Green Ai: Advancing Environmentally Sustainable Artificial Intelligence

Abstract

Artificial Intelligence (AI) has become a transformative force across various sectors, yet itsenvironmental impact—particularly from energy consumption and carbon emissions—is increasinglyconcerning. Training and deploying large-scale models like BERT and GPT require immense computationalresources, contributing to significant power use and environmental degradation. In response, Green AI hasemerged to promote energy-efficient and environmentally sustainable AI development. This paper exploresthe core principles, techniques, and applications of Green AI. It highlights the environmental costs ofconventional AI, including the carbon footprint of training, data center energy demands, and hardwarelifecycle impacts. Green AI promotes efficiency alongside accuracy, transparency in reporting energy andcarbon metrics, and lifecycle-based evaluations. Key techniques such as model pruning, quantization, andknowledge distillation are discussed for their role in reducing computational complexity. Efficientarchitectures like Mobile Net and Tiny ML, and innovations in edge computing and low-power hardware (e.g.,TPUs, FPGAs) are examined for their sustainability benefits. Tools and metrics like ML CO2 Impact andperformance-per-watt benchmarks support the evaluation of sustainable AI.Real-world applications in smartagriculture, energy management, and urban planning illustrate the practical relevance of Green AI. The paperconcludes by addressing ethical and policy considerations, advocating for responsible, low-impact AI as both atechnical necessity and a moral imperative.