我国海洋渔业碳汇研究现状与热点的知识图谱分析

Knowledge Graph Analysis of Status Quo and Hot Topics of Marine Fishery Carbon Sink Research in China

海洋渔业碳汇是海洋碳汇的主要组成部分,是实现海洋碳增汇的有效途径之一。为进一步梳理海洋渔业碳汇领域的研究现状与热点动态,利用CiteSpace软件,分别以“海洋渔业碳汇”和“碳汇渔业”为主题,剔除学位论文、会议、年鉴等文献,对CNKI数据库中2010年1月-2024年2月的文献开展知识图谱分析,总结了国内海洋渔业碳汇的研究趋势、机构分布、核心作者分布及关键词突现。结果表明:(1)海洋渔业碳汇的研究经历了从提出到深入研究的过程,中间呈现出一定波动性,近年虽发文量激增,但总量仍然偏少,体现出较大的研究价值和应用前景。(2)研究机构以高校和各地区水产研究机构为主且合作频繁,受地域等因素的限制,作者之间的合作关系仍以同一机构内合作为主,呈现出“整体分散,局部聚焦”的状态。(3)高频关键词为“碳汇渔业”“碳汇”“海洋渔业”等,关键词聚类结构明显和合理,形成了具有较强汇聚特征的集群研究网络。

Marine fishery carbon sink is the main component of marine carbon sink and one of the effective ways to realize marine carbon sink.In order to further sort out the current research status and hot topics in the field of marine fishery carbon sink,with“marine fishery carbon sink”and“carbon sink fishery”as the theme respectively,and academic papers,conferences,yearbooks and other literature being excluded,and knowledge graph analysis on literature in CNKI database from January 2010 to February 2024 being carried out,Citespace software was utilized to summarize the research trends,institutional distribution,core author distribution and keywords emergence of marine fishery carbon sink in China.The results showed that:(1)The study of marine fishery carbon sinks has experienced a process from proposal to indepth research,though with a certain fluctuation in the middle.The total amount is still small,despite the surge of published papers in recent years,reflecting a greater research value and application prospect.(2)Research institutions are mainly universities and regional aquatic research institutions with frequent cooperation with each other.Due to the limitation of geographical factors,the cooperation between authors is still based on the cooperation within the same institution,showing a state of“overall dispersion and local focus.”(3)High-frequency keywords are carbon sink fisheries,carbon sink,marine fisheries,etc.The keyword clustering structure is significant and reasonable,forming a cluster research network with strong convergence characteristics.