Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium
Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and gove...
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Frontiers Media S.A.
2024-12-01
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| Series: | Earth Science, Systems and Society |
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| Online Access: | https://www.lyellcollection.org/doi/10.3389/esss.2024.10109 |
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| author | Christopher J. M. Lawley Marcus Haynes Bijal Chudasama Kathryn Goodenough Toni Eerola Artem Golev Steven E. Zhang Junhyeok Park Eleonore Lèbre |
| author_facet | Christopher J. M. Lawley Marcus Haynes Bijal Chudasama Kathryn Goodenough Toni Eerola Artem Golev Steven E. Zhang Junhyeok Park Eleonore Lèbre |
| author_sort | Christopher J. M. Lawley |
| collection | DOAJ |
| description | Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and governance (ESG) rather than geological scarcity. Herein we combine public geospatial data as mappable proxies for key ESG indicators (e.g., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. Knowledge-driven methods are the least impacted by population bias and are used to calculate an ESG rating that is then applied to a global dataset of lithium occurrences as a case study. We demonstrate that giant lithium brine deposits (i.e., >10 Mt Li2O) are restricted to regions with higher spatially situated risks relative to a subset of smaller pegmatite-hosted deposits that yield higher ESG ratings (i.e., lower risk). Our results reveal trade-offs between the sources of lithium, resource size, and spatially situated risks. We suggest that this type of geospatial ESG rating is broadly applicable to other CRM and that mapping spatially situated risks prior to mineral exploration has the potential to improve ESG outcomes and government policies that strengthen supply chains. |
| format | Article |
| id | doaj-art-56bf1e9541304401a2ce2feeb86d0522 |
| institution | Kabale University |
| issn | 2634-730X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Earth Science, Systems and Society |
| spelling | doaj-art-56bf1e9541304401a2ce2feeb86d05222025-01-10T14:04:55ZengFrontiers Media S.A.Earth Science, Systems and Society2634-730X2024-12-014110.3389/esss.2024.10109Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of LithiumChristopher J. M. Lawley0Marcus Haynes1Bijal Chudasama2Kathryn Goodenough3Toni Eerola4Artem Golev5Steven E. Zhang6Junhyeok Park7Eleonore Lèbre81Natural Resources Canada, Geological Survey of Canada, Ottawa, ON, Canada2Geoscience Australia, Canberra, ACT, Australia3Geological Survey of Finland, Espoo, Finland4British Geological Survey, The Lyell Centre, Edinburgh, United Kingdom3Geological Survey of Finland, Espoo, Finland5Geological Survey of Queensland, Brisbane, QLD, Australia1Natural Resources Canada, Geological Survey of Canada, Ottawa, ON, Canada6Korea Institute of Geoscience and Mineral Resources, Daejeon, Republic of Korea7Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD, AustraliaDisruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and governance (ESG) rather than geological scarcity. Herein we combine public geospatial data as mappable proxies for key ESG indicators (e.g., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. Knowledge-driven methods are the least impacted by population bias and are used to calculate an ESG rating that is then applied to a global dataset of lithium occurrences as a case study. We demonstrate that giant lithium brine deposits (i.e., >10 Mt Li2O) are restricted to regions with higher spatially situated risks relative to a subset of smaller pegmatite-hosted deposits that yield higher ESG ratings (i.e., lower risk). Our results reveal trade-offs between the sources of lithium, resource size, and spatially situated risks. We suggest that this type of geospatial ESG rating is broadly applicable to other CRM and that mapping spatially situated risks prior to mineral exploration has the potential to improve ESG outcomes and government policies that strengthen supply chains.https://www.lyellcollection.org/doi/10.3389/esss.2024.10109machine learningsustainabilitycritical mineralsustainable developmentmineral potentialgeoscience |
| spellingShingle | Christopher J. M. Lawley Marcus Haynes Bijal Chudasama Kathryn Goodenough Toni Eerola Artem Golev Steven E. Zhang Junhyeok Park Eleonore Lèbre Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium Earth Science, Systems and Society machine learning sustainability critical mineral sustainable development mineral potential geoscience |
| title | Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium |
| title_full | Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium |
| title_fullStr | Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium |
| title_full_unstemmed | Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium |
| title_short | Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium |
| title_sort | geospatial data and deep learning expose esg risks to critical raw materials supply the case of lithium |
| topic | machine learning sustainability critical mineral sustainable development mineral potential geoscience |
| url | https://www.lyellcollection.org/doi/10.3389/esss.2024.10109 |
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