Papers Containing Keywords(s): 'electricity'
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Authors
Gale Boyd - 6
Matthew Doolin - 3
Viewing papers 11 through 15 of 15
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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 PaperEnergy Intensity, Electricity Consumption, and Advanced Manufacturing Technology Usage
July 1993
Working Paper Number:
CES-93-09
This paper reports on the relationship between the usage of advanced manufacturing technologies (AMTs) and energy consumption patterns in manufacturing plants. Using data from the Survey of Manufacturing Technology and the 1987 Census of Manufactures, we model the energy intensity and the electricity intensity of plants as functions of AMT usage and plant age. The main findings are that plants which utilize AMTs are less energy intensive than plants not using AMTs but consume proportionately more electricity as a fuel source. Additionally, older plants are generally more energy intensive and rely on fossil fuels to a greater extent than younger plants.View Full Paper PDF
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Working PaperInter Fuel Substitution And Energy Technology Heterogeneity In U.S. Manufacturing
March 1993
Working Paper Number:
CES-93-05
This paper examines the causes of heterogeneity in energy technology across a large set of manufacturing plants. This paper explores how regional and intertemporal variation in energy prices, availability, and volatility influences a plant's energy technology adoption decision. Additionally, plant characteristics, such as size and energy intensity, are shown to greatly impact the energy technology adoption decision. A model of the energy technology adoption is developed and the parameters of the model are estimated using a large, plant-level dataset from the 1985 Manufacturing Energy Consumption Survey (MECS).View Full Paper PDF
