Over 400 million metric tons of plastic waste are generated globally each year, resulting in pollution and lost resources. Recycling strategies can recapture this wasted material, but there is a lack of quantitative and transparent data on the capabilities and impacts of these processes. Here, we develop a data set of material quality, material retention, circularity, contamination tolerance, minimum selling price, greenhouse gas emissions, energy use, land use, toxicity, waste generation, and water use metrics for closed-loop polymer recycling technologies, including mechanical recycling and solvent-based dissolution of polyethylene, polyethylene terephthalate (PET), and polypropylene, as well as enzymatic hydrolysis, glycolysis, and vapor methanolysis of PET. Mechanical recycling and PET glycolysis display the best economic (9%−73% lower than competing technologies) and environmental (7%−88% lower) performances, while dissolution, enzymatic hydrolysis, and methanolysis provide the best recyclate material qualities (2%−27% higher). We identify electricity, steam, and organic solvents as top process contributors to these metrics and apply sensitivity and multicriteria decision analyses to highlight key future research areas. The estimates derived in this work provide a quantitative baseline for comparing and improving recycling technologies, can help reclaimers identify optimal end-of-life routes for given waste streams, and serve as a framework for assessing future innovations.

The goal of the circular economy (CE) is to transition from today's take-make-waste linear pattern of production and consumption to a circular system in which the societal value of products, materials, and resources is maximized over time. Yet circularity in and of itself does not ensure social, economic, and environmental performance (i.e., sustainability). Sustainability of CE strategies needs to be measured against their linear counterparts to identify and avoid strategies that increase circularity yet lead to unintended externalities. The state of the practice in quantitatively comparing sustainability impacts of circular to linear systems is one of experimentation with various extant methods developed in other fields and now applied here. While the proliferation of circularity metrics has received considerable attention, to-date, there is no critical review of the methods and combinations of methods that underlie those metrics and that specifically quantify sustainability impacts of circular strategies. Our critical review herein analyzes identified methods according to six criteria: temporal resolution, scope, data requirements, data granularity, capacity for measuring material efficiency potentials, and sustainability completeness. Results suggest that the industrial ecology and complex systems science fields could prove complementary when assessing the sustainability of the transition to a CE. Both fields include quantitative methods differing primarily with regard to their inclusion of temporal aspects and material efficiency potentials. Moreover, operations research methods such as multiple-criteria decision-making (MCDM) may alleviate the common contradictions which often exist between circularity metrics. This review concludes by suggesting guidelines for selecting quantitative methods most appropriate to a particular research question and making the argument that while there are a variety of existing methods, additional research is needed to combine existing methods and develop a more holistic approach for assessing sustainability impacts of CE strategies.

By 2050, the cumulative mass of end-of-life photovoltaic (PV) modules may reach 80 Mt globally. The impacts could be mitigated by module recycling, repair and reuse; however, previous studies of PV circularity omit the consideration of critical social factors. Here we used an agent-based model to integrate social aspects with techno-economic factors, which provides a more realistic assessment of the circularity potential for previously studied interventions that assesses additional interventions that cannot be analysed using techno-economic analysis alone. We also performed a global sensitivity analysis using a machine-learning metamodel. We show that to exclude social factors underestimates the effect of lower recycling prices on PV material circularity, which highlights the relevance of considering social factors in future studies. Interventions aimed at changing customer attitudes about used PV boost the reuse of modules, although used modules can only satisfy one-third of the US demand during 2020–2050, which suggests that reuse should be complemented by recycling.

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