2.1. The Web of Science Database
This paper now moves to demonstrate the emergence of, and the relations between these terms—mitigation, adaptation, resilience—in the academic literature related to climate change. We use 1991 as out starting point, as already discussed, and conclude with material published in 2019, as publications were continually being added during data collection and included “in press” publications dated 2021 and even 2022.
We employed the Web of Science database (WoS), which is acknowledged to be a robust resource in this context [
24]. It excludes technical reports and publications in journals whose standards do not meet specific criteria, primarily relating to peer review. WoS is a curated database with holdings in several languages, although its content is primarily in English. It aggregates publications into a number of indexes, notably Science, Social Science, Arts and Humanities and a new category of content in emerging journals which, in a field such as this, will tend towards specialized focus or broad interdisciplinarity [
25]. In order to display the relative amounts of published material available for analysis in this context,
Table 1 shows the results from a search in WoS (search term = climate change). It should be noted that a single publication can be assigned to more than one of these indexes but we have examined this overlap and do not see evidence of systematic bias in this research. This is discussed again in the
Appendix A.
The coverage within WoS reflects the distribution of authorship around the globe. In the context of climate change, the national origins of those publishing in the field show a concentration in Europe, followed by North America and China (see
Table 2).
An advantage of WoS is that it aligns well with the software VOSviewer, used to visualize the bibliographic networks and which accepts WoS, Scopus, Dimensions and PubMed files [
26]. Neither PubMed nor Dimensions offers comprehensive citation analysis. WoS was chosen over the Scopus database because it provides citation analyses such as “highly cited papers”, which can be compared between searches. Highly cited papers have been identified as an analytical resource within the scientometric literature [
27]. Such publications are in the top one percent in each of the different subject areas identified within WoS and are based on 10 years of publications, in this instance 2010–2019.
For our collection of data, we searched titles, abstracts and keywords, outlined in
Table 1 and
Table 3, in the WoS core collection. This collection includes Science Citation Index Expanded (1900–present), Social Sciences Citation Index (1956–present), Arts & Humanities Citation Index (1975–present), and Emerging Sources Citation Index (2015–present). In WoS, an “all database” search option was available, which added SciELO Citation Index, Russian Science Citation Index and KCI-Korean Journal Database. However, a simple test search of “climate change adaptation” with these databases showed publications published before 1991, typically on adaptation in a biological context only, outside the focus of this study.
As
Table 1 shows, there has been a large number of publications linked to the various aspects of climate change. Many of these papers come from a wide range of disciplines, especially in the case of resilience. In STEM publications, papers with narrow biological and ecological meanings may be only tangentially connected to a broader discussion of climate change. In order to minimize the heterogeneity of our search results, we observed slightly different protocols, and details of these can be found in the
Appendix A.
When downloading the files, they were saved as tab-delimited txt files with full records and citation content for use in the VOSviewer visualization software and trendline creation in Excel. Some terms have the same meaning but different spellings. Examples include variations of climate change such as climate changing, climate-change and changing climate or words such as floods, flood and flooding. To combine terms with the same meaning in our datasets, a standardized thesaurus was created using Excel. The list of terms combined can be found in the
Appendix A.
2.2. VOSviewer
In our study, we used the software tool VOSviewer to analyze and visualize our datasets. VOSviewer builds and visualizes bibliographic networks and is commonly used in bibliographic analyses of fields such as sustainability [
28]. Items such as terms, keywords, authors, research journals, countries or researchers can be employed to form bibliographic networks. Networks are formed from an input of bibliographic database files (i.e., WoS, Scopus, Dimensions and PubMed) or reference manager files (i.e., RIS, EndNote and RefWorks files) [
29]. To compare items within these networks, VOSviewer can complete citation, bibliographic coupling, co-citation, co-occurrence or co-authorship links. Each of our maps included only one type of item and most focused on keywords found within the title, abstract or text of our dataset of publications from WoS; in situations where the algorithm found too few keywords, a full text option was employed.
We used the co-occurrence analysis, which creates links between terms. On the maps shown below, lines connect different terms. Link strength indicates the number of publications in which two terms occur together and thicker lines represent a stronger link. A weighted link score—the “link attribute score”—can indicate the number of co-occurrences for links of a given term with other terms. VOSviewer determines that items are closely related when the software calculates association strengths between items that are similar, where association strength “is a proportion of total co-occurrences between items to the expected total co-occurrences between those items, assuming they are statistically independent” [
29]. Once items are mapped, those that are closer together are taken to be strongly related compared to items that are far apart. From these visualizations, keywords found in different clusters were used to label and interpret the dataset.
This analysis was also used to visualize the co-citations between different journals. Lines in this analysis represent when papers from one journal have cited papers in a different journal. Journals that appear prominently have larger numbers of citations in relation to other journals. The co-citation network of journals shows the disciplinary landscape of science and social science and identifies the production of knowledge and public policy as they relate to climate change.