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Given the scale of plastic generation, its persistent presence in the environment, and the urgent need to transition to a net-zero emissions paradigm, managing plastic waste has gained increasing attention globally. Developing an effective strategy for plastic waste management requires a comprehensive assessment of the potential benefits offered by different solutions, particularly with respect to their environmental impact. This study employs the life cycle assessment (LCA) methodology to evaluate the environmental impact of two alternative scenarios to the As-Is scenario for managing plastic waste in the province of British Columbia in Canada. The LCA results suggest that the Zero Plastic Waste scenario, which heavily relies on chemical recycling, may not inherently result in a reduced environmental footprint across all impact categories. This is notable when the focus is solely on end-of-life treatment processes, without considering the produced products and energy. The Intermediate scenario reduces the amount of plastic waste sent to landfills by directing more end-of-life plastic to mechanical recycling facilities. This scenario provides immediate benefits for resource conservation, with a minimal increase in the environmental burden resulting from treatment processes. Nonetheless, achieving a net-zero transition requires combining traditional and emerging recycling technologies. The current study could offer some guidance to policymakers on strategies for fostering more sustainable management of plastic waste. [ABSTRACT FROM AUTHOR]

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions. [Display omitted] [ABSTRACT FROM AUTHOR]