A text mining analysis of the climate change literature in industrial ecology. (13th March 2020)

• Record Type: Journal Article
• Authors: Dayeen, Fazle Rabbi; Sharma, Abhinav S.; Derrible, Sybil
• Other Names: Chester Mikhail guestEditor.; Bakshi Bhavik guestEditor.; Baynes Tim guestEditor.; Cheah Lynette guestEditor.; Derrible Sybil guestEditor.; Eckelman Matthew guestEditor.; Heidrich Oliver guestEditor.; Liu Beibei guestEditor.; Samaras Constantine guestEditor.
• Is Part Of: Journal of industrial ecology. Volume 24:Number 2(2020:Apr.)
• Page Start: 276
• Page End: 284
• Publication Date: 2020-03-13
• Subjects: academic publishing -- climate change -- industrial ecology -- resilience -- text mining -- topic modeling; Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.12998
• Languages: English
• ISSNs: 1088-1980

Abstract

Abstract: The literature on climate change research has evolved tremendously since the 1990s. The goal of this study is to use text mining to review the climate change literature and study the evolution of the main trends over time. Specific keywords from articles published in the special issue " Industrial Ecology for Climate Change Adaptation and Resilience" in the Journal of Industrial Ecology are first selected. Details of over 35, 000 publications containing these keywords are downloaded from the Web of Science from 1990 to 2018. The number of publications and co‐occurrence of keywords are analyzed. Moreover, latent Dirichlet allocation (LDA)—a probabilistic approach that can retrieve topics from large and unstructured text documents—is applied on the abstracts to uncover the main topics (consisting of new terms) that naturally emerge from them. The evolution in time of the importance of some emerging topics is then analyzed on the basis of their relative frequency. Overall, a rapid growth in climate change publications is observed. Terms such as "climate change adaptation" appear on the rise, whereas other terms are declining such as "pollution." Moreover, several terms tend to co‐occur frequently, such as "climate change adaptation" and "resilience." The database collected and the LiTCoF (Literature Topic Co‐occurrence and Frequency) Python‐based tool developed for this study are also made openly accessible. This article met the requirements for a gold – gold JIE data openness badge described http://jie.click/badges .