For other versions of this document, see http://wikileaks.org/wiki/CRS-RL34272 ------------------------------------------------------------------------------ ¢ ¢ ¢ Prepared for Members and Committees of Congress ¢ ¢ Instituting policies to manage or reduce greenhouse gas (GHG) emissions would likely impact different states differently. Understanding these differences may provide for a more informed debate regarding potential policy approaches. However, multiple factors play a role in determining impacts, including alternative design elements of a GHG emissions reduction program, the availability and relative cost of mitigation options, and the regulated entities' abilities to pass compliance costs on to consumers. Three primary variables drive a state's human-related GHG emission levels: population, per capita income, and the GHG emissions intensity. GHG emissions intensity is a performance measure. In this report, GHG intensity is a measure of GHG emissions from sources within a state compared with a state's economic output (gross state product, GSP). The GHG emissions intensity driver stands apart as the main target for climate change mitigation policy, because public policy generally considers population and income growth to be socially positive. The intensity of carbon dioxide (CO2) emissions largely determines overall GHG intensity, because CO2 emissions account for 85% of the GHG emissions in the United States. As 98% of U.S. CO2 emissions are energy-related, the primary factors that shape CO2 emissions intensity are a state's energy intensity and the carbon content of its energy use. Energy intensity measures the amount of energy a state uses to generate its overall economic output (measured by its GSP). Several underlying factors may impact a state's energy intensity: a state's economic structure, personal transportation use in a state (measured in vehicle miles traveled per person), and public policies regarding energy efficiency. The carbon content of energy use in a state is determined by a state's portfolio of energy sources. States that utilize a high percentage of coal, for example, will have a relatively high carbon content of energy use, compared to states with a lower dependence on coal. An additional factor is whether a state is a net exporter or importer of electricity, because CO2 emissions are attributed to electricity-producing states, but the electricity is used (and counted) in the consuming state. Between 1990 and 2000, the United States reduced its GHG intensity by 1.6% annually. Assuming that population and per capita income continue to grow as expected, the United States would need to reduce its GHG intensity at the rate of 3% per year in order to halt the annual growth in GHG emissions. Therefore, achieving reductions (or negative growth) in GHG emissions would necessitate further declines in GHG intensity. ¢ Introduction ..................................................................................................................................... 1 Greenhouse Gas Emission Drivers.................................................................................................. 2 Greenhouse Gas Emissions Intensity .............................................................................................. 4 Greenhouse Gas Emissions Intensity in the States.................................................................... 5 Carbon Dioxide Intensity and Its Drivers........................................................................................ 6 Energy Intensity ........................................................................................................................ 6 Economic Structure............................................................................................................. 7 Personal Transportation ...................................................................................................... 8 Public Policy ....................................................................................................................... 8 State Climate....................................................................................................................... 9 Gross State Product............................................................................................................. 9 Conclusions....................................................................................................................... 10 Carbon Content of Energy Use ............................................................................................... 10 Electricity Generation ........................................................................................................11 Electricity Exports/Imports............................................................................................... 12 Consequences of Differences in State Emissions Drivers in the Context of a Federal Greenhouse Gas Emissions Reduction Program ........................................................................ 13 Greenhouse Gas Intensity Levels in the Context of an Emissions Reduction Program ................ 16 Table 1. Comparison of GHG Emission Drivers for the 10 U.S. States with the Highest GHG Emissions Levels in 2003 ................................................................................................... 3 Table 2. Average Annual Rates of Change for GHG Emissions and Drivers for the Entire United States: 1990-2000 ............................................................................................................. 3 Table 3. States with the Five Highest and Five Lowest GHG Intensity Levels (2003)................... 5 Table 4. States with the Five Highest and Five Lowest Energy Intensity Levels (2003 data).............................................................................................................................................. 6 Table 5. States with High Percentages of Gross State Product Based on High- or Low- Energy Intensive Sectors (2003 data)........................................................................................... 7 Table 6. States with the Five Highest and Five Lowest Vehicle Miles Traveled Per Capita (2003) ........................................................................................................................................... 8 Table 7. States With the Five Highest and Five Lowest Carbon Contents of Energy Use (2003) ......................................................................................................................................... 10 Table 8. States with the Highest Percentage of In-State Electricity Generated from Coal and Zero-Emission Energy Sources (2003).................................................................................11 Table 9. States with High Percentages of Exported and Imported Electricity in Terms of Overall Energy Use (2003)......................................................................................................... 12 Table 10. GHG Emissions Intensity Average Annual (Negative) Growth Rates (1990- 2003) for the 10 States with the Most GHG Emissions in 2003 ................................................ 17 ¢ Table A-1. GHG Emissions and GHG Emissions Drivers for All 50 States, Listed Alphabetically (2003 data) ......................................................................................................... 18 Table A-2. GHG Emissions and GHG Emissions Drivers for All 50 States, Ranked by GHG Emissions (2003 data) ...................................................................................................... 20 Table A-3. Average Annual Growth Rates (1990-2003) for GHG Emissions and GHG Emissions Drivers for All 50 States............................................................................................ 21 Table A-4. CO2 Emissions Intensity and CO2 Emissions Intensity Drivers for All 50 States, Listed Alphabetically (2003 data)................................................................................... 23 Table A-5. CO2 Emissions Intensity and CO2 Emissions Intensity Drivers for All 50 States, Ranked by CO2 Emissions Intensity (2003 data)............................................................ 25 ¡ Appendix. Select Tables with Data for All 50 States..................................................................... 18 Author Contact Information .......................................................................................................... 