Critical mineral acquisition has been key to decarbonization technologies. Transition to decarbonization technologies like electric vehicles requires large quantities of critical minerals including nickel, lithium, cobalt, copper, and aluminum.1 Market price and supply of such critical minerals are closely connected to policy constraints and social resistance from the supplying countries and will result in significant environmental justice impacts. However, the interconnections between policies, society, environment, and the economic market remained unexplored. Consequential life cycle analysis (CLCA) takes account of direct and indirect consequences of changes in the product to evaluate its environmental impacts.2 In this work, we propose a toy model to carry out CLCA on critical mineral mining to predict how major supplying countries will respond to mineral price fluctuations based on their policy, social, and environmental constraints. The proposed model consists of three parts: market supply and demand forecasting, policy and social constraint multiplier, and environmental impact evaluation. Mineral price forecasting has been challenging due to its highly non-linear nature. This model employs Artificial Neural Networks (ANN) as a machine learning algorithm that takes in historical critical mineral prices from major supplying countries as inputs and then formulate a complex underlying relationship to predict the future market price fluctuation. Market supply and demand change of critical minerals is then calculated as a response to price fluctuation and price elasticity. In addition to pure market force, the critical mineral market is also highly sensitive to regional policy change and social resistance. Therefore, an elasticity multiplier that quantifies the extent of policy constraints and social resistance of a given region is added to market elasticity to model a more comprehensive response. Aside from the economic, political, and social consequences of mining, the proposed model also assesses the environmental impacts of mining based on different geological locations and mining types, such as underground mining and open pit mining. This model also includes environmental parameters such as GHG emissions and carbon loss in vegetation due to land clearing to compare the significance of environmental consequences between mining in different countries. As more intensive mining on critical minerals is taking place to achieve global decarbonization, this toy model offers a perspective on the potential subsequent changes from mining.

References

1. IRENA (2023), Geopolitics of the energy transition: Critical materials, International Renewable Energy Agency, Abu Dhabi.

2. Renewable Fuel Standard Program (RFS2) Regulatory Impact Assessment. (2010).

Transitioning to green energy technologies requires more sustainable and secure rare earth elements (REE) production. The current production of rare earth oxides (REOs) is completed by an energy and chemically intensive process (beneficiation, leaching, separation by solvent extraction, and refinement) from the mining of REE ores. Investigations into a more sustainable supply of REEs from secondary sources, such as toxic phosphogypsum (PG) waste, is vital to securing the REE supply chain. PG is a waste byproduct of fertilizer production produced at hundreds of millions of tonnes per year. This PG is stored indefinitely in ‘stacks’ which are vulnerable to release of toxic and radioactive waste into the environment. The extraction of REEs from this waste may make remediation feasible. However, it is unclear how to recover the dilute REEs from PG waste (conventional solvent extraction is inefficient and has high environmental impact). In this work, we propose a route for the recovery of REEs from PG using a bio-inspired adsorptive separation, and we assess its financial viability and life cycle environmental impacts via life cycle assessment (LCA), techno-economic analysis (TEA), and identify targeted improvement opportunities through global uncertainty/sensitivity analysis and scenario analysis. We determined that this system can be profitable (net present value of $200 million, internal rate of return of 17%, and minimum selling price (MSP) of $48/kg REO) and shows reduced environmental impact in several ReCiPe 2016 Midpoint impact categories (land occupation and transformation, eutrophication, particulate matter formation, terrestrial ecotoxicity, and ionizing radiation) when compared to conventional REO production and PG stacking. However, the REEPS system underperforms in other impact categories (global warming, freshwater and marine ecotoxicity, human toxicity, terrestrial acidification, and metal and fossil depletion). The life cycle environmental impacts and financial viability are primarily driven by chemical consumption in the leaching, concentration, and wastewater treatment process sections ($15/kg REO). In addition to high chemical costs, the large capital cost of the selective adsorption resin ($18/kg REO) limits profitability. Scenario analysis shows that the system is profitable at capacities larger than 100,000 kg/hr PG with a PG REE content above 0.5 wt%. The most dilute PG sources (0.02-0.1 wt% REE) are inaccessible using the current process scheme (limited by the cost of acid and subsequent neutralization) requiring further examination of new process schemes and improvements in technological performance. Overall, this study evaluates the sustainability of a first-of-its-kind REE recovery process from PG and uses these results to provide clear direction for advancing sustainable REE recovery from secondary sources.

