Green Artificial Intelligence for Energy-Efficient Computing Systems

Regina Barzilay

Green Artificial Intelligence for Energy-Efficient Computing Systems

Author(s)	Regina Barzilay
Country	United States
Abstract	Green Artificial Intelligence (Green AI) has emerged as a transformative paradigm that emphasizes the development, deployment, and operation of Artificial Intelligence (AI) systems with minimal energy consumption, reduced carbon emissions, and sustainable computational practices. As AI models become increasingly complex and computationally intensive, the environmental impact of large-scale data centres, high-performance computing (HPC), cloud platforms, and machine learning training processes has become a significant global concern. Green AI integrates energy-efficient algorithms, model optimization, edge computing, cloud computing, federated learning, TinyML, hardware acceleration, renewable energy integration, carbon-aware scheduling, and sustainable software engineering to balance computational performance with environmental sustainability. By reducing energy usage while maintaining high accuracy and efficiency, Green AI supports sustainable digital transformation across industries including healthcare, finance, manufacturing, transportation, agriculture, education, and smart cities. This study investigates Green Artificial Intelligence for energy-efficient computing systems through a multidisciplinary perspective. A qualitative and analytical research methodology based on secondary data from computer science, Artificial Intelligence, electrical engineering, environmental science, information systems, sustainability studies, and public policy literature is employed to examine Green AI technologies, implementation frameworks, governance mechanisms, ethical considerations, and future research opportunities. Particular emphasis is placed on energy-efficient machine learning, explainable AI, cloud and edge computing, carbon-aware computing, data centre optimization, hardware accelerators, and circular digital economy principles. The findings indicate that Green AI significantly improves computational efficiency, reduces energy consumption, lowers carbon emissions, enhances data centre sustainability, and supports climate-resilient digital infrastructure. Intelligent optimization techniques enable efficient model training, resource allocation, workload balancing, and renewable energy utilization while maintaining high-performance computing capabilities. However, challenges including computational trade-offs, hardware limitations, algorithm complexity, lack of standardized sustainability metrics, cybersecurity concerns, implementation costs, and policy uncertainty remain major barriers to widespread adoption. The study concludes that integrating Green AI principles with energy-efficient hardware, sustainable cloud infrastructure, renewable energy systems, and interdisciplinary collaboration provides a comprehensive pathway toward environmentally responsible and resilient computing ecosystems.
Keywords	: Green Artificial Intelligence, Energy-Efficient Computing, Sustainable AI, Cloud Computing, Edge Computing, Carbon-Aware Computing, Green Data Centres, Machine Learning.
Field	Engineering
Published In	Volume 3, Issue 6, November-December 2021
Published On	2021-12-29

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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 for Energy-Efficient Computing Systems

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