Papers Containing Keywords(s): 'utility'
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Viewing papers 1 through 10 of 10
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Working PaperThe Impact of Industrial Opt-Out from Utility Sponsored Energy Efficiency Programs
October 2023
Working Paper Number:
CES-23-52
Industry accounts for one-third of energy consumption in the US. Studies suggest that energy efficiency opportunities represent a potential energy resource for regulated utilities and have resulted in rate of return regulated demand-side management (DSM) and energy efficiency (EE) programs. However, many large customers are allowed to self-direct or opt-out. In the Carolinas (NC and SC), over half of industrial and large commercial customers have selected to opt out. Although these customers claim they invest in EE improvements when it is economic and cost-effective to do so, there is no mechanism to validate whether they actually achieved energy savings. This project examines the industrial energy efficiency between the program participants and non participants in the Carolinas by utilizing the non-public Census of Manufacturing data and the public list of firms that have chosen to opt out. We compare the relative energy efficiency between the stay-in and opt-out plants. The t-test results suggest opt-out plants are less efficient. However, the opt-out decisions are not random; large plants or plants belonging to large firms are more likely to opt out, possibly because they have more information and resources. We conduct a propensity score matching method to account for factors that could affect the opt-out decisions. We find that the opt-out plants perform at least as well or slightly better than the stay-in plants. The relative performance of the opt-out firms suggest that they may not need utility program resources to obtain similar levels of efficiency from the stay-in group.View Full Paper PDF
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Working PaperThe U.S. Manufacturing Sector's Response to Higher Electricity Prices: Evidence from State-Level Renewable Portfolio Standards
October 2022
Working Paper Number:
CES-22-47
While several papers examine the effects of renewable portfolio standards (RPS) on electricity prices, they mainly rely on state-level data and there has been little research on how RPS policies affect manufacturing activity via their effect on electricity prices. Using plant-level data for the entire U.S. manufacturing sector and all electric utilities from 1992 ' 2015, we jointly estimate the effect of RPS adoption and stringency on plant-level electricity prices and production decisions. To ensure that our results are not sensitive to possible pre-existing differences across manufacturing plants in RPS and non-RPS states, we implement coarsened exact covariate matching. Our results suggest that electricity prices for plants in RPS states averaged about 2% higher than in non-RPS states, notably lower than prior estimates based on state-level data. In response to these higher electricity prices, we estimate that plant electricity usage declined by 1.2% for all plants and 1.8% for energy-intensive plants, broadly consistent with published estimates of the elasticity of electricity demand for industrial users. We find smaller declines in output, employment, and hours worked (relative to the decline in electricity use). Finally, several key RPS policy design features that vary substantially from state-to-state produce heterogeneous effects on plant-level electricity prices.View Full Paper PDF
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Working PaperNeighborhood Income and Material Hardship in the United States
January 2022
Working Paper Number:
CES-22-01
U.S. households face a number of economic challenges that affect their well-being. In this analysis we focus on the extent to which neighborhood economic conditions contribute to hardship. Specifically, using data from the 2008 and 2014 Survey of Income and Program Participation panel surveys and logistic regression, we analyze the extent to which neighborhoods income levels affect the likelihood of experiencing seven types of hardships, including trouble paying bills, medical need, food insecurity, housing hardship, ownership of basic consumer durables, neighborhood problems, and fear of crime. We find strong bivariate relationships between neighborhood income and all hardships, but for most hardships these are explained by other household characteristics, such as household income and education. However, neighborhood income retains a strong association with two hardships in particular even when controlling for a variety of other household characteristics: neighborhood conditions (such as the presence of trash and litter) and fear of crime. Our study highlights the importance of examining multiple measures when assessing well-being, and our findings are consistent with the notion that collective socialization and community-level structural features affect the likelihood that households experience deleterious neighborhood conditions and a fear of crime.View Full Paper PDF
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Working PaperEnergy Prices, Pass-Through, and Incidence in U.S. Manufacturing*
January 2016
Working Paper Number:
CES-16-27
This paper studies how increases in energy input costs for production are split between consumers and producers via changes in product prices (i.e., pass-through). We show that in markets characterized by imperfect competition, marginal cost pass-through, a demand elasticity, and a price-cost markup are suffcient to characterize the relative change in welfare between producers and consumers due to a change in input costs. We and that increases in energy prices lead to higher plant-level marginal costs and output prices but lower markups. This suggests that marginal cost pass-through is incomplete, with estimates centered around 0.7. Our confidence intervals reject both zero pass-through and complete pass-through. We and heterogeneous incidence of changes in input prices across industries, with consumers bearing a smaller share of the burden than standards methods suggest.View Full Paper PDF
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Working PaperWater Use and Conservation in Manufacturing: Evidence from U.S. Microdata
June 2015
Working Paper Number:
CES-15-16R
Water can be a scarce resource, particularly in certain places at certain times. Understanding both water use and conservation efforts can help ensure that limited supplies can meet the demands of a growing population and economy. This paper examines water use and recirculation in the U.S. manufacturing sector, using newly recovered microdata from the Survey of Water Use in Manufacturing, merged with establishment-level data from the Annual Survey of Manufactures and the Census of Manufactures. Results suggest that water use per unit of output is largest for larger establishments, in part because larger establishments use water for more purposes. Larger establishments are also found to recirculate water more ' satisfying demand (water use) without necessarily increasing water intake. Various costs also appear to play a role in water recirculation. In particular, the water circulation rate is found to be higher when water is purchased from a utility. Relatively low (internal) prices for self-supplied water could suppress the incentive to invest in recirculation. Meanwhile, establishments with higher per-gallon intake treatment costs also recirculate more, as might be expected. The cost associated with water discharge ' due to regulation or otherwise ' also increases circulation rates. The aridity of a locale is found to have little effect on circulation rates.View Full Paper PDF
