State Greenhouse Gas Emissions: Comparison and Analysis

^Pr^ep^ar^{ed f}^o^r^M^e^m^b^ers^an^d^Com^mⁱ^t^t^ees^of^C^ong^r^e^ss

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.

Introduc tion ..................................................................................................................................... 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 ¹
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 ²
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 ³
overall economic output, or gross state product. 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^T^h^is^r^e^por^t^doe^s^{not a}^ddr^e^s^s^the^de^ba^te^s^a^s^s^o^cⁱ^a^t^e^d^w^ith^c^lim^a^t^e^c^h^aⁿ^g^e^sc^ie^nc^e^{or the}^r^o^le^of^hum^aⁿ^a^c^tiv^ityⁱⁿ
^c^lim^a^t^e^c^h^aⁿ^g^e^{. For}^a^dis^c^us^sⁱ^on^of^the^s^e^is^s^u^e^s^{, s}^e^e^C^R^S^R^e^por^{t R}^L³³⁸⁴^9,^C^lim^ate^C^h^an^ge^:^Sc^ie^nc^e^and^Po^lic^y
^I^m^plic^ati^ons^,^by^J^a^ne^A^.^L^e^g^g^e^tt.
²^Se^e^C^R^S^R^e^por^{t R}^L³³⁸²⁶^,^C^lim^ate^C^h^an^ge^:^The^K^y^o^t^o^Pr^ot^oc^ol,^Bali^"Ac^tio^{n P}^l^aⁿ^,"^a^{nd I}ⁿ^te^rⁿ^a^tⁱ^ona^{l Ac}^ti^ons^{, by}
^Sus^a^{n R}^.^Fle^t^c^h^e^r^aⁿ^{d L}^a^r^r^y^P^a^r^k^e^r^{; C}^R^S^R^e^por^t^R^L³¹⁹³¹^,^C^lim^a^t^e^C^han^ge^:^Fe^de^r^a^l^Law^s^a^{nd P}^o^li^cⁱ^e^s^Re^late^{d to}
^G^r^e^e^nhous^e^G^a^s^Re^duc^ti^ons^,^by^B^r^eⁿ^{t D}^.^Y^a^c^o^b^u^c^cⁱ^a^{nd L}^a^r^r^y^P^a^r^k^e^r^{; C}^R^S^R^e^por^{t R}^L³³⁸¹^2,^C^lim^ate^C^h^an^ge^:^Ac^tioⁿ
^by^St^ate^s^To^Ad^dr^e^s^s^G^r^e^e^nhous^e^G^a^s^Em^is^sⁱ^oⁿ^s^{, by}^J^ona^thaⁿ^L^.^R^a^m^s^e^u^r^.
³^G^H^G^emⁱ^s^sⁱ^ons^inte^nsⁱ^t^y^is^a^pe^r^f^or^m^a^nc^e^m^e^a^s^ur^e^.^W^h^eⁿ^look^ing^a^t^e^m^is^sⁱ^ons^{on a}ⁿ^e^c^o^nom^y^-^wⁱ^de^s^c^a^l^e^,^g^r^os^s
^dom^e^s^tic^pr^oduc^t⁽^G^D^P⁾^or^g^r^os^s^s^t^a^t^e^pr^od^uc^{t (}^G^SP⁾^is^ty^pic^a^l^l^y^us^e^d^{. H}^o^w^e^v^e^r^,^othe^r^e^c^onom^ic^ou^tputs^,^s^u^c^h^a^s^a
^tons^of^s^t^e^e^l^or^c^e^m^eⁿ^{t, m}^a^y^be^use^d^{to a}ⁿ^a^l^y^z^e^the^e^m^issⁱ^ons^inte^ns^ity^of^s^p^e^cⁱ^fⁱ^c^s^our^c^e^s^or^e^c^onom^ic^s^e^c^t^or^s^.^A
^hⁱ^gh^{er G}^H^Gⁱⁿ^t^eⁿ^sⁱ^t^y^v^a^l^u^{e (co}^m^p^a^red^t^o^o^t^h^e^{r st}^at^{es) i}ⁿ^dⁱ^cat^{es t}^h^at^{a st}^at^{e gen}^e^rat^e^s^m^o^{re e}^mⁱ^ssi^oⁿ^s^p^e^{r eco}^no^mⁱ^c
^out^put⁽ⁱ^.e^{., G}^S^P⁾^t^h^aⁿ^othe^r^s^t^a^t^e^s^.

Greenhouse Gas Emissions Data in This Report
^Green^{house ga}^{s (GH}^{G) emissions data can}^{be described in}^several^{different ways, w}^{hich may}^{lead to inconsistencies}
^{when com}^{paring data f}^{rom differ}^{ent sourc}^es.
^{In this}^{report, G}^{HG emissions in}^{clude the following gases: carbon}^{dioxide (CO}²⁾^,^{nitrous oxide, methane,}
^perfluorocar^{bons, hydrofluoroca}^{rbons, and s}^ulfur^he^{xafluoride. Only emissions from h}^uma^{n-related activities are}
^{included. To examine the emissions data in aggregate, data f}^{rom th}^{e six gases are}^{converted (}^based^{on the global}
^{warming potential of the ga}^{s) int}^{o a single unit of meas}^{ure: million}^{metric tons of car}^{bon dioxide-equivalents}
^(MMTCO²^{E). One}^milli^{on metric}^{tons eq}^{uals one teragra}^{m (10}¹²^gr^{ams), a measure}^{used b}^y^{some so}^{urces to desc}^ribe
^{emission levels. Moreover, other reports may}^{provide emissions data in metric tons of ca}^rbon-e^qui^{valents. To}
^{convert car}^bon-eq^{uivalents to C}^O²^{-equivalents, multiply car}^bon-e^quivalents^{by 4}^4/1^2.
^{Unless otherwise noted, the data}^{in this re}^{port come from t}^{he W}^{orld Resources Institute’s Climate Analysis}
^{Indicators Tool (CAIT}^{). The CAIT state data are compiled using t}^{he E}^nvironme^{ntal Protection Agency’s State}
^Inventor^{y Tool and default data for each state. Ma}^{ny state}^{s have}^p^{repared t}^{heir own emissions inve}^{ntories with more}
^{precise data, but}^{most of thes}^{e in}^{ventories only cover 1}⁹⁹⁰^emissi^{ons. Although the}^{re may}^{be slight}^data
^{discrepancies}^{between CA}^{IT and}^{the state inve}^{ntories, CAIT serve}^{s as a homogeneou}^{s data source,}^providing
^{estimates for all states and all G}^{HGs t}^hroug^{h 2}⁰⁰^3.
^{This report does not include land}^{use, land use}^cha^{nges, or forestr}^{y (LULUCF) in emissions or inten}^{sity data. Data}
^{from these}^sources^{are ge}^{nerally considered less robust t}^han^{data from other sou}^rces.

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:
^{Equation 1:}
^{GHG Emissions}⁼^Population^X^{Per Capita Inco}^me^X^{GHG Intensit}^y
^(MMTCO²^E)^(Persons)^(GSP/Person)^(MMTCO²^{E /}^GSP)
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 ⁴
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.

⁴^For^f^u^r^t^he^r^dis^c^us^sⁱ^oⁿ^s^e^e^C^R^S^R^e^por^{t R}^L³³⁹⁷^0,^G^r^e^eⁿ^h^ous^e^G^a^s^Em^is^sⁱ^{on D}^r^iv^e^r^s^:^Pop^u^l^a^tio^n,^Ec^onom^ic
^Devel^o^p^m^en^t^an^d^Gro^w^t^h^,^an^d^Eⁿ^erg^y^Use^{, by}^J^{ohn B}^l^odg^e^{tt a}ⁿ^d^L^a^r^r^y^P^a^r^k^e^r^{; s}^e^e^a^l^s^o^K^e^vⁱ^{n B}^a^um^e^r^{t, e}^t^a^l^{., 2}⁰⁰⁵^,
^N^a^vⁱ^gatⁱⁿ^{g t}^h^e^N^u^m^b^e^r^s^:^G^r^e^eⁿ^h^ous^e^G^a^s^D^a^ta^an^{d I}ⁿ^te^r^nat^io^nal^C^lim^ate^Po^lic^y^{, W}^o^{rld Re}^so^urc^e^s^Institute^.