26 ¢ There is a broad agreement in the scientific community that the earth's climate is changing and that the primary cause over the past few decades is an increasing concentration of greenhouse gases (GHGs) in the atmosphere. Most climate scientists have concluded that human activities-- e.g., fossil fuel combustion, land clearing, and industrial and agricultural operations--have played a central role in climate change, particularly in recent decades.1 A variety of efforts that seek to address climate change are currently underway or being developed on the international, national, and sub-national level (e.g., individual state actions or regional partnerships). These efforts cover a wide spectrum, from research initiatives to GHG emission reduction regimes.2 If Congress establishes a federal program to manage or reduce GHG emissions, the emission requirements would likely impact different states differently. However, predicting the different impacts of policies is a complicated task, because multiple factors play a role. Such factors include alternative design elements of a GHG emissions reduction program, the availability and relative cost of mitigation options, and the regulated entities' abilities to pass compliance costs on to consumers. Underlying climate change policy discussions are GHG emissions and the factors that determine their levels and growth. One of the primary factors is GHG emissions intensity. In this report, GHG emissions intensity is a measure of GHG emissions from state sources divided by the state's overall economic output, or gross state product.3 Because carbon dioxide (CO2) is the primary GHG in the vast majority of states, the report focuses on CO2 emissions intensity and its determining factors. These factors vary significantly across state lines. An analysis of these factors and how they compare among the states may contribute to a more informed debate regarding potential policy approaches. 1 This report does not address the debates associated with climate change science or the role of human activity in climate change. For a discussion of these issues, see CRS Report RL33849, Climate Change: Science and Policy Implications, by Jane A. Leggett. 2 See CRS Report RL33826, Climate Change: The Kyoto Protocol, Bali "Action Plan," and International Actions, by Susan R. Fletcher and Larry Parker; CRS Report RL31931, Climate Change: Federal Laws and Policies Related to Greenhouse Gas Reductions, by Brent D. Yacobucci and Larry Parker; CRS Report RL33812, Climate Change: Action by States To Address Greenhouse Gas Emissions, by Jonathan L. Ramseur. 3 GHG emissions intensity is a performance measure. When looking at emissions on an economy-wide scale, gross domestic product (GDP) or gross state product (GSP) is typically used. However, other economic outputs, such as a tons of steel or cement, may be used to analyze the emissions intensity of specific sources or economic sectors. A higher GHG intensity value (compared to other states) indicates that a state generates more emissions per economic output (i.e., GSP) than other states. Navigating the Numbers: Greenhouse Gas Data and International Climate Policy, World Resources Institute. Development and Growth, and Energy Use, by John Blodgett and Larry Parker; see also Kevin Baumert, et al., 2005, 4 For further discussion see CRS Report RL33970, Greenhouse Gas Emission Drivers: Population, Economic table for all 50 states is included in the Appendix to this report. 2003. These 10 states accounted for almost 50% of total U.S. GHG emissions in 2003. A similar Table 1 shows this relationship for the 10 U.S. states with the highest GHG emission levels in substantially among the states and play varying roles when determining a state's GHG emissions. The three variables--population, per capita income, and GHG emissions intensity--differ of one another: a change in one variable may influence another variable.4 constant, GHG emissions will increase. The three emissions drivers do not operate independently GHG emissions. For instance, if one of these variables increases, while the other two remain The equation indicates that each of the variables can play a significant role in shaping a state's )PSG / E2OCTMM( )nosreP/PSG( )snosreP( )E2OCTMM( ytisnetnI GHG X emocnI atipaC reP X noitalupoP = snoissimE GHG :1 noitauqE relationship: approximated by multiplying together these three variables. Equation 1 expresses this income, and GHG emissions intensity of the economy. A state's GHG emission levels can be Three broad factors influence GHG emission levels in a nation or state: population, per capita .secruos rehto morf atad naht tsubor ssel deredisnoc yllareneg era secruos eseht morf ataD .atad ytisnetni ro snoissime ni )FCULUL( yrtserof ro ,segnahc esu dnal ,esu dnal edulcni ton seod troper sihT .3002 hguorht sGHG lla dna setats lla rof setamitse gnidivorp ,ecruos atad suoenegomoh a sa sevres TIAC ,seirotnevni etats eht dna TIAC neewteb seicnapercsid atad thgils eb yam ereht hguohtlA .snoissime 0991 revoc ylno seirotnevni eseht fo tsom tub ,atad esicerp erom htiw seirotnevni snoissime nwo rieht deraperp evah setats ynaM .etats hcae rof atad tluafed dna looT yrotnevnI etatS s'ycnegA noitcetorP latnemnorivnE eht gnisu delipmoc era atad etats TIAC ehT .)TIAC( looT srotacidnI sisylanA etamilC s'etutitsnI secruoseR dlroW eht morf emoc troper siht ni atad eht ,deton esiwrehto sselnU 2 .21/44 yb stnelaviuqe-nobrac ylpitlum ,stnelaviuqe- OC ot stnelaviuqe-nobrac trevnoc oT .stnelaviuqe-nobrac fo snot cirtem ni atad snoissime edivorp yam stroper rehto ,revoeroM .slevel noissime 2 21 ebircsed ot secruos emos yb desu erusaem a ,)smarg 01( margaret eno slauqe snot cirtem noillim enO .)E OCTMM( stnelaviuqe-edixoid nobrac fo snot cirtem noillim :erusaem fo tinu elgnis a otni )sag eht fo laitnetop gnimraw labolg eht no desab( detrevnoc era sesag xis eht morf atad ,etagergga ni atad snoissime eht enimaxe oT .dedulcni era seitivitca detaler-namuh morf snoissime ylnO .ediroulfaxeh ruflus dna ,snobracoroulfordyh ,snobracoroulfrep 2 ,enahtem ,edixo suortin ,) OC( edixoid nobrac :sesag gniwollof eht edulcni snoissime GHG ,troper siht nI .secruos tnereffid morf atad gnirapmoc nehw seicnetsisnocni ot dael yam hcihw ,syaw tnereffid lareves ni debircsed eb nac atad snoissime )GHG( sag esuohneerG tropeR sihT ni ataD snoissimE saG esuohneerG ¢ ¢ 1 elbaT eht htiw setatS .S.U 01 eht rof srevirD noissimE GHG fo nosirapmoC . 3002 ni sleveL snoissimE GHG tsehgiH snoissimE GHG noitalupoP emocnI atipac reP ytisnetnI GHG etatS noillim$ / E2OCT E2OCTMM s000,1 ni nosrep/PSG PSG fo saxeT 287 431,22 738,43 510,1 ainrofilaC 354 664,53 787,73 833 ainavlysnneP 103 153,21 422,33 437 oihO 992 834,11 471,33 803,1 adirolF 172 289,61 845,03 325 sionillI 962 056,21 818,73 165 anaidnI 962 291,6 280,33 513,1 kroY weN 442 832,91 137,14 403 nagihciM 212 860,01 062,43 416 anaisiuoL 012 184,4 573,92 195,1 lla rof egarevA 231 207,5 404,53 129 setatS 05 etutitsnI secruoseR dlroW eht morf atad htiw )SRC( ecivreS hcraeseR lanoissergnoC yb deraperP :ecruoS .looT srotacidnI sisylanA etamilC ,)IRW( Table 1 provides a snapshot of information. Annual changes (or growth rates, which can be either positive or negative) in the GHG emission drivers will influence whether GHG emissions rise or fall. In order to reduce emissions, the sum of the three variable rates--population, income, and intensity--must be negative. To put this goal in perspective, consider the annual average rates of change for the United States between 1990 and 2000 (Table 2): 2 elbaT eht rof srevirD dna snoissimE GHG rof egnahC fo setaR launnA egarevA . 0002-0991 :setatS detinU eritnE snoissimE GHG noitalupoP emocnI atipaC reP ytisnetnI GHG %4.1 = %2.1 + %8.1 + %6.1- .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS Table 2 reveals that the growth rates were positive for both U.S. population and per capita income during the 1990s. Although GHG intensity decreased during that time period, the decline was not enough to offset the increases from the other two variables, and GHG emission levels increased by 1.4% annually. Annual growth rates for GHG emissions and the emission drivers vary significantly among the U.S. states. The Appendix contains a table listing the growth rates for all 50 states. In some states, GHG intensity declines were well above average declines, but these annual reductions were offset by increases in population, per capita income, or a combination of the two. ¢ ¢ Of the three GHG emission drivers--population, per capita income, and GHG emissions intensity--the most relevant in terms of climate change policy is GHG intensity. Decreases in population and/or per capita income would contribute to lowering a state's GHG emissions. However, growth in population and personal income is generally considered a positive social outcome, and policies that would seek to directly limit these emissions drivers are essentially outside the bounds of public policy. GHG intensity is a simple measure of GHG emissions per unit of output. Although most GHG reduction regimes address actual emissions,5 the national target in the United States--as announced by the Bush Administration--aims to reduce the GHG emissions intensity of the