Outlook for Biomass Ethanol Production and Demand

[1] D. Hanson, “MTBE: Villain or Victim,” Chemical and Engineering News (October 18, 1999), p. 49.

[2] Energy Information Administration, The National Energy Modeling System: An Overview, DOE/EIA-0581 (Washington, DC, May 1998).

[3] U.S. Department of Energy, Office of Fuels Development, Biofuels Program, “History of Biofuels,” web site www.ott.doe.gov/ biofuels/history.html.

[4] D.V. Hunt, The Gasohol Handbook (New York, NY: Industrial Press, 1981).

[5] D.V. Hunt, The Gasohol Handbook (New York, NY: Industrial Press, 1981).

[6] U.S. Department of Energy, Office of Fuels Development, Biofuels Program, “History of Biofuels,” web site www.ott.doe.gov/ biofuels/history.html.

[7] D.V. Hunt, The Gasohol Handbook (New York, NY: Industrial Press, 1981).

[8] U.S. National Alcohol Fuels Commission, Fuel Alcohol: An Energy Alternative for the 1980’s (Washington, DC, 1981).

[9] Renewable Fuels Association, Ethanol Industry Outlook 1999 and Beyond (Washington, DC, 1999).

[10] B. Kelly, “Alternative Fuels,” National Petroleum News, Vol. 90, No. 12 (November 1998), pp. 44-50.

[11] “Ethanol-Diesel Blend Can Expand Market,” Successful Farming, Vol. 97, No. 1 (January 1999), p. 48.

[12] K. Greenberg, “Ethanol Manufacturers Remain Confident,” Chemical Market Reporter, Vol. 255, No. 21 (May 24, 1999), p. 4.

[13] Downstream Alternatives, Inc., The Use of Ethanol in California Clean Burning Gasoline: Ethanol Supply/Demand and Logistics (Bremen, IN, May 1999).

[14] Energy Information Administration, Renewable Energy Annual 1996, DOE/EIA-0603(96) (Washington, DC, March 1997).

[15] National Renewable Energy Laboratory, “The Cost of Cellulase Enzymes,” in Bioethanol From the Corn Industry, DOE/GO-10097-577 (Golden, CO, May 1998).

[16] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[17] R. Wooley, M. Ruth, D. Glassner, and J. Sheehan, “Process Design and Costing of Bioethanol Technology: A Tool for Determining the Status and Direction of Research and Development,” Biotechnology Progress, Vol. 15, No. 5 (September-October 1999), pp. 794-803.

[18] M. McCoy, “Biomass Ethanol Inches Forward,” Chemical And Engineering News (December 7, 1998), p. 29.

[19] D.V. Hunt, The Gasohol Handbook (New York, NY: Industrial Press, 1981).

[20] U.S. Department of Energy, Office of Fuels Development, Biofuels Program, “History of Biofuels,” web site www.ott.doe.gov/ biofuels/history.html.

[21] C. Cooper et al., “A Renewed Boost for Ethanol,” Chemical Engineering, Vol. 106, No. 2 (February 1999), p. 35.

[22] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[23] C. Cooper et al., “A Renewed Boost for Ethanol,” Chemical Engineering, Vol. 106, No. 2 (February 1999), p. 35.

[24] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[25] C. Cooper et al., “A Renewed Boost for Ethanol,” Chemical Engineering, Vol. 106, No. 2 (February 1999), p. 35.

[26] M. McCoy, “Biomass Ethanol Inches Forward,” Chemical And Engineering News (December 7, 1998), p. 29.

[27] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[28] C. Keplinger, “55 Million Gallons-Per-Year of Ethanol Capacity On-Line in CA,” Oxy-Fuel News, Vol. 11, No. 32 (August 23, 1999), p. 1.

[29] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[30] “Corn Stover to Ethanol—A Plan for Commercializing Collection,” Biofuels News, Vol. 2, No. 2 (Spring 1999), p. 3.