Table 1. Comparison of GHG Emission Drivers for the 10 U.S. States with the
Highest GHG Emissions Levels in 2003
^{GHG Emissions}^Population^{Per capita Inc}^ome^{GHG Intensit}^y
^State^TCO²^E^{/ $million}
^MMTCO²^E^{in 1,00}^0s^GSP/person^{of GSP}
^{Texas 782}^22,1³⁴^34,8³⁷^1,01⁵
^{California 453}^35,4⁶⁶^37,7⁸⁷³³⁸
^{Pennsylvania 301}^12,3⁵¹^33,2²⁴⁷³⁴
^{Ohio 299}^11,4³⁸^33,1⁷⁴^1,30⁸
^{Florida 271}^16,9⁸²^30,5⁴⁸⁵²³
^{Illinois 269}^12,6⁵⁰^37,8¹⁸⁵⁶¹
^{Indiana 269}^6,19²^33,0⁸²^1,31⁵
^{New York}²⁴⁴^19,2³⁸^41,7³¹³⁰⁴
^{Michigan 212}^10,0⁶⁸^34,2⁶⁰⁶¹⁴
^{Louisiana 210}^4,48¹^29,3⁷⁵^1,59¹
^{Average fo}^{r all}^{132 5,70}²^35,4⁰⁴⁹²¹
^{50 Sta}^tes
^Source:^Prepa^{red by Cong}^{ressional Research Service (CRS) with}^{data from the World Resources}^I^nstitute
^{(WRI), C}^{limate Analysis Indicators Tool}^.
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. Average Annual Rates of Change for GHG Emissions and Drivers for the
Entire United States: 1990-2000
^{GHG Emissions}^Population^{Per Capita Inco}^me^{GHG Intensit}^y
^1.4%^{= 1.2% +}^1.8%⁺^-1.6%
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
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 ⁵
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 ⁶
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 ⁷
“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 ⁸
emission levels. For example, the Administration has stated that meeting the U.S. emissions ⁹
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

⁵^For^e^x^a^m^ple^,^the^Eur^ope^aⁿ^Uⁿ^io^n’^s^Em^is^sⁱ^ons^T^r^a^d^ing^Sc^he^m^e^aⁿ^{d the}^K^y^oto^P^r^ot^oc^{ol r}^e^q^u^ir^e^a^c^t^u^a^{l em}^is^sⁱ^on
^r^e^duc^tio^ns^{. R}^e^d^u^c^tioⁿ^pr^og^r^a^m^s^u^nde^r^de^v^e^lo^pm^eⁿ^{t a}^t^the^s^t^a^t^e^le^v^e^{l a}^l^s^o^r^e^q^u^ir^e^a^c^t^ua^{l r}^e^d^u^c^tio^ns⁽^e^.^g^{., C}^a^lif^or^nia^a^nd
^the^R^e^gⁱ^ona^{l G}^r^e^e^nhous^e^G^a^s^Iⁿ^it^ia^tiv^e⁾^.
⁶^A^lthoug^{h t}^h^e^A^d^mⁱ^nis^t^r^a^tion’^s^s^upp^or^ti^ng^d^o^c^u^m^e^{nt us}^e^s^18%^{, t}^h^e^doc^um^eⁿ^{t a}^l^s^o^s^t^a^t^e^s^tha^t^t^h^e^g^o^a^{l is}^{to r}^e^d^u^c^e
^inte^ns^ity^f^r^o^m^{183 t}^o¹⁵¹^(m^e^t^ric^tons^of^c^a^r^{bon e}^q^uiv^a^le^nt^pe^{r m}^illion^do^lla^rs^of^g^r^os^s^dom^e^s^tic^prod^uc^{t), a}¹⁷^.⁵^%
^r^e^duc^tio^n.
⁷^Se^e^H^e^rz^og^{, T}ⁱ^m^o^thy^,^e^t^a^l^{., 200}^6,^T^a^r^g^e^t^Iⁿ^te^ns^ity^:^Aⁿ^A^naly^s^is^o^f^G^r^e^e^nhous^e^G^a^s^Iⁿ^te^ns^ity^Tar^g^e^t^s^{, W}^R^I^R^e^por^t,
^pp.^15-¹⁶^.
⁸^S^ee,^P^e^w^Cen^t^{er o}ⁿ^G^l^o^b^a^l^Clⁱ^m^at^{e Ch}^an^ge,^Ana^l^y^s^is^{of Pr}^e^s^ideⁿ^t^Bus^h^’^s^C^l^im^ate^C^h^a^nge^Pl^an^{, a}^t
^http:^//w^w^w^.pe^w^c^lⁱ^m^a^t^e^.^or^g^/^pol^ic^y^_^ce^nte^r^/a^na^ly^s^e^s^/^r^e^s^pons^e^_^bus^h^polic^y^.^c^f^m^.
⁹^T^h^e^Ex^e^c^u^tiv^e^Sum^m^a^r^y^de^s^c^ribi^ng^theⁱⁿ^te^ns^ity^ta^rg^e^t^s^t^a^t^e^s^{: “}^t^he^P^r^e^s^ideⁿ^t’s^c^o^m^m^it^m^e^{nt w}^{ill a}^c^h^ie^v^e^{100 m}^illion
^m^e^tric^{tons of}^re^duc^e^d^e^m^{issions in 2}⁰¹²^a^l^one^{, w}^{ith m}^o^re^thaⁿ⁵⁰^{0 m}^{illion m}^e^tric^{tons i}ⁿ^c^u^m^u^la^tiv^e^sa^vⁱ^ng^{s ov}^e^r^the
^en^{tire d}^ecad^{e.” See h}^t^t^p^://w^ww^.^w^h^iteh^o^u^s^e.g^o^v^/ⁿ^e^w^s^/^r^eleases/²⁰⁰²^/⁰²^/^clim^ate^c^h^aⁿ^g^e.h^t^m^l^.

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.
Table 3. States with the Five Highest and Five Lowest GHG Intensity Levels (2003)
^{States wi}^{th Five}^{GHG Intensit}^y^{States wi}^{th Five Lowest}^{GHG Intensit}^y
^{Highest GHG Int}^ensity^(TCO²^{E / $million o}^f^{GHG Intensit}^{y Lev}^els^(TCO²^{E / $million o}^f
^Levels^GSP)^GSP)
^{Wyoming 3,79}⁹^Connecticut²⁸⁶
^{West Virginia}^3,09⁷^{New York}³⁰⁴
^{North Dakota}^2,88⁵^Massach^usetts³²⁷
^{Montana 1,75}^California³³⁸
^{Alaska 1,66}²^Rhode^Island³⁴⁹
^{Average for all 50}^{states: 9}⁷⁹
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
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 ¹¹
nation’s GHG emissions, excluding land use changes and forestry. In all but four states, 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 ¹²
were to rank states based on CO² emissions intensity, the results would be nearly identical. 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^Se^e^H^e^rz^og^{, T}ⁱ^m^o^thy^,^e^t^a^l^{., 200}^6,^T^a^r^g^e^t^Iⁿ^te^ns^ity^:^Aⁿ^A^naly^s^is^o^f^G^r^e^e^nhous^e^G^a^s^Iⁿ^te^ns^ity^Tar^g^e^t^s^{, W}^R^I^R^e^por^t,
^pp.^15-¹⁶^.
¹¹^T^h^e^f^our^s^t^a^t^e^s^tha^t^e^m^{it r}^e^la^tiv^e^l^y^la^r^g^e^pe^r^c^eⁿ^ta^ge^s^of^non-^C^O²^G^H^G^em^is^sⁱ^ons^inc^l^u^d^e^S^out^{h D}^a^k^o^ta⁽⁴^7%⁾^,^I^d^a^h^o
⁽^38%⁾^,^N^e^br^a^s^k^a⁽^32%⁾^,^aⁿ^{d I}^o^w^a⁽^26%⁾^.
¹²^Wy^o^m^ing^,^W^e^s^t^Vⁱ^rg^inia^{, N}^o^rth^D^a^k^o^ta^{, A}^l^a^s^k^a^{, a}^{nd L}^ouisⁱ^aⁿ^a^ra^nk¹^st^t^h^r^o^u^{gh 5}^th^(Moⁿ^tan^a⁶^th^);^t^h^{e f}ⁱ^{ve st}^at^e^s
^w^{ith the}^low^e^{st CO}²^e^m^{issions int}^e^nsity^a^r^e^ideⁿ^tic^a^l^{, but Ca}^lif^ornⁱ^a^a^nd^Ma^ssa^c^hus^e^{tts sw}^itc^{h positio}^ns.

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 ¹⁴
content of its energy use (or fuel mix). The relationship between CO² emissions intensity,
energy intensity and carbon content of energy use is shown in Equation 2.
^{Equation 2:}
^CO²^{Emissions Intensity}⁼^{Energy Intensity}^X^Carbo^{n Co}^{ntent of E}^nergy
^(CO²^/GSP)^(toe/GSP)^(TCO²^/toe)
^Note:^{The units cited above include gross state}^{product (GSP), tons of carbon}^{dioxide-equivalent (TCO}²^{), and}
^{tons of oil equivalent (toe).}
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.
Table 4. States with the Five Highest and Five Lowest Energy Intensity Levels (2003
data)
^{States wi}^{th Five}^Highest^{Energy Intensity (toe}^{States wi}^{th Five Lowest}^{Energy Intensity (toe}
^{Energy Intensities}^{/ $million of GSP)}^{Energy Intensities}^{/ $million of GSP)}
^{Louisiana 0.71}^New^York^0.13
^{Alaska 0.69}^Connecticut^0.14
^{Wyoming 0.61}^Massach^{usetts 0.14}
^{North Dakota}^0.50^California^0.15
^{West Virginia}^0.56^{Rhode Island}^0.16
^{Average for all 50}^{states: 0.2}⁹
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.

¹³^T^h^e^othe^r^por^ti^on⁽²^.⁵^%⁾^c^a^m^e^f^r^om^indus^tr^ia^{l a}^c^tiv^ity^{. T}^h^is^e^s^ti^m^a^te^e^x^c^l^ude^s^la^{nd us}^e^c^h^aⁿ^g^e^s^.^W^R^I^,^C^lim^a^t^e
^Aⁿ^a^l^y^sⁱ^s^I^ndic^a^t^or^s^T^ool^.
¹⁴^W^h^eⁿ^no^n-^C^O²^gases—e.^g.^,^m^e^t^h^an^e,ⁿⁱ^t^r^ou^{s o}^xⁱ^d^{e—are p}^a^rt^o^f^t^h^{e G}^H^Gⁱⁿ^t^eⁿ^sⁱ^t^y^cal^cu^l^u^s,^ot^h^e^r^f^a^ct^o^r^{s co}^m^eⁱⁿ^t^o
^pla^y^.^A^ppr^ox^im^a^t^e^l^y^50%^of^non-^C^O²^G^H^{Gs a}^r^e^g^e^ne^r^a^te^{d by}^a^g^ri^c^u^ltura^{l a}^c^tiv^itie^{s, a}ⁿ^{d t}^h^e^s^e^e^m^is^{sion le}^v^e^{ls m}^a^y^be
^inf^l^ue^nc^e^d^by^c^h^aⁿ^g^e^s^{in r}^e^la^te^{d e}^c^onom^ic^m^a^r^k^e^t^s^.

