Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
For grid operators and policymakers, this paper reveals that current renewable procurement strategies (RECs/PPAs) are ineffective for mitigating AI's grid impacts, and that spatial and operational design of data centers is critical.
AI-driven data center demand increases fossil generation, wholesale electricity prices (up to 25% in treated PJM zones), and outage frequency (0.5–1 additional outages per year), while renewable energy certificates fail to mitigate these impacts due to a timing wedge between consumption and credited generation. Colocation with storage or behind-the-meter generation can substantially reduce grid externalities.
Data centers now account for 4.4% of United States electricity demand, yet the grid-level effectiveness of the renewable energy certificates (RECs) and power purchase agreements (PPAs) hyperscalers use to claim carbon neutrality remains unclear. We develop a game-theoretic model in which a data center operator chooses among RECs, PPAs, and behind-the-meter colocation while generators make entry decisions under endogenous financing costs. The model identifies a timing wedge -- the mismatch between consumption and credited renewable generation -- as a central mechanism through which AI demand degrades reliability, raises prices, and increases emissions even when RECs cover 100% of annual consumption. Colocation with storage addresses this wedge directly and induces the greatest renewable entry by eliminating generator revenue risk. We test these predictions by exploiting the staggered release of large language models as a natural experiment, using difference-in-differences on a novel dataset linking AI activity to local grid outcomes. AI demand significantly increases fossil generation, wholesale prices (up to 25% in treated PJM zones), and outage frequency (0.5--1 additional outages per year) near data centers, with impacts scaling in model size. Data centers with on-site generation exhibit a sign reversal in power-quality effects, consistent with the model's prediction that behind-the-meter capacity absorbs demand spikes. Counterfactual analyses show that edge inference, spatial reallocation, and colocated storage each substantially mitigate grid impacts, while REC-only strategies do not. Together, our results demonstrate that the externalities of AI to the grid are tightly coupled to procurement design and the spatial organization of data center infrastructure.