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Room R
Workshop
In person
LeadersGoldDiscovery

From standards to science to action: Implementing and scaling sustainable AI across its lifecycle

    • Date
    7 July 2026
    Timeframe
    09:00 - 12:15 CEST
    Duration
    3h 15 minutes
      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.
      Schedule

      Presents the academic and scientific perspective, addressing key methodological challenges such as system boundaries, data availability, and comparability, and demonstrating how research supports robust and credible assessment approaches.

      Moderator(s):

      Showcases real-world case studies from industry and academia, focusing on tools, methodologies, and practical challenges, and highlighting lessons learned in implementing environmental impact assessments for AI systems.

      Speaker(s)
      Moderator(s):

      Provides an overview of emerging methodologies and ongoing work across different organizations and initiatives, alongside recent scientific and academic developments in assessing AI environmental impact. The session highlights the need for alignment, interoperability, and scientifically grounded approaches.

      Presentation

       

      Speaker(s)
      Moderator(s):

      Explores practical strategies for building and operating more sustainable AI systems across their lifecycle, including energy efficiency, data centre optimization, cooling technologies, and materials and resource considerations.

      Moderator(s):

      A discussion focused on scaling implementation, identifying priorities for action, and exploring opportunities for collaborative pilots, data-sharing, and future initiatives. The session will also consider how policy and regulatory frameworks can support the responsible and scalable deployment of sustainable AI, including through enabling conditions for collaboration, interoperability, and broader alignment with global efforts.

      Speaker(s)
      Moderator(s):
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