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Addressing plastic pollution and waste flows: Insights from South Africa’s experience
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South Africa applied the 'Pathways' plastic flow modeling tool—developed by Pew Charitable Trusts and Oxford University—at the national level, finding that plastic pollution could almost double by 2040 without intervention, while meeting existing extended producer responsibility targets could avoid 33% of projected pollution. An optimized system change scenario could prevent 63% of total plastic pollution between 2023 and 2040.
The Pew Charitable Trust's 2020 report 'Breaking the Plastic Wave', indicates that existing technologies could support an 80% reduction in plastic leakage relative to business as usual by 2040. Therefore, South Africa became the first country to work with the Pew Charitable Trust and Oxford University to test and apply 'Pathways', a modelling framework and software tool which stemmed and evolved from the Pew report, at country level. The tool calculates the flows of plastics in the economy and the impact of various strategies to reduce future plastic pollution. The Scenario Builder within the Pathways tool allows the user to optimise flows in the plastics value chain to satisfy a set of defined objectives in order to achieve an optimal solution. Three major findings have emerged from the application of Pathways at country level for South Africa. Firstly, plastic pollution is set to almost double by 2040 if no interventions are implemented. Secondly, meeting the newly legislated extended producer responsibility (EPR) targets set for plastic packaging can avoid 33% of projected total pollution over the period of 2023-2040. Lastly, an optimal system change can avoid 63% of total plastic pollution over the period 2023-2040. Thus, applying Pathways at country level in South Africa has proven to be valuable by setting a baseline against which progress towards reducing plastic pollution can be measured; determining the outcome of meeting the legislated EPR targets over time, and informing policy decisions by allowing users to model different scenarios towards an optimal system change scenario.
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