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The deployment of a sustainable recycling network for electric vehicle batteries requires the development of an infrastructure to collect and deliver batteries to several locations from which they can be transported to companies for repurposing or recycling. This infrastructure is still not yet developed in North America, and consequently, spent electric vehicle batteries in Canada are dispersed throughout the country. The purpose of this reverse logistics study is to develop a spatial modeling framework to identify the optimal locations of battery pack dismantling hubs and recycling processing facilities in Canada and quantify the environmental and economic impacts of the supporting infrastructure network for electric vehicle lithium-ion battery end-of-life management. The model integrates the geographic information system, material flow analysis for estimating the availability of spent battery stocks, and the life cycle assessment approach to assess the environmental impact. To minimize the costs and greenhouse gas emission intensity, three regional recycling clusters, including dismantling hubs, recycling processing, and scrap metal smelting facilities, were identified. These three clusters will have the capacity to satisfy the annual flow of disposed batteries. The Quebec–Maritimes cluster presents the lowest payload distance, life-cycle carbon footprint, and truck transportation costs than the Ontario and British Columbia–Prairies clusters. Access to end-of-life batteries not only makes the battery supply chain circular, but also provides incentives for establishing recycling facilities. The average costs and carbon intensity of recycled cathode raw materials are CAD 1.29/kg of the spent battery pack and 0.7 kg CO2e/kg of the spent battery pack, respectively, which were estimated based on the optimization of the transportation distances.

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The decision-making landscape to maximize the use of sustainable biomass resources, and achieve long-term environmental and socioeconomic benefits, is complex with a high level of uncertainty in biomass supply and logistics, technical and economic performance of the biorefinery routes, lifecycle performance of the finished products, and other sustainability criteria. Numerous decision-making support models have been developed but these models usually assess only a few specific aspects of technology, regulations, economic, environment, and society. Decision-making support models with a limited capability to capture environmental and socioeconomic performance of the biorefining pathways are not able to identify the best available biorefining routes. This study reviews and discusses recent progress on the harmonization, standardization, and integration of the existing decision-making support models that aim to improve the comparability of the results of these models when different pathways are being assessed and align the decisions made at the strategic, tactical and operational levels. With the growing number of climate-change policies and greenhouse gas (GHG) emission reduction targets, national and international efforts to harmonize the input databases, the model assumptions and system boundaries, and the integration of the existing models have been increasing. However, the deployment of the integrated frameworks among the bioeconomy stakeholders that are capable of evaluating and identifying the promising biorefining routes with significant economic, social and environmental benefits is still not a common practice. This study proposes an integrated decision-making support framework to identify cost-competitive, low-carbon fuel production pathways that are technically viable and can potentially provide maximum GHG emission reduction.