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Fossil fuel carbon dioxide (CO2) emissions in the US decreased by about 11% between 2007 and 2013, from 6,023 to 5,377 Mt CO2 (1 Mt = 106 metric tons). This decline in emissions has been widely attributed to a shift from coal to natural gas in US electricity production. However, the factors driving the decline—and the decade of increasing emissions that preceded it—have not been quantitatively evaluated; the role of natural gas in the decline therefore remains speculative. Here, we analyze for the first time the factors affecting US emissions 1997 2013. Prior to 2007, rising emissions were driven by economic growth: 71% of the increase between 1997 and 2007 was due to increases in US consumption of goods and services, with the remainder of the increase due to population growth. Emissions decreased with the global economic recession: 77% of the decrease 2007-2009 was due to decreased consumption and changes in the production structure of the US economy, with just 18% related to changes in the fuel mix of the energy sector. During the ongoing economic recovery, 2009-2013, the overall decrease in US emissions has been small (less than 1%), with nearly equal contributions from changes in the fuel mix of the energy sector (26%), decreases in energy use per unit of GDP (25%), further changes in US production structure (25%), and changes in consumption patterns (24%). From these results, we conclude that changes in fuel mix—the primary means by which substitution of gas for coal affects emissions—have had a relatively minor role in the reduction of US CO2 emissions since 2007. Energy-climate policies such as the proposed US US-EPA rules may therefore be necessary to lock-in the recent emissions reductions and drive further decarbonization of the US energy system as its economy recovers and grows
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ARTICLE
Received 8 Oct 2014 |Accepted 3 Jun 2015 |Published 21 Jul 2015
Drivers of the US CO
2
emissions 1997–2013
Kuishuang Feng1, Steven J. Davis2,3, Laixiang Sun1,4,5 & Klaus Hubacek1
Fossil fuel CO
2
emissions in the United States decreased by B11% between 2007 and 2013,
from 6,023 to 5,377 Mt. This decline has been widely attributed to a shift from the use of coal
to natural gas in US electricity production. However, the factors driving the decline have not
been quantitatively evaluated; the role of natural gas in the decline therefore remains
speculative. Here we analyse the factors affecting US emissions from 1997 to 2013. Before
2007, rising emissions were primarily driven by economic growth. After 2007, decreasing
emissions were largely a result of economic recession with changes in fuel mix (for example,
substitution of natural gas for coal) playing a comparatively minor role. Energy–climate
policies may, therefore, be necessary to lock-in the recent emissions reductions and drive
further decarbonization of the energy system as the US economy recovers and grows.
DOI: 10.1038/ncomms8714 OPEN
1Department of Geographical Sciences, University of Maryland, College Park, Maryland 20742, USA. 2Department of Earth System Science, University of
California, Irvine, Irvine, California 92697, USA. 3Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. 4Department of
Financial and Management Studies, SOAS, University of London, London WC1H 0XG, UK. 5International Institute for Applied Systems Analysis (IIASA),
A-2361 Laxenburg, Austria. Correspondence and requests for materials should be addressed to K.H. (email: hubacek@umd.edu).
NATURE COMMUNICATIONS | 6:7714 | DOI: 10.1038/nc omms8714 | www.nature.com/naturecommunications 1
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The CO
2
emissions from the burning of fossil fuels are the
primary cause of anthropogenic climate change1, and
the United States emits more CO
2
each year than any
other country except China. In the decade before 2007, US CO
2
emissions grew by an average 0.7% per year. However, beginning
in 2007, US emissions decreased, reaching a minimum of
5,284 Mt CO
2
in 2012—12% lower than 2007 levels and 5%
lower than 1997 levels2. This recent decline is good news and
is consistent with the Obama administration’s stated goal of
reducing CO
2
emissions by 17% in 2020 and 83% in 2050 relative
to 2005 levels3. Assuming no change in emissions outside the
power sector, the new rules proposed by the US Environmental
Protection Agency in June 2014 to limit CO
2
emissions from
power plants will require US emissions to decrease to 4,200 Mt
CO
2
in 2030—a further 20% reduction from 2013 levels4.
