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The Impact of Energy Prices on Technology Choice in the United States Steel Industry

Gale A. Boyd and Stephen H. Karlson

Year: 1993
Volume: Volume 14
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol14-No2-3
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Abstract:
In the last 30 years, U.S. steel producers have replaced their aging open hearth steel furnaces with basic oxygen (BOF) or large electric are furnaces (LEF). This choice of technology creates the opportunity to substitute electricity for fossil fuels. We extend earlier research to investigate whether energy prices affect this type of technology adoption. The econometric model uses the "seemingly unrelated Tobit" method to capture the effects of the industry's experience with both technologies, technical change, and potential cost reductions, as well as energy prices, on adoption. Men we include the prices of electricity and coking coal as explanatory variables, the four energy price coefficients have the signs predicted by the law of demand, but the magnitude of the coefficients is such that the non-price terms are more important, e.g. a 50% increase in electricity prices would delay LEF adoption by only 12 days. Our results suggest that the adoption of LEF represents a form of major process technical change (factor biased - electricity using), rather than a price-induced technological innovation.



A Note on the Fisher Ideal Index Decomposition for Structural Change in Energy Intensity

Gale A. Boyd and Joseph M. Roop

Year: 2004
Volume: Volume 25
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol25-No1-5
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Abstract:
Index numbers have been used to decompose aggregate trends in energy intensity, i.e., the ratio of energy use to activity. By making a direct appeal to the theory underlying price index numbers used by the energy decomposition literature, this note proposes the chain weighted Fisher Ideal Index as a formula that solves the `residual problem.' The connection to index number theory also allows us to illustrate that the measures of activity used to define energy intensity need not be additive across the sectors that are involved in the decomposition. We give an empirical example using recent U.S. manufacturing data of the Fisher Ideal Index, compared to the T"rnqvist Divisia index, a popular index in the energy literature.



Estimating Plant Level Energy Efficiency with a Stochastic Frontier

Gale A. Boyd

Year: 2008
Volume: Volume 29
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol29-No2-2
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Abstract:
A common distinguishing feature of engineering models is that they explicitly represent best practice technologies, while parametric/statistical models represent average practice. It is more useful to energy management or goal setting to have a measure of energy performance capable of answering the question, �How close is observed performance from the industry best practice?� This paper presents a parametric/statistical approach to measure best practice. The results show how well a plant performs relative to the industry. A stochastic frontier regression analysis is used to model plant level energy use, separating energy into systematic effects, inefficiency, and random error. One advantage is that physical product mix can be included, 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 milling plants.





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