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Measuring the performance and maturity of the plastic recycling value chain system: implications and prospects

Abstract

The sustainability of plastic materials and products requires the continuous improvement of the circular pathways for the material. A key strategy in the circularity of plastic is plastic recycling. Improving the circular pathways requires an understanding of the maturity level of the plastic recycling system. This study evaluated the maturity of the plastic recycling system in South Africa across the plastic value chain. Both secondary and primary data were collected, analysed and cross-validated. The results put the maturity of the country’s system at “Visionary” (Level 3) for the value chain stages of primary plastic production, product manufacturing and recycling, whereas waste generation, collection and handling, sorting and recyclate market were rated as “Structured” (Level 2). Furthermore, a set of initiatives to advance the maturity of the system to the desired level of “Connected and Dynamic” (Level 5) were identified. The paper provides a benchmark of performance and determines the stages of the system requiring additional attention. This is aimed at providing insight into policymaking to advance plastic recycling and circularity.

Introduction

The plastic industry has developed and contributed substantially to the global economy over the years. Globally, in 2021, plastic production was estimated at 390.7 million tons [1], and this volume was projected to be almost double by 2035 and to quadruple by 2050 [2]. The continued increase in plastic use contributes considerably to the total mass of generated waste within the environment. Globally, a significant percentage of plastics is being wasted, with the major share of this generated waste either landfilled, incinerated, or leaked into the ecosystem, and a mere 9% effectively recycled [3]. In the specific area of focus for this study, namely, South Africa, as of 2020, 90% of waste generated was still ending up in the landfill [4].

For the sustainability of plastic as a resource, the circular pathways for the material must be improved. Plastic circularity has been described as a feasible and beneficial alternative to the linear economy (where plastic is simply produced, consumed and disposed). Recycling is one of the many available circular solutions. Although inferior to other measures or strategies found in the waste management hierarchy, it is useful in mitigating the negative impact of plastic waste.

This study shares the perspective that the current performance and level of maturity of the individual stages across the plastic value chain, as well as the whole system, first need to be understood and established to improve plastic circularity. Therefore, this study aimed to determine the level of maturity of the plastic recycling system in South Africa to identify pathways to improvement and increase plastic circularity and sustainability. According to Hahladakis and Iacovidou [5], each stage along the plastic value chain needs to be considered separately when assessing the development and sustainability of the system, as all the stages have impacts or implications.

Equally, this study is driven by three basic considerations. First, the complete development of a plastic value chain cannot be achieved by focusing on a single stage in the value chain, but rather on all practices and activities in the different stages of the value chain. Second, no known study has measured the maturity of the plastic recycling system, with specific focus on the value chain stages, as shown in the following section of the study. Third, unlike many studies, this study does not propose a new maturity model, but applied an existing model (Plastic Recycling Value Chain Maturity Model), developed specifically for the plastic system, to a real-life situation and tested its applicability. Most studies tend to propose new maturity models instead of applying existing models to resolve real-life situations.

The maturity measurement of the plastic value chain in South Africa produces a basis for improving the country’s recycling system and the respective value chain stages and processes, determining areas requiring additional efforts and developing action plans for bridging the gaps between the current and desired maturity levels. It will contribute to the debate of optimising the plastic recycling option and circular economy in general.

Literature review

Fraser et al. [6] describe the concept of maturity as the growth or progression of a system from a preliminary status to a very advanced status. It involves a sequence of stages forming a desired pathway to an objective or goal [7]. These stages are represented by maturity models where processes are defined, applied and subsequently improved [8]. Pullen [9] describes a maturity model as a structured group made up of elements that define the attributes of processes required at various stages of growth or development. It is an enabler for achieving optimal performance. However, Pöppelbuß and Röglinger [7] propose that the primary focus of the model should not be only on the developmental stages, but also on the set of elements driving the growth process to an improved position.

Maturity models serve various useful purposes for a system, organisation or country. Battista et al. [10] and Van Dyk [11] describe them as tools for assessing an existing situation to guide the formulation of a control and improvement roadmap. Netland and Alfnes [12] indicate that they are applied by organisations or establishments to compare the level of maturity of their system processes with that of best practices. In addition, maturity models assist in benchmarking similar or comparable systems or processes in other organisations, countries or regions [7]. Schumacher et al. [13] maintain that they are appropriate for assessing the degree of resources in place to adopt necessary action.

The roots of maturity models are traceable to the quality maturity grid developed in 1979 by Crosby [14]. This measured organisational processes along the five maturity levels or stages, in ascending order, of uncertainty (stage 1), awakening (stage 2), enlightenment (stage 3), wisdom (stage 4) and certainty (stage 5) [14]. Mettler [15] further developed a framework that classifies maturity models according to the different dimensions they address. These include process maturity (the level of a process being unambiguously defined, measured, managed and controlled), people maturity (the level of people achieving knowledge development and proficiency improvement) and technology maturity (the level of a technology reaching a threshold). However, the majority of maturity models focus on process dimension [16].

Since 1993 when the Capability Maturity Model (CMM) was developed [17], maturity models have continued to enjoy increasing popularity and acceptance. Today, different maturity models have been developed for system or organisational assessment in various disciplines, including supply chain [18,19,20], energy management [21], product development [6], project management [22], technology [23] and software development [24]. Adopting maturity models has undoubtedly driven huge scholarly interest [25]. Their application in different fields of study, including the plastic system, is expected to continue to increase [26].

Furthermore, plastic sustainability as a resource demands that the circular pathways for the material must be continuously improved across all the stages of the plastic value chain. The objective will be to maximise the value of the plastic material at every value chain stage considering the focus is to keep plastic products and material at their best utility every time in a circular economy [3]. Plastics can potentially be recycled multiple times without compromising their mechanical properties too much, depending on the recovered plastic waste quality, thus contributing substantially to achieving a circular economy. While it is relatively normal to obtain extra value from plastic waste through recycling, this is usually not taken into consideration in the product design mechanism [27]. This situation demands a more complete approach or process all through the value chain stages of plastics production, consumption and end-of-life. Overproduction of plastic needs to be minimised or eliminated. Similarly, the designing and manufacturing of plastic products to facilitate reuse, repair or remanufacture will lead to less plastic products going into the plastic waste stream [3]. Shifting towards circularity of plastic should involve considering all dimensions or aspects of sustainability when closing the loops, while noting that the transition towards circularity can be hindered by different barriers, including regulatory, economic and technical [4].

The development of the circular pathways of plastic requires an understanding of the performance and maturity level of the plastic value chain system, which further requires the assessment or measurement of the system. The South African plastic waste management and recycling system is currently at a developing stage, with a low plastic recycling rate of approximately 13% recorded in 2017 [27]. The country still lags behind in shifting from waste landfilling to recycling, as an estimated 90% of generated waste is being landfilled [4]. Therefore, the level of performance or maturity of the plastic recycling system in South Africa needs to be assessed to inform an improvement roadmap.

