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Edge computing further supports sustainable computing by processing data closer to where it is generated instead of continuously sending information to centralized data centers. This reduces network traffic, lowers latency, and decreases energy consumption associated with data transmission. Smart cities, autonomous vehicles, industrial IoT systems, and healthcare devices increasingly rely on edge AI to deliver real-time intelligence with improved energy efficiency. Organizations are also adopting responsible AI development practices by measuring the environmental costs of their AI projects. Researchers now evaluate models not only based on accuracy but also on energy usage, training time, and carbon emissions. This shift encourages developers to create AI systems that balance performance with sustainability, promoting long-term environmental benefits alongside technological advancement. Green AI also supports business objectives beyond environmental responsibility. 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