A state’s economic structure likely plays an important role. For instance, a primary economic ¹⁵
factor is whether the state’s economy is based more on high-energy industries or low-energy ¹⁶
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.
Table 5. States with High Percentages of Gross State Product Based on High- or
Low-Energy Intensive Sectors (2003 data)
^State^{Percentage of}^State^{Percentage of}
^{GSP from}^{GSP from}
^High-Energy^Low-Ener^gy
^Sectors⁰^Sectors^b
^{Wyoming 32}^Delaware⁷⁹
^{Louisiana 23}^Hawaii⁷⁶
^{Alaska 22}^New^York⁷⁵
^{West Virginia}¹⁷^Maryland⁷¹
^Texas¹⁴^{Connecticut / Rhode Island}⁷⁰
^{50-State Average}^7%^{50-State Average}^61%
^Source:^Prepa^{red by CRS with d}^{ata from Burea}^{u of E}^{conomic An}^{alysis, at http://bea.gov/index.htm.}
^a.^{For this table, as for the}^{rest of t}^{his report, high-e}^nergy^{sectors in}^{clude the following North Ameri}^can
^Industr^{y Classification System (NAICS) primary and se}^{condary gro}^{upings: mining, utilities, primary}^metal
^manufa^cturing,^paper^man^ufact^uri^{ng, petroleum a}^{nd coal products}^manufa^{cturing, a}^{nd chemical}
^manufa^cturing.
^b.^{For this table, as for the}^{rest of t}^{his report, low-energy sector}^{s in}^{clude the following North Ameri}^can
^Industr^{y Classification System (NAICS) primary grou}^{ps: information; finance}^{and ins}^ura^{nce; real esta}^te;
^{professional/technical services;}^m^{anagement of}^{companies; adminis}^{tration and waste se}^{rvices; educa}^tion;
^{health care a}^{nd social assistance;}^{arts, entertainme}^{nt, recr}^{eation; accomodation and food; other services;}
^{and governme}^nt.

¹⁵^For^t^h^is^r^e^p^o^r^t^,^hig^h^-^e^ne^r^g^y^s^e^c^t^o^r^s^inc^l^u^d^e^the^f^o^ll^ow^ing^N^o^r^t^{h A}^m^e^r^ic^aⁿ^I^ndus^tr^y^C^l^a^s^sⁱ^fⁱ^c^a^{tion S}^y^s^t^em⁽^N^A^I^C^S⁾
^prim^a^r^y^a^{nd s}^e^c^onda^ry^g^r^oupi^ng^s^:^mⁱ^ning^{, util}^itie^s^,^pr^im^a^r^y^m^e^ta^l^m^a^nuf^a^c^t^uring^,^pa^pe^{r m}^a^nuf^a^c^t^uriⁿ^g^,^pe^tro^l^e^u^m^a^nd
^c^o^a^l^pr^o^duc^ts^m^a^nuf^a^c^t^ur^ing^,^a^nd^c^h^e^mⁱ^c^a^l^m^a^nuf^ac^turⁱ^ng^.
¹⁶^For^t^h^is^r^e^p^o^r^t^,^low^-^eⁿ^e^r^gy^s^e^c^t^o^r^s^inc^l^u^d^e^the^f^o^ll^ow^ing^N^o^r^t^{h A}^m^e^r^ic^aⁿ^I^ndus^tr^y^C^l^a^s^sⁱ^fⁱ^c^a^{tion S}^y^s^t^em⁽^N^A^I^C^S⁾
^prim^a^r^y^g^r^oups^{: i}ⁿ^f^o^rm^a^{tion; f}ⁱ^na^nc^e^a^{nd i}ⁿ^s^u^ra^nc^e^;^re^a^l^e^s^ta^te^{; pr}^o^f^e^s^sⁱ^ona^l/te^c^hnic^a^l^s^e^rv^ic^e^s^{; m}^a^na^g^e^m^e^{nt of}
^co^m^p^anⁱ^e^s;^ad^mⁱⁿⁱ^st^ratⁱ^oⁿ^an^{d wast}^{e servi}^c^es;^ed^u^catⁱ^oⁿ^;^h^eal^t^h^ca^{re an}^d^so^ci^al^assi^st^an^ce;^art^s^,^en^t^e^rt^aiⁿ^m^en^t^,
^recreatⁱ^oⁿ^;^acco^m^o^d^a^tⁱ^oⁿ^and^f^o^od^;^ot^h^e^{r servi}^ces;^an^d^go^vern^m^eⁿ^t^.

The transportation sector accounts for over a quarter (28%) of total energy consumption in the ¹⁷
United States. Within the transportation sector, personal transportation—i.e., cars, light trucks, ¹⁸
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 ¹⁹
in 2003.
Table 6. States with the Five Highest and Five Lowest Vehicle Miles Traveled Per
Capita (2003)
^{States of Highest Rank}^{Vehicle Mil}^es^{States of Lowest}^{Vehicle Mil}^es
^{Traveled Per}^Rank^{Traveled Per}
^Capita^Capita
^{Wyoming 18,3}⁶⁷^New^York^7,02⁰
^{Vermont 13,4}³²^Hawaii^7,47⁶
^{Oklahoma 13,0}⁴⁸^Alaska^7,63⁰
^{Alabama 13,0}⁴⁵^Rhode^Island^7,78³
^{Mississippi 13,0}³⁶^New^Jers^ey^8,08
^{Average for all 50}^{states: 1}^0,57¹
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
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 ²⁰

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 ^th
intensity (ranks 15). 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^T^h^e^ind^u^s^t^rⁱ^a^l⁽³^2%⁾^,^r^e^sⁱ^de^nt^ia^l⁽^22%⁾^,^aⁿ^{d c}^o^m^m^e^r^cⁱ^a^l⁽^18%⁾^s^e^c^t^or^s^c^ons^um^e^d^the^r^e^m^a^ining^pr^o^por^ti^ons^.^Se^e
^C^R^S^R^e^por^t^R^L³¹⁸^49,^Eⁿ^erg^y^:^Sel^ect^ed^F^a^ct^{s an}^d^N^u^mb^ers^{, by}^C^a^r^o^{l G}^l^ov^e^r^a^{nd C}^a^r^l^{E. B}^e^hr^eⁿ^s^.
¹⁸^U^.^{S. D}^e^pa^r^t^m^e^{nt of}^Ene^r^gy^{, 200}^7,^Tr^ans^p^o^r^t^a^tio^{n E}ⁿ^e^r^gy^D^a^t^a^B^ook⁽^E^diti^on²⁶⁾^{, ta}^ble^2.^6.
¹⁹^B^a^s^e^{d on W}^R^I^C^A^I^T^da^ta^.
²⁰^Lⁱ^ke^wⁱ^s^e^,^o^f^t^h^{e 2}⁵^st^at^{es w}ⁱ^t^h^hi^gh^{er en}^erg^yⁱⁿ^t^eⁿ^sⁱ^t^y^l^e^vel^s^,¹⁷^ar^{e al}^so^am^oⁿ^g^t^h^{e 2}⁵^st^at^{es w}ⁱ^t^h^hi^gh^er
^VM^T^/^pe^r^s^oⁿ^.

supporting improvements in efficiency standards for electricity generation, buildings, and/or
appliances. For example, 12 states have established energy efficiency standards for appliances ²¹
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 ²²
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 ²³
ranked highly by the ACEEE scorecard. Conversely, the states with high energy intensities ²⁴
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 ²⁵
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 ²⁶
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

