Evaluating the Circularity of Manufactured Products: Proposition of a Unified Index Based on Existing Indicators

Abstract

The issue of waste in oceans has become a major global concern since the 1970s. As a result, there has been an increased focus on addressing its environmental and economic impact. In this context, a circularity index has been proposed to evaluate the effectiveness of reuse strategies for manufactured products, with a specific emphasis on plastic materials. To develop this index, a comprehensive literature review was conducted, which identified 11 microlevel indicators related to circularity. These indicators were analyzed against six crucial criteria outlined by the Ellen MacArthur Foundation. Based on this analysis, a theoretical circularity index was created by combining three indicators that cover all the essential criteria, namely energy, lifespan, and reuse. To illustrate the application of the proposed index in the real world, the article presents an example of a plastic industrial pallet, highlighting areas for improvement in achieving greater circularity in product lifecycles. The main implication is that now managers and practitioners of manufacturing industries can measure their impact on essential circular issues with a single metric.

Introduction

In recent decades, there has been a growing public concern about the rising amount of manufactured waste in our oceans. It was not until 1972 that the United Nations Environmental Assembly officially recognized the severe threat posed by plastic materials to the environment and humanity. The damage caused by plastic waste alone is estimated to reach up to $13 billion (Akenji et al., 2020). Understanding the nature of this waste and its potential consequences is crucial for decision-makers to implement measures to mitigate its impact (Jambeck et al., 2015). The vast amount of waste produced each year from manufactured products is causing significant environmental harm across multiple domains. Some experts argue that materials like plastics should be classified as hazardous waste due to their inherent toxicity and pollutant-absorbing abilities (Worm et al., 2017).

The current linear consumption model, which involves producing, consuming, and disposing of products, has negative impacts on the environment (Miller et al., 2018). To address these issues, the circular economy (CE) has emerged as a solution. By designing products and processes with waste reduction in mind, the CE promotes reuse and reduces the need for new raw materials while minimizing environmental pollution. This approach allows for the extraction of local resources from postconsumer materials, making the CE a valuable tool for addressing manufactured waste (Geissdoerfer et al., 2016).

Measuring material circularity is important, even though complete circularity is not achievable due to entropy and energy dissipation. However, striving for complete circularity can enhance resource utilization and guide sustainability efforts. Therefore, this study aims to identify the most appropriate circularity indicators for evaluating manufactured materials and making informed decisions regarding their recovery.

Assessing the effectiveness of activities that promote material circularity can transform the human and environmental relationship with raw materials, such as metal, wood, glass, or plastic. Managing such measurements can help minimize energy requirements, waste production, and landfill dumping, as well as maximize the reuse and recycling of returning materials. Nonetheless, to the best of our research, we did not find a unified, single index that could embrace at the same time energy efficiency, lifespan, and rate of return of materials. Finding a unified index is the research gap this study aims to bridge by answering the following research question: How can we evaluate, at the same time, with a single index, energy efficiency, lifespan, and return of materials concerning a manufactured product?

The purpose of the article is to propose a circularity index that could inform how efficient the reuse strategy for a manufactured product is. The research methodology is quantitative modeling. The main novelty is the proposition of a unified index, a single metric that could embrace all relevant issues concerning the circular process of reusing a manufacturing product. Usually, practitioners must handle multiple indicators to ensure all the factors surrounding circular reusing are focused in their environmental strategy. The rest of the article is structured in three more sections: materials and methods, results, and conclusions.

Searching for Indicators in the Literature

A systematic literature review (SLR) can map and evaluate the relevant literature in order to answer a research question and develop scientific knowledge. This research is conducted using transparent and replicable methods, following methodological rigor (Tranfield et al., 2003). The SLR process involves defining the database, search logic, search criteria, criteria for article selection, and descriptive and content analysis. −

Two electronic databases were used to locate seed articles for the portfolio composition: Scopus and Web of Science. These databases were chosen due to their significant array of well-regarded articles that are acceptable to academia.

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The search logic for this study involved the use of databases to ensure that all relevant articles fitting the search criteria were considered. The search terms used were “circular economy” and “indicators” to focus on indicators associated with the CE.

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The defined criteria for database research were: (1) no restriction on publication period; (2) consideration of article studies; (3) consideration of articles in the final publication stage; and (4) consideration of articles only in English to allow the global audience to access and eventually triangulate findings.

