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Replication Data for: Setting with the Sun: The impacts of renewable energy on conventional generation
No full textThese files provide code to replicate results from the paper “Setting with the Sun: The impacts of renewable energy on conventional generation” published in the Journal of the Association of Environmental and Resource Economists
Replication Code for: Building Codes Do Save Energy: Evidence from Hourly Smart-Meter Data
No full textWe provide the code necessary to replicate all of the results in the tables and figures of the article and online appendix. In addition we provide the data and code used for the replication of Levinson (2016). The README file describes our data sources and provides information for how to access them
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California Public Schools & Electrification: Financial Impacts and Policy Implications
Get PDFAs California's building decarbonization efforts move beyond traditional efficiency measures with guaranteed financial returns, state policies and funding programs must become more nuanced. Building electrification will ensure decarbonization, but the complexities of fuel substitution could have unintended consequences. This research investigates the potential financial implications of statewide electrification programs for California public schools. It models two policy pathways for the electrification of public schools in California. The first pathway models the change in school utility burden that could stem from not electrifying public schools as statewide fuel substitution drives down natural gas demand. The second pathway models school utility cost changes at current utility rates should they receive upfront electrification subsidies. This research finds that without policy changes, public schools not included in electrification funding programs could see annual utility burden increases of 975 by 2035. If schools do electrify, this research found high variability in changes to utility costs. Over half of the more than 800 whole-building school fuel substitution scenarios modeled show an increase in utility costs after electrification. California's public schools will experience increased utility burdens if left behind as the energy market transitions. Still, electrification will increase utility burdens for many schools without changes to the state's utility rate structures. This research suggests that the financial complexities of fuel substitution and the variety of energy needs across the state necessitate electrification policies that are designed with a focus on community-level equity rather than state-level equality
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Essays on Energy and Natural Resource Economics
No full textThis dissertation consists of two essays on how residential consumers respond to a range of different electricity price structures and one on the supply side of the energy sector.The first essay examines what information households respond to in their monthly energy bills and the implications of their behavioral responses to electricity pricing. Previous work has uncovered evidence that households largely respond to the average price they pay for energy in the previous billing period. In this essay, I re-examine whether households indeed pay attention solely to the average price. Using detailed hourly consumption data from over 100,000 households in Sacramento, California, I measure the impact of surpassing the first threshold of nonlinear tariff structures in a billing month on households' average daily electricity consumption in subsequent months. My empirical results illustrate that households that consumed enough to be subjected to a higher marginal price in the previous billing period reduced their electricity consumption in the succeeding billing month. This finding illustrates one of the many inefficiencies that arise from tiered rate structures: households' response to prices that are not reflecting current supply conditions but rather the household's past consumption levels.The second essay studies how households respond to Time-Of-Use (TOU) electricity prices that vary throughout the course of the day. The primary purpose of the time-varying pricing scheme is to reshape households' electricity consumption in and near peak-demand hours---more specifically, to reduce their consumption during peak hours and shift some of their consumption to off-peak hours. The existing literature presents evidence that under TOU tariff structures, residential consumers reduced their electricity consumption during peak price periods, but these reductions were insensitive to the marginal changes in peak-hour prices. In this essay, I re-examine the impact of TOU rates but with a different strategy. Rather than estimating how aggregate consumption responds to TOU rates, I decompose household electricity consumption into two distinct categories: consumption for non-temperature-control and temperature-control uses. My empirical analysis shows that households indeed responded to the magnitude of the price increase in the peak rate period; however, the response was not the same for the two consumption categories. In particular, while non-temperature-control-driven consumption during the peak hours markedly fell as the peak price increased, temperature-control-driven consumption fell prior to the peak hours, and actually increased during the peak hours, relative to the reduction in pre-peak hours, as the peak price increased. Ultimately, the differences in the responses across these two channels masked households' high price sensitivity. This also illustrates that the two types of electricity consumption evolved differently, and