²¹^E^P^A^,^M^a^p^:^S^t^at^{e E}ⁿ^ergy^E^f^fⁱ^ci^en^c^y^A^c^tⁱ^oⁿ^s^{- S}^t^at^{e A}^p^p^lⁱ^aⁿ^{ce E}^f^fⁱ^ci^en^c^y^S^t^an^d^a^rd^{s (as o}^f¹^/¹^/²⁰⁰⁷⁾^,^at
^http:^//www^.e^pa^.g^ov^/^c^le^aⁿ^eⁿ^e^r^gy^/^s^ta^te^a^ndloc^a^l^/^a^c^tiv^itie^s.ht^m^.
²²^A^m^e^r^ic^aⁿ^Counc^il^f^o^{r a}ⁿ^Ene^r^g^y^-^Effⁱ^cⁱ^eⁿ^{t Ec}^onom^y^(A^{CEEE), 20}^07,^The^Sta^t^e^Ene^r^gy^E^ffic^ie^nc^y^S^c^ore^c^a^{rd for}
²⁰⁰⁶^,^at^h^t^t^p^:^/^/^aceee.^o^r^g.
²³^Inc^l^u^d^ing^tie^{s, Ca}^lif^ornⁱ^a^a^{nd C}^o^nne^c^tic^ut^r^a^nk^e^d^fⁱ^r^s^{t; Ma}^s^s^a^c^hus^e^tts^r^a^nk^e^d⁴^th^{; N}^e^w^Y^o^r^k^r^a^nke^{d 7}^th^{; a}^{nd R}^h^ode
^I^s^la^{nd 9}^th^.
²⁴^L^ouisⁱ^aⁿ^a^w^a^s^r^a^nk^e^d⁴⁰^th^{; A}^l^a^s^ka^r^a^nk^e^d⁴¹^st^{; W}^y^o^m^ing^ra^nk^e^d⁴⁹^th^{; N}^o^r^t^{h D}^a^k^o^ta^r^a^nk^e^d⁵¹^st^{; a}ⁿ^{d W}^e^{st V}ⁱ^rg^inia
^r^a^nk^e^d³⁵^th^.
²⁵^T^h^{e “d}^egree-d^a^{y” i}^s^a^m^e^t^rⁱ^c^u^s^e^d^t^o^{assess t}^h^{e d}^e^m^aⁿ^d^f^o^{r h}^eatⁱ^{ng an}^d^/^o^r^c^o^o^lⁱⁿ^{g n}^eed^s.^B^o^t^h^h^eatⁱⁿ^{g d}^e^{gree d}^a^y^s
^{(HDDs) an}^d^co^o^lⁱⁿ^{g d}^e^{gree d}^a^y^s^{(CDDs) are b}^a^sed^oⁿ^dⁱ^f^f^e^r^eⁿ^ces^f^r^o^m^{a te}^m^p^eratu^r^{e o}^f⁶⁵^°^F^,^{a b}^a^{se te}^m^p^eratu^r^e
^coⁿ^s^id^ered^to^h^a^{ve n}^e^ith^{er h}^eati^{ng n}^o^r^coo^lⁱⁿ^{g n}^eed^s.^F^o^{r exam}^p^l^e,¹⁰^{HDDs are gen}^e^rated^f^o^{r a d}^a^{y w}^ith^an^averag^e
^d^a^ily^te^m^p^eratu^r^{e o}^f⁵⁵^°^F^.^High^{er HDDs (e.}^g^.^,^A^l^as^{ka) an}^d^{CDDs (e.}^g^.^,^F^l^o^rⁱ^d^{a) ind}ⁱ^{cate greater h}^eatin^{g o}^r^c^o^o^lⁱⁿ^g
ⁿ^eed^s,^resp^ectⁱ^v^el^y^.
²⁶^T^h^{ree o}^f^t^h^{e f}ⁱ^v^e^st^at^{es (se}^e^Ta^b^l^{e 6}^{) w}^{ith hig}^h^eⁿ^e^r^gy^inte^nsitie^s^—^Wy^o^m^ing^,^A^l^a^s^ka^{, a}^{nd Nort}^h^Da^k^o^ta^—a^re^{in the}
^{top f}ⁱ^v^e^f^o^r^num^be^r^of^de^g^r^e^e^day^s^.

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 ^th^th
rank near the bottom in terms of absolute GSP (in 2003): Alaska (45), Wyoming (50), North ^th^th
Dakota (48), and West Virginia (40). 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 ^th
example, Louisiana, the state with the highest energy intensity, ranked 24 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 ^th^th
days, last (50) for absolute GSP, and 49 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).
Table 7. States With the Five Highest and Five Lowest Carbon Contents of Energy
Use (2003)
^{States wi}^{th Highest}^Carbo^{n Co}^{ntent of}^{States wi}^{th Lowest}^Carbo^{n Co}^{ntent of}
^Carbo^{n Co}^{ntents of}^{Energy Use (TC}^O²^/^Carbo^{n Co}^{ntents of}^{Energy Use (TC}^O²^/
^{Energy Use}¹⁰⁰^{0 toe)}^{Energy Use}¹⁰⁰^{0 toe)}
^{West Virginia}^5,78⁰^Idaho^1,21⁰
^{Wyoming 5,46}^Oregon^1,54
^{North Dakota}^4,77⁰^Washington^1,66⁰
^{Montana 3,48}^Vermont^1,66
^{Utah 3,47}⁰^Connecticut^1,89⁰
^{Average for all 50}^{states: 2,5}²⁷
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.

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 ²⁷
CO² (per unit of energy) as a natural gas-fired facility. Some energy sources—e.g., ²⁸
hydropower, 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 ²⁹
not demonstrate as much variance in their CO² 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 ³⁰
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.
Table 8. States with the Highest Percentage of In-State Electricity Generated from
Coal and Zero-Emission Energy Sources (2003)
^State^{Percentage of In-State}^State^{Percentage of In-State Electricity}
^{Electricity Generated from}^{Generated from Zero-}^{Emissions E}^nergy
^Coal^Sources
^West^{98% Vermont}¹⁰⁰^%
^Virginia
^{Wyoming 97%}^Idaho^96%

²⁷^T^h^e^Ene^r^gy^Iⁿ^f^o^r^m^a^{tion A}^d^mⁱⁿⁱ^s^t^r^a^{tion w}^e^bs^ite^pr^ovⁱ^d^e^s^a^ta^ble^lⁱ^s^ting^the^a^m^ount^of^C^O²^g^e^ne^r^a^{ted pe}^r^uni^{t of}
^eⁿ^e^r^gy^f^o^{r dif}^f^e^r^eⁿ^{t e}ⁿ^e^r^gy^sourc^e^s^{, a}^t^http^://w^ww^.^e^ia^.doe^.g^ov^/oia^f^/¹⁶^05/^c^o^e^f^fⁱ^cⁱ^eⁿ^ts.htm^l.
²⁸^Som^e^s^t^udie^s^ha^v^e^f^ound^tha^t^hy^dr^oe^le^c^t^rⁱ^c^da^m^s^m^a^y^be^a^s^our^c^e^of^G^H^G^em^is^sⁱ^ons^{. D}^a^m^r^e^s^e^r^v^oir^s^c^aⁿ^e^m^it
^m^e^thaⁿ^e^thro^ug^h^plaⁿ^t^de^c^o^m^position^,^b^u^{t t}^h^{is e}^f^f^e^c^t^v^a^rie^s^b^y^loc^a^t^ion,^beⁱⁿ^g^m^o^re^prono^unc^e^d^{in w}^a^r^m^e^r^c^lⁱ^m^a^t^e^s^.
^Se^e^e^.^g^{., W}^o^{rld Com}^mⁱ^s^s^{ion o}ⁿ^D^a^m^s^{, 2000}^{, T}^h^e^Re^port^of^the^W^o^{rld C}^o^m^m^is^sⁱ^{on on}^D^a^m^s^{, a}^t^http://w^w^w^.dam^s^.^org^/
^r^e^por^t/.
²⁹^Iⁿ²⁰^03,^pe^tr^o^l^e^u^m^a^c^c^ounte^d^f^o^r^97%^of^the^eⁿ^e^r^gy^c^ons^um^e^d^{in t}^h^e^U^.^{S. t}^r^aⁿ^s^p^or^ta^tio^{n s}^e^c^t^or^.^EI^A^,^Ene^r^g^y^P^o^w^e^r
^Mont^hly^,^Ma^rc^{h 2}⁰⁰⁴^{, T}^a^ble^2.^5,^a^t^http^://w^ww^.^e^ia^.doe^.g^ov^/.
³⁰^T^h^e^s^e^a^dditio^na^{l s}^o^urc^e^s^inc^l^u^d^e^w^{ood a}ⁿ^d^ot^he^{r w}^{ood w}^a^s^t^e^,^bla^c^k^liqu^o^r,^biog^eⁿ^ic^m^unicⁱ^pa^{l s}^o^lid^w^a^s^t^e^,^la^ndf^ill
^g^a^s^,^s^l^udg^e^w^a^s^t^e^,^a^g^rⁱ^c^u^ltur^e^by^pr^od^uc^ts^{, a}ⁿ^d^ot^he^r^bi^om^a^s^s⁽^E^I^A^{, Ele}^c^t^rⁱ^c^Pow^e^r^Mont^hly^,^Ma^r^c^{h 2}⁰⁰⁴^{, T}^a^ble
¹^.¹³^B).^A^l^t^h^ou^gh^t^h^{ese so}^u^r^{ces do}^yⁱ^el^d^CO²^e^mⁱ^ssi^oⁿ^s^w^h^en^u^s^{ed as f}^u^el^s,^t^h^ei^{r co}^m^b^u^s^tⁱ^o^{n do}^{es no}^t^p^r^o^vⁱ^d^e
^add^itio^na^l^CO²^e^m^is^sⁱ^ons^{to t}^h^e^a^t^m^o^s^phe^r^e⁽ⁱ^.e^{., the}^y^w^{ould ha}^v^e^pr^o^duc^e^d^C^O²^e^m^is^sⁱ^ons^a^t^s^o^m^e^poi^{nt v}ⁱ^a^na^t^u^r^a^l
^pr^oc^e^s^s^e^s⁾^{. T}^hus^{, f}^o^r^this^r^e^p^o^r^t^t^h^e^y^a^r^e^c^ounte^d^a^s^z^e^r^o-^e^m^is^sⁱ^on^eⁿ^e^r^gy^s^our^c^e^s^.

^State^{Percentage of In-State}^State^{Percentage of In-State Electricity}
^{Electricity Generated from}^{Generated from Zero-}^{Emissions E}^nergy
^Coal^Sources
^{Indiana 94%}^Washington^82%
^North^{94% Oregon}^70%
^Dakota
^{Utah 94%}^New^66%
^Hamps^hire
^Source:^Prepa^{red by CRS with d}^{ata from E}^nergy^{Information Ad}^{ministration, Electric Power Mon}^{thly (Marc}^h
²⁰⁰^{4), at htt}^{p://www.eia.d}^oe.gov/^.
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 ³¹
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.
Table 9. States with High Percentages of Exported and Imported Electricity in Terms
of Overall Energy Use (2003)
^State^{Percentage of}^State^{Percentage of}
^{Energy C}^onsumed^{Energy C}^onsumed
^{That Is}^Exported^{That Is}^Imported
^Electricity^Electricity
^{West Virginia}^44%^Idaho^41%
^{Wyoming 42%}^Delaware^28%
^{North Dakota}^36%^{Rhode Island}^22%

³¹^F^r^o^m^{a mat}^h^e^m^atⁱ^cal^p^e^rsp^ectⁱ^v^e^,ⁱⁿ^aⁿ^e^t^exp^o^r^tⁱⁿ^g^st^at^{e t}^h^eⁿ^u^m^erat^o^r^(t^CO²⁾^of^the^e^qua^ti^{on (}^t^C^O²^{/ toe}⁾^w^oul^d
^inc^r^e^a^s^e^,^{but t}^h^e^de^nom^ina^t^or⁽^t^oe⁾^w^{ould r}^e^m^a^{in the}^s^a^m^e^.^T^h^e^r^e^ve^r^s^e^w^ould^oc^c^u^rⁱⁿ^im^por^ting^s^t^a^t^e^s^.

