Australia risks losing its world-leading advantage in critical and rare minerals used for clean energy, electric vehicles and batteries for solar energy, unless it embraces artificial intelligence in the mining sector, according to research from Monash University and the University of Tasmania.

In a paper published in Nature Communications, the researchers argue artificial intelligence will revolutionize the mining of copper, lithium, nickel, zinc, cobalt and rare earth minerals used to produce clean energy technologies.

Australia is in a prime position to benefit with the world's largest proven reserves of nickel and zinc, the second largest proven reserves of cobalt and copper and the world's third largest proven reserves of bauxite. It is also the world's largest producer of bauxite and lithium and is the third largest producer of cobalt.

Co-researcher Deputy Dean, Research, Professor Russell Smyth, from the Department of Economics at Monash University said to take advantage of these resources, Australia must embrace AI through all stages of the mining process.

"With the right policies and technological advancements, AI has the potential to transform the mining industry, making it more efficient, cost effective, less risky, and environmentally friendly," said Professor Smyth.

Critical and rare minerals are a crucial part of achieving net zero emissions by 2050. But the International Energy Agency (IEA) has identified it takes 12.5 years from exploration to production, meaning investors see it as too risky.

In order to achieve global net zero by 2050, the IEA estimates investment of US $360-450 billion will be necessary by 2030, leading to an anticipated supply between US $180-220 billion. This implies an investment shortfall of up to US $230 billion.

Such a shortfall could lead to insufficient supply in the future, making decarbonization efforts more costly and potentially slowing them down. Professor Smyth said their research could help address a number of these issues.

"AI could improve processes such as mineral mapping by using drone-based photogrammetry and remote sensing; more accurately calculate the life of the mine and improve mining productivity including drilling and blasting performance," said Professor Smyth.

"AI can also be used to reduce the required rate of return on investment by forecasting the risk of cost blow-outs, as well as equipment planning and predictive maintenance and management of equipment to minimize repairs."

Co-researcher Associate Professor Joaquin Vespignani, from the Tasmanian School of Business and Economics at the University of Tasmania, said their theory suggests that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors, which they term the back-ended risk premium.

"We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We proposed that the back-ended risk premium may also reduce the gains in productivity expected from AI technologies in the mining sector," Associate Professor Vespignani said.

"Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

"Without significant investment by governments around the world in AI within the mining industry to increase productivity and improve environmental practices, there is a high risk that the clean energy transition will become costly for communities, potentially slowing down decarbonization efforts."