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As artificial intelligence continues to scale globally, its environmental footprint—across energy, water, materials, and infrastructure—has become a critical area of focus. While recent years have seen progress in developing methodologies and frameworks, the priority now is to translate these into scientifically grounded, measurable, and scalable implementation. A growing number of initiatives are shaping this space, including emerging methodologies such as the standard “Guidelines for assessing the environmental impact of artificial intelligence systems” (ITU-T L.1801/ ETSI ES 204 135), parallel work across international organizations, and ongoing scientific and academic research. At the same time, increasing attention is being given to how AI systems can be designed and operated more sustainably across their full lifecycle, from materials and hardware to energy use and infrastructure. This workshop brings together experts from research, academia, industry, and technical communities to explore how methodologies, scientific insights, and real-world implementation can be combined to both measure and reduce the environmental impact of AI systems. Building on previous discussions, the session will focus on moving from frameworks to practice by: examining current approaches to assessing AI environmental impact integrating scientific and academic perspectives to ensure methodological robustness showcasing implementation experiences and lessons learned exploring practical strategies for improving sustainability across AI systems, infrastructure, and resources The workshop is also in line with broader global efforts, such as the Green Digital Action (GDA) initiative, particularly its focus on improving the environmental performance of AI and digital infrastructure through collaboration and innovation. The objective is to support a more coherent, science-based, and implementation-driven approach to sustainable AI—enabling stakeholders to move from measurement to meaningful impact.
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