[31] M. Wang, C. Saricks, and D. Santini, Effects of Fuel Ethanol Use on Fuel-Cycle Energy and Greenhouse Gas Emissions, ANL/ESD-38 (Argonne, IL: Argonne National Laboratory, Center for Transportation Research, January 1999).

[32] M. Wang, C. Saricks, and D. Santini, Effects of Fuel Ethanol Use on Fuel-Cycle Energy and Greenhouse Gas Emissions, ANL/ESD-38 (Argonne, IL: Argonne National Laboratory, Center for Transportation Research, January 1999).

[33] Includes a small amount of ethanol from cheese whey, wheat starch, and potato and brewery waste.

[34] B. Blackburn et al., Evaluation of Biomass-to-Ethanol Fuel Potential in California, Draft Report P500-99-011 (Sacramento, CA: California Energy Commission August 1999).

[35] Energy Information Administration, Annual Energy Outlook 2000, DOE/EIA-0383(2000) (Washington, DC, December 1999).

[36] U.S. Department of Agriculture, USDA Agricultural Baseline Projections to 2008, Staff Report WAOB-99-1 (Washington, DC, February 1999).

[37] M. Price et al., The Impact of Increased Corn Demand for Ethanol in Planted Cropland (Washington, DC: U.S. Department of Agriculture, 1998).

[38] Energy Information Administration, Renewable Fuels Module of the National Energy Modeling System, Model Documentation 2000, DOE/EIA-M069(2000) (Washington, DC, January 2000).

[39] U.S. Department of Agriculture, Forest Services of the United States, 1992, General Technical Report RM-234 (Washington, DC, June 1994).

[40] Antares Group, Inc., Biomass Residue Supply Curves for the U.S., Report Prepared for the National Renewable Energy Laboratory (Landover, MD, November 1998).

[41] M.E. Walsh, R.L. Perlack, D.A. Becker, A. Turhollow, and R.L. Graham, Evolution of the Fuel Ethanol Industry: Feedstock Availability and Price, Draft Report (Oak Ridge, TN: Oak Ridge National Laboratory,  April 1998).

[42] R.L. Graham, L.J. Allison, and D.A. Becker, “ORECCL—The Oak Ridge Energy Crop County Level Database,” in Proceedings, BIOENERGY ‘96 (Nashville, TN, September 15-20, 1996).

[43] R. Wooley, M. Ruth, D. Glassner, and J. Sheehan, “Process Design and Costing of Bioethanol Technology: A Tool for Determining the Status and Direction of Research and Development,” Biotechnology Progress, Vol. 15, No. 5 (September-October 1999), pp. 794-803.

[44] U.S. Department of Agriculture, Ethanol: Economic and Policy Tradeoffs, Agricultural Economic Report No. 585 (Washington, DC, 1988).

[45] M. Wang, C. Saricks, and M. Wu, Fuel-Cycle Fossil Energy Use and Greenhouse Gas Emissions of Fuel Ethanol Produced from U.S. Midwest Corn, Sponsor Report to the Illinois Department of Commerce and Community Affairs (Argonne, IL: Argonne National Laboratory, Center for Transportation Research, December 1997).

[46] R. Wooley, M. Ruth, D. Glassner, and J. Sheehan, “Process Design and Costing of Bioethanol Technology: A Tool for Determining the Status and Direction of Research and Development,” Biotechnology Progress, Vol. 15, No. 5 (September-October 1999), pp. 794-803.

[47] National Renewable Energy Laboratory, Bioethanol Multi-Year Technical Plan, Preliminary Draft (Golden, CO,  July 1999).

[48] E. Mansfield, “Technical Change and the Rate of Imitation,” Econometrica, Vol. 29, No. 4 (1961), pp. 741-765.

[49] A.W. Blackman, “The Market Dynamics of Technological Substitution,” Technological Forecasting and Social Change, Vol. 6 (1974), pp. 41-63.

