Three essays on the economics of energy efficiency and conservation
type: CreativeWork
description: "In this dissertation, I analyze investment decisions and consumer behavior related to energy efficiency and conservation. This research is motivated by evidence that the benefits from energy savings can extend well beyond private monetary gains to consumers. By providing insight on these topics, I contribute to a broad literature on environmental economics. This dissertation is constituted of three chapters as follows. For the first chapter, I introduce a methodological contribution for statistical evaluation of the impact of policy changes, interventions, or general ""treatments."" Specifically, I focus on estimating treatment effect heterogeneity in event studies with staggered adoption: panel data settings where observational units experience treatment at different points in time. I propose using highly flexible machine learning algorithms to predict counterfactuals (unobserved outcomes in absence of treatment) in those settings. With simulations, I show that my proposed method can recover nuanced effects with more accuracy and with better statistical efficiency than traditional econometric models (such as fixed effects regressions). I conclude that chapter with an application of the ML approach to real data from the Weatherization Assistance Program (WAP), which is one of the largest residential energy efficiency programs in the US. I identify how energy savings differ substantially depending on housing structure and on which types of upgrades were performed. For example, I document how complete furnace replacements are associated with significantly higher savings than furnace repairs. Also, I show that measures related to wall, attic, and foundation insulation are among the strongest contributors to energy savings. Finally, I assess measure-specific cost-effectiveness. Those results are informative for efforts to improve the allocation of program funds. The second chapter of this dissertation consists of a randomized control trial (RCT) to assess if behavioral…
abstract: "In this dissertation, I analyze investment decisions and consumer behavior related to energy efficiency and conservation. This research is motivated by evidence that the benefits from energy savings can extend well beyond private monetary gains to consumers. By providing insight on these topics, I contribute to a broad literature on environmental economics. This dissertation is constituted of three chapters as follows. For the first chapter, I introduce a methodological contribution for statistical evaluation of the impact of policy changes, interventions, or general ""treatments."" Specifically, I focus on estimating treatment effect heterogeneity in event studies with staggered adoption: panel data settings where observational units experience treatment at different points in time. I propose using highly flexible machine learning algorithms to predict counterfactuals (unobserved outcomes in absence of treatment) in those settings. With simulations, I show that my proposed method can recover nuanced effects with more accuracy and with better statistical efficiency than traditional econometric models (such as fixed effects regressions). I conclude that chapter with an application of the ML approach to real data from the Weatherization Assistance Program (WAP), which is one of the largest residential energy efficiency programs in the US. I identify how energy savings differ substantially depending on housing structure and on which types of upgrades were performed. For example, I document how complete furnace replacements are associated with significantly higher savings than furnace repairs. Also, I show that measures related to wall, attic, and foundation insulation are among the strongest contributors to energy savings. Finally, I assess measure-specific cost-effectiveness. Those results are informative for efforts to improve the allocation of program funds. The second chapter of this dissertation consists of a randomized control trial (RCT) to assess if behavioral…
author: Nogueira Meirelles De Souza, Mateus
language: en
keywords: energy economics, energy efficiency, causal inference, environmental economics, impact evaluation, machine learning, randomized control trial, observational study, applied econometrics, behavioral economics
date published: 2020-10-07T20:59:40Z
date modified: 2026-07-22T22:24:48Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/108459