AI & Environment Resource HubXiao, Tianqi; Nerini, Francesco Fuso; Matthews, H. Damon; Tavoni, Massimo; You, Fengqi · 2025-11-10
This paper projects that U.S. AI server expansion could generate up to 1,125 million m³ of annual water use and 44 Mt CO₂-equivalent emissions by 2030, making net-zero goals unlikely without major reliance on offsets. It highlights that while best practices could cut impacts substantially, real progress depends on faster grid decarbonization, improved infrastructure, and coordinated public–private action.
Xiao, Tianqi; Nerini, Francesco Fuso; Matthews, H. Damon; Tavoni, Massimo; You, Fengqi. 2025-11-10. Environmental Impact and Net-Zero Pathways for Sustainable Artificial Intelligence Servers in the USA. https://www.nature.com/articles/s41893-025-01681-y (AI & Environment Resource Hub; record paper-408; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Built environment and infrastructure
AI’s rapid growth is driving an unprecedented surge in energy and water demand, raising serious environmental justice concerns, especially in marginalized communities, while sparking debate over whether climate benefits can outweigh the harms without stronger accountability and sustainable innovation.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Built environment and infrastructure
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.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Built environment and infrastructure
The rapid expansion of data centers has created new political challenges. Governments want to build more data centers to reap the benefits from the artificial intelligence (AI) economy and achieve their digital sovereignty agendas. However, data centers require enormous amounts of electricity and water, threaten emission-reduction targets, and often raise local electricity prices. As a result, local communities increasingly mobilize against data center construction. We use a vignette and conjoint survey experiment in Germany to evaluate how publics think about the environmental, economic, and (geo)political tradeoffs that data centers entail. We find that directly priming people with digital sovereignty concerns only marginally increases support for building more data centers. Yet, support varies substantially based on characteristics that people do have strong views about: decarbonization, geopolitics, and local environmental and economic impact.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Water; Built environment and infrastructure
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.