Coinciding with the post-2007 decline in emissions,
innovations in hydraulic fracturing technology have dramatically
increased domestic supplies of gas5,6. Commentators in the
scientific community and media have linked the two trends,
celebrating the climate benefits of the gas boom7–9. Recently, the
Third National Climate Assessment of the United States
Global Change Research Program also adopted this conclusion,
stating that the decrease in US CO
2
emissions was ‘ylargely
due to a shift from coal to less CO
2
-intensive natural gas for
electricity production’10. Yet, despite potentially significant
implications for US climate and energy policy, there has been
no quantitative analysis of whether the gas boom and changes
in the fuel mix of the power sector are indeed driving the decrease
in US CO
2
emissions.
Here, we use input–output structural decomposition analysis
(SDA) to assess sources of change in US CO
2
emissions over
a decade of mostly increasing emissions, 1997–2007, and then
over the period of mostly decreasing emissions, 2007–2013.
Our analysis quantifies the contribution of six different factors to
changes in US emissions. These factors are: population growth;
changes in consumption volume caused exclusively by changes
in per capita consumption of goods and services; shifts in
consumption patterns or the types of goods and services being
consumed; adjustments in production structure or the mix of
inputs (for example, labour, domestic and imported materials)
required to produce US goods and services; changes in fuel mix as
reflected by the CO
2
emitted per unit of energy used; and changes
in energy intensity or the energy used per inflation-adjusted unit
of economic output. The SDA in this research is based on the
additive decomposition of the changes in emission determined by
six multiplicative factors acting as accelerators or retardants of the
emission dynamics. Each term in the decomposition is a product
of the change in one explicative factor and the level values of the
other five factors, and thus represents the contribution of one
explicative factor to the total change in emission. For example, in
the term where population is the explicative factor, the values of
consumption volume, production structure, consumption
patterns, energy intensity and fuel mix are held unchanged and
only population varies. In this way, the SDA method allows us to
quantify the contribution of each of the assessed factors to the
trend in emissions. Details of our methodology and data sources
are in the Methods section (including Supplementary Methods).
We find that before 2007, rising emissions were driven by
economic growth: 71% of the increase between 1997 and 2007
was due to increases in US consumption of goods and services,
with the remainder of the increase due to population growth.
Concurrent with the global economic recession, 83% of the
decrease during 2007–2009 was due to decreased consumption
and changes in the production structure of the US economy, with
just 17% related to changes in the fuel mix. During the economic
recovery, 2009–2013, the decrease in US emissions has been small
(o1%), with nearly equal contributions from changes in the fuel
mix, decreases in energy use per unit of GDP, changes in US
production structure, and changes in consumption patterns. We
conclude that substitution of gas for coal has had a relatively
minor role in the emissions reduction of US CO
2
emissions since
2007.
Results
Growing emissions from 1997 to 2007. Between 1997 and 2007,
US emissions increased by 7.3% (Fig. 1, black curve). Our
analysis shows that the main factor behind this increase was an
increase in consumption volume caused by growth in per capita
consumption of goods and services in the United States. Indeed,
increases in such consumption volume correspond to a
contribution of a 21.8% increase in emissions over this decade
(Fig. 1, red curve). The next most important factor influencing
CO
2
emissions over the same period was population growth.
Immigration and natural growth have resulted in steady
population growth at a rate of B1% per year since 1997. These
population gains contributed to an 8.9% increase in emissions
between 1997 and 2007 (Fig. 1, yellow curve).
