Proposes a rebound-informed framework with five tests (metric, boundary, reinvestment, burden shifting, governance) showing that AI datacenter sustainability claims often rely on relative efficiency gains without proving absolute reductions in energy, water, and other burdens.
Bargagli-Stoffi
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
AI data center waste heat upgraded by heat pumps can drive direct air capture to achieve net CO2 removal and offset operational emissions in several US states under current and 2030 scenarios.
Staggered difference-in-differences estimates suggest AI model releases increase fossil generation and wholesale prices near data centers, but the paper's model and price calculations are internally inconsistent.
Hyper-datafication—industrialized production of AI training data—creates growing storage-energy, carbon, and labor costs that fall disproportionately on data workers and under-represented languages.
citing papers explorer
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Plateau That Never Comes: When Efficiency Claims in Datacenters and AI Become Greenwashing
Proposes a rebound-informed framework with five tests (metric, boundary, reinvestment, burden shifting, governance) showing that AI datacenter sustainability claims often rely on relative efficiency gains without proving absolute reductions in energy, water, and other burdens.
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Recasting AI Data Centers as Engines for Carbon Removal
AI data center waste heat upgraded by heat pumps can drive direct air capture to achieve net CO2 removal and offset operational emissions in several US states under current and 2030 scenarios.
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Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
Staggered difference-in-differences estimates suggest AI model releases increase fossil generation and wholesale prices near data centers, but the paper's model and price calculations are internally inconsistent.
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How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
Hyper-datafication—industrialized production of AI training data—creates growing storage-energy, carbon, and labor costs that fall disproportionately on data workers and under-represented languages.