Kyoto Testimony

Summary of the Kyoto Report

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[1] Intergovernmental Panel on Climate Change, Climate Change 1995: The Science of Climate Change (Cambridge, UK: Cambridge University Press, 1996).

[2] Greenhouse gases differ in their impacts on global temperatures. For comparison of emissions from the various gases, they are often weighted by global warming potential (GWP), established by the Intergovernmental Panel on Climate Change, which is a measure of the impact of each gas on global warming relative to that of carbon dioxide, which is defined as having a GWP equal to 1.

[3] Energy Information Administration, Emissions of Greenhouse Gases in the United States 1996, DOE/EIA-0573(96) (Washington, DC, October 1997).

[4] Energy Information Administration, Annual Energy Outlook 1998, DOE/EIA-0383(98) (Washington, DC, December 1997).

[5] Energy Information Administration, International Energy Outlook 1998, DOE/EIA-0484(98) (Washington, DC, April 1998).

[6] President William J. Clinton and Vice President Albert Gore, Jr., The Climate Change Action Plan (Washington, DC, October 1993).

[7] Carbon dioxide is absorbed by growing vegetation and soils. Defining the total impacts of CCAP as net reductions accounts for the increased sequestration of carbon dioxide as a result of the forestry and land-use actions in the program.

[8] Energy Information Administration, Emissions of Greenhouse Gases in the United States 1996, DOE/EIA-0573(96) (Washington, DC, October 1997).

[9] Energy Information Administration, Mitigating Greenhouse Gas Emissions: Voluntary Reporting, DOE/EIA-0608(96) (Washington, DC, October 1997).

[10] The text of the Kyoto Protocol is available at web site www.unfccc.de.

[11] Australia, Austria, Belgium, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Estonia, European Community, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Liechtenstein, Lithuania, Luxembourg, Monaco, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland, and United States of America. Turkey and Belarus are Annex I nations that have not ratified the Convention and did not commit to quantifiable emissions targets.

[12] Hydrofluorocarbons are a non-ozone-depleting substitute for CFCs; perfluorocarbons are byproducts of aluminum production and are also used in semiconductor manufacturing; and sulfur hexafluoride is used as an insulator in electrical equipment and in semiconductor manufacturing.

[13] See Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98) (Washington, DC, February 1998), for a summary description. Detailed documentation is available through the National Energy Information Center at 202/586-8800 or on the EIA web site at www.eia.doe.gov.

[14] U.S. Department of State, Office of Global Change, Climate Action Report, Department of State Publication 10496 (Washington, DC, July 1997).

[15] Energy Information Administration, Annual Energy Outlook 1995, DOE/EIA-0383(95) (Washington, DC, January 1995)

[16] See web site www.state.gov/www/global/oes/fs_kyoto_climate_980115.html.

[17] See web site www.house.gov/commerce/database.htm.

[18] The discussion about the resolution can be accessed in the Congressional Record of July 25, 1997, from web site www.access.gpo.gov/ su_docs/aces/aces150.html.

[19] A permit auction system is identical to a carbon tax as long as the marginal abatement reduction cost is known with certainty by the Federal Government. If the target reduction is specified, as in this analysis, then there is one true price, which represents the marginal cost of abatement, and this also becomes the appropriate tax rate. In the face of uncertainty, however, the actual tax rate applied may over- or undershoot the carbon reduction target. Auctioning of the permits by the Federal Government is evaluated in this report. To investigate a system of allocated permits would require an energy and macroeconomic modeling structure with a highly detailed sectoral breakout beyond those represented in the NEMS and DRI models. For a comparison of emissions taxes and marketable permit systems, see R. Perman, Y. Ma, and J. McGilvray, Natural Resources and Environmental Economics (New York, NY: Longman Publishing, 1996), pp. 231-233.

[20] Energy Information Administration, Annual Energy Outlook 1993, DOE/EIA-0383(93) (Washington, DC, January 1993).

[21] A.E. Smith, J. Platt, and A.D. Ellerman, “The Cost of Reducing SO₂,” Public Utilities Fortnightly (May 15, 1998).