The Electric Transmission Network: A Multi-Region Analysis

[1]  Energy Information Administration, “An Exploration of Network Modeling: The Case for NEPOOL,” Issues in Midterm Analysis and Forecasting 1998, DOE/EIA-0607(98) (Washington, DC, July 1998), web site www.eia.doe.gov/oiaf/issues98/modtech.html.

[2]  Strictly, out-of-order dispatching refers to operating a generator whose costs of production exceed those of another idle generator.

[3]  As with the events of July 6 and July 19, 1999, in the eastern half of the Pennsylvania-Jersey-Maryland (PJM) interconnection. Voltages were severely reduced because of inadequate incentives for generators to produce reactive power. See U.S. Department of Energy, Report of the U.S. Department of Energy’s Power Outage Study Team (Washington, DC, March 2000), pp. 5 and 10.

[4]  Buses represent points where major pieces of electrical equipment connect to the grid or where major transmission lines meet. In the model described herein, buses represent any point where power flow equations must be balanced.

[5]  The analysis accounts for alternating current indirectly by checking to assure that voltage levels are maintained for the power flow solution. Because of the detailed level of this analysis, it was not possible to include alternating current explicitly due to the computational difficulties that occur when both of the components of alternating current (real and reactive power) are modeled.

[6]  For a description of the EMM see Energy Information Administration, The Electricity Market Module of the National Energy Modeling System: Model Documentation Report, DOE/EIA-M068(2000) (Washington, DC, January 2000), web site ftp://ftp.eia.doe.gov/pub/pdf/ model.docs/m068(2000).pdf. For an overview of NEMS see Energy Information Administration, The National Energy Modeling System: An Overview 2000, DOE/EIA-0581(2000) (Washington, DC, March 2000), web site www.eia.doe.gov/oiaf/aeo/overview/index.html.

[7]  Energy Information Administration, The Electricity Market Module of the National Energy Modeling System: Model Documentation Report, DOE/EIA-M068(2000) (Washington, DC, January 2000).

[8]  The regions used in this analysis are part of the NERC regions that were formed to assure the reliability of the electricity transmission network. One of the NERC regions is the Northeast Power Coordinating Council. NYPP and NEPOOL are subregions of this Council.

[9]  “Equivalencing” is a method used to reduce computations when analyzing electrical networks. It involves replacing a network with a simplified representation that has the same electrical properties as the original network.

[10]  The distribution network, the system of lower voltage lines (usually less than 69 kilovolts) that distribute power locally, is not included because it imposes computational complexity and offers little additional information on the interregional transmission network.

[11]  Federal Energy Regulatory Commission, Form FERC 715, “Annual Transmission Planning and Evaluation Report,” case nepp97s.raw.

[12]  Significant Canadian imports are also delivered to both NYPP and ECAR.

[13]  For June 15, 2000, ISO New England reported Canadian capacity deliveries of 2,508 megawatts, in order to meet a peak load of 16,675 megawatts. See web site www.iso-ne.com/power_system/morning_report_external.html.

[14]  The assumption of constant fuel costs per unit output between minimum and maximum operating levels is an approximation. Operating costs are U-shaped, that is, they are relatively high at the extremes of the operating range and lower in the region between the extreme points.

[15]  Industrial customers, however, are frequently forced to adhere to strict reactive power constraints and are charged explicitly for their inability to do so.

[16]  Some configurations of generator outputs will meet real power demands but fail to provide enough reactive power to deliver useable power: voltages are either too high or too low. The procedure used here weeds out such seemingly feasible solutions. See the 1998 EIA study for a discussion of the consequences of ignoring the effects of reactive power in trade with Canada.

[17]  For example, respondents to Form FERC 715 planned on a peak load for MAAC of 47,687 megawatts. Actual peak load for summer 1997, according to the PJM Independent System Operator, was 49,406 megawatts (July 15, hour ending 5 P.M.), which at the time constituted the all-time high demand in the region. Actual peak load was 44,302 megawatts in 1996 and 48,524 megawatts in 1995. See web site www.pjm.com.

