Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values
Electrification 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 potentia...
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Frontiers Media S.A.
2022-12-01
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| Series: | Earth Science, Systems and Society |
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| Online Access: | https://www.lyellcollection.org/doi/10.3389/esss.2022.10064 |
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| author | Christopher J. M. Lawley Matthew G. E. Mitchell Diana Stralberg Richard Schuster Eliot McIntire Joseph R. Bennett |
| author_facet | Christopher J. M. Lawley Matthew G. E. Mitchell Diana Stralberg Richard Schuster Eliot McIntire Joseph R. Bennett |
| author_sort | Christopher J. M. Lawley |
| collection | DOAJ |
| description | Electrification 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 resources, carbon, nature-based recreation, species at risk, climate-change refugia) and gaps in the current protected-area network to identify areas of high geological potential with lower ecological risk. New prospectivity models were trained on high-resolution geological 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. 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. Managing hotspots with multiple land-use priorities would necessarily involve partnerships with both Indigenous peoples whose traditional lands are affected, and other impacted communities. We suggest that prospectivity models and other machine learning methods can be used as part of natural resources management strategies to balance critical mineral development with conservation and biodiversity values. |
| format | Article |
| id | doaj-art-7291cc5783974b83a31f5605558ffe19 |
| institution | Kabale University |
| issn | 2634-730X |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Earth Science, Systems and Society |
| spelling | doaj-art-7291cc5783974b83a31f5605558ffe192025-01-10T14:04:52ZengFrontiers Media S.A.Earth Science, Systems and Society2634-730X2022-12-012110.3389/esss.2022.10064Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity ValuesChristopher J. M. Lawley0Matthew G. E. Mitchell1Diana Stralberg2Richard Schuster3Eliot McIntire4Joseph R. Bennett51Geological Survey of Canada, Natural Resources Canada, Ottawa, ON, Canada2Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada3Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, Edmonton, AB, Canada4Nature Conservancy of Canada, Toronto, ON, Canada5Pacific Forestry Centre, Canadian Forest Service, Natural Resources Canada, Victoria, BC, Canada6Department of Biology, Institute of Environmental and Interdisciplinary Science, Carleton University, Ottawa, ON, CanadaElectrification 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 resources, carbon, nature-based recreation, species at risk, climate-change refugia) and gaps in the current protected-area network to identify areas of high geological potential with lower ecological risk. New prospectivity models were trained on high-resolution geological 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. 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. Managing hotspots with multiple land-use priorities would necessarily involve partnerships with both Indigenous peoples whose traditional lands are affected, and other impacted communities. We suggest that prospectivity models and other machine learning methods can be used as part of natural resources management strategies to balance critical mineral development with conservation and biodiversity values.https://www.lyellcollection.org/doi/10.3389/esss.2022.10064machine learningcritical mineralssustainable developmentconservation prioritiesecosystem servicesmineral potential |
| spellingShingle | Christopher J. M. Lawley Matthew G. E. Mitchell Diana Stralberg Richard Schuster Eliot McIntire Joseph R. Bennett Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values Earth Science, Systems and Society machine learning critical minerals sustainable development conservation priorities ecosystem services mineral potential |
| title | Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values |
| title_full | Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values |
| title_fullStr | Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values |
| title_full_unstemmed | Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values |
| title_short | Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values |
| title_sort | mapping canada s green economic pathways for battery minerals balancing prospectivity modelling with conservation and biodiversity values |
| topic | machine learning critical minerals sustainable development conservation priorities ecosystem services mineral potential |
| url | https://www.lyellcollection.org/doi/10.3389/esss.2022.10064 |
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