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The article selection criteria involved the exclusion of articles without full text, abstracts, and keywords unrelated to manufactured materials and indicators, texts where the CE is only used as a future direction for study, and finally, CE cited only as expressions.

The snowball method was utilized to make a sample of articles (Atkinson & Flint, 2001). The snowball method is a research approach for systematic literature studies using a reference list from an article or the article’s citations to identify new sources. In this research, the article by Rizwan et al. (2018) was selected from the Scopus database, and the article by Bobba et al. (2023) was selected from the Web of Science database. After the SLR stages and the use of the snowball method, the results revealed a total of 53 circularity indicators identified in the selected articles. Initially, 39 articles were thoroughly read, starting with the most recent ones, which led to the inclusion of 20 additional articles in the database through the snowball method. This last inclusion demonstrates the effectiveness of the method in expanding the database and identifying new relevant sources for research.

Upon the sampled articles, the study retrieved indicators and criteria for measuring CE related to manufactured materials. Indicators for activities supporting a CE can be classified into three levels according to Kirchherr et al. (2017): micro (focusing on aspects related to organizations, products, and consumers), meso (centered on industrial parks), and macro (encompassing cities, provinces, regions, and countries). In this study, the focus is solely on indicators situated at the first level of the CE, as the primary objective is to investigate manufactured products. The complete list of the 53 indicators can be found in Barbosa (2023, p. 52). After analyzing the 53 indicators identified in the review, only 11 of them were considered relevant to the microlevel category and will be used in this research. This study adopted the relationship of indicators with six crucial criteria from the Ellen MacArthur Foundation’s report titled “A Vision of Circular Economy for Plastic” to assess the relevance of the metrics. The criteria are recirculation, reuse, recycling, energy efficiency, reduction, and hazardous substances. They were used to evaluate the 11 indicators, focusing on the most critical aspects of circularity for materials.

Finally, a structured evaluation recommendation was followed to establish a clear definition of what is intended to be measured. The authors stressed the importance of defining the guiding model based on a strict definition since the data were obtained from existing literature, representing a direct circularity. The strict definition focuses on closing the product cycle, extending both its lifespan and that of its components. The equations employed for constructing the theoretical CE indicator involved indices that led to composite indicators through technological cycles.

Results

Analysis of indicators and criteria retrieved from the literature

The SLR identified 11 microlevel indicators (IEC1 to IEC11) addressing different aspects of circularity concerning manufactured products. The following text discusses the indicators.

IEC1 evaluates the reuse of materials in products or components (Di Maio & Rem, 2015), while IEC2, also known as the circular economy index, measures the economic value of materials in consumer products (Cullen, 2017). IEC3, or circular economy performance indicator (CEPI), calculates the ideal environmental effectiveness based on available waste treatment options in a specific region (Ghisellini et al., 2016). IEC1, IEC2, and IEC3, although directly related to the CE, address different aspects of circularity. IEC1 evaluates the reuse of materials in products or components. This indicator is part of the “cradle-to-cradle” (C2C) approach, which aims for sustainability and material reuse. On the contrary, IEC2 focuses on measuring the economic value of materials in consumer products, considering strategic, economic, and environmental aspects. As for IEC3, it calculates the ideal environmental effectiveness based on available waste treatment options in a specific region. IEC4 is the circularity material cycles that evaluate the percentage of production based on returned material related to the maximum production in a given time lapse (Pauliuk et al., 2017). While some indicators relate to metallic materials in the literature (Pauliuk, 2018), their use can also be extended to other materials since crucial information for this indicator relates to the purity, quality, and material recovery capacity during use. IEC5 is the closed-loop calculator, which evaluates how close a product’s production cycle is. It relies on subindicators that take into account the design of the product, the type of use, and the manufacturing process. More than an indicator, it is a tool to evaluate the degree of circularity in a CE or a specific industrial sector, focusing on the product level, which places it at the micro level (Saidani et al., 2019). IEC6 is the end-of-life recycling rate; there is the percentage of a discarded material recycled or returned to a new cycle of production (Graedel et al., 2011). IEC7, or material circularity indicator, evaluates the circularity of a product’s component materials. Although evaluating the performance of systems and processes related to the CE, it focuses on the circularity of a product concerning its component materials, the use of virgin raw materials, the generation of unrefusable waste, and the product’s utility. Even if sometimes related to IEC1, it focuses on the broader approach of C2C, aiming to transform the production and consumption system into something regenerative and sustainable (Kroon et al., 2020). IEC8 (product-level circularity metric) operates based on using economic value as the basic unit to aggregate product parts into a circularity metric at the product level, where circularity is defined as the fraction of a product originating from other used products (European Commission, 2020). This fraction, considered as “recirculated” economic value, varies on a scale from 0 to 1 (or from 0% to 100% recirculated parts). The IEC9 (resource duration indicator—RDI), better known in the literature as a longevity indicator, was developed to express the longevity of resources within a set of aggregated resources (Franklin-Johnson et al., 2016). On the contrary, IEC10 (reuse potential indicator—RPI), like IEC9, measures how longevity uses time to measure the duration of a material within a product system, where longer retention means that resource use is maximized (Matos et al., 2023). Thus, IEC10 is a circularity metric that represents the amount of material that can be reused through available technologies, fractionated by its revenue from selling processed materials. IEC10 seeks to encompass the sustainability tripod, focusing on quantifying the economic usability of transforming waste into materials in terms of technological development. Finally, IEC11 (recycling rates) is the only indicator found that describes the amount of material recovered from a waste management system, such as landfills (Park & Chertow, 2014).

