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Critical minerals are increasingly essential for modern technologies like wind turbines, electric vehicles, solar panels, energy storage systems, and others. However, their extraction and processing generate significant greenhouse gas emissions (GHGE). Modern AI technologies can reduce GHGE by optimizing energy systems and transportation, enhancing environmental monitoring, and supporting a sustainable mineral value chain. Accordingly, this study is among the first to empirically quantify the individual and interactive impacts of critical minerals and AI technologies on GHGE. This study uses an advanced Kernel-Based Regularized Least Squares (KRLS) machine learning technique to analyze G7 nations’ panel data due to its flexibility in handling nonlinear relationships and multicollinearity. Robust and reliable findings from the three KRLS models show that minerals have a positive marginal impact on GHGE, which intensifies as mineral use increases, highlighting the significant environmental burden of mineral-related activities like extraction and processing. Conversely, AI technologies have a negative marginal effect on GHGE, indicating their beneficial role in mitigating emissions. The combined effect of AI and minerals (AI x MNL) is also negative, confirming that AI can moderate the environmental impact of minerals and improve environmental quality. This effect remains negative across most levels, suggesting that higher AI integration leads to greater GHGE mitigation. The study also found that economic growth increases GHGE at all levels, while both renewable energy and economic globalization help reduce them. Based on these findings, the research provides policy recommendations such as AI-driven monitoring systems and sustainable mineral management to achieve net-zero emissions.
Zahoor Ahmed (Wed,) studied this question.