However, other factors slowed the growth of emissions
between 1997 and 2007: decreases in the energy intensity of
GDP; changes in the consumption patterns of US consumers;
shifts in production structure; and decreases in the use of coal as
an energy source. For instance, over this period, the energy used
per dollar of economic output decreased by 17% (Fig. 2a, black
curve), the share of consumer spending on manufactured goods
decreased by B4% (Fig. 2b), the share of imported inputs to the
US industry sectors increased (for example, imports to petroleum
and coal products sector increased by 6.7%, and imports to the
chemical products, primary metals and textile sectors increased
by 2.7%, 2.5% and 2.1%, respectively)11, and the share of US
electricity generated from coal decreased by B5% while the share
generated from natural gas increased by 8% (Fig. 2c). All of these
trends exerted a downward influence on emissions. Between 1997
and 2007, changes in energy intensity, consumption patterns,
production structure and fuel mix contributed to retarding
1997 1999 2001 2003 2005 2007 2009 2011 2013
Year
–25%
–20%
–15%
–10%
–5%
5%
10%
15%
25%
20%
0
Contributions of different factors
to changes in CO2 emissions
Energy
intensity
Consumption
patterns
Production
structure
Fuel mix
Emissions
Population
Consumption
volume
Figure 1 | Contributions of different factors to changes in the US CO
2
emissions between 1997 and 2013. Using 1997 as base year, the solid
black line shows the percentage change in total CO
2
emissions. The
other lines show the contribution to the change in emissions from
consumption volume (red), population (yellow), consumption patterns
(green), production structure (blue), energy intensity (purple) and fuel mix
(orange).
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8714
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emissions of 7.4, 6.9, 4.9 and 3.6%, respectively (Fig. 1, purple,
green, blue and orange curves, respectively).
Declining emissions from 2007 to 2013.USCO
2
emissions
stopped growing in 2007, and decreased by B11% between 2007
and 2013 (Fig. 1, black curve). Looking at this time period in
aggregate, the only factor which acted to increase emissions over
the period was continued and steady population growth ( þ3.7%)
(Fig. 1, yellow curve). However, the upward influence of
population growth was overwhelmed by the downward influence
of changes in production structure ( 6.1%), fuel mix ( 4.4%),
consumption volumes triggered by per capita consumption
(3.9%), energy intensity of GDP ( 0.5%) and changing con-
sumption patterns ( 0.4%; Fig. 1, blue, orange, red, purple and
green curves, respectively).
Although all of the analysed factors except population
contributed to the decrease in emissions during 2007–2013,
different factors dominated over shorter periods. Figure 3
subdivides 2007-2013 into 2-year periods, showing that emissions
fell by 9.9% from 2007 to 2009, increased by 1.3% between 2009
to 2011 and decreased again by 2.1% between 2011 and 2013.
More than half (53%) of the initial and most substantial
decrease in emissions, between 2007 and 2009, was due to a sharp
drop in the volume of consumed goods as a result of reduction in
per capita consumption during the global economic recession
(Fig. 3, red bar). In particular, Fig. 4 shows that sharp decreases in
the volume of capital expenditures and exported goods between
2007 and 2009 drove down associated emissions by 25% and 18%,
respectively. Changes in the production structure of the US
economy (that is, the volume and type of intermediate goods
demanded) and the fuel mix of the energy sector contributed 30%
and 17% of the initial (2007–2009) decrease in emissions,
respectively, while increases in the energy intensity of the US
economy and changing consumption patterns exerted modest
upward influences on emissions during the same period.
As the US economy had slowly recovered from the global
economic recession, between 2009 and 2013, the average
annual change in US emissions was small: a 0.2% decrease.
Economic recovery is reflected by the upward influence of the
volume of goods consumed on emissions during both 2009–2011
and 2011–2013. Between 2009 and 2011, rising consumption
volume, population growth, and increasing energy intensity urged
emissions up by a combined 4.0% (2.2%, 1.5% and 0.3%,
respectively), which was only partly offset by the changes in
consumption patterns ( 1.1%), production structure ( 1.0%)
and fuel mix ( 0.6%), resulting in an actual increase in
emissions of 1.3% (Fig. 3). However, between 2011 and 2013,
the upward influence of consumption volume and population on
emissions was less ( þ1.2% and þ1.2%, respectively) and the
energy intensity of the economy decreased ( 2.1%). When
combined with changes in the fuel mix of the energy sector
(1.2%) and shifting consumption patterns ( 0.2%), the net
effect was a 2.1% decrease in emissions during 2011–2013 (Fig. 3).