[18]  See web site www.powerworld.com for more information about the PowerWorld® software.

[19]  All data are available on request from Tom Leckey (202-586-9413, thomas.leckey@eia.doe.gov).

[20]  Heat rates are the quantities of energy, usually expressed in British thermal units (Btu), needed to produce 1 kilowatthour of electricity. It takes about 3 Btu of energy input to produce 1 Btu of electricity from a typical baseload fossil-fuel unit. In this analysis the heat rate was multiplied by the cost of the fuel to approximate the variable cost of producing electricity. Other costs that contribute to the total generating cost include the O&M costs, which usually are small relative to the fuel costs and were omitted from this analysis.

[21]  The operating cost of a generator is calculated using the general formula (a + bg + cg² + dg³) × unit fuel cost. The parameter a is a constant, and b represents a fuel use coefficient. In the formula, c and d are the curvature parameters and are assumed to be zero. The quantity in parentheses is the amount of fuel required to generate g kilowatthours of electricity. The product is the fuel cost for producing g kilowatthours of electricity. Capital costs and variable O&M costs are not included in the cost calculation. Capital costs were excluded because they are not expected to be included when generation services are bid into competitive markets. The variable O&M costs were excluded because they are small and it is difficult to obtain data for individual generators. Small fossil units may fall below the reporting threshold of Form FERC 1 and Form EIA-412. All costs are reported in nominal dollars. Because of these simplifying assumptions, especially with regard to the c and d coefficients, costs reported here are, presumably, low estimates.

[22]  Ideally, it is desirable to have costs for every generator in the system.

[23]  Form EIA-867 has recently been redesignated as Form EIA-860B.

[24]  The Millstone units were shut down by the U.S. Nuclear Regulatory Commission in 1996 because of design configuration issues and safety concerns. Units 2 and 3 returned to service in 1998.

[25]  The 20-percent reduction results in loads of 38,169 megawatts in MAAC and 17,089 megawatts in NEPOOL. Load duration data reported by the two independent system operators indicate that median (4,380th greatest) hourly load for 1999 was 29,319 megawatts in MAAC and 13,532 megawatts in NEPOOL.

[26]  Nationally, the average monthly demand in July and August 1997 was 16 percent higher than the average demand in the other months. Energy Information Administration, Monthly Energy Review, DOE/EIA-0035(1999/12) (Washington, DC, December 1999), Table 7.1.

[27]  The engineering solution was validated by Dr. Tom Overbye, Professor of Electrical Engineering, University of Illinois. A few lines in NYPP exceeded their limits, a result attributed to several small generators that were reported to the FERC as being out of service.

[28]  Marginal costs are computed using the average of the marginal costs at the buses in the transmission network. Detailed data are available upon request from Tom Leckey (202-586-9413, thomas.leckey@eia.doe.gov).

[29]  Some of the lower voltage buses are excluded from the contour.

[30]  Marginal cost is reduced $0.05 per megawatthour, too small an increment to show as a reduction in Table 4.

[31]  Of the four NERC regions at issue, only NEPOOL’s marginal cost remains above that of the super region, the hypothetical aggregation of all four regions, which is $19.4 per megawatthour.

[32]  The hourly system cost is a measure of all the operating costs of all the generators for a single hour.

[33]  System cost in the four NERC regions declines by 0.1 percent.

[34]  Exports increase even more at the peak, to 936 megawatts. NYPP does not, however, become a net exporter under any of the trade scenarios. There are 800 megawatts of power imported from Hydro Quebec, specified as an exogenous input.

[35]  Transactions with Canada are included at a fixed level based on Form FERC 715. In these cases, Canadian generators have no costs assigned and are not dispatched. They play no role in the optimal power flow solution.

