In the fifth lecture, we will talk about the potential uneven impacts of the energy transition, with a focus on the electricity sector.
In the practical session, we will learn how to combine machine learning tools applied to smart meter data with Census data to estimate the heterogeneity impacts of energy policies.
Slides 9: slides9.pdf
Slides 10: slides9.pdf
❗ Save the Jupyter notebook (.ipynb) and the data CSV file in the same folder. Then, open the Jupyter Notebook to start exploring.
You will present your own project based on the model that you have learned.
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Borenstein, S., & Davis, L. W. (2016). The Distributional Effects of US Clean Energy Tax Credits. Https://Doi.Org/10.1086/685597, 30(1), 191–234. https://doi.org/10.1086/685597
Borenstein, S. (2017). Private Net Benefits of Residential Solar PV: The Role of Electricity Tariffs, Tax Incentives, and Rebates. Https://Doi.Org/10.1086/691978, 4(S1), S85–S122. https://doi.org/10.1086/691978
Burger, S. P., Knittel, C. R., Pérez-Arriaga, I. J., Schneider, I., & Vom Scheidt, F. (2020). The efficiency and distributional effects of alternative residential electricity rate designs. Energy Journal, 41(1), 199–239. https://doi.org/10.5547/01956574.41.1.SBUR
Feger, F., Pavanini, N., & Radulescu, D. (2020). Welfare and Redistribution in Residential Electricity Markets with Solar Power. Working Paper.
Leslie, G. and Pourkhanali, A. & Roger, G. (2021). Can Real-Time Pricing Be Progressive? Identifying Cross-Subsidies under Fixed-Rate Electricity Tariffs. working paper
Wang, Reguant, Fabra, and Cahana (2021). The Distributional Impacts of Real-Time Pricing. Work in progress.
Wolak, F. (2016). Designing Nonlinear Price Schedules for Urban Water Utilities to Balance Revenue and Conservation Goals. National Bureau of Economic Research. https://doi.org/10.3386/w22503