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Quantification of plastic recovery by the informal waste sector through a probabilistic approach

Journal of Industrial Ecology 2026 Score: 50 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Nicolas Navarre, Sowmya Marriyapillai Ravisandiran, Nicolas Navarre, José M. Mogollón Stefano Cucurachi, Stefano Cucurachi, José M. Mogollón

Summary

Researchers used material flow analysis with Monte Carlo simulations to estimate plastic waste management in Chennai, India, finding that the informal waste sector recovers 13–20% of the city's plastic while at least 51% of total plastic waste is mismanaged — with recyclable polymers achieving far higher recovery rates than non-recyclable ones.

Abstract Plastic pollution poses a significant threat to human health and the environment, with its impacts expected to worsen due to rising global plastic production. Mismanagement of plastic waste is particularly high in low-middle-income countries like India, which ranks as the 12th largest source of plastic leakage globally. Paradoxically, India also has one of the highest plastic recovery rates in the world, largely due to the role of the informal waste sector (IWS). In this study, we estimate the contribution of the IWS to plastic recovery and assess plastic waste mismanagement and recovery rates in Chennai, India, using a Material Flow Analysis (MFA). To address variability in input data and identify their valid solution spaces, we employ Monte Carlo Simulations. This approach narrows down the collection quantity of the waste pickers to 14% of the prior input range. Our findings indicate that the IWS is responsible for anywhere between 13 and 20% of Chennai’s total plastic recovery. The city mismanages at least 51% of its plastic waste, recovering only 22–34% for processing. However, recyclable plastic polymers have a high recovery rate of at least 43%, while non-recyclable polymers have a much lower rate of 11–20%, highlighting the need to improve collection and processing of these waste streams to reduce littering and plastic leakage. Our study highlights the potential of using probabilistic methods in addressing data gaps in dynamic and under-studied systems like the IWS and provides critical insights for improving plastic waste management in Chennai.

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