MDPI Acta Medica Okayama 1996-1073 16 15 2023 Features and Evolution of Global Energy Trade Patterns from the Perspective of Complex Networks 5677 EN Yingnan Cong Business School, China University of Political Science and Law Yufei Hou Business School, China University of Political Science and Law Jiaming Jiang Center for Artificial Intelligence and Mathematical Data Science, Okayama University Shuangzi Chen School of Economics, Hebei University Xiaojing Cai Graduate School of Humanities and Social Sciences, Okayama University As an integral part of economic trade, energy trade is crucial to international dynamics and national interests. In this study, an international energy trade network is constructed by abstracting countries as nodes and representing energy trade relations as edges. A variety of indicators are designed in terms of networks, nodes, bilaterals, and communities to analyze the temporal and spatial evolution of the global energy trade network from 2001 to 2020. The results indicate that network density and strength have been steadily increasing since the beginning of the 21st century. It is observed that the position of the United States as the core of the international energy market is being impacted by emerging developing countries, thus affecting the existing trade balance based on topological analysis. The weighted analysis of bilateral relations demonstrates that emerging countries such as China, Brazil, and Saudi Arabia are pursuing closer cooperation. The community analysis reveals that an increasing number of countries possess strong energy trade capabilities, resulting in a corresponding increase in energy trade volumes. No potential conflict of interest relevant to this article was reported.

Elsevier Acta Medica Okayama 2352-3409 44 2022 The dataset of Japanese patents and patents' holding firms in green vehicle powertrains field 108524 EN Jiaming Jiang Graduate School of Humanities and Social Science, Okayama University Kensuke Baba Cyber-Physical Engineering Informatics Research Core, Okayama University Yu Zhao School of Management, Department of Management, Tokyo University of Science Junshi Feng Graduate School of Humanities and Social Science, Okayama University Sou Kumagai Department of Electrical and Communication Engineering, Faculty of Engineering, Okayama University In 2020, the Government of Japan declared "2050 carbon neutral" and launched a long-term strategy to create a "virtuous cycle of economy and environment".(1) Japanese firms possess many technologies that contribute to decarbonization, which is important to expand investment for Green Technology (environmental technology) development. As automobiles are major contributors to greenhouse gas emissions [1], the technological shift towards vehicle powertrain systems is an attempt to lower problems like emissions of carbon dioxide, nitrogen oxides [2]. On the other hand, patent data are the most reliable business performance for applied research and development activities when investigating the knowledge domains or the technology evolution (Wand, 1997). Our paper describes a Japanese patents dataset of the vehicle powertrain systems for hybrid electric vehicle (HEV), battery electric vehicle (BEV) and fuel cell electric vehicles (FCEV). In this paper we create a method of bombinating international patent classification (IPC) and keywords to define "green" patents in vehicle powertrains field, using patent data which were applied to Japan Patent Office recorded on EPO's PATSTAT database during 2010 similar to 2019 year. When analyze patents, it is necessary to consider the social situation of each country including language background, we collect patents description documents (abstracts and titles) not only written in English but also in Japanese. Finally, we build a database includes 6025 green patents' description documents and 266 patents' holding firms. With which we then identify 3756 HEV patents, 1716 BEV patents, and 553 FCEV patents. Data about patent holding firms is also appended. The full dataset may be useful to researchers who would like to do further search like natural language processing and machine learning on patent description documents, statistical data analysis for empirical economics. No potential conflict of interest relevant to this article was reported.