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.
4
Pith papers citing it
years
2026 4representative citing papers
Game-theoretic modeling and difference-in-differences analysis using LLM releases show AI data center demand increases fossil generation, wholesale prices, and outages near data centers unless mitigated by behind-the-meter capacity.
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.
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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Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
Game-theoretic modeling and difference-in-differences analysis using LLM releases show AI data center demand increases fossil generation, wholesale prices, and outages near data centers unless mitigated by behind-the-meter capacity.
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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.