[22] The modeling approach assumes perfect foresight of carbon prices for capacity planning in the electricity industry. Perfect foresight, in this context, means that the carbon prices that are anticipated during planning are later realized. An algorithm solves for the path of carbon prices in which anticipated and realized carbon prices are approximately the same, while ensuring that the carbon prices clear the carbon permit market each year. In the end-use demand sectors, foresight is assumed not to have a material influence on energy equipment decisions, and such decisions are modeled on the basis of prices in effect at the time of the decision.

[23] A related factor influencing the effect of carbon prices on gasoline demand is that the price of gasoline already includes Federal and State excise taxes averaging 37 cents per gallon in 1996, equivalent to a carbon permit price of $155 per metric ton. When additional carbon permit prices are included in the delivered price of gasoline, the percentage increase in price is not as high as it would be if gasoline were untaxed initially. In turn, the percentage change in gasoline demand due to the carbon price is not as high as it would be if gasoline were not already taxed.

[24] The “hurdle rate” for evaluating energy efficiency investments has also been referred to as the “implicit discount rate” (i.e., the empirically based rate required to simulate actual purchases—the one implicitly used). These rates are often much higher than would be expected if financial considerations alone were their source. Among the reasons often cited for relatively high apparent hurdle rates are uncertainty about future energy prices and future technologies, lack of information about technologies and energy savings, additional costs of adoption not included in the calculations, relatively short tenure of residential home ownership, hesitancy to replace working equipment, attributes other than energy efficiency that may be more important to consumers, limited availability of investment funds, renter/owner incentive differences, and builder incentives to minimize construction costs. For a good discussion of potential market barriers and the economics of energy efficiency decisions, see Jaffe and Stavins, “Energy Efficiency Investments and Public Policy,” The Energy Journal, Vol. 15, No. 2 (1994), pp. 43-65.

[25] The long-run elasticities reflect the effects of altered prices after 20 years for the last year of the forecast, 2020.

[26] U.S. Bureau of the Census, Construction Reports, series C20.

[27] Association of Home Appliance Manufacturers, Fact Book 1996.

[28] These standards represent updates to previous standards authorized by the National Appliance Energy Conservation Act of 1987.

[29] U.S. Department of Energy, Progress in Residential Retrofit, Based on Owens-Corning Marketing Research.

[30] Energy Information Administration, Technology Forecast Updates—Residential and Commercial Building Technologies, Draft Report (Arthur D. Little, Inc., June 1998).

[31] Energy Information Administration, Housing Characteristics 1993.

[32] Assumptions include lowering hurdle rates to 15 percent real, increasing the price sensitivity parameters to switch fuels, increasing short-run price elasticities from -0.25 to -0.40, and decreasing miscellaneous electricity penetration.

[33] Energy Information Administration, Technology Forecast Updates—Residential and Commercial Building Technologies, Draft Report (Arthur D. Little, Inc., June 1998).

[34] Intensity here is the average annual consumption of electricity for water heating in homes with electric water heaters.

[35] General characteristics of the commercial sector provided in the above paragraphs are from Energy Information Administration, A Look at Commercial Buildings in 1995: Characteristics, Energy Consumption, and Energy Expenditures, DOE/EIA-0318(95) (Washington, DC, September 1998).

[36] The hurdle rates consist of both financial and nonfinancial components, as described for the residential sector.

[37] For the purposes of this study, the financial portion of the hurdle rates is considered to be 15 percent in real terms.

[38] Current assumptions use an analysis of data from EIA’s 1992 commercial buildings survey. Sources for data on consumer behavior are listed on page A-18 of Energy Information Administration, Model Documentation Report: Commercial Sector Demand Module of the National Energy Modeling System, DOE/EIA-M066(98) (Washington, DC, January 1998).

[39] As in the residential model, the long-run elasticities are for 2020 and represent the effects after 20 years of altered price regimes.

[40] Energy Information Administration, Technology Forecast Updates—Residential and Commercial Building Technologies, Draft Report (Arthur D. Little, Inc., June 1998).

[41] Calculated form U.S. Department of Commerce, 1995 Annual Survey of Manufactures, pp. 1-7 and 1-36.

