Creating an academic landscape of sustainability science: an analysis of the citation network

• Published in: Sustainability science Vol. 2; no. 2; pp. 221 - 231
• Main Authors: Kajikawa, Yuya, Ohno, Junko, Takeda, Yoshiyuki, Matsushima, Katsumori, Komiyama, Hiroshi
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.10.2007
• Subjects: Agricultural research; Agriculture; Agroforestry; Biotechnology; Climate change; Ecological economics; Economics; Fisheries; Forestry; Landscape; Livestock; Natural language processing; Rainforests; Rural sociology; Science; Sustainability; Sustainability science; Sustainable development; Tropical forests; Urban planning; Wildlife
• ISSN: 1862-4065; 1862-4057; 1862-4057
• DOI: 10.1007/s11625-007-0027-8

Issue Title: Policy sciences for sustainable development Sustainability is an important concept for society, economics, and the environment, with thousands of research papers published on the subject annually. As sustainability science becomes a distinctive research field, it is important to define sustainability clearly and grasp the entire structure, current status, and future directions of sustainability science. This paper provides an academic landscape of sustainability science by analyzing the citation network of papers published in academic journals. A topological clustering method is used to detect the sub-domains of sustainability science. Results show the existence of 15 main research clusters: Agriculture, Fisheries, Ecological Economics, Forestry (agroforestry), Forestry (tropical rain forest), Business, Tourism, Water, Forestry (biodiversity), Urban Planning, Rural Sociology, Energy, Health, Soil, and Wildlife. Agriculture, Fisheries, Ecological Economics, and Forestry (agroforestry) clusters are predominant among these. The Energy cluster is currently developing, as indicated by the age of papers in the cluster, although it has a relatively small number of papers. These results are compared with those obtained by natural language processing. Education, Biotechnology, Medical, Livestock, Climate Change, Welfare, and Livelihood clusters are uniquely extracted by natural language processing, because they are common topics across clusters in the citation network.[PUBLICATION ABSTRACT]