With artificial intelligence (AI) doing everything from writing computer code to creating on-demand recipes based on what’s in your kitchen, it’s tempting to wonder if this technology could solve the biggest problem of all: the existential threat posed by climate change and a warming planet.
So, are countries and research institutions already using this powerful technology to meet this monumental scientific and policy challenge? Joanna Lewis, Provost’s Distinguished Associate Professor of Energy and Environment and director of the Science, Technology and International Affairs Program (STIA) at SFS, recently wrote an open-access article in Springer Nature titled “Climate change and artificial intelligence: assessing the global research landscape” in which she attempts to gauge the extent to which AI is being put to use in this area.
We had a few questions for her.
Q. Why is the use of AI technologies in climate change research important to quantify and compare?
A. Quantifying and comparing the use of AI technologies in climate change research is crucial for pinpointing where these methods are applied, whether in emissions estimations or climate system modeling. This analysis also identifies the countries leading AI-driven climate studies, revealing global research and application trends. Importantly, it uncovers gaps and opportunities for future work, guiding the development of this evolving field to address critical areas and optimize the application of AI in combating climate change.
Q. The article goes into depth about your methodology. Without asking you to restate that here, in general, how did you and your co-authors arrive at the filtering and sorting mechanisms you used?
A. The methodology for this analysis aimed to provide a more comprehensive review than traditional bibliometric analyses by creating research clusters based on relationships within a merged corpus. The selected papers were categorized by the AI methods employed, (e.g., reinforcement learning, neural networks, natural language processing,) and the climate issues addressed, such as environmental modeling or carbon emissions. This structured approach enabled a broad yet focused assessment, capturing the intersection of AI and climate change to offer a detailed overview of the research landscape