Increases in the supply of natural gas affect two of the factors in
our analysis: the fuel mix of the energy sector and, to a lesser
extent, the energy intensity of the US economy. By decreasing gas
prices, abundant gas encourages a shift in the fuel mix from more
carbon-intensive coal to gas. In turn, a shift to gas may contribute
to decreased energy intensity because gas-fired power plants are
on average 20% more efficient at converting fuel energy to
electricity than coal plants12.
The boom of natural gas from breakthroughs in hydraulic
fracturing of shale deposits had only just begun to affect US gas
supplies in 2009 (ref. 5). Thus, the decrease in emissions from
changes in the fuel mix of the energy sector prior 2009 reflects an
1997 2001 2005 2009 2013
1997 2001 2005 2009 2013
1997 2001 2005 2009 2013
Coal
Gas
Oil
Nuclear
Renewables
Services
Manufactured goods
Construction
Services
Mean
Manufact.
Transport.
Utilities
Energy intensity
Ye a r
Ye a r
Ye a r
Consumption patterns
0%
40%
60%
80%
100%
20%
0%
40%
60%
80%
100%
20%
20%
10%
–10%
–20%
–30%
–40%
–50%
0%
Fuel mix
Percent change in energy
used per $ of output
Share of final
consumption (%)
Share of electricity
generation (%)
a
b
c
Figure 2 | Trends underlying the decomposed factors. (a) Per cent
changes in the energy intensity (energy used per dollar (US$) of output) of
key sectors in the US economy, (b) shares of final demand made up of
manufactured goods (that is, food, clothing, agriculture, paper and printing,
chemical manufacture (manufact.), petroleum refining, metal
manufacturing, machinery and equipment, utilities and construction) and
services (that is, retail, hotel, transport, shipping, real estate, public
administration, defense, education, health, community and social work, and
household employment.) and (c) changes in the fuel mix of the US
electricity sector.
NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8714 ARTICLE
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&2015 Macmillan Publishers Limited. All rights reserved.
independent and longer-term trend of the declining use of coal in
the US energy sector (see, for example, Fig. 2c). However, as seen
in Fig. 3, changes in the US fuel mix from 2007 to 2009 alone
would not have caused a decrease in US emissions.
Although the decreases in emissions since 2009 have been
relatively small, the influence of shale gas is visible. For example,
about half of the 2.1% decrease in emissions during 2011–2013 is
related to changes in the fuel mix of the energy sector ( 1.2%,
orange bar in Fig. 3). Yet the decrease in the energy intensity of
the US economy was nearly twice as strong an influence on
emissions over the same period (purple bar in Fig. 3).
Although a drop in the energy intensity (exajoule per dollar
output) of the energy sector in 2013 accounts for roughly a third
of the observed decrease in US energy intensity in 2011–2013, the
remaining two-thirds relate to changes in energy used by the
transport and service sectors (Fig. 2a). Three unrelated trends
underlie the decreasing energy intensity of these sectors. First,
high gasoline prices during 2011–2013 (the average price of
gasoline had remained above $3.40 per gallon during this period,
in contrast to the average price of $2.50 per gallon in 2005) have
contributed to both a reduction in per capita miles driven
(Supplementary Fig. 1a) and an increase in average fuel efficiency
of vehicles (Supplementary Fig. 1b), and thus a 33% decrease in
US gasoline consumption during 2011–2013. Second, a mild
winter in 2012 meant less energy was used for heating and thus
reduced energy intensity of the service sector (households also
used less energy for home heating, which accounts for part of the
drop in consumption volume)13 (Supplementary Fig. 2). Last,
there is evidence that manufacturing in the United States became
more energy efficient: energy use by manufacturing was nearly
constant 2011–2013 despite average annual growth in GDP of
2.3% per year over the same period.
