Center for Robust Decision Making on Climate and Energy Policy

Undergraduate Research

Research Opportunities for Undergraduates

We are always looking for qualified local summer undergraduate students to participate in research at RDCEP.  Students will conduct interdisciplinary research in the areas of climate and energy policy. Students with skills in computer programming, statistics, web development/ design, or communicating research results to the general public are preferred.

If you are interested send your resume to Alison Brizius: abrizius AT ci.uchicago.edu

Potential research projects include:


Potential Research Areas:

Due to the interdisciplinary nature of the Center's research, all projects are collaborative in nature: the participant will engage with a several other researchers (Faculty, Postdoctoral Scholars, Graduate Students and/or other Undergraduates) to work towards the completion of the project.

The feasibility of Renewable Portfolio Standards in Illinois and other U.S. states: The Illinois renewable portfolio standard (RPS) will, if fully implemented, require that 25% of Illinois electric power be obtained from qualified renewables in Illinois and adjacent states by 2025. Implementation is limited, however, by a “cost cap” that freezes requirements if costs to the consumers grow beyond 2% of 2010 retail electrical costs. Illinois legislation, like that of many other U.S. states, requires renewable electricity, but only if it is cheap.  A group of students led by Professor Moyer will study the feasibility of the Illinois RPS as well as similar RPS enacted in several other states.

Development of interactive policy analysis tools: The complex interfaces associated with many economic modeling tools are a major barrier to their use by non-IT-savvy researchers, policy makers, and the general public. To address this issue, and RDCEP group led by Professor Weisbach is working to provide an intuitive interactive interface to simple models that represent the economic impact of climate change. The goal is a web-accessible, user-friendly integrated assessment model in which users can change various model parameters and see, via graphical displays, how these changes alter the results. Ultimately, the target is to allow users to do not only individual simulations but also policy-specific optimizations and Monte Carlo runs to test for parameter sensitivity.

Use of shipping data to validate international trade volumes: International trade data developed by the Global Trade Analysis Project (GTAP) are used in a wide range of research and policy studies, including those conducted by RDCEP. The raw data from which GTAP constructs its database are ultimately self-reported and of variable quality. International shipping data represents an opportunity to evaluate the quality of GTAP trade data. This project will involve working with a large database obtained from Lloyds containing comprehensive records of all shipping over a four-year period, and determining whether this data can be used to conduct such an evaluation.

Text mining for extraction of economic model parameters: In economics as in other fields, estimates of important elasticities and other economic model parameters are reported over a broad literature. While various papers have reviewed this literature, none provides a comprehensive collection of estimates. New text mining methods, including methods pioneered (in other fields, notably biomedicine) at UChicago, can be used to perform more comprehensive reviews. The research project will include application of these tools to the economics literature, with the goal of both collecting as many estimates as possible and seeking to understand factors that result in different estimates. For example, are different estimates due to different input data? Do different research groups use different estimates?


Assessment of regional aggregation of impacts: Impacts evaluation is complicated by uncertainty in regional climate predictions. While predictions of global warming are robust, predictions on smaller spatial scales are highly uncertain, and in agriculture, for example, crop yields depend on highly local conditions. This project will investigate to what extent variability within and across models affects climate impact assessments as a function of level of aggregation. County-by-county predictions are likely too uncertain to be meaningful. However, for impacts aggregated to the level of the United States, uncertainties should be less important. The student will use agriculture as a test case and run existing agricultural yield models with climate data from a variety of models and develop distributions of model variability in impacts at various scales of aggregation. The study is feasible and relatively simple because of pre-existing work by RDCEP researchers with agricultural models, but will be important in informing uncertainty assessments.


Statistical models of crop yields: Inherently inaccurate low-input crop models may in fact be outperformed by statistical representation of crop yields driven by empirical data. This project will center on generating a suitable statistical representation and comparing with existent process-based models. The work will involve compiling and downscaling historical climate data (or reanalyses) through existing methods, compiling agricultural yield data, and evaluating the key climate parameters affecting yields (often maxima and minima rather than monthly mean values). Students with experience in statistics could extend the work, in collaboration with graduate students, to developing and validating a statistical model.

Assessing the carbon abatement potential of electric vehicles: Many view vehicle electrification as a promising way of reducing greenhouse gas emissions from the transportation sector, but with the current electricity system, electrification represents a switch from gasoline-powered to coal-powered motor vehicles. The impact on net carbon dioxide emissions depend on patterns of vehicle charging (because location and time of day dictate which power plants are used to generate the electricity). An research project will quantify the greenhouse gas emissions from vehicle electrification by combing readily available government survey data on vehicle use and a model of the electricity system, such as Argonne National Laboratory’s ENPEP-BALANCE model, to simulate electric vehicle use. The project will explore the policy implications of uncertainty in estimates, and characterize how the carbon implications of electrification vary across regions of the country. Participants will gain experience in working with data and systems modeling tools.

Assessing how much consumers should be willing to pay for energy efficient appliances: Policy analysts continue to debate whether consumers appear to demand too little energy efficiency. Some believe that consumers are subject to a cognitive bias that makes them unwilling to trade-off additional upfront cost when buying energy-consuming durable goods for future energy savings, in ways that are inconsistent with neoclassical economic theory. Most research in this area has included only a cursory treatment of uncertainty about energy cost savings, meaning that results may inadequately account for risk assessments in consumer decisions. This project will quantify the uncertainties in energy prices, in appliance longevity, and in energy savings to quantify consumer uncertainty and verify the rationality of consumer behavior. Parameter value distributions can be estimated from data (on electricity prices, on energy efficiency, and on appliance use) from the Energy Information Administration and the Environmental Protection Agency. Research will include performing simple Monte Carlo simulations in order to characterize uncertainty and understand whether consumers misvalue energy efficiency.