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TitleMapping Canada's green economic pathways for battery minerals
AuthorLawley, C J MORCID logo; Mitchell, M; Stralberg, DORCID logo; Schuster, R; McIntire, E; Bennett, J
SourceEarth Science, Systems and Society (ES3) 2022 p. 1-20, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20220190
PublisherGeological Society of London
Mediadigital; on-line
File formatpdf; html
ProvinceCanada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65; 66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560
SubjectsScience and Technology; mineralogy; minerals; mineral potential; machine learning; Sustainable development; Ecosystem Services
Illustrationslocation maps; diagrams; tables; graphs
ProgramTargeted Geoscience Initiative (TGI-6) Digital Geoscience and Method Development Project
Released2022 12 06
AbstractElectrification of Canada's energy and transport sectors is essential to achieve net-zero emissions by 2050 and will require a vast amount of raw materials. A large proportion of these critical raw materials are expected to be sourced from as yet undiscovered mineral deposits, which has the potential to accelerate environmental pressures on natural ecosystems. Herein we overlay new prospectivity model results for a major source of Canada's battery minerals (i.e., magmatic Ni ± Cu ± Co ± PGE mineral systems) with five ecosystem services (i.e., freshwater, carbon, nature-based recreation, species at risk, climate-change refugia) and gaps in the protected-area network to identify areas of high geological potential with lower ecological risk. New prospectivity models were trained on high-resolution geological datasets and geophysical survey compilations using spatial cross-validation methods. The area under the curve for the receive operating characteristics (ROC) plot and the preferred gradient boosting machines model is 0.972, reducing the search space for more than 90% of deposits in the test set by 89%. Using the inflection point on the ROC plot as a threshold, we demonstrate that 16% of the most prospective model cells partially overlap with the current network of protected and other conserved areas, further reducing the search space for new critical mineral deposits. Moreover, the vast majority of the remaining high prospectivity cells correspond to ecoregions with less than half of the protected areas required to meet national conservation targets. Poorly protected ecoregions with one or more of the five ecosystem services are interpreted as hotspots with the highest potential for conflicting land-use priorities in the future, including parts of southern Ontario and Québec, western Labrador, and northern Manitoba and Saskatchewan. Planning and managing hotspots with multiple land-use priorities would necessarily involve partnerships with Indigenous peoples and other communities. We suggest that prospectivity models based on advanced machine learning methods can be used as part of natural resources management strategies to balance critical mineral development with conservation and biodiversity values.
Summary(Plain Language Summary, not published)
Machine learning methods are used to predict areas with high geological potential for battery minerals and lower ecological risk.

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