The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 20 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual. electricity consumption | peak load | climate change | economic impacts | extreme events I ntegrated Assessment Models (IAMs) used to estimate the US government's social cost of carbon include large costs due to changes in electricity demand resulting from climate change (1-3). The Climate Framework for Uncertainty, Negotiation, and Distribution (FUND), for example, estimates the majority of the costs of climate change to result from the additional cost of cooling (4). However, FUND and the other IAMs rely on a highly simplified and outdated estimate of the relationship between rising temperatures and heating and cooling costs (5, 6). At the same time, future capital investments in electric generation capacity require accurate, region-specific forecasts of future electricity demand. Many aspects of these forecasts are well understood: electricity demand tends to rise with population, income, and the presence of energy-intensive industries (7). However, because electricity use by residential, commercial, industrial, and agricultural customers is strongly affected by ambient temperature, climate change-induced changes in temperature are likely to significantly affect future generation, transmission, and distribution requirements relative to a world with a stationary climate. As the electricity grid is designed for maximum load days, which tend to be the hottest days in many areas, the increasing intensity of extreme heat days will require additional investments in peak generation capacity, transmission, or storage.Prior work has examined the relationship between electricity load, i.e., the quantity of electricity demanded, and tem...

for helpful comments. We also thank UC Berkeley's Energy Institute at Haas, where Rapson was a visitor while this research was conducted. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework that differ from the more standard cross-sectional RD framework. First, many applications (particularly in environmental economics) lack cross-sectional variation and are estimated using observations far from the temporal threshold. This common empirical practice is hard to square with the assumptions of a cross-sectional RD, which is conceptualized for an estimation bandwidth shrinking even as the sample size increases. Second, estimates may be biased if the time-series properties of the data are ignored (for instance, in the presence of an autoregressive process), or more generally if short-run and long-run effects differ. Finally, tests for sorting or bunching near the threshold are often irrelevant, making the framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical examples using air quality data, we offer suggestions for the empirical researcher wishing to use the RD in time framework.