As stated earlier, no known study has measured the maturity of the plastic recycling system, with specific focus on the value chain stages. Fletcher et al. [28] developed a framework to assess the maturity of technological applications in plastic waste management and the adoption of circularity principles in hospitals. Montoya et al. [29] adapted a model for measuring reverse logistics maturity in both small and medium organisations, with respect to sustainable solid waste management. Hynds et al. [30] proposed a technique to assess organisational maturity with respect to producing products that are sustainable and also eco-design inclined. Sehnem et al. [31] assessed the models that organisations use for conducting business so as to identify the respective sectors involved in the circular economy and their levels of maturity.

For the maturity measurement of the plastic recycling system, the country’s identified plastic recycling stages of the value chain include plastic production, product manufacturing, waste generation, sorting, recycling and recyclate market. The approach to maturity measurement applied in this study is centred upon a maturity model, the Plastic Recycling Value Chain Maturity Model, recently developed by Olatayo et al. [32], based on the importance of the plastic value chain.

Methodology

The different levels of the developed maturity model, in increasing order of maturity, are Level 1: Ad Hoc and Unstructured; Level 2: Structured; Level 3: Visionary; Level 4: Integrated; Level 5: Connected and Dynamic. The assessment indicators (maturity elements) used for measurement include customer satisfaction, process costs, quality data and database, structured processes, performance targets, chain actors’ integration and system performance review. Table 1 provides the descriptions of the maturity elements [32]. The application of the maturity elements of the maturity model to the different stages of the plastic value chain involved assessing each of the value chain stages separately against all of the elements. A particular stage is measured or rated based on how it performs when all the elements are considered or applied individually according to the definition of each element.

Table 1 Criteria for assessment of the plastic recycling system maturity level

A dual research process, involving both secondary and primary data analysis, was utilised to generate reliable results regarding the performance and maturity of the plastic recycling system. The process is shown in Fig. 1.

Fig. 1
figure 1

Maturity measurement process

Secondary data

In this phase of the methodological process the performance and maturity of the various value chain stages of the plastic recycling system were evaluated, with reference to the defined maturity elements, using existing secondary data. The approach used for the secondary data collection and analysis followed the guidelines described by Ivarsson [33].

Secondary data was collected on all the value chain stages of the plastic recycling system, for each of the defined maturity elements. This data was relevant information (e.g. measures, factors, regulations, plans, indices, values, quantities, qualities, outcomes, estimates, volumes, projections, technology and infrastructure) that described and defined the current status and situations of every stage of the South African value chain. The secondary data was sourced through a desktop review of relevant documents, including government documents, plastic organisations and industry reports, and journal publications. These reports contained information relating to the various value chain stages. The list of the 16 reports analysed for the measurement of the maturity of the plastic recycling system is included in Table S1 of Supplementary Information.

The analysis of the data involved rating the performance of all the value chain stages on a performance scale, in reference to each of the maturity elements. The specified ratings on the performance scale were 1—Very poor; 2—Poor; 3—Average; 4—Good; 5—Excellent. The collated data was analysed by the main researcher and three independent reviewers. The data, as well as the maturity element descriptions and performance scale, were shared with the three reviewers for independent review and quantitative analysis. These reviewers are researchers in this field of study. The activity involved each reviewer making informed decisions and scoring (based on the shared data) the performance of all stages of the value chain on the scale, for each of the defined maturity elements. Where there was no data for any of the maturity elements for any particular stage, the scores were computed based on the number of elements with data.

Subsequently, the four sets of performance scores awarded by the reviewers for all the value chain stages were collated and subjected to further statistical analysis and computation. This involved using multivariate descriptive statistics including the mean, standard deviation, skewness and kurtosis. This followed the approach applied in similar studies involving the Likert scale data collection technique [34, 35]. The mean and standard deviation helped to identify and understand the responses of the participants and determine the level of performance and maturity of the value chain stages; the skewness and kurtosis were analysed to determine the symmetry and the degree of distribution of the data [35]. Thereafter, factor analysis and Cronbach’s alpha test were applied to analyse the extent and differences in the scores by the reviewers and to establish the internal consistency and validity of the results [35].

Primary data

This phase involved the collection of data from main actors in the value chain through an online questionnaire and the statistical analysis of this primary data, according to the guidelines described by Forza [36]. The questionnaire was a Likert scale of scores: 1—Very poor; 2—Poor; 3—Average; 4—Good; 5—Excellent. The target population for data collection consisted of stakeholders from the private sector, government and industry associations. They comprised government parastatals (national, provincial and local), manufacturers, distributors, recyclers, collection enterprises, buy-back centres and industry associations.

The questionnaire survey had limitations, as the potential number of stakeholders from the target population with sufficient required knowledge on the subject matter was limited. As expected, getting an adequately large sample of participants was challenging. Initially, about 70 potential participants were identified and contacted, but only 13 experts responded positively and participated in this study, and 8 completed the questionnaire satisfactorily. These satisfactory responses were from two participants belonging to the plastic production/product manufacturing cluster and six to the recycling/recyclate market cluster. While there was limited expert participation in the questionnaire survey, the sample is satisfactory to some degree in the study context, as the participants were deeply engaged in the subject focus and possessed a wealth of experience and knowledge in the required areas. This scenario is acknowledged and upheld by Ma et al. [37], who encountered a similar limitation of a sufficiently large sample of knowledgeable participating experts. Their study was on plastic reduction in fast-moving consumer goods firms in Europe. It is important to emphasise that the need for and dependence on sufficient expert participation could be a potential hurdle in carrying out a maturity evaluation, especially in smaller, local systems.

The collection of data was subsequently followed by analysis, where the performance and maturity of the plastic value chain stages were analysed, with respect to the maturity elements. The primary data enriched the research study and helped validate findings from secondary data and increased validity.

Cross-validation of data

This phase involved the cross-validation of the results of both the primary and secondary data analysis to determine and validate the performance and maturity of the plastic recycling system in South Africa. The cross-validation increased the credibility of these research findings and helped to give a more balanced and detailed representation of the development of this recycling system and its value chain stages in South Africa.

Results

The findings of both the secondary and primary data analysis are presented in this section and the cross-validation of both analyses is given to determine the performance and maturity of the stages of the value chain of the South African plastic recycling system. This is with respect to the maturity elements of customer satisfaction, process costs, quality data and database, structured processes, performance targets, chain actors’ integration and system performance review.

Secondary data analysis

The analysis of the secondary data involved computing the descriptive statistics, which comprises the mean, standard deviation, kurtosis and skewness. The statistical analysis, provided in Tables 2, 3, 4, 5, 6, 7, 8, was done for each of the maturity elements, and for all the value chain stages to capture the whole plastic value chain system.

