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The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field

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 firms possess many technologies that contribute to decarbonization, which is important to expand investment for Green Technology (enviro...

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Detalles Bibliográficos
Autores principales: Jiang, Jiaming, Baba, Kensuke, Zhao, Yu, Feng, Junshi, Kumagai, Sou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418797/
https://www.ncbi.nlm.nih.gov/pubmed/36039080
http://dx.doi.org/10.1016/j.dib.2022.108524
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author Jiang, Jiaming
Baba, Kensuke
Zhao, Yu
Feng, Junshi
Kumagai, Sou
author_facet Jiang, Jiaming
Baba, Kensuke
Zhao, Yu
Feng, Junshi
Kumagai, Sou
author_sort Jiang, Jiaming
collection PubMed
description 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 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∼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.
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spelling pubmed-94187972022-08-28 The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field Jiang, Jiaming Baba, Kensuke Zhao, Yu Feng, Junshi Kumagai, Sou Data Brief Data Article 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 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∼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. Elsevier 2022-08-08 /pmc/articles/PMC9418797/ /pubmed/36039080 http://dx.doi.org/10.1016/j.dib.2022.108524 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Jiang, Jiaming
Baba, Kensuke
Zhao, Yu
Feng, Junshi
Kumagai, Sou
The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title_full The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title_fullStr The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title_full_unstemmed The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title_short The dataset of Japanese patents and patents’ holding firms in green vehicle powertrains field
title_sort dataset of japanese patents and patents’ holding firms in green vehicle powertrains field
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9418797/
https://www.ncbi.nlm.nih.gov/pubmed/36039080
http://dx.doi.org/10.1016/j.dib.2022.108524
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