introduction, the ‘R’-philosophies of CE frameworks were presented, with a more detailed elaboration available elsewhere (Jawahir and Bradley, 2016 ; Potting et al., 2017 ). Tables 2 – 12 showed how the various studies considered each ‘R’ for each sector. Here, we discuss the aggregated view, as shown in Fig. 4 . To maximise value retention of MCPs, focus should be on the upper-part of the ‘R’-based CE-tree (‘R0’), instead of the lower-part (‘R9’), where there are increased risks of value loss (Ellen MacArthur Foundation, 2013 ; Kirchherr et al., 2023 ). However, most of the studies considered here have focused on the lower-part rather than the upper-part. Refuse and rethink were each considered only once, despite being considered as key goals of a circular economy. However, indeed these terms were some of the more recent additions to the ‘R’ lexicon (Kirchherr et al., 2017 ), and so some of the earlier studies would have predated their introduction. Also, much of the literature considers materials which have already entered the anthroposphere before circular economy ideas were popularised. Thus, the situation may change over the coming years as industry adopts circular design principles, incorporating durability, repairability, and recyclability. Groups the studies based on the applications of system dynamics and ABMS, Fig. 5 shows that system dynamics is typically far more prevalent, apart from in the case of industrial symbiosis, where the behaviour of individual agents is of key importance, with greater consideration of reuse, recycle and recovery. The focus in the bio-based sector is on recycling, reuse and recovery. The absence of repair, refurbish and remanufacture is not surprising given the nature of the sector. In the construction sector, the focus is on reduce and recycle, reflecting perhaps greater awareness of the need to improve management of construction and demolition waste arising from existing building stocks. While there is interest in reuse, repair, refurbish and repurpose within the construction industry, it is more at the design and construction stage than at end-of-life. Focus in the higher-value electrical and electronics products sector is on reuse, recycle, and recovery options. Similarly, studies focussing on the manufacturing sector primarily considered reuse and refurbish, again reflecting the focus of the studies being products more than systems. The focus of the studies on single materials was recycle and recovery, reflecting the relatively high-value materials being considered in many of the studies. Finally, the miscellaneous sectors mostly focused on reuse, recycle and recovery options. Additionally, from the ABMS perspective, recycle was mostly widely considered, followed by reuse, and recovery. Interestingly, refuse, rethink, and repair are missing from all studies. 5 Future Research Directions Based on the analysis and discussion, the following future research directions could be suggested: Need for holistic consideration for identification of leverage points in the circular value-chain: This paper started with a discussion of systems thinking and its associated complexity. The purpose of systems thinking is identifying root causes of challenges and solutions. In the CE context, this entails closing, slowing, or narrowing the materials and energy loops. The systematic literature review showed how previous literature has considered systems thinking, thereby identifying benefits of different CE related metrics, and quantifying them. Various metrics have been considered in the value-chain of the materials, components, or products (MCPs) by using system dynamics and ABMS. However, information is still lacking along the whole value-chain of MCPs from the raw material excavation to end-of-life processing, and reintroduction into the value-chain. The values (i.e., economic, environmental, social, and technical) generated/destroyed/transferred need to be considered at each process, and thereafter identifying the key leverage points (Iacovidou et al., 2017b; Millward-Hopkins et al., 2018). This will enable informed policy decisions, which will act to disrupt the current system, and in doing so reveal further leverage points, promoting further interventions, and so on in a continuous process (Boral et al., 2024). At this point, not only will the MCPs be flowing in a circular way, but so will the decisions and associated impacts. This cannot be visualised without the aid of simulation approaches. Furthermore, the review has highlighted the interdisciplinarity of circular economy research. This should be continued and encouraged for more informed circular economy decision making. Use of multiple simulation approaches for holistic consideration: Circular processes cannot be modelled by a single simulation approach. Each approach has its advantages and drawbacks, as elaborated in S.2.2 and S.2.3 . These simulation approaches are also sufficiently flexible to be integrated with other decision-making tools, enabling more informed decision-making. The integration of different tools and techniques requires experienced systems thinkers and analysts who are also conversant with circular economy. However, each implementation software has its own benefits and limitations, with some more suited to system dynamics and others to ABMS. The ‘PySD’ package in Python has the capability to model the system dynamics architecture, and the ‘Mesa’ package in the same platform can incorporate ABMS. However, despite being free to use, to date, no work has highlighted their integration. Without this, any holistic considerations will be limited to theoretical frameworks, limiting widespread (industrial) adoption. Modelling external shocks and interventions: Interventions from governments or policy makers, and external shocks (e.g., COVID pandemic) can distort, positively or negatively simulation outputs. The impacts of interventions cannot be quantified in a deterministic way, but the stochastic and emergent properties of ABMS can model this. Also, techniques such as Poisson process (where shocks are random and independent), renewal process (where shocks occur at constant rate), and Gamma process (shocks occur at a rate that increases with time) can model shocks. Data availability, uncertainty, quality: Circular economy research suffers from a lack of complete data in terms of materials, energy flows, etc (Boral and Black, 2024; Lysaght et al., 2023; Velis et al., 2021; Wang et al., 2024, 2022). Moving across scales, from ‘micro’ level analysis to ‘meso’ or ‘macro’ level, another challenge is data aggregation, assuming data is available. Then, when system boundaries expand, modelling data uncertainty and quality are further challenges. Although fuzzy, and Bayesian MFA are options to account for data uncertainty, they can’t handle the system dynamicity, and thus their application to holistic circular economy systems is still lacking. This can only be solved through open data and software platforms capable of multiple modelling approaches. 6 Conclusions This paper is the first to highlight how different circularity metrics have been quantified using system dynamics and ABMS approaches. Overall, our review and analysis provide insights into the strengths and limitations of these methodologies, paving the way for further research in circular economy. Systems thinking is not new, but its application to circular economy is still nascent. This sectoral review highlights studies modelling the associated complexities via system thinking analytical and computational approaches. System dynamics, a top-down approach, considers the aggregated view of the system by modelling the complexities through causal loops, stocks and flows, delays, temporal scale, etc.; and ABMS takes a bottom-up approach for modelling granular information of the system, considering the heterogeneity, adaptiveness, emergence, temporal and spatial scales, and dynamic interactions. This latter approach is more recent and so is still developing. Consequently, the combination of system dynamics and ABMS has not yet been applied in a circular economy context, while also considering the value metrics from different value domains, despite the strong potential for enhanced circularity quantification. Further combining system-level quantification with other (traditional) decision-support tools could enable more informed decisions on advancing circular economy theory and practice by industry and policymakers. Declarations Funding This research was funded as part of the UKRI Interdisciplinary Circular Economy Centre for Mineral-based Construction Materials (ICEC-MCM), by the Engineering and Physical Sciences Research Council (EPSRC) - Grant reference: EP/V011820/1. CRediT Author Statement Soumava Boral: Conceptualisation, Methodology, Writing – Original draft preparation, Formal analysis, Data Curation, Visualization; Leon Black: Conceptualisation, Writing – Review and Editing, Visualisation, Supervision, Funding acquisition; Costas Velis: Conceptualisation, Methodology, Writing – Review and Editing, Supervision, Project administration, Funding acquisition. Data Availability Statement All data associated with this study are available in the article and in the Supplementary Material (SM) . Acknowledgements We acknowledge the support of UKRI Interdisciplinary Circular Economy Centre for Mineral-based Construction Materials (EP/V011820/1). 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Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data. J. Ind. Ecol. Xing, L., Lin, T., Hu, Y., Lin, M., Liu, Y., Zhang, G., Ye, H., Xue, X., 2023. Reducing food-system nitrogen input and emission through circular agriculture in montane and coastal regions. Resour. Conserv. Recycl. 188, 106726. Yang, H., Xia, J., Thompson, J.R., Flower, R.J., 2017. Urban construction and demolition waste and landfill failure in Shenzhen, China. Waste Manag. 63, 393–396. Yazan, D.M., Fraccascia, L., 2020. Sustainable operations of industrial symbiosis: an enterprise input-output model integrated by agent-based simulation. Int. J. Prod. Res. 58, 392–414. Yu, Y., Yazan, D.M., Bhochhibhoya, S., Volker, L., 2021. Towards Circular Economy through Industrial Symbiosis in the Dutch construction industry: A case of recycled concrete aggregates. J. Clean. Prod. 293, 126083. Zhao, Y., Yu, M., Xiang, Y., Chang, C., 2023. An approach to stimulate the sustainability of an eco-industrial park using coupled emergy and system dynamics. Environ. Dev. Sustain. 25, 11531–11556. Additional Declarations The authors declare potential competing interests as follows: C.A.V. consults for organizations active in the waste, resources and circular economy sphere. He has received funding to the University of Leeds from UKRI, GCRF, NERC, ESRC, BBSRC, Royal Academy of Engineering, British Council, Innovate UK, EC H2020, World Bank Group, OECD, GIZ, UN-Habitat, UNESCAP, UNOPS, The Pew Charitable Trusts, IGES, ISWA, GRID-Arendal, Swedish EPA, MARS and SYSTEMIQ. He is affiliated with the International Solid Waste Association (ISWA), International Waste Working Group (IWWG), the Scientists Coalition for an Effective Plastics Treaty and the Innovation Alliance for a Global Plastics Treaty. The University of Leeds has memorandums of understanding with the Alliance To End Plastic Waste and the United Nations Environment Global Partnership on Plastic Pollution and Marine Litter (GPML), which refer to plastic pollution databases. Supplementary Files SupplementaryMaterialBoralBlackVelisCircularEconomySystemsQuantification.pdf SupplementaryMaterial_Boral_Black_Velis_CircularEconomySystemsQuantification Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5844499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":403719665,"identity":"d88ac72f-bc9e-4a88-9536-d19184daeb6f","order_by":0,"name":"Soumava Boral","email":"","orcid":"https://orcid.org/0000-0001-9616-3397","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Soumava","middleName":"","lastName":"Boral","suffix":""},{"id":403719666,"identity":"d32aba0d-60d2-4452-ab8a-673eb8b181c5","order_by":1,"name":"Leon Black","email":"","orcid":"https://orcid.org/0000-0001-8531-4989","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Leon","middleName":"","lastName":"Black","suffix":""},{"id":403719667,"identity":"22ed9ccd-fdb9-40a8-ba9e-ef4dfdcfd98c","order_by":2,"name":"Costas Velis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie2Rv4rCQBCHRxZiM7CdrCj6ChME7XyWlYBpolgKB3Ft7rp7gfNhEha00d4yIlxlkevuwMKNp2KTxFJwv+rHMh87fwAslmclIWg550TQ/H+KShRJ0Lkq+KACMFCXXK5whW4iJ6H/WdvE++mkj1DVCcN1viIi7JAkPXpvjD13TR4CDonhNl8hcIZCUmSUoFtXphggAIZpoeL/Sgp9pxH0/hTNEPihTGFLszEmjdKtKNIIIvuloDGhmTaNaTebxTS2Qkd8U7woGJ9/zOdpegzb7a9R/KOOby3OvV1yWOYrwG7pcpHsplGBcA8+WGexWCwvxwn7JEPndJrUaQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1906-726X","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Costas","middleName":"","lastName":"Velis","suffix":""}],"badges":[],"createdAt":"2025-01-16 20:26:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5844499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5844499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75696487,"identity":"c3a3117f-e126-43b9-9ad1-6e7d28f37cb6","added_by":"auto","created_at":"2025-02-07 08:24:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":587887,"visible":true,"origin":"","legend":"\u003cp\u003eA: Materials flows in system dynamics modelling approach. B: Materials flows in ABMS approach. Materials identity is lost in SD models but retained in ABMS modelling, as indicated by the colour visualisation.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/76d2cacb876fed65f2f22971.jpg"},{"id":75696489,"identity":"3735c9ce-7fd6-4fef-a838-27b738949893","added_by":"auto","created_at":"2025-02-07 08:24:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307038,"visible":true,"origin":"","legend":"\u003cp\u003eConsideration of different circularity metrics in system dynamics studies. The colours reflect the different sectors, while studies considering ‘flow’ are shown as undelined. Apart from the one study in bio-based sector, and two studies in electrial and electrnoics products sector, none of the reviewed system dynamics studies jointly considered the quantification of environmental, economic, and social metrics.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/ab13cc3f6e019194cec6006a.jpg"},{"id":75696499,"identity":"7cf24147-6832-41ce-b59f-1d7bf3e65a37","added_by":"auto","created_at":"2025-02-07 08:24:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213592,"visible":true,"origin":"","legend":"\u003cp\u003eConsideration of different circularity metrics in ABMS studies. The colours reflect the different sectors, while studies considering MCP flow are shown in bold italics. None of the reviewed ABMS studies jointly considered the environmental, economic and social metrics.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/da25f9d93ba9df20cdd17327.jpg"},{"id":75696498,"identity":"cd56afd5-d5a5-458b-85c0-d3e6fd86d95a","added_by":"auto","created_at":"2025-02-07 08:24:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2725879,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of ‘R’-based strategies according to each of the sectors investigated in this study. Most of the studies are focusing at the lower end of a hypothetical CE hierarchy – i.e. more extended effort cycles (i.e. recycle), where chances of value loss are perceived as being (substantially) higher.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/025fbdfa89906ab7e910c147.jpg"},{"id":75696722,"identity":"e055a46a-086d-4f92-80ba-89f1bc04ef40","added_by":"auto","created_at":"2025-02-07 08:32:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":705125,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of R-types (as stated in key) considered in studies pertaining to each sector, in adopting system dynamics (SD) or Agent-based modelling and simulation (ABMS) approaches.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/63e8d9b50e84494cb08e8737.jpg"},{"id":75697472,"identity":"06c1ebd4-9e99-4080-b32e-1141975adaa9","added_by":"auto","created_at":"2025-02-07 08:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7276194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/bb01d47b-0055-461c-8ed5-7d8712472bc9.pdf"},{"id":75696493,"identity":"4b86e753-3eaf-41e8-8c81-9494a2a9024f","added_by":"auto","created_at":"2025-02-07 08:24:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1324143,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementaryMaterial_Boral_Black_Velis_CircularEconomySystemsQuantification\u003c/p\u003e","description":"","filename":"SupplementaryMaterialBoralBlackVelisCircularEconomySystemsQuantification.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5844499/v1/c25d475a7b8d51d180906dfb.