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. 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. 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. Because carbon dioxide (CO 2 ) is the primary GHG in the vast majority of states, the report focuses on CO 2 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. Three broad factors influence GHG emission levels in a nation or state: population, per capita income, and GHG emissions intensity of the economy. A state's GHG emission levels can be approximated by multiplying together these three variables. Equation 1 expresses this relationship: The equation indicates that each of the variables can play a significant role in shaping a state's GHG emissions. For instance, if one of these variables increases, while the other two remain constant, GHG emissions will increase. The three emissions drivers do not operate independently of one another: a change in one variable may influence another variable. The three variables—population, per capita income, and GHG emissions intensity—differ substantially among the states and play varying roles when determining a state's GHG emissions. Table 1 shows this relationship for the 10 U.S. states with the highest GHG emission levels in 2003. These 10 states accounted for almost 50% of total U.S. GHG emissions in 2003. A similar table for all 50 states is included in the Appendix to this report. 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 ): 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, 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%; 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." 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. For example, the Administration has stated that meeting the U.S. emissions intensity target would lead to GHG emission reductions. 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 whether it applies to emissions intensity or absolute emissions. 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. What factors determine a state's intensity and lead to the wide variances among the states? In the United States, carbon dioxide (CO 2 ) emissions have historically accounted for 85% of the nation's GHG emissions, excluding land use changes and forestry. In all but four states, CO 2 emissions accounted for at least 80% of the state's GHG emissions in 2003. As the dominant GHG, the intensity of CO 2 emissions significantly impacts the overall GHG intensity. If Table 3 were to rank states based on CO 2 emissions intensity, the results would be nearly identical. Due to the dominance of CO 2 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 CO 2 emissions intensity and its drivers for all 50 states). Approximately 98% of the U.S. CO 2 emissions in 2003 were from energy use. The primary factors that determine CO 2 emissions intensity in a state are its energy intensity and the carbon content of its energy use (or fuel mix). The relationship between CO 2 emissions intensity, energy intensity and carbon content of energy use is shown in Equation 2 . 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. 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 industries or low-energy industries. 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. The transportation sector accounts for over a quarter (28%) of total energy consumption in the United States. Within the transportation sector, personal transportation—i.e., cars, light trucks, and motorcycles—accounts for the majority of energy use (64% in 2004). 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. 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. 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 15 th ). 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, 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. The American Council for an Energy-Efficient Economy (ACEEE) published an energy efficiency scorecard that ranks the states based on their energy efficiency policies. 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. Conversely, the states with high energy intensities received low ACEEE rankings. 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). 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, 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 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 (45 th ), Wyoming (50 th ), North Dakota (48 th ), and West Virginia (40 th ). 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 24 th 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 (50 th ) for absolute GSP, and 49 th 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 CO 2 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 CO 2 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 CO 2 per tons of oil equivalent, toe). 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 CO 2 emissions in many states and is the highest emitting sector in the United States, accounting for approximately 40% of U.S. CO 2 emissions. Electricity can be generated from a variety of energy sources, which vary significantly by their ratio of CO 2 emissions per unit of energy. A coal-fired power plant emits almost twice as much CO 2 (per unit of energy) as a natural gas-fired facility. Some energy sources—e.g., hydropower, nuclear, wind, or solar—do not directly release any CO 2 emissions. Although the transportation sector contributes a significant percentage of CO 2 emissions in most states (and 33% of U.S. CO 2 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 CO 2 emissions per unit of energy. 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. 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. 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 CO 2 emissions are attributed to the generating state, but the energy use is attributed to the consuming state. 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. 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). This electricity is counted as energy use in Idaho, while the CO 2 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. 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 CO 2 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. 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. 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. 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 financially. 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. 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 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. 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 110 th 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%. Reducing GHG emissions in the United States would necessitate further declines in GHG intensity. Several legislative proposals in the 110 th Congress would require GHG emissions to return to 1990 levels by 2020. To meet this objective, national GHG intensity would need to decline annually (starting in 2010) by 5.0%. 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%. Equation 1:

Summary: 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.