Brian C. Prest
Fellow; Director, Social Cost of Carbon Initiative
Brian Prest is an economist and Fellow at Resources for the Future specializing in climate change, oil and gas, and energy economics. Prest uses economic theory and econometric models to improve energy and environmental policies by assessing their impacts on markets and pollution outcomes. His recent work includes economic modeling of federal oil and gas leasing policies, including implementing carbon pricing into federal oil and gas royalties. He is also working to improve the scientific basis of the social cost of carbon by establish an empirical basis for determining discount rates and developing probabilistic Bayesian projections of country-level energy intensity. He also uses machine learning techniques to improve understanding of household electricity demand and time-varying pricing. His past work includes econometric modeling of the US oil and gas industry, evaluating the effects of coal subsidies on emissions, understanding the economic effects of rising temperatures, modeling the market dynamics of climate change policy under policy uncertainty. His work has appeared in peer-reviewed journals including the Journal of the Association of Environmental and Resource Economists, Journal of Environmental Economics and Management, Energy Economics, and The Energy Journal.
Prior to joining RFF, Prest earned his PhD at Duke University and previously worked in both the public and private sectors. At the Congressional Budget Office, he developed economic models of various energy sectors to analyze the effects of proposed legislation, including the 2009 Waxman-Markey cap-and-trade bill and related Clean Electricity Standards. At NERA Economic Consulting, he conducted electricity market modeling, project valuation, and discounted cash flow analysis of various infrastructure investments in the United States, Latin America, Europe, Africa, and Southeast Asia, with a focus on the power sector.
Education
- PhD in environmental and resource economics, Duke University, 2018
- BA in economics, Williams College, 2009
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Testimony and Public Comments — Jan 14, 2022
Comments to EPA on Updated Estimates of the Social Cost of Methane for Usage in Regulatory Analysis
Comments to EPA on Updated Estimates of the Social Cost of Methane for Usage in Regulatory Analysis
Working Paper — Sep 29, 2021
RCTs Against the Machine: Can Machine Learning Prediction Methods Recover Experimental Treatment Effects?
RFF researchers examine how well machine learning counterfactual prediction tools can estimate causal treatment effects.
Working Paper — Sep 9, 2021
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
This paper, coauthored by scholars at RFF and top research universities, examines the key methods and challenges involved in estimating a more accurate social cost of carbon.
Issue Brief — Sep 9, 2021
Methane Fees' Effects on Natural Gas Prices and Methane Leakage
This issue brief examines the economic effects of a range of methane fees, finding that incentivizing natural gas companies to reduce methane leaks could create benefits at relatively low cost.
Testimony and Public Comments — Jan 14, 2022
Comments to EPA on Updated Estimates of the Social Cost of Methane for Usage in Regulatory Analysis
Comments to EPA on Updated Estimates of the Social Cost of Methane for Usage in Regulatory Analysis
Working Paper — Sep 29, 2021
RCTs Against the Machine: Can Machine Learning Prediction Methods Recover Experimental Treatment Effects?
RFF researchers examine how well machine learning counterfactual prediction tools can estimate causal treatment effects.
Issue Brief — Sep 9, 2021
Methane Fees' Effects on Natural Gas Prices and Methane Leakage
This issue brief examines the economic effects of a range of methane fees, finding that incentivizing natural gas companies to reduce methane leaks could create benefits at relatively low cost.
Working Paper — Sep 9, 2021
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
This paper, coauthored by scholars at RFF and top research universities, examines the key methods and challenges involved in estimating a more accurate social cost of carbon.
Media Highlight — Nov 26, 2021
Oil and Gas Companies Should Pay More to Drill on Public Lands and Waters, Interior Department Says
Oil and Gas Companies Should Pay More to Drill on Public Lands and Waters, Interior Department Says
Media Highlight — Nov 25, 2021
Will An Extra 50 Million Barrels of Crude Lower US Gasoline Prices?
Will An Extra 50 Million Barrels of Crude Lower US Gasoline Prices?
Media Highlight — Nov 15, 2021
Rising Natural-Gas Prices Pose Hurdle for Methane Tax
Rising Natural-Gas Prices Pose Hurdle for Methane Tax
Media Highlight — Oct 14, 2021
Biden's Winter Energy Chill
Biden's Winter Energy Chill
Resources Magazine — Oct 21, 2021
Working Toward a New Social Cost of Carbon
The social cost of carbon (SCC) can have a major impact on the US government's climate regulations, but estimating its value can get complicated. RFF researchers aim to make the best possible estimate using cutting-edge empirical and scientific data.
Resources Magazine — Oct 21, 2021
Improving Discounting in the Social Cost of Carbon
The social cost of carbon (SCC) is a critical number for the influence it can have on US climate policies and regulations. New RFF research aims to improve the SCC by carefully and empirically determining the most appropriate discount rate.
Resources Magazine — Oct 21, 2021
Celebrating Nature with #MyResources
In addition to their economic benefits, outdoor spaces also provide physical and psychological perks. Share in the fun of RFF staff and scholars who like getting out and about and have reflected on some of their favorite natural spots and national parks.
Common Resources — Sep 29, 2021
Can We Use Machine Learning to Predict Energy Savings?
Randomized controlled trials can reveal the effect of a change in policy. But trials can be expensive and difficult. RFF research shows that a different approach, using machine learning, can link cause and effect without major investments in experiments.