[36]  The first 43,000 megawatts of supply in MAAC are available at 33 mills per kilowatthour or less; the next 1,000 megawatts of load raises the cost to nearly 43 mills per kilowatthour.

[37]  One of these, Norwalk Harbor 138, is reported as “open” on Form FERC 715 and is not available to the model. Interestingly, when this line is closed, the model indicates that low-cost NEPOOL generators in Southern Connecticut are able to supply NYPP with nearly 250 additional megawatts, thereby reducing the marginal cost in NYPP slightly, increasing the marginal cost in NEPOOL by 4 percent, and increasing the marginal cost over the entire super region from $19.4 to $19.7 per megawatthour. Source: Office of Integrated Analysis and Forecasting, PowerWorld® model run FREE80W8NPTIES.D061500.

[38]  Pleasant Valley-Long Mountain 398.

[39]  Much of this power comes from the nuclear units James Fitzpatrick and Nine Mile Point in western New York.

[40]  The New York ISO reports the same available transmission capability for that interface. See web site http://mis.nyiso.com/public/htm/atc_ttc. NEPOOL ISO reports slightly more capacity.

[41]  Energy Information Administration, Annual Energy Outlook 2000, DOE/EIA-0383(2000) (Washington, DC, December 1999), AEO2000 National Energy Modeling System run AEO2K.D100199A (reference case).

[42]  The EMM allows for “economy trades” between NEPOOL and Canada, with opportunities for trade arising from cost differences, but the bulk of the imports in EMM are fixed “contract” trades.

[43]  Imports in the shoulder demand super region case were 995 megawatts.

[44]  The competitive pricing algorithm sums four components: reliability, tax, transmission and distribution, and energy.

[45]  Thus simulating PowerWorld®, as NEPOOL turns to regional sources of generation.

[46]  See web site www.iso-ne.com.

[47]  Another 700 megawatts comes from New Brunswick through Maine.

[48]  ISO New England, Inc., Monthly Market Report (May 1999), p. 20, Figure 17, web site www.iso-ne.com.

[49]  See also web site http://mis.nyiso.com/public/pdf/atc_ttc/, where the New York ISO reports transmission capacity of 1,600 megawatts.

[50]  See web site www.iso-ne.com/economic_and_load_forecasting/monthly_1999.txt.

[51]  Estimated as total monthly net interchange less estimated Canadian monthly imports of 1.8 gigawatthours × 730. This yields an estimate for Canadian imports of 15,811 gigawatthours, basically consistent with the NEMS estimate.

[52]  ISO New England, Inc., Monthly Market Report (February 2000), p. 6, Figure 3, web site www.iso-ne.com.

[53]  Another possibility is that EMM yields a price for NEPOOL that is roughly consistent with power flow models but underestimates the NYPP price. PowerWorld® model runs that raised NYPP exports to 1,377 megawatts (the hourly equivalent of 12,060 gigawatthours of imports in the AEO2000 reference case) produced negligible marginal cost reductions in NEPOOL but increased the marginal cost in NYPP by nearly 6 percent. Source: Office of Integrated Analysis and Forecasting, PowerWorld® model run REF80-HINYPPIMP.D061500.

[54]  Losses are reflected as increased generation.

Annual Energy Outlook Forecast Evaluation

[1] Energy Information Administration, Annual Energy Outlook 2001, DOE/EIA-0383(2000)(Washington, DC, December 2000), www.eia.doe.gov/oiaf/aeo/index.html, is the most recent AEO.

Modeling Distributed Electricity Generation in the NEMS Buildings Models

[1]  The value of transmission and distribution network savings and any benefits from reduced congestion are not estimated in this paper.

[2]  For detailed information on the National Energy Modeling System (NEMS), see Energy Information Administration, National Energy Modeling System: An Overview 2000, DOE/EIA-0581(2000) (Washington, DC, April 2000), web site ftp://ftp.eia.doe.gov/pub/pdf/ multi.fuel/0581(2000).pdf.

