Abstract* 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.” Japanese corporations possess many technologies that contribute to decarbonization, which is important for expanding investments in green transformation technology inventory (GXTI) development. Patent data are the most reliable measure of business performance for applied research and development activities when investigating knowledge domains or technological evolution. Our paper describes a Japanese patent dataset of Japanese corporations’ green transformation (GX) patent applications on the Japan Platform for Patent Information, using a search method of bombinating International Patent Classification (IPC) codes and keywords. The dataset contains 37,476 GX patent applications from 298 corporations during the period 1999–2022.

This paper adds to research on the international drivers of corruption and anti-corruption by focusing on the influence of regional trade agreements (RTAs). Using cross-national data and employing a social network analysis, we provide descriptions of the evolution of deep integration of RTAs' network in anti-corruption and transparency with a comprehensive view in which we utilise the network's Closeness, Harmonic, Betweenness, Eigenvector, and PageRank centralities to demonstrate the positions of member countries. We examine the relations of the positions of member countries in the deep RTAs' network and the performance of those countries in fighting corruption. Our empirical results suggest that the countries which hold dominant positions in the deep RTAs' network may be associated with better performance in fighting corruption. At the same time, countries which may have traditionally lagged in the enforcement of anti-bribery and corruption laws will benefit through the transfer of best practices and technical expertise in rulemaking and enforcement by using their positions in the deep RTAs' network. Furthermore, our results indicate that the provisions related to anti-corruption or transparency in RTAs will play more important roles in fighting public sector corruption for

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.

As automobiles are major contributors to greenhouse gas emissions, the technological shift towards vehicle powertrain systems is an attempt to lower problems such as emissions of carbon dioxide and nitrogen oxides. Patent data are the most reliable measure of business performance for applied research and development activities when investigating knowledge domains or technology evolution. This is the first study on Japanese patent citation data of the green vehicle powertrains technology industry, using the social network analysis method, which emphasizes centrality estimates and community detection. This study not only elucidates the knowledge by visualizing flow patterns but also provides a precious and congregative method for verifying important patents under the International Patent Classification system and grasping the trend of the new technology industry. This study detects leading companies, not only in terms of the number of patents but also the importance of the patents. The empirical result shows that the International Patent Classification (IPC) class that starts with "B60K", which includes hybrid electric vehicle (HEV) and battery electric vehicle (BEV), is more likely to be the technology trend in the green vehicle powertrains industry.

The knowledge and innovation generated by researchers at universities is transferred to industries through patent licensing, leading to the commercialization of academic output. In order to investigate the development of Chinese university–industry technology transfer and whether this kind of collaboration may affect a firm’s innovation output, we collected approximately 6400 license contracts made between more than 4000 Chinese firms and 300 Chinese universities for the period between 2009 and 2014. This is the first study on Chinese university–industry knowledge transfer using a bipartite social network analysis (SNA) method, which emphasizes centrality estimates. We are able to investigate empirically how patent license transfer behavior may affect each firm’s innovative output by allocating a centrality score to each firm in the university–firm technology transfer network. We elucidate the academic–industry knowledge by visualizing flow patterns for different regions with the SNA tool, Gephi. We find that innovation capabilities, R&D resources, and technology transfer performance all vary across China, and that patent licensing networks present clear small-world phenomena. We also highlight the Bipartite Graph Reinforcement Model (BGRM) and BiRank centrality in the bipartite network. Our empirical results reveal that firms with high BGRM and BiRank centrality scores, long history, and fewer employees have greater innovative output.

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. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Whereas technical standards and Standard Setting Organizations (SSOs) are omnipresent and essential to mass production and communications, relatively little is formally known about the propensity of firms’ decisions to belong to certain SSOs. An understanding of such propensities can explain why some firms join SSOs (and others do not) and have implications for the regulation of SSOs. This paper uses a social network analysis technique to categorize/place firms in SSO communities and then empirically analyzes their propensities to belong to SSOs. We concentrate our study on standard setting organizations’ features and their intellectual property rights (IPR) policies such as licensing rules, disclosure requirements, as well as the features of the decision process of standards. Using data on more than 1060 member firms as participants in 28 SSOs, we are able to uniquely graph the membership of firms in SSOs by highlighting some important characteristics through community detection. The results provide some novel insights into why firms might choose certain SSO communities over others.

Technology standards are considered important tools for increasing bargaining power and licensing revenues by combining the strategies of firms with the standard-setting organizations (SSOs) standardization processes. The essential patents declared by members of the SSOs play a critical role in such standardization processes. Some former researchers have found that, when using network analysis for measuring the knowledge positions in the “main-path” of standards-based markets, the essential patents did not match very well with the actual knowledge positions of the firms, in most cases. In this paper, we focus on the essential patents declared by the member firms in JTC1, an SSO that provides a standards development environment related to the development of the worldwide information and communication technology (ICT) standards for business and consumer applications, and that employs social network analysis techniques to investigate the knowledge positions of the patents, not only in the “main-path” discussed in the earlier literature, but also in the brokerage processes. We found that the brokerage-process approach helped us to better understand the roles of the essential patents that dominate transactions, relations, and the exchange of knowledge in the patent citation network than that of the main-path. Our findings suggest that claiming essentiality depends on the strategic behavior not only of the patents’ owners, but also of the SSOs.

Using patent citations as an indicator of knowledge flows, this paper examines the effects of firms’ global patent social networks on knowledge flows from business method software patents. Patent social networks are considered along several dimensions, including relative centrality, structural equivalence and brokerage roles. Identifying 19,385 software patents applications to the USPTO by 37 countries during 1995–2012, results show that firms positioned with a relative centrality or situated within the same structural equivalent cluster have more citations to their counterpart firms’ patents. Further, among the different brokerage roles, we find positive promotion to knowledge transfer when the citing and cited firms both serve the role of an itinerant as well as that of a gatekeeper/representative, while firms that act as gatekeeper/representative (alone) cite less patents from firms that do not enact this kind of a role. These unique insights provide a better understanding of channels of knowledge transmission and have implications for the pace of technological change.