^State^{Percentage of}^State^{Percentage of}
^{Energy C}^onsumed^{Energy C}^onsumed
^{That Is}^Exported^{That Is}^Imported
^Electricity^Electricity
^{Montana 28%}^Maryland^20%
^{New Ham}^pshire^23%^Virginia^17%
^Source:^Prepa^{red by CRS with d}^{ata from E}^nergy^{Information Ad}^{ministration, State Energy Data S}^ystem
^{http://www.eia.doe}^.gov/^emeu/^{states/_seds.html.}
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 ³²
receiving some amount of coal-fired electricity from Wyoming (and Oregon and Nevada). 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 ³³
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 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^Id^ah^o^P^o^w^e^r,^w^hⁱ^c^h^{serves cu}^st^o^m^{ers i}ⁿ^Id^aho^,ⁱ^s^a^p^a^rtⁱ^a^l^o^wⁿ^e^r^o^f^co^al^-fⁱ^r^ed^p^o^w^{er p}^l^an^t^sⁱⁿ^t^h^es^{e st}^at^es.^S^{ee E}^I^A^,
^A^nnua^{l Ele}^c^t^rⁱ^c^G^e^ne^r^a^tor^R^e^por^t⁽^D^a^t^a^b^a^s^e⁸⁶⁰⁾^,^a^t^ht^tp:/^/w^w^w^.eⁱ^a^.doe^.g^ov^{; s}^e^e^a^l^s^o^ht^tp:/^/w^w^w^.ida^hop^ow^e^r^.c^o^m^.
³³^P^e^r^te^le^ph^one^c^o^nv^e^r^s^a^{tion w}^ith^EI^A^of^fⁱ^cⁱ^a^l^{, J}^u^l^y^{30, 2}⁰⁰⁷^.

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 ³⁴
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, ³⁵
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

³⁴^For^m^o^r^e^dis^c^us^sⁱ^oⁿ^of^the^s^e^is^s^u^e^s^{, s}^e^e^C^R^S^R^e^por^{t R}^L³³⁷⁹^9,^C^lⁱ^m^ate^C^h^a^nge^:^D^e^sⁱ^gⁿ^A^ppr^oac^he^s^for^a
^G^r^e^e^nhous^e^G^a^s^Re^duc^ti^on^Pr^ogr^am^{, by}^L^a^rr^y^P^a^rk^e^r^.
³⁵^R^a^y^m^{ond K}^o^pp,²⁰⁰⁷^,^G^r^e^eⁿ^h^o^u^s^e^G^a^s^Re^gul^ati^{on i}ⁿ^t^h^e^Uⁿ^ite^{d S}^t^ate^s^{, R}^e^s^our^c^e^s^f^o^r^the^Fut^u^r^e^Dⁱ^s^c^us^sⁱ^on^P^a^pe^r^.

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 ³⁷
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

³⁶^In^{a m}^a^rket^-b^as^ed^syst^e^m^(e.^g^.^,^c^a^p^-^an^d^-^t^r^ad^e),^emⁱ^ssi^oⁿ^al^l^o^w^aⁿ^c^{es can}^b^e^u^s^ed^t^o^co^m^p^l^y^wⁱ^t^h^anⁱⁿ^dⁱ^vi^du^al
^co^m^p^an^y^’^{s cap}^o^r^so^l^d^t^o^o^t^h^e^r^p^a^rtⁱ^e^{s su}^b^j^ect^t^o^t^h^{e cap.}^A^s^su^ch^,^al^l^o^w^aⁿ^{ces are a}^f^o^r^m^o^f^cu^rren^c^y^an^d^w^o^u^l^d
^pr^ov^ide^aⁿ^inf^u^sⁱ^oⁿ^of^f^unds^.
³⁷^T^h^e^v^a^s^t^m^a^jorⁱ^t^y^of^em^issⁱ^ons^a^llow^a^nc^e^s^w^e^r^e^dis^t^rⁱ^b^u^te^{d f}^o^r^f^r^e^e^unde^r^the^E^u^r^o^pe^aⁿ^pr^og^r^a^m^.^Se^e^N^a^tiona^l
^C^o^m^m^is^sⁱ^{on on E}ⁿ^e^r^g^y^P^o^lic^y^,²⁰⁰⁷^,^All^o^c^a^tⁱⁿ^g^All^o^w^a^nc^e^sⁱⁿ^a^G^r^e^e^nhous^e^G^a^s^Tr^adi^ng^Sy^s^t^e^m^{, 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 ³⁸
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 ³⁹
and 2003; the average decline among all states was 1.7%.
Reducing GHG emissions in the United States would necessitate further declines in GHG ^th
intensity. Several legislative proposals in the 110 Congress would require GHG emissions to ⁴⁰
return to 1990 levels by 2020. To meet this objective, national GHG intensity would need to ⁴¹
decline annually (starting in 2010) by 5.0%.

³⁸^Se^e^N^a^tiona^{l R}^e^ne^w^a^ble^Ene^r^g^y^L^a^bor^a^t^or^y^,^Ma^{p of}^U^.^{S. A}^nnua^l^A^v^e^r^ag^e^Wⁱ^nd^Pow^e^r^,^a^t^http:/^/r^r^e^dc^.nr^e^l.g^o^v^/
^wⁱ^nd/pu^bs^/^a^tla^s^/^m^a^ps^.htm^l#2-^6.
³⁹^T^h^e^c^ontr^a^s^t^be^tw^e^eⁿ^indivⁱ^dua^l^s^t^a^t^e^inte^ns^ity^le^v^e^ls^a^{nd the}^s^t^a^t^e^s^’^a^v^e^r^ag^e^le^ve^{l is}^only^f^o^r^c^o^m^p^a^r^is^on^pur^pos^e^s^.
^W^h^en^cal^cu^l^a^tⁱⁿ^{g t}^h^{e st}^at^{es’ average i}ⁿ^t^eⁿ^sⁱ^t^y^l^e^vel^,^al^l^st^at^{es are c}^o^uⁿ^t^e^d^equ^a^l^l^y^.^Becau^{se o}^f^t^h^{e si}^gnⁱ^fⁱ^can^t^vari^an^ce
ⁱⁿ^emⁱ^ssi^oⁿ^s^b^e^t^w^een^l^a^{rge an}^d^smal^l^st^at^es,^t^h^{e st}^at^{es’ av}^e^r^a^g^e^inte^nsity^le^v^e^l^m^a^y^{not c}^o^incⁱ^de^w^{ith the}^na^tio^na^l
^inte^nsity^le^v^e^{l. T}^e^{n sta}^t^e^s^c^o^m^p^ris^e^a^pprox^im^a^t^e^l^y^50%^of^{U.S. G}^H^G^e^mⁱ^{ssions. T}^h^e^a^c^tions^of^the^s^e^sta^t^e^s^w^{ill lik}^e^l^y
^ha^v^e^g^r^e^a^t^e^r^e^f^f^e^c^t^{on the}^na^tio^na^l^G^H^G^inte^nsity^.
⁴⁰^For^e^x^a^m^ple^,^{S. 2}^{80 (}^L^ie^be^r^m^aⁿ⁾^,^S.³⁰^{9 (}^S^a^nde^r^s⁾^,^S^.⁴⁸⁵^(Kerry⁾^,^H.R.⁶²⁰^(Olv^{er), an}^d^H.R.¹⁵⁹⁰^(W^ax^m^aⁿ⁾^.
⁴¹^T^h^is^c^a^l^c^u^la^{tion a}^s^s^u^m^e^s^:^{(1) U}^.^{S. p}^o^p^u^la^tio^{n w}^{ill g}^r^ow^a^nnua^lly^by^0.9%^(U^{.S. Ce}^ns^us^Bure^a^u^,^a^t
^http:^//w^w^w^.c^eⁿ^s^u^s^.^g^o^v^/^c^g^i-bin/^ip^c^/idbs^um^.pl?^c^t^y⁼^U^S^{)); (2) inc}^o^m^e^s^w^{ill inc}^r^e^a^s^e^a^nnua^lly^b^y^2.1%^(the^ra^te^of
^inc^r^e^a^s^e^f^r^o^m^{1975 t}^o²⁰^03,^W^R^I^,^C^lim^a^t^e^Aⁿ^a^l^y^s^is^I^ndic^a^t^or^s^T^o^o^l⁾^;⁽³⁾^G^H^G^emⁱ^s^sⁱ^ons^w^e^r^e^6,240^MMT^C^O²^{E in}
⁽^c^onti^nue^d.^..)

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%.
Table 10. GHG Emissions Intensity Average Annual (Negative) Growth Rates (1990-
2003) for the 10 States with the Most GHG Emissions in 2003
^State^{GHG Emissions Intensity Average}^{Annual G}^r^o^{wth Rates (1990-}²⁰⁰³⁾
^{Texas -2.5}
^{California -1.9}
^{Pennsylvania -2.1}
^{Ohio -1.7}
^{Florida -1.6}
^{Illinois -1.6}
^{Indiana -2.1}
^{New York}^-1.6
^{Michigan -2.6}
^{Louisiana -0.6}
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.

