%0 Journal Article %A Li HUANG %A Mi ZHOU %T International ecosystem service research dynamics and regional differences: A bibliometric analysis based on Web of Science data %D 2020 %R 10.18402/resci.2020.04.02 %J Resources Science %P 607-620 %V 42 %N 4 %X

Ecosystem service research has been widely concerned by the Chinese and international scholars. Revealing the current international research hotspots and development trends will provide a reference for ecosystem service research and practice in China. Based on bibliometric analysis and CiteSpace, this study took the Science Citation Index Expanded (SCI-E) and Social Science Citation Index (SSCI) databases of the Web of Science Core Collection as sample data sources to systematically analyze the basic characteristics, main research impacts, and research hotpots in the field of ecosystem service research. The activity index (AI) and the attraction index (AAI) were used to evaluate the research efficiency and academic influence of different countries or regions in this field over time. The research results show that: (1) The number of publications and citations of international ecosystem service research increased significantly with time, especially after 2012, and the number of scholars focusing on this issue has continued to increase; (2) The concentration of published articles in relevant journals is strong; the number of published articles in the top 10 journals accounts for 40% of the total number of published articles; (3) In recent years, China’s research strength in ecosystem services has been continuously enhanced, but it is still lower than the global average. (4) The assessment framework and research method framework of ecosystem services are currently hot topics in this research field. Particular attention should be paid to integrating social needs, human well-being, and ecosystem regulation services into the analysis framework of ecosystem services while focusing on the value and role of cultural ecosystem services, and making full use of innovative methods such as machine learning and big data mining to solve complex social and ecological problems.

%U https://www.resci.cn/EN/10.18402/resci.2020.04.02