文章基本信息

标题：The Evolution of Green Port Research: A Knowledge Mapping Analysis
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作者：Lin, Chun-Yu ; Dai, Gui-Lin ; Wang, Su 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2022
卷号：14
期号：19
页码：1-25
DOI：10.3390/su141911857
语种：English
出版社：MDPI, Open Access Journal
摘要：Green port research integrates numerous theoretical and empirical studies of topics of important concern over the years, involving comprehensive scientific analyses. Learning about the global evolution of research on green ports can improve the understanding and evaluation of green ports by scholars and practitioners. In this paper, a total of 1051 papers from the Web of Science core collection for the period during 1992–2021 are collected as sample data. Based on the database, an analysis is conducted with CiteSpace software to map the knowledge regarding the field of green ports and explore the current research trends; the cooperation networks among countries (regions), institutions, and authors; and the evolution and mutation of keywords. The results indicate that the overall number of green port papers has increased over time. The countries with the highest numbers of papers published and the greatest influence are China and the United States, respectively. Additionally, the cooperative networks among authors, institutions, and countries are not mature enough and need to be strengthened further. Existing studies focus on the environmental impacts, evaluation, and construction of green ports. Recent trends indicate that researchers have become increasingly focused on reducing emissions, optimizing operations, evaluating policies, and identifying post-pandemic health issues associated with green ports. This study contributes to knowledge by assessing the state of the most significant studies published in the field of green port research, and the results contribute to a better understanding of green port research, which can further provide directions for the sustainable development of green ports.
关键词：green port; CiteSpace; visual analysis; knowledge mapping; bibliometric