Shifts in the production structure of the US economy between
2007 and 2013 have consistently exerted a downward influence
on US emissions, as the volume and type of intermediate goods
used by various industry sectors has evolved and become more
efficient (blue bars in Fig. 3). Yet this structural shift also reflects
the progressive offshoring of emissions-intensive industries to
China and other developing countries over the analysed period14.
For instance, between 2009 and 2011, when changes in domestic
production structure exerted a downward influence on US CO
2
emissions ( 1%, blue bar in Fig. 3), we calculated that the net
import of emissions embodied in US trade increased by 32%
(Supplementary Fig. 3). Trade data for the 2011–2013 period is
not yet available.
Between 2009 and 2013, the share of US consumption of
manufactured goods increased relative to services (Fig. 2b), but
the net effect of changes in consumption patterns was to decrease
emissions (by 1.1% between 2009 and 2011 and by 0.2% between
2011 and 2013; green bars in Fig. 3). This result reveals that
changes in the types of goods being consumed over time can have
a significant impact on emissions15,16, and that it is not as simple
as the balance of manufactured goods and services.
Discussion. Between 1997 and 2007, US emissions grew steadily
(0.7% per year) as increases related to population growth
and consumption volume (per capita consumption) outpaced
the downward influence of improving energy intensity,
shifting consumption patterns and production structure and
decarbonizing fuel mix.
The large decrease (9.9%) in US CO
2
emissions between 2007
and 2009 was primarily the result of the economic recession,
evidenced by large decreases in household consumption, energy-
intensive capital expenditures and export (Figs 1, 3 and 4). The
recessionary belt-tightening may also have contributed to the
significant efficiency gains in production structure.
Since 2009, the slow recovery of the US economy has urged
emissions backup, but has been closely balanced by decreases in
2007 2009 2011 2013
–2.1%–9.9% +1.3%
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
4.8
+1.0%
+1.2%
+1.2%
+1.2%
+1.3% –2.3%
–4.0%
–7.1%
+0.3%
+1.5%
+2.2% –0.6% –1.0%–1.1%
–0.2% –1.0%–1.2%
–2.1%
Annual emissions (Gt CO
2
)
Population
Population
Population
Energy intensity
Energy intensity
Energy intensity
Consumption pattern
Consump. pattern
Consump. pattern
Fuel mix
Fuel mix
Fuel mix
Production structure
Prod. structure
Prod. structure
Consump. volume
Consump. volume
Consump. volume
Figure 3 | Contributions of different factors to the decline in US CO
2
emissions 2007–2009 and 2009–2011 and 2011–2013. Between 2007 and 2009,
decreases in the volume of goods and services consumed during the economic recession (red) was the primary contributor to the nearly 10% drop in
emissions. But between 2009 and 2011, consumption (consump.) volume rebounded, population grew and the energy intensity of output increased, driving
up emissions by 1.3% against modest decreases in the carbon intensity of the fuel mix and shifts in production structure and consumption patterns.
Between 2011 and 2013, increases in population and consumption volume again pushed emissions upward, but overall emissions decreased by 2.1% due to
further changes in production (prod.) structure, consumption patterns, decreasing use of coal and decreases in energy intensity of output. Not shown here,
emissions increased by 1.7% between 2012 and 2013, driven primarily by increases in consumption volume.
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8714
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&2015 Macmillan Publishers Limited. All rights reserved.
energy intensity, especially in the transport, manufacturing and
service sectors (Fig. 2a), as well as changes in the fuel mix of the
energy sector. The net effect has been very little change in
emissions; between 2009 and 2013; US emissions have decreased
by an average of 0.2% per year. Contrary to conventional wisdom,
our decomposition analysis shows that changes in the fuel mix of
the energy sector (including those related to the shale gas boom)
account for a relatively small portion of this decrease.