The next step is to pinpoint criteria to support the judgment of the indicators. EMF outlined six criteria—recirculation, reuse, recycling, reduction, energy efficiency, and hazardous substances—to analyze the circularity aspects addressed by each indicator: −

Recirculation (C1): The study suggests a strategic approach to eliminate problematic or unnecessary packaging through redesign, innovation, and novel delivery models. The focus is on phasing out specific products in the market to achieve a CE. Furthermore, the study acknowledges situations where packaging can be avoided without compromising utility.

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Reuse (C2): Emphasizing the adoption of reuse models whenever relevant, this criterion aims to reduce the dependence on single-use packaging. While improving recycling is vital, the study suggests exploring reuse-based business models as a preferred option whenever possible, thereby lessening the demand for disposable materials.

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Recycling (C3): The primary goal of this criterion is to prevent any plastic from being inadequately disposed of in the environment, emphasizing the importance of effective recycling processes.

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Reduction (C4): This criterion seeks to eradicate landfills, incineration, and waste-to-energy conversion. Achieving this involves reducing the overall use of problematic raw materials (for instance, plastic) and exploring environmentally friendly alternatives to replace petroleum-based materials.

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Energy efficiency (C5): Shifting from virgin raw material resources to renewable sources is the focus of this criterion, contingent on proving their environmental effectiveness and responsible management sources.

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Hazardous substances (C6): Fundamental to circularity, this criterion underscores the elimination of hazardous chemicals in the manufacturing and recycling of packaging. It highlights the importance of avoiding substances that pose risks to the environment and human health.

Table 1 lists which criterion each indicator addresses, according to the literature assigned in the last column. The table also informs the number of indicators (out of 11) that address each criterion.

Table 1.

Relationships Between Indicators and Criteria

Tag	Indicator	C1	C2	C3	C4	C5	C6	References
IEC1	Material reutilization part (C2C)	x	x		x			Di Maio and Rem (2015)
IEC2	Circular economy index (CEI)			x				Cullen (2017)
IEC3	Circular economy performance indicator (CEPI)			x		x		Ghisellini et al. (2016)
IEC4	Circularity material cycles (CIRC)			x				Pauliuk et al. (2017)
IEC5	Closed-loop calculator (CLC)		x	x				Saidani et al. (2019)
IEC6	End-of-life recycling rates (EoL-RRs)			x				Graedel et al. (2011)
IEC7	Material circularity indicator (MCI)	x						Kroon et al. (2020)
IEC8	Product-level circularity metric (PCM)	x						European Commission (2020)
IEC9	Resource duration indicator (RDI)	x	x					Franklin-Johnson et al. (2016)
IEC10	Reuse potential indicator (RPI)		x			x		Matos et al. (2023)
IEC11	Recycling rates (RRs)			x				Park and Chertow (2014)
Number of addressing indicators (out of 11)		4	4	6	1	2	0

Three criteria [C1, C2, C3] simultaneously appear in the same study (Di Maio et al., 2017), which is a clue for future research: recirculation, reuse, and reduction should be addressed at the same time by a single strategic action. The same indicator addresses other pairs of criteria simultaneously: [C3, C5], [C2, C3], [C1, C2], and [C3, C5]. Particularly, the pair [C3, C5] is addressed by three indicators, IEC1, IEC5, and IEC10, which means that single actions on these indicators can leverage both reuse and energy efficiency. Surprisingly, no indicator addressed C6 (hazardous substances), which is also a clue for future research.