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: C.A.V. consults for organizations active in the waste, resources and circular economy sphere. He has received funding to the University of Leeds from UKRI, GCRF, NERC, ESRC, BBSRC, Royal Academy of Engineering, British Council, Innovate UK, EC H2020, World Bank Group, OECD, GIZ, UN-Habitat, UNESCAP, UNOPS, The Pew Charitable Trusts, IGES, ISWA, GRID-Arendal, Swedish EPA, MARS and SYSTEMIQ. He is affiliated with the International Solid Waste Association (ISWA), International Waste Working Group (IWWG), the Scientists Coalition for an Effective Plastics Treaty and the Innovation Alliance for a Global Plastics Treaty. The University of Leeds has memorandums of understanding with the Alliance To End Plastic Waste and the United Nations Environment Global Partnership on Plastic Pollution and Marine Litter (GPML), which refer to plastic pollution databases. ","formattedTitle":"Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe concept and practice of circular economy (CE)/circularity intend to go beyond the established practices of waste management and resource recovery, towards an even more sustainable management of material resources. Numerous circular economy/circularity definitions have been proposed, as summarised in detail by Kircherr and co-workers (Kirchherr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, the United Nations Environment Programme (UNEP) described CE as \u003cem\u003e\u0026ldquo;an economy that reduces consumption of resources and the generation of waste, and reuses and recycles waste throughout the production, distribution and consumption processes\u0026rdquo;\u003c/em\u003e (United Nations Environment Programme (UNEP), \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). An alternative definition proposes that \u003cem\u003e\u0026ldquo;[\u0026hellip; circular economy] is restorative and regenerative by design and aims to keep products, components, and materials at their highest utility and value at all times, distinguishing between technical and biological cycles\u0026rdquo;\u003c/em\u003e (Ellen MacArthur Foundation, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) encourages a shift from \u0026lsquo;cradle-to-grave\u0026rsquo; thinking to a \u0026lsquo;cradle-to-cradle\u0026rsquo; philosophy, thus promoting the idea of maximising the positive \u0026lsquo;value\u0026rsquo; associated with material, components and products (MCPs). This wording emphasises the concept of MCPs \u003cem\u003e\u0026lsquo;\u003c/em\u003evalue\u003cem\u003e\u0026rsquo;\u003c/em\u003e regeneration and redistribution through their entire lifecycle, and multiple iterations thereof.\u003c/p\u003e \u003cp\u003eNonetheless, this approach does not define the inherently multi-dimensional and therefore complex meaning of \u003cem\u003e\u0026lsquo;\u003c/em\u003evalue\u003cem\u003e\u0026rsquo;\u003c/em\u003e, which usually goes beyond the monetary loss/benefits, to incorporate environmental benefits, social equity and prosperity, and minimal technical performance loss (e.g., design for reuse) (Iacovidou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Millward-Hopkins et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such a value transforms: is created/destroyed/transferred between places and owned by organisations as MCPs are physically moved and transformed in the extraction of resources, materials, components, semi-finished goods and goods are manufactured, retailed, used and becoming after-use/waste and entering the waste and resource recovery/disposal part of the cycle. Measuring this value as an attempt to quantify circularity is therefore feasible only in a comparative way, within a system at which the value carried by MCPs takes specific \u0026lsquo;states\u0026rsquo;/\u0026lsquo;levels\u0026rsquo; at different points.\u003c/p\u003e \u003cp\u003eFor example, simplified typologies on the mode of circularity have been historically narrated via \u0026lsquo;R\u0026rsquo;-type based frameworks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They offer a simple conceptual hierarchy of circularity modalities applicable at various MCPs lifecycle stages, focusing on slowing, closing, or narrowing resource flows. Similarly, quantifying the nature and degree of circularity achieved by these R-type modalities, requires as a bare minimum definition of ratios between parts of a material flows system. From a whole system\u0026rsquo;s perspective, the implementation of each \u0026lsquo;R\u0026rsquo; philosophy is highly time-dependent, short- and long-term impacts (i.e., time dynamicity and delays) with potential benefits (e.g., diversion from landfill) or drawbacks (e.g., insufficient recycling capabilities driving increased disposal), and it depends on stakeholder activity along value chain (Walzberg et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), i.e. in material flow systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA variety of \u0026lsquo;R\u0026rsquo; concept-based frameworks proposed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lsquo;R\u0026rsquo;- type content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-Rs-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce, Reuse, and Recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrennan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-Rs-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce, Reuse, Recycle, Recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYang et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-Rs-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce, Redesign, Recover, Reuse, Remanufacture, Recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJawahir and Bradley (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-Rs-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefuse, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, and Recover energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVan Buren et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10-Rs-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, Recover and Refuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKirchherr et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn such material flow systems, multiple variables may cause system dynamicity: i.e., qualitative and quantitative, endogenous and exogeneous, linear or non-linear. Understanding stocks and flows of material, energy, finance, etc. is key to sufficiently describe such a CE system, where multiple stakeholders are involved along the entire value-chain, sometimes in closed loops introducing feedback in the system, each aiming to maximise its own benefits, therefore transforming the monetised or wider perceived \u0026lsquo;value\u0026rsquo; of MCPs at each system point over time. As a result, such systems feature numerous complexities, and feature trade-offs among aspects of \u0026lsquo;value\u0026rsquo; which would be very difficult, if at all possible, to capture/summarise solely through traditional analytical, equations-based, approaches (Sun et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, system boundaries (time, geographical, administrative, scale) may play a crucial role in systems thinking, and while the actions of system agents may not reveal impacts at a \u003cem\u003e\u0026lsquo;micro\u0026rsquo;\u003c/em\u003e scale, they may do so at \u003cem\u003e\u0026lsquo;meso\u0026rsquo;\u003c/em\u003e or \u003cem\u003e\u0026lsquo;macro\u0026rsquo;\u003c/em\u003e-scales. For example, even where systems tend towards a balanced state, external agents all along the value chain, such as early adopters of new technologies, can disrupt this state until a new balanced state develops. A series of system-level failures have been proposed as preventing the creation and operation of genuine circular economy (Velis, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2018\u003c/span\u003e): description, verification and quantification of such phenomena, however, would necessitate a systems approach. Quantifiable metrics of circularity could be exhibiting highly non-linear behaviours, as a system emerges through the adaptive nature of agents.\u003c/p\u003e \u003cp\u003eTherefore, we argue that ultimately, effective qualification and quantification of circularity by default can be only achieved through a so-called \u0026lsquo;systems thinking\u0026rsquo; approach. Simply put, systems thinking adopts holistic and interconnected approaches to understanding and addressing inherent complexities of an entire system, rather than its isolated components (Arnold and Wade, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bassi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Demartini et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Richmond (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) defined systems thinking as the art and science of making trustworthy decisions about behaviour by developing deep understanding of underlying structures. Meanwhile, Sterman (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) defined systems thinking as an approach and mindset for understanding and analysing complex systems as interconnected and dynamic entities, and thus asserting the principle of dynamic complexity for solving systemic challenges. It involves recognizing that a system is more than the sum of its parts; and that its behaviours and properties emerge from interactions and interdependencies among its components.\u003c/p\u003e \u003cp\u003eIn this context, such a methodologically analytical approach has the potential to identify leverage points in the system under a multiplicity of conditionalities. Systems thinking and its pertinence to CE are discussed in detail \u003cb\u003eS.2.1\u003c/b\u003e and \u003cb\u003eS.3\u003c/b\u003e of the \u003cb\u003eSupplementary Material\u003c/b\u003e (denoted as \u003cb\u003eS\u003c/b\u003e for tables, figures, and sections). Two prominent approaches (schools of thought) in this regard are system dynamics modelling and agent-based modelling and simulation (ABMS). System dynamics and ABMS approaches can to a variable degree address challenges around static and liner descriptions of circularity: \u003cb\u003eSections S.2.2\u003c/b\u003e and \u003cb\u003eS.2.3\u003c/b\u003e offer more details of system dynamics and ABMS theorising.\u003c/p\u003e \u003cp\u003eTo date, applied research on CE and systems thinking has often focused on complexity modelling of the \u003cem\u003e\u0026lsquo;micro\u0026rsquo;\u003c/em\u003e scale. For example, addressing the meaning of systems thinking in a CE context, but not the inherent complexities and dynamicity of the whole system in \u0026lsquo;macro\u0026rsquo; scale (Iacovidou et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recent research has advanced CE, but often still omitting systems thinking and even more so its corresponding quantification (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which, as we argue, could provide a sound base for more holistic, meaningful, and reliable circularity analytics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReviews on systems thinking and its quantification from a CE perspective.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScope limitations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrated review of CE concerning construction and the built environment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmitted systems thinking and dynamic quantification of CE.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026Ccedil;imen (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFocused on common modelling approaches to analyse industrial symbiosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not focus on dynamics of circularity metrics and parameter quantification.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemartini et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReviewed life cycle assessment studies of a biorefinery system.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmitted quantification of systems thinking by complexity modelling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVance et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive review on adoption of system dynamics and agent-based models in construction waste management.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDid not focus on the necessity of systems thinking, its complexity, and thereafter dynamic quantification of circularity metrics.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDing et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo the best of our knowledge, there are no reviews and analyses with a comprehensive scope on the quantification of circularity through systems thinking and its associated complexity modelling. Therefore, here we adopt a systematic scoping review approach according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews), concerning the application of systems thinking through system dynamics and ABMS. Out approach and scope can offer novel insights on: (a) considering how complexity arises in CE systems, highlights the relevance of systems thinking applied to CE, and particularly its quantification; (b) by the way of a case study, it applies a magnifying lens on applications of system dynamics and agent-based modelling in quantifying different aspects of CE in the built environment, specifically also considering circularity metrics and wider parameters; and, (c) by describing and analysing how other decision-support tools have been integrated with system dynamics and ABMS aiming at more refined analytics. Additionally, we also consider case study geography, along with the significance of policy and governance aspects.\u003c/p\u003e \u003cp\u003eThe paper is structured as follows: \u003cb\u003eSection 2\u003c/b\u003e highlights the methodology (research questions and literature review approach); \u003cb\u003eSection 3\u003c/b\u003e describes applications of system dynamics and ABMS to CE in series of main sectors/categories, including bio-based, construction, electrical and electronics equipment, single materials, industrial symbiosis, along with miscellaneous; \u003cb\u003eSection 4\u003c/b\u003e provides critical observations and quantified insights; \u003cb\u003eSection 5\u003c/b\u003e identifies key future research avenues; and \u003cb\u003eSection 6\u003c/b\u003e offers overarching conclusions.\u003c/p\u003e"},{"header":"2 Methodological Approach to Systematic Literature Review","content":"\u003cp\u003eWe conducted a systematic scoping review following the PRISMA-ScR guidelines (Peters et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the associated preferred reporting items checklist (Tricco et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) (\u003cstrong\u003eTable S.3\u003c/strong\u003e). The review explored three specific questions:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eRQ1: What were the contexts/necessities of applying system dynamics modelling and ABMS approaches in CE?\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eRQ2: Which types of decisions were provided through system dynamics and ABMS approaches?\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eRQ3: Were any other tools/methods applied in conjunction with system dynamics, and ABMS? If so, why?\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe literature search terms are listed in \u003cstrong\u003eSection S.5\u003c/strong\u003e. Searching was confined to articles featured in the SCOPUS bibliographic database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.scopus.com\u003c/span\u003e\u003c/span\u003e), given that it is multidisciplinary and futures comprehensive main research outputs coverage (peer-reviewed journal articles). Conference articles and book chapters were excluded here, to ensure peer-reviewed content and avoid duplication of ideas. The search was practically restricted to recently published articles, arbitrarily defined as in the last 8 years at the point of the start of the review: 2016\u0026ndash;2023. As a matter of testing for previous period outputs, indeed only 3 relevant articles were retrieved from 2003\u0026ndash;2015, and these were not useful to answer the research questions. The detailed inclusion-exclusion criteria are shown in \u003cstrong\u003eSection S.6\u003c/strong\u003e, with the selection of sources and data charting presented in \u003cstrong\u003eSection S.7\u003c/strong\u003e.