national economy. In 2002, the Bush Administration set a voluntary target of reducing the ratio of U.S. GHG emissions to the U.S. Gross Domestic Product (GDP) by 18% by 2012. According to the Administration, meeting this target would reduce intensity beyond that of intensity reductions expected under a business-as-usual scenario. Based on data available in 2002, GHG emissions intensity was projected to decline by 14% under a business-as-usual scenario. Critics of the Administration's intensity target have pointed out that (1) the intensity target is more precisely quantified at 17.5%;6 and (2) more recent data indicate that the U.S. intensity declined by 16.2% between 1990 and 2002. Thus, some observers have described the effect of the intensity target as "negligible."7 Intensity targets are sometimes viewed with skepticism, because the intensity target proponents may imprecisely describe (or overstate) how reductions in emissions intensity would affect actual emission levels.8 For example, the Administration has stated that meeting the U.S. emissions intensity target would lead to GHG emission reductions.9 Arguably, such a description can be misleading, because the reductions would occur within the context of increasing U.S. emissions. In other words, U.S. emissions would continue to increase, but if the intensity target is met, the emissions increase would be less than business-as-usual. Moreover, there is some uncertainty as to whether the "reductions" will be achieved at all. The Administration's projected reductions are based on GDP forecasts. If the GDP increases at higher than projected rates, absolute emissions can increase beyond business-as-usual scenario, while still meeting the intensity target. Although some have questioned the environmental efficacy of intensity targets (i.e., their ability to lower GHGs), the effectiveness of an emissions target depends primarily on its stringency, not 5 For example, the European Union's Emissions Trading Scheme and the Kyoto Protocol require actual emission reductions. Reduction programs under development at the state level also require actual reductions (e.g., California and the Regional Greenhouse Gas Initiative). 6 Although the Administration's supporting document uses 18%, the document also states that the goal is to reduce intensity from 183 to 151 (metric tons of carbon equivalent per million dollars of gross domestic product), a 17.5% reduction. 7 See Herzog, Timothy, et al., 2006, Target Intensity: An Analysis of Greenhouse Gas Intensity Targets, WRI Report, pp.15-16. 8 See, Pew Center on Global Climate Change, Analysis of President Bush's Climate Change Plan, at http://www.pewclimate.org/policy_center/analyses/response_bushpolicy.cfm. 9 The Executive Summary describing the intensity target states: "the President's commitment will achieve 100 million metric tons of reduced emissions in 2012 alone, with more than 500 million metric tons in cumulative savings over the entire decade." See http://www.whitehouse.gov/news/releases/2002/02/climatechange.html. ¢ whether it applies to emissions intensity or absolute emissions.10 Meeting an aggressive intensity target can result in actual emission reductions, if the intensity decrease outpaces the combined increases in population and per capita income. In fact, if the United States is to reduce its emissions, while maintaining population and per capita income growth rates, a stringent reduction in GHG emissions intensity would be required. ¢ The GHG intensity levels display a considerable range among the 50 states. Table 3 lists the states with the five highest and five lowest GHG intensity values (based on 2003 data). The table shows that the ends of the spectrum differ by more than an order of magnitude. )3002( sleveL ytisnetnI GHG tsewoL eviF dna tsehgiH eviF eht htiw setatS .3 elbaT eviF htiw setatS ytisnetnI GHG tsewoL eviF htiw setatS ytisnetnI GHG ytisnetnI GHG tsehgiH fo noillim$ / E2OCT( sleveL ytisnetnI GHG fo noillim$ / E2OCT( sleveL )PSG )PSG gnimoyW 997,3 tucitcennoC 682 ainigriV tseW 790,3 kroY weN 403 atokaD htroN 588,2 sttesuhcassaM 723 anatnoM 557,1 ainrofilaC 833 aksalA 266,1 dnalsI edohR 943 979 :setats 05 lla rof egarevA .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS What factors determine a state's intensity and lead to the wide variances among the states? In the United States, carbon dioxide (CO2) emissions have historically accounted for 85% of the nation's GHG emissions, excluding land use changes and forestry. In all but four states,11 CO2 emissions accounted for at least 80% of the state's GHG emissions in 2003. As the dominant GHG, the intensity of CO2 emissions significantly impacts the overall GHG intensity. If Table 3 were to rank states based on CO2 emissions intensity, the results would be nearly identical.12 Due to the dominance of CO2 emissions in the vast majority of states, this report focuses on its role in driving overall GHG emissions intensity, and thus GHG emissions. (Note that the Appendix contains a table listing CO2 emissions intensity and its drivers for all 50 states). 10 See Herzog, Timothy, et al., 2006, Target Intensity: An Analysis of Greenhouse Gas Intensity Targets, WRI Report, pp.15-16. 11 The four states that emit relatively large percentages of non-CO2 GHG emissions include South Dakota (47%), Idaho (38%), Nebraska (32%), and Iowa (26%). 12 Wyoming, West Virginia, North Dakota, Alaska, and Louisiana rank 1st through 5th (Montana 6th); the five states with the lowest CO2 emissions intensity are identical, but California and Massachusetts switch positions. ¢ ¡ ¢ Approximately 98% of the U.S. CO2 emissions in 2003 were from energy use.13 The primary factors that determine CO2 emissions intensity in a state are its energy intensity and the carbon content of its energy use (or fuel mix).14 The relationship between CO2 emissions intensity, energy intensity and carbon content of energy use is shown in Equation 2. :2 noitauqE ytisnetnI snoissimE 2OC = ytisnetnI ygrenE X ygrenE fo tnetnoC nobraC )PSG/2OC( )PSG/eot( )eot/2OCT( dna ,)2OCT( tnelaviuqe-edixoid nobrac fo snot ,)PSG( tcudorp etats ssorg edulcni evoba detic stinu ehT :etoN .)eot( tnelaviuqe lio fo snot ¢ ¢ Energy intensity is the amount of energy a state consumes--typically measured in tons of oil equivalent (toe)--per its level of economic output (gross state product). Table 4 shows the states with highest and lowest energy intensity levels in 2003. A comparatively high energy intensity figure indicates a states uses more energy (toe) per economic output (GSP) than other states. There is wide gulf (a factor of five) between states at either end of the spectrum. Multiple factors influence a state's energy intensity. This section of the report compares energy intensity levels with five potential drivers: economic structure, transportation use, public policy, state climate, and gross state product. An overall assessment of the factors and their interactions with energy intensity is provided at the end of this section. 3002( sleveL ytisnetnI y grenE tsewoL eviF dna tsehgiH eviF eht htiw setatS .4 elbaT )atad tsehgiH eviF htiw setatS eot( ytisnetnI ygrenE tsewoL eviF htiw setatS eot( ytisnetnI ygrenE seitisnetnI ygrenE )PSG fo noillim$ / seitisnetnI ygrenE )PSG fo noillim$ / anaisiuoL 17.0 kroY weN 31.0 aksalA 96.0 tucitcennoC 41.0 gnimoyW 16.0 sttesuhcassaM 41.0 atokaD htroN 05.0 ainrofilaC 51.0 ainigriV tseW 65.0 dnalsI edohR 61.0 92.0 :setats 05 lla rof egarevA .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS 13 The other portion (2.5%) came from industrial activity. This estimate excludes land use changes. WRI, Climate Analysis Indicators Tool. 14 When non-CO2 gases--e.g., methane, nitrous oxide--are part of the GHG intensity calculus, other factors come into play. Approximately 50% of non-CO2 GHGs are generated by agricultural activities, and these emission levels may be influenced by changes in related economic markets. ¢ A state's economic structure likely plays an important role. For instance, a primary economic factor is whether the state's economy is based more on high-energy industries15 or low-energy industries.16 A state with a GSP based on a high ratio of high-energy industries is likely to have a higher overall energy intensity than a state with proportionately more low-energy sectors (e.g., finance, professional services). Table 5 lists (1) the five states with the highest percentages of their GSP resulting from high- energy intensive industries; and (2) the five states with the highest percentages of their GSP based on low-energy intensive industries. A comparison of Table 4 and Table 5 indicates a correspondence between energy intensity and a state's economic structure. The top-three highest energy intensity states are also the top-three in percentage of their GSP from high-energy sectors; three of the top-five lowest