Science for whom? Examining the data quality, themes, and trends in 30 years of public funding for global climate change and energy research

Public spending for research and development is undoubtedly one of the most powerful tools for government policy in the areas of climate change and energy systems and technology innovation. However, existing datasets are currently fragmented, incomplete, and partial in their coverage. This study presents results from a more comprehensive, granular, and descriptive attempt to compile a dataset of global funding patterns on energy and climate research. To do so, it identified 114,201 potential projects funded by 154 research councils across 17 countries and the European Commission from 1990 to 2020 (with projected funding up until 2026). A smaller sample of 1000 illustrative projects were examined in greater detail. It finds that there are difficulties with accessible and available public data, including an inaccuracy of data on published websites or inadequate tracking and updating of project details. Research on energy and climate change is supported by a surprisingly broad base of inquiry, including research from the social sciences and economics but also the arts and humanities, engineering and technology, life sciences and medicine, and natural and physical sciences. Climate change adaptation research is the most funded general area, followed by climate mitigation via energy systems, transportation and mobility, geo/climate engineering, and industrial decarbonization. Funding has been allocated unevenly in favor of some specific technologies, e.g. resilience and adaption, energy efficiency, and electric vehicles. Publicly funded research benefits a very particular set of disciplines, e.g. communication studies, economics, computer science, and chemical engineering. Moreover, the funded projects reveal a striking diversity of methods, including literature reviews, surveys and original data collection, the development of intellectual property, case studies, qualitative research and energy modeling