Data, Power and Emissions: The Environmental Cost of AI

We study the environmental impact of artificial intelligence (AI) using a novel dataset that links measures of AI penetration, the location of data centers and power plants, and CO2 emissions across US commuting zones between 2002 and 2022. Our analysis yields four main findings. First, exploiting a shift–share identification strategy, we show that localities more exposed to AI experience relatively faster emissions growth. Second, decomposition results indicate that scale effects dominate, while changes in industrial composition exert at most a weak mitigating effect; at the same time, electricity generation becomes more carbon intensive. Third, AI penetration raises dependence on non-renewable electricity. Fourth, proximity to data centers is a key driver of this effect, as nearby power plants shift toward greater fossil fuel use. These findings suggest that, absent a rapid decarbonization of power generation, the diffusion of AI is likely to exacerbate environmental externalities through the energy demand of data centers.