[3]  For detailed information on distributed generation modeling in the NEMS residential and commercial buildings modules, see Energy Information Administration, Residential Sector Demand Module of the National Energy Modeling System: Model Documentation 2000, DOE/EIA-067(2000) (Washington, DC, January 2000), web site ftp://ftp.eia.doe.gov/pub/pdf/model.docs/m067(2000).pdf; and Commercial Sector Demand Module of the National Energy Modeling System: Model Documentation 2000, DOE/EIA-066(2000) (Washington, DC, January 2000), web site ftp://ftp.eia.doe.gov/pub/pdf/model.docs/m066(2000).pdf.

[4]  For building sector distributed generation investments in AEO2000, the only tax credit incorporated in the forecast is a business energy tax credit for PV units of 10 percent of the installed purchase costs up to $25,000 in any one year, as provided in the Energy Policy Act of 1992.

[5]  For detailed information on the distributed generation market, see ONSITE SYCOM Energy Corporation, The Market and Technical Potential for Combined Heat and Power in the Commercial/Industrial Sector (Washington, DC, January 2000).

[6]  For example, DOE’s Million Solar Roofs program is a voluntary program that aims at enlisting utility and State agency participation in demonstrating the efficacy of solar power.

[7]  The differences between the cost decline assumptions stem partly from the level of maturity of each technology. Commercially applicable fuel cells have been tested and marketed only in the past few years, and building-size microturbines are in the demonstration phase. PV technology, in contrast, has been deployed commercially for more than 15 years and, thus, is less likely to experience near-term cost declines.

[8]  Currently, fuel cells are most frequently available in packaged units of 200 kilowatts. Thus, for buildings with smaller average demand, there is a potential to supply electricity to the grid if the price received is high enough to compensate for the costs of generating the electricity.

[9]  For a more detailed discussion of these and other components of the incentives, see Energy Information Administration, Analysis of the Climate Change Technology Initiative: Fiscal Year 2001, SR/OIAF/2000-01 (Washington, DC, April 2000).

[10] Lower capital costs prompt adoption by consumers with smaller electric and thermal loads, leading to less efficient fuel cell use.

[11] This is of course not the case for fuel-consuming, carbon-dioxide-emitting distributed generation. If the carbon dioxide efficiency and transmission and distribution loss savings of the distributed resource do not exceed the marginal carbon dioxide emissions of utility generation resources, distributed generation can increase carbon dioxide emissions. 

Impact of Technological Change and Productivity on the Coal Market

[1]  Energy Information Administration, Annual Energy Review 1998, DOE/EIA-0384(98) (Washington, DC, July 1999), Table 7.6.  Energy Information Administration, Annual Energy Review 1998, DOE/EIA-0384(98) (Washington, DC, July 1999), Table 7.6.

[2]  Details of the econometric specification for coal minemouth pricing are presented in the documentation report, Coal Market Module of the National Energy Modeling System, DOE/EIA-M060(2000) (Washington, DC, January 2000).  Details of the econometric specification for coal minemouth pricing are presented in the documentation report, Coal Market Module of the National Energy Modeling System, DOE/EIA-M060(2000) (Washington, DC, January 2000).