energy intensity states are also among the top-six states for GSP based on low-energy sectors. Of the 25 states with the highest percentages of their GSPs based on high- energy sectors, 19 of these states are ranked in the top-25 for energy intensity. ro -hgiH no desaB tcudorP etatS ssorG fo segatnecreP hgiH htiw setatS .5 elbaT )atad 3002( srotceS evisnetnI ygrenE-woL etatS fo egatnecreP etatS fo egatnecreP morf PSG morf PSG ygrenE-hgiH ygrenE-woL 0srotceS bsrotceS gnimoyW 23 erawaleD 97 anaisiuoL 32 iiawaH 67 aksalA 22 kroY weN 57 ainigriV tseW 71 dnalyraM 17 saxeT 41 dnalsI edohR / tucitcennoC 07 egarevA etatS-05 %7 egarevA etatS-05 %16 .mth.xedni/vog.aeb//:ptth ta ,sisylanA cimonocE fo uaeruB morf atad htiw SRC yb deraperP :ecruoS naciremA htroN gniwollof eht edulcni srotces ygrene-hgih ,troper siht fo tser eht rof sa ,elbat siht roF .a latem yramirp ,seitilitu ,gninim :sgnipuorg yradnoces dna yramirp )SCIAN( metsyS noitacifissalC yrtsudnI lacimehc dna ,gnirutcafunam stcudorp laoc dna muelortep ,gnirutcafunam repap ,gnirutcafunam .gnirutcafunam naciremA htroN gniwollof eht edulcni srotces ygrene-wol ,troper siht fo tser eht rof sa ,elbat siht roF .b ;etatse laer ;ecnarusni dna ecnanif ;noitamrofni :spuorg yramirp )SCIAN( metsyS noitacifissalC yrtsudnI ;noitacude ;secivres etsaw dna noitartsinimda ;seinapmoc fo tnemeganam ;secivres lacinhcet/lanoisseforp ;secivres rehto ;doof dna noitadomocca ;noitaercer ,tnemniatretne ,stra ;ecnatsissa laicos dna erac htlaeh .tnemnrevog dna 15 For this report, high-energy sectors include the following North American Industry Classification System (NAICS) primary and secondary groupings: mining, utilities, primary metal manufacturing, paper manufacturing, petroleum and coal products manufacturing, and chemical manufacturing. 16 For this report, low-energy sectors include the following North American Industry Classification System (NAICS) primary groups: information; finance and insurance; real estate; professional/technical services; management of companies; administration and waste services; education; health care and social assistance; arts, entertainment, recreation; accomodation and food; other services; and government. ¢ The transportation sector accounts for over a quarter (28%) of total energy consumption in the United States.17 Within the transportation sector, personal transportation--i.e., cars, light trucks, and motorcycles--accounts for the majority of energy use (64% in 2004).18 A measure that tracks personal transportation use in a state is vehicle miles traveled (VMT) per person. A state's per capita VMT is another factor that likely impacts a state's energy intensity. As Table 6 indicates, there is a significant range between states with the most and least VMT/person. The five states--New York, Hawaii, Alaska, Rhode Island, and New Jersey--on the low end of the spectrum averaged 7,598 VMT/person in 2003; the five states--Wyoming, Vermont, Alabama, Oklahoma, and Mississippi--on the other end averaged 14,186 VMT/person in 2003.19 reP delevar seliM elciheV tsewoL eviF dna tsehgiH eviF eht htiw setatS .6 elba T T )3002( atipaC knaR tsehgiH fo setatS seliM elciheV tsewoL fo setatS seliM elciheV reP delevarT knaR reP delevarT atipaC atipaC gnimoyW 763,81 kroY weN 020,7 tnomreV 234,31 iiawaH 674,7 amohalkO 840,31 aksalA 036,7 amabalA 540,31 dnalsI edohR 387,7 ippississiM 630,31 yesreJ weN 380,8 175,01 :setats 05 lla rof egarevA .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS There is a general correspondence between a state's per capita VMT and energy intensity. Of the 25 states with the lowest energy intensity levels, 17 of them are also in the group of 25 states with the fewest VMT/person.20 However, there are several dramatic exceptions to this correlation. For example, Alaska ranks third for lowest VMT/person, but second for highest energy intensity. Conversely, Vermont has the second highest VMT/person, but has a relatively low energy intensity (ranks 15th). Such exceptions demonstrate that multiple factors play a role and that energy intensity drivers may have varying impacts in different states. ¢ States can seek to reduce energy intensity through public policy action. Some states have enacted policies or regulations that are more stringent or broader in scope than federal standards, 17 The industrial (32%), residential (22%), and commercial (18%) sectors consumed the remaining proportions. See CRS Report RL31849, Energy: Selected Facts and Numbers, by Carol Glover and Carl E. Behrens. 18 U.S. Department of Energy, 2007, Transportation Energy Data Book (Edition 26), table 2.6. 19 Based on WRI CAIT data. 20 Likewise, of the 25 states with higher energy intensity levels, 17 are also among the 25 states with higher VMT/person. ¢ supporting improvements in efficiency standards for electricity generation, buildings, and/or appliances. For example, 12 states have established energy efficiency standards for appliances that are more stringent than federal requirements.21 The American Council for an Energy-Efficient Economy (ACEEE) published an energy efficiency scorecard that ranks the states based on their energy efficiency policies.22 The ACEEE scores show a relationship with highest and lowest energy intensity levels among the states. Of the states with low energy intensity levels, all were ranked highly by the ACEEE scorecard.23 Conversely, the states with high energy intensities received low ACEEE rankings.24 In addition, of the 25 states ranked highly by ACEEE for public policy, 19 of the states are among the 25 states with the lowest energy intensities. Natural factors, such as a state's climate, may influence energy intensity in some states, but the degree of influence is difficult to determine. A state's overall climate helps determine the amount of energy needed to heat or cool residential, commercial, and industrial buildings. A measurement used to evaluate this concept is the "degree day," which includes heating degree days (HDDs) and cooling degree days (CDDs).25 In the United States, HDDs outnumber CDDs by a factor of five to one, thus states in colder climates generally have the most degree days. An examination of energy intensity and degree days for all 50 states does not indicate an overall correlation between these two measures. While several states rank highly for both degree days and energy intensity,26 many of the states with low energy intensities--e.g., New York, Connecticut, and Massachusetts--are among the top 25 states in terms of degree days. In addition, many of the states with few degree days are among the top 25 states in terms of energy intensity. The lack of an overall correlation between degree days and energy intensity does not rule out the influence of climate. Climate may play a supplemental role that is perhaps obscured by more influential factors. The size of a state's economy (the denominator of energy intensity) can be an important part of the equation. Of the states with the 25 lowest GSPs, 17 of the states are in the top-25 for energy intensity. A sudden increase/decrease in a variable that alters energy consumption will likely yield 21 EPA, Map: State Energy Efficiency Actions - State Appliance Efficiency Standards (as of 1/1/2007), at http://www.epa.gov/cleanenergy/stateandlocal/activities.htm. 22 American Council for an Energy-Efficient Economy (ACEEE), 2007, The State Energy Efficiency Scorecard for 2006, at http://aceee.org. 23 Including ties, California and Connecticut ranked first; Massachusetts ranked 4th; New York ranked 7th; and Rhode Island 9th. 24 Louisiana was ranked 40th; Alaska ranked 41st; Wyoming ranked 49th; North Dakota ranked 51st; and West Virginia ranked 35th. 25 The "degree-day" is a metric used to assess the demand for heating and/or cooling needs. Both heating degree days (HDDs) and cooling degree days (CDDs) are based on differences from a temperature of 65 °F, a base temperature considered to have neither heating nor cooling needs. For example, 10 HDDs are generated for a day with an average daily temperature of 55 °F. Higher HDDs (e.g., Alaska) and CDDs (e.g., Florida) indicate greater heating or cooling needs, respectively. 