Green Artificial Intelligence: Reducing the Energy and Environmental Footprint of Intelligent Computing Systems

Melissa Schilling

Green Artificial Intelligence: Reducing the Energy and Environmental Footprint of Intelligent Computing Systems

Author(s)	Melissa Schilling
Country	United States
Abstract	The rapid expansion of Artificial Intelligence (AI) has created significant opportunities for scientific discovery, economic development, automation, and digital transformation. At the same time, the increasing computational requirements of large AI models have generated concerns regarding electricity consumption, carbon emissions, water use, electronic waste, and the broader environmental footprint of intelligent computing systems. Green Artificial Intelligence (Green AI) has emerged as an important research direction that seeks to improve the environmental sustainability of AI throughout its lifecycle. This study examines the concept of Green AI and evaluates technological and organisational approaches for reducing the energy and environmental footprint of AI systems. A qualitative and conceptual methodology based on secondary literature is adopted to analyse energy-efficient model architectures, efficient training, hardware optimisation, data-centre efficiency, model compression, edge AI, renewable energy integration, carbon-aware computing, and lifecycle management. The study proposes an integrated Green AI framework connecting efficient algorithms, sustainable hardware, intelligent infrastructure, renewable energy, and responsible model deployment. The analysis indicates that environmental efficiency should be considered alongside model accuracy when evaluating AI systems. Techniques such as pruning, quantisation, knowledge distillation, efficient architectures, workload scheduling, and carbon-aware computing can reduce computational requirements, while edge AI can reduce data transmission and centralised processing requirements in appropriate applications. However, efficiency improvements may be offset by increasing model scale and AI adoption, creating a potential rebound effect. The study concludes that sustainable AI requires a shift from a narrow focus on computational performance towards a broader optimisation of accuracy, energy efficiency, carbon intensity, resource utilisation, and environmental impact. Green AI should therefore become an integral principle of AI research, deployment, governance, and infrastructure planning.
Keywords	Green AI, Sustainable Artificial Intelligence, Energy Efficiency, Carbon Footprint, Green Computing, Machine Learning, Sustainable Computing, Data Centres, Edge AI, Environmental Sustainability.
Field	Engineering
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-07-23

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Green Artificial Intelligence: Reducing the Energy and Environmental Footprint of Intelligent Computing Systems

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