Research

Working Papers

U.S. Electricity Interconnections: Technology, Potential, and Investment 

Job Market Paper

Since 1978, the U.S. federal government has enacted various Production Tax Credit (PTC) and Investment Tax Credit (ITC) policies to stimulate investment in electricity generation capacity. As solar and wind technology costs have rapidly declined, making them among the cheapest electricity generation technologies available, utilities have invested heavily in these resources since the early 2000s. However, the contiguous United States is heterogeneous in average wind speed and solar irradiance, resulting in regional disparities in renewable capacity factors and, consequently, clean electricity generation shares. To quantify the effects of these subsidies, I develop a two-region dynamic general equilibrium model of the U.S. electricity sector, featuring technology-specific capital costs and region-specific energy productivity for solar, wind, gas, and coal generation. I solve the model with and without federal subsidies to compute counterfactual transition paths beginning in 1990, finding that clean electricity generation growth was 1.7 times greater in the Eastern Interconnection than in the Western Interconnection, as the East's lower baseline renewable potential left greater room for policy-driven expansion. A counterfactual analysis further shows that reaching 80% electricity generation from solar and wind in each Interconnection by 2030 requires a subsidy rate 19% larger in the East than in the West.

AI-integrated models for assessing agricultural resilience

with Joshua R. Waite, Dana Golden, Kevin Camp, Mojdeh Saadati, Shannon Regan, Pat Schnable, Baskar Ganapathysubramanian, Carlos D. Messina, Suzanne Thornsbury, Soumik Sarkar (Under Review)

Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We develop a novel AI-powered tool that integrates economic models (GTAP) with biophysical models (APSIM) to analyze supply chain shocks, enabling policymakers and market participants to assess cross-disciplinary impacts through queries and responses written in natural language. We present a conceptual vulnerability framework that categorizes the agricultural supply chain as a social-ecological system with biophysical and economic exposures and a working prototype that couples domain models through a lightweight AI orchestration layer. Our framework is an important first step in orchestrating existing domain models to illustrate the power of model coordination. An empirical example illustrates both the power of the tool and remaining gaps.

Agentic AI orchestration of heterogeneous economic models for rapid, multi-scenario analysis of energy crises

with Dana Golden, Lav R. Varshney, Suzanne Thornsbury 

Rigorous economic models can take months to construct, yet energy crises demand decisions from policymakers within days or even hours. Any disruption in energy markets is not isolated but rapidly disseminates through interlinked global systems. Off-the-shelf models that already exist typically focus only on limited aspects and are distributed across research groups, programming languages and incompatible formats. Integrating these models manually can take longer than the crisis itself, forcing analysts to rely on whichever models are easiest to connect and leaving consequential scenarios unexplored. Policymakers must make rapid decisions with obstructed and limited information. We show that large language models can perform the critical integration directly, revealing connections in a way that is timely and computationally efficient. The system constructs internally consistent scenarios, translates assumptions into model-specific inputs, executes existing economic and physical models in dependency order, and synthesizes outputs for policymakers. The language model generates no quantitative results: every reported value is reproduced directly from an underlying model run, remains traceable to its source and is subject to analyst approval at each stage.

U.S. Clean Electricity Generators: Census Tracts and Income, Poverty, Population Dynamics

U.S. investment in utility-scale wind and solar electricity generation has grown rapidly since the early 2000s. Unlike coal and natural gas generators, clean energy technologies require vast land areas optimized for solar irradiance or wind speeds, making siting decisions both geographically and socioeconomically heterogeneous. Using Energy Information Administration Form 860 and 2019 American Community Survey data, I analyze the demographic characteristics of U.S. census tracts hosting solar, wind, and fossil fuel electricity generators. Using state-normalized demographic comparisons across 5,000+ census tracts, I find that fossil fuel tracts have 25%, 20%, and 12% higher shares of low-income, high-poverty, and majority non-White tracts than solar tracts, and 62%, 89%, and 101% higher shares of low-income, high-poverty, and majority non-White tracts than wind tracts. Across census tracts containing either clean or fossil fuel generating technologies, a higher share of tracts in the Western Interconnection is classified as low-income, high-poverty, and non-White than in the Eastern Interconnection.