26 Three of the five states (see Table 6) with high energy intensities--Wyoming, Alaska, and North Dakota--are in the top five for number of degree days. ¢ a more pronounced effect in states with lower GSPs. In contrast, the effects of drastic changes may be less pronounced in states with larger GSPs. Four of the states with high energy intensities rank near the bottom in terms of absolute GSP (in 2003): Alaska (45th), Wyoming (50th), North Dakota (48th), and West Virginia (40th). Conversely, California and New York, which are among the top five states with lowest energy intensities, are ranked first and second, respectively. However, in the other states listed above (Table 4), the size of GSP may play a lesser role. For example, Louisiana, the state with the highest energy intensity, ranked 24th for total GSP in 2003. Other than a state's climate, each of the factors discussed above shows a relationship with energy intensity. Most of the states with high energy intensity levels are at the extreme end of the range for more than one of the underlying factors; many of the states with low intensities also have corresponding rankings with one or more underlying factors. However, there are sometimes dramatic exceptions. The exceptions highlight the diversity among the states and indicate the difficulty in making conclusions that apply in all states. In addition, for states that have multiple factors steering towards higher energy intensity, it is difficult to determine which factor is dominant. Perhaps the most extreme example of this difficulty is Wyoming, which has the third highest energy intensity. Wyoming ranks first for percentage of energy-intensive industries, first for VMT/person, fourth for number of degree days, last (50th) for absolute GSP, and 49th in ACEEE's public policy scorecard. All of these rankings point towards increased energy intensity, thus creating a challenge to identify the primary influence in states such as Wyoming. ¢ The second driver of CO2 emissions intensity is the carbon content of energy use in a state. Energy sources vary in the amount of carbon released per unit of energy supplied (e.g., British Thermal Unit). A state that uses a greater proportion of high-carbon energy sources will have higher CO2 emissions per unit of energy use than a state that utilizes more low-carbon energy sources. Table 7 shows the states with the five highest and five lowest carbon contents of energy use (measured in tons of CO2 per tons of oil equivalent, toe). y grenE fo stnetnoC nobraC tsewoL eviF dna tsehgiH eviF eht htiW setatS .7 elbaT )3002( esU tsehgiH htiw setatS fo tnetnoC nobraC tsewoL htiw setatS fo tnetnoC nobraC fo stnetnoC nobraC esU ygrenE 2OCT( / fo stnetnoC nobraC esU ygrenE 2OCT( / esU ygrenE )eot 0001 esU ygrenE )eot 0001 ainigriV tseW 087,5 ohadI 012,1 gnimoyW 064,5 nogerO 045,1 atokaD htroN 077,4 notgnihsaW 066,1 anatnoM 084,3 tnomreV 066,1 hatU 074,3 tucitcennoC 098,1 725,2 :setats 05 lla rof egarevA .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS ¢ ¢ A state's electricity sector is especially important in the context of a state's carbon content of energy use. The electricity sector produces a substantial portion of CO2 emissions in many states and is the highest emitting sector in the United States, accounting for approximately 40% of U.S. CO2 emissions. Electricity can be generated from a variety of energy sources, which vary significantly by their ratio of CO2 emissions per unit of energy. A coal-fired power plant emits almost twice as much CO2 (per unit of energy) as a natural gas-fired facility.27 Some energy sources--e.g., hydropower,28 nuclear, wind, or solar--do not directly release any CO2 emissions. Although the transportation sector contributes a significant percentage of CO2 emissions in most states (and 33% of U.S. CO2 emissions in 2003--the second highest sector), this sector utilizes a more homogenous fuel portfolio. In contrast to fuels used to generate electricity, transportation fuels do not demonstrate as much variance in their CO2 emissions per unit of energy.29 Thus for the purposes of examining a state's carbon content of energy use, this report focuses on the electricity sector. Compared to the other states, the five states with high carbon contents in their fuel mix utilized a relatively large percentage of coal for electricity generation in 2003. Conversely, the five states with the lowest levels generated electricity from a relatively high percentage of zero-emission energy sources in 2003. In general, hydropower and nuclear power dominate the zero-emission subcategory in terms of use, but the zero-emission sources also include wind, solar, geothermal, and the sources that fall within the Energy Information Administration's (EIA) "other renewables" category.30 Table 8 lists the states that utilized the greatest percentages of coal to generate electricity and the states with the highest percentages of zero-emission energy sources. morf detareneG yticirtcelE etatS-nI fo egatnecreP tsehgiH eht htiw setatS .8 elbaT )3002( secruoS ygrenE noissimE-oreZ dna laoC etatS etatS-nI fo egatnecreP etatS yticirtcelE etatS-nI fo egatnecreP morf detareneG yticirtcelE ygrenE snoissimE-oreZ morf detareneG laoC secruoS tseW %89 tnomreV %001 ainigriV gnimoyW %79 ohadI %69 27 The Energy Information Administration website provides a table listing the amount of CO2 generated per unit of energy for different energy sources, at http://www.eia.doe.gov/oiaf/1605/coefficients.html. 28 Some studies have found that hydroelectric dams may be a source of GHG emissions. Dam reservoirs can emit methane through plant decomposition, but this effect varies by location, being more pronounced in warmer climates. See e.g., World Commission on Dams, 2000, The Report of the World Commission on Dams, at http://www.dams.org/ report/. 29 In 2003, petroleum accounted for 97% of the energy consumed in the U.S. transportation sector. EIA, Energy Power Monthly, March 2004, Table 2.5, at http://www.eia.doe.gov/. 30 These additional sources include wood and other wood waste, black liquor, biogenic municipal solid waste, landfill gas, sludge waste, agriculture byproducts, and other biomass (EIA, Electric Power Monthly, March 2004, Table 1.13B). Although these sources do yield CO2 emissions when used as fuels, their combustion does not provide additional CO2 emissions to the atmosphere (i.e., they would have produced CO2 emissions at some point via natural processes). Thus, for this report they are counted as zero-emission energy sources. ¢ etatS etatS-nI fo egatnecreP etatS yticirtcelE etatS-nI fo egatnecreP morf detareneG yticirtcelE ygrenE snoissimE-oreZ morf detareneG laoC secruoS anaidnI %49 notgnihsaW %28 htroN %49 nogerO %07 atokaD hatU %49 weN %66 erihspmaH hcraM( ylhtnoM rewoP cirtcelE ,noitartsinimdA noitamrofnI ygrenE morf atad htiw SRC yb deraperP :ecruoS ./vog.eod.aie.www//:ptth ta ,)4002 ¢ ¡ Another important factor that affects a state's carbon content of energy use is whether the state is a net importer or exporter of electricity. States consume fuels (e.g., coal, natural gas, etc.) to generate electricity, but the electricity may be exported to and used in another state. The method for accounting for these exchanges influences the level of a state's carbon content of energy use. In the above carbon content of energy data (Table 7), if one state uses an energy source (e.g., coal) to generate electricity and then sells the electricity to a consumer in a second state, the CO2 emissions are attributed to the generating state, but the energy use is attributed to the consuming state.31 Table 9 lists the states in which electricity exports accounted for high percentages of energy use. Likewise, the table lists the states in which imported electricity accounted for high percentages of energy use. The import/export factor is especially prominent for states with high carbon content levels. The top four states for carbon content of energy use in 2003--West Virginia, Wyoming, North Dakota, and Montana--exported substantial portions of electricity in that year. Of the five states with low carbon content levels, the import/export factor appears most relevant in Idaho, where imported electricity accounted for 41% of its total energy use in 2003. smreT ni yticirtcelE detropmI dna detropxE fo segatnecreP hgiH htiw setatS .9 elbaT )3002( esU ygrenE llarevO fo etatS fo egatnecreP etatS fo egatnecreP demusnoC ygrenE demusnoC ygrenE detropxE sI tahT detropmI sI tahT yticirtcelE yticirtcelE ainigriV tseW %44 ohadI %14 gnimoyW %24 erawaleD %82 atokaD htroN %63 dnalsI edohR %22 31 From a mathematical perspective, in a net exporting state the numerator (tCO2) of the equation (tCO2 / toe) would increase, but the denominator (toe) would remain the same. The reverse would occur in importing states. ¢ etatS fo egatnecreP etatS fo egatnecreP demusnoC ygrenE demusnoC ygrenE detropxE sI tahT detropmI sI tahT yticirtcelE yticirtcelE anatnoM %82 dnalyraM %02 erihspmaH weN %32 ainigriV %71 metsyS ataD ygrenE etatS ,noitartsinimdA noitamrofnI ygrenE morf atad htiw SRC yb deraperP :ecruoS .lmth.sdes_/setats/ueme/vog.eod.aie.www//:ptth Some may argue that this characteristic of the data artificially inflates the carbon content of energy use in exporting states, while artificially lowering the measure in states that import a significant amount of electricity. Consider Wyoming and Idaho, two states at opposite extremes of the carbon contents of energy use range. Two coal-fired power plants located in Wyoming are partially owned by electricity providers