A large global workforce of informal entrepreneurs has historically dominated circular economy practices in the Global South, collecting many millions of tonnes of waste for recycling, and supplementing insufficiencies in struggling municipal waste management systems. Ongoing negotiations for a ‘Legally Binding Instrument on Plastic Pollution’ implicitly recognise the substantial contribution made by the informal recycling sector (IRS - waste pickers) to plastic pollution mitigation. To this, we carried out a systematic scoping review of IRS prevalence and productivity following the PRISMA-ScR method followed by extensive data analytics. Waste pickers represent median 0.2% (interquartile range – IQR: 0.1-0.5%) of the urban population worldwide, collecting between 20 kg and 80 kg of engineered materials for recycling each day, of which 30% (mean wt. ar) are plastics. We identify substantial shortcomings in most methodologies used to gather data on the IRS, introducing epistemic uncertainty into some previous estimates of the sector’s activity. We recommend development of a standardised resource-efficient method of sampling and data gathering, suitable for implementation at municipal/local scale. Our work offers verifiable quantitative knowledge on the sector’s activities to date, suitable for use in plastic pollution quantification models and local/national action plans required to baseline and monitor progress towards multilateral targets.
Systematic Review
Informal recycling sector contribution to plastic pollution mitigation: A systematic scoping review and analysis of prevalence and productivity
https://doi.org/10.21203/rs.3.rs-3791652/v1
This work is licensed under a CC BY 4.0 License
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published 30 Jun, 2024
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Informal recycling sector
Plastic pollution
Circular economy
IRS
Waste pickers
Worldwide, a substantial workforce of informal recyclers recovers many millions of tonnes of engineered materials from waste each year (Cook and Velis, 2020). The retention of this mass within the circular economy reduces the need to extract resources, reduces carbon emissions (Vergara et al., 2016; Wilson et al., 2009) and assists countries with achieving their SDG11.6.1 target to increase the mass of waste sent to controlled facilities (UN-Habitat, 2021). Yet, in many societies, participants in the informal recycling sector (IRS) suffer from social stigmatisation, existing on the margins of mainstream society and even being criminalised for their vocation (Dias, 2016; Scheinberg et al., 2016; Velis et al., 2012; White, 2013). Their work also results in exposure to a range of hazards, not least from the materials and substances which they encounter amongst waste of unknown composition, but also from musculoskeletal attrition and exposure to adverse meteorological conditions (Zolnikov et al., 2021).
As IRS activities are most prevalent in the Global South, where comprehensive waste management provision is often lacking (Wilson, 2023), it is self-evident that their work also prevents the emission of plastic into the environment (Velis et al., 2022). This contribution to plastic pollution mitigation has been acknowledged by international non-governmental organisations (NGOs) (GAIA, 2021; Tearfund, 2023), large multi-national corporations (APEW, 2023; Berger, 2023) and in negotiations for a Legally Binding Instrument on Plastic Pollution (hereafter, the ‘Plastics Treaty’) (Adam et al., 2022; UNEP, 2022). The draft, under negotiation, calls for IRS participants to be legally recognised, protected and supported as part of a sustainable plastics value chain (UNEP, 2023b) and advocates for the sector (Schröder, 2020) argue for a so-called ‘Just Transition’ according to fundamental principles (Shift, 2022) which aim to protect waste pickers’ rights, safety and independence (IKHAPP, 2023). These aspirations echo previous research (Velis et al., 2012), and policy approaches (Silva de Souza Lima and Mancini, 2017) which advocate for their integration into formal waste management planning and place the IRS at the centre of efforts required to upscale resource recovery in the Global South (Cook et al., 2022; O'Hare, 2020; O’Hare, 2019).
The transformative change required to support and integrate the activities of the IRS requires knowledge of the sector’s activities to establish how many people are involved and to quantify the impact they have on plastic pollution and the circular economy (Velis et al., 2022). Recent efforts to make such estimates (Wilson et al., 2015) and model the sector’s productivity (Cook and Velis, 2020; Lau et al., 2020; OECD, 2022a, b) indicate 10–20 million workers collecting 50–90 million metric tonnes (Mt) of recyclate each year worldwide. Some of the source data underlying these estimates comes from Linzner and Lange (2013), who reviewed the prevalence, productivity and modality of the IRS, the only quantitative global review of the sector’s activities to date. They tentatively estimated that the IRS makes up 0.6% (0.5–2%) of the population, the upper end of the range in agreement with earlier estimates (Medina, 2000), and that the IRS could reclaim as much as 45% of all municipal solid waste generated. A conservative extrapolation of these data (Section S1) would mean a Global IRS population of 40 million people, collecting 390 Mt waste per annum, considerably more than recent modelling suggests (Cook and Velis, 2020; Lau et al., 2020; OECD, 2022a, b). The substantial difference between these estimates is indicative of the uncertainty amongst source data used to estimate IRS activities, frustrating efforts to assess the true current and potential future impact of the IRS on plastic pollution mitigation.
Focussing on the South African circular economy, Godfrey (2021) reviewed and modelled the productivity of the IRS and estimated its contribution to the fiscal economy, estimating waste picker income at 47 million USD (range 23–84 million USD) and 1.1 Mt of recyclate collected in 2017. Aside from Linzner and Lange (2013), only Morais et al. (2022) has reviewed informal sector productivity with a worldwide scope, focussing on poverty alleviation and the circular economy. However, a systematic and qualitative assessment of global productivity and prevalence is absent.
Parties to the Plastics Treaty, will require high resolution estimates of plastic emissions. These estimates will be impacted by the IRS and negotiations for a Just Transition conditions towards safer and fairer working conditions (IWAP, 2023) must be based on strong data which can be used for inclusion in local and national action plans. To this, we consolidate more than 20 years of data on the prevalence and productivity of the IRS via a systematic scoping review (PRISMA-ScR) followed by data analytics. We assess the methodological basis on which data were collected and present the most detailed assessment of IRS activities in 32 countries worldwide. We propose that methods for data collection are improved to ensure that future data collection efforts result in outcomes support the IRS to continue the valuable function in global society and help its participants achieve recognition for their work.
Our scope covers evidence relating to the activities of ‘primary collectors’, defined here as people engaged in the separate collection of recyclates. We deliberately exclude information relating to informal workers who collect the whole fraction of municipal solid waste with the intention of reclaiming materials afterwards to avoid double counting ‘sorters’ who we also consider primary collectors. We define ‘secondary collectors’ as material buyers and exclude them from our analysis except where information about them assisted with understanding the activities of primary collectors.
2.1 Systematic review
We systematically reviewed academic literature relating to IRS productivity and prevalence between 2000 and 2020 obtained from Scopus in English, Portuguese, and Chinese languages. We followed the PRISMA-ScR method (Tricco et al., 2018), and have included the checklist of preferred reporting items in Supplementary Information (SI), Table S1. We sought data which quantified or categorised IRS activities according to: (1) the number of active primary collectors; (2) the proportion who are men, women and children; (3) the composition of materials collected: (4) the productivity of primary collectors; and (5) the operating context (for example whether they collected waste door-to-door, or from land disposal sites) (Detailed research questions in Table S2).
Due to the wide scope of our research questions, we chose to search for all articles that contained information about the IRS, regardless of the discipline (search terms in Table S3). This meant a considerable number of publications had to be screened, but also that we reduced the chance of missing relevant information.
Publications were screened by a single reviewer and marked for inclusion, exclusion or for further discussion amongst the project team. A second reviewer periodically blind-reviewed batches of articles which were compared with the assessment of the first reviewer to ensure consistency of approach. Unresolvable disputes were arbitrated by other members of the project team. We deliberately implemented lenient inclusion criteria (Table S5) to enable us to assess the breadth of type and strength of methodological approaches taken.
We supplemented the academic search with a structured search of grey literature (i.e., not peer-reviewed or of academic origin) from websites of non-governmental organisations, supra-national organisations, and specialist consultancies (Table S4). Snowball searching was used to identify research not already identified using bibliographies and the ‘cited by’ function in Scopus. We attempted to track down, verify and cite the original research in studies that reported secondary data. However, in cases where the original study was unobtainable, or ambiguous, we reported the data as presented, assuming that the author had verified the source correctly. Data from all sources were charted (extracted) and categorised according to the variables listed in Table S6.
