About

I'm a computational economist specializing in economic modeling of energy, environmental, and infrastructure policy.

At The Brattle Group, I lead the firm’s economic impact assessment work and develop large-scale modeling frameworks to evaluate the effects of economic shocks. My work combines computable general equilibrium (CGE) modeling with electricity market and power sector analysis to assess how regulatory changes, market design, technology adoption, and investment decisions affect economic activity and long-term planning outcomes. I lead the development of BEYOND, Brattle’s economic impact assessment model, as well Brattle's power sector capacity expansion model, gridSIM. 

I am also part of the teaching faculty in the Department of Economics at University of Maryland, College Park, where I teach Microeconomics for the Applied Economics program.   

In my academic research, I research optimal climate policies using dynamic programming and numerical optimization. Below I summarize my current research and model development: 

Economic Impacts of New York’s Cap and Invest Program (NYCI) 

NYCI is a transformative policy aimed to reduce greenhouse gas emissions through a declining economy-wide emissions cap. The cap is set so that the state achieves the emissions reduction goals set forth in the Climate Leadership and Community Protection Act (CLCPA); the program’s emissions allowance auction is used to price emissions based on the supply (equal to the cap) and economy-wide demand for emissions allowances; the revenue generated through allowance auctions is invested to facilitate the state’s decarbonization efforts in an equitable way. Our study for Environmental Defense Fund examines the power sector and economy-wide impacts of NYCI. Using an integrated modeling framework that couples Brattle’s electricity capacity expansion model and macroeconomic model of the US, we study the interdependencies between carbon pricing, electricity generation, energy consumption and macroeconomic growth. The report can be found here: Link

How Learning Helps Mitigate the Worst When the Downside of Climate Change is Extreme (Link)

Deep uncertainty in equilibrium climate sensitivity (ECS), the eventual temperature increase in response to changes in GHG concentrations, has persistently been a critical issue in determining an effective yet feasible hedging strategy against low-probability, high-consequence climate outcomes. We examine how learning efforts to reduce uncertainty in ECS affects the optimal policy when the downside of climate change is catastrophic. Using stochastic dynamic programming, we investigate the role of learning under a “triple worst-case” scenario, in which climate damages are catastrophic at high temperatures; the true ECS, unknown to the world, exceeds the range of current scientific beliefs; and negative emissions technologies do not materialize at scale for deployment. We find that learning primarily contributes to welfare improvements through mid-course corrections in short-run emission abatement and long-run capital investment decisions.