^(...c^on^tin^ue^d)
¹⁹⁹^{0 (}^U^.^S^{. E}^P^A^,²⁰⁰⁷^,^U^.^S.^Iⁿ^v^eⁿ^tor^y^o^f^G^r^e^e^nho^us^e^G^a^s^Em^is^sⁱ^oⁿ^s^a^nd^Sink^s¹⁹^90-²⁰⁰⁵^,^a^t^ht^tp:/^/^w^w^w^.^e^pa^.g^ov^/
^clⁱ^m^at^ech^an^ge),^an^d^{are p}^r^o^j^ect^{ed t}^o^b^e⁷^,⁶³²^MM^T^C^O²^Eⁱⁿ²⁰¹⁰⁽^b^ased^oⁿ^{a 1}^.⁰^%^an^nu^al^{average g}^r^o^w^t^h^rat^e
^be^tw^e^eⁿ¹⁹⁹⁰^aⁿ^d²⁰⁰⁵⁾^.

Table A-1. GHG Emissions and GHG Emissions Drivers for All 50 States, Listed
Alphabetically (2003 data)
^GHG^Population^{Per capita}^GHG^Intensit^y
^Emissions^Income
^State
^MMTCO²^E^{in 1,00}^0s^GSP/person^TCO²^E^{/ $million of}^GSP
^Alabama^{164 =}^4,49^{5 X}^27,1^{40 X}^1,34³
^Alaska^{46 =}^{648 X}^42,7^{84 X}^1,66²
^Arizona^{96 =}^5,58^{2 X}^31,2^{94 X}⁵⁵¹
^Arkansas^{81 =}^2,72^{4 X}^25,9^{71 X}^1,13⁸
^California^{453 =}^35,4^{66 X}^37,7^{87 X}³³⁸
^Colorad^o^{107 =}^4,54^{6 X}^39,1^{44 X}⁶⁰⁰
^Connecticut^{46 =}^3,48^{2 X}^45,8^{75 X}²⁸⁶
^Delaware^{19 =}^{817 X}^54,6^{67 X}⁴²⁶
^Florida^{271 =}^16,9^{82 X}^30,5^{48 X}⁵²³
^Georgia^{186 =}^8,75^{0 X}^34,2^{28 X}⁶²¹
^Hawaii^{23 =}^1,24^{6 X}^34,1^{80 X}⁵⁵⁰
^Idaho^{24 =}^1,36^{7 X}^26,9^{06 X}⁶⁵¹
^Illinois^{269 =}^12,6^{50 X}^37,8^{18 X}⁵⁶¹
^Indiana^{269 =}^6,19^{2 X}^33,0^{82 X}^1,31⁵
^Iowa^{108 =}^2,94^{2 X}^32,4^{81 X}^1,13³
^Kansas^{101 =}^2,72^{7 X}^31,6^{68 X}^1,16⁶
^Kentucky^{164 =}^4,11^{4 X}^28,7^{39 X}^1,38⁵
^Louisiana^{210 =}^4,48^{1 X}^29,3^{75 X}^1,59¹
^Maine^{26 =}^1,30^{7 X}^28,6^{32 X}⁶⁹³
^Maryland^{90 =}^5,50^{7 X}^36,1^{64 X}⁴⁵⁰
^Massach^usetts^{92 =}^6,44^{0 X}^43,8^{50 X}³²⁷
^Michigan^{212 =}^10,0^{68 X}^34,2^{60 X}⁶¹⁴
^Minnesota^{120 =}^5,05^{9 X}^39,1^{46 X}⁶⁰⁶
^Mississippi^{76 =}^2,87^{4 X}^23,2^{81 X}^1,13¹
^Missouri^{163 =}^5,71^{2 X}^32,1^{23 X}⁸⁸⁶
^Montana^{41 =}^{917 X}^25,3^{89 X}^1,75⁵
^Nebras^ka^{65 =}^1,73^{7 X}^34,5^{93 X}^1,08⁸
^Nevada^{48 =}^2,24^{1 X}^36,9^{33 X}⁵⁷⁴
^New^{22 =}^1,28^{6 X}^35,8^{21 X}⁴⁶⁹
^Hamps^hire
^New^Jers^ey^{137 =}^8,63^{3 X}^42,4^{35 X}³⁷³

^GHG^Population^{Per capita}^GHG^Intensit^y
^Emissions^Income
^State
^MMTCO²^E^{in 1,00}^0s^GSP/person^TCO²^E^{/ $million of}^GSP
^New^Me^xico^{66 =}^1,87^{8 X}^28,5^{90 X}^1,23⁶
^New^York^{244 =}^19,2^{38 X}^41,7^{31 X}³⁰⁴
^North^Carolina^{168 =}^8,41^{6 X}^34,2^{88 X}⁵⁸¹
^North^Dakota^{57 =}^{633 X}^31,4^{64 X}^2,88⁵
^Ohio^{299 =}^11,4^{38 X}^33,1^{74 X}⁷⁸⁸
^Oklahoma^{124 =}^3,50^{4 X}^27,0^{47 X}^1,30⁸
^Oregon^{51 =}^3,56^{1 X}^32,8^{25 X}⁴³⁵
^Pennsylvania^{301 =}^12,3^{51 X}^33,2^{24 X}⁷³⁴
^Rhode^Island^{13 =}^1,07^{5 X}^33,9^{04 X}³⁴⁹
^South^Carolina^{92 =}^4,14^{2 X}^28,8^{09 X}⁷⁷¹
^South^Dakota^{27 =}^{764 X}^33,6^{71 X}^1,06⁰
^Tennessee^{141 =}^5,83^{4 X}^32,5^{23 X}⁷⁴⁵
^Texas^{782 =}^22,1^{34 X}^34,8^{37 X}^1,01⁵
^Utah^{69 =}^2,35^{6 X}^30,1^{15 X}⁹⁷⁷
^Vermont^{8 =}^{619 X}^31,6^{93 X}³⁹⁹
^Virginia^{143 =}^7,37^{6 X}^38,1^{08 X}⁵⁰⁷
^Washington^{95 =}^6,13^{0 X}^36,6^{12 X}⁴²¹
^West^Virginia^{133 =}^1,80^{9 X}^23,7^{08 X}^3,09⁷
^Wisconsin^{123 =}^5,46^{7 X}^33,7^{99 X}⁶⁶⁶
^Wyoming^{72 =}^{501 X}^37,8^{57 X}^3,79⁹
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
^Note:^{The calculations above are based on t}^he^E^{quation 1}^(pro^{vided again below), but the u}^nits^{have be}^en
^{altered to make the figu}^{res more}^prese^{ntable a}^{nd easier to compa}^{re. In pa}^{rticular, note that}^{in the}^{above table}
^{the pop}^{ulation figure for ea}^{ch sta}^{te is in 1,00}^{0s; and t}^he^GHG^inte^{nsity figure is}^prese^{nted in met}^{ric tons (instead}
^{of mill}^{ion metric tons) of CO}²^{E a}^{nd in million do}^{llars of GSP (instead of one dollar of GSP)}^.
Equation 1:
^{GHG Emissions}⁼^Population^X^{Per Capita Inco}^me^X^{GHG Intensit}^y
^(MMTCO²^E)^(Persons)^(GSP/Person)^(MMTCO²^{E /}^GSP)

Table A-2. GHG Emissions and GHG Emissions Drivers for All 50 States, Ranked by
GHG Emissions (2003 data)
^GHG^Population^{Per capita}^GHG^Intensit^y
^Emissions^Income
^{State Rank}
^MMTCO²^E^{in 1,00}^0s^GSP/person^TCO²^E^{/ $million}^{of GSP}
^{Texas 1}⁷⁸²⁼^22,1³⁴^X^34,8³⁷^X^1,01⁵
^{California 2}⁴⁵³⁼^35,4⁶⁶^X^37,7⁸⁷^X³³⁸
^{Pennsylvania 3}³⁰¹⁼^12,3⁵¹^X^33,2²⁴^X⁷³⁴
^{Ohio 4}²⁹⁹⁼^11,4³⁸^X^33,1⁷⁴^X⁷⁸⁸
^{Florida 5}²⁷¹⁼^16,9⁸²^X^30,5⁴⁸^X⁵²³
^{Illinois 6}²⁶⁹⁼^12,6⁵⁰^X^37,8¹⁸^X⁵⁶¹
^{Indiana 7}²⁶⁹⁼^6,19²^X^33,0⁸²^X^1,31⁵
^{New York}⁸²⁴⁴⁼^19,2³⁸^X^41,7³¹^X³⁰⁴
^{Michigan 9}²¹²⁼^10,0⁶⁸^X^34,2⁶⁰^X⁶¹⁴
^{Louisiana 10}²¹⁰⁼^4,48¹^X^29,3⁷⁵^X^1,59¹
^{Georgia 11}¹⁸⁶⁼^8,75⁰^X^34,2²⁸^X⁶²¹
^North^{12 168}⁼^8,41⁶^X^34,2⁸⁸^X⁵⁸¹
^Carolina
^{Alabama 13}¹⁶⁴⁼^4,49⁵^X^27,1⁴⁰^X^1,34³
^{Kentucky 14}¹⁶⁴⁼^4,11⁴^X^28,7³⁹^X^1,38⁵
^{Missouri 15}¹⁶³⁼^5,71²^X^32,1²³^X⁸⁸⁶
^{Virginia 16}¹⁴³⁼^7,37⁶^X^38,1⁰⁸^X⁵⁰⁷
^{Tennessee 17}¹⁴¹⁼^5,83⁴^X^32,5²³^X⁷⁴⁵
^{New Jers}^ey¹⁸¹³⁷⁼^8,63³^X^42,4³⁵^X³⁷³
^{West Virginia}¹⁹¹³³⁼^1,80⁹^X^23,7⁰⁸^X^3,09⁷
^{Oklahoma 20}¹²⁴⁼^3,50⁴^X^27,0⁴⁷^X^1,30⁸
^{Wisconsin 21}¹²³⁼^5,46⁷^X^33,7⁹⁹^X⁶⁶⁶
^{Minnesota 22}¹²⁰⁼^5,05⁹^X^39,1⁴⁶^X⁶⁰⁶
^{Iowa 23}¹⁰⁸⁼^2,94²^X^32,4⁸¹^X^1,13³
^Colorad^{o 24}¹⁰⁷⁼^4,54⁶^X^39,1⁴⁴^X⁶⁰⁰
^{Kansas 25}¹⁰¹⁼^2,72⁷^X^31,6⁶⁸^X^1,16⁶
^{Arizona 26}⁹⁶⁼^5,58²^X^31,2⁹⁴^X⁵⁵¹
^{Washington 27}⁹⁵⁼^6,13⁰^X^36,6¹²^X⁴²¹
^{South Carolina}²⁸⁹²⁼^4,14²^X^28,8⁰⁹^X⁷⁷¹
^Massach^{usetts 29}⁹²⁼^6,44⁰^X^43,8⁵⁰^X³²⁷
^{Maryland 30}⁹⁰⁼^5,50⁷^X^36,1⁶⁴^X⁴⁵⁰
^{Arkansas 31}⁸¹⁼^2,72⁴^X^25,9⁷¹^X^1,13⁸
^{Mississippi 32}⁷⁶⁼^2,87⁴^X^23,2⁸¹^X^1,13¹