For each indicator, a value of 1 is assigned if it covers a given dimension; otherwise, a value of 0 is assigned. This process results in a matrix, which is then used to calculate the similarity between each pair of indicators by applying the Jaccard coefficient. The Jaccard index measures the degree of similarity by comparing how many dimensions are shared between two indicators relative to the total dimensions they have. Past software version 2.17c, a specialized tool for statistical analysis, was used to calculate the Jaccard index. Additionally, the software generated a dendrogram—a diagram that groups similar multidimensional indicators into clusters, helping to visualize which indicators are most alike. Figures 1 and 2 synthesize the relationships between criteria and between indicators employing two dendrograms based on the Jaccard similarity index. In Figure 1, both dendrograms show [C1, C2, C4] and [C3, C5] as closely related criteria, as well as C6 as an isolated criterion. In Figure 2, both dendrograms show that [IC2, IC4, IC6, IC11] and [IC7, IC8] are closely related.

Figure 1.

Dendrograms based on Jaccard similarity index clustering the criteria: (left) single linkage and (right) paired group

Figure 2.

Dendrograms based on Jaccard similarity index clustering the indicators: (A) single linkage and (B) paired group

Proposition of a circularity index

A theoretical circularity index specifically focused on manufactured materials was developed based on the indicators and criteria previously studied. Table 1 shows that the three-tuple [IC3, IC9, IC10] is the combination with a small number of indicators that cover all five criteria (the 60 criterion is not covered by any indicator and from now on is removed from the study). Therefore, this study should employ a combination of those indicators to propose a new one that is wider than the primitive ones. IEC3 (CEPI) stood out as the only one with a direct and significant practical application concerning plastic materials (Habib, 2019). In essence, CEPI is a metric that assesses how effective and sustainable a waste treatment is by comparing the obtained result to an ideal based on environmental quality criteria (current benefit/ideal benefit). According to Huysman et al. (2017), the ideal benefit is the avoided energy impact for the virgin production of a given material represented in energy units as megajoules. Even so, CEPI needs to address essential social and economic aspects of the CE. The indicator also does not consider issues such as costs, employment in the recycling industry, economic impacts, and social justice in the value chain. IEC9 (RDI) evaluates how long a material remains within a system, which is important for maximizing resource utilization, including material reuse and recycling. RDI is expressed in time units, involving the initial lifespan A, which is the useful time of an item in its original state; the recovering lifespan B, which is the useful time of a refurbished item; and the recycling lifespan C, which is the useful time of a recycled item. Finally, IEC10 (RPI) ranges from 0 to 1, equaling 0 when the entire amount of materials of an item are discarded after the useful life and equaling 1 when all materials route to a new use. It enters as Y in the new indicator. Equation (1) combines elements of the three indicators to advance a new indicator more completely, as it considers not only environmental but also economic and social concerns. $I E C_{T} = μ_{1} . μ_{2} . μ_{3} = [\frac{(EfV - EfR)}{EfV}] . [\frac{A + B + C}{Benchmark}] . Y$ (1)

Where: −

In µ₁, EfV is the energy required to process a virgin unit, and EfR is the energy required to process a returned unit (inspired in CEPI);

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In µ₂, A, B, and C are time lapses, and the benchmark is a reference lifespan for the basic material extracted from reliable databases (inspired by RDI); and

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In µ₃, Y is the fraction of that kind of material that routes to new use in the region of the application (inspired in RPI).

A real-world example: An industrial pallet made of plastic

A real-world example can help illustrate the proposition.

As an example, this study considers the case of polyethylene (PE) plastic industrial packaging (pallets), whose closed-loop recycling process shows a 1:1 ratio, which means that 1 kg of returned material produces 1 kg of new material. Processing returned material requires 32 percent of processing virgin material (ABIPLAST, 2022). Typically, the lifespan of an original industrial pallet (A) is 6 years, the refurbished lifespan is 3 years (B) (Adlmaier & Sellitto, 2007), and the lifespan of a new product (typically plastic furniture for houses and offices), made from recycled material from useless pallets, is 15 years (C). The sum of the three lifespans yields 24 years. According to the annual report of the European Commission titled Global Action for Plastics, for PE, A equals 54 years, B corresponds to 17 years, and C corresponds to 12 years, totaling 83 years (European Commission, 2020). Therefore, 83 years is the benchmark for the calculation. Finally, according to the Brazilian Plastic Industry Association report, the annual recycling rate of PE in Brazil is 66.25 percent, which means that 66.25 percent of the produced PE is likely to be recycled within 1 year (ABIPLAST, 2022). The remaining fraction routes to landfills. Based on such parameters, applying Equation (1) yields 13 percent, resulting in Equation (2). $I E C_{T} = μ_{1} . μ_{2} . μ_{3} = [68 %] . [\frac{24}{83}] . 66.2 % = 13 %$ (2)

