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.

Enzymatic hydrolysis of poly(ethylene terephthalate) (PET) is a chemical recycling approach intended to enable a circular economy for polyesters. To quantitatively compare this technology to other recycling and synthesis approaches...

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Moving the current linear economy toward circularity is expected to have environmental, economic, and social impacts. Various modeling methods, including economic input-output modeling, life cycle assessment, agent-based modeling, and system dynamics, have been used to examine circular supply chains and analyze their impacts. This work describes the newly developed Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework, which is designed to model how the impacts of supply chains might change as circularity increases. We first establish the framework with a discussion of modeling capabilities that are needed to capture circularity transitions; these capabilities are based on the fact that supply chains moving toward circularity are dynamic and therefore not at steady state, may encompass multiple industrial sectors or other interdependent supply chains and occupy a large spatial area. To demonstrate the capabilities of CELAVI, we present a case study on end-of-life wind turbine blades in the U.S. state of Texas. Our findings show that depending on exact process costs and transportation distances, mechanical recycling could lead to 69% or more of end-of-life turbine blade mass being kept in circulation rather than being landfilled, with only a 7.1% increase in global warming potential over the linear supply chain. We discuss next steps for framework development.

The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.