Table 2 Plastic production statistical analysis of secondary data
Table 3 Product manufacturing statistical analysis of secondary data
Table 4 Waste generation statistical analysis of secondary data
Table 5 Collection and handling statistical analysis of secondary data
Table 6 Sorting statistical analysis of secondary data
Table 7 Recycling statistical analysis of secondary data
Table 8 Recyclate market statistical analysis of secondary data

The standard deviation ranged between 0 and 1.785, which represented a relatively normal variation of the reviewer scores for all the value chain stages. For the skewness and kurtosis analyses, all the data analysed ranged between ± 2.0 and − 6.0 to + 4.0. According to the criteria by Byrne [54] and Curran et al. [55], data is referred to as normal or has moderate normality when the skewness and the kurtosis are between ± 2.0 and ± 7.0. None of the skewness and kurtosis values were beyond the specified ranges, suggesting that the data analysed was fairly free from skewness and was not awkwardly distributed.

The mean of the performance scores by the reviewers for each maturity element in the different stages of the system was computed to determine their performance ratings. The element of customer satisfaction apparently had good performance at the sorting and collection and handling stages of the value chain, with a mean score of 3.5 and 2.75, respectively. Process costs are seemingly well-managed at the stages of plastic production (3.25) and product manufacturing (3.25). For quality data and database, the stages of plastic production and product manufacturing apparently performed very well in the value chain at 4.5 and 4.25, respectively. Quality recyclables and products were best (3.75) at the recycling stage and performance was worst (1.5) at the collection and handling stage. The stage of plastic production seemed to have the best structured processes with a mean of 3.25, whereas product manufacturing appeared to be the best at setting performance targets for system improvement at 3.25. The element of chain actors’ integration was generally low across the value chain, although product manufacturing came top at 1.75. System performance review was carried out well at the stages of plastic production (4.0), product manufacturing (3.75), collection and handling (3.75), sorting (3.75) and recycling (3.0).

The collective performance ratings for the different value chain stages, in reference to the secondary data, are presented in Table 9. The performance scores were calculated using the direct mean data in Tables 2, 3, 4, 5, 6, 7, 8.

Table 9 Performance of the value chain stages (secondary data)

Primary data analysis

The responses of the participating experts were analysed and statistical values were computed for the maturity elements for all the plastic value chain stages as shown in Table 10. Table S2 of Supplementary Information shows the questionnaire that was administered for the study. As stated earlier, the limited participation in the survey was compensated for by the deep wealth of experience and knowledge of the eventual participants on the subject focus, an argument which is made by Ma et al. [37] for a similar study on plastics reduction.

Table 10 Statistical analysis of primary data

The standard deviation ranged between 0.25 and 1.0, signifying normal variation. The ranges of the skewness and the kurtosis analyses were from − 0.666 to + 0.999 and − 1.310 to + 0.586, respectively, indicating that the values were within the normal range.

From the mean computation of the different variables, the joint stages of plastic production and product manufacturing had customer satisfaction with the best performance at 3.5 and structured processes scored least at 2.0. Similarly, for the collective stages of recycling and recyclate market, quality data and database topped the varied performances of the maturity elements at 4.08, whereas structured processes had the lowest score of 3.67. Regrettably, the joint stages of waste generation, collection and handling, and sorting were not scored, as particular stakeholders for these stages were not responsive to the questionnaire.

Similarly, the collective performance ratings of the stages, with respect to the primary data, are presented in Table 11. Performance scores were computed using the direct mean data in Table 10.

Table 11 Performance of the value chain stages (primary data)

Cross-validation of analysis

The results from the primary and secondary analyses were cross-validated to determine the aggregate performances of the plastic value chain stages and maturity of the whole system. The two analyses were comparatively assessed and the findings are presented in Table 12.

Table 12 Cross-validation of the primary and secondary data analysis results

An important observation in the cross-analysis result is the deviation between the results of the primary and secondary data analysis for the recycling/recyclate market. A possible factor is that responses from willing experts could be a self-selection of those parts of the system, where recycling is best established. Follow-on studies with different sets of experts will help to throw more light on these possibilities. On the other hand, recent improvements in the sector, which were not recorded in previous or existing literature (secondary data), could be revealed by the expert participation.

The performance and maturity of the different value chain stages of the system are further illustrated in Fig. 2.

Fig. 2
figure 2

Maturity levels of the various stages of the plastic recycling system

The cross-validation of both analyses established the maturity of the value chain stages of primary plastic production, product manufacturing and recycling to be “Visionary” (Level 3). For waste generation, collection and handling, sorting and recyclate market, the maturity was “Structured” (Level 2).

According to the model developed by Olatayo et al. [32], the features associated with the maturity level of “Visionary” (Level 3) include existence of platforms to formally support recycling activities development; existence of established owners of various processes of the plastic value chain, who are tasked with performance; targets for performance are mostly realised; process costs start to reduce along the value chain; and consumers’ inclusion in the improvement of processes starts to produce customer satisfaction. The features of “Structured” (Level 2) are that processes of the value chain begin to be structured; elementary processes are being defined and documented; traditional jobs and practices still exist; quality data pertaining to the system is available in little quantity; targets for performance are rarely realised, even though defined; recyclables and waste are rarely traced; high process costs still exist; improving, but still low customer satisfaction.

Discussion of plastic value chain maturity level

Plastic production

The primary plastic production sector in South Africa can be described largely as “Visionary” (Level 3) in terms of maturity level. The customer satisfaction was “Good” for the primary data analysis, but secondary data was unavailable. The process costs maturity was “Average” for both the primary and secondary data analysis. There are balanced operational costs to corresponding productivity. According to Mofo [38], South Africa is a huge producer of plastic polymers. Despite the high cost of production, the plastic production stage has substantial capabilities for producing polymers, and pricing of polymers (polypropylene and propylene) as inputs for plastic product manufacturing is high [38]. The performance of quality data and database was rated “Average” and “Excellent” for the primary and secondary data analysis, respectively. There has been consistent availability of varied data, monthly and annual, for total and per capita plastic production and consumption (virgin and recyclate), plastics gross domestic product (GDP) contribution, percentage contribution to GDP, percentage contribution to manufacturing and other important statistics regularly shared by Plastics SA, the Department of Trade, Industry and Commerce, GreenCape, Statistics SA and other relevant departments and agencies. The rating for quality recyclables and products was “Average” and “Good” for the primary and secondary data analysis, respectively. Sasol, a major producer of monomers and polymers in the country, and other players such as Safrisol, supply plastics to both local and international markets [38]. This suggests the capacity for the production of quality outputs. For structured processes, the sector was rated “Poor” and “Excellent” for the primary and secondary data analysis, respectively. The polymer production industry has been described as structured and well-established, with access to raw materials as a result of the abundance of gas reserves in the country [38]. The value chain stage was rated “Average” and “Poor” for chain actors’ integration in the primary and secondary data analysis, respectively. While there is some level of collaboration as the sector feeds the local market for plastic product manufacturing, each step of the long value chain adds costs, and each stakeholder operates separately [39]. The rating for system performance review was “Poor” and “Very good” for the primary and secondary data analysis, respectively. Available data shows evidence of review of the system performance, such as records of operating companies and workforce, performance level, market pricing and other relevant audits [38, 39].