[3]  Labor productivity is calculated by dividing total coal production by the total direct hours worked by all employees engaged in production, preparation, processing, development, maintenance, repair, and shop or yard work at mining operations, but excluding office workers. For 1997 and prior years, as well as the AEO2000 forecast years, the measure also includes hours for all technical and engineering personnel. Increased productivity may be related in part to reducing staff other than miners. For 1998 and future years, EIA will obtain coal production and employment data through a data-sharing agreement with the Mine Safety and Health Administration (MSHA). MSHA has a separate category for office workers, which includes both professional and clerical employees. Employee hours in this category will not be included in the productivity statistic, beginning in 1998. The coal forecasts appearing in AEO2001 and in subsequent reports will be based on the productivity definition used by MSHA and published by EIA in its Coal Industry Annual.  Labor productivity is calculated by dividing total coal production by the total direct hours worked by all employees engaged in production, preparation, processing, development, maintenance, repair, and shop or yard work at mining operations, but excluding office workers. For 1997 and prior years, as well as the AEO2000 forecast years, the measure also includes hours for all technical and engineering personnel. Increased productivity may be related in part to reducing staff other than miners. For 1998 and future years, EIA will obtain coal production and employment data through a data-sharing agreement with the Mine Safety and Health Administration (MSHA). MSHA has a separate category for office workers, which includes both professional and clerical employees. Employee hours in this category will not be included in the productivity statistic, beginning in 1998. The coal forecasts appearing in AEO2001 and in subsequent reports will be based on the productivity definition used by MSHA and published by EIA in its Coal Industry Annual.

[4]  Bureau of Labor Statistics, web site http://stats.bls.gov/ceshome.htm. Earnings include premium pay for overtime, but exclude irregular bonuses, various welfare benefits, and payroll taxes paid by employers.  Bureau of Labor Statistics, web site http://stats.bls.gov/ceshome.htm. Earnings include premium pay for overtime, but exclude irregular bonuses, various welfare benefits, and payroll taxes paid by employers.

[5]  A.D. Ellerman, T.M. Stoker, and E.R. Berndt, Sources of Productivity Growth in the American Coal Industry, MIT-CEEPR 98-004 WP (Cambridge, MA: Massachusetts Institute of Technology, March 1998).  A.D. Ellerman, T.M. Stoker, and E.R. Berndt, Sources of Productivity Growth in the American Coal Industry, MIT-CEEPR 98-004 WP (Cambridge, MA: Massachusetts Institute of Technology, March 1998).

[6]  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt. The BLS productivity measure, expressed in output per employee hour, uses a chained output index based on the production tonnages of four ranks of coal (bituminous, subbituminous, lignite, and anthracite) that are weighted by their corresponding minemouth prices.  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt. The BLS productivity measure, expressed in output per employee hour, uses a chained output index based on the production tonnages of four ranks of coal (bituminous, subbituminous, lignite, and anthracite) that are weighted by their corresponding minemouth prices.

[7]  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt.  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt.

[8]  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Manufacturing Industries, 1987-97, web site ftp://146.142.4.23/pub/news.release/prin2.txt. Note that the end year is 1997 for Manufacturing Industries, compared with 1998 for Service-Producing and Mining Industries.  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Manufacturing Industries, 1987-97, web site ftp://146.142.4.23/pub/news.release/prin2.txt. Note that the end year is 1997 for Manufacturing Industries, compared with 1998 for Service-Producing and Mining Industries.

[9]  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt.  U.S. Department of Labor, Bureau of Labor Statistics, Productivity and Costs: Service-Producing and Mining Industries, 1987-98, web site ftp://146.142.4.23/pub/news.release/prin.txt.

[10]  A.D. Ellerman, T.M. Stoker, and E.R. Berndt, Sources of Productivity Growth in the American Coal Industry, MIT-CEEPR 98-004 WP (Cambridge, MA: Massachusetts Institute of Technology, March 1998).  A.D. Ellerman, T.M. Stoker, and E.R. Berndt, Sources of Productivity Growth in the American Coal Industry, MIT-CEEPR 98-004 WP (Cambridge, MA: Massachusetts Institute of Technology, March 1998).

[11]  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 40.  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 40.

[12]  The values for production and productivity for 1998 shown in Tables 3 and 4 are forecasts, which are consistent with AEO2000. Final historical values for 1998 were published in June 2000 in EIA’s Coal Industry Annual 1998, DOE/EIA-0584(98), after the December 1999 release of AEO2000. The historical values for regional production and productivity differ slightly from the forecast values for 1998 that appear in this paper.  The values for production and productivity for 1998 shown in Tables 3 and 4 are forecasts, which are consistent with AEO2000. Final historical values for 1998 were published in June 2000 in EIA’s Coal Industry Annual 1998, DOE/EIA-0584(98), after the December 1999 release of AEO2000. The historical values for regional production and productivity differ slightly from the forecast values for 1998 that appear in this paper.

