Climate Change Mitigation Using Machine Learning: Predictive Analytics for Sustainable Policy Making

Abstract

Climate change is one of the most pressing global challenges, and data-driven solutions are critical for effective policy-making. This paper explores how machine learning can aid in climate modeling, emissions forecasting, and sustainable policy development. We analyze supervised and unsupervised ML techniques to predict temperature trends, assess the impact of carbon emissions, and optimize renewable energy deployment. Case studies on AI-driven climate initiatives, such as carbon credit trading and forest conservation monitoring, are discussed. The findings suggest that ML-driven insights can support governments and organizations in making informed decisions for long-term environmental sustainability.