Product manufacturing

The maturity level for the product manufacturing stage was ranked “Visionary” (Level 3). The element of customer satisfaction was not rated for the secondary data analysis as a result of non-availability of data, but it was “Good” for the primary data analysis. The process costs performance was rated “Average” and “Poor” for the primary and secondary data analysis, respectively. The sector experiences high operational costs with respect to exorbitant electricity costs from production processes that are energy-intensive, the very high cost of polymers, which are used as input in production, and other related expenses, although there are substantial capacities and capabilities for plastic products manufacturing [38]. Quality data and database was rated “Average” and “Good” for the primary and secondary data analysis, respectively. Some relevant data is available, often shared by Plastics SA, Statistics SA and other related agencies. Similarly, quality recyclables and products was rated “Average” and “Good” for the primary and secondary data analysis, respectively. There is a suggestion of the capacity to manufacture quality products, but the low margins in the sector are a challenge for investment in modern equipment and technology for the local manufacture of plastic products [38]. Structured processes was rated “Poor” and “Good” for the primary and secondary data analysis, respectively. The sector is equally well-established, although there are insufficient skilled employees, minimal advanced manufacturing activities and practices and slow technology upgrade [38]. Regarding performance targets, the ratings were “Average” and “Poor” for the primary and secondary data analysis, respectively. While targets in other performances seem non-existent, there are targets for major retailers in the sector to manufacture recyclable packaging, as research in alternatives to plastic bags and single-use packaging to address plastic impact is being promoted [38]. The performance of chain actors’ integration was “Average” and “Poor” for the primary and secondary data analysis, respectively. There is some level of integration among the polymer producers, although each step of the value chain adds no value, but costs, and every stakeholder operates separately [39]. System performance review was rated “Poor” and “Good” for the primary and secondary data analysis, respectively. Available data reveals the review of different performances in the system such as sales growth, production, import and other relevant audits [38, 39]. According to the literature, the sustainable design of plastics to facilitate reuse, repair or remanufacture would result in minimal plastic waste entering into the plastic waste stream [3]. Moving from linearity to circularity of plastic must entail all processes and practices in the production stage of the value chain.

Waste generation

The South African waste generation sector was ranked “Structured” (Level 2) in terms of maturity. Not all the maturity elements for this stage were rated for primary data analysis as some data was not available. For the secondary data analysis, the maturity elements of customer satisfaction, process costs and chain actors’ integration were rated “Very poor”. With respect to customer satisfaction, the sector is challenged by the absence of incentives (or disincentives) for households to practise separation at source [41] and a dearth of national awareness campaigns and public and private sector programmes on waste separation at source, although some metropolitan areas are implementing the programmes [42, 43]. The stage is also challenged by littering, illegal dumping and waste service delivery backlogs [41]. For process costs, there are increasing operational costs due to increasing waste generation from population growth, urbanisation and increased plastic consumption [44, 45]. Economically, South Africa lost an estimated R17 billion worth of resources in 2012 as a result of waste disposal to the landfills [46]. The assessment of the chain actors’ integration shows insufficient collaboration and integration among stakeholders; Van Jaarsveldt [46] reports the lack of awareness and education of stakeholders operating within the waste management sector. Furthermore, the performances of the elements of quality data and database, quality recyclables and products, structured processes and performance targets were rated “Poor”. There is relative availability of data records for plastic waste generation, as shown by Plastics SA, DFFE and other studies, although data reliability with respect to waste stream volumes and waste stream types is a challenge as a result of municipalities and the industry not implementing, monitoring and reporting waste management plans [42]. Apparently, there is low quality of waste due to lack of separation at source programmes nationally, although such programmes exist in some metropolitan areas despite challenges [42, 46]. In addition, structured processes were largely lacking, as there is no national separation at source programme, except in some provinces with larger urban populations [46, 47].

The sector has problems of littering, illegal dumping, minimal separation at source and waste service delivery backlogs [41]. In addition, both the industrial and commercial waste generators are responsible for their own waste management [41]. Some performance targets were set for this stage, although many provincial and local government authorities are lagging behind. This is demonstrated by the Waste Act which specifies targets for national waste management systems, yet some provincial and local government authorities have not domesticated the process or targets [42]. The Plastic Bag Regulation of 2002/2003 was projected to reduce plastic bag waste generation by 50% [49]. The element of system performance review was rated “Average”, as records show reviews of waste management performances by local government, access of households to waste management and household separation rates [39, 41, 42]. As supported by the literature, a more complete approach must be implemented through all the plastic value chain stages of plastics, particularly monitoring, controlling and minimising the quantity of plastic waste being generated and entering the waste management system through actions and practices that reduce the usage of plastic [26].

Collection and handling

The maturity of the collection and handling stage was ranked as “Structured” (Level 2). According to the assessments, not every maturity element for this stage was rated for primary data analysis as some data was not available. For the secondary data analysis, customer satisfaction was rated “Very poor”, as the stage is challenged by littering, illegal dumping and waste service delivery backlogs [41]. The weekly waste removal practice is below the minimum threshold [48]. A rating of “Very poor” was given to process costs, as total costs of waste management have risen with respect to equipment hire, maintenance, overtime and other related expenses; plants and fleet are ageing and infrastructure is under strain [48]. Quality data and database was rated “Average”, considering the availability of certain data for plastic waste collected and managed, as shown by Plastics SA, GreenCape and DFFE, although there are challenges regarding waste stream types [42]. The rating for quality recyclables and products was “Poor” as a result of the apparent low quality of waste collected due to the lack of programmes such as separation at source, except in a number of metropolitan areas [42, 46]. Structured processes was rated “Poor” due to the sector not being prioritised and the implementation of the least cost collection and disposal method rather than the integrated waste management system [42]. In addition, there is huge dependence on the informal waste collectors [42, 46].

Performance targets was rated “Poor”. Some performance targets are set, but some levels of government authorities are underperforming [42]. The rating for chain actors’ integration was “Very poor” because collaboration and integration among stakeholders in the sector are inadequate [46]. System performance review was rated “Average”. Available data shows different reviews of government’s waste management plans and households’ waste management practices and activities [39, 41, 42].

Sorting

The sorting stage of the value chain was ranked as “Structured” (Level 2) with respect to the maturity of the recycling system. Not all maturity elements were rated for primary data analysis as some data was not available. For the secondary data analysis, the performance assessments rated customer satisfaction as “Good”. It can be inferred that there is considerable customer satisfaction as recyclers are mostly supplied with well-sorted waste through the buy-back centres. These centres buy waste from mostly the informal sector and thereafter sort, clean and bale it, thereby adding valuable quality to the recyclables before selling to the big recyclers or recycling companies [50]. Process costs was rated “Very poor” as there has been an increase in the overall costs of managing waste, including equipment hire, maintenance and other related expenses. The rating for quality data and database was “Poor” considering the availability of very little data regarding this value chain stage. Quality recyclables and products was rated “Average”. While there is a supply of quality recyclables because the waste is mostly well-sorted by the buy-back centres and value is added, most on-site sorting processes and activities are labour intensive and not technology driven [50]. Structured processes received a rating of “Poor” as the sector is on average well-structured. While the buy-back centres play an essential and major role in this stage, the supply of recyclables is largely dependent on the informal sector [50], and the relevant cooperatives working in this stage face infrastructure problems [47]. The element of performance targets could not be rated due to non-availability of data. Chain actors’ integration was rated “Very poor” due to the inadequate collaboration and integration among stakeholders within the stage [46]. The rating for system performance review was “Average” as data revealed the existence of certain reviews of system performance [47, 50].

