Machine Learning to Mitigate the Vulnerability of the Mining Industry to Climate Change Impacts

Dear Colleagues,

Mining is a sector that is particularly vulnerable to climate change. Awareness of the potential negative impacts of climate change on the mining sector has grown in the past years. The effects of extreme weather events such as recurring droughts, floods and rising temperatures experienced in some of the world's leading mining regions, such as Australia, Chile and Mongolia, have led the mining industry to start thinking about their vulnerabilities and the risks climate change could pose. Climate change presents physical risks to mining and metallurgy as these industries are often located in challenging locations, rely on fixed assets with long lifetimes, involve global supply chains, manage climate-sensitive water and energy resources, and balance the interest of several stakeholders. Increased temperatures, changes in precipitation, sea level rise and extreme events may become additional stressors with the potential to increase existing risks.

There has been little research and debate that takes a more comprehensive look at the links between climate change and mining. Machine Learning (ML) is a form of AI which extracts patterns from data and has been widely used for decades across different industries, but cases related to climate change effects on mining operations have not been explored much yet. Artificial Intelligence can definitely contribute to climate change mitigation, e.g., through energy efficiency or by reducing emissions from transportation and industry but on the other hand, AI can also help us adapt to climate change's impacts by improving the ability to predict extreme weather events and providing decision-support tools allowing for more effectively responses, and increased resilience by helping to identify risk factors and develop mitigation plans.

This Special Issue of Atmosphere will open a broad debate on how Artificial Intelligence and Machine Learning applications may be used to mitigate the consequences of climate change in mining operations and at the same time what is being done to incorporate these applications in the Re-Thinking Mining concept.

Dr. Maria De Lurdes Dinis
Guest Editor

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• artificial intelligence
• modeling
• machine learning
• monitoring data
• environmental remediation
• mining operations
• climate change
• impacts
• extreme weather events
• risks