that serve customers in Idaho. Idaho customers are receiving some amount of coal-fired electricity from Wyoming (and Oregon and Nevada).32 This electricity is counted as energy use in Idaho, while the CO2 emissions are attributed to Wyoming (or Oregon or Nevada). From another perspective, the example is less a critique of the carbon content of energy measure, and more a highlight of how electricity generation and use is measured. There is no system in place to physically track electricity upon generation. Therefore, it is impossible to precisely attribute imported electricity to its energy source.33 Moreover, exported electricity may come from energy sources other than coal. States may export electricity generated from low- or zero- carbon energy sources, such as hydropower or nuclear. This factor adds another layer of complexity to the accounting. As the above Wyoming/Idaho example demonstrates, rough approximations might be established based on ownership data, but it may be difficult (if not impossible) to precisely assign the CO2 emissions from an exporting state to the importing state. Thus, states that appear to be using low-carbon energy sources, may be importing high-carbon energy, in the form of electricity. ¡ As noted above, the states have, in some cases, vastly different levels of GHG emissions intensity and related underlying variables. If Congress were to enact a federal GHG emissions reduction program, these differences may lead to a wide range of impacts in the states. The range of impacts would depend on the logistics of the emissions reduction program and the ability of regulated entities to spread compliance costs. 32 Idaho Power, which serves customers in Idaho, is a partial owner of coal-fired power plants in these states. See EIA, Annual Electric Generator Report (Database 860), at http://www.eia.doe.gov; see also http://www.idahopower.com. 33 Per telephone conversation with EIA official, July 30, 2007. ¢ If Congress creates a mandatory GHG emissions reduction regime, the program would assign (directly or indirectly) a cost to emissions of carbon (or carbon-equivalents in the case of some GHGs). The stringency, scope, and design of the reduction regime would play a large role in determining costs and how the costs are distributed. For instance, Congress could include specific provisions--e.g., a safety-valve or revenue recycling--that would control costs or ease the burden on particular groups.34 Regardless of how Congress might design a GHG reduction program, a mandatory GHG reduction regime would affect states differently. In particular, the states' different energy intensities and carbon content of energy use indicate the states would experience different effects. States with relatively high levels of carbon content in their energy use (Table 7) would likely see higher energy prices. These states typically use a high percentage of coal to generate electricity, thus electricity prices would likely increase in these states.35 The consumers' responses to these price increases would help determine impacts. Consumers may choose to conserve energy use or switch to alternative sources. The carbon price imposed by the emission reduction regime would provide incentives to switch from high-carbon to low-carbon fuel (e.g., from coal to natural gas). However, such a switch may be limited by the technology and infrastructure existing in a state, particularly in the electricity generation sector. Conventional coal-fired power plants in operation today, which account for approximately 50% of all electricity generation, cannot simply switch to another fuel source. The producers of coal-fired electricity may be able to pass along the additional carbon costs to consumers, but some state regulations may hinder a company's ability to include the additional costs in electricity prices. Differences in the states' regulatory structures may influence which groups ultimately pay for the additional carbon costs. In states with tighter regulatory control over prices, power companies may bear a relatively higher cost; in other states, consumers of electricity may bear a higher percentage of the costs, where companies are less constrained in passing costs along to customers in the form of higher prices. Depending on particular design elements of the emissions reduction program, some of these potential disproportionate effects might be alleviated. For example, if producers are expected to pay a higher percentage of the additional carbon costs, some of the emission allowances might be provided for free. If consumers are anticipated to pay a higher proportionate cost, the allowances could be auctioned. The auction's revenues could be returned to consumers, particularly to low- income households, which would be especially impacted by higher electricity bills. As discussed above, a state's import/export ratio of electricity may influence its carbon content of energy use (or fuel mix). This component adds a further layer of complexity when assessing the potential impacts of a carbon price. For example, depending on how emission allowances might be distributed under a federal cap-and-trade system, states that are net energy providers may receive financial gains, at least in the short-term. For instance, if power plants can pass along the mitigation costs (of carbon reduction) in higher electricity prices and receive their emission allowances for free (often referred to as "grandfathering") the companies may benefit 34 For more discussion of these issues, see CRS Report RL33799, Climate Change: Design Approaches for a Greenhouse Gas Reduction Program, by Larry Parker. 35 Raymond Kopp, 2007, Greenhouse Gas Regulation in the United States, Resources for the Future Discussion Paper. ¢ financially.36 These potential gains to the likely regulated entities (e.g., coal-fired power plants) have been described as "windfall profits," and have been recently observed in the European Union's Emission Trading System.37 The gains would be temporary, because under most cap-and- trade proposals, the cap decreases over time; thus, regulated entities would receive fewer allowances as the program progresses. If Congress enacts an emissions reduction program, states with high levels of energy intensity are likely to face higher costs than states with low energy intensity levels. As Table 4 shows, the high and low energy intensity levels can differ by a factor of four, which suggests that the impacts between the states at the ends of the spectrum could vary dramatically. Energy intensity levels are shaped by multiple factors. Some of these factors may be based on behavior or actions. These factors may be altered through public policy. For example, states could initiate policies or support programs that seek to change the driving behavior (i.e., VMT) of its citizens. Other factors--especially a state's ratio of high and low carbon intensive industries--are more structural, and thus more difficult (if not impractical) to alter through public policy. In addition, depending on the degree to which a state's energy intensity is influenced by its climate, a newly-imposed carbon price may have a greater impact. In these states, the demand for energy may be less elastic (i.e., responsive to price changes) than other states, because energy is more critical for daily life necessities, such as home heating. Low-income citizens may face a disproportionate burden, as a share of income, of price increases in states with substantial heating and/or cooling needs. States with high energy intensity may have a high percentage of carbon-intensive industries (e.g., manufacturing). These industries would likely see an increase in their operational costs due to the new carbon price, but they may be able to include the additional carbon costs in the price of their products (e.g., paper, cement, steel), thus spreading the costs to consumers in other states. However, passing along the carbon price to consumers may not be financially viable for producers. The ability of producers to pass along the carbon price would be determined by the competitiveness of the market and consumers' willingness to pay higher prices or forego purchases for a particular good. Consumers may seek out product substitutes or lower cost suppliers (which could include foreign producers not subject to a domestic carbon price). From another perspective, higher levels in emissions drivers, particularly the energy intensity variable, may suggest a state has comparatively more "low hanging fruit" or lower-cost options to meet emission reduction requirements. As noted above, the states with high energy intensities were also ranked poorly by ACEEE's energy efficiency scorecard. Although these states' energy intensity levels are primarily due to economic structure, there may be room for improvement-- via "no regrets" energy efficiency policies--within the framework of their economic structure. Along these lines, states that currently use a substantial percentage of high-carbon fuels for energy purposes (particularly for electricity generation) may have more options in a carbon- constrained regime than states that are already utilizing a high percentage of low-carbon energy 36 In a market-based system (e.g., cap-and-trade), emission allowances can be used to comply with an individual company's cap or sold to other parties subject to the cap. As such, allowances are a form of currency and would provide an infusion of funds. 