2.2 Data preparation
Data indicating the number of primary collectors were separated according to whether they collected waste from multiple locations (‘multi-source’), for example from the streets or households, and those who collected waste from single locations (‘single-source’) where waste has been concentrated, for example land disposal sites and waste transfer stations. For data quality assurance, boxplots were graphed to visually examine the data spread and rapidly identify potential outliers that may have resulted from errors in our analysis or from source material. Cases identified were further scrutinised, resulting in the removal of three data points, we confidently considered as likely to be typographical errors in the original material.
For the purposes of graphing and data analytics, the study periods (year) were simplified as follows: 1) where no study period was reported, we used the date of publication; 2) where a study year was reported, we used that year; and 3) where a range of years were reported, for example in a panel survey, we used the most recent year.
2.3 Data analysis
A full, detailed description of the data analysis steps is presented in the SI, Section S3; and all data used in this study are in the Supplementary Data File. Briefly, we used OriginPro, version 2023b statistical software to explore potential associations and fit models (curves) between the proportion of IRS primary collectors in the population (dependent variable) and a range of socioeconomic indicators (independent variable), following Velis et al. (2023). Models were ranked using the Akaike Information Criterion (AIC) Test score (Akaike, 1974) and other statistical metrics to assess the goodness of fit (Table ST07a). Transport modality and operational context were simplified, and in some cases assumed (Table ST02a and b) to enable comparability in data presentation. Working time data presented as averages (means, medians or as a non-specified averages) were treated separately to categorical grouped data. The latter were distributed uniformly between the hours or days within each category, and an overall mean calculated for each time-step. Means were then graphed following the same model fitting process as the prevalence. Data on the composition of waste collected for recycling by the IRS were simplified into five main (basic) groups: plastics, paper, metals, glass and other. The term ‘mean’ denotes arithmetic mean; other measures of central tendency (e.g., median) are also used, as needed. Where data were available, plastic items were further classified according to their (main) polymer type.
3.1 Prevalence of primary collectors
Median prevalence of IRS primary collectors is just over 0.2% (IQR 0.1–0.5%) of the population in sub-national administrative areas (Fig. 1), with the highest proportions in Easter Asia (median 0.8%; IQR 0.4–0.9%), mainly China where recovery of recyclate from waste has supported the urban poor over many decades (Linzner and Salhofer, 2014; Steuer et al., 2017). National scale mean estimates of total number of waste pickers in China are ca 4 million (Fig. 2A), but with substantial differences (0.2–0.7% population) reported (Fig. 2B). China’s rapid attainment of economic prosperity in recent years (Ding et al., 2019) has come with and substantial improvements to its waste management system (Wilson, 2023; Xiao et al., 2023). Therefore, it is noteworthy that six of the data points for China, and one from Mongolia, are pre-2010, including two which report primary collector prevalence at > 1% of the population. We did not find a recent review of the IRS in China, but it is conceivable that numbers have dwindled in the context of China’s contemporary economic status.
Approximately 23% of the data points obtained for prevalence relate to the countries of Southern Asia, where IRS participants represent a median of 0.4% (IQR 0.2–0.7%) of the population. Alongside Brazil, India has received considerable attention in literature for its thriving IRS, as it has historically combined a large population of urban poor with a waste management system that is insufficient to cope with its rising population and prosperity (Hasan and Ghosal, 2023). It is therefore unsurprising that 70% of the data points for Southern Asia are for Indian administrative areas, with studies dating back to 1992. Excluding one outlier (ca. 1.6%), data from studies of waste picker prevalence in Southern Asia showed a large range of between 0.02% and 1.3% of the population. This contrasts with studies from Latin America and the Caribbean where the data were much more tightly clustered (median 0.17%; IQR 0.1–0.2%). Both Southern Asia and Latin America and the Caribbean regions have strong uptake of collaborative practices amongst the IRS, and many of the data points we obtained are based on association memberships and cooperatives. In Brazil, where waste pickers enjoy formal recognition in law (Bouvier and Dias, 2021), IRS participants (‘Cartoneros’) are enumerated through a national census which provides a detailed assessment of their prevalence and activities (Silva et al., 2013). Census enumeration is by definition complete; however, this high level of precision has been criticized by some authors for its lack of accuracy (Dias and Ogando, 2015). For example, in Brazil, the National Movement of Waste Pickers (MNCR) estimates that the waste picker population is between 800,000 and 1,000,000 people, at odds with the official national census which estimates 388,000 (Silva et al., 2013). In Indonesia, the Indonesian Waste Pickers Union reports 3,700,000 members (IAWP, nd) which, based on 2023 population (273 million) would mean that 1.4% of all Indonesians work as a waste picker.
3.1.1 Trends in primary collector prevalence
The median proportion of waste pickers in the population of sub-national administrative areas appears to decline with increased prosperity, from ca. 0.4% in LICs to 0.1% in HICs, though only two data points were available for HICs (Fig. 1). None of the models which we explored demonstrated a strong association between socio-economic indicators and prevalence (Supplementary Data File ST-07a). Nonetheless, we have picked two examples (Fig. 2C and D) that demonstrate a weak downward trend, for example when plotted against GNI per capita PPP, and demonstrating how it could be modelled with a 2nd order polynomial curve (Fig. 2C). In this example, there are few studies of waste picker prevalence in wealthier nations and hence few data points above $18,000, resulting in a wider confidence interval for administrative areas in countries with higher incomes. Given this data paucity, we deliberately excluded the estimate for Paris from the analysis shown in Fig. 2C, as it was just introducing to much uncertainty and leverage on the curve fitting (for demonstration purposes, graph including Paris is shown in Fig. S3A).
We also identified a weak downward trend between the proportion of IRS participants in the population and increased Social Progress Opportunity Index (SPI-3) (Social Progress Imperative, 2022) (Fig. 2D). Compared with SPI-0 (the combined social progress indicator), SPI-3 provided better explanatory power for waste picker prevalence (the dependent variable). Velis et al. (2023) also found SPI-3 to be a better predictor of waste management performance indicators compared to SPI-0; inferring that improvements to ‘personal rights’, ‘tolerance and inclusion’ and ‘access to advanced education’ were also can be correlated to some aspects of waste management development. As with wealth, it is possible that as populations have greater access to opportunity, fewer people might choose to endure the risk and hardship associated with waste picking.
The downward trend in the presence of informal actors in the waste management system of higher income countries is supported by Wilson et al. (2012), and partially supports the qualitative conceptualization of ‘development bands’ proposed by Whiteman et al. (2021), which indicates decreasing influence of IRS participants on waste management systems as standards and governance increase.
In a study of cities in Latin America and the Caribbean, Espinoza et al. (2011) also found similar correlations between waste management performance and GDP, Gini coefficient, unemployment, poverty and indigence.
Sub-Saharan Africa is incongruous with the narrative that waste picker prevalence is higher in poorer nations, with the lowest median proportion of primary collectors (0.03%) across its sub-national administrative areas (Fig. 1). One explanation for the comparatively small IRS community is that despite poor waste management provision (Kaza et al., 2018) and large source of accessible materials, it may be more challenging to sell materials compared to countries with a more developed industrial sector. Even in the context of considerable fiscal challenges and widespread poverty (International Monetary Fund, 2023), the lack of market for secondary materials could serve to restrict the proliferation of the sector, or at least restrict it to certain areas.
Despite the correlation between development and IRS prevalence, it is not inevitable that waste picker numbers will always dwindle with improved wealth and progress. Countries such as Chile (See Santiago de Chile in Fig. 2C), Uruguay (see Montevideo in Fig. 2C), and Argentina (See Fig. 2A and Buenos Aires in Fig. 2B-C) have all retained strong IRS population despite achieving HIC status in recent years. Though we have little evidence here, it is possible that countries with a pre-existing and substantial IRS contingent may see a much slower decline in numbers and even a persistence of the IRS community for much longer.
