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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.



Energy and Agricultural Commodity Markets Interaction: An Analysis of Crude Oil, Natural Gas, Corn, Soybean, and Ethanol Prices

Song-Zan Chiou-Wei, Sheng-Hung Chen, and Zhen Zhu

Year: 2019
Volume: Volume 40
Number: Number 2
DOI: 10.5547/01956574.40.2.schi
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Abstract:
This paper broadens the analysis of the interactions between energy and agricultural commodity markets by focusing on five major commodities: oil, natural gas, soybean, corn, and ethanol, and intends to provide more updated information regarding the degree of the connection among the markets. We estimate a DCC-MGARCH model to accommodate the dynamic and changing degree of interconnections among the five markets with respect to price levels and price volatilities. In doing so, we control for additional economic variables including oil and gas inventories, interest rate spread, exchange rate and economic activities. Our empirical evidence suggests that there are varying degrees of interconnections among the energy and agricultural commodities in the long term as well as the short term, but the interactions among the agricultural commodities and ethanol are generally higher than the interactions between oil and gas and agricultural markets. In addition, we reveal some weak evidence of commodity market speculation. The estimated conditional volatility correlations suggest that volatility spillovers among the markets were time dependent and dynamic.





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