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 sciences^2,3. 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.

关于气候变化的科学文献的大量扩展¹对全球环境评估以及我们对这些评估如何运作的理解提出了挑战。大数据和机器学习可以帮助我们处理大量科学文本，使评估的产生更容易处理，并让我们更好地了解过去的评估与文献的关系。我们使用主题建模来绘制来自 Web of Science 的 400,000 多篇关于气候变化的出版物的主题图或地形图。我们更新了关于 IPCC 的当前知识，表明与确定的文献基线相比，社会科学实际上在最近的评估报告中被过度代表。与解决方案相关的技术知识——尤其是在农业和工程领域——的代表性不足。我们建议此类地图的各种其他应用，^2,3。评估报告中缺乏社会科学知识并不一定意味着 IPCC 存在偏见，而是表明需要更多的社会科学研究，重点关注气候解决方案的技术主题。