Artificial Intelligence and Machine Learning Applications in Climate Change Research: A Bibliometric Analysis

Climate change is considered one of today's most pressing problems. Increasing risks and negative impacts have profound social, economic, and developmental implications. Addressing and mitigating these impacts has made technological innovation a fundamental necessity. Among emerging approaches, artificial intelligence (AI) and machine learning (ML), which provide timely and context-specific insights using online data, stand out for their capacity to generate potential solutions. AI- and ML-based models, which have become fundamental tools for assessing environmental impacts and making predictive forecasts, are enhancing climate adaptation capacity. They facilitate the analysis of complex, hard-to-separate dynamics in climate variables and support robust impact assessments. By integrating datasets, these technologies enable more precise predictions of extended climate scenarios. The primary objective of this study is to investigate the characteristics of AI and ML techniques and their potential role in addressing climate change, a threat that causes significant losses in natural systems. The study examines the role of AI and ML in combating climate change through bibliometric analysis, focusing on the evolution of academic publications in the field over time. The findings reveal that AI and ML can offer both advantages and disadvantages in combating climate change. While their benefits are far more significant, weighing the balance between benefits and risks is crucial.