A model for Long-term Industrial Energy Forecasting (LIEF) [report]

M. Ross, R. Hwang
1992 unpublished
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more » ... rights. Reference here.ta to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or The Regents of the University of California and shaU not be und for advertising or product endorsement purposes. "Ibis report has been reproduced directly from the best available copy. Available to DOE and DOE Contr_,_tors from th_, Office of Scientific and Technical [nformation P.O. Box 62, Oak Ridge, TN 37831 Prices available from (615) 576-&_01, FTS 626-8401 Available to the public' from the National Technical InformaLion _ervice ,w purpose of this repo_ is to establish the content and structural validity of the model, and to provide estimates for ,the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector." Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: 1) trends in total production; 2) sectoral or structural shift, that is, changes in the mix of industrial output from energyintensive to energy non-i3tensive sectors; and 3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. Proper description of production activities is a key to reasonable forecasting of industrial energy use. Sectoral shifts and changes in real energy intensity can only be properly characterized if careful attention is paid to the kind of sectoral disaggregafion (Section II) as well as to data series (Section IV). lt is often more important to the forecast than the description of efficiency improvement. Disaggregation into 2-digit Standard Industrial Classifications (SICs) is not satisfactory for long-term forecasting, neither for energyintensive industries nor for energy-non-intensive industries. Section II describes a new approach. This requires, of course, some additional information or assumptions, beyond tha_Ifor the 2-digit sectors. " The long-term, real energy intensity forecasting technique proposed here rests on the concept of a hierarchy of industrial decision making: 1) choice of fundamental production , process, which is autonomous in the sense that it is not sensitive to energy prices, 2) choice of energy-related technologies which is sensitive to energy price, and 3) operational decisions. It is assumed for long-term forecasts that operational decisions, for example the important choices made during recessions, are not of interest. Thus, the modeling effort ¢r
doi:10.2172/10129664 fatcat:z3uqqfbmxbbmxghjzqtx2zqmpa