Recasting AI Data Centers as Engines for Carbon Removal

TL;DR

This paper explores repurposing AI data center waste heat with heat pumps to enhance direct air capture of CO2, potentially turning data centers into net-negative carbon emitters in certain regions.

Contribution

It introduces a thermodynamically integrated DAC-AIDC system and provides a region-specific assessment of its climate and economic benefits across the US.

Findings

AIDC waste heat can significantly improve CO2 removal efficiency.

Integration can turn DAC from net-positive to net-negative emissions in carbon-intensive regions.

Several states could achieve net-negative removal ratios above 1 by 2030.

Abstract

AI data centers (AIDCs) are rapidly increasing electricity demand and associated CO2 emissions, yet they also generate continuous low-grade waste heat. Here, we assess whether this heat can be upgraded by heat pumps to drive direct air capture (DAC) and reduce the climate impact of AI infrastructure. We develop a thermodynamically integrated DAC-AIDC system and conduct a region-resolved assessment across the United States, accounting for AIDC capacity, server composition, local climate, electricity prices, and grid carbon intensity. We find that AIDC waste heat can substantially improve net CO2 removal and lower the levelized cost of capture. In carbon-intensive regions, integration can flip DAC from net-positive to net-negative. Under a 2030 scenario with more GPU-intensive AIDCs and cleaner grids, several states achieve removal ratios above 1, indicating that integrated systems can offset their own operational emissions and deliver additional carbon removal.