3.1.2 Gender
In general, in the sub-national and national areas which reported gender prevalence, more men than women are engaged in primary collection activities, though most of the data related to Latin America and the Caribbean region (n = 19) and data points (n = 7) in sub-Saharan Africa (Fig. 2E). Given this regional bias, consolidating the data to calculate averages does not provide a meaningful outcome. Nonetheless, we can observe that the ratio across most of the study locations is in the region of 70–80% men in most contexts, except for Grand Vitoria and Structural City in Brazil, the latter IRS population of which has more women than men. We did not find any sources which used more gender inclusive descriptors other than ‘men’ or ‘women’.
3.1.3 Data provenance
Of the 69 studies (127 data points) which reported IRS prevalence at sub-national level, 15 (56 data points) purposively collected data on the number of waste pickers operating. Of these, nine (10 data points – 8%) met our criteria for representative sampling; carried out a complete enumeration of the IRS population in a clearly defined area (saturated sample); or analysed a secondary source which carried out complete enumeration. The remaining 54 studies (71 data points) either reported estimates from a secondary source which was not always cited or provided an estimate based on expert knowledge which was occasionally qualified but mostly unsubstantiated. This lack of representativeness, or even corroboration, has a substantial impact on the certainty of our results. The implication is that our global understanding of the IRS numbers has relatively low reliability.
All sub-national studies reported prevalence in urban areas, limiting our ability to extrapolate the findings to rural areas. Speculatively, IRS activities may be less intense in places where the quantity of available engineered materials is low, meaning they have to cover a larger area to obtain sufficient material to make a living. Furthermore, IRS participants do not necessarily have access to vehicles. Without access to materials markets within walking or cycling distance, IRS primary collection becomes unviable. For these reasons, it is recommended that further work investigates the prevalence of the IRS in rural areas to establish the relative probability of people engaging in waste picking.
3.2 Productivity of primary collectors
IRS primary collectors that operate in single-source collection contexts, achieve higher daily yields compared to those who work in multi-source contexts, though the evidence base for the former is considerably smaller with just 19 studies for multi-source compared with 78 studies for single-source (Fig. 3A). Those who work on foot in the streets collect the least material, with a median of 20 kg·d− 1 (IQR 15–37 kg·d− 1). The carrying capacity and range capability of wheeled transport provides IRS primary collectors with a substantial advantage, enabling those who work on the streets to collect between 35 kg·d− 1 and 60 kg·d− 1, effectively doubling or tripling the daily yield. Itinerant buyers achieve higher yields still with median of 40–73 kg·d− 1, presumably because the currency transaction enables them to source larger quantities from fewer waste generators, optimising the material to source ratio.
One of the drawbacks of analysis of average productivity across multiple datasets (Fig. 3A), is that it does not provide understanding of the range of productivity data reported within each cohort. Of the 99 studies that reported primary collector productivity, 23 reported a range (Fig. 3B). Comparison of the data from these studies and that from the aggregated dataset of averages in Fig. 3A, shows much wider variation between productivity amongst groups of primary collectors in sub-national administrative areas. For example, several studies of street collectors on foot indicated productivity in excess of 60 kg·d− 1, three times greater than the median of averages indicated in Fig. 3A.
No clear picture emerged to indicate whether productivity is higher for men or women. Just 12 datapoints reported productivity by gender, with mostly incongruous operating context and transport modalities. Three data points for children were comparable, indicating that they collected around half to two thirds of the quantities of adults, but given the complexities of IRS activities and lives, it is conceivable that these data points might not be generalisable to other contexts.
3.3 Primary collector working time
Very little information was found to indicate how many months of the year IRS primary collectors work for. Just two studies of waste pickers in Pune (India) and Durban (South Africa) indicate that most waste pickers work between 11 and 12 months per year, but some work as few as 4 months, suggesting that some IRS workers have another vocation and that the work is seasonal (Fig. 4A).
The median working days per month was just below 24 (IQR 21.5–26), but with one study reporting 18 days per month worked which is equivalent to a 4-day working week (Fig. 4B). It was unclear from any of the studies whether the monthly (sometimes reported as weekly) working time accounted for annual holidays, sickness, or other life events. A study of waste pickers (n = 200) on a dumpsite in Mumbai (not included in our systematic review), found that, they lost 18 days per year of productive time due to ill health compared to 11 in a control group (n = 103) (Chokhandre et al., 2017). Therefore, true working days may be slightly more or less than those reported.
Daily working time was reported as an average (median, mean or not stated) or as categorical proportions of a sample. Analysis of data from the former, shown in Fig. 4C, indicates that IRS work on average 8 hours per day (IQR 6.5-9 hours) with those working on single-source sites working very slightly less. The range was large, with some IRS members working as few as five hours per day and some as many as 11 on average. Analysis of data-points in the two studies of Pune and Durban shows a much clearer difference between the daily working hours of men and women, with women generally working slightly fewer hours (range 4.5–7.5 h·d− 1) than men (range 1.5–11.5 h·d− 1) (Fig. 4D). One explanation could be that women have caring responsibilities or other sources of income. The exception is the group of men who work just 1.5 hours per day, suggesting that they collect waste from the streets to supplement another source of income.
We fitted a Gaussian function to the mean of the data which were reported categorically to demonstrate the spread of daily working time invested by IRS primary collectors (Fig. 4E). The mode, roughly 7.5 hours, is similar to the median of the daily working times reported in Fig. 4C. Analysis of the sources which reported categorical daily working time provides additional insight into the working practices of some waste pickers which is not obvious from studies which only report mean data. For example, we can see that around 10% of primary collectors work between 1 and 3 hours per day, indicating that waste collection is either a supplement or that they have other activities which occupy the rest of their time.
The working week of waste pickers was based on three categorical data points. This means there is high uncertainty and low generalisability. Nonetheless, we fitted a third order polynomial to the averages which explains three core aspects of the data: (1) That nearly a third of primary collectors work 7 days per week; (2) that 15–20% of informal primary collectors work just 1 or 2 days per week; and (3) That there is notable group within the cohort that work 3 days per week. Though based on a fairly limited dataset, both the polynomial and Gaussian functions can be used for modelling these aspects of the activity of IRS primary collectors.
3.4 Composition of material collected for recycling
3.4.1 Materials basket composition
We reveal a diverse range of materials collected by the IRS worldwide, dominated by the four main materials groups: plastics, paper (including cardboard), metals and glass (Fig. 5A and C). Paper, plastics, and metals are represented strongly in most locations (the full sub-national administrative area dataset is presented in Fig. S6). By comparison, glass is less commonly collected, which is unsurprising given its much lower market value. For example, in South Africa, glass was relatively recently sold by waste pickers for between 5 USD and 19 USD per tonne (Godfrey, 2021). Speculatively, the low cost to weight ratio and risk of injury if it breaks could contribute to the low level of targeting by the IRS.
In our analysis, the typical waste picker basket contains 4–88% wt. (ar) plastic based items (Fig. 5C) with an overall mean of approximately 30% wt. (ar) (Fig. 5A). A linear model best explained the variability in plastic composition over time, with a weak non-significant upward trend indicating that plastics are increasingly targeted (Fig. 5B). The proportion of paper in the baskets of primary collectors also appears to be increasing (Fig. S5A) and a gradual decrease in collection of metal (Fig. S5B), glass (Fig. S5C) and other material (Fig. S5D) is evident. Of these, only glass showed a statistically significant temporal trend (p < 0.0001).
Within the plastic fraction, and in contrast to Global North typical target material profiles, we find a diverse array of plastic types collected, including material which is relevant to locally specific markets such as shoe-soles, PVC, and PS (Fig. S7). Flexible plastics such as LDPE, HDPE, and PP foils are widely targeted and represent a median of approximately 60% of the basket in LMCs and 32% in UMCs.
Evidence indicates the existence of an IRS going back several centuries (Velis et al., 2009) and it is likely that people have always sought to recover value from waste, even before modern concepts of informality existed. However, during the second half of the 20th century, a relatively rapid increase in engineered materials placed on the market in the Global South outpaced the capability and capacity of governments to cope with the associated increased waste generation (Melosi, 1982; Wilson, 2023). Combined with rising unemployment and poverty, the contemporary IRS arose during the 1960s and 1970s, capitalising on the shortcomings of waste management authorities and improving their economic circumstances (van Zeeland, 2014).