\u003c/p\u003e"},{"header":"3 Sector-wise Applications of SD and ABMS in Quantitative Circular Economy","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Bio-based Sector\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 System dynamics modelling in bio-based sector\u003c/h2\u003e \u003cp\u003eArticles cover a diverse range of contexts, such as; food waste reduction in the food, energy, water, and climate (FEWC) nexus (Parsa et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or through an online food sharing application (Ranjbari et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the flow of chicken and associated waste products in the economy (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), addressing reactive nitrogen\u0026rsquo;s societal contribution (Xing et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), utilising waste cooking oil as second-generation fuel (de Carvalho Freitas et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), examining urban sustainability transition in food, energy, water, and waste (FEWW) sectors (Valencia et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), waste oil flow in the motor industry and its circularity (Viruega Sevilla et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and scrutinising phosphorus recycling process (El Wali et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe initial step of system dynamics modelling involves the creation of causal loop diagrams, offering a qualitative understanding of intricate relationships between different parts. Afterwards, stocks and flows within the system are identified, and quantitative analysis is executed by stocks and flows diagrams. Some authors exclusively presented the causal loop diagrams before developing the stocks and flows diagrams (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; de Carvalho Freitas et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ranjbari et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xing et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas some incorporated the causality of different variables within the stocks and flows diagram (Valencia et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some did not report the stocks and flows diagram directly, but presented the flows of materials, energy, or finance through Sankey diagrams (Parsa et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStocks and flows diagrams were used to illustrate the dynamic interactions of flows of food, energy, water and climate changes (FEWC) between different sectors in an agri-food supply chain (Parsa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to highlight socio-economic interactions coupled with material and finance flows (Parsa et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and to integrate the forward and reverse value-chain mapping in chicken farming (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, they aided the understanding and mapping of flows within various scenarios: for example reactive nitrogen flows from food waste and its associated impact on the nitrogen cycle (Xing et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); waste cooking oil generation pattern by households, and its impact on water pollution (de Carvalho Freitas et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); food, energy, water and waste flow (Valencia et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and, flow of waste oil in the motor industry (Viruega Sevilla et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Some authors even modelled material flows (viz., surplus food) with the knowledge of the system entities (viz., population) (Ranjbari et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and coupled materials flow with social metrics (El Wali et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSystem dynamics model is in principle able to identify key leverage points of systems, and in scenario generation and metrics forecasting, which, in turn, could help policy recommendations. Key leverage points may not be obvious in baseline scenarios but emerge through examining various alternative scenarios. For instance, crop planting and aquaculture were identified through scenario generation as the main sectors for emitting reactive nitrogen (Xing et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In de Carvalho Freitas et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), household cooking oil consumption patterns were identified as the key influence on water pollution. Meanwhile, food waste and environmental footprints could be minimised by reducing waste generation at consumer and redistribution levels (Parsa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Children\u0026rsquo;s knowledge of a food-sharing app was identified as a key lever to reducing food waste (Ranjbari et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Parsa et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) the key sectors for food waste minimisation were identified which, in turn, benefitted the socio-economic metrics. Some studies demonstrated self-sustainability through landfill gas and stormwater reuse (Valencia et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Holistic linkages between small and medium-scale farms have been emphasised for sustainable development (Abbasi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the practice of CE enhanced global phosphorus security and social prosperity in low and middle-income countries as found in El Wali et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, all these studies mapped stocks and flows over extended time periods, across various geographical scales (El Wali et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and coupled them with various CE metrics (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), not only to map the current situation, but also to forecast future scenarios. The variables considered in the models were often non-linear. For details refer to \u003cb\u003eTable S6-S7\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in system dynamics applications within the bio-based sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood waste reduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate cost, total GVA, and sectoral GVA, severe food insecurity rate, food waste generation, redistribution cost, redistribution benefit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, reuse, recycle, recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParsa et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood waste reduction in food, energy, water, and climate (FEWC) nexus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood footprint, food waste generation, energy footprint, water footprint, and carbon footprint.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, reuse, recycle, recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup model building to create, refine, and restructure the system dynamics model with stakeholders\u0026rsquo; interventions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParsa et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood-waste mitigation strategy by food-sharing platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal amount of food waste prevented, total CO\u003csub\u003e2\u003c/sub\u003e emissions prevented, and food-sharing platform performance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRethink, reduce, and reuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBass diffusion model to account the adoption of food-sharing app by the population.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRanjbari et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehaviour of egg laying chicken, egg, hatchery, and day-old chick stocks, poultry litter and poultry litter to banana farm stock, chicken flow to slaughterhouse, waste flow to fish farm, total income, profit, and total cost of the stakeholders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, repurpose, recycle, recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbbasi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood and agricultural system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal reactive nitrogen output, air nitrogen emission, soil reactive nitrogen emission, water reactive nitrogen emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse and recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXing et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuel: waste cooking oil to second generation fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold consumption and industrial consumption of vegetable oil, waste oil not recycled, water pollution, and oil to recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ede Carvalho Freitas et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood, energy, water, waste nexus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReclaimed water utilization, food wate reuse, waste to energy generation, food production, carbon footprint, water footprint, water resilience index, food supply index and different types of wastes produced.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, recycle, reuse, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTOPSIS (Technique for order of preference by similarity to ideal solution), a MCDM tool was used to identify the most suitable scenario based on sustainability and resilience indicators.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValencia et al. (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor industry residues (waste oil, spent solvent, battery waste, and dirty wipes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutflows of waste oil, emissions (CO2), human toxicity, energy cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eViruega Sevilla et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (P) recycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorker safety, employment equality, employment rate, poverty rate, child labour, water use, nutrition supply, P efficiency and security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEl Wali et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 ABMS in bio-based sectors\u003c/h2\u003e \u003cp\u003eABMS is a bottom-up system modelling approach. In Farahbakhsh et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) it was utilised to identify the barriers to adopting emerging technologies in waste treatment plants, exploring potential contribution of chemical recycling of carbon-containing waste of municipal solid waste (MSW). It has also been used for dynamic life-cycle sustainability assessment to contribute to UN sustainable development goals (UN SDGs) (Voss et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and to investigate households\u0026rsquo; intentions regarding recycling (Tong et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, we sense that ABMS is preferred over systems dynamics when the heterogeneities of the stochastic agents are essential to modelling. In Farahbakhsh et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) it was induced by agents\u0026rsquo; (viz., waste treatment plants) environmental awareness, investment required to adopt emerging technologies, and expected returns, etc. In Voss et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) agents were characterised based on their geographic locations, annual residual MSW (rMSW) production volume, treatment capacity, etc. In Tong et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), households\u0026rsquo; intentions towards recycling and/or landfilling were considered as heterogeneities.\u003c/p\u003e \u003cp\u003eThese heterogeneous agents sustain by dynamically adapting to their environment. For example, in the case of valorising organic waste into high-end products, the agents adapted within a global market in response to economic, social and environmental pressures (Farahbakhsh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Strict sustainability regulations led agents (viz., administrative areas, and waste treatment plants) to adapt in Voss et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) regarding circular utilisation of organic residual MSW as a chemical feedstock via gasification. While in Tong et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) waste separation and recycling were agents triggered by changes in consumer behaviour.\u003c/p\u003e \u003cp\u003eInteractions between agents and with the environment can lead to the emergence of new, not easily foreseeable, behaviours. This can result from social pressure provided by other agents to adopt CE technologies (Farahbakhsh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or be reward-driven (Tong et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Temporal (Farahbakhsh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Voss et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and spatial considerations (Tong et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Voss et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) offer a nuanced understanding of dynamic processes. This consideration over temporal scales led to scenarios analysis and metrics quantification as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (for further details see \u003cb\u003eTable S20-S22\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications within the bio-based sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics and/or parameters quantified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean organic waste treatment facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolatile fatty acid platform (VFAP) adoption rate by changing the: (a) subsidies (both investment and operational), (b) market growth in polyhydroxyalkanoates (PHA) segment, (c) improvement in technological efficiencies, (d) social pressure.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarahbakhsh et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon containing waste of MSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate change, terrestrial acidification, fossil resource scarcity, system cost, and impact on local environment for different defined scenarios.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoss et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold recyclable wastes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipation rate of households regarding three choices: landfilling, placing waste at a central container, or being collected, every day for a period.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheory of planned behaviour (TPB) was used to identify key influencing factors affecting the residents\u0026rsquo; inclination to recycling.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTong et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction Sector\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 System dynamics modelling in the construction sector\u003c/h2\u003e \u003cp\u003eSystem dynamics has been used for a range of applications in the construction sector. It was used to examine the circularity potential of recycled paver blocks (RPB) (Gandhi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the decarbonisation potential of US commercial buildings (Eissa and El-adaway, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ghufran et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explored enablers for the CE transition in the construction sector, focusing on causal interdependencies, while Kliem et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) specifically concentrated on policy. Mostert et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) meanwhile estimated and forecast construction and demolition materials flows in the German construction sector.\u003c/p\u003e \u003cp\u003eCausal loop diagrams have been constructed to depict the dynamic interdependencies between cost components when manufacturing recycled paver blocks (Gandhi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to examine causal links between CE enablers in the construction industry (Ghufran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and public policy instruments\u0026rsquo; impacts on CE business model (Kliem et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Stocks and flows diagrams meanwhile aided quantification of the temporal evolution of manufacturing cost of recycled paver blocks (Gandhi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), flows of newly built floorspace, demolished floorspace, total carbon emissions associated with concrete and emissions per unit floorspace (Eissa and El-adaway, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They have also been used to examine organisational incentive schemes, policy supports, and sustainable development (Ghufran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), flows of demolished materials in the economy (Mostert et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and in modelling sand and gravel quarry and disposal volumes (Kliem et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, stocks and flows diagram prove versatile, not only capturing material flows, but also evolving policy variables (Ghufran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll these studies considered the temporal evolution of dynamic systems, and subsequent scenarios analysis led to recommendations based on reported metrics (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and identifying the key leverage points. For instance, Gandhi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified contractor profit, overhead expenses, and labour costs as the main barriers to using RPBs in India. Eissa and El-Adaway (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggested that a comprehensive CE policy in the US could deliver a 52% decarbonisation potential by 2050, while policy support and incentive schemes have been recognised as key factors for CE transition in Ghufran et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDelay modelling, another key feature of system dynamics, as adopted by Kliem et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Mostert et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The CE transition in the construction sector not only requires adequate recycling, but also a constant supply of secondary materials. While Ghufran et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) considered global participants, all other studies were conducted at the micro or meso-scale. For further information see \u003cb\u003eTable S8-S9\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantified metrics, consideration of 'R' principles in system dynamics applications within the construction sector\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecycled paver block circularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturing cost of recycled paver block\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGandhi et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecarbonisation potential of US commercial buildings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual commercial floorspace, embodied emissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, reduce,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEissa and El-adaway (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy impacts on CE transition within the construction industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy supports, organizational incentive scheme, and sustainable development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGhufran et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircularity potential of recycled building materials to replace virgin materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemand for aggregates for concrete, demand for recycled aggregates for concrete, savings in sand and gravels, unused recycled aggregates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMostert et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarriers in adopting CE transition in the construction industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecycling and recovering quota of CDW and excavation materials, primary gravel price, disposal price\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKliem et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Electrical and Electronics Products Sector\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 System dynamics modelling in the electrical and electronics sectors\u003c/h2\u003e \u003cp\u003eL\u0026auml;hdesm\u0026auml;ki et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) modelled the CE potential of lithium in a global context, while Guzzo et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed how CE policy interventions facilitated nationwide electrical and electronics waste collection and treatment. Llerena-Riascos et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) explored the operative-strategic interdependency in improving the representation and performance of waste electrical and electronics equipment (WEEE) collection and processing steps. Salim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) delved into socio-technical transition pathways for end-of-life management of rooftop photovoltaic solar panels. In both works Chaudhary and Vrat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Sinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) used systems dynamics to investigate circularity regarding mobile phones. Chaudhary and Vrat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlighted the drivers for proper circular flow of gold in mobile phones, while Sinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) drew attention to the potential of closing material flow loops in global mobile phone product systems, addressing sustainability challenges of material recovery.