Climate change represents one of the most pressing global challenges of the 21st century, demanding innovative and scalable solutions to mitigate its adverse impacts. Artificial Intelligence (AI) has emerged as a transformative tool in environmental science, offering advanced predictive capabilities that can enhance climate change mitigation efforts. This paper explores the pivotal role of AI-driven predictive models in advancing sustainability by enabling accurate forecasting, risk assessment, and optimization of mitigation strategies. We discuss the various AI techniques, including machine learning, deep learning, and reinforcement learning, which harness diverse data sources such as satellite imagery, sensor networks, and climate databases to model complex environmental systems. Key applications examined include carbon emission prediction, renewable energy management, climate disaster forecasting, and resource-efficient agricultural practices. Despite these promising developments, challenges such as data quality, model interpretability, computational demands, and ethical considerations remain critical obstacles. The paper highlights ongoing research directions aiming to address these issues, emphasizing the need for multidisciplinary collaboration, transparent AI models, and policy integration. By bridging technological innovation with environmental stewardship, AI-powered predictive models hold significant promise to accelerate global efforts toward a sustainable and resilient future.
Pratihari et al. (2024) studied this question.