4.1 Evidence for IRS prevalence
Around the 1990s and 2000s, literature began to emerge on the activities of the IRS, and some early estimates suggested that they represent 2% of Global South populations (Medina, 2000). Later, systematic and evidence-based efforts suggested that the proportion was closer to 0.6% (range 0.5-2%) (Linzner and Lange, 2013). However now we find that the numbers, though still very large on an absolute basis, are perhaps 10 times less as a proportion of population, than those suggested by Medina (2000). But, in the absence of sufficiently detailed methodological explanations in more than half of the studies, and the lack of representativeness in virtually all of the others, it is challenging to determine where the true (i.e., unbiased) numbers lie within the large ranges reported. Therefore, we have deliberately refrained from a ‘back-of-the envelope' extrapolation of prevalence data presented here and would caution against such efforts which could be misleading without consideration of the large heterogeneity and uncertainty in the evidence base.
The fact that so many studies report numbers based on expert judgement adds uncertainty to our IRS prevalence dataset. Even if these specialists have a good understanding of the IRS in a particular area, we were not able to locate evidence that they have published a scientifically implemented enumeration process and no method for expert opinion elicitation, for example Hemming et al. (2018), was used by any author. Regardless of the method, few studies appeared to consider the potential limitations of their estimates or methodological approach, making it hard to determine whether they had comprehensively enumerated a particular area. We did not systematically collate a list of methodological shortcomings; however, we suggest here several potential reasons why numbers of IRS participants may be inaccurately estimated (Table 1).
|
Overestimates |
Both |
Underestimates |
|---|---|---|
|
• Estimates based on size of organisational membership which may not necessarily reflect the number of operationally active participants. • Inclusion of participants who are not primary collectors. |
• Inaccurate or non-specification of geographical study boundary and hence wider population. • Non-consideration of seasonality and part-time working. • Waste pickers are banned or unwelcome in some areas (Fergutz et al., 2011), meaning that enumeration activities in one area are not generalisable to another. • Proportion of waste pickers in population is small, so with a small sample there is greater probability of error (Bouvier and Dias, 2021). |
• Enumeration based on census data may miss those of no fixed abode or itinerant workers in temporary accommodation (Bouvier and Dias, 2021; Dias and Ogando, 2015). • Non-consideration of operational areas or materials markets frequented by waste pickers and hence non-enumeration (Bouvier and Dias, 2021). |
One of the core inconsistencies noted across all studies is that the number of IRS collectors reported did not appear to consider seasonality and part-time working. For example, the typical method of comparison for formal employees uses the concept of ‘full-time equivalence’ (also ‘whole time equivalence’), enabling comparison of business size or productivity according to a standardised metric (Eurostat, 2020). But no such metric was used to report prevalence in any of the published research, despite evidence showing that some workers only practice primary collection for part of the day, week, month, and year. Therefore, most numbers reported are likely to reflect the number of IRS collectors but cannot be used directly to calculate productivity unless they are standardised to full-time equivalence.
4.2 Productivity and related metrics
Evidence on IRS productivity was sufficient to indicate the relative differences in capability between participants working in a variety of contexts and transport modalities. Combined with other relevant characteristics such as working time, material composition, operational context and prevalence, these metrics can be used, for example, in modelling to determine the plastic pollution mitigation potential of the sector (Cottom et al., 2023).
The lack of contextual data to accompany findings on productivity-related metrics has resulted in some uncertainty in many of the studies investigated. For example, of the 50 studies (99 data points) which reported productivity, 12 (21 data points) did not detail the operational context and 21 (25 data points) did not detail the mode of transport used by primary collectors. In many cases, productivity was not the primary focus of studies, yet even in the case of health-related surveys or studies which evaluated local waste systems, inclusion of context would assist with interpretation of results. In another example, the analysis of working time, none of the seven studies that reported days worked per month indicated whether they had accounted for sickness, holiday (vacation) time, or whether the data were averaged across the year. This oversight could have a substantial impact on the annual productivity of an IRS worker and any extrapolatable productivity estimate.
Few studies that reported productivity or composition data base their findings on field measurements. Just two (Abledu and Amfo-Otu, 2018; Moniruzzaman et al., 2011) used scales to weigh waste obtained by primary collectors and another three examined invoices from collectors (Hayami et al., 2006; Sasaki and Araki, 2014; Sasaki et al., 2018). A further eight obtained productivity or composition data from expert interviews with cooperatives, which theoretically, but not necessarily, ought to have been derived from measurements. Obtaining data from written records are substantially more certain because they involve assessment of the validity of measured observations. Conversely, many of the studies relied on testimonials from waste pickers about both composition and productivity. Where measured data are unavailable, verbal surveys may be the only remaining strategy, however, there is a substantial risk of recall bias associated with such investigative techniques (Coughlin, 1990).
4.3 Plastic pollution mitigation and circular economy
In the Global North, recycling of post-consumer plastics recycling is a relatively new concept, having been introduced in parts of Europe since the late nineties and in many other countries such as the UK and US over the following decade (Stokes et al., 2013). Though collection of plastics for recycling has increased steadily, the plastics reprocessing sector in HICs has struggled to sustain itself, and has been subject to plant closures, high reject rates, and huge uncertainty (Lerpiniere and Cook, 2018). This has meant that many HICs have relied on trading plastics recyclate with businesses in the Global South to meet regulatory obligations and recover monetary value from this complex mix of materials (Brooks et al., 2018; Velis, 2014; Velis, 2015). Whereas the composition of plastics collected for recycling in HICs is diverse (Chruszcz and Reeve, 2018), post-consumer plastics reprocessors in the Global North are almost entirely focussed on HDPE and PET, as they are the most prevalent, predictable and easy to separate materials (Vogt et al., 2021). More recent efforts have seen an increase in PP and LDPE reprocessing, but these are still small (Arnold et al., 2022; Palmer and Baker, 2023).
In the Global South, there is evidence that plastics recycling has been active since at least the early 1990s (Kaseva and Gupta, 1996; van Beukering, 1994; van Beukering et al., 1999) and speculatively since the mid-late 1980s. We also found evidence that post-consumer flexible plastics were being collected for recycling by the IRS in the early 1990s (Di Gregorio, 1994) contrary to the prevailing assumption that recycling flexible plastics is unprofitable and unviable (Niaounakis, 2020). In the late 2010s and 2020s, we find that plastics are increasingly targeted by the IRS, and several Global South collection baskets are dominated by flexible post-consumer material (Hayami et al., 2006; Sasaki and Araki, 2014; Sasaki et al., 2020).
IRS primary collectors are relatively unconstrained by the types of health and safety regime that exist in formal settings. Unless they work in an organisational construct, for example for a boss, or in a cooperative, they are mostly free of overheads. This enables them to capitalise on collecting sparsely distributed, niche materials that a formal business might be reluctant to invest time into selecting, storing, and processing. For example, we found a variety of non-mainstream, specialist materials being targeted. Indicatively: medical waste plastics (Black et al., 2019); footwear including sandals (Gill, 2009), slippers (Afon, 2012), plastic shoes (Kamran et al., 2015); woven polypropylene bags (Sasaki and Araki, 2014); mattresses (Sasaki et al., 2018); and less commonly recovered polymers such as nylon (Afon, 2012), ‘rigid foam’ (Zeng et al., 2016), polyurethane and acrylonitrile butadiene styrene (ABS) (Vaidya et al., 2016).
The role, exiting and potential, of the IRS in plastic pollution mitigation has been acknowledged in negotiations for the Plastics Treaty. However, the fact that the (unorganised) informal waste collection business model is made viable at the expense of personal safety is widely criticised as being unsustainable and ethically unconscionable (Velis, 2023). The Just Transition movement (Leroy et al., 2022; UN-Habitat and NIVA, 2022; UNEP, 2023a) advocates for a fair and inclusive process which considers the rights and safety of waste pickers during transition to a circular economy (Schröder, 2020). Regardless of the objectives, both the Plastics Treaty Negotiations and Just Transition movement will require factual and detailed intelligence on the core technical aspects of the role, size and productivity of the IRS for reporting and modelling possible inclusion/ transition futures (Velis et al., 2022).