\u003c/p\u003e \u003cp\u003eCausal loop diagrams have been key in offering an explicit representation of dynamic interdependencies (Alamerew and Brissaud, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chaudhary and Vrat, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Salim et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while others presented causality passively (Guzzo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sinha et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Facilitation of CE was found to be not only dependent on cost and revenues, but also on strategic regulatory decisions, and proper policy implementation (Alamerew and Brissaud, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chaudhary and Vrat, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas, circular value-chain mapping of gold, e-waste, and mobile phones with their sustainability benefits were evident in Chaudhary and Vrat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Sinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where sustainability indicators were causally linked. Additionally, inappropriate disposal of hazardous waste was addressed in Salim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), urging proper recovery and recycling.\u003c/p\u003e \u003cp\u003eStocks and flows diagrams were prevalent throughout the literature, effectively representing material flows inside system boundaries. However, Guzzo et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used them to represent the flow of technology adoption processes, and Salim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for recycling fund flow. Delay and lifetime distribution modelling through different distributions were shown in Guzzo et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, Guzzo et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) presented an inductive-deductive system dynamics model, where the empirical theory was constructed in the inductive stage, and then deductive theory was tested in another case-study.\u003c/p\u003e \u003cp\u003eAll the studies reviewed in this sub-section explored the common capabilities of system dynamics modelling - temporal dynamics, scenario analysis, metrics quantification, and thereby recommendations (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The work of Sinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was a global-scale study. In scenario analysis, multiple previous scenarios can be superimposed (Guzzo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; L\u0026auml;hdesm\u0026auml;ki et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, L\u0026auml;hdesm\u0026auml;ki et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) recommended to implement a combined CE policy, informed by such multiple scenarios. Guzzo et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggested the systemic change for CE implementation. While, Salim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that there were high uncertainties in waste collection, recovery performance, and landfill disposal.\u003c/p\u003e \u003cp\u003eSystem dynamics can also handle uncertainties of the parameters, and can provide decisions accounting, as shown by Salim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Chaudhary and Vrat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) underscored the value of consumer awareness and stakeholder incentives for better CE implementation, while their earlier work (Chaudhary and Vrat, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) emphasised the policy interventions for addressing challenges associated with the informal handling of e-waste without environmental and health protection. Meanwhile Sinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recommended closed-loop recycling through improved collection systems, longer mobile phone retention time, improved recycling in developing countries, and shorter phone hibernation times. This work proposed reducing informal recycling of e-waste, as they claim that it leads to lower resource recovery along with higher pollution. Further details are in \u003cb\u003eTable S10\u003c/b\u003e - \u003cb\u003eS11\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in system dynamics applications within the electrical and electronics products sectors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste lithium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLithium demand by application, lithium content in waste stream, and lithium accumulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, refuse, repurpose, remanufacture, recycle, and reuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u0026auml;hdesm\u0026auml;ki et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste from Electrical and Electronics equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOfficial EEE collection, inadequate disposal of EEE, ratio of material treated in last years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, reuse, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuzzo et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste from Electrical and Electronics equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterial extraction, official EEE collection, inadequate disposal of EEE, ratio of material treated, and ratio of material lost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuzzo et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste from Electrical and electronic equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailability of raw material, material extraction, EEE commissioning, total EEE in use, disposal of EEE as WEEE, WEEE recycled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, remanufacture, repair, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBass diffusion model to model the diffusion of technology in the system.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuzzo et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste from Electrical and Electronics equipment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvoided environmental burden (AEB), repurchase price, WEEE generated, profits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepair, recycle, remanufacture, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed-integer non-linear programming approach was used to fine tune the system dynamics parameters.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLlerena-Riascos et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOL management of photovoltaic solar cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollection fraction, collection rate, total recovered materials, landfill amount, payback period and number of dwellings with PV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, repair, recycling, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantitative-qualitative triangulation method was used to capture the mental models of the stakeholders.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalim et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGold recovery from cell phone recycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepletion/savings of gold reserve, gold from E waste, gold from discarded cell phones, economic benefits, environmental benefits, social benefits (job creations), collection efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, refurbish, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChaudhary and Vrat (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE-waste reverse supply chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepletion of material reserve, material conversion rate, extracted material, economic, environmental, and social benefits, increase of collection coverage, and pre-processing capacity, demand of recycled materials, change in recycled products with change in refurbish and reuse rate, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, refurbish, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChaudhary and Vrat (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric vehicle batteries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross benefit of remanufacturing, remanufacturing margin, and price for remanufactured and new electric vehicle batteries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, remanufacture, repurpose, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlamerew and Brissaud (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile phone recycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGold use by phone manufacturers and gold recovery at the end of life of phone, loop leakage, closed loop efficiency of global mobile phone product systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, refurbish, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpt Quest optimiser to optimise high and low sensitive parameters.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSinha et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 ABMS in the electrical and electronics sectors\u003c/h2\u003e \u003cp\u003eABMS has been employed to assess the influence of techno-economic and social factors on the circularity of hard-disk drives (HDDs) (Walzberg et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The importance of considering social factors in empirical CE analysis has been underscored, alongside techno-economic considerations (Walzberg et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ABMS was also adopted to model waste household appliance recovery (Luo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mashhadi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) stressed that product-service-systems (PSS), such as mobile phone leasing, can overcome CE implementation barriers. ABMS was instrumental in quantifying demand behaviour for circular business models (leasing or functional sales) (Lieder et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). Another study by the same authors employed multi-method simulation approach combining ABMS and discrete event simulation (DES) to quantify design efforts for circular options (reuse, remanufacture, and recycle) and supply chain settings (buy-back, pay-per-use, and leasing) (Lieder et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). The details of each study and the types of agents can be found in \u003cb\u003eTable S23-S25\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe ability of ABMS to capture the agents\u0026rsquo; distinct characteristics, attributes, behaviours, and decision-making rules, i.e. heterogeneity, has been useful in tracking agents\u0026rsquo; specific CE pathways (Walzberg et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By integrating the theory of planned behaviour (TPB) and its associated factors, Walzberg et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) determined how the agents\u0026rsquo; decisions at the individual level led to changes at the systemic level, specifically in their adoption of circular economy (CE) pathways. The heterogeneity of residents, governments, and recycling agents was considered by Luo et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who included factors such as waste appliance generation, industrial standards development, tax incentives, distances from residents, etc.. The \u0026lsquo;memorising\u0026rsquo; capability of agents was demonstrated by Mashhadi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Lieder et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e) where previous positive experiences influenced the subsequent decisions regarding adopting CE options (e.g., buying/leasing/pay-per-use, etc.). Meanwhile, heterogeneity demonstrated a single component\u0026rsquo;s four distinct stages (Lieder et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e): manufactured, assembled in product, disassemble after use, and material recovered.\u003c/p\u003e \u003cp\u003eWith more knowledge of the positive impacts of CE, agents can be self-encouraged, i.e. show adaptation. While agents always try to choose a position where their benefits are maximised, the rewards are dependent on the path taken (Luo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Adaptation has been demonstrated due to socio-economic and social status (Walzberg et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), environment-friendly awareness, and peer-pressure (Mashhadi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), socio-demographics, social networks, and product utility (Lieder et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), and proper marketing and pricing strategy (Lieder et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e). Complex interactions between agents, and their stochastic natures, were consistently emphasised in each work. ABMS has been employed to represent the flow of materials (Lieder et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Walzberg et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and also spatial considerations (Luo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Walzberg et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Meanwhile, agent heterogeneity was obtained from survey data by Mashhadi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and used within an ABMS framework.\u003c/p\u003e \u003cp\u003eScenarios analysis led to recommendations, as evidenced by metric quantification, for achieving increased circularity (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For instance, the reuse of hard disk drives was found to be more environmentally friendly (Walzberg et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, CE practice was found to be facilitated by proper education on recycling and recovery options, effective regulatory frameworks, technological innovations, improving product eco-design, and establishment of waste appliance recovery networks (Luo et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Scenarios analysis also revealed agents\u0026rsquo; previous positive experience on buying/leasing affects further buying/leasing decisions (Mashhadi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The \u003cem\u003e\u0026lsquo;pay-per-use\u0026rsquo;\u003c/em\u003e approach supported by advertisements provided environmental friendliness and service-orientation. This was further aligned with a \u003cem\u003e\u0026lsquo;buy-back\u0026rsquo;\u003c/em\u003e scenario for more returns after a certain period in Lieder et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications within the electrical and electronics products sectors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics and/or parameters quantified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhoto-voltaic cell circularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecycling costs, material recycling rate, material recovery rate, initial recycling costs, recyclers\u0026rsquo; net income, reuse rate, landfill costs, societal costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, reuse, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheory of planned behaviour (TPB) was used to model decision-making for PV cell purchase/reuse.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWalzberg et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd of management of magnets of hard-disk drives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecovered mass of rare earth material (REE), material value, and avoided greenhouse gas (GHG) emission by recycling, hard disk drives (HDD) reuse, and magnet reuse, enhancement of trust towards data wiping.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheory of planned behaviour (TPB) was used to model customers\u0026rsquo; intention to reuse/recycle the hard-disk drives. Data uncertainties and qualities were modelled through data pedigree matrix.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWalzberg et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaste electrical appliance recovery industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaste appliance generated per capita, extent of environmental pollution perceived by residents, service level of each recycler perceived by the residents, residents\u0026rsquo; recycling tendency with respect to each recycler, recovery rate, policy costs, profits of the agents.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPB was used to model residents\u0026rsquo; recycling behaviour.