^GHG^Population^{Per capita}^GHG^Intensit^y
^Emissions^Income
^{State Rank}
^MMTCO²^E^{in 1,00}^0s^GSP/person^TCO²^E^{/ $million}^{of GSP}
^{Wyoming 33}⁷²⁼⁵⁰¹^X^37,8⁵⁷^X^3,79⁹
^{Utah 34}⁶⁹⁼^2,35⁶^X^30,1¹⁵^X⁹⁷⁷
^{New Me}^xico³⁵⁶⁶⁼^1,87⁸^X^28,5⁹⁰^X^1,23⁶
^Nebras^{ka 36}⁶⁵⁼^1,73⁷^X^34,5⁹³^X^1,08⁸
^{North Dakota}³⁷⁵⁷⁼⁶³³^X^31,4⁶⁴^X^2,88⁵
^{Oregon 38}⁵¹⁼^3,56¹^X^32,8²⁵^X⁴³⁵
^{Nevada 39}⁴⁸⁼^2,24¹^X^36,9³³^X⁵⁷⁴
^{Alaska 40}⁴⁶⁼⁶⁴⁸^X^42,7⁸⁴^X^1,66²
^{Connecticut 41}⁴⁶⁼^3,48²^X^45,8⁷⁵^X²⁸⁶
^{Montana 42}⁴¹⁼⁹¹⁷^X^25,3⁸⁹^X^1,75⁵
^{South Dakota}⁴³²⁷⁼⁷⁶⁴^X^33,6⁷¹^X^1,06⁰
^{Maine 44}²⁶⁼^1,30⁷^X^28,6³²^X⁶⁹³
^{Idaho 45}²⁴⁼^1,36⁷^X^26,9⁰⁶^X⁶⁵¹
^{Hawaii 46}²³⁼^1,24⁶^X^34,1⁸⁰^X⁵⁵⁰
^New^{47 22}⁼^1,28⁶^X^35,8²¹^X⁴⁶⁹
^Hamps^hire
^{Delaware 48}¹⁹⁼⁸¹⁷^X^54,6⁶⁷^X⁴²⁶
^{Rhode Island}⁴⁹¹³⁼^1,07⁵^X^33,9⁰⁴^X³⁴⁹
^{Vermont 50}⁸⁼⁶¹⁹^X^31,6⁹³^X³⁹⁹
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
Table A-3. Average Annual Growth Rates (1990-2003) for GHG Emissions and GHG
Emissions Drivers for All 50 States
^State^{GHG Emissions}^Population^{Per capita Inc}^ome^{GHG Intensit}^y
^Alabama^1.4%⁼^0.8%^{+ 1.9% +}^-1.2%
^Alaska^1.9%⁼^1.2%^{+ -2.3% +}^3.1%
^Arizona^2.5%⁼^3.2%^{+ 2.7% +}^-3.3%
^Arkansas^1.6%⁼^1.1%^{+ 2.4% +}^-1.8%
^California^0.7%⁼^1.3%^{+ 1.3% +}^-1.9%
^Colorad^o^2.2%⁼^2.5%^{+ 2.7% +}^-2.9%
^Connecticut^0.4%⁼^0.4%^{+ 1.5% +}^-1.5%
^Delaware^-0.2%⁼^1.5%^{+ 2.1% +}^-3.7%
^Florida^2.1%⁼^2.1%^{+ 1.7% +}^-1.6%
^Georgia^1.6%⁼^2.3%^{+ 2.0% +}^-2.6%
^Hawaii^0.1%⁼^0.9%^{+ -0.6% +}^-0.2%

^State^{GHG Emissions}^Population^{Per capita Inc}^ome^{GHG Intensit}^y
^Idaho^2.2%⁼^2.3%^{+ 2.6% +}^-2.7%
^Illinois^1.2%⁼^0.8%^{+ 2.0% +}^-1.6%
^Indiana^1.4%⁼^0.8%^{+ 2.6% +}^-2.1%
^Iowa^1.1%⁼^0.4%^{+ 2.6% +}^-1.9%
^Kansas^0.9%⁼^0.7%^{+ 1.8% +}^-1.6%
^Kentucky^1.4%⁼^0.8%^{+ 2.1% +}^-1.5%
^Louisiana^0.0%⁼^0.5%^{+ 0.1% +}^-0.6%
^Maine^1.6%⁼^0.5%^{+ 1.4% +}^-0.3%
^Maryland^0.9%⁼^1.1%^{+ 1.4% +}^-1.5%
^Massach^usetts^0.3%⁼^0.5%^{+ 2.3% +}^-2.5%
^Michigan^0.4%⁼^0.6%^{+ 2.4% +}^-2.6%
^Minnesota^1.5%⁼^1.1%^{+ 2.7% +}^-2.2%
^Mississippi^2.1%⁼^0.8%^{+ 2.0% +}^-0.7%
^Missouri^1.9%⁼^0.8%^{+ 2.0% +}^-0.9%
^Montana^1.1%⁼^1.1%^{+ 1.8% +}^-1.7%
^Nebras^ka^1.6%⁼^0.7%^{+ 2.4% +}^-1.5%
^Nevada^2.8%⁼^4.8%^{+ 0.8% +}^-2.7%
^New^Ham^pshire^2.4%⁼^1.1%^{+ 2.8% +}^-1.5%
^New^Jers^ey^0.7%⁼^0.8%^{+ 1.6% +}^-1.7%
^New^Me^xico^1.0%⁼^1.6%^{+ 3.2% +}^-3.7%
^New^York^0.4%⁼^0.5%^{+ 1.4% +}^-1.6%
^North^Carolina^2.3%⁼^1.8%^{+ 2.1% +}^-1.7%
^North^Dakota^1.4%⁼^-0.1%^{+ 3.1% +}^-1.6%
^Ohio^0.7%⁼^0.4%^{+ 2.1% +}^-1.7%
^Oklahoma^1.1%⁼^0.8%^{+ 1.5% +}^-1.2%
^Oregon^2.1%⁼^1.7%^{+ 3.1% +}^-2.6%
^Pennsylvania^0.2%⁼^0.3%^{+ 2.0% +}^-2.1%
^Rhode^Island^2.3%⁼^0.5%^{+ 1.7% +}^0.0%
^South^Carolina^2.3%⁼^1.3%^{+ 1.9% +}^-0.9%
^South^Dakota^1.6%⁼^0.7%^{+ 3.6% +}^-2.5%
^Tennessee^1.3%⁼^1.4%^{+ 2.5% +}^-2.5%
^Texas^1.4%⁼^2.0%^{+ 2.0% +}^-2.5%
^Utah^1.2%⁼^2.4%^{+ 2.3% +}^-3.4%
^Vermont^1.3%⁼^0.7%^{+ 2.0% +}^-1.5%
^Virginia^0.8%⁼^1.3%^{+ 1.8% +}^-2.2%
^Washington^0.9%⁼^1.7%^{+ 1.6% +}^-2.4%
^West^Virginia^0.1%⁼^0.1%^{+ 1.9% +}^-1.8%