Examining the application shows that the partial values are μ₁ = 68 percent, μ₂ = 29 percent, and μ₃ = 66 percent. The partial values offer valuable insights for guiding subsequent strategic actions. The primary concern lies with μ₂, indicating a need for strategic interventions to bolster the metric. The focus should be on extending all time lapses, particularly those associated with the initial usage of the product, to address the issue effectively. The current lifespans of 6 and 3 years for plastic usage in pallet manufacturing are notably short when compared to the benchmark of 83 years for the plastic life cycle. Actions should be directed toward enhancing the design to increase the time to failure for pallets. Additionally, improvements in the refurbishing process can extend the useful life of refurbished items. Finally, efforts should also be made to explore more robust applications for recycled materials, ensuring prolonged utility for the recycled units. These strategic initiatives will contribute to an overall improvement in the examined metrics and promote sustainable practices.

Conclusion

The CE model is gaining momentum as a crucial strategy for addressing the growing environmental problems caused by plastic waste. However, evaluating the effectiveness of material reuse and recycling strategies can be challenging. To address this issue, an innovative and comprehensive framework known as the circularity index was proposed. This framework provides an efficient approach to evaluating reuse strategies for manufactured products, particularly those made from plastic materials.

The circularity index is designed to provide a holistic assessment of circularity by combining multiple indicators and criteria that take into account environmental, economic, and social aspects. It offers a practical and actionable way to identify opportunities to improve product design, extend product lifespan, and encourage the use of recycled materials. By providing insights into the circularity of products, the index enables stakeholders to make informed decisions and take targeted actions to improve sustainability metrics.

The circularity index has been effectively applied in real-world situations to identify opportunities for improvement. By analyzing the circularity of a particular product, the index identified several areas where interventions could be made to improve circularity metrics. These interventions included improving product design to make it more easily recyclable, extending the product’s lifespan through maintenance and repair, and encouraging the use of recycled materials in production. The primary, though not the only, application of the index is in industrial activities, including supply chain management, manufacturing, distribution, and reverse logistics. The proposed circularity index serves as a valuable tool for encouraging sustainable practices in material reuse and recycling. By using this index, stakeholders can identify areas for improvement and implement specific actions to advance sustainability and CE goals. Additionally, non-governmental organizations and public entities can utilize the index to assess the environmental performance of activities within a specific region, helping to guide policy and decision-making for enhanced environmental stewardship.

Further research should focus on the refinement of the index, which may be helpful in advancing CE initiatives and curbing the environmental impact of manufactured waste. Furthermore, to comprehensively explore how the index should function across an entire economic sector, we propose conducting a survey within a specific industry, such as the plastic packaging sector. Such survey should use a sufficiently large sample size, calculated by statistical means. This approach mirrors the methodology employed by Baierle et al. (2020), who utilized surveys and mathematical tools to deeply delve into strategic issues within extensive networks of industrial companies, including entire industries. Finally, the presence of hazardous substances was not factored into the index. Future research should also address this important issue, as including hazardous substances could significantly impact the ecological footprint of industrial applications. Considering these substances would provide a more comprehensive understanding of the environmental risks associated with industrial processes.

Footnotes

Acknowledgments

The authors would like to thank the Brazilian Research Agency, CNPq, for funding this research.

Authors’ Contributions

All authors have read and agreed with the published version of the article. Conceptualization: R.F.M.B. and L.M.S.C. Methodology: R.F.M.B. and L.M.S.C. Validation: G.S.R. and M.A.S. Formal analysis: G.S.R. and M.A.S. Writing—original draft preparation: R.F.M.B. Writing—review and editing: M.A.S. Supervision: L.M.S.C. Project administration: L.M.S.C. Funding acquisition: L.M.S.C.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This research was funded by the CNPq, the Brazilian Agency for Research, under grant agreement No. 303496/2022-3.

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