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLuo et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell phone leasing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal buy/lease decision count, tendency to lease next cell phone if the lease term is increased, percentage of leases over time for different new product prices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscrete choice analysis to predict future market demand considering social influences of new product adoption.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMashhadi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashing machine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarket share, customer satisfaction, price, environment friendliness, service.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRefurbish, reuse, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLieder et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashing machine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of customers served, aggregated lifecycle cost, aggregated lifecycle impact, and material savings.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, remanufacture, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscrete event simulation was used to model the closed loop-supply chain movements.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLieder et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Circularity of Single Materials\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 System dynamics modelling for circularity of single materials\u003c/h2\u003e \u003cp\u003eIn a 'learning-by-doing'-based system dynamics model, Ghosh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored end-of-life pathways for PET bottles, calculating circularity indicators considering technological, economic, and policy constraints. Meanwhile Saidani et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) employed a multi-method simulation approach to identify, classify and assess key parameters and action levers to close the material cycle loop of platinum in catalytic converters. System dynamics also modelled key endogenous policy impacts on the development of a circular model for zinc manufacturing growth (Ojha and Vrat, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), considering exploration, demand, collection, recycling, repair and reuse. Finally, Pfaff et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) applied system dynamics to examine sectoral primary and secondary copper flows and stocks in Germany.\u003c/p\u003e \u003cp\u003eCausal loop diagrams were presented by Ojha and Vrat (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Saidani et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to illustrate the dynamic connections between collection, recovery, circularity rate, and influence of linear and circular economy factors affecting manufacturing output growth. Ghosh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) didn\u0026rsquo;t show a stock-and-flow diagram, but conveyed it implicitly to perform the lifecycle and techno-economic analyses, which considered the flow of plastics. Stocks and flows diagrams have been presented to illustrate the flow of platinum (Saidani et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), zinc (Ojha and Vrat, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and copper (Pfaff et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Temporal evolution of material flows was intrinsic in each study. However, spatial aspects were considered less frequently (Ghosh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pfaff et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the exception of Saidani et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), all studies conducted scenarios analysis, leading to recommendations through metrics quantification (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For instance, optimal PET circularity was when \u003cem\u003e\u0026lsquo;recyclate glycolysis\u0026rsquo;\u003c/em\u003e was adopted with improved collection access, due to increased replacement of virgin materials with recycled resin (Ghosh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meanwhile, increased geological exploration, improved collection facilities, cost-effective remanufacturing technologies, and better incentive schemes were recommended for improving zinc circularity (Ojha and Vrat, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, Pfaff et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) concluded that imported primary copper had detrimental environmental impacts. However, this work was contingent on extensive data requirements. See \u003cb\u003eTable S12-S13\u003c/b\u003e for more details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications concerning circularity of single materials.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlastics (PET bottles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCircularity (closed loop circularity, open loop circularity, upcycling circularity, incineration circularity, average inflow outflow circularity, landfill diversion circularity) environmental impact (GWP in kg CO\u003csub\u003e2\u003c/sub\u003e eq.) and life cycle costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGhosh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatinum in vehicular catalytic converters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatinum circularity, potential collection, collection of catalytic converters, recovery of platinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFuzzy cognitive mapping to identify action levers, drivers, and parameters responsible for loop-closure. Functional analysis system technique (FAST) and matrix impact cross-reference multiplication applied to a classification (MICMAC) to list the potential action levers and to select and cluster the key variables, respectively.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaidani et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc recycling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioural trends of four variables: green reserve, extraction material, manufacturing output and total refeed for different developed scenarios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepair, reuse, remanufacture, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOjha and Vrat (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper production and recycling sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd of life (EOL) collection rate and recycling rate, total copper input to domestic production of finished goods, total collected domestic EOL copper scrap, primary copper input to domestic production of finished goods.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASTRA based macroeconomic model was combined with system dynamics to evaluate the economic aspects of the sector.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePfaff et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Manufacturing Sector\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 System dynamics modelling applications in the manufacturing sector\u003c/h2\u003e \u003cp\u003eA multi-method simulation approach was proposed for a circular manufacturing system (CMS) of washing machines (Roci et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), integrating ABMS, discrete event simulation (DES), and system dynamics modelling. Franco (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated the impacts of product design (i.e., slowing resource flows through design for longevity, ease of maintenance and repair; and by design for disassembly and recycling), business models (i.e., slowing resource flow by product-service-system and maintenance, and reuse), and post-use strategies on facilitating CE. In a theoretical study on data-driven circular economy Charnley et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) developed the concept of certainty of product quality (CPQ) and applied it to electric motor circularity through remanufacturing, by integrating DES and system dynamics modelling.\u003c/p\u003e \u003cp\u003eNeither Charnley et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) nor Roci et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) developed causal loop diagrams, but they have been presented to highlight the causality between different indices (e.g., disassembly index, recyclability index, functional risk, etc.). Stock-and-flow diagrams accounted material flows (Charnley et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Franco, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as dynamic evolutions of finance, and emission flows (Roci et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith Austria as their system boundary, scenarios analysis and recommendations driven by metrics quantification revealed the benefits of circular economy for washing machines (Roci et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (see Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The work also found that manufacturing cost dominated lifecycle costs, followed by installation, refurbishment, and deinstallation costs. However, environmental impact was dominated by the use phase, possibly due to the long lifespan of white goods. Franco (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed that when perceived functional risks of recycled or reused products were low, it positively impacted circularity. Additionally, increased CPQ was helpful in promoting the CE as shown in Charnley et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). See \u003cb\u003eTable S14-15\u003c/b\u003e for more information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications within the manufacturing sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain of case-study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite goods manufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLifecycle costs, environmental performance (kg CO\u003csub\u003e2\u003c/sub\u003e eq.), revenue streams and profit over time.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, refurbish, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eABMS was adopted to describe the heterogeneity of the agents (i.e., manufacturers, customers, etc.). DES was utilised to describe the processes flow (e.g., manufacturing processes and logistic activities).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRoci et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothetical manufacturing firm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducts collected for recycling, recycled products inventory, recycling waste rate, recycling rate, recycled products sales rate, purchase rate, products in use, landfill products, products collected for recycling, etc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepair, reuse, refurbish, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBass diffusion model was adopted to model the shape of the distribution of the short and long-life product.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFranco (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemanufacturing process of electric motors (rotor and shaft)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime spent for remanufacturing based on the certainty of product quality (CPQ) value, effect of CPQ on reusable products.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, repair, refurbish, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDES for enumerating the sequence of operations in a remanufacturing process.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCharnley et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Industrial Symbiosis\u003c/h2\u003e \u003cp\u003eIndustrial symbiotic networks (ISNs) represent clusters of firms engaged in exchanging residual materials, energy, and information, with the overarching goals of fostering economic prosperity, social advancements, and mitigating environmental impacts. They have been considered as \u0026lsquo;complex adaptive systems\u0026rsquo; for compelling reasons: actors emerge into coherent forms over time, their adaptive nature reflecting their ability to change over time, self-organisation because of their ability to find new partners and collaborations based on mutual utility, path-dependency and non-linearity (Fraccascia and Yazan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 System dynamics modelling in industrial symbiosis\u003c/h2\u003e \u003cp\u003eWhile ABMS is considered as more adept at capturing symbiotic patterns with heterogeneous agents in ISNs, some authors tried to model it through system dynamics models. A dynamic empirical evaluation model for capturing sustainable development of eco-industrial parks was introduced by Zhao et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), using an emergy index. Meanwhile, Morales et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) modelled a meso-scale CE implementation for bio-based industrial symbiosis (BBIS) of the sugar-beet value chain, emphasising the concept of viable value chain (VVC) during disruptive events (e.g., COVID-19, climate change). In a different context, Dong et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposed a system dynamics model to understand the long-term materials flow inside a coal power plant and a cement production plant for integrated circularity.\u003c/p\u003e \u003cp\u003eThe inclusion of a causal loop diagram in Zhao et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) facilitated capture of the dynamics of different industrial sectors. Causality of economy and ecology by sharing of the (waste) materials was presented in Dong et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The concept of emergy consists of money, material, and energy flows, and either or both were captured through stocks and flows diagrams in all studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles in system dynamics applications for the manufacturing sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-industrial park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic development (EDR\u003csup\u003e1\u003c/sup\u003e, EYR\u003csup\u003e2\u003c/sup\u003e), environmental compatibility (EWR\u003csup\u003e3\u003c/sup\u003e, ELR\u003csup\u003e4\u003c/sup\u003e), social acceptability (ED\u003csup\u003e5\u003c/sup\u003e, and CP\u003csup\u003e6\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot mentioned.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZhao et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar-beet value chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugar production, CO\u003csub\u003e2\u003c/sub\u003e emission, stillage, and bioethanol production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMorales et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCement and coal production plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural resource saving (e.g., gypsum, clay, limestone, coal consumption, water consumption), reduction of pollutants (SO\u003csub\u003e2\u003c/sub\u003e, smoke dust emission), carbon emission from calcination, costs savings, sales revenue, increase of costs of water, supply demand difference (SDD) of fly ash, de-sulphurised gypsum and slag generation, electricity power yield, cement yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, recycle, repurpose, recover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDong et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEDR is the ratio of total emergy use and industrial added value of the park in one year.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEYR is the ratio of the total emergy output to the emergy purchased from society (e.g., fuels, goods and services).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEWR is the ratio of the sum of emergy, with three wastes (viz., waste gas, wastewater, and solid waste) to the total emergy, which was used to measure the pressure of wastes on the ecosystem.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eELR is the ratio of purchased and non-renewable local emergy to the free/renewable resources emergy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eED is the ratio of emergy created by production processes to the area of the eco-industrial park.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCP is the ratio of available and per capita emergy usage.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAll of these studies considered system boundaries at the meso-scale and included temporal dynamics. Through scenarios analysis, and metrics quantification (see Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) these studies provided insights for decision-making. Zhao et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) adopted a science and technology-driven scenario to deliver optimal sustainable development. Systematic consideration of the value chain in industrial symbiosis to prevent supply chain collapse during shocks was underscored in Morales et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with their forecasts unveiling complexities overlooked by linear economic models. Meanwhile, efficient supply chain eco-design emerged as critical for circularity of two mutually dependent sectors (Dong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). See \u003cb\u003eTable S16 \u0026ndash; S17\u003c/b\u003e for details.