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@article{203819,
        author = {Prisha Bankar and Riya Shah and Vilas Ghonge and Shweta Pawar},
        title = {Green AI: A Study on Carbon Footprint and Water Consumption of Artificial Intelligence Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2622-2626},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203819},
        abstract = {Developments in machine learning, cloud computing, and artificial intelligence (AI) techniques have led to the development of AI technologies in recent times. Despite the positive effects AI has on automation, productivity, and decision-making, AI has raised concerns regarding its environmental impacts such as carbon emissions, energy consumption, and water usage. AI systems require large models that should be trained using complex and intensive computation capabilities provided by high-powered computers that require energy to function effectively. This paper explores the environmental impacts of Artificial Intelligence systems, particularly carbon emissions and water consumption. The paper will analyze how AI training, AI inference processes and cloud computing impact environmental degradation. In addition, this paper explains how data centers, GPUs, and cooling systems contribute to the increase in energy consumption. Moreover, this paper looks at the concept of green AI, which promotes sustainability in AI technology. Some of the ways suggested include model optimization, utilization of renewable energy sources, efficient use of hardware and effective data center management.},
        keywords = {Green AI, Carbon Footprint, Water Consumption, Artificial Intelligence, Sustainable Computing, Data Centers, Energy Efficiency, Generative AI, Environmental Impact.},
        month = {June},
        }