nonlinearly, according to daily heating degree days and the point electricity was consumed in time. These findings suggest that adopting autonomous heating control systems or augmenting additional across-day flexibility to the typical structure of TOU electricity pricing is required to maximize the benefits of the pricing scheme.The third essay develops a discrete choice dynamic programming (DCDP) framework in continuous time by formulating fracking firms' drilling decisions as an optimal stopping problem. In a recent paper, Hotelling's classic model of nonrenewable resource extraction is recast as a drilling problem to explain observations in Texas that drilling activity responds to oil prices sensitively, while oil production from existing wells does not respond. The model in this prior paper, however, cannot rationalize the empirical phenomenon that firms in North Dakota drilled wells in both low- and high-quality locations. The DCDP model uses cost shocks to rationalize the simultaneous drilling of resources with heterogeneous quality. Further, the model can be estimated empirically using microeconomic data and also solved analytically for an aggregate solution. In the limit, the model converges to the classic Hotelling model
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An Approach to Estimate Household Energy Savings from Weatherization Program
No full textThe scope of this study is to estimate the change in household energy consumption before and after the Low-Income Weatherization Program. In chapter 1, we econometrically estimated daily energy savings by detailed weatherization types and hourly energy savings. We use California ISO’s wholesale electric price to estimate the savings at electric market perspective. We conducted robustness tests and confirmed that our approach could be used to estimate the impact of weatherization. We compare average daily energy savings from treated households to control to evaluate inconsistencies. Calculated savings were 1.9 kWh to 2 kWh daily on average. We find that weatherized homes in the sample reduced electricity consumption during afternoon peak hours when the cost of supplying the energy is high. Electricity savings were also greater when ambient temperature was high.In chapter 2, we present three steps analysis to understand weatherization program participation and savings heterogeneity. Our approach is to use households’ prior energy consumption and home characteristics. We estimated household specific usage pattern from pre-weatherization energy use. We found that households with greater temperature sensitivity (i.e., had higher energy demand on hot days), was associated with greater reduction in energy consumption after the weatherization. This study contributes to understanding how households’ prior energy use and home characteristics can explain heterogeneity in household-level savings. Our study also shows not necessarily that households with greater saving potential are opting into the program. Although the direction of the effect is as expected, sensitivity to temperature was not a significant indicator for predicted probability of participation. Other home characteristics appears to explain participation more. There are also non-participating and non-responding households who would have saved energy.
In chapter 3, we study how targeting, and the scale of energy efficiency programs affect the energy savings achieved by energy efficiency program. Our study tries to further close the gap between literatures on targeting and opt-in decisions to energy efficiency program. We find that households with highest predicted probability of participation is driven by variables such as home year built, but household with demand sensitive to high temperatures has most potential to save. Our results implicate that allowing the households to select into the program without targeting (opting-in) or relying on the attributes that drives the choice to participate does not achieve the highest per household energy savings. We find that energy savings from weatherization could be increased if policymakers and utilities could effectively target treatment towards households whose energy use is closely tied to outdoor temperature. Our findings also indicates that the difference between the savings achieved by targeting and the existing opt-in program design diminishes as the program scales up
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Three Essays on Utility Policy in the Face of Climate Change
Get PDFThis dissertation uses econometric methods to explore the interaction between utility policy and climate change on consumers. Energy and water utilities play an important role in all consumers' lives. As such, the decisions that regulators make with respect to these services can have large ripple effects through the economy. It is important for regulators to understand the full impact of their policy decisions. The difficulty of these decisions is further compounded by climate change. Both greater variation in temperatures and changes to historical precipitation patterns will create difficult trade-offs for policy makers. I leverage multiple large data sets in order to address these trade offs. The first essay looks at the impact of extreme heat on consumer credit scores. A change in a consumer's credit score can have a wide ranging influence on their financial opportunities. Household energy bills are the most obvious example of how extreme temperatures can impact credit scores. While we stay agnostic about the exact causal mechanism, the results of this analysis reflect patterns important to recognize in utility policy. We take a random nation-wide sample of credit score data from 500,000 consumers over a nearly 10 year period and pair