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 ABMS in industrial symbiosis\u003c/h2\u003e \u003cp\u003eABMS modelled actors\u0026rsquo; behavioural patterns (viz., waste suppliers, waste processor), and their intricate relationships towards ISN robustness (in terms of network survival for a particular time period, and cash flows per tonne of waste) (Lange et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). While the viability of ISN survivability was examined for two business models (Lange et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), namely circular waste management and waste as by-product. The dynamics of the construction supply chain, focusing on recycled concrete aggregate in an ISN was presented by Yu et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Geographically oriented symbiotic relationships were examined in Raimbault et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and material flows in a complex stakeholders' involvement network in Fernandez-Mena et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Several aspects of ISN survival through information sharing (Fraccascia and Yazan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), cost-sharing negotiations between companies (Yazan and Fraccascia, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), redundancy strategy (Fraccascia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the impact of online platforms (Fraccascia, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have been shown.\u003c/p\u003e \u003cp\u003eAgent heterogeneity (\u003cb\u003eTable S27\u003c/b\u003e) has been captured in terms of changing supply chain quantity, physical degradation of waste and chemical composition (Lange et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Agents were also heterogeneous in terms of their quantity, location, storage capacity, material flow rate (destination agent), and various attributes for \u003cem\u003e\u0026lsquo;vehicle\u0026rsquo;\u003c/em\u003e agent (Yu et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and through their demand-offer functions (Raimbault et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, agents were heterogeneous due to industry type plus waste processor (Fernandez-Mena et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003eb\u003c/span\u003e) or input-output characteristics (Fraccascia, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fraccascia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fraccascia and Yazan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yazan and Fraccascia, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmergent patterns due to agents\u0026rsquo; adaptation capability ranged from leaving an ISN network (Raimbault et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), choosing new partners (Lange et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), and navigating uncertain encounters with the environment (Fraccascia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Materials flow (Fernandez-Mena et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003eb\u003c/span\u003e), and cash flow (Lange et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) between agents have also been quantified. In Raimbault et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Yu et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) material flows were modelled stochastically. The spatial dispersions of the agents (Fernandez-Mena et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were also contemplated through ABMS, proving beneficial over system dynamics.\u003c/p\u003e \u003cp\u003eThe memorising capability of agents in ABMS was showcased through \u0026lsquo;fitness to process the waste\u0026rsquo;, \u0026lsquo;leaving threshold\u0026rsquo; in Lange et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). A similar \u0026lsquo;fitness function\u0026rsquo;, based on economic benefits, encouraged agents to stay in ISNs (Yazan and Fraccascia, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). How the agents\u0026rsquo; activities were triggered by realising the material transfer from the vehicle partner was presented in Yu et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Note that, unlike with system dynamics, all the agents in the ABMS are not required to function during the entire simulation period. For instance, allocation of materials to agents only when they have the capacity to accommodate it (Fernandez-Mena et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScenarios analysis, leading to recommendations through metrics quantification (see Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) was conducted across most studies, apart from (Lange et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Raimbault et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, changes in scenarios in ABMS allowed observations at both micro and system levels, presenting its unique advantage over system dynamics. Additionally, during scenario analysis in ABMS, new emergent patterns could develop, which is not the case in system dynamics modelling. For instance, economic behaviour preceded behavioural patterns, such as ISN partners leaving networks due to insufficient waste supply (Lange et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Yu et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggested that increasing upcycling efficiency and subsiding stakeholders plays a pivotal role in RCA circularity. Distances between agents were key for ISN survival (Fernandez-Mena et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Raimbault et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A circularity scenario has been reported to mitigate GHG emissions, but at a substantial trade-off between food production and livestock number (Fernandez-Mena et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Sensitive information sharing through gradual trust-building was emphasised in Fraccascia and Yazan (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Minimising the differences between input-output quantities of waste facilitated the ISN survivability (Yazan and Fraccascia, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Transaction cost played a pivotal role in economic and environmental benefit provided by ISN (Fraccascia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While Fraccascia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that when over 60% of ISN members engaged with online platform sharing, partners who did not participate in the ISN were disadvantaged, and adversely impacted the environmental performance. For more information, see \u003cb\u003eTable S26 \u0026ndash; S28\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles and use of other tools in agent-based modelling applications for industrial symbiosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics and/or parameters quantified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISN partners: (a) large scale agricultural area, (b) small-scale urban agricultural area focused on sustainable food production and recreation, (c) a business park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCash flow per each industrial symbiotic network actor, and failure or success of network (robustness).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheory of planned behaviour (TPB) was used to model agents\u0026rsquo; negotiation and self-evaluation process.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLange et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio-based material suitable for anaerobic digestion for processing local waste and energy production from biogas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCircular business model survival rate percentage, value captured or lost per actor for each of the scenario.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPB was used for bilateral negotiations between waste processor and suppliers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLange et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecycled concrete aggregate ISN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelivered recycled concrete aggregates, reduced CO\u003csub\u003e2\u003c/sub\u003e emissions, and space of cooperation/industrial symbiosis probability between firms involved in the network for different scenarios.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGIS was adopted to articulate the complex spatial relationships of industrial actors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYu et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothetical symbiotic network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal waste flow and relative cost.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMulti-objective optimization was adopted to minimize the cost and waste products given that agents were distributed in a different geographical location.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaimbault et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro-food network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen flow, CO\u003csub\u003e2\u003c/sub\u003e emissions, number of local flows, CO\u003csub\u003e2\u003c/sub\u003e eq.\u0026nbsp;emitted per gigagram of protein and tera-calorie of metabolizable energy in food production, crop production, meat and milk production, animal feeding district balance, biogas, and electricity production.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGIS was adopted to highlight the distances between the agents.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFernandez-Mena et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro-food network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifferent local material flows within the network (e.g., local fertilization flow, animal requirements flow, and energy flows), the average distance in exchanges of manure and grass, and the CO\u003csub\u003e2\u003c/sub\u003e emission from material transport.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMulti-criteria assessment to compare the performances of different scenarios.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFernandez-Mena et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothetical industrial symbiosis case studies comprising marble waste and concrete production, and alcohol slops used for fertilizer production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic performance indicator = (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{economic\\:benefits\\:created\\:by\\:IS\\:}{production\\:costs\\:of\\:firms}\\)\u003c/span\u003e\u003c/span\u003e), and environmental performance measure =\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{total\\:waste\\:diverted\\:from\\:landfill\\:}{\\begin{array}{c}primary\\:inputs\\:saved+total\\:waste\\:\\\\\\:produced\\\\\\:+required\\:primary\\:inputs\\end{array}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical and monetary flows were modelled through enterprise input-output analysis (EIOA).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFraccascia and Yazan (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA hypothetical marble-concrete industrial symbiosis case-study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic benefits, probability of implementation of industrial symbiosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYazan and Fraccascia (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA hypothetical marble-concrete industrial symbiosis case-study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic performance indicator (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{economic\\:benefits\\:created\\:by\\:IS\\:}{production\\:costs\\:of\\:firms}\\)\u003c/span\u003e\u003c/span\u003e), and environmental performance measure\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{total\\:waste\\:diverted\\:from\\:landfill\\:}{\\begin{array}{c}primary\\:inputs\\:saved+total\\:waste\\:\\\\\\:produced\\\\\\:+required\\:primary\\:inputs\\end{array}}\\)\u003c/span\u003e\u003c/span\u003e),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFraccascia et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothetical industrial symbiosis case studies comprising marble waste and concrete production, and alcohol slops used for fertilizer production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of waste exchange by each company involved in the IS, platform usage rate, and amount of saved residuals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, recycle and recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFraccascia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Miscellaneous Sectors\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1 System dynamics modelling applications in miscellaneous sectors\u003c/h2\u003e \u003cp\u003eTerritorial competitiveness index (TCI) has emerged as a significant metric for gauging sustainable growth. Sezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) projected the development trajectory of Izmir in Turkey through TCI. Meanwhile, recognising the role of extended producer responsibility towards promoting CE, Kuo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) developed a system dynamics model to determine the optimal subsidy (i.e., minimisation of cost and maximisation of recycling rate) between stakeholders pertaining to aseptic paper packaging. A parallel approach has been adopted to quantify and evaluate the implementation and effectiveness of regional CE (Gao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tracking material and energy flows between industries considering the impact of environmental fragility-economic poverty vicious cycles (FPVC) were shown in Cheng et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, Asif et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) developed an integrated ABMS and systems dynamics approach to understand the dynamic relationships between business approaches, supply chains and product-design, along their influences on economic and environmental performance.\u003c/p\u003e \u003cp\u003eCausal loop diagrams outlined the causal interplay between sustainability indicators and TCI in Sezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A similar diagram was utilised in Kuo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to represent the dynamics of waste generation, incorporating interventions related to production, costs, and recycling. When the multiple metrics for a particular value domain in CE is considered, there are always some cross-domain interconnectedness as presented in Asif et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Gao et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where resource consumption subsystem, environmental impact subsystem, etc. were causally linked.\u003c/p\u003e \u003cp\u003eWith the exception of Sezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), all studies in this sub-section developed stocks and flows diagrams to represent material flows. Sezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used it for showing the accumulation of GDP growth, alongside material flows (i.e., waste generation). Temporal consideration was integral in all studies, leading to scenarios analysis and recommendations through metrics quantification (see Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). For instance, Sezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) projected a peak of TCI in 2020, followed by a moderate decline to 2022, then a subsequent rise to 2027, due to development of sustainable policies, efficient resource utilisation, GDP increase, etc. While, Kuo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified collection cost and recycler capacity were the most sensitive parameters in the system, highlighting the decreased collection costs increased EPR fund generation, and decreased recycler capacity led to increased landfilled waste. Gao et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified that, along with growth of CE, GDP also increased, contingent to slight decrease in birth rate and development of tertiary industries. Cheng et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showed that CE improved the ecological and economic benefits in terms of improved livelihoods and reduced pollution of the considered system. For more information, see \u003cb\u003eTable S18-S19\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-study domains, quantified metrics, consideration of 'R' principles and use of other tools in system dynamics applications for miscellaneous sectors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase-study domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics quantified through system dynamics model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable development of a city in Turkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual GDP growth rate, social well-being, sustainable land use, CO\u003csub\u003e2\u003c/sub\u003e emission, renewable energy production, technology innovation index, waste generation, and territorial competitiveness index.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, reduce, recycle, recover.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSezer et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAseptic paper packaging waste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollection rate, recycling rate, extended producer responsibility fund, collection flow, recycling flow and waste in landfill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycle, repurpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKuo et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional CE of Guangdong province in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMass of total and direct material input and output components, Resource consumption and waste emission (biological substance consumption, fossil fuel consumption, solid waste consumption), intensity efficiency index (total material input of 10,000 RMB of GDP, and total material output of 10,000 RMB of GDP), building material consumption, and industrial exhaust emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGao et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA meso-scale implementation of a FPVC area in China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits of livestock faeces recycling (e.g., biogas production rate, recycling amount of faeces, conversion amount of organic fertilizer, pollution free fruits and vegetables output), effects of water savings (e.g., water saving amount, annual recycling of wastewater, terrace area, total number of water cellar), effects of waste recycling (e.g., utilization amount of potato residue, annual straw utilization and burning, mulching fil remained, feeding beef cattle), effects of energy savings (e.g., fossil energy decreases, CO\u003csub\u003e2\u003c/sub\u003e emissions reduction).