GAIA-DC: A Feasibility-First Energy, Grid-Flexibility, and Environmental Carrying-Capacity Framework for Sustainable AI Data Centers
Naresh Somara · 2026-08-21
AI data centers are becoming grid-scale flexible loads whose siting and operation affect electricity demand, carbon emissions, cooling energy, water use, and regional infrastructure adequacy. This paper presents GAIA-DC, a feasibility-first framework for sustainable AI data centers that couples facility energy performance, grid flexibility, regional environmental carrying capacity, and workload placement.
Naresh Somara. 2026-08-21. GAIA-DC: A Feasibility-First Energy, Grid-Flexibility, and Environmental Carrying-Capacity Framework for Sustainable AI Data Centers. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7323938 (AI & Environment Resource Hub; record paper-508; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Land use, heat, and noise; 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.
Nathaniel Burola
Shared topics: Cross-cutting sustainability; 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.
Climate One
Shared topics: Cross-cutting sustainability; Energy and electricity; Climate and greenhouse gases; Water; Built environment and infrastructure
Machine learning or "AI" technologies are increasingly integrated into consumer software. Proponents claim that these tools - especially generative AI such as ChatGPT - will make our lives easier, but any benefits come at a steep price. The energy and water demands of the requisite data centres are enormous, and in a grid yet to fully decarbonise, this comes with a large carbon footprint. In fact, in many ways the current AI boom is a microcosm of the climate crisis itself.
youtube.com
Shared topics: Cross-cutting sustainability; Energy and electricity; Water; Land use, heat, and noise; Built environment and infrastructure
Rapid growth in artificial intelligence (AI) and cloud-based services has accelerated data center development, creating new pressures on electricity systems, water resources, land use, and local governance. Largely ignored by citizens and policymakers in the past, data centers are now highly visible, energy-intensive facilities whose siting and operation raise significant community and policy challenges.
