nep-ene New Economics Papers
on Energy Economics
Issue of 2006‒08‒26
three papers chosen by
Roger Fouquet
Imperial College, UK

  1. The Comovement between Fuel Prices and the General Price level in Australia By Lei Lei Song
  2. Energy-Efficient Renovation of Educational Buildings By Heike Erhorn-Kluttig; Ove Mørck

  1. By: Lei Lei Song (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)
    Abstract: This paper examines the relationship between the general price level and the relative price of fuel by measuring correlation from VAR forecast errors. The results suggest a significant positive correlation between quarterly changes in the relative price of fuel and the CPI, at least in the short to medium term from two to four years. The finding has important implications for measuring the long-term trend in inflation as relative price changes in fuel contain important information about future inflation.
    JEL: E31 E37
    Date: 2006–08
  2. By: Heike Erhorn-Kluttig; Ove Mørck
    Abstract: Case studies demonstrating energy-efficient renovation of educational buildings collected by the International Energy Agency (IEA) provide information on retrofit technologies, energy-saving approaches and ventilation strategies. Some general findings are presented here along with one case study, Egebjerg School in Denmark, which shows how natural ventilation can be incorporated into a refurbishment project.
    Keywords: environment, Denmark
    Date: 2005–06
  3. By: Rhys T. Dale; Wallace E. Tyner (Department of Agricultural Economics, College of Agriculture, Purdue University)
    Abstract: Ethanol, the common name for ethyl alcohol, is fuel grade alcohol that is predominately produced through the fermentation of simple carbohydrates by yeasts. In the United States, the carbohydrate feedstock most commonly used in the commercial production of ethanol is yellow dent corn (YDC). The use of ethanol in combustion engines emits less greenhouse gasses than its petroleum equivalent, and it is widely hoped that the increased substitution of petroleum by ethanol will reduce US dependence on imported oil and decrease greenhouse gas emissions. Production of ethanol within the United States is expected to double, from 3.4 billion gallons in 2004, to about seven billion gallons in the next five years. Two processes currently being utilized to produce ethanol from YDC are dry milling and wet milling. The wet mill process is more versatile than the dry mill process in that it produces a greater variety of products; starch, corn syrup, ethanol, Splenda, etc., which allows for the wet mill to better react to market conditions. However, the costs of construction and operation of a wet mill are much greater than those of a dry mill. If ethanol is the target product, then it can be produced at a lower cost and more efficiently in a dry mill plant than in a wet mill plant, under current economic conditions. Of the more than 70 US ethanol plants currently in production, only a few are of the wet mill variety. A descriptive engineering spreadsheet model (DM model) was developed to model the dry mill ethanol production process. This model was created to better understand the economics of the ethanol dry mill production process and how the profitability of dry mill plants is affected under different conditions. It was also developed to determine the economic and environmental costs and benefits of utilizing new and different technologies in the dry mill process. Specifically, this model was constructed to conduct an economic analysis for novel processes of obtaining greater alcohol yields in the dry mill process by conducting a secondary fermentation of sugars converted from lignocellulosics found in the dry mill co-product, distiller’s grains. This research is being conducted at Purdue University, Michigan State, Iowa State, USDA, and NCAUR under a grant from the US Department of Energy. The DM model is more technically precise, and more transparent, than other models of the dry mill process that have been constructed for similar purposes. The Tiffany and Eidman model (TE model) uses broad generalities of the dry mill process, based on the current state of production, to approximate the sensitivities of the process to changes in variables. The TE model parameters were well researched, but the model suffers from several drawbacks. The main limitations of this model are that the results are very sensitive to the input values chosen by the user. Unlike the DM model, complex manipulations, such as determining the effect of new technologies would require accurate parameter estimates using the TE model. The McAloon model [11].uses highly technical engineering software (ASPEN) that acts essentially as a “black box” in the dry mill production process. This very exact model does not allow for a more general examination of the dry mill process. Changes in the production process would necessitate precise engineering plans. Similar to the TE and McAloon models, the DM model is a spreadsheet model, but unlike the McAloon model it is completely self-contained. The DM model is a feed backward model, input requirements (corn, enzymes, chemicals, utilities, etc) are calculated based on the user entered values for annual production and process parameters. The mass flow rates, in pounds per hour were then calculated and used in estimating the size, in dimension or power, of each major piece of equipment. The cost associated with each piece of major equipment was then calculated as an exponential function of its corresponding size. Total capital costs associated with a dry mill plant were then estimated using the percentage of equipment costs method [13]. It was found that the DM model estimates of the total capital costs associated with medium to large dry mill plants (those with the capacity to produce between 10 and 100 million gallons of ethanol a year) were within 5% of total fixed costs estimated by BBI [2]. Operating costs were compared with the 2002 USDA survey results and also found to be very close [15]. A companion document, “Economic and Technical Analysis of Dry Milling: Model User’s Manual,” staff paper no 06-05, explains how the model is used to conduct analysis of dry milling alternatives.
    Keywords: Ethanol, DDGS, Dry Milling, Biochemical Process Engineering, Economic Modeling, Financing, Fermentation Process Modeling
    Date: 2006–04

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