AI AND CLIMATE CHANGE: DATA-DRIVEN SOLUTIONS FOR ENVIRONMENTAL SUSTAINABILITY

Abstract

Climate change represents one of the most pressing challenges of the twenty-first century, demanding urgent, scalable, and intelligent solutions. Artificial intelligence (AI), with its capacity for processing enormous datasets, recognizing patterns, and optimizing complex systems, has emerged as a powerful enabler in the fight against environmental degradation and greenhouse gas emissions. This research paper explores the intersection of AI and climate science, systematically examining how machine learning, deep learning, computer vision, and natural language processing are being applied across energy systems, agriculture, transportation, urban infrastructure, and carbon monitoring. Through a comprehensive review of methodologies, case studies, empirical datasets, and comparative analyses, this study demonstrates that AI-driven interventions can reduce sector-specific energy consumption by 12–25%, improve renewable energy forecast accuracy by up to 23 percentage points, and enable precision environmental monitoring at scales previously unattainable. The paper further evaluates the limitations of current AI deployments—including algorithmic bias, data scarcity, computational energy costs, and governance gaps—while proposing a forward-looking framework that aligns AI development with global sustainability goals articulated in the Paris Agreement and the United Nations Sustainable Development Goals (SDGs). The findings underscore the imperative for interdisciplinary collaboration, equitable data access, and responsible AI governance to fully realize the potential of data-driven environmental solutions