it with ZIP code level weather data to estimate the impact of unexpected temperature fluctuations on consumer credit scores. These temperature spikes are unlikely to be anticipated by consumers since they fall outside of weather norms for the quarter and ZIP code. As a secondary analysis, we use a distributed lag model to see how the effect of temperatures influences credit scores over time. We find that high temperatures lead to modest, but tangible, decreases in credit scores on average. We also find that these effects linger, but dissipate over the course of a year. The average effect of this decrease is relatively modest, however our analysis does not preclude larger heterogeneous effects. To address these outcomes, lawmakers can create policies that target the immediate short-term financial difficulties and long-term impacts to consumer credit following extreme temperature spikes. The second essay measures the outcomes and cost-effectiveness of four Californian water demand management (WDM) programs. WDM programs aim to reduce water consumption, and by doing so, also reduce energy use and greenhouse gas (GHG) emissions. Energy is consumed by water utilities to pump, convey, distribute, and treat water and by consumers to heat water. WDM programs are widely used, but the outcomes predicted by the governments and utilities implementing them are sometimes based on optimistic assumptions about the efficacy of the upgraded appliances. Rather than relying on ex ante deemed savings approaches, we present ex post empirical estimates of the water and energy savings achieved by four urban water retrofit programs. We start by estimating the water and energy savings incurred by the programs to evaluate their cost effectiveness, then look at the distinct cost savings for households, utilities, and government agencies. We find that these programs often saved much less water and energy than was estimated by deemed savings methods. Therefore, the programs where only sometimes cost effective on a household level and not cost effective for utilities based on internal cost savings. This result largely stems from how water is priced in California. Additionally, only one of the four programs was cost effective in reducing energy usage and GHG emissions as it reduced the amount of water heated by consumers while the other programs focused on reducing cold water consumption. The third essay summarizes various methods for evaluating water and energy savings programs. Government agencies, utilities, and community organizations have used water and energy savings programs in order to conserve resources and delay capital investments for many years. These programs are often evaluated ex ante or ex post in order to determine their effectiveness. Often, these evaluations are done internally by the same organizations which administer the programs. As discussed in the second essay, these methods can sometimes rely on strong assumptions about consumer behavior and the efficacy of upgraded fixtures. This paper explains the assumptions made by various popular evaluation methods and describes in which settings these methods can be appropriately applied and what data they require. This paper serves as an aid to program administrators so that they can better plan, execute, and evaluate water and energy saving programs
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Dynamically Regulating Emissions of Stock Pollutants
Get PDFAir pollution has plagued cities around the world for years. Major metropolitan areas often install air pollution regulations that vary in stringency over time. Regulations on polluters are lax on days when the air quality is good. For days with high ambient air pollution, governments impose stricter measures to avoid exacerbating the already poor air quality. This study focuses specifically on the Heavy Air Pollution Emergency Plan (HAPEP) of Chengdu, China. During the winter months, Chengdu frequently faces periods of sustained, high levels of ambient particulate matter (PM). Importantly, these high levels of ambient PM are not ruled by temporary increases in the flow of PM being emitted each day. Instead, it is driven largely by the weather conditions. For example, during periods of stagnant wind conditions, the stock of PM grows in the air. This accumulation process continues until the weather conditions change -- i.e., the wind picks up -- and the stock of PM in the air is cleared out. This pollution pattern is representative of the cities which are in a closed basin terrain. The Chengdu HAPEP requires monitoring short-term PM forecasts. If the predicted PM exceeds certain concentration and duration thresholds, an air pollution alert is issued. Subsequently, a set of measures intended to lessen air pollution are imposed, including driving restrictions and production suspensions.In the first chapter, I develop a dynamic optimization framework taking into account the characteristics of PM pollution by directly modeling the stock nature of pollutants and the cleanup process of pollutants. The model leads to an important conclusion: the optimal amount of pollution emission should always increase over time if there is no pollutant dispersion. Although in reality pollution dissipation is never absolutely zero, this conclusion indicates an incentive to delay the pollution for social welfare improvement. However, the variations in expected pollution dissipation should also be incorporated when deciding the optimal action.In the second chapter, I empirically estimate the dynamic process of pollution dissipation by identifying how daily weather conditions drive the change in ambient PM levels from one day to the next. I also show that the weather conditions in the near future can be forecasted