AI for Energy, Water and Waste Management Conference
Institut Veolia · 2026-02-25
Conference held on the 19th of February 2026, organised by the Veolia Institute, hosted at the Collège des Bernardins, on the role of AI for the main environmental challenges. This event followed the joint publication of our last report, coproduced with Microsoft, called "AI for Energy, Water and Waste Management". Our experts explored the potential benefits of AI for the ecological transition in relation to concerns raised by its carbon footprint and growing energy, water, and precious metal requirements.
Institut Veolia. 2026-02-25. AI for Energy, Water and Waste Management Conference. https://www.youtube.com/watch?v=FIXUdoShvas (AI & Environment Resource Hub; record video-192; collection snapshot 2026-09-15).
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Matthew Riemland examines how translation professionals can confront the environmental costs of AI, including carbon emissions, water consumption, and rare mineral extraction, through 'eco-translation practice.' He argues these harms stem from structural power imbalances rather than individual choices, and proposes vocational and structural strategies of resistance, from supporting data center activism to demanding transparency from AI developers.
Encounters in Translation
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems
In the Environmental Impacts of Data Centers 101 course, you will learn to analyze the environmental impacts of data centers using a life cycle assessment (LCA) perspective that goes beyond what you see in the news. This will include energy, water, land use, carbon emissions, global supply chains, e-waste concerns, ecological impacts, and environmental justice case studies. Whether you work in tech, sustainability, policy, or are just curious about how AI systems operate behind the scenes, this course gives you the clarity and frameworks to understand these impacts from end-to-end.
Nathaniel Burola
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems
In this CXO Bytes episode, Sanjay Podder and May Yap, SVP & CIO of Jabil, discuss the intersection of green IT, responsible AI, and sustainable manufacturing, highlighting Jabil’s integration of renewable energy, circular economy principles, and AI-driven solutions into its operations, its commitment to carbon neutrality by 2045, and key initiatives like energy-efficient manufacturing, water conservation, and e-waste management, while emphasizing the role of green IT practices and AI in driving sustainable innovation.
youtube.com
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Biodiversity and ecosystems
Artificial intelligence (AI) is in the media spotlight for its potential to transform the economic and research sectors, among others. This drives funding bodies to support AI-based innovation, with for example the Horizon Europe and Digital Europe programmes run by the European Union, or France’s investment strategy France 2030 (national strategy for AI). On the other hand, the environmental impacts of AI are now better understood, and we cannot ignore the role of AI on electricity and water usage, mineral resource depletion, and greenhouse gas emissions1,2. To bring together innovation and sustainability, the French Department for the Environment (Ministère en charge de la Transition Écologique) has decided to require the use of the Green Algorithms tool for funding applications on the topic of AI and climate change. Applicants now have to include estimates of the carbon footprint and energy usage of the different development phases of the proposed AI solution. This was tested on a first funding call “Demonstrators of frugal AI for sustainable development of local communities”. The first applications were received in December 2023, with positive feedback from the different stakeholders. Applicants in particular approved of this new criterion, as they understood its necessity, found the tool easy to use, and did not consider this to slow down innovation. Following this successful implementation in a first funding call, it was decided to include the Green Algorithms tool more systematically in the application guidelines of other AI-related funding calls run by the Department. The goal of this piece is to reflect on the inclusion of environmental criteria in AI funding calls and share the lessons learned with other funding bodies internationally to promote similar initiatives across the AI ecosystem.