^State^{GHG Emissions}^Population^{Per capita Inc}^ome^{GHG Intensit}^y
^Wisconsin^1.2%⁼^0.8%^{+ 2.6% +}^-2.2%
^Wyoming^0.9%⁼^0.8%^{+ 1.0% +}^-0.8%
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
^Note:^{The sum of the}^GH^{G emi}^{ssions driver rates ma}^{y not p}^reci^{sely equal the}^{rate of}^GHG^emiss^{ions in all}
^{cases. Nevert}^{heless, the ge}^neral^{relationship holds true.}
Table A-4. CO² Emissions Intensity and CO² Emissions Intensity Drivers for All 50
States, Listed Alphabetically (2003 data)
^CO²^Emissions^{= Energy}^Intensity^X^Carbo^{n Co}^{ntent of}
^Intensity^{Energy Use}
^State
^TCO²^/^$million⁼^{toe / $million GSP}^X^TCO²^{/ 1000 toe}
^{of GSP}
^{Alabama 1,17}⁸⁼^0.42^X^2,69⁰
^{Alaska 1,62}⁴^0.69^2,30
^{Arizona 517}⁼^0.20^X^2,57⁰
^{Arkansas 933}^0.40^2,18
^{California 295}⁼^0.15^X^1,90⁰
^Colorad^{o 509}^0.19^2,62
^{Connecticut 269}⁼^0.14^X^1,89⁰
^{Delaware 392}^0.18^2,14
^{Florida 478}⁼^0.21^X^2,26⁰
^{Georgia 569}^0.25^2,22
^{Hawaii 502}⁼^0.18^X^2,74⁰
^{Idaho 404}^0.32^1,21
^{Illinois 497}⁼^0.21^X^2,32⁰
^{Indiana 1,22}¹^0.36^3,14
^{Iowa 839}⁼^0.31^X^2,64⁰
^{Kansas 935}^0.33^2,78
^{Kentucky 1,24}⁷⁼^0.40^X^3,03⁰
^{Louisiana 1,50}⁸^0.71^2,08
^{Maine 647}⁼^0.32^X^1,94⁰
^{Maryland 411}^0.20^2,05
^Massach^{usetts 308}⁼^0.14^X^2,17⁰
^{Michigan 557}^0.23^2,32
^{Minnesota 510}⁼^0.23^X^2,22⁰
^{Mississippi 993}^0.45^2,10
^{Missouri 770}⁼^0.25^X^2,95⁰
^{Montana 1,44}²^0.41^3,48

^CO²^Emissions^{= Energy}^Intensity^X^Carbo^{n Co}^{ntent of}
^Intensity^{Energy Use}
^State
^TCO²^/^$million⁼^{toe / $million GSP}^X^TCO²^{/ 1000 toe}
^{of GSP}
^Nebras^{ka 737}⁼^0.27^X^2,63⁰
^{Nevada 528}^0.20^2,61
^{New Ham}^pshire⁴⁴⁶⁼^0.18^X^2,49⁰
^{New Jers}^ey³⁴⁶⁼^0.18^X^1,94⁰
^{New Me}^xico^1,08⁸⁼^0.31^X^3,44⁰
^{New York}²⁷¹⁼^0.13^X^2,03⁰
^{North Carolina}⁵¹¹⁼^0.23^X^2,19⁰
^{North Dakota}^2,40⁰⁼^0.50^X^4,77⁰
^{Ohio 728}^0.26^2,62
^{Oklahoma 1,11}⁴⁼^0.40^X^2,74⁰
^{Oregon 356}^0.23^1,54
^{Pennsylvania 678}⁼^0.24^X^2,69⁰
^{Rhode Island}³³⁰⁼^0.16^X^1,99⁰
^{South Carolina}⁷⁰¹⁼^0.34^X^1,99⁰
^{South Dakota}⁵⁵⁸⁼^0.26^X^2,04⁰
^{Tennessee 672}^0.30^2,15
^{Texas 933}⁼^0.40^X^2,27⁰
^{Utah 885}^0.25^3,47
^{Vermont 332}⁼^0.20^X^1,66⁰
^{Virginia 443}^0.22^2,00
^{Washington 365}⁼^0.22^X^1,66⁰
^{West Virginia}^2,71⁹⁼^0.46^X^5,78⁰
^{Wisconsin 571}^0.25^2,26
^{Wyoming 3,47}³⁼^0.61^X^5,46⁰
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
^Note:^{In all but four stat}^{es, the}^p^{roduct of the e}^nergy^{intensity val}^{ue and ca}^{rbon co}^{ntent of e}^nergy^{use value is}
^{slightly lower than the CO}²^emis^{sions intensity value. This differe}ⁿ^{ce reflects t}^{he small perc}^{entage (}^{on average}
^{2%) of the}^{states’ CO}²^emissions^{that come from so}^{urces o}^{utside the ene}^{rgy sector (e.g., agricultura}^l).

Table A-5. CO² Emissions Intensity and CO² Emissions Intensity Drivers for All 50
States, Ranked by CO² Emissions Intensity (2003 data)
^CO²^Emissions⁼^Energy^X^Carbo^{n Co}^{ntent of}
^Intensity^Intensity^{Energy Use}
^State^Rank
^TCO²^/^$million⁼^{toe / $million}^X^TCO²^{/ 1000 toe}
^{of GSP}^GSP
^{Wyoming 1}^3,47³⁼^0.61^X^5,46⁰
^{West Virginia}²^2,71⁹⁼^0.46^X^5,78⁰
^{North Dakota}³^2,40⁰⁼^0.50^X^4,77⁰
^{Alaska 4}^1,62⁴⁼^0.69^X^2,30⁰
^{Louisiana 5}^1,50⁸⁼^0.71^X^2,08⁰
^{Montana 6}^1,44²⁼^0.41^X^3,48⁰
^{Kentucky 7}^1,24⁷⁼^0.40^X^3,03⁰
^{Indiana 8}^1,22¹⁼^0.36^X^3,14⁰
^{Alabama 9}^1,17⁸⁼^0.42^X^2,69⁰
^{Oklahoma 10}^1,11⁴⁼^0.40^X^2,74⁰
^{New Me}^xico¹¹^1,08⁸⁼^0.31^X^3,44⁰
^{Mississippi 12}⁹⁹³⁼^0.45^X^2,10⁰
^{Kansas 13 935}⁼^0.33^X^2,78⁰
^{Texas 14}⁹³³⁼^0.40^X^2,27⁰
^{Arkansas 15}⁹³³⁼^0.40^X^2,18⁰
^{Utah 16}⁸⁸⁵⁼^0.25^X^3,47⁰
^{Iowa 17}⁸³⁹⁼^0.31^X^2,64⁰
^{Missouri 18}⁷⁷⁰⁼^0.25^X^2,95⁰
^Nebras^{ka 19}⁷³⁷⁼^0.27^X^2,63⁰
^{Ohio 20}⁷²⁸⁼^0.26^X^2,62⁰
^{South Carolina}²¹⁷⁰¹⁼^0.34^X^1,99⁰
^{Pennsylvania 22}⁶⁷⁸⁼^0.24^X^2,69⁰
^{Tennessee 23}⁶⁷²⁼^0.30^X^2,15⁰
^{Maine 24}⁶⁴⁷⁼^0.32^X^1,94⁰
^{Wisconsin 25}⁵⁷¹⁼^0.25^X^2,26⁰
^{Georgia 26}⁵⁶⁹⁼^0.25^X^2,22⁰
^{South Dakota}²⁷⁵⁵⁸⁼^0.26^X^2,04⁰
^{Michigan 28}⁵⁵⁷⁼^0.23^X^2,32⁰
^{Nevada 29}⁵²⁸⁼^0.20^X^2,61⁰
^{Arizona 30}⁵¹⁷⁼^0.20^X^2,57⁰
^{North Carolina}³¹⁵¹¹⁼^0.23^X^2,19⁰
^{Minnesota 32}⁵¹⁰⁼^0.23^X^2,22⁰
^Colorad^{o 33}⁵⁰⁹⁼^0.19^X^2,62⁰

^CO²^Emissions⁼^Energy^X^Carbo^{n Co}^{ntent of}
^Intensity^Intensity^{Energy Use}
^{State Rank}
^TCO²^/^$million⁼^{toe / $million}^X^TCO²^{/ 1000 toe}
^{of GSP}^GSP
^{Hawaii 34}⁵⁰²⁼^0.18^X^2,74⁰
^{Illinois 35}⁴⁹⁷⁼^0.21^X^2,32⁰
^{Florida 36}⁴⁷⁸⁼^0.21^X^2,26⁰
^{New Ham}^pshire³⁷⁴⁴⁶⁼^0.18^X^2,49⁰
^{Virginia 38}⁴⁴³⁼^0.22^X^2,00⁰
^{Maryland 39}⁴¹¹⁼^0.20^X^2,05⁰
^{Idaho 40}⁴⁰⁴⁼^0.32^X^1,21⁰
^{Delaware 41}³⁹²⁼^0.18^X^2,14⁰
^{Washington 42}³⁶⁵⁼^0.22^X^1,66⁰
^{Oregon 43}³⁵⁶⁼^0.23^X^1,54⁰
^{New Jers}^ey⁴⁴³⁴⁶⁼^0.18^X^1,94⁰
^{Vermont 45}³³²⁼^0.20^X^1,66⁰
^{Rhode Island}⁴⁶³³⁰⁼^0.16^X^1,99⁰
^Massach^{usetts 47}³⁰⁸⁼^0.14^X^2,17⁰
^{California 48}²⁹⁵⁼^0.15^X^1,90⁰
^{New York}⁴⁹²⁷¹⁼^0.13^X^2,03⁰
^{Connecticut 50}²⁶⁹⁼^0.14^X^1,89⁰
^Source:^Prepa^{red by CRS with d}^{ata from the WRI, Climate Analy}^{sis Indicators Tool}^.
^Note:^{In all but four stat}^{es, the}^p^{roduct of the e}^nergy^{intensity val}^{ue and ca}^{rbon co}^{ntent of e}^nergy^{use value is}
^{slightly lower than the CO}²^emis^{sions intensity value. This differe}ⁿ^{ce reflects t}^{he small perc}^{entage (}^{on average}
^{2%) of the}^{states’ CO}²^emissions^{that come from so}^{urces o}^{utside the ene}^{rgy sector (e.g., agricultura}^l).
^Jon^a^t^h^aⁿ^L^.^R^a^m^s^e^u^r
^An^al^y^s^{t i}ⁿ^En^vⁱ^r^oⁿ^m^eⁿ^{tal P}^o^lic^y
^j^r^am^s^e^u^r^@^c^rs^.l^oc.^g^ov^{, 7-}⁷⁹¹⁹