4.4 Insights for future data collection
Achieving a representative sample requires prior knowledge of the population being investigated and creates a challenge for researching the IRS who often have little opportunity, interest, or compulsion to document their activities or have them documented by others (Linzner and Lange, 2013). Even if characteristic data are available for an IRS population, it may not always be suitable for the stratification of samples (Stephan, 1941). For example, Sabedot and Pereira Neto (2017) used the age variance of 150 waste pickers to select a sample of 29 informal workers structured interviews on composition and productivity. Whilst it is plausible that age is a factor in the ability of an individual to collect and carry material, our productivity assessment shows that there are strong associations with transport modality and operating context. Six studies stratified samples by area (block sampling), to reduce the resource burden of total enumeration (Di Gregorio, 1994; Mitchell, 2008; Putri et al., 2018; Sasaki et al., 2018; Steuer et al., 2017; UAESP, 2012). This may be the only feasible solution in a project with limited resources, and these projects were not all focussed on activities related to productivity. However, we argue that it is challenging to ensure that the variability of characteristics in the population is adequately represented in only area-stratified samples, because of the heterogeneity of IRS activities; particularly if the study area is small or if the results are intended for generalising across a much larger area.
Without prior knowledge of the population, relevant to a study’s objectives, or more certainty that area sampling will sufficiently represent the heterogeneous characteristics of an IRS cohort, researchers are left with few options to achieve a representative sample. A national population census provides a near comprehensive enumeration; however, as suggested in Table 1, IRS participants may be over-represented amongst those who might avoid or be systematically excluded from such an exercise (Bouvier and Dias, 2021). If the target population is in a relatively controlled location, total enumeration is straightforward. For example, both Cruvinel et al. (2020) and Bonini-Rocha et al. (2021) were able to count the number of waste pickers working at a land disposal site in Structural City, Brazil, with reasonably high reliability, and then carry out near-complete enumeration of the waste picker population. However, waste pickers operate in a diverse set of contexts and using a wide range of equipment and transport modalities throughout the built environment; within waste facilities (including land disposal sites); and may even be symbiotic with businesses and institutions. Methods which aim to enumerate and measure IRS characteristics must ensure that this diversity is proportionally represented.
Oduro-Appiah et al. (2021) solved many of these data gathering shortcomings by carrying out a target census of the IRS population across the city of Accra in Ghana. Using a similar strategy to Steuer et al. (2017) and (2018), they approached IRS participants as they marketed materials at the city’s ‘junk dealers’ (materials markets), as well as at focal points for primary material collection such as at transfer stations and informal dumpsites. A basic census was carried out of every primary and secondary collector in the city, each of which was asked to provide basic demographic information. This was used to create strata from which to select a representative sub-sample of waste pickers, each of whom were invited to participate in an in-depth interview. The sub-sample participants were asked questions about productivity, materials, and monetary economics.
The method adopted by Oduro-Appiah et al. (2021) appears to have addressed many of the challenges of representativeness faced by other studies. However, we argue that the sub-sample stratification characteristics, could be tailored more closely to those which impact productivity, namely operational context, and transport modality. The strategy of engagement at materials markets is also compelling. All primary collectors must sell their materials, so engaging them at the point-of-sale appears to be an effective strategy. However, more research is required to ascertain whether this method would enable total enumeration, or whether there are other types of market that are challenging to identify. For example, Schenck et al. (2019) reported that of the 365 waste pickers surveyed across 10 dumpsites in South Africa, 8% sold materials to private individuals rather than buy-back centres. Whether those individuals subsequently sold material on to a buy-back centre is unknown, but it could interfere with the objective of total enumeration if they were not identified.
We reveal here the breadth and scale of information on IRS prevalence and productivity, which for the first time provides a statistically assessed quantitative indication of the profound impact that its participants have on the global circular economy and in the fight against plastic pollution. As waste and resources management systems develop, understanding how many IRS primary collectors are active worldwide is vital to ensure that they are equitably represented and considered into future waste and resources management planning. Yet, despite it being a most widely reported variable, we find here IRS prevalence to be the least reliable, with the majority of evidence based on the judgement of individuals or repetition of information without verifiable provenance. Data were almost entirely obtained from studies in the Global South. Whilst we agree that IRS activity is likely to be less prevalent in the Global North, there is lack of quantifiable evidence which may reveal the scale of unreported material flowing into the circular economy.
Despite the uncertainty over the number of IRS primary collectors, our review and analysis findings support the widely acknowledged premise that the sector has a substantial positive impact in the fight against plastic pollution. This contribution is already recognised in Plastics Treaty negotiations, which call for legal protection, recognition, and inclusion of the IRS into a ‘safe plastics value chain’. If parties agree to these obligations, local and national action plans will require accurate assessment of IRS activities and data on prevalence and productivity will be required by plastic pollution quantification models, which often use the quantity of ‘at-risk’ material to assess the probability of emission into the environment. Beyond plastics pollution, the quantification of IRS activities is also critical for estimating the indicator SDG 11.6.1, the proportion of waste managed in controlled facilities; and more widely for establishing their contribution to circularity across the Global South and in the globalised secondary materials supply chains.
To this, we propose the development of a new, standardised approach for quantifying the contribution of the IRS to the circular economy and plastic pollution mitigation. Such a method would build on the valuable experiences of the 120 studies reviewed here, addressing previous shortcomings of representativeness through complete enumeration of the sector, and which could be set within a clearly defined administrative boundary. Stratification should be relevant to productivity and prevalence, and census participants engaged at the point of material sale to achieve a saturated sample. We suggest that the approach must have the flexibility to be tailored to different cultures and contexts; be achievable with the typically limited / modest monetary resources that are realistically obtainable in the Global South; and be comprehensible enough to be implemented by practitioners.
The knowledge gap and uncertainty around the extent, nature, and effect of waste picker operations has historically damaged the prospects of an inclusive and just transformation of the sector. We aspire that our findings and insights on prevalence and productivity of the IRS could form a new, defensible baseline, and trigger a much-needed additional effort in this direction. Ultimately, a community that still suffers from social and societal exclusion (despite offering tangible environmental services) may become critical to the achievement of internationally agreed targets. Rather than ideologically driven opinions or unsubstantiated statements, a reliably quantified evidence base can only assist in that direction of change.
Author contributions
E.C.: Conceptualization; Methodology; Validation; Formal analysis; Investigation; Data Curation; Writing - Original Draft; Writing - Review & Editing; Visualization. N.S.S.LC.: Conceptualization; Methodology; Validation; Formal analysis; Investigation; Data Curation. C.A.V.: Conceptualization; Methodology; Validation; Formal analysis; Investigation; Data Curation; Writing - Original draft; Writing - Review & Editing; Visualization; Supervision; Resources; Funding acquisition.
Acknowledgements
We would like to thank: Xue Mei for assistance with searching and screening data in Chinese; Josh W. Cottom for assistance with data analysis; Kwaku Oduro-Appiah, David C. Wilson, and Remi Jaligot for their assistance with clarification of findings in their research; and Angeliki Savvantoglou for illustration of the graphical abstract.
Funding
This work was partly supported by GRID-Arendal and NORAD under project number 205275, with additional in-kind support by the University of Leeds; and the University of Leeds Visiting Researcher Fund supporting Nathalia Silva de Souza Lima Cano’s research placement in Leeds. The views expressed in this article are those of the authors’ alone and do not necessarily represent the views or policies of GRID-Arendal or NORAD.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability statement
All data relating to this study are available in the main text and supplementary information and supplementary data file.
- Abledu, E.S., Amfo-Otu, R., 2018. Contribution of Informal Sector Recycling Workers to Sustainable Landfill Management: The Case of Kpone Landfill Site in the Greater Accra Region. PUCG Appl. Res. J. 4, 1-11.
- Adam, H.N., Pille-Schneider, L., Enge, C., Nøklebye, E., Bharat, G.K., Abraham, M., Dkhar, N., 2022. Important but ignored?: The role of the informal recycling sector in a prospective international agreement on plastic pollution, International Knowledge Hub Against Plastic Pollution (IKHAPP), Oslo, Norway https://ikhapp.org/ikhapp-publication/microplastic-pollution-in-coastal-ecosystem-off-mumbai-coast-india/.
