资源环境科技发展态势分析平台

The massive expansion of scientific literature on climate change(1) 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.

The rapid growth of climate change research presents challenges for IPCC assessments and their stated aim of being comprehensive, objective and transparent. Here the authors use topic modelling to map the climate change literature, and assess how well it is represented in IPCC reports.