[42] For a variety of views, see Boyd et al., “Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach,” The Energy Journal, Vol. 8, No. 2 (1987); Doblin, “Declining Energy Intensity in the U.S. Manufacturing Sector,” The Energy Journal, Vol. 9, No. 2 (1988); Howarth, “Energy Use in U.S. Manufacturing: The Impacts of the Energy Shocks on Sectoral Output, Industry Structure, and Energy Intensity,” The Journal of Energy and Development, Vol. 14, No. 2 (1991); Jacard, Nyober, and Fogwill, “How Big is the Electricity Conservation Potential in Industry?” The Energy Journal, Vol. 14, No. 2 (1993); Steinmeyer, “Energy Use in Manufacturing,” in Hollander, ed., The Energy-Environmental Connection (Island Press, 1992), Chapter 10; and U.S. Department of Energy, Comprehensive National Energy Strategy (Washington, DC, April 1998), pp. 13-14.

[43] The refining industry is modeled separately in the Petroleum Market Module of NEMS.

[44] Council of the Economic Advisers, Economic Report of the President (Washington, DC, February 1995), p. 381.

[45] For example, see Boyd and Karlson, “Impact of Energy Prices on Technology Choice in the U.S. Steel Industry,” The Energy Journal, Vol. 14, No. 2 (1993). More general discussion can be found in Berndt and Wood, “Energy Price Shocks and Productivity Growth: A Survey,” in Gordon et al., eds., Energy: Markets and Regulation (Cambridge, MA: MIT Press, 1987); and Berndt, “Energy Use, Technical Progress and Productivity Growth: A Survey of Economic Issues,” Journal of Productivity Analysis, Vol. 2 (1990).

[46] Energy Information Administration, Manufacturing Consumption of Energy 1994, DOE/EIA-0512(94) (Washington, DC, December 1997), p. 168.

[47] The decomposition is done with the divisia index. For an explanation of the calculation of the index, see Boyd et al., “Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach,” The Energy Journal, Vol. 8, No. 2 (1987). Alternative decomposition methods are discussed in Greening et al., “Comparison of Six Decomposition Methods: Application of Aggregate Energy Intensity for Manufacturing in Ten OECD Countries,” Energy Economics, Vol. 19 (1997). Note that using different time periods or subsector aggregations may also yield different results.

[48] The high technology sensitivity case is based in part on an analysis prepared by Arthur D. Little, Inc., Aggressive Technology Strategy for the NEMS Model (1998).

[49] Federal Highway Administration, National Personal Travel Survey: 1990 NPTS Databook, Vol. I (Washington, DC, November 1993), p. 3-18.

[50] U.S. Department of Energy, Office of Policy, Planning, and Program Evaluation, The Climate Change Action Plan: Technical Supplement (Washington, DC, March 1994).

[51] This secondary effect has been estimated at about 10 to 12 percent. See L.A. Greening and D.L. Greene Energy Use, Technical Efficiency, and the Rebound Effect: “A Review of the Literature,draft report prepared for the Office of Policy Analysis and International Affairs ”, U.S. Department of Energy (Washington, DC, November 6, 1997).

[52] High technology assumptions were derived from the following sources: light-duty vehicle conventional technology attributes from J. DeCicco and M. Ross, An Updated Assessment of the Near-Term Potential for Improving Automotive Fuel Economy, American Council for an Energy-Efficient Economy (Washington, DC, November 1993); light-duty alternative fuel vehicle cost and performance attributes from U.S. Department of Energy, Office of Transportation Technologies, Program Analysis Methodology: Final Report—Quality Metrics 98 Revised (Washington, DC, April 1997); freight trucks from U.S. Department of Energy, Office of Transportation Technologies, OHVT Technology Roadmap (Washington, DC, October 1997), and conversations with Frank Stodolsky, Argonne National Laboratory, and Mr. Suski, American Trucking Association; air from conversations with Glenn M. Smith, National Aeronautics and Space Administration.

[53] U.S. Department of Energy, Office of Transportation Technologies, Program Analysis Methodology: Final Report—Quality Metrics 98 (Washington, DC, April 16, 1997).