Artificial Intelligence in Climate Change Mitigation: Exploring AI-Based Models for Environmental Monitoring, Renewable Energy Optimization, and Sustainable Policy Development

Abstract

This study investigates the transformative role of artificial intelligence (AI) in climate change mitigation through three core domains: environmental monitoring, renewable energy optimization, and sustainable policy development. Adopting a mixed-methods approach, the research integrates secondary data from global climate repositories (2000–2022) with simulated AI model outputs using machine learning frameworks. Key findings reveal that convolutional neural networks (CNNs) enhance deforestation detection accuracy by 28% over traditional satellite methods, while reinforcement learning optimizes wind farm energy yield by 19% under variable conditions. Policy simulation models using natural language processing (NLP) predict 15–22% higher compliance rates in carbon pricing frameworks. The study identifies critical gaps in AI ethics, data equity, and long-term scalability. Conclusions emphasize AI’s potential as a force multiplier in achieving net-zero targets by 2050, provided governance and inclusivity challenges are addressed. Implications extend to policymakers, technologists, and environmental scientists.