Recycling

The maturity of the recycling sector of the value chain was ranked as “Visionary” (Level 3). All maturity elements for this stage were rated “Good” for primary data analysis. For the secondary data analysis, the elements of customer satisfaction and performance targets were not rated due to non-availability of data. The rating for process costs was “Very poor” considering that recyclers are operating in an environment that is increasingly difficult for business and facing tight margins due to the high costs of operation, rising electricity costs and an economy downturn generally [46]. Quality data and database was rated “Excellent”, considering the availability of varied data pertaining to recyclate regularly shared by Plastics SA, GreenCape and other relevant agencies. Quality recyclables and products was rated “Average”. Investments in quality assurance are helping to improve the quality of recyclates, as the establishment of quality assurance laboratories has resulted in improved quality, despite the separation at source of recyclables being low [46, 51]. Structured processes was rated “Average” as the structure is of average standard. The sector has limited infrastructure for recycling and recycling processors, and it is based largely on the informal sector collecting recyclables and selling for recycling purposes. However, there has been substantial support from industry-funded associations [39, 42, 44, 47, 52]. The rating of chain actors’ integration was “Very poor” as collaboration and integration among stakeholders within the stage are inadequate, with each stakeholder operating individually [39, 46]. System performance review was rated “Average” as data shows that there are some reviews regarding the performance of the system [41, 42]. As expressed in the literature, plastic circularity needs to be consistently improved in every stage of the plastic value chain, as the value of plastic material must be maximised at all these stages [3]. It has been argued that plastic has the potential to be recycled many times, and this can be explored to contribute significantly to realising a circular economy, while taking into consideration the sustainable product design [26].

Recyclate market

The value chain stage of the recyclate market was ranked as “Structured” (Level 2). The respective maturity elements for this stage were rated “Good” for primary data analysis. For the secondary data analysis, customer satisfaction, process costs and performance targets were not rated due to non-availability of data. Quality data and database was rated “Very poor”, as data on the trading size and number of available end-markets for recyclates is lacking [42]. Quality recyclables and products was rated “Average”, as investments have been made in quality assurance to improve the quality of recyclates, even while the separation at source of recyclables is low [46, 51]. The quality of the recyclates produced has enabled the reuse or application of these recyclates in the production of similar products, particularly in the food packaging sector where packaging waste is recycled and the recyclates are reused for new packaging applications [51].

Structured processes was rated “Very poor” due to the lack of structures and end-markets [42, 53]. Similarly, chain actors’ integration was rated “Very poor” considering the absence of end-markets for recyclates and poor collaboration and integration among stakeholders within the system [39, 42, 46]. System performance review was rated “Very poor” as data shows a negligible review of system performance [41, 42].

Performance implications

The above results from the assessment are crucial for the future policymaking of South Africa regarding the advancement of plastic waste recycling, especially in respect of the increasing plastic circularity and sustainability across the respective value chain stages of the system. Significant findings that support the improvement of the various aspects of the plastic value chain stages, with respect to the defined maturity elements (customer satisfaction, process costs, quality data and database, structured processes, performance targets, chain actors’ integration and system performance review) are presented and discussed. These findings originated from a detailed review of initiatives that have influenced the development of the various stages of the plastic recycling system, as shown in Table 13.

Table 13 Initiatives for development of plastic recycling stages

The prospect of a high-performing plastic recycling value chain in the country would benefit from the adoption of these outlined initiatives in the respective stages of the system. These initiatives are recorded to have had positive impacts on the system. Major initiatives that cut across the value chain stages in the table include legislation, digitalisation, training and skills, education, facilities, reward system, technology and standards.

Achieving an increase in plastic sustainability and circularity through legislation at the production stage of plastics requires manufacturers to comply with regulations or requirements on the minimum content of recyclate that must be included in a certain share of their products or packaging for products. Similarly, integrated legislation will strengthen the generation, collection, handling, transportation, as well as disposal of plastic waste in South Africa. Enacting suitable legislation involving licensing, guidelines, processes, fees and punishment regarding these activities is the first crucial step towards effective plastic waste collection and handling. There is also the need to regulate and set standards (equipment, test and industry) for the plastic recycling sector as it expands in terms of the number of plastic recyclers and the different recyclates or recycled products. This will protect the sector and equally guarantee the value or quality of recycled materials and products. The existence of regulations ensures fair practices in the sector, stabilises the market and enables a market mechanism for the plastic recyclers, operators and buyers.

Digitalisation and data automation in plastic production enable productivity in the sector, as overproduction can be avoided and wastage minimised. Digital platforms can be used in plastic waste collection and handling to connect residents directly to waste collection operators. This involves residents or businesses taking pictures or giving verbal illustrations of the waste generated and uploading them on apps for analysis and advice on where to dispose of it. This enables increased rates of plastic waste collected and recovered for recycling, and also assists the waste generators in reducing their waste collection levy. Digitalising the recycling sector helps business entities to scale up for plastic waste recycling.

Training and skills are required to advance the plastic value chain, as manufacturers, recyclers, waste pickers and waste collectors should have appropriate and sufficient knowledge of the quality, quantity, environmental benefits, best use and administration of plastic recycling and circularity. Providing training to operators in the value chain to acquire skills and competence would improve the effectiveness and efficiency of waste separation, collection, sorting and recycling of plastic. In addition, there should be a good reward system for operators in the industry to further develop the system. Waste pickers are examples of important stakeholders that could be considered for employment, reasonable wages, proper training and health insurance.

The provision of education to the consumers and general public regarding the benefits of sustainable plastic consumption, separation at source and proper disposal of plastic waste needs to be promoted sufficiently. This will ensure that waste generators and the public are adequately aware and conscious of their expected roles in advancing plastic sustainability and circularity in the country. Improved behaviour towards waste management could yield a high-performing plastic value chain. Research reports can be made available to government officials, waste managers, recyclers and the general public on resource and waste management plans and strategies, hazards, policy proposals and recycling activities and practices.

Technology is highly important and very valuable in plastic waste sorting and recycling. Advanced technologies could be in the form of sorting, material recovery, recycling or information technology, and they would provide sustainable and lasting solutions. Sorting, recovery or recycling technologies help to advance economic and environmental sustainability; information technology provides solutions for evaluating, monitoring and improving the whole plastic recycling value chain system.

Facilities improvement for plastic waste collection, transportation and storage is a requirement for a sustainable plastic recycling value chain system. Both profit and non-profit collection facilities or stations should be established for the collection of post-consumer plastic waste. The facilities should be accessible to residents, businesses and peddlers. Sorting centres should equally be promoted for pre-treatment before moving to recyclers. A model for transportation allocation for waste collection could be developed for quick collection and to reduce the average time spent queuing at disposal facilities.