- Afon, A., 2012. A survey of operational characteristics, socioeconomic and health effects of scavenging activity in Lagos, Nigeria. Waste Manage. Res. 30(7), 664-671.
- Akaike, H., 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716-723.
- APEW, 2023. Progress Report 2022: Catalysing Impact, Alliance to End Plastic Waste (APEW), Delaware, USA https://endplasticwaste.org/en/our-stories/-/media/94253BB91D3D4F379080B5A17F5E2722.ashx.
- Arnold, M., Manoochehri, S., Wahlström, M., Nelen, D., Colgan, S., 2022. Flexible plastics in Europe’s circular economy, European Environment Agency European Topic Centre – Circular economy and resource use https://circulareconomy.europa.eu/platform/sites/default/files/2022-12/Flexible%20Plastics.pdf.
- Berger, R., 2023. The Plastic Waste Management Framework, Alliance to End Plastic Waste (APEW), Delaware, USA https://endplasticwaste.org/en/our-stories/rb-recycling-framework.
- Black, M., Karki, J., Lee, A.C.K., Makai, P., Baral, Y.R., Kritsotakis, E.I., Bernier, A., Fossier Heckmann, A., 2019. The health risks of informal waste workers in the Kathmandu Valley: a cross-sectional survey. Public Health 166, 10-18.
- Bonini-Rocha, A.C., de Oliveira, R.A.C., Bashash, M., do Couto Machado, G., Cruvinel, V.R.N., 2021. Prevalence of musculoskeletal disorders and risk factors in recyclable material waste pickers from the dump of the structural city in Brasília, Brazil. Waste Manage. 125, 98-102.
- Bouvier, M., Dias, S., 2021. Statistical Brief No 29 - Waste Pickers in Brazil: A Statistical Profile, WIEGO, Manchester, UK https://www.wiego.org/sites/default/files/publications/file/WIEGO_Statistical_Brief_N29_Brazil_WPs.pdf.
- Brooks, A.L., Wang, S., Jambeck, J.R., 2018. The Chinese import ban and its impact on global plastic waste trade. Sci. Adv. 4(6), eaat0131.
- Chokhandre, P., Singh, S., Kashyap, G.C., 2017. Prevalence, predictors and economic burden of morbidities among waste-pickers of Mumbai, India: a cross-sectional study. Journal of Occupational Medicine and Toxicology 12(1), 30.
- Chruszcz, A., Reeve, S., 2018. Composition of plastic waste collected via kerbside: Results of a waste compositional analysis of plastics at MRFs and PRFs, Waste and Resources Action Programme (WRAP), Banbury https://wrap.org.uk/resources/report/composition-plastic-waste-collected-kerbside#download-file.
- Cook, E., Velis, C.A., 2020. Global review on safer end of engineered life, Royal Academy of Engineering, London, UK https://doi.org/10.5518/100/58.
- Cook, E., Velis, C.A., Cottom, J.W., 2022. Scaling up resource recovery of plastics in the emergent circular economy to prevent plastic pollution: Assessment of risks to health and safety in the Global South. Waste Manage. Res. 40(12), 1680-1707.
- Cottom, J.W., Cook, E., Velis, C.A., 2023. A local-to-global emissions inventory of macroplastic pollution. PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.3.rs-3376960/v1.
- Coughlin, S.S., 1990. Recall bias in epidemiologic studies. Journal of Clinical Epidemiology 43(1), 87-91.
- Cruvinel, V.R.N., Zolnikov, T.R., Takashi Obara, M., Oliveira, V.T.L.d., Vianna, E.N., Santos, F.S.G.d., Oliveira, K.C.d., Scott, J.A., 2020. Vector-borne diseases in waste pickers in Brasilia, Brazil. Waste Manage. 105, 223-232.
- Di Gregorio, M., 1994. Urban harvest: Recycling as a peasant industry in Northern Vietnam, East-WEst Center, Honolulu, USA https://scholarspace.manoa.hawaii.edu/bitstream/10125/21620/1/ENVop017UrbanHarvest1994%5Bpdfa%5D.PDF.
- Dias, S.M., 2016. Waste pickers and cities. Environment and Urbanization 28(2), 375-390.
- Dias, S.M., Ogando, A.C., "Engendering Waste Pickers Cooperatives in Brazil" in Cooperatives and the World of Work ILO Research Conference – Antalya, 9-10 November 2015 (International Labour Organization, 2015).
- Ding, Y., McQuoid, A., Karayalcin, C., 2019. Fiscal decentralization, fiscal reform, and economic growth in china. China Econ. Rev. 53, 152-167.
- Espinoza, P.T., Arce, E.M., Daza, D., Faure, M.S., Terraza, H., 2011. Regional Evaluation on Urban Solid Waste Management in Latin America and the Caribbean: 2010 Report, Pan American Health Organization (PAHO), Inter-American Development Bank (IDB), Inter-American Association of Sanitary and Environmental Engineering (AIDIS) https://publications.iadb.org/en/regional-evaluation-urban-solid-waste-management-latin-america-and-caribbean-2010-report.
- Eurostat, 2020. Glossary: Full-time equivalent (FTE). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Statistics_Explained:General_disclaimer. (Accessed 4 December 2023).
- Fergutz, O., Dias, S., Mitlin, D., 2011. Developing urban waste management in Brazil with waste picker organizations. Environment and Urbanization 23(2), 597-608.
- GAIA, 2021. Strengthening Waste Picker Organising in Africa, Global Alliance for Incinerator Alternatives (GAIA), Durban, South Africa https://drive.google.com/file/d/1JuAAa8psN1D37lr9-sHq464EkOZowYX3/view?usp=drive_link.
- Gill, K., 2009. Of Poverty and Plastic: Scavenging and Scrap Trading Entrepreneurs in India's Urban Informal Economy. Oxford University Press, Delhi.
- Godfrey, L., 2021. Quantifying economic activity in the informal recycling sector in South Africa. S. Afr. J. Sci. 117(9/10).
- Hasan, M.S., Ghosal, S., 2023. Informal plastic waste recycling firms in rural eastern India: Implications for livelihood and health. Clinical Epidemiology and Global Health 21, 101286.
- Hayami, Y., Dikshit, A.K., Mishra, S.N., 2006. Waste pickers and collectors in Delhi: Poverty and environment in an urban informal sector. The Journal of Development Studies 42(1), 41-69.
- Hemming, V., Burgman, M.A., Hanea, A.M., McBride, M.F., Wintle, B.C., 2018. A practical guide to structured expert elicitation using the IDEA protocol. Methods in Ecology and Evolution 9(1), 169-180.
- IAWP, nd. Ikatan Pemulung Indonesia (Indonesia Waste-pickers Union- IPI). https://globalrec.org/organization/ikatan-pemulung-indonesia-indonesia-waste-pickers-union-ipi/. (Accessed 13 November 2023).
- IKHAPP, 2023. Inclusion of the Informal Recycling Sector in a Global Agreement on Plastic Pollution, International Knowledge Hub Against Plastic Pollution (IKHAPP), Oslo, Norway https://ikhapp.org/wp-content/uploads/2023/05/IKHAPP_WP1-1.pdf.
- International Monetary Fund, 2023. Regional economic outlook. Sub-Saharan Africa : light on the horizon?, International Monetary Fund, Washington DC, USA https://www.imf.org/en/Publications/REO/SSA/Issues/2023/10/16/regional-economic-outlook-for-sub-saharan-africa-october-2023.
- IWAP, 2023. IAWP’s Vision for a Just Transition for Waste Pickers under the UN Plastics Treaty, International Alliance of Waste Pickers https://globalrec.org/wp-content/uploads/2023/11/english_position-paper_IAWP-vision-just-transition_resolution150ppi.pdf.
- Kamran, A., Chaudhry, M.N., Batool, S.A., 2015. Role of the informal sector in recycling waste in eastern lahore. Pol. J. Environ. Stud. 24(2), 537-543.
- Kaseva, M.E., Gupta, S.K., 1996. Recycling — an environmentally friendly and income generating activity towards sustainable solid waste management. Case study — Dar es Salaam City, Tanzania. Resour. Conserv. Recycl. 17(4), 299-309.
