Lab Seminar: Robert Reaser and Leslie Sanchez

This week, Econ PhD candidate Robert Reaser and ARE postdoc Leslie Sanchez each presented their ongoing work to the Lab.

Rob's work focuses on the effect of AI data centers on energy markets. After two decades of stagnation, U.S. electricity demand is growing rapidly, driven, in large part, by the proliferation of energy-intensive data centers for Artificial Intelligence (AI) training. Rob and his coauthors estimate the impact of this load expansion on wholesale electricity generation costs and emissions using a detailed economic dispatch model covering the continental U.S. They estimate that by September 2025, data center load growth was already meaningfully increasing total generation costs relative to a counterfactual world without it. Increases in costs and wholesale prices are driven by the convexity of the aggregate supply curve. As constant, high-utilization data center demand consumes excess generating capacity, the market clears at increasingly steep portions of the curve. As a result, the authors find that marginal impacts are often the highest during shoulder seasons rather than traditional summer peaks, as data center baseload exacerbates supply tightness during scheduled generator maintenance windows. Forward-looking simulations suggest these patterns will intensify, which may slow the rate of grid decarbonization by necessitating increased reliance on thermal generation to bridge renewable intermittency.

Leslie's work focuses on the spatial and temporal economics of groundwater use. California’s Groundwater Sustainability Agencies (GSAs) face statutory deadlines under the Sustainable Groundwater Management Act (SGMA) to set sustainable pumping levels and implement corrective policies. In overdrafted basins, these include demand management measures such as pumping limits and supply-side actions such as managed aquifer recharge. GSAs, however, often lack information on how the costs of pumping reductions vary across users and may underestimate hydrologic delays associated with recharge. Using the Turlock Groundwater Basin as a case study, Leslie and coauthors combine data on surface water deliveries, remotely sensed evapotranspiration, crop decisions, and aquifer properties to construct scalable measures for estimating the marginal value of surface and groundwater use in agriculture at the field-level. They then use these estimates to better understand the potential effects of increased pumping costs across irrigators, and analyze potential gains from trade in groundwater pumping rights under a simplified aquifer model with instantaneous transmission.  Leslie and her coauthors compare these results with outcomes under models that incorporate directional groundwater flow and transmission lags. The study aims to quantify heterogeneity in groundwater values across pumpers, assess efficiency gains from reallocating pumping rights and spatially targeted recharge under simplified hydrologic assumptions, and examine how those gains change when realistic aquifer dynamics are taken into account.