ScholarWorks@경상국립대학교

초록

As global demand for critical minerals intensifies with the expansion of energy transition technologies and advanced manufacturing, developing sustainable and efficient exploration strategies has become a national priority. In this shift, AI-driven exploration technologies are emerging as a transformative force, reshaping how mineral resources are discovered, assessed, and managed. This study analyzes the global research landscape in critical mineral exploration by examining patent and scientific publication data to evaluate both research efficiency and knowledge spillover capacity. Using data envelopment analysis and super-efficiency modeling, we compare national R&D performance, identify leading countries, and quantify diffusion dynamics. The results reveal significant disparities: countries such as the United States, South Korea, and Canada demonstrate high research efficiency and strong spillover effects, supported by active innovation ecosystems and technological adoption. In contrast, resource-rich nations including China, Australia, and Russia show limited diffusion due to weaker AI-based innovation incentives and insufficient industry-academia collaboration. Italy stands out as an effective model of policy-driven R&D utilization and technological diffusion. These findings highlight the strategic importance of combining AI-enabled exploration, qualitative research impact, and international cooperation. The study offers policy implications for countries seeking to strengthen resource security and enhance competitiveness through sustainable and innovation-driven mineral exploration.

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