- Kaza, S., Yao, L., Bhada-Tata, P., Van Woerden, F., 2018. What a waste 2.0: a global snapshot of solid waste management to 2050. World Bank Publications,.
- Lau, W.W.Y., Shiran, Y., Bailey, R.M., Cook, E., Stuchtey, M.R., Koskella, J., Velis, C.A., Godfrey, L., Boucher, J., Murphy, M.B., Thompson, R.C., Jankowska, E., Castillo, A.C., Pilditch, T.D., Dixon, B., Koerselman, L., Kosior, E., Favoino, E., Gutberlet, J., Baulch, S., Atreya, M.E., Fischer, D., He, K.K., Petit, M., Sumaila, U.R., Neil, E., Ochocki, M., Lawrence, K., Palardy, J.E., 2020. Evaluating scenarios toward zero plastic pollution. Science https://doi.org/10.1126/science.aba9475(369), 1455–1461.
- Leroy, M.L.N., Rivoal, M., Quertamp, F., Luong, N.T., 2022. Inclusion of Informal Waste Workers (IWWs) in the transition to sustainable waste management: Issue Brief, United Nations Development Programme (UNDP), Hanoi, Vietnam https://www.undp.org/vietnam/publications/inclusion-informal-waste-workers-iwws-transition-sustainable-waste-management.
- Lerpiniere, D., Cook, E., 2018. Improving markets for recycled plastics: Trends, prospects and policy responses, Paris, France http://dx.doi.org/10.1787/9789264301016-en.
- Linzner, R., Lange, U., 2013. Role and size of informal sector in waste management – a review. Proceedings of the Institution of Civil Engineers - Waste and Resource Management 166(2), 69-83.
- Linzner, R., Salhofer, S., 2014. Municipal solid waste recycling and the significance of informal sector in urban China. Waste Manage. Res. 32(9), 896-907.
- Medina, M., 2000. Scavenger cooperatives in Asia and Latin America. Resour. Conserv. Recycl. 31(1), 51-69.
- Melosi, M.V., 1982. Garbage in the Cities: Refuse, Reform, and the Environment, 1880-1980. Texas A&M University Press.
- Mitchell, C.L., 2008. Altered landscapes, altered livelihoods: The shifting experience of informal waste collecting during Hanoi's urban transition. Geoforum 39(6), 2019-2029.
- Moniruzzaman, S.M., Bari, Q.H., Fukuhara, T., 2011. Recycling practices of solid waste in Khulna City, Bangladesh. J. Solid Waste Technol. Manage. 37(1), 1-15.
- Morais, J., Corder, G., Golev, A., Lawson, L., Ali, S., 2022. Global review of human waste-picking and its contribution to poverty alleviation and a circular economy. Environ. Res. Lett. 17(6), 063002.
- Niaounakis, M., 2020. Recycling of flexible plastic packaging. William Andrew, Oxford, England.
- O'Hare, P., 2020. ‘We Looked after People Better when We Were Informal’: The ‘Quasi-Formalisation’ of Montevideo's Waste-Pickers. Bull. Latin Amer. Res. 39(1), 53-68.
- O’Hare, P., 2019. ‘The landfill has always borne fruit’: precarity, formalisation and dispossession among Uruguay’s waste pickers. Dialectical Anthropology 43(1), 31-44.
- Oduro-Appiah, K., Scheinberg, A., Afful, A., de Vries, N., 2021. The contribution of participatory engagement strategies to reliable data gathering and inclusive policies in developing countries: Municipal solid waste management data in the Greater Accra Metropolitan Area of Ghana. Afr J Sci Technol In 13(6), 735-746.
- OECD, 2022a. Global plastics outlook: Economic drivers, environmental impacts and policy options, OECD Publishing, Paris, France https://www.oecd-ilibrary.org/content/publication/de747aef-en.
- OECD, 2022b. Global plastics outlook: Policy scenarios to 2060, OECD Publishing, Paris, France https://www.oecd-ilibrary.org/environment/global-plastics-outlook_aa1edf33-en.
- Palmer, G., Baker, T., 2023. Plastic Market Situation Report 2022, Waste and Resources Action Programme (WRAP), Banbury, UK https://wrap.org.uk/resources/report/plastics-market-situation-report-2022#download-file.
- Putri, A.R., Fujimori, T., Takaoka, M., 2018. Plastic waste management in Jakarta, Indonesia: evaluation of material flow and recycling scheme. J. Mater. Cycles Waste Manage. 20(4), 2140-2149.
- Sabedot, S., Pereira Neto, T., 2017. Desempenho ambiental dos catadores de materiais recicláveis em Esteio (RS). Engenharia Sanitaria e Ambiental 22, 103-109.
- Sasaki, S., Araki, T., 2014. Estimating the possible range of recycling rates achieved by dump waste pickers: The case of Bantar Gebang in Indonesia. Waste Manage. Res. 32(6), 474-481.
- Sasaki, S., Watanabe, K., Lee, K., Widyaningsih, N., Baek, Y., Araki, T., 2020. Recycling contributions of dumpsite waste pickers in Bantar Gebang, Indonesia. J. Mater. Cycles Waste Manage. 22(5), 1662-1671.
- Sasaki, S., Watanabe, K., Widyaningsih, N., Araki, T., 2018. Collecting and dealing of recyclables in a final disposal site and surrounding slum residence: the case of Bantar Gebang, Indonesia. J. Mater. Cycles Waste Manage. 21, 375–393.
- Scheinberg, A., Nesić, J., Savain, R., Luppi, P., Sinnott, P., Petean, F., Pop, F., 2016. From collision to collaboration – Integrating informal recyclers and re-use operators in Europe: A review. Waste Manage. Res. 34(9), 820-839.
- Schenck, C.J., Blaauw, P.F., Swart, E.C., Viljoen, J.M.M., Mudavanhu, N., 2019. The management of South Africa’s landfills and waste pickers on them: Impacting lives and livelihoods. Devel. Southern Africa 36(1), 80-98.
- Schröder, P., 2020. Promoting a Just Transition to an Inclusive Circular Economy, Programme, C.H.E.E.a.R., London, UK https://www.chathamhouse.org/sites/default/files/2020-04-01-inclusive-circular-economy-schroder.pdf.
- Shift, 2022. Principles for Corporate Engagement on Human Rights with the Informal Waste Sector: Applying the UN Guiding Principles on Business and Human Rights to the Plastic Packaging Recycling Value Chain, Shift Project, New York, USA https://faircircularity.org/app/uploads/2022/11/Principles-for-Corporate-Engagement-on-Human-Rights-with-the-Informal-Waste-Sector.pdf.
- Silva de Souza Lima, N., Mancini, S.D., 2017. Integration of informal recycling sector in Brazil and the case of Sorocaba City. Waste Manage. Res. 35(7), 721-729.
- Silva, S.P., Goes, F.L., Alvarez, A.R., 2013. Situação Social das Catadoras e dos Catadores de Material Reciclável e Reutilizável: Brasil [Social Situation of Recyclable and Reusable Material Waste Pickers: Brazil], IPEA - Instituto de Pesquisa Econômica Aplicada, Brasília, Brazil https://www.ipea.gov.br/portal/images/stories/PDFs/situacao_social/131219_relatorio_situacaosocial_mat_reciclavel_brasil.pdf.
- Social Progress Imperative, Social progress index 2022. The Social Progress Imperative (2022); https://www.socialprogress.org/download/.
- Stephan, F.F., 1941. Stratification in Representative Sampling. J. Marketing 6(1), 38-46.
- Steuer, B., Ramusch, R., Part, F., Salhofer, S., 2017. Analysis of the value chain and network structure of informal waste recycling in Beijing, China. Resour. Conserv. Recycl. 117, 137-150.
- Steuer, B., Ramusch, R., Salhofer, S.P., 2018. Can Beijing's informal waste recycling sector survive amidst worsening circumstances? Resour. Conserv. Recycl. 128(October 2017), 59-68.
- Stokes, R.G., Koster, R., Sambrook, S.C., 2013. The business of waste : Great Britain and Germany, 1945 to the present. Cambridge University Press, New York.
