Supplementary materials for the publication “Technological Sovereignty as a Current Energy Security Challenge. Preliminary analysis”

The paper is planned to be published on https://www.preprints.org/ and further in the journal Energy Systems Research (https://esrj.ru/index.php/esr).! Use the JSON files at https://app.vosviewer.com/.Abstract. Energy security is often interpreted as independence from fossil fuels, but a one-sided approach can lead to dependence on high-value-added technologies. The development of artificial intelligence, which requires high energy consumption, chips and servers, is shifting competition in manufacturing and services from energy security to technological sovereignty. With the development of technology, sovereignty has shifted from military independence to freedom from economic coercion by other states and large corporations. The aim of this study was to identify suitable tools for analyzing abstract texts from tens of thousands of bibliometric records and pre-assessing relevant topics related to the energy sector to effectively analyze trends in technological sovereignty issues. In this paper 10 thousand bibliometric records for the year 2024, sorted by relevance and exported from the open abstract database Scilit on the query: “energy AND technology” in [Title, Abstract, Keyword], Content Type: JOURNAL-ARTICLE, English. Filters were applied on the “Subject” category most related to technology: Power Systems & Electric Vehicles, Energy Systems & Technologies, Electrical Energy Management, and Nuclear Technology & Instrumentation. The main theme of the bibliometric data analyzed was renewable energy. Twelve clusters were identified based on keywords, of which three were closest to the topic for which this research was funded: hydrogen, heat energy storage and greenhouse gas emissions. These clusters reflect keywords derived from both Yake! and PatternRank. The Yake! program outperforms PatternRank in terms of run time and representation of found keywords in abstract texts. The feasibility of using AnyAscii for text preprocessing is demonstrated. Using artificial intelligence to create text based on key phrases speeds up text processing, but the need for manual editing remains. The study showed that there is a need to expand data sources, e.g. using OnePetro for oil and gas topics, IEEE Xplore for energy systems issues, Semantic Scholar to evaluate the role of AI in the energy sector.