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, reuse, recycle, repurpose, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCheng et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircular product systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental performance, cost-based economic performance, and profit-based economic performance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemanufacturing, reuse, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eABMS was coupled with system dynamics to capture the market information (population, income of the population, etc) and offer attributes (price to offer, convenience of the offer, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAsif et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.a.: not applicable\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.7.2 ABMS applications for miscellaneous sectors\u003c/h2\u003e \u003cp\u003eABMS has been adopted to examine circularity of wind power generation, considering end-of-life options for turbine blades (Walzberg et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Whereas, circularity via fashion renting, i.e., a product-service system has been examined using ABMS to represent customers\u0026rsquo; behaviour and interactions (Fani et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth studies showed the heterogeneity of agents in terms of behaviour. This led to different paths for material circularity. With theory of planned behaviour (TPB) used to represent the behaviours of the agents\u0026rsquo;, ABMS was crucial in representing how micro level changes of the TPB parameters impacted the whole dynamics of the system. In each study, all of the agents were interconnected, and connected with the environment. Changes in any of them led to the emergence of new behavioural patterns to adapt them in the system. Spatial and temporal considerations were inherent, along with the stochastic natures of the agents. The quantified metrics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. Furthermore, Walzberg et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) found that regulatory pressure and attitudes positively impacted recycling, and agents were more prone to recycle when the recycling facilities were located close-by. For more information, see \u003cb\u003eTable S29-S31\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDomains of case-studies, quantified metrics, consideration of 'R' principles, and use of other tools in agent-based modelling applications for miscellaneous sector.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain of case-study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics and/or parameters quantified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhich \u0026lsquo;R\u0026rsquo;s were considered?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse of other tools\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnshore wind turbine blades circularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional cumulative mean landfill rate according to different transportation costs, landfill behaviour under logistic barriers, adoption of thermoplastic blade design and the dissolution recycling pathway.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduce, reuse, repurpose, recycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTPB was adopted to represent agents\u0026rsquo; behaviour and its effects on the neighbouring agents.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWalzberg et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFashion renting process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCustomers\u0026rsquo; attitude towards fashion renting, performance of the service store, and experience of the customer.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReuse, refurbish, recovery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDES was used to model the fashion renting process.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFani et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Critical Observations","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Major Themes of System Dynamics and ABMS Approaches\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Why is system dynamics modelling favoured in CE quantification?\u003c/h2\u003e \u003cp\u003eThe preferences of system dynamics modelling in quantifying CE can be attributed to several key themes identified in the earlier discussions. These themes encompass causal loop diagrams, stock and flow diagrams, consideration of non-linearity or unpredictable variable evolution, temporal scale, spatial considerations, delay modelling, scenario generation and recommendations achieved through quantification of metrics.\u003c/p\u003e \u003cp\u003eA circular economy system is naturally complex, with lots of uncertainty and variability due to its constantly changing patterns. Thus, organisations seek an approach that can model systems and analyse their behaviour before actual implementation. System dynamics has both qualitative and quantitative analysis capabilities (Sumari et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The qualitative capability is exemplified through causal loop diagrams (e.g., reinforcing and balancing) to account for the causal relationships between system variables. Quantification involves the transformation of causal loop diagrams into stocks and flows diagrams. The usual workflow for developing a system dynamics model is shown in \u003cb\u003eFigure S1\u003c/b\u003e. It shows that initial models often cannot capture reality, and system dynamics modelling is therefore an iterative approach, where the model is continually updated based on inputs from the system thinker. This was clearly stated by Sterman (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe stock and flow diagrams in the literature are not only being used to represent the flow of materials, but also to model flow of money, energy, policy variables, etc. The diagrams represent the aggregation and disaggregation of stocks and flows in a continuous time domain through various differential equations, accounting for the causality of other system variables. Thus, compared to dynamic MFA by system dynamics models, static MFA and Bayesian MFA do not allow extrapolation and exploration of future scenarios, but they rather provide snapshots of systems at a given time, and do not consider non-deterministic causality and/or interdependency of other system variables. However, this aggregation in a continuous time domain leads to loss of individual properties, and a perfect mixing condition becomes prevalent in terms of dynamic MFA (Guerrero et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The (commercially) established/ standardised dynamic MFA performing platforms, often associated with life-cycle assessment approaches (e.g., STAN, Umberto, and OpenLCA, SimaPro) do not consider the internal dynamics within the system.\u003c/p\u003e \u003cp\u003eAdditionally, stocks and flows within any circular economy system (or complete value-chain) may be a stop-start process. For example, products may stay in a process, e.g., use phase, for some period, causing delays. This delay modelling is a unique characteristic of system dynamics modelling and has been incorporated either stochastically or deterministically (Guzzo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This delay modelling can give rise to non-linearity, as can causal loops, where one parameter may have a non-linear relationship with other parameters, in turn affecting stocks and flows. Each of the subsections have shown examples of system dynamics being used to demonstrate changes over time.\u003c/p\u003e \u003cp\u003eWhile both ABMS and system dynamics can perform spatial analysis, the former requires more, finer system details than the latter. Conversely, the aggregation property of the latter provides decisions at a system level and may not provide finer details at the local level. While finer details may be possible, this comes at the cost of increased computational complexity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Why is ABMS favoured in CE quantification?\u003c/h2\u003e \u003cp\u003eThe ABMS literature revealed major themes: heterogeneity, adaptation, agent-agent and agent-environment interactions (sometimes leading to emergent properties), spatial considerations, flow modelling, stochasticity of agents and scenarios analysis.\u003c/p\u003e \u003cp\u003eIn terms of individuality modelling, ABMS is superior to system dynamics as it can handle finer details (Borshchev and Filippov, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The memorising capability of ABMS leads to adaptation potential through path dependency. In other words, if agents are rewarded for following a particular path, then their future behaviour is more likely to also follow that particular path. This rewarding information is sometimes shared with neighbouring agents. Thus, discrete-time disaggregated agents interact with each other and/or their environment at each time-step, and based on pre-defined state-chart rules, they either move to the next state or stay in the current state. The interaction space between agents is usually user-defined (e.g., circle (Fernandez-Mena et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e)) and can be adjusted as per the case-study (Guerrero et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, some interesting phenomena in the context of CE systems, such as, technology adoption, word-of-mouth, and residents\u0026rsquo; intention lends authors to ABMS. Although these can be modelled with a system dynamics approach, it is at the cost of time and complexity (i.e., by incorporating different variables and thereafter setting their values and causal relationships).\u003c/p\u003e \u003cp\u003eAnother benefit of ABMS for simulating CE scenarios is its capability to incorporate the spatial scale. This has been attempted with a spatial system dynamics model (Neuwirth and Peck, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), (in different context) by integration of the GIS system in the model, but its use is still not widespread. As shown in \u003cb\u003eSection 3.6.2\u003c/b\u003e and in Raimbault et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the clustering of industrial symbiotic partners is highly relevant to circular economy systems at the meso- (regional) or macro- (national or global) scales.\u003c/p\u003e \u003cp\u003eThe delay modelling in system dynamics and triggering of a particular event in ABMS are ostensibly analogous, but quite different in mechanism. While the former is part of the model\u0026rsquo;s internal process and is governed by mathematical equations, the latter signifies the activation of a particular agent, governed by rules. Furthermore, the former does not produce emergent properties, rather it influences the flows, while the latter may produce emergent properties, but cannot be predicted in advance. Thus, depending on the rules, which are generally stochastic in nature, an event is triggered in the ABMS, while at the system level properties are generated.\u003c/p\u003e \u003cp\u003eConversely, ABMS can deal with qualitative data which may be represented in terms of scale values. For instance, considering the technological yield increase probability and attention to social reasoning as discrete values between [0,1] (Farahbakhsh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and incorporating these in simulations and scenario analysis. Thus, when such parameters are not easily quantifiable, ABMS can be considered a suitable approach.\u003c/p\u003e \u003cp\u003eScenario analysis in system dynamics and ABMS yield are both feasible, but from different perspectives. While system dynamics considers aggregate properties at the system level, in ABMS emergent properties are observed, which are in turn dependent on the agents and their dynamic interactions with each other and their environment. System dynamics scenario analysis often yields results from a single simulation run for a given parameter set, while ABMS typically requires multiple iterations to account for stochasticity and derive robust calculations. To elaborate, a single simulation run in system dynamics provide a complete picture of the system\u0026rsquo;s behaviour for a given set of initial conditions and parameters. However, the scenario analysis through system dynamics requires multiple parameters\u0026rsquo; adjustments, which in turns require to re-run the model with the adjusted parameters to get the complete understanding of the system. Thus, each scenario typically involves a deterministic simulation unless uncertainty is explicitly incorporated. However, in ABMS, due to stochasticity of the individual agents, and their associated rules, the system is itself stochastic. To understand the system\u0026rsquo;s behaviour, multiple iterations or simulation are required to account for the variability and derive statistical properties (e.g. confidence intervals).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Flow consideration through system dynamics and ABMS\u003c/h2\u003e \u003cp\u003eFlows are critical to revealing circularity, be they materials, energies, money, or policy. However, their modelling in ABMS in system dynamics models differ, as the former retains individuality, while it is lost in system dynamics, as shown in \u003cb\u003eFigs.\u0026nbsp;1a-1b\u003c/b\u003e (considering the example of material flows).\u003c/p\u003e \u003cp\u003eTo explain further, consider the work of Walzberg et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), where agents were triggered in the material exchanges by economics or peer pressure. Additionally, at each time-step the agent decided whether the waste material was landfilled or recycled, based on the cost-constraint. Similarly, in Raimbault et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), at each time-step, each agent looked whether their waste outputs could be another agent\u0026rsquo;s input. They then considered spatial and economic perspectives (because increased inter-agent distances increased associated transportation costs). Only if these requirements were fulfilled, did material exchange take place. Conversely, material exchange in Fernandez-Mena et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) was completely stochastic in nature, because the preference coefficient was not given as an input by the modeller, but randomly selected by the simulator. Thus, ABMS modelling, considers material exchange both from the micro-level perspective, but also after meeting various criteria. While this can also be modelled by system dynamics models, it is at the cost of modelling complexity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Joint Consideration of Metrics System Dynamics and Agent-Based Models\u003c/h2\u003e \u003cp\u003eCE has been considered a practical approach to progress towards a sustainable future (Geissdoerfer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kirchherr et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), adopting the three pillars of sustainability, as first proposed by Brundtland \u0026ndash; economic gains, cleaner environment, and societal prosperity (Brundtland, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows how various studies in the different sectors considered \u0026lsquo;value\u0026rsquo;-associated metrics from each of these three pillars. Half of the studies considered more than one metric, yet only around 10% considered value metrics covering all three of environmental, economic and social domains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, most studies focused on environmental and then economic aspects. However, there were differences between sectors. Studies in the bio-based sector had a greater emphasis on social and environmental metrics, while studies in the electrical and electronics sectors had a greater emphasis on economic aspects.\u003c/p\u003e \u003cp\u003eSimilarly, the metrics studied by ABMS can be grouped into economic, environmental, social value, others, and MCP flows (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Focussing on environmental, economic, and social value metrics, economic metrics dominated, followed by environmental aspects. Meanwhile, only one study, in the electrical and electronic products sector, considered social aspects, in this case in conjunction with economic metrics. While about half of the system dynamics studies considered multiple metrics, far fewer ABMS studies considered more than a single metric. Aside from studies in the electrical and electronic products sector only one study, in the bio-based sector, considered more than one metric.