[13]  The forecast values for average annual percent change in coal production by region are based on coal tonnage. If the computation were based on the total energy content of the coal produced, slightly different growth rates would result. For example, the growth rate of U.S. coal production, based on energy content, is projected to increase at an annual rate of 0.6 percent over the period, compared to the 0.7-percent growth in coal tonnage.  The forecast values for average annual percent change in coal production by region are based on coal tonnage. If the computation were based on the total energy content of the coal produced, slightly different growth rates would result. For example, the growth rate of U.S. coal production, based on energy content, is projected to increase at an annual rate of 0.6 percent over the period, compared to the 0.7-percent growth in coal tonnage.

[14]  The values for production and productivity for 1998 shown in Tables 3 and 4 are forecasts, which are consistent with AEO2000. Final historical values for 1998 were published in June 2000 in EIA’s Coal Industry Annual 1998, DOE/EIA-0584(98), after the December 1999 release of AEO2000. The historical values for regional production and productivity differ slightly from the forecast values for 1998 that appear in this paper.  The values for production and productivity for 1998 shown in Tables 3 and 4 are forecasts, which are consistent with AEO2000. Final historical values for 1998 were published in June 2000 in EIA’s Coal Industry Annual 1998, DOE/EIA-0584(98), after the December 1999 release of AEO2000. The historical values for regional production and productivity differ slightly from the forecast values for 1998 that appear in this paper.

[15]  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 2.  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 2.

[16]  Energy Information Administration, The U.S. Coal Industry in the 1990’s: Low Prices and Record Production, DOE/EIA-0631(99) (Washington, DC, September 1999), Table 1.  Energy Information Administration, The U.S. Coal Industry in the 1990’s: Low Prices and Record Production, DOE/EIA-0631(99) (Washington, DC, September 1999), Table 1.

[17]  Alan Greenspan, U.S. Federal Reserve, web site www.federalreserve.gov/boarddocs/speeches/2000/20000613.htm.  Alan Greenspan, U.S. Federal Reserve, web site www.federalreserve.gov/boarddocs/speeches/2000/20000613.htm.

[18] “U.S. Longwall Census ’99,” Coal Age (1999). “U.S. Longwall Census ’99,” Coal Age (1999).

[19]  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 54.  Energy Information Administration, Coal Industry Annual 1997, DOE/EIA-0548(97) (Washington, DC, December 1998), Table 54.

[20]  P&H Mining Equipment, web site www.phmining.com/products/index.html.  P&H Mining Equipment, web site www.phmining.com/products/index.html.

[21]  “Ultra-Class Haul Trucks,” Coal Age, web site www.coalage.com/feature1.html.  “Ultra-Class Haul Trucks,” Coal Age, web site www.coalage.com/feature1.html.

[22]  Harnischfeger Industries, 1998 Annual Report, p. 6.

[23]  Energy Information Administration, Annual Energy Review 1998, DOE/EIA-0384(98) (Washington, DC, July 1999), Table 7.8.

[24]  U.S. Bureau of Census, web site www.census.gov/prod/2000pubs/ace-98.pdf, and prior versions.

[25]  CONSOL Energy, Securities and Exchange Commission Form 10-Q (September 30, 1999).

[26]  Energy Information Administration, Annual Energy Outlook 2000, DOE/EIA-0383(2000) (Washington, DC, December 1999), Table A16.

[27]  Energy Information Administration, Annual Energy Outlook 2000, DOE/EIA-0383(2000) (Washington, DC, December 1999), pp. 221 and 241 and Table F16.

[28]  This is the standard deviation of the moving average of year to year productivity growth rates over the period 1980-1995, calculated separately for surface and deep mines.