Furthermore, standards are applicable to production output and these entail guaranteeing the quality, safety and reliability for the manufacturers, recyclers and consumers. Considering that plastics have numerous applications, maintaining standards involves attending to a broad collection of plastic products or diverse plastic material properties, for example biodegradability of the material.

Conclusion

The plastic recycling value chain and the respective stages in South Africa were separately measured to critically assess the current performance of the value chain stages and the maturity of the whole system, using a developed maturity model, with a set of maturity elements as assessment indicators. This was in response to improving the circularity of plastics in the country. The result of the primary and secondary data analysis showed that the maturity levels of the stages of primary plastic production, product manufacturing and recycling of the plastic value chain in the country can be rated as “Visionary” (Level 3). The individual stages of waste generation, collection and handling, sorting, and recyclate market are rated as “Structured” (Level 2). In addition, a number of initiatives that have influenced the development of the various stages of the plastic recycling system, with respect to the defined maturity elements, were identified from a detailed review of previous works and discussed. These initiatives could assist in advancing the system in South Africa.

The study contributed to the academic community in the areas of identifying critical elements required to be prioritised for the maturity of the plastic value chain. It presented fundamentals for the improvement of the plastic value chain and recycling system of any country. It identified areas, processes and action plans that could be useful in bridging the gap between low maturity level to the highest maturity level. The study highlighted the importance and contribution of every stage of the plastic value chain in achieving total maturity of the plastic system. It contributed to knowledge that would optimise the plastic recycling option and circular economy as a whole. In addition, it extended the body of literature in the areas of maturity of a system and maturity models, as well as the entrenchment of circularity in all stages of the plastic value chain.

Furthermore, considering that the country’s plastic value chain still requires future development at every stage of the value chain, it is important for the country to be part of the discussion and development of a globally binding treaty being proposed and championed by the United Nations Environment Programme (UNEP) to curb plastic pollution. This legally binding instrument, being discussed by the Member States of the organisation, is expected to utilise a comprehensive approach to tackle the complete life cycle of plastic, comprising design, production, use and disposal.

Availability of data and materials

All data will be provided on request.

References

  1. Plastics Europe (2022) Plastics: the facts 2022. Plastics Europe, Brussels. https://plasticseurope.org/knowledge-hub/plastics-the-facts-2022/. Accessed 27 Mar 2024

  2. WEF (2016) The new plastics economy rethinking the future of plastics. World Economic Forum. http://www3.weforum.org/docs/WEF_The_New_Plastics_Economy.pdf. Accessed 2 July 2023.

  3. OECD (2022) Plastic pollution is growing relentlessly as waste management and recycling fall short, says OECD. Organisation for Economic Co-operation and Development. https://www.oecd.org/environment/plastic-pollution-is-growing-relentlessly-as-waste-management-and-recycling-fall-short.htm. Accessed 2 Oct 2023.

  4. SAPRO (2020) Tackle the landfill crisis. South African Plastic Recycling Organisation. https://www.plasticrecyclingsa.co.za/. Accessed 17 June 2020.

  5. Hahladakis JN, Iacovidou E (2019) An overview of the challenges and trade-offs in closing the loop of postconsumer plastic waste (PCPW): focus on recycling. J of Hazard Mater 380(120887):1–10. https://doi.org/10.1016/j.jhazmat.2019.120887

    Article  CAS  Google Scholar 

  6. Fraser P, Farrukh C, Gregory M (2003) Managing product development collaborations: a process maturity approach. Proc Inst Mech Eng Pt B J Eng Manuf 217(11):1499–1519

    Article  Google Scholar 

  7. Pöppelbuß J, Röglinger M (2011) What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management. Ecis. 28. http://aisel.aisnet.org/ecis2011/28/. Accessed 2 July 2023.

  8. Clark T, Jones R (1999) Organisational interoperability maturity model for C2. In: Proceedings of the 1999 command and control research and technology symposium. Pp 1–13.

  9. Pullen W (2007) A public sector HPT maturity model. Perform Improv 46(4):9–15. https://doi.org/10.1002/pfi.119

    Article  Google Scholar 

  10. Battista C, Fumi A, Schiraldi MM (2012) The Logistic Maturity Model: Guidelines for logistic processes continuous improvement. In: Proceedings of the POMS 23rd annual conference. Chicago, Illinois, USA. http://www.pomsmeetings.org/ConfProceedings/025/FullPapers/FullPaper_files/025-1329.pdf. Accessed 28 Sept 2023.

  11. Van Dyk L (2013) The development of a telemedicine service maturity model. PhD thesis, Stellenbosch University, Stellenbosch. Accessed 20 July 2023.

  12. Netland T, Alfnes E (2008) A practical tool for supply chain improvement - experiences with the supply chain maturity assessment test (SCMAT). https://www.sintef.no/upload/Teknologi_og_samfunn/intrans/NetlandandAlfnes(2008)Apracticaltoolforsupplychainmprovement_submittedtoPOMTokyo%5B1%5D.pdf. Accessed 28 Sept 2023.

  13. Schumacher A, Erol S, Sihn WA (2016) Maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52:161–166. https://doi.org/10.1016/j.procir.2016.07.040

    Article  Google Scholar 

  14. Crosby C (1979) Quality is free. McGraw-Hill, New York

    Google Scholar 

  15. Mettler T (2011) Maturity assessment models: a design science research approach. Int J Soc Syst Sci 3(12):81–98. https://doi.org/10.1504/IJSSS.2011.038934

    Article  Google Scholar 

  16. Mettler T, Rohner P (2009) Situational maturity models as instrumental artifacts for organizational design. In: Proceedings of the 4th international conference on design science research in information systems and technology. ACM. 22. https://doi.org/10.1145/1555619.1555649

  17. Paulk MC, Curtis B, Chrissis MB, Weber CV (1993) Capability maturity model for software, Version 1.1. Carnegie Melon University, Pennsylvania. https://doi.org/10.1109/52.219617

    Book  Google Scholar 

  18. De Oliveira MPV, Ladeira MB, McCormack KP (2011) The supply chain process management maturity model – SCPM3. Supply Chain Manag Pathways Res Pract. https://doi.org/10.5772/18961

    Article  Google Scholar 

  19. Payne T (2010) Aligning AMR’s DDVN and Garner’s Chaos tolerant supply chain maturity frameworks. Gartner, New York

    Google Scholar 

  20. Lockamy A, McCormack K (2004) The development of a supply chain management process maturity model using the concepts of business process orientation. Supply Chain Manag An Int J 9(4):272–278. https://doi.org/10.1108/13598540410550019

    Article  Google Scholar 

  21. Antunes P, Carreira P, DaSilva MM (2014) Towards an energy management maturity model. Energy Policy 73:803–814. https://doi.org/10.1016/j.enpol.2014.06.011

    Article  Google Scholar 

  22. Ibbs CW, Kwak YH (2000) Assessing project management maturity. Proj Manag J 31(1):32–43. https://doi.org/10.1177/875697280003100106

    Article  Google Scholar 

  23. Peters W, Doskey S, Moreland J Jr (2017) Technology maturity assessments and confidence intervals. Syst Eng. https://doi.org/10.1002/sys.21389

    Article  Google Scholar 

  24. Harter DE, Krishnan MS, Slaughter SA (2000) Effects of process maturity on quality, cycle time and effort in software product development. Manage Sci 46(4):451–466. https://doi.org/10.1287/mnsc.46.4.451.12056

    Article  Google Scholar 

  25. Becker J, Niehaves B, Pöppelbuß J, Simons A (2010) Maturity Models in IS Research. Paper presented at the 18th European Conference on Information Systems (ECIS 2010), Pretoria, South Africa. https://www.researchgate.net/publication/221408759_Maturity_Models_in_IS_Research. Accessed 17 June 2020.

