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Lithium-ion battery (LIB) pack is the core component of electric vehicles (EVs). As the demand is continuously increasing, it puts a lot of strain on the battery raw material supply chains. Likewise, the large quantity of spent LIBs from different sources will add to the complexity of end-of-life (EoL) management. Battery recycling processing is a potential source of critical cathode precursor materials as an alternative to virgin raw material sourcing. Indeed, metal sulfates (nickel, cobalt, and manganese) and lithium carbonate could be recovered through EoL processing. This study aims to provide an economic and environmental life cycle sustainability assessment of recycled battery materials. This assessment is based on a bottom-up approach considering geographical boundaries and process data inputs. The two sources of critical cathode battery materials, virgin and recycled battery materials, are compared based on economic and environmental indicators. This study identified the province of Quebec in Canada as the geographical boundary where several battery processing plants have been recently announced. The best available recycling process (hydrometallurgy) was selected. For the virgin materials, this study considers the option of importing from other jurisdictions by using global average supply chain values. Furthermore, a comparison of alternative supply chain configurations was performed using a spatially differentiated approach. The main findings of this study are as follows: (i) the environmental credit of recycled cathode active materials (CAMs) is estimated as −6.46 kg CO2e/kg CAM, and (ii) the overall cost and environmental impacts of producing LIB cathode active material from recycled battery materials can be 48% and 54% lower than production from virgin materials, respectively, considering the upstream, midstream, and downstream stages of the CAM supply chain. The main drivers for the reduction in these financial costs and emissions are the local transportation and the hydrometallurgical process. The assessment results provide insights to support the development of appropriate policies and R&D solutions adapted to local considerations as well as offer additional possibilities to improve the design of sustainable supply chains for LIB recycling. [ABSTRACT FROM AUTHOR]

The main objective of this study was to develop models for accurate prediction of municipal solid waste (MSW) generation and diversion based on demographic and socio-economic variables, with planned application of generating Canada-wide MSW inventories. Models were generated by mapping residential MSW quantities with socio-economic and demographic parameters of 220 municipalities in the province of Ontario, Canada. Two machine learning algorithms, namely decision trees and neural networks, were applied to build the models. Socio-economic variables were derived from Canadian Census data at regional and municipal levels. A data pre-processing and integration framework was developed in Matlab® computing software to generate datasets with sufficient data quantity and quality for modeling. Results showed that machine learning algorithms can be successfully used to generate waste models with good prediction performance. Neural network models had the best performance, describing 72% of variation in the data. The approach proposed in this study demonstrates the feasibility of creating tools that helps in regional waste planning by means of sourcing, pre-processing, integrating and modeling of publically available data from various sources. [ABSTRACT FROM AUTHOR]

Carbon capture, utilization, and storage (CCUS) is an attractive technology for the decarbonization of global energy systems. However, its early development stage makes impact assessment difficult. Moreover, rising popularity in carbon pricing necessitates the development of a methodology for deriving carbon abatement costs that are harmonized with the price of carbon. We develop, using a combined bottom-up analysis and top-down learning curve approach, a levelized cost of carbon abatement (LCCA) model for assessing the true cost of emissions mitigation in CCUS technology under carbon pricing mechanisms. We demonstrate our methodology by adapting three policy scenarios in Canada to explore how the implementation of CO2-to-diesel technologies could economically decarbonize Canada's transportation sector. With continued policy development, Canada can avoid 932 MtCO2eq by 2075 at an LCCA of CA$209/tCO2eq. Technological learning, low emission hydroelectricity generation, and cost-effective electricity prices make Quebec and Manitoba uniquely positioned to support CO2-to-diesel technology. The additional policy supports beyond 2030, including an escalating carbon price, CO2-derived fuel blending requirements, or investment in low-cost renewable electricity, which can accelerate market diffusion of CO2-to-diesel technology in Canada. This methodology is applicable to different jurisdictions and disruptive technologies, providing ample foci for future work to leverage this combined technology learning + LCCA approach. [ABSTRACT FROM AUTHOR]

Comprehensive evaluation of greenhouse gas emission reductions from alternative vehicle powertrains requires the use of a life cycle assessment methodology, which considers manufacturing, utilization and end-of-life phases of the vehicle and fuel production. However, vehicle powertrain technologies and regional electricity and fuel production characteristics are evolving rapidly, making such studies performed using current data obsolete in few years. For example, regional electricity generation mixes across the Canadian provinces are largely different, and they are likely to evolve with the introduction of renewable electricity. Hence, there is a general need for adaptable and flexible life cycle assessment frameworks for the transportation sector for rapid evaluation of GHG reductions using up-to-date data. The objective of this study is to develop an adaptive life cycle assessment framework for light duty vehicles that can incorporate critical new developments in vehicle powertrain technologies and changes in electricity and fuel supply. Conventional, hybrid, plug-in hybrid, battery electric and fuel cell powertrain types are included in the framework as parametrized models. Critical parameters related to vehicle size and battery capacity, fuel consumption, mileage, battery chemistry, vehicle hybridization, and electricity and fuel emission intensity are incorporated. The framework was demonstrated by applying it in the Canadian context with the analysis of scenarios associated with electricity grid emission intensity, mileage, battery capacity and battery chemistry. [ABSTRACT FROM AUTHOR]