Mapping the Landscape of Artificial Intelligence in Climate Change Research: A Meta-Analysis on Impact and Applications (Proposals Track)
Christian Burmester (Osnabrück University); Teresa Scantamburlo (UniversityofVenice)
Abstract
This proposal advocates a comprehensive and systematic analysis aimed at mapping and characterizing the intricate landscape of Artificial Intelligence and Machine Learning applications and their impacts within the domain of climate change research, both in adaption and mitigation efforts. Notably, a significant upswing in this interdisciplinary intersection has been observed since 2020. Utilizing advanced topic clustering techniques and qualitative analysis, we have discerned 12 distinct macro areas that supplement, enrich, and expand upon those identified in prior research. The primary objective of this undertaking is to furnish a data-rich panoramic view and informative insights regarding the functions and tools of the mentioned disciplines. Our intention is to offer valuable guidance to the scholarly community and propel further research endeavors, encouraging meticulous examinations of research trends and gaps in addressing the formidable challenges posed by climate change and the climate crisis.