The massive expansion of scientific literature on climate change1 poses challenges for global environmental assessments and our understanding of how these assessments work. Big data and machine learning can help us deal with large collections of scientific text, making the production of assessments more tractable, and giving us better insights about how past assessments have engaged with the literature. We use topic modelling to draw a topic map, or topography, of over 400,000 publications from the Web of Science on climate change. We update current knowledge on the IPCC, showing that compared with the baseline of the literature identified, the social sciences are in fact over-represented in recent assessment reports. Technical, solutions-relevant knowledge—especially in agriculture and engineering—is under-represented. We suggest a variety of other applications of such maps, and our findings have direct implications for addressing growing demands for more solution-oriented climate change assessments that are also more firmly rooted in the social sciences2,3. The perceived lack of social science knowledge in assessment reports does not necessarily imply an IPCC bias, but rather suggests a need for more social science research with a focus on technical topics on climate solutions.
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The datasets from the study are available as Supplementary Information files.
The code used to produce this paper is available at https://github.com/mcallaghan/cc-topography.
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M.C. is supported by a PhD stipend from the Heinrich Böll Stiftung. J.M. acknowledges funding from the German Federal Ministry of Education and Research within the PEGASOS project (grant reference: 01LA1826A).
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Robin Haunschild, Hannah Hughes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Coloured bars show the proportion of each topic made up of papers from each disciplinary category. Crosses show the Disciplinary Entropy of each topic (see methods for details).
The Doc Term Matrix shows the number of occurrences of each term in the document. The Topic Term Matrix shows the topic score of each term-topic combination. The Doc Topic Matrix shows the document-topic score for each topic. This topic makeup of the document shown is illustrated by the bars in the top left. Words highly associated with each topic that occur in the document are highlighted. All values are real, although the doc-term matrix is scaled by the inverse-document frequency before being used in the model.
Representation is the share of the subset of documents being cited by the IPCC divided by the share of the subset in the whole literature. We plot on a log scale so that 0.5 is equally distant to 1 as 2; plot labels show real values.
Representation is the share of the subset of documents being cited by the IPCC divided by the share of the subset in the whole literature. Social science proportion shows the proportion of the total document-topic score coming from documents in the social sciences.
Topics in the upper or lower 6.66th percentile of either dimension are labelled. Representation is the share of the subset of documents being cited by the IPCC divided by the share of the subset in the whole literature. Assessment period occurrence refers to the center of a topic’s distribution across assessment periods (see methods for further details).
Supplementary Table 1.
A list of the documents considered in this study, along with basic metadata and their position on the map. For copyright reasons, the full metadata from WoS cannot be published. To reproduce the analysis, it would be necessary to download the abstracts for the papers shown, either using the WoS IDs provided or the query documented in ref. 5.
A list of the topics, along with their features discussed in this paper. The top ten words associated with each topic are also shown.
A list of document-topic scores, which can be cross-referenced with the document and topic IDs in docs.csv and topics.csv.
Models with different numbers of topics. It was used to select the topic model used for analysis in this paper.
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Callaghan, M.W., Minx, J.C. & Forster, P.M. A topography of climate change research. Nat. Clim. Chang. 10, 118–123 (2020). https://doi.org/10.1038/s41558-019-0684-5
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