\u003c/p\u003e \u003cp\u003eThe dominance of single value metric studies using ABMS can be understood by recognising that this approach is mostly focused on how the changes in the micro level agents\u0026rsquo; behaviours impact the whole system. Possibly, researchers have avoided to jointly quantify the CE value metrics through ABMS due to increased computational complexity and traceability of models\u0026rsquo; microstructures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, describing circular economy requires a holistic approach, where overall economic, environmental and social benefits must be quantified from the systems thinking perspective for informed decision making. This enables targeted policy implementation through identification of key leverage points of the whole system. Furthermore, while analysis of individual systems has proved useful, these systems have still been somewhat isolated from a global perspective, and there have been limited endeavours to couple them and quantify metrics from a global perspective. This is possibly because there is, as yet, no global consensus on quantification of circular economy value metrics. Furthermore, value optimisation at key leverage points in the various systems considered here, is missing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Utilisation of External Tools along with System Dynamics and ABMS\u003c/h2\u003e \u003cp\u003eSystem dynamics can be considered as both rigid and flexible in its approach. It is rigid in terms of maintaining its fundamental principles as outlined in \u003cb\u003eTable S1\u003c/b\u003e, yet flexible in terms of its integration capability with other analytical tools. The inputs to the model can be optimised or tweaked, but there can be no changes in the basic principles of a system dynamics model. Meanwhile, outputs from system dynamics models can be further analysed using other tools, for example for optimal CE scenario selection, plus cost and benefits of each process, or environmental impacts through LCA. In this context, DES has been used to quantify flows but requires more abstract knowledge of the system, which can be resource intensive. This is possibly the reason why some studies resort to describing their models through hypothetical case-studies.\u003c/p\u003e \u003cp\u003eABMS can also be coupled with external tools. Many studies considered the Theory of Planned Behaviour (TPB) for modelling agents\u0026rsquo; thought process/behaviours, which influenced the state transition of the ABMS. It has also been adopted to incorporate regulatory behaviour and logistic constraints. Numerous studies have considered material flows in terms of enterprise input-output analysis (EIOA), with metrics quantified through employing stochastic- and/or indicator-based ABMS. DES has been employed, but as mentioned earlier, requires extensive abstract data, which is difficult to obtain. Additionally, data quality, uncertainty and selection of pertinent parameters have been considered in ABMS models.\u003c/p\u003e \u003cp\u003eThus, both SD and ABMS are flexible in modelling the complexities of system thinking, by integrating various tools and techniques to quantify different aspects of circular economy, which in turn leads to more informed decision-making. However, this integration requires judicious thinking and case-specific challenges, which solely depends upon the modeller\u0026rsquo;s expert judgment.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4 Where we are in CE tree \u0026ndash; \u0026lsquo;R\u0026rsquo;-based value retention?\u003c/b\u003e \u003c/p\u003e\u003cp\u003eIn the introduction, the \u0026lsquo;R\u0026rsquo;-philosophies of CE frameworks were presented, with a more detailed elaboration available elsewhere (Jawahir and Bradley, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Potting et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e showed how the various studies considered each \u0026lsquo;R\u0026rsquo; for each sector. Here, we discuss the aggregated view, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. To maximise value retention of MCPs, focus should be on the upper-part of the \u0026lsquo;R\u0026rsquo;-based CE-tree (\u0026lsquo;R0\u0026rsquo;), instead of the lower-part (\u0026lsquo;R9\u0026rsquo;), where there are increased risks of value loss (Ellen MacArthur Foundation, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kirchherr et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, most of the studies considered here have focused on the lower-part rather than the upper-part. Refuse and rethink were each considered only once, despite being considered as key goals of a circular economy. However, indeed these terms were some of the more recent additions to the \u0026lsquo;R\u0026rsquo; lexicon (Kirchherr et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and so some of the earlier studies would have predated their introduction. Also, much of the literature considers materials which have already entered the anthroposphere before circular economy ideas were popularised. Thus, the situation may change over the coming years as industry adopts circular design principles, incorporating durability, repairability, and recyclability.\u003c/p\u003e \u003cp\u003eGroups the studies based on the applications of system dynamics and ABMS, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that system dynamics is typically far more prevalent, apart from in the case of industrial symbiosis, where the behaviour of individual agents is of key importance, with greater consideration of reuse, recycle and recovery. The focus in the bio-based sector is on recycling, reuse and recovery. The absence of repair, refurbish and remanufacture is not surprising given the nature of the sector. In the construction sector, the focus is on reduce and recycle, reflecting perhaps greater awareness of the need to improve management of construction and demolition waste arising from existing building stocks. While there is interest in reuse, repair, refurbish and repurpose within the construction industry, it is more at the design and construction stage than at end-of-life. Focus in the higher-value electrical and electronics products sector is on reuse, recycle, and recovery options. Similarly, studies focussing on the manufacturing sector primarily considered reuse and refurbish, again reflecting the focus of the studies being products more than systems. The focus of the studies on single materials was recycle and recovery, reflecting the relatively high-value materials being considered in many of the studies. Finally, the miscellaneous sectors mostly focused on reuse, recycle and recovery options. Additionally, from the ABMS perspective, recycle was mostly widely considered, followed by reuse, and recovery. Interestingly, refuse, rethink, and repair are missing from all studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Future Research Directions","content":"\u003cp\u003eBased on the analysis and discussion, the following future research directions could be suggested:\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cem\u003eNeed for holistic consideration for identification of leverage points in the circular value-chain:\u0026nbsp;\u003c/em\u003eThis paper\u003cem\u003e\u0026nbsp;\u003c/em\u003estarted with a discussion of systems thinking and its associated complexity. The purpose of systems thinking is identifying root causes of challenges and solutions. In the CE context, this entails closing, slowing, or narrowing the materials and energy loops. The systematic literature review showed how previous literature has considered systems thinking, thereby identifying benefits of different CE related metrics, and quantifying them. Various metrics have been considered in the value-chain of the materials, components, or products (MCPs) by using system dynamics and ABMS. However, information is still lacking along the whole value-chain of MCPs from the raw material excavation to end-of-life processing, and reintroduction into the value-chain. The values (i.e., economic, environmental, social, and technical) generated/destroyed/transferred need to be considered at each process, and thereafter identifying the key leverage points (Iacovidou et al., 2017b; Millward-Hopkins et al., 2018). This will enable informed policy decisions, which will act to disrupt the current system, and in doing so reveal further leverage points, promoting further interventions, and so on in a continuous process (Boral et al., 2024). At this point, not only will the MCPs be flowing in a circular way, but so will the decisions and associated impacts. This cannot be visualised without the aid of simulation approaches. Furthermore, the review has highlighted the interdisciplinarity of circular economy research. This should be continued and encouraged for more informed circular economy decision making.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eUse of multiple simulation approaches for holistic consideration:\u0026nbsp;\u003c/em\u003eCircular processes cannot be modelled by a single simulation approach. Each approach has its advantages and drawbacks, as elaborated in \u003cstrong\u003eS.2.2\u003c/strong\u003e and \u003cstrong\u003eS.2.3\u003c/strong\u003e. These simulation approaches are also sufficiently flexible to be integrated with other decision-making tools, enabling more informed decision-making. The integration of different tools and techniques requires experienced systems thinkers and analysts who are also conversant with circular economy. However, each implementation software has its own benefits and limitations, with some more suited to system dynamics and others to ABMS. The \u0026lsquo;PySD\u0026rsquo; package in Python has the capability to model the system dynamics architecture, and the \u0026lsquo;Mesa\u0026rsquo; package in the same platform can incorporate ABMS. However, despite being free to use, to date, no work has highlighted their integration. Without this, any holistic considerations will be limited to theoretical frameworks, limiting widespread (industrial) adoption.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eModelling external shocks and interventions:\u0026nbsp;\u003c/em\u003eInterventions from governments or policy makers, and external shocks (e.g., COVID pandemic) can distort, positively or negatively simulation outputs. The impacts of interventions cannot be quantified in a deterministic way, but the stochastic and emergent properties of ABMS can model this. Also, techniques such as Poisson process (where shocks are random and independent), renewal process (where shocks occur at constant rate), and Gamma process (shocks occur at a rate that increases with time) can model shocks. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eData availability, uncertainty, quality:\u0026nbsp;\u003c/em\u003eCircular economy research suffers from a lack of complete data in terms of materials, energy flows, etc (Boral and Black, 2024; Lysaght et al., 2023; Velis et al., 2021; Wang et al., 2024, 2022). Moving across scales, from \u0026lsquo;micro\u0026rsquo; level analysis to \u0026lsquo;meso\u0026rsquo; or \u0026lsquo;macro\u0026rsquo; level, another challenge is data aggregation, assuming data is available. Then, when system boundaries expand, modelling data uncertainty and quality are further challenges. Although fuzzy, and Bayesian MFA are options to account for data uncertainty, they can\u0026rsquo;t handle the system dynamicity, and thus their application to holistic circular economy systems is still lacking. This can only be solved through open data and software platforms capable of multiple modelling approaches.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis paper is the first to highlight how different circularity metrics have been quantified using system dynamics and ABMS approaches. Overall, our review and analysis provide insights into the strengths and limitations of these methodologies, paving the way for further research in circular economy.\u003c/p\u003e \u003cp\u003eSystems thinking is not new, but its application to circular economy is still nascent. This sectoral review highlights studies modelling the associated complexities via system thinking analytical and computational approaches. System dynamics, a top-down approach, considers the aggregated view of the system by modelling the complexities through causal loops, stocks and flows, delays, temporal scale, etc.; and ABMS takes a bottom-up approach for modelling granular information of the system, considering the heterogeneity, adaptiveness, emergence, temporal and spatial scales, and dynamic interactions. This latter approach is more recent and so is still developing. Consequently, the combination of system dynamics and ABMS has not yet been applied in a circular economy context, while also considering the value metrics from different value domains, despite the strong potential for enhanced circularity quantification. Further combining system-level quantification with other (traditional) decision-support tools could enable more informed decisions on advancing circular economy theory and practice by industry and policymakers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded as part of the UKRI Interdisciplinary Circular Economy Centre for Mineral-based Construction Materials (ICEC-MCM), by the Engineering and Physical Sciences Research Council (EPSRC) - Grant reference: EP/V011820/1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoumava Boral:\u003c/strong\u003e Conceptualisation, Methodology, Writing \u0026ndash; Original draft preparation, Formal analysis, Data Curation, Visualization; \u003cstrong\u003eLeon Black:\u003c/strong\u003e Conceptualisation, Writing \u0026ndash; Review and Editing, Visualisation, Supervision, Funding acquisition; \u003cstrong\u003eCostas Velis:\u003c/strong\u003e Conceptualisation, Methodology, Writing \u0026ndash; Review and Editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data associated with this study are available in the article and in the \u003cstrong\u003eSupplementary Material (SM)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support of UKRI Interdisciplinary Circular Economy Centre for Mineral-based Construction Materials (EP/V011820/1). We thank Ed Cook from University of Leeds for his constructive suggestions while drafting this article. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, I.A., Shamim, A., Shad, M.K., Ashari, H., Yusuf, I., 2024. Circular economy-based integrated farming system for indigenous chicken: Fostering food security and sustainability. J. Clean. Prod. 436, 140368.\u003c/li\u003e\n\u003cli\u003eAlamerew, Y.A., Brissaud, D., 2020. Modelling reverse supply chain through system dynamics for realizing the transition towards the circular economy: A case study on electric vehicle batteries. J. Clean. Prod. 254, 120025.\u003c/li\u003e\n\u003cli\u003eArnold, R.D., Wade, J.P., 2015. A definition of systems thinking: A systems approach. Procedia Comput. Sci. 44, 669\u0026ndash;678.\u003c/li\u003e\n\u003cli\u003eAsif, F.M., Lieder, M., Rashid, A., 2016. Multi-method simulation based tool to evaluate economic and environmental performance of circular product systems. 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Sustain. 25, 11531\u0026ndash;11556.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"5260bd39-4413-4c8e-896d-bcd06bd1d6f4","identifier":"10.13039/100014013","name":"UK Research and Innovation","awardNumber":"EP/V011820/1","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Leeds","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Circular economy, Systems thinking, Complexity modelling, Agent-based modelling and simulation, System dynamics, Circularity, Metrics","lastPublishedDoi":"10.21203/rs.3.rs-5844499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5844499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCircular economy (CE) quantification features intrinsic complexity, mandating the application of systems thinking and associated methodologies to navigate multifaceted and dynamic intricacies; posing challenges for science-policy interfacing. Well-established approaches such as System Dynamics (SD) and emergent Agent-Based Modeling and Simulation (ABMS) are adept at interrogating such complexities within intricate systems. While SD employs a macroscopic, top-down lens, ABMS delves into a microscopic, bottom-up perspective. However, to date there are no comprehensive reviews quantifying circularity through systems thinking and its associated complexity modelling. Here, we analyse this topic through a systematic scoping review using PRISMA-ScR. Our analysis has identified core limitations in existing approaches, regarding the extent to which CE complexity has been captured holistically. Although both SD and ABMS can address circularity\u0026rsquo;s dynamic interactions and feedback loops, they are predominantly applied in isolation due to the absence of standardised platforms that can integrate both approaches, and to reduce computational costs. Exploration of the potential synergies from combining these two approaches and coupling them with traditional decision-support tools such as life-cycle and multi-criteria ones are minimal. Such a fragmented approach limits their ability to model internal dynamics; in turn restricting their utility to inform system-wide decision-support. The review also accentuates the lack of standardised metrics and the need for a more holistic evaluation framework for CE incorporating economic, environmental, social, and technical value metrics. A more unified approach to support sustainable, informed decisions in the pursuit of circularity is imperative for improving evidence-based policymaking and empowering industrial adoption of circularity.\u003c/p\u003e","manuscriptTitle":"Conceptualizing systems thinking and complexity modelling for circular economy quantification: A systematic review and critical analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-07 08:23:57","doi":"10.21203/rs.3.rs-5844499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efc5ae46-4c6e-45f9-822b-397b882c0660","owner":[],"postedDate":"February 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43048154,"name":"Environmental Engineering"},{"id":43048155,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-02-07T08:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-07 08:23:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5844499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5844499","identity":"rs-5844499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}