  26. Scott JE (2007) Mobility, business process management, software, sourcing and maturity model trends: propositions for the IS organization of the future. Inf Syst Manag 24(2):139–145

    Article  Google Scholar 

  27. Olatayo KI, Mativenga PT, Marnewick AL (2021) Comprehensive evaluation of plastic flows and stocks in South Africa. Resour Conserv Recycl 170(105567):1–11. https://doi.org/10.1016/j.resconrec.2021.105567

    Article  Google Scholar 

  28. Fletcher CA, Clair RS, Sharmina M (2021) A framework for assessing the circularity and technological maturity of plastic waste management strategies in hospitals. J Clean Prod 306(127169):1–8

    Google Scholar 

  29. Montoya CC, Bouzon M, Vidal-Holguin CJ (2020) Assessment of maturity of reverse logistics as a strategy to sustainable solid waste management. Waste Manag Res J Sustain Circ Econ. https://doi.org/10.1177/0734242X19897131

    Article  Google Scholar 

  30. Hynds EJ, Brandt V, Burek S, Jager W, Knox P, Parker JP, Schwartz L, Taylor J, Zietlow M (2014) A maturity model for sustainability in new product development. Res Technol Manag 57(14):50–57. https://doi.org/10.5437/08956308X5701143

    Article  Google Scholar 

  31. Sehnem S, Campos LM, Julkovski DJ, Cazella CF (2019) Circular business models: level of maturity. Manag Decis 57(4):1043–1066. https://doi.org/10.1108/MD-07-2018-0844

    Article  Google Scholar 

  32. Olatayo KI, Mativenga PT, Marnewick AL (2023) Plastic value chain and performance metric framework for optimal recycling. J Ind Ecol. https://doi.org/10.1111/jiec.13384

    Article  Google Scholar 

  33. O’Leary Z (2021) The essential guide to doing your research project, 4th edn. Sage, London

    Google Scholar 

  34. Ivarsson L (2023) Principals’ perceptions of gifted students and their education. Soc Sci Hum Open 7(100400):1–6

    Google Scholar 

  35. Cheng C, Lay K, Hsu Y, Tsai Y (2021) Can Likert scales predict choices? Testing the congruence between using Likert scale and comparative judgment on measuring attribution. Methods Psychol 5(100081):1–11. https://doi.org/10.1016/j.metip.2021.100081

    Article  CAS  Google Scholar 

  36. Forza C (2002) Survey research in operations management: a process-based perspective. Intern. J Oper Prod Manag 22(2):152–194. https://doi.org/10.1108/01443570210414310

    Article  Google Scholar 

  37. Ma X, Park C, Moultrie J (2020) Factors for eliminating plastic in packaging: the European FMCG experts’ view. J Clean Prod 256(120492):1–11. https://doi.org/10.1016/j.jclepro.2020.120492

    Article  Google Scholar 

  38. Mofo L (2020) Future-proofing the plastics value chain in Southern Africa. WIDER working paper 2020/148. United Nations University World Institute for Development Economics Research. https://doi.org/10.35188/UNU-WIDER/2020/905-1

  39. Plastics SA (2022) Overview of Plastics SA and the South African plastics industry. Midrand: Plastics SA. https://www.plasticsinfo.co.za/wp-content/uploads/2022/12/PSA-AR-2122_interactive.pdf. Accessed 17 June 2023.

  40. Euromap (2016) Plastics resin production and consumption in 63 countries worldwide. European Plastics and Rubber Machinery. http://www.euromap.org/en/markets/market-data. Accessed 4 July 2020.

  41. GreenCape (2019) Waste: 2019 market intelligence report. GreenCape. https://www.greencape.co.za/assets/uploads/waste-market-intelligence-report-web.pdf. Accessed 7 Sept 2022.

  42. DFFE (2020) National waste management strategy 2020. Pretoria: Department of Forestry, Fisheries and the Environment. https://www.dffe.gov.za/sites/default/files/docs/nationalwaste_management_strategy.pdf. Accessed 18 Sept 2023.

  43. GDARDWM (2022) Parliamentary DFFE Portfolio Committee presentation. Pretoria: Gauteng Department of Agriculture and Rural Development Waste Management. https://pmg.org.za/tabled-committee-report/4890/. Accessed 18 Sept 2023.

  44. DEA (2019) 2019 Revised and updated National Waste Management Strategy. Pretoria: Department of Environmental Affairs. https://www.gov.za/sites/default/files/gcis_document/201912/42879gon1561.pdf. Accessed 18 Sept 2023.

  45. Mofokeng M, du Plessis A, du Plessis L (2022) Uncovering the Nexus between the Informal Waste Economy and Municipal Solid Waste Management in the Mangaung Metropolitan Municipality. Administratio Publica. 30; 1. https://journals.co.za/doi/pdf/10.10520/ejc-adminpub_v30_n1_a2

  46. Van Jaarsveldt D (2016) Market analysis: waste management and recycling in South Africa. Southern African—German chamber of commerce and industry NPC. Accessed 20 Aug 2022. https://mediafra.admiralcloud.com/customer_609/11f6f63a-f61f-48a5-a3e8-bc8bbdd98a65?response-content-disposition=inline%3B%20filename%3D"Short_Market_Analysis_Waste_South_Africa.pdf.pdf"&Expires=1717760519&Key-Pair-Id=K3XAA2YI8CUDC&Signature=bFRCoE3v6TIwBaXtzRm-gYvDIF9VX6JbFYtAmSC6iR-Bz0z8ZDL1dijQxNatxGBoolbgadub5UTQO94YfIj6V0bqXOKadQap~XfAuuPxtdORS6qj1Ps7yJSUTAXBEwZRGW28tDZniKVeOhcB8fUq~MlCMiz0EncP4MEQKfrNuA3BwloxwL1bTiBmLmz1vDaA2PtMN8LKjKGupH7s5ykxvSv75lDLoS0nDQNiIP1eaNSRzcw-fSFePNRZBvcibUAQdJbJwzb-zP~6elT0JGwIWPUDUOLp79tUxR~O8-VFDOuk00h-VV9vSECLw5gAknBfgjsNYVp7maAHRd4FdSrmFA__

  47. Godfrey L, Oelofse S (2017) Historical review of waste management and recycling in South Africa. Resources 6(4):57. https://doi.org/10.3390/resources6040057

    Article  Google Scholar 

  48. PC-EFF (2022) eThekwini waste management. Portfolio Committee on Environment, Forestry and Fisheries. eThekwini, KwaZulu-Natal. https://pmg.org.za/tabled-committee-report/4691/. Accessed 20 Aug 2022.

