Understanding the Human Effects of Climate Change

Climate change has already begun to profoundly alter the relationship between/nhumans and their environment for the vast majority of the world’s population. How-/never, history has demonstrated that human are nothing if not responsive: as the/nclimate changes, so too will economies, governments, and individuals. This disser-/ntation examines impacts and responses to climate change with an eye towards un-/nderstanding how future societies might adapt to substantial climatic changes. The/nfirst chapter measures the welfare cost of changes in amenity values due to climate/nchange by proxying for temperature preferences using contemporaneous changes in/nmood, as detected from posts on the social media platform Twitter. The second/nchapter examines the response of electricity demand to changes in temperature as/na means to project patterns of future energy consumption and large-scale capital/ninvestments. The third chapter makes a methodological contribution to test three/nquasi-experimental methods of estimating electricity savings in dynamic pricing pro-/ngrams versus an empirical “gold standard”: the results from this chapter will aid/npolicymakers in quantifying the effects these programs on curbing future increases/nin electricity generation due to climate change./nThe first chapter is motivated by a gap in the climate impacts literature: the/nchange in amenity values resulting from temperature increases may be a substantial/nunaccounted-for cost of climate change. Without an explicit market for climate, prior/nwork has relied on cross-sectional variation or survey data to identify this cost. This/npaper presents an alternative method of estimating preferences over nonmarket goods/nwhich accounts for unobserved cross-sectional and temporal variation and allows forprecise estimates of nonlinear effects. Specifically, I create a rich panel dataset on/nhedonic state: a geographically and temporally dense collection of updates from the/nsocial media platform Twitter, scored using a set of both human- and machine-trained/nsentiment analysis algorithms. Using this dataset, I find strong evidence of a sharp/ndeclines in hedonic state above and below 20 ◦ C (68 ◦ F). This finding is robust across/nall measures of hedonic state and to a variety of specifications./nThe second chapter simulates the effect of climate change on future electricity/ndemand in the United States. We combine fine-scaled hourly electricity load data/nwith observations of weather to estimate the response of both average and peak/nelectricity demand to changes in temperature. Applying these estimates to a set of/nlocally downscaled climate projections, we project regional end-of-century changes/nin electricity load. The results document increases in average hourly load across the/ncountry, with more pronounced changes occurring in the southern United States./nImportantly, we find changes in peak demand to be larger than changes in aver-/nage demand, which has implications for public policy choices around future capital/ninvestment./nThe third chapter compares quasi-experimental designs to experimental designs in/nthe context of a dynamic pricing setting designed to encourage customers to save en-/nergy. Randomized controlled trials (RCTs) are widely viewed as the “gold standard”/nfor evaluating the effectiveness of an intervention. However, because are percieved/nto be prohibitively expensive and challenging to implement successfully, they are/nnot broadly executed in policy settings. In particular, analysis of the effect of energy/npricing has largely been conducted through a two commonly used quasi-experimental/nmethodologies: difference-in-differences and propensity score matching. Using a rare/nset of large-scale randomized field evaluations of electricity pricing, we compare the/nestimates obtained from these quasi-experimental designs and from a regression dis-/ncontinuity design to the true estimates obtained through the experimental method./nWe demonstrate empirical evidence in favor of four stylized facts that highlight the/nimportance of understanding selection bias and spillover effects in this context. First,/ndifference-in-differences and propensity-score methods mis-estimate the true effect/nby up to 5% of mean peak hour usage. Second, propensity score estimates resemble/ndifference-in-difference findings, but standard errors tend to be larger and point esti-/nmates are more biased for opt-out models. Third, regression discontinuity methods/ncan be heavily biased relative to the true average treatment effect. Finally, we find/nstrong evidence that biases are more pronounced in opt-in vs. opt-out designs.

Advisor: Auffhammer, Maximilian