See allcott.stanford.edu/teaching/EE for the class webpage and promo video. Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. The final paper is a great way to generate ideas for and get started on a senior thesis.
4-5 units · Letter (ABCD/NP) · GER: WAY-AQR, WAY-SI
See allcott.stanford.edu/teaching/EE for the class webpage and promo video. Are you interested in environmental and energy policy? Do you want to improve your data science skills? If so, Environmental Economics is for you. In the first few weeks of class, you'll use data and microeconomic modeling to quantify the harms from pollution, including estimating the social cost of carbon emissions. For the rest of the quarter, you'll use more data and microeconomic modeling to evaluate major environmental policies such as pollution taxes, cap-and-trade programs, and subsidies for clean technologies. You will consider overall benefits and costs as well as distributional equity, which can inform discussions of environmental justice. You will learn and practice useful data science skills, including applied econometrics/causal inference methods (e.g., difference-in-differences, instrumental variables, and regression discontinuity) and equilibrium modeling. The class has weekly problem sets involving data analysis in R, plus a final paper. Class sessions feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. The final paper is a great way to generate ideas for and get started on a senior thesis.
Offered in Autumn 2025 at Stanford University.