- Tearfund, 2023. Fair Circularity Principles. https://faircircularity.org/fair-circularity-principles/. (Accessed 22 November 2023).
- Tricco, A.C., Lillie, E., Zarin, W., O'Brien, K.K., Colquhoun, H., Levac, D., Moher, D., Peters, M.D.J., Horsley, T., Weeks, L., Hempel, S., Akl, E.A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M.G., Garritty, C., Lewin, S., Godfrey, C.M., Macdonald, M.T., Langlois, E.V., Soares-Weiser, K., Moriarty, J., Clifford, T., Tunçalp, Ö., Straus, S.E., 2018. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine 169(7), 467-473.
- UAESP, 2012. Informe final censo de recicladores, bogotá humana 2012: Informe final revisado y ajustado, Unidad administrativa especial de servicios públicos, Bogotá, Colombia https://www.anrcolombia.org/index.php?option=com_phocadownload&view=category&download=133:uaesp-informe-final-censo-de-recicladores-bogota-2012&id=27:formalizacion-censo-recicladores&Itemid=148.
- UN-Habitat, 2021. Waste Wise Cities Tool: Step by Step Guide to Assess a City's Municipal Solid Waste Management Performance through SDG indicator 11.6.1 Monitoring, UN-Habitat, Nairobi, Kenya https://unhabitat.org/sites/default/files/2021/02/Waste%20wise%20cities%20tool%20-%20EN%203.pdf.
- UN-Habitat and NIVA, 2022. Leaving no one behind: How a global instrument to end plastic pollution can enable a just transition for the people informally collecting and recovering waste, United Nations Human Settlements Programme (UN-Habitat), Norwegian Institute for Water Research (NIVA), Nairobi, Kenya https://unhabitat.org/leaving-no-one-behind-how-a-global-instrument-to-end-plastic-pollution-can-enable-a-just-transition.
- UNEP, 2022. Resolution 5/14 - End plastic pollution: Towards an international legally binding instrument, United Nations Environment Assembly of the United Nations Environment Programme, Nairobi https://wedocs.unep.org/bitstream/handle/20.500.11822/39812/OEWG_PP_1_INF_1_UNEA%20resolution.pdf.
- UNEP, 2023a. Topic Sheet: Just Transition, United Nations Environment Programme (UNEP), Nairobi, Kenya https://wedocs.unep.org/handle/20.500.11822/42231.
- UNEP, 2023b. Zero draft text of the international legally binding instrument on plastic pollution, including in the marine environment, United Nations Environment Assembly of the United Nations Environment Programme, Nairobi https://wedocs.unep.org/bitstream/handle/20.500.11822/43239/ZERODRAFT.pdf.
- Vaidya, P., Kumar, R., Sharma, D., 2016. Economics and environmental impacts of plastic waste recycling: A case study of Mumbai. J. Solid. Waste. Tech. Manage. 42(4), 287-297.
- van Beukering, P., 1994. An economic analysis of different types of formal and informal entrepreneurs, recovering urban solid waste in Bangalore (India). Resour. Conserv. Recycl. 12(3), 229-252.
- van Beukering, P., Sehker, M., Gerlagh, R., Kumar, V., 1999. Analysing Urban Solid Waste in Developing Countries: a Perspective on Bangalore, India, International Institute for Environment and Development (IIED), Amsterdam, The Netherlands https://www.academia.edu/download/100429222/creed24e.pdf.
- van Zeeland, A.J.W.M., 2014. The interaction between popular economy, social movements and public policies: A case study of the waste pickers' movement, United Nations Research Institute for Social Development (UNRISD), Geneva, Switzerland https://www.econstor.eu/handle/10419/148785.
- Velis, C.A., 2014. Global recycling markets: plastic waste: A story for one player – China, International Solid Waste Association, Vienna, Austria http://wedocs.unep.org/handle/20.500.11822/19316.
- Velis, C.A., 2015. Circular economy and global secondary material supply chains. Waste Manage. Res. 33(5), 389-391.
- Velis, C.A., 2023. Chapter 3: Waste pickers: keeping after-use plastics circular, but at high personal cost, in: Hardesty, B.D., Willis, K., Barrett, J., Wilcox, C. (Eds.), Ending Plastic Waste: Community Actions About the World. CSIRO Publishing, Australia, pp. 11-17.
- Velis, C.A., Hardesty, B.D., Cottom, J.W., Wilcox, C., 2022. Enabling the informal recycling sector to prevent plastic pollution and deliver an inclusive circular economy. Environmental Science & Policy 138, 20-25.
- Velis, C.A., Wilson, D.C., Cheeseman, C.R., 2009. 19th century London dust-yards: A case study in closed-loop resource efficiency. Waste Manage. 29(4), 1282-1290.
- Velis, C.A., Wilson, D.C., Gavish, Y., Grimes, S.M., Whiteman, A., 2023. Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning. Sci. Total Environ. 872, 161913.
- Velis, C.A., Wilson, D.C., Rocca, O., Smith, S.R., Mavropoulos, A., Cheeseman, C.R., 2012. An analytical framework and tool (‘InteRa’) for integrating the informal recycling sector in waste and resource management systems in developing countries. Waste Manage. Res. 30(9), 43-66.
- Vergara, S.E., Damgaard, A., Gomez, D., 2016. The Efficiency of Informality: Quantifying Greenhouse Gas Reductions from Informal Recycling in Bogotá, Colombia. J. Ind. Ecol. 20(1), 107-119.
- Vogt, B.D., Stokes, K.K., Kumar, S.K., 2021. Why is Recycling of Postconsumer Plastics so Challenging? ACS Applied Polymer Materials 3(9), 4325-4346.
- White, R., 2013. Global Environmental Harm: Criminological Perspectives. Taylor & Francis.
- Whiteman, A., Webster, M., Wilson, D.C., 2021. The nine development bands: A conceptual framework and global theory for waste and development. Waste Manage. Res. 39(10), 1218-1236.
- Wilson, D.C., 2023. Learning from the past to plan for the future: An historical review of the evolution of waste and resource management 1970–2020 and reflections on priorities 2020–2030 – The perspective of an involved witness. Waste Manage. Res. 10.1177/0734242X231178025, 0734242X231178025.
- Wilson, D.C., Araba, A.O., Chinwah, K., Cheeseman, C.R., 2009. Building recycling rates through the informal sector. Waste Manage. 29(2), 629-635.
- Wilson, D.C., Rodic, L., Modak, P., Soos, R., Rogero, A.C., Velis, C., Iyer, M., Simonett, O., 2015. Global waste management outlook, United Nations Environment Programme, Nairobi, Kenya https://wedocs.unep.org/bitstream/handle/20.500.11822/9672/-Global_Waste_Management_Outlook-2015Global_Waste_Management_Outlook.pdf.pdf?sequence=3&%3BisAllowed=.
- Wilson, D.C., Rodic, L., Scheinberg, A., Velis, C.A., Alabaster, G., 2012. Comparative analysis of solid waste management in 20 cities. Waste Manage. Res. 30(3), 237-254.
- Xiao, W., Liu, T., Tong, X., 2023. Assessing the carbon reduction potential of municipal solid waste management transition: Effects of incineration, technology and sorting in Chinese cities. Resour. Conserv. Recycl. 188, 106713.
- Zeng, C., Huang, C.J., Niu, D.J., Zhou, M., Zhao, Y.C., 2016. Management of rural domestic waste based on investigations of informal waste recycling: A case study of Guangdong Province. Journal of Ecology and Rural Environment 32(6), 946-950.
- Zolnikov, T.R., Furio, F., Cruvinel, V., Richards, J., 2021. A systematic review on informal waste picking: Occupational hazards and health outcomes. Waste Manage. 126, 291-308.
The authors declare potential competing interests as follows: Dr Costas Velis reports financial support was provided by Lloyd’s Register Foundation. Dr Costas Velis reports a relationship with International Solid Waste Association (ISWA) that includes: consulting or advisory, funding grants, speaking and lecture fees, and travel reimbursement. Both Dr Costas Velis and Mr Ed Cook are actively involved in the waste, resources and circular economy sector in research, advising, scholarship and consultancy capacities.
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