{"id":835,"date":"2024-10-09T09:16:25","date_gmt":"2024-10-09T09:16:25","guid":{"rendered":"https:\/\/www.itu.int\/initiatives\/green-digital-action\/?page_id=835"},"modified":"2025-12-10T10:07:56","modified_gmt":"2025-12-10T10:07:56","slug":"green-computing","status":"publish","type":"page","link":"https:\/\/www.itu.int\/initiatives\/green-digital-action\/impact\/green-computing\/","title":{"rendered":"Green computing"},"content":{"rendered":"
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\n Lower environmental footprint by embracing green computing<\/strong> <\/h2>\n

\n Cloud computing has become an essential part of the digital infrastructure for government and businesses worldwide, enabling scalable and efficient operations. However, this widespread adoption comes with a significant environmental cost, including increased energy consumption and carbon emissions from data centers. As organizations look to align their operations with sustainability goals, green cloud computing is gaining attention as a solution to reduce the environmental footprint of digital services. <\/p>\n <\/div>\n

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\n AI and the environment <\/h3>\n

\n Continuous innovation in AI<\/a> and its applications is crucial for addressing global challenges like climate change. However, the computational power needed to sustain AI’s growth is doubling roughly every 100 days, leading to a significant rise in energy consumption. Consequently, the energy required to run AI tasks is also escalating, with annual growth rates between 26 percent and 36 percent. As organizations increasingly rely on cloud computing to support their AI initiatives, it becomes vital to establish proper guidelines and standards that mitigate the environmental impact of these technologies.

The GDA sub-working group on Sustainable AI <\/strong>is identifying gaps in measuring and testing different AI workloads to better understand the environmental footprint of this transformative technology.
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\n Energy-Efficient and Sustainable Software<\/strong> <\/h3>\n

\n This workstream aims to establish comprehensive, action-oriented guidelines for developing energy-efficient and sustainable software, enabling organisations to adopt practices that reduce environmental impact, enhance efficiency, and deliver measurable benefits. Key outcomes include alignment with global standards, quantification of sustainability opportunities, and the creation of accessible resources to drive adoption across the industry. <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Advancing green computing and AI <\/h3>\n<\/div><\/div>\n<\/div>\n\n
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Share best practices<\/strong><\/strong><\/p>\n

Share your organization\u2019s reporting challenges on energy usage and GHG emissions from AI, and exchange best practices for how AI can positively impact energy consumption and emissions in the ICT sector and beyond.<\/p>\n <\/div>\n <\/div>\n <\/div>\n \n \n

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Join working group<\/strong>(s)<\/p>\n

Email us to join the GDA working group on Green Computing or specific workstreams\/sub-working groups on sustainable AI, green software, data centers or the cloud.<\/p>\n <\/div>\n <\/div>\n <\/a>\n \n \n

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Do you have an innovative idea or are you a start-up using AI for climate change? <\/strong><\/p>\n

Participate in the AI for Climate Action Innovation Factory in one of four categories: emission reduction, renewable energy integration and efficiency, adaptation and monitoring of the impacts of climate change through nuclear science, and climate action agricultural practices.
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