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Working PaperThe Impact of Heterogeneous NOx Regulations on Distributed Electricity Generation in U.S. Manufacturing
April 2015
Working Paper Number:
CES-15-12
The US EPA's command-and-control NOx policies of the early 1990s are associated with a 3.1 percentage point reduction in the likelihood of manufacturing plants vertically integrating the electricity generation process. During the same period California adopted a cap-and-trade program for NOx emissions that resulted in no significant impact on distributed electricity generation in manufacturing. These results suggest that traditional command-and-control approaches to air pollution may exacerbate other market failures such as the energy efficiency gap, because distributed generation is generally recognized as a more energy efficient means of producing electricityView Full Paper PDF
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Working PaperThe Effect of Power Plants on Local Housing Values and Rents: Evidence from Restricted Census Microdata
July 2008
Working Paper Number:
CES-08-19
Current trends in electricity consumption imply that hundreds of new fossil-fuel power plants will be built in the United States over the next several decades. Power plant siting has become increasingly contentious, in part because power plants are a source of numerous negative local externalities including elevated levels of air pollution, haze, noise and traffic. Policymakers attempt to take these local disamenities into account when siting facilities, but little reliable evidence is available about their quantitative importance. This paper examines neighborhoods in the United States where power plants were opened during the 1990s using household-level data from a restricted version of the U.S. decennial census. Compared to neighborhoods farther away, housing values and rents decreased by 3-5% between 1990 and 2000 in neighborhoods near sites. Estimates of household marginal willingness-to-pay to avoid power plants are reported separately for natural gas and other types of plants, large plants and small plants, base load plants and peaker plants, and upwind and downwind households.View Full Paper PDF
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Working PaperElectricity Pricing to U.S. Manufacturing Plants, 1963-2000
October 2007
Working Paper Number:
CES-07-28
We construct a large customer-level database and use it to study electricity pricing patterns from 1963 to 2000. The data show tremendous cross-sectional dispersion in the electricity prices paid by manufacturing plants, reflecting spatial price differences and quantity discounts. Price dispersion declined sharply between 1967 and 1977 because of erosion in quantity discounts. To estimate the role of cost factors and markups in quantity discounts, we exploit differences among utilities in the purchases distribution of their customers. The estimation results reveal that supply costs per watt-hour decline by more than half over the range of customer-level purchases in the data, regardless of time period. Prior to the mid 1970s, marginal price and marginal cost schedules with respect to annual purchase quantity are remarkably similar, in line with efficient pricing. In later years, marginal supply costs exceed marginal prices for smaller manufacturing customers by 10% or more. The evidence provides no support for a standard Ramsey-pricing interpretation of quantity discounts on the margin we study. Spatial dispersion in retail electricity prices among states, counties and utility service territories is large, rises over time for smaller purchasers, and does not diminish as wholesale power markets expand in the 1990s.View Full Paper PDF
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Working PaperEstimating the Distribution of Plant-Level Manufacturing Energy Efficiency with Stochastic Frontier Regression
March 2007
Working Paper Number:
CES-07-07
A feature commonly used to distinguish between parametric/statistical models and engineering models is that engineering models explicitly represent best practice technologies while the parametric/statistical models are typically based on average practice. Measures of energy intensity based on average practice are less useful in the corporate management of energy or for public policy goal setting. In the context of company or plant level energy management, it is more useful to have a measure of energy intensity capable of representing where a company or plant lies within a distribution of performance. In other words, is the performance close (or far) from the industry best practice? This paper presents a parametric/statistical approach that can be used to measure best practice, thereby providing a measure of the difference, or 'efficiency gap' at a plant, company or overall industry level. The approach requires plant level data and applies a stochastic frontier regression analysis to energy use. Stochastic frontier regression analysis separates the energy intensity into three components, systematic effects, inefficiency, and statistical (random) error. The stochastic frontier can be viewed as a sub-vector input distance function. One advantage of this approach is that physical product mix can be included in the distance function, avoiding the problem of aggregating output to define a single energy/output ratio to measure energy intensity. The paper outlines the methods and gives an example of the analysis conducted for a non-public micro-dataset of wet corn refining plants.View Full Paper PDF
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Working PaperIdentifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application
January 2007
Working Paper Number:
CES-07-03
Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naive estimates of both individual and group effects. This paper proposes a non-parametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in the canonical vertical sorting model of Epple and Platt (1998), that there is a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a non-parametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. This control function converts the problem to a model with one unobservable so that traditional instrumental variables solutions may be applied. In our application, we instrument for each individual.s observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals. The neighborhood effects model is estimated using confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks.View Full Paper PDF
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Total Results: 10
