Green AI: Energy-Efficient Machine Learning for Sustainable Infrastructure - Middle East Context

Abstract

The exponential growth of artificial intelligence (AI) systems has precipitated unprecedented energy consumption and carbon
emissions, threatening global sustainability goals. This article examines the convergence of Green AI-energy-efficient machine
learning methodologies—with sustainable infrastructure development in the Middle East. Through comprehensive analysis
of 60 peer-reviewed studies, we synthesize current Green AI strategies including model compression, quantization, pruning,
neuromorphic computing, and carbon-aware scheduling, while examining their application to Middle Eastern energy landscapes
characterized by ambitious renewable energy transitions and smart city initiatives. Our findings reveal that Green AI techniques can
reduce energy consumption by 35-45% with minimal accuracy degradation, while Middle Eastern nations increasingly deploy AIdriven solutions for solar forecasting, smart grid optimization, and sustainable urban infrastructure. However, significant barriers
persist, including limited regional research, infrastructure constraints, extreme climate conditions, and policy gaps. We propose
an integrated framework combining technical Green AI innovations with region-specific policy recommendations, emphasizing
international collaboration, capacity building, and adaptive governance. This research contributes to environmentally responsible
AI discourse while addressing unique opportunities and challenges facing Middle Eastern nations in pursuing sustainable
development goals.