37 The vast majority of emissions allowances were distributed for free under the European program. See National Commission on Energy Policy, 2007, Allocating Allowances in a Greenhouse Gas Trading System, p.11. ¢ sources. For instance, if states in both categories were required to reduce current emissions by a set percentage, states using high-carbon fuels may seek low-carbon fuel substitutes, but states using low-carbon fuels would be limited in this regard. This comparison does not suggest that switching to low-carbon fuels will be easy (or inexpensive), but these states may have more ways to find emission reductions. Moreover, low-carbon fuel substitutes may not be distributed evenly across the states. Some states that currently use large proportions of high-carbon energy sources may be in better positions--in terms of natural resource endowments and geography--than other states looking for low-carbon substitutes. For example, there is more wind energy potential in the western and mid-western states than in states in the Southeast.38 The above comparison also highlights the importance of selecting a baseline year for an emission reduction program. If emissions caps are compared to 1990 levels, it would reward states for reductions made during the 1990s. If the reduction program's baseline is 2000, for example, the reductions made before that year would not count, and these states may have more difficulty finding lower-cost options. ¢ ¡ Several members in the 110th Congress have introduced proposals that would establish a nation- wide GHG reduction program. Any emissions reduction regime would necessitate declines in GHG intensity. The declines needed would depend on the level of absolute reductions mandated by the enacted program. To stabilize national GHG emission growth, the entire United States would need to achieve annual reductions in GHG intensity of approximately 3% (assuming population and income continue to grow at a combined rate of 3%). Only four states--Delaware (3.7%), New Mexico (3.7%), Utah (3.4%), and Arizona (3.3%)--exceeded this annual rate of decline between 1990 and 2003; the average decline among all states was 1.7%.39 Reducing GHG emissions in the United States would necessitate further declines in GHG intensity. Several legislative proposals in the 110th Congress would require GHG emissions to return to 1990 levels by 2020.40 To meet this objective, national GHG intensity would need to decline annually (starting in 2010) by 5.0%.41 38 See National Renewable Energy Laboratory, Map of U.S. Annual Average Wind Power, at http://rredc.nrel.gov/ wind/pubs/atlas/maps.html#2-6. 39 The contrast between individual state intensity levels and the states' average level is only for comparison purposes. When calculating the states' average intensity level, all states are counted equally. Because of the significant variance in emissions between large and small states, the states' average intensity level may not coincide with the national intensity level. Ten states comprise approximately 50% of U.S. GHG emissions. The actions of these states will likely have greater effect on the national GHG intensity. 40 For example, S. 280 (Lieberman), S. 309 (Sanders), S. 485 (Kerry), H.R. 620 (Olver), and H.R. 1590 (Waxman). 41 This calculation assumes: (1) U.S. population will grow annually by 0.9% (U.S. Census Bureau, at http://www.census.gov/cgi-bin/ipc/idbsum.pl?cty=US)); (2) incomes will increase annually by 2.1% (the rate of increase from 1975 to 2003, WRI, Climate Analysis Indicators Tool); (3) GHG emissions were 6,240 MMTCO2E in (continued...) ¢ To put this goal in perspective, consider the 10 states that emitted the most GHGs in 2003 (accounting for approximately 50% of total U.S. emissions) and the GHG intensity annual average rates of change (between 1990 and 2003) for these states (Table 10). These states would likely need to make further reductions in GHG intensity if the national GHG intensity levels are to decline annually by 5% starting in 2010. Many of these states would need to more than double their current annual GHG intensity declines to reach a negative growth rate of 5%. -0991( setaR htworG )evitageN( launnA egarevA ytisnetnI snoissimE GHG .01 elbaT 3002 ni snoissimE GHG tsoM eht htiw setatS 01 eht rof )3002 etatS egarevA ytisnetnI snoissimE GHG )3002-0991( setaR htworG launnA saxeT 5.2- ainrofilaC 9.1- ainavlysnneP 1.2- oihO 7.1- adirolF 6.1- sionillI 6.1- anaidnI 1.2- kroY weN 6.1- nagihciM 6.2- anaisiuoL 6.0- .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS (...continued) 1990 (U.S. EPA, 2007, U.S. Inventory of Greenhouse Gas Emissions and Sinks 1990-2005, at http://www.epa.gov/ climatechange), and are projected to be 7,632 MMTCO2E in 2010 (based on a 1.0% annual average growth rate between 1990 and 2005). 373 X 534,24 X 336,8 = 731 yesreJ weN 964 X 128,53 X 682,1 = 22 erihspmaH weN 475 X 339,63 X 142,2 = 84 adaveN 880,1 X 395,43 X 737,1 = 56 aksarbeN 557,1 X 983,52 X 719 = 14 anatnoM 688 X 321,23 X 217,5 = 361 iruossiM 131,1 X 182,32 X 478,2 = 67 ippississiM 606 X 641,93 X 950,5 = 021 atosenniM 416 X 062,43 X 860,01 = 212 nagihciM 723 X 058,34 X 044,6 = 29 sttesuhcassaM 054 X 461,63 X 705,5 = 09 dnalyraM 396 X 236,82 X 703,1 = 62 eniaM 195,1 X 573,92 X 184,4 = 012 anaisiuoL 583,1 X 937,82 X 411,4 = 461 ykcutneK 661,1 X 866,13 X 727,2 = 101 sasnaK 331,1 X 184,23 X 249,2 = 801 awoI 513,1 X 280,33 X 291,6 = 962 anaidnI 165 X 818,73 X 056,21 = 962 sionillI 156 X 609,62 X 763,1 = 42 ohadI 055 X 081,43 X 642,1 = 32 iiawaH 126 X 822,43 X 057,8 = 681 aigroeG 325 X 845,03 X 289,61 = 172 adirolF 624 X 766,45 X 718 = 91 erawaleD 682 X 578,54 X 284,3 = 64 tucitcennoC 006 X 441,93 X 645,4 = 701 odaroloC 833 X 787,73 X 664,53 = 354 ainrofilaC 831,1 X 179,52 X 427,2 = 18 sasnakrA 155 X 492,13 X 285,5 = 69 anozirA 266,1 X 487,24 X 846 = 64 aksalA 343,1 X 041,72 X 594,4 = 461 amabalA PSG nosrep/PSG s000,1 ni E2OCTMM fo noillim$ / E2OCT etatS ytisnetnI GHG emocnI noitalupoP snoissimE atipac reP GHG )atad 3002( yllacitebahplA detsiL ,setatS 05 llA rof srevirD snoissimE GHG dna snoissimE GHG .1-A elbaT ¡ ¢ )PSG / E2OCTMM( )nosreP/PSG( )snosreP( )E2OCTMM( ytisnetnI GHG X emocnI atipaC reP X noitalupoP = snoissimE GHG Equation 1: .)PSG fo rallod eno fo daetsni( PSG fo srallod noillim ni dna E2OC fo )snot cirtem noillim fo daetsni( snot cirtem ni detneserp si erugif ytisnetni GHG eht dna ;s000,1 ni si etats hcae rof erugif noitalupop eht elbat evoba eht ni taht eton ,ralucitrap nI .erapmoc ot reisae dna elbatneserp erom serugif eht ekam ot deretla neeb evah stinu eht tub ,)woleb niaga dedivorp( 1 noitauqE eht no desab era evoba snoitaluclac ehT :etoN .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS 997,3 X 758,73 X 105 = 27 gnimoyW 666 X 997,33 X 764,5 = 321 nisnocsiW 790,3 X 807,32 X 908,1 = 331 ainigriV tseW 124 X 216,63 X 031,6 = 59 notgnihsaW 705 X 801,83 X 673,7 = 341 ainigriV 993 X 396,13 X 916 = 8 tnomreV 779 X 511,03 X 653,2 = 96 hatU 510,1 X 738,43 X 431,22 = 287 saxeT 547 X 325,23 X 438,5 = 141 eessenneT 060,1 X 176,33 X 467 = 72 atokaD htuoS 177 X 908,82 X 241,4 = 29 aniloraC htuoS 943 X 409,33 X 570,1 = 31 dnalsI edohR 437 X 422,33 X 153,21 = 103 ainavlysnneP 534 X 528,23 X 165,3 = 15 nogerO 803,1 X 740,72 X 405,3 = 421 amohalkO 887 X 471,33 X 834,11 = 992 oihO 588,2 X 464,13 X 336 = 75 atokaD htroN 185 X 882,43 X 614,8 = 861 aniloraC htroN 403 X 137,14 X 832,91 = 442 kroY weN 632,1 X 095,82 X 878,1 = 66 ocixeM weN PSG nosrep/PSG s000,1 ni E2OCTMM fo noillim$ / E2OCT etatS ytisnetnI GHG emocnI noitalupoP snoissimE atipac reP GHG ¢ 131,1 X 182,32 X 478,2 = 67 23 ippississiM 831,1 X 179,52 X 427,2 = 18 13 sasnakrA 054 X 461,63 X 705,5 = 09 03 dnalyraM 723 X 058,34 X 044,6 = 29 92 sttesuhcassaM 177 X 908,82 X 241,4 = 29 82 aniloraC htuoS 124 X 216,63 X 031,6 = 59 72 notgnihsaW 155 X 492,13 X 285,5 = 69 62 anozirA 661,1 X 866,13 X 727,2 = 101 52 sasnaK 006 X 441,93 X 645,4 = 701 42 odaroloC 331,1 X 184,23 X 249,2 = 801 32 awoI 606 X 641,93 X 950,5 = 021 22 atosenniM 666 X 997,33 X 764,5 = 321 12 nisnocsiW 803,1 X 740,72 X 405,3 = 421 02 amohalkO 790,3 X 807,32 X 908,1 = 331 91 ainigriV tseW 373 X 534,24 X 336,8 = 731 81 yesreJ weN 547 X 325,23 X 438,5 = 141 71 eessenneT 705 X 801,83 X 673,7 = 341 61 ainigriV 688 X 321,23 X 217,5 = 361 51 