  49. Dikgang J, Leiman A, Visser M (2012) Analysis of the plastic bag levy in South Africa. Resour Conserv Recycl 66:59–65. https://doi.org/10.1016/j.resconrec.2012.06.009

    Article  Google Scholar 

  50. Viljoen J, Blaauw D, Schenck C (2019) The opportunities and value-adding activities of buy-back centres in South Africa’s recycling industry: a value chain analysis. Local Econ 34(3):294–315. https://doi.org/10.1177/0269094219851491

    Article  Google Scholar 

  51. Plastics SA (2018) 2017/18 annual review. https://www.plasticsinfo.co.za/wp-content/uploads/2019/09/Plastics_SA-AR-2017_18-100pages-96dpi.pdf. Accessed 25 June 2020.

  52. Hoffman M, Schenck C (2020) The value chain and activities of polyethylene terephthalate plastics in the South African waste economy. Local Econ 35(5):523–535. https://doi.org/10.1177/0269094220931697

    Article  Google Scholar 

  53. UNEP (2018) Africa waste management outlook. United Nations Environment Programme, London

    Google Scholar 

  54. Byrne BM (2010) Structural equation modeling with AMOS: basic concepts, applications, and programming. Routledge, New York

    Google Scholar 

  55. Curran PJ, West SG, Finch JF (1996) The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods 1(1):16–29

    Article  Google Scholar 

  56. European Commission (2018) Communication from the Commission to the European parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: a European strategy for plastics in a circular economy. COM(2018) 28 Final 1–17.

  57. Mathews JA, Tan H (2016) Circular economy: lessons from China. Nature 531:440–442. https://doi.org/10.1038/531440a

    Article  CAS  Google Scholar 

  58. Chauhan A, Jakhar SK, Chauhan C (2021) The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal. J Clean Prod 279(123854):1–8

    Google Scholar 

  59. Sun M, Yang X, Huisingh D, Wang R, Wang Y (2015) Consumer behavior and perspectives concerning spent household battery collection and recycling in China: a case study. J Clean Prod 107:775–785

    Article  Google Scholar 

  60. Rioux L (2011) Promoting pro-environmental behaviour: collection of used batteries by secondary school pupils. Environ Educ Res 17(3):353–373

    Article  Google Scholar 

  61. Hansmann R, Loukopoulos P, Scholz RW (2009) Characteristics of effective battery recycling slogans: a Swiss field study. Resour Conserv Recycl 53(4):218–230

    Article  Google Scholar 

  62. Blumberga A, Timma L, Romagnoli F, Blumberga D (2015) Dynamic modelling of a collection scheme of waste portable batteries for ecological and economic sustainability. J Clean Prod 88:224–233

    Article  Google Scholar 

  63. Nižetić S, Šolić P, González-de-Artaza DL, Patrono L (2020) Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. J Clean Prod 274(122877):1–9

    Google Scholar 

  64. Plastic Soup Foundation (2018) Reduce your plastic footprint. https://www.plasticsoupfoundation.org/en/download-my-little-plastic-footprint/. Accessed 10 Sept 2023.

  65. Watkins E, Gionfra S, Schweitzer JP, Pantzar M, Janssens C, Brink PT (2017) EPR in the EU plastics strategy and the circular economy: a focus on plastic packaging. Institute for European Environmental Policy, Brussel

    Google Scholar 

  66. Xue W, Cao K, Li W (2015) Municipal solid waste collection optimization in Singapore. Appl Geogr 62:182–190

    Article  Google Scholar 

  67. Zhu J, Yu X, Zhang Q, Li Y, Tan S, Li D, Yang Z, Wang J (2019) Cetaceans and microplastics: first report of microplastic ingestion by a coastal delphinid, Sousa chinensis. Sci Total Environ 659:649–654

    Article  CAS  Google Scholar 

  68. Wagner TP, Toews P, Bouvier R (2013) Increasing diversion of household hazardous wastes and materials through mandatory retail take-back. J Environ Manag 123:88–97

    Article  Google Scholar 

  69. Zhang S, Wang J, Liu X, Qu F, Wang X, Wang X, Li Y, Sun Y (2019) Microplastics in the environment: A review of analytical methods, distribution, and biological effects. TrAC Trends Anal Chem 111:62–72. https://doi.org/10.1016/j.trac.2018.12.002

    Article  CAS  Google Scholar 

  70. Zhou L, Xu Z (2012) Response to waste electrical and electronic equipment in China: legislation, recycling system, and advanced integrated process. Environ Sci Tech 46(9):4713–4724. https://doi.org/10.1021/es203771m

    Article  CAS  Google Scholar 

  71. Zeng X, Li J (2013) Implications for the carrying capacity of lithium reserve in China. Resour Conserv Recycl 80:58–63

    Article  Google Scholar 

  72. Zeng X, Li J (2015) On the sustainability of cobalt utilization in China. Resour Conserv Recy 104:12–18. https://doi.org/10.1016/j.resconrec.2015.09.014

    Article  Google Scholar 

  73. Tamma P (2018) China’s trash ban forces Europe to confront its waste problem. POLITICO

    Google Scholar 

  74. OECD. 2018. Improving plastics management: Trends, policy responses, and the role of international co-operation and trade. OECD Environment Policy Paper No. 12. Paris: Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/environment/improving-plastics-management_c5f7c448-en. Accessed 30 Sept 2023.

  75. Watkins E, Schweitzer J, Leinala E, Börkey P (2019) Policy approaches to incentivise sustainable plastic design-environment working paper N°149. Organisation for Economic Co-operation and Development. https://one.oecd.org/document/ENV/WKP%282019%298/En/pdf. Accessed 30 Sept 2023.

  76. EMF (2017) Rethinking the future of plastics and catalysing action. https://www.ellenmacarthurfoundation.org/assets/downloads/publications/NPEC-Hybrid_English_22-11-17_Digital.pdf. Accessed 30 Sept 2023.

  77. Closed Loop Partners (2018) Closed Loop Partners 2018 impact report. https://www.closedlooppartners.com/wp-content/uploads/2020/01/Closed-Loop-Partners-Impact-Report-2018-1.pdf. Accessed 3 Oct 2023.

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KIO processed the methodology, data analysis, software, original draft and investigation. PTM and ALM handled the conceptualisation, supervision, validation, review and editing. All the authors approved the final manuscript.

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Olatayo, K.I., Mativenga, P.T. & Marnewick, A.L. Measuring the performance and maturity of the plastic recycling value chain system: implications and prospects. Environ Sci Eur 36, 112 (2024). https://doi.org/10.1186/s12302-024-00937-2

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