A text mining analysis of the climate change literature in industrial ecology

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.

Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure

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Past and future uses of text mining in ecology & evolution

“…For example, Anderson et al (2021) [18] analyzed over 130,000 articles to explore the increasing diversity of ecological hypotheses and theories published over the past 80 years. Similar studies of publishing trends have explored ecological topics in high impact journals [19], showed the emergence of conservation biology as a separate discipline from ecology [20], analyzed the growth of interdisciplinarity in biodiversity science [21], tracked shifting popularity of topics within industrial ecology [22] and fish ecology [23], identified research themes in disease ecology [24], and pinpointed critical research gaps in conservation science [25] and pollination ecology [26]. Outside of academic articles, text mining can reveal important trends for environmental management and biodiversity conservation [27].…”

Past and future uses of text mining in ecology and evolution

“…[22] analysed over 130 000 articles to explore the increasing diversity of ecological hypotheses and theories published over the past 80 years. Similar studies of publishing trends have explored ecological topics in high impact journals [23], showed the emergence of conservation biology as a separate discipline from ecology [24], analysed the growth of interdisciplinarity in biodiversity science [25], tracked shifting popularity of topics within industrial ecology [26] and fish ecology [27], identified research themes in disease ecology [28], and pinpointed critical research gaps in conservation science [29] and pollination ecology [30]. Outside of academic articles, text mining can reveal important trends for environmental management and biodiversity conservation [31].…”