iruossiM 583,1 X 937,82 X 411,4 = 461 41 ykcutneK 343,1 X 041,72 X 594,4 = 461 31 amabalA 185 X 882,43 X 614,8 = 861 21 aniloraC htroN 126 X 822,43 X 057,8 = 681 11 aigroeG 195,1 X 573,92 X 184,4 = 012 01 anaisiuoL 416 X 062,43 X 860,01 = 212 9 nagihciM 403 X 137,14 X 832,91 = 442 8 kroY weN 513,1 X 280,33 X 291,6 = 962 7 anaidnI 165 X 818,73 X 056,21 = 962 6 sionillI 325 X 845,03 X 289,61 = 172 5 adirolF 887 X 471,33 X 834,11 = 992 4 oihO 437 X 422,33 X 153,21 = 103 3 ainavlysnneP 833 X 787,73 X 664,53 = 354 2 ainrofilaC 510,1 X 738,43 X 431,22 = 287 1 saxeT PSG fo nosrep/PSG s000,1 ni E2OCTMM noillim$ / E2OCT knaR etatS ytisnetnI GHG emocnI noitalupoP snoissimE atipac reP GHG )atad 3002( snoissimE GHG yb deknaR ,setatS 05 llA rof srevirD snoissimE GHG dna snoissimE GHG .2-A elbaT ¢ %2.0- + %6.0- + %9.0 = %1.0 iiawaH %6.2- + %0.2 + %3.2 = %6.1 aigroeG %6.1- + %7.1 + %1.2 = %1.2 adirolF %7.3- + %1.2 + %5.1 = %2.0- erawaleD %5.1- + %5.1 + %4.0 = %4.0 tucitcennoC %9.2- + %7.2 + %5.2 = %2.2 odaroloC %9.1- + %3.1 + %3.1 = %7.0 ainrofilaC %8.1- + %4.2 + %1.1 = %6.1 sasnakrA %3.3- + %7.2 + %2.3 = %5.2 anozirA %1.3 + %3.2- + %2.1 = %9.1 aksalA %2.1- + %9.1 + %8.0 = %4.1 amabalA ytisnetnI GHG emocnI atipac reP noitalupoP snoissimE GHG etatS setatS 05 llA rof srevirD snoissimE GHG dna snoissimE GHG rof )3002-0991( setaR htworG launnA egarevA .3-A elbaT .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS 993 X 396,13 X 916 = 8 05 tnomreV 943 X 409,33 X 570,1 = 31 94 dnalsI edohR 624 X 766,45 X 718 = 91 84 erawaleD 964 X 128,53 X 682,1 = 22 74 erihspmaH weN 055 X 081,43 X 642,1 = 32 64 iiawaH 156 X 609,62 X 763,1 = 42 54 ohadI 396 X 236,82 X 703,1 = 62 44 eniaM 060,1 X 176,33 X 467 = 72 34 atokaD htuoS 557,1 X 983,52 X 719 = 14 24 anatnoM 682 X 578,54 X 284,3 = 64 14 tucitcennoC 266,1 X 487,24 X 846 = 64 04 aksalA 475 X 339,63 X 142,2 = 84 93 adaveN 534 X 528,23 X 165,3 = 15 83 nogerO 588,2 X 464,13 X 336 = 75 73 atokaD htroN 880,1 X 395,43 X 737,1 = 56 63 aksarbeN 632,1 X 095,82 X 878,1 = 66 53 ocixeM weN 779 X 511,03 X 653,2 = 96 43 hatU 997,3 X 758,73 X 105 = 27 33 gnimoyW PSG fo nosrep/PSG s000,1 ni E2OCTMM noillim$ / E2OCT knaR etatS ytisnetnI GHG emocnI noitalupoP snoissimE atipac reP GHG ¢ %8.1- + %9.1 + %1.0 = %1.0 ainigriV tseW %4.2- + %6.1 + %7.1 = %9.0 notgnihsaW %2.2- + %8.1 + %3.1 = %8.0 ainigriV %5.1- + %0.2 + %7.0 = %3.1 tnomreV %4.3- + %3.2 + %4.2 = %2.1 hatU %5.2- + %0.2 + %0.2 = %4.1 saxeT %5.2- + %5.2 + %4.1 = %3.1 eessenneT %5.2- + %6.3 + %7.0 = %6.1 atokaD htuoS %9.0- + %9.1 + %3.1 = %3.2 aniloraC htuoS %0.0 + %7.1 + %5.0 = %3.2 dnalsI edohR %1.2- + %0.2 + %3.0 = %2.0 ainavlysnneP %6.2- + %1.3 + %7.1 = %1.2 nogerO %2.1- + %5.1 + %8.0 = %1.1 amohalkO %7.1- + %1.2 + %4.0 = %7.0 oihO %6.1- + %1.3 + %1.0- = %4.1 atokaD htroN %7.1- + %1.2 + %8.1 = %3.2 aniloraC htroN %6.1- + %4.1 + %5.0 = %4.0 kroY weN %7.3- + %2.3 + %6.1 = %0.1 ocixeM weN %7.1- + %6.1 + %8.0 = %7.0 yesreJ weN %5.1- + %8.2 + %1.1 = %4.2 erihspmaH weN %7.2- + %8.0 + %8.4 = %8.2 adaveN %5.1- + %4.2 + %7.0 = %6.1 aksarbeN %7.1- + %8.1 + %1.1 = %1.1 anatnoM %9.0- + %0.2 + %8.0 = %9.1 iruossiM %7.0- + %0.2 + %8.0 = %1.2 ippississiM %2.2- + %7.2 + %1.1 = %5.1 atosenniM %6.2- + %4.2 + %6.0 = %4.0 nagihciM %5.2- + %3.2 + %5.0 = %3.0 sttesuhcassaM %5.1- + %4.1 + %1.1 = %9.0 dnalyraM %3.0- + %4.1 + %5.0 = %6.1 eniaM %6.0- + %1.0 + %5.0 = %0.0 anaisiuoL %5.1- + %1.2 + %8.0 = %4.1 ykcutneK %6.1- + %8.1 + %7.0 = %9.0 sasnaK %9.1- + %6.2 + %4.0 = %1.1 awoI %1.2- + %6.2 + %8.0 = %4.1 anaidnI %6.1- + %0.2 + %8.0 = %2.1 sionillI %7.2- + %6.2 + %3.2 = %2.2 ohadI ytisnetnI GHG emocnI atipac reP noitalupoP snoissimE GHG etatS ¢ 084,3 X 14.0 = 244,1 anatnoM 059,2 X 52.0 = 077 iruossiM 001,2 X 54.0 = 399 ippississiM 022,2 X 32.0 = 015 atosenniM 023,2 X 32.0 = 755 nagihciM 071,2 X 41.0 = 803 sttesuhcassaM 050,2 X 02.0 = 114 dnalyraM 049,1 X 23.0 = 746 eniaM 080,2 X 17.0 = 805,1 anaisiuoL 030,3 X 04.0 = 742,1 ykcutneK 087,2 X 33.0 = 539 sasnaK 046,2 X 13.0 = 938 awoI 041,3 X 63.0 = 122,1 anaidnI 023,2 X 12.0 = 794 sionillI 012,1 X 23.0 = 404 ohadI 047,2 X 81.0 = 205 iiawaH 022,2 X 52.0 = 965 aigroeG 062,2 X 12.0 = 874 adirolF 041,2 X 81.0 = 293 erawaleD 098,1 X 41.0 = 962 tucitcennoC 026,2 X 91.0 = 905 odaroloC 009,1 X 51.0 = 592 ainrofilaC 081,2 X 04.0 = 339 sasnakrA 075,2 X 02.0 = 715 anozirA 003,2 X 96.0 = 426,1 aksalA 096,2 X 24.0 = 871,1 amabalA eot 0001 / 2OCT X PSG noillim$ / eot = PSG fo noillim$ / 2OCT etatS esU ygrenE X ytisnetnI ygrenE = ytisnetnI fo tnetnoC nobraC snoissimE 2OC )atad 3002( yllacitebahplA detsiL ,setatS 05 llA rof srevirD ytisnetnI snoissimE 2OC dna ytisnetnI snoissimE 2OC .4-A elbaT .eurt sdloh pihsnoitaler lareneg eht ,sselehtreveN .sesac lla ni snoissime GHG fo etar eht lauqe ylesicerp ton yam setar revird snoissime GHG eht fo mus ehT :etoN .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS %8.0- + %0.1 + %8.0 = %9.0 gnimoyW %2.2- + %6.2 + %8.0 = %2.1 nisnocsiW ytisnetnI GHG emocnI atipac reP noitalupoP snoissimE GHG etatS ¢ .)larutlucirga ,.g.e( rotces ygrene eht edistuo secruos morf emoc taht snoissime 2OC 'setats eht fo )%2 egareva no( egatnecrep llams eht stcelfer ecnereffid sihT .eulav ytisnetni snoissime 2OC eht naht rewol ylthgils si eulav esu ygrene fo tnetnoc nobrac dna eulav ytisnetni ygrene eht fo tcudorp eht ,setats ruof tub lla nI :etoN .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS 064,5 X 16.0 = 374,3 gnimoyW 062,2 X 52.0 = 175 nisnocsiW 087,5 X 64.0 = 917,2 ainigriV tseW 066,1 X 22.0 = 563 notgnihsaW 000,2 X 22.0 = 344 ainigriV 066,1 X 02.0 = 233 tnomreV 074,3 X 52.0 = 588 hatU 072,2 X 04.0 = 339 saxeT 051,2 X 03.0 = 276 eessenneT 040,2 X 62.0 = 855 atokaD htuoS 099,1 X 43.0 = 107 aniloraC htuoS 099,1 X 61.0 = 033 dnalsI edohR 096,2 X 42.0 = 876 ainavlysnneP 045,1 X 32.0 = 653 nogerO 047,2 X 04.0 = 411,1 amohalkO 026,2 X 62.0 = 827 oihO 077,4 X 05.0 = 004,2 atokaD htroN 091,2 X 32.0 = 115 aniloraC htroN 030,2 X 31.0 = 172 kroY weN 044,3 X 13.0 = 880,1 ocixeM weN 049,1 X 81.0 = 643 yesreJ weN 094,2 X 81.0 = 644 erihspmaH weN 016,2 X 02.0 = 825 adaveN 036,2 X 72.0 = 737 aksarbeN eot 0001 / 2OCT X PSG noillim$ / eot = PSG fo noillim$ / 2OCT etatS esU ygrenE X ytisnetnI ygrenE = ytisnetnI fo tnetnoC nobraC snoissimE 2OC ¢ 026,2 X 91.0 = 905 33 odaroloC 022,2 X 32.0 = 015 23 atosenniM 091,2 X 32.0 = 115 13 aniloraC htroN 075,2 X 02.0 = 715 03 anozirA 016,2 X 02.0 = 825 92 adaveN 023,2 X 32.0 = 755 82 nagihciM 040,2 X 62.0 = 855 72 atokaD htuoS 022,2 X 52.0 = 965 62 aigroeG 062,2 X 52.0 = 175 52 nisnocsiW 049,1 X 23.0 = 746 42 eniaM 051,2 X 03.0 = 276 32 eessenneT 096,2 X 42.0 = 876 22 ainavlysnneP 099,1 X 43.0 = 107 12 aniloraC htuoS 026,2 X 62.0 = 827 02 oihO 036,2 X 72.0 = 737 91 aksarbeN 059,2 X 52.0 = 077 81 iruossiM 046,2 X 13.0 = 938 71 awoI 074,3 X 52.0 = 588 61 hatU 081,2 X 04.0 = 339 51 sasnakrA 072,2 X 04.0 = 339 41 saxeT 087,2 X 33.0 = 539 31 sasnaK 001,2 X 54.0 = 399 21 ippississiM 044,3 X 13.0 = 880,1 11 ocixeM weN 047,2 X 04.0 = 411,1 01 amohalkO 096,2 X 24.0 = 871,1 9 amabalA 041,3 X 63.0 = 122,1 8 anaidnI 030,3 X 04.0 = 742,1 7 ykcutneK 084,3 X 14.0 = 244,1 6 anatnoM 080,2 X 17.0 = 805,1 5 anaisiuoL 003,2 X 96.0 = 426,1 4 aksalA 077,4 X 05.0 = 004,2 3 atokaD htroN 087,5 X 64.0 = 917,2 2 ainigriV tseW 064,5 X 16.0 = 374,3 1 gnimoyW eot 0001 / 2OCT X PSG = PSG fo noillim$ / eot noillim$ / 2OCT knaR etatS esU ygrenE X ytisnetnI = ytisnetnI fo tnetnoC nobraC ygrenE snoissimE 2OC )atad 3002( ytisnetnI snoissimE 2OC yb deknaR ,setatS 05 llA rof srevirD ytisnetnI snoissimE 2OC dna ytisnetnI snoissimE 2OC .5-A elbaT ¢ jramseur@crs.loc.gov, 7-7919 Analyst in Environmental Policy Jonathan L. Ramseur .)larutlucirga ,.g.e( rotces ygrene eht edistuo secruos morf emoc taht snoissime 2OC 'setats eht fo )%2 egareva no( egatnecrep llams eht stcelfer ecnereffid sihT .eulav ytisnetni snoissime 2OC eht naht rewol ylthgils si eulav esu ygrene fo tnetnoc nobrac dna eulav ytisnetni ygrene eht fo tcudorp eht ,setats ruof tub lla nI :etoN .looT srotacidnI sisylanA etamilC ,IRW eht morf atad htiw SRC yb deraperP :ecruoS 098,1 X 41.0 = 962 05 tucitcennoC 030,2 X 31.0 = 172 94 kroY weN 009,1 X 51.0 = 592 84 ainrofilaC 071,2 X 41.0 = 803 74 sttesuhcassaM 099,1 X 61.0 = 033 64 dnalsI edohR 066,1 X 02.0 = 233 54 tnomreV 049,1 X 81.0 = 643 44 yesreJ weN 045,1 X 32.0 = 653 34 nogerO 066,1 X 22.0 = 563 24 notgnihsaW 041,2 X 81.0 = 293 14 erawaleD 012,1 X 23.0 = 404 04 ohadI 050,2 X 02.0 = 114 93 dnalyraM 000,2 X 22.0 = 344 83 ainigriV 094,2 X 81.0 = 644 73 erihspmaH weN 062,2 X 12.0 = 874 63 adirolF 023,2 X 12.0 = 794 53 sionillI 047,2 X 81.0 = 205 43 iiawaH eot 0001 / 2OCT X PSG = PSG fo noillim$ / eot noillim$ / 2OCT knaR etatS esU ygrenE X ytisnetnI = ytisnetnI fo tnetnoC nobraC ygrenE snoissimE 2OC ¢ ------------------------------------------------------------------------------ For other versions of this document, see http://wikileaks.org/wiki/CRS-RL34272