with high accuracy. By altering the timing of historical interventions according to both existing conditions and expected upcoming weather patterns, a 12.1% more PM pollution reduction and a 25.5% more bronchitis hospital visit reduction can be achieved within a period in 2018.In the third chapter, I estimate the health effects of PM pollution. I find that pollution exposures up to six days ago are associated with contemporaneous bronchitis hospitalization. I also assess the curvature of the response function of respiratory hospitalization to PM pollution. I fail to find any convincing evidence that supports the presence of nonlinearity. One caveat of this conclusion is that I only consider possible non-linear effects of the contemporaneous pollution concentration but ignore the possible non-linear effects of the lagged pollution concentration, due to the complexity of the specification and the difficulty in identification if both of them are incorporated. Although this simplification might introduce a bias in an unclear way, it is of less concern since prediction instead of specific point estimates is the focus of this analysis
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Essays on Tax Policy Assessment Considering Electric Vehicle Growth
Get PDFIn recent years, electric vehicles (EVs) have continued to grow. According to one forecast, EV share among registered cars will reach about 7% within a decade. The high share of EVs will accelerate the deficit in government revenues from gasoline taxes. Therefore, a vehicle miles traveled (VMT) tax is being discussed as an alternative to the automobile fuel tax. The government is considering the policy shift from a gasoline tax to a VMT tax, that affects almost populations, but few studies have evaluated the new policy while taking into account the growth of EVs. This paper bridges the gap between assessing the VMT tax and considering increasing EV penetration.Chapter 1 discusses the distributional effect of a VMT tax when considering the penetration of electric vehicles. It studies the impact of a VMT tax introduction on vehicle choice and utilization in a two-period model that links two decisions on vehicle choice and subsequent driving. Also, it examines the consumer surplus changes in the short term due to policy shifts from a gasoline tax to a VMT tax according to vehicle types, fuel economy, and household attributes. The results show that the revenue-neutral VMT tax would increase consumer surplus by a modest $2 per vehicle per year. It also suggests that even if the government imposes the same federal VMT tax rate, each state could be a winner or loser depending on the average MPG, miles driven, and VMT elasticity. Ultimately, the results show that shifting policy towards a VMT tax becomes more efficient as the penetration of EVs grows. When the EV share reaches 5%, the incremental EV share generates an additional surplus equal to twice the surplus from adopting the revenue-neutral VMT tax. It can also reduce revenue and expenditure discrepancies without changing the tax rate.Chapter 2 explores a VMT tax to increase government revenues as electric vehicle penetration grows and the long-term impact of that tax on consumer surplus. By taking the estimates from the two-period structural model for vehicle choice and utilization, it conducts a counterfactual analysis to increase government revenue by 30%. Based on the results, the following sensitivity analyses target the cases where government revenue increases from 30% to 100%, and the share of electric vehicles increases from 3% to 10%. As a result, the VMT tax generates a small but positive net consumer surplus compared to the gasoline tax when moderately increasing government revenues. Net consumer surplus from the VMT tax rises incrementally as the growth rate in government tax revenue increases. If the proportion of electric vehicles rises, a relatively more significant additional net surplus will occur. It implies that when raising revenue, the tax rate significantly affects consumer surplus more than the tax type. However, as the proportion of electric vehicles increases, tax type also becomes important in determining a tax rate and changing the consumer surplus.Chapter 3 discusses the second-best optimal taxes by the three tax types considering the expansion of EVs in the US: gasoline tax, VMT tax, and combined tax of gas and VMT taxes. According to the model developed in this paper, the second-best optimal tax rates of flat VMT and flat VMT+gas taxes, converted in cents per gallon, are higher than the second-best optimal gas tax. However, both rates can improve social welfare more than the gas tax. The flat VMT+gas tax generates a significant welfare benefit as much as the VMT tax at the second-best optimal rates. In the early stage of introducing electric vehicles, among the best revenue-neutral taxes to maintain the current government revenue, only the flat VMT+gas tax provides positive welfare benefits. As long as the EV share does not exceed 40%, the flat VMT+gas tax is more favorable than the flat VMT tax. The EV VMT+gas tax is more advantageous regardless of the EV share than the VMT tax types capped with the current government revenue.These three papers contribute to understanding of how the share of electric vehicles affects the surplus from the policy shift from a gasoline tax to a VMT tax. The cost of switching tax policies makes policymakers hesitant to adopt the new tax policy. However, the results of this dissertation provide evidence that adopting the new tax policy can benefit both private and public alike if sustained EV growth is to be expected
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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