The geothermal brines in the Salton Sea Known Geothermal Resource Area (SS-KGRA) in California are unique in that they are highly saline brines that contain high concentrations of lithium. These brines are being brought to the surface, used to produce geothermal energy, and then they are reinjected back into the subsurface to maintain pressure in the geothermal reservoir. Before reinjection, there is opportunity to extract the lithium, which is a promising development in securing a domestic supply chain of lithium. In our previous work (LBNL-2001557), the environmental impacts of the lithium extraction process and the associated geothermal expansion that would be needed to support this, were evaluated using publicly available data. Based on expected water cuts in the region, caused by declining water levels in the Colorado River, combined with the expanded geothermal/lithium industry has the potential to impact allocations to agricultural water in the region by around 50%. This is a worst-case scenario estimate and the allocation is largely driven by the water cuts, but there is very limited information on availability of actual water use needed for the direct lithium extraction (DLE) process.

The work herein builds upon the existing water analysis to develop models that better characterize uncertainties around water allocation scenarios and the unit processes involved in DLE. To do this, I built a model of DLE unit process trains starting from a recent review paper from Vera et al. 2023, which identified papers that describe DLE processes, and their associated water use. I then incorporated industry and patent data for DLE processes. With this information I develop a python-based model with interchangeable unit processes, where appropriate, and ranges of water use for each of these processes. This allowed us to evaluate uncertainties in water usage from DLE given different extraction process scenarios. I also expanded the water allocation scenarios to represent a range of water cuts and not just a low (10%) and high (40%) estimate as was assumed previously. I will present the DLE models that were developed and an assessment of potential water usage and its impact on water allocations in the region.

As global demand for clean energy grows, wind energy stands out as a sustainable option with substantial potential for expansion. In Canada, supportive policies drive the growth of wind energy projects. However, challenges such as supply chain constraints for critical materials like Rare Earth Elements (REEs) and the aging of the first generation of wind turbines necessitate improved waste management. This study aims to develop the first open-source dynamic material flow analysis (dMFA) model within the wind energy system and conduct Canada’s first comprehensive analysis of material demand and end-of-life management within the wind energy sector. Additionally, this study evaluates carbon emissions and energy consumption associated with wind turbine material production.

This project employs a comprehensive modeling approach, encompassing four sub-models, two energy capacity scenarios, three technological development scenarios, two end-of-life management scenarios, seven wind turbine sub-technologies, and ten types of material. The results indicate that the Global Net Zero scenario emphasizes a more substantial increase in onshore wind energy compared to the GCAM scenario. By 2050, the cumulative material demand in the Global Net Zero scenario is 1.8 times that of GCAM. Despite the predicted increase in average capacity per wind turbine from 2020 to 2050, the material intensity will rise by 26% due to larger turbine sizes. Closed-loop recycling is expected to reduce the demand for virgin metal materials by 20-30% over the next 30 years, but it will only decrease the demand for REEs by less than 2%. The substantial increases in material requirements will not be significantly offset by domestic closed-loop recycling within the first half of the century. By 2050, the demand for Nd in Canadian wind turbine construction will be 7.96 times the current levels, and for Dy, it will be 6.71 times, representing 64% and 30% of global production in 2022, respectively. The supply of REEs will likely become a significant constraint on the future development of wind energy in Canada. From a material availability perspective, smaller wind turbines equipped with doubly fed induction or squirrel cage induction generators are a preferable option for Canada. Moreover, technological advancements are crucial in minimizing environmental impacts, with advanced technology scenarios outpacing optimistic recycling projections in reducing environmental impacts from 2030 to 2050.

Overall, this research is poised to significantly influence both policy-making and practical applications, contributing to broader sustainability goals in the wind energy sector.