As deep underground digital twins have not yet been established worldwide, this study extracted keywords from national or city-led digital twin practices and elements of digital twins and through these keywords selected research papers and topics that could contribute to the establishment of deep underground digital twins in the future. We applied the concept of digital twins in geology and underground research to collect 1702 papers from the Web of Science and conducted semantic network analysis and topic modeling. The keywords digital, three dimensions, and real time were placed in the middle and have many links in the word network. Artificial intelligence, deep learning, and neural networks all showed a low degree of centrality. As a result of topic modeling using Latent Dirichlet allocation (LDA), topics related to topography, geological structure, and rock distribution, which are the basic data for building a deep underground digital twin, were noted, and topics related to earthquakes/vibrations, landslides, groundwater, and volcanoes were identified. Energy resources and space utilization have emerged as the main themes.

Eun-Young Ahn. Econ. Environ. Geol. 2025;58:193-200. https://doi.org/10.9719/EEG.2025.58.2.193

This study analyzes carbon markets in the U.S. and Canada, including enhanced oil/gas recovery (EOR/EGR) projects. The American Carbon Registry (ACR), a voluntary carbon market in California, and the Canadian Alberta Emission Offset System have methodologies for EOR and storage in saline aquifers. ACR also presented EGR. EOR-CCS projects demonstrated significant CO 2 reductions, ranging from 1,748 to 7,675 million tons in the U.S. and 30 to 3,040 million tons in Canada. The EOR and hydrogen-production-linked CCS projects are representative CCS projects in Canada. CCS infrastructure construction, R&D, and demonstration projects of oil/natural gas-CCS-hydrogen are actively underway in the net-zero policies by the U.S. and Canada. CCS projects in the U.S. and Canada, which are linked to the supply of natural gas and hydrogen energy in Korea, can secure new overseas energy sources and dispose of the CO 2 captured in Korea.

In this study, a quantitative analysis was conducted on the contribution of domestic industry-targeting companies that received technology transfer and technical consulting from KIGAM, as well as KIGAM startups and cooperative companies. The analysis was based on product classification in the Korean input-output table and the production inducement effect by sector. Since 2019, the greatest contribution from KIGAM's R&D in mineral and energy resources and geological information has been observed in the professional, scientific, and technical services sector. For 380 basic sectors, those were scientific and technological services, research and development industries, software publishing, architectural and landscape services, materials recycling services, smelting, refining, and alloys of non-ferrous metals, non-ferrous metal ores, construction, special-purpose machinery, and measuring and testing instruments. The largest quantitative effects, in terms of company sales, KIGAM's contribution rates, and production inducement coefficients, were identified in software publishing, materials recycling services, and scientific and technological services.

Eun-Young Ahn. Econ. Environ. Geol. 2026;59:913-22. https://doi.org/10.9719/EEG.2026.59.5.913

Text data analysis of Basic Mining Plans can yield quantitative results that differ from those obtained through expert analysis methods. This study conducted a quantitative analysis of the changes in the following Basic Mining Plans: the 2nd Plan in 2014, the 3rd Plan in 2020, and the 4th Plan in 2024. The commonalities and differences among these plans were examined through word frequency and topic modeling analyses based on natural language processing techniques. The 2nd Basic Mining Plan emphasized planning and strategy development, while the 3rd Plan expanded to include safety, education, materials, and issues related to North Korea. The 4th Plan highlighted the topics of foreign employment and smart mining, along with the stockpiling keyword, which indicates an increase in instability within the global raw material supply chain. This study systematically traced the evolution of topics and keywords over time through topic modeling based on natural language processing.