New study finds that Australia risks losing critical mineral edge without AI adoption

A new study has found that Australia is at risk of losing its world-leading advantage in critical and rare minerals if it doesn’t leverage artificial intelligence (AI).

A paper by Monash University and University of Tasmania researchers published in Nature Communications suggests that AI can revolutionise the mining of copper, lithium, nickel, zinc, cobalt and rare earth minerals used to produce clean energy technologies.

Those critical metals are growing in importance because of their use in clean energy, electric vehicles and batteries for solar energy.

Prime position

Co-researcher Professor Russell Smyth, Deputy Dean of Research at Monash University’s Department of Economics, said Australia – with the world’s largest proven reserves of nickel and zinc, the second-largest of cobalt and copper and the third-largest of bauxite – is in a prime position to benefit from this.

However, he noted that for Australia to take full advantage of these resources, it must embrace AI throughout all stages of the mining process.

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

“Critical and rare minerals are crucial to achieving net-zero emissions by 2050.”

“However, the International Energy Agency (IEA) has identified that it takes 12.5 years from exploration to production, meaning investors see it as too risky.”

Looming shortfall

Based on current figures, the IEA believs a shortfall is looming in the billions of dollars of investment required to achieve global net zero by 2050.

Professor Smyth said such a shortfall could lead to insufficient supply, making decarbonisation efforts more costly and potentially slowing them down.

“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,” he said.

“AI can also be used to reduce the required rate of return on investment by forecasting the risk of cost blowouts, as well as improving equipment planning, predictive maintenance and management of equipment to minimise repairs.”

Reducing risks

Co-researcher Associate Professor Joaquin Vespignani from the University of Tasmania’s School of Business and Economics said AI can help reduce the risks associated with back-ended critical mineral projects that have unaddressed technical and non-technical barriers.

“We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector,” he said.

“We propose that the back-ended risk premium may also reduce the gains in productivity expected from AI technologies in the mining sector.”

“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.”

“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 decarbonisation efforts.”