In addition to a more robust understanding of the factors
influencing US emissions during 1997–2013, our analysis may be
helpful in assessing the efficacy of different forces to reduce US
emissions in the future. For example, the modest effect of changes
in the fuel mix of the energy sector on emissions in recent years
suggests that further increase in the use of natural gas may be of
limited benefit in decreasing emissions. This is because barring
technology-specific policies (for example, Renewable Portfolio
Standards), recent studies have shown that gas does not substitute
for coal only; growth of emission-free technologies such as solar,
wind and nuclear energy is also limited while gas is cheap17,18.In
these studies, future increases in natural gas use act to both
reduce domestic coal use and slow the growth of renewable
energy, resulting in little net change to cumulative CO
2
emissions17,19–21. Moreover, CO
2
emissions are not the only
consideration; a growing number of studies also show that
increased leakage of methane from new natural gas infrastructure
can offset CO
2
reductions relative to coal22,23. Third, decreases
in residential gas prices (Supplementary Fig. 4) may lead to
rebound effects if people spend some of the money they saved
heating their home on carbon- and energy-intensive goods24.
And finally, decreased domestic demand for coal has enabled an
increase in US coal exports to eager and growing overseas
markets. The US power sector consumed 170 million fewer
metric tons of coal in 2013 than in 2007, during which period coal
exports doubled even as coal prices rose (Supplementary Fig. 5).
Although CO
2
emissions from US coal burned elsewhere are
generally attributed to the country where those emissions occur,
the emissions nonetheless contribute to global climate change
(and in fact less energy may be produced per unit of CO
2
emissions when the coal is burned in countries with less-efficient
power plants). For all these reasons, further increases in the use of
natural gas in the United States may not have a large effect on
global greenhouse gas emissions and warming.
Similarly, further emissions reductions due to decreases in
energy intensity are not inevitable. As can be seen in Fig. 2a, the
energy intensity of utilities increased between 2009 and 2013,
perhaps because such utilities chose to pass the cost savings
related to cheap gas along to their customers25. The energy
intensity of other industry sectors also shows no long-term
decreasing trend (Fig. 2a). In contrast, any gas-driven recovery of
US manufacturing, such as in the production of vehicles and
heavy machinery26, will tend to increase the average energy
intensity of the US economy.
1997 1999 2001 2003 2005 2007 2009 2011 2013
Ye a r
1997 1999 2001 2003 2005 2007 2009 2011 2013
Ye a r
1997 1999 2001 2003 2005 2007 2009 2011 2013
Ye a r
1997 1999 2001 2003 2005 2007 2009 2011 2013
Ye a r
–25%
–20%
–20%
–30%
–40%
–15%
–10%
–10%
–5%
5%
10%
10%
20%
30%
40%
15%
15%
25%
25%
35%
20%
0
–35%
–15%
–25%
–5%
5%
0
0
Contributions of different factors
to changes in CO2 emissions
Contributions of different factors
to changes in CO2 emissions
–25%
–20%
–15%
–10%
–5%
5%
10%
15%
25%
20%
0
Contributions of different factors
to changes in CO2 emissions
Contributions of different factors
to changes in CO2 emissions
ab
cd
Household Government
Capital Exports
Consumption
volume
Emissions
Population
Fuel mix
Energy
intensity
Consumption
patterns
Production
structure
Figure 4 | Contributions of different factors to changes in US CO
2
emissions specific to different final demand components 1997–2013. Shown are
changes in emissions related to household expenditures (a), government expenditures (b), capital investment (c) and exports (d). In each panel, the solid
black line shows the percentage change in CO
2
emissions triggered by changes in the corresponding final demand component, and the other lines show the
contribution to the change in emissions from consumption volume (red), population (yellow), consumption patterns (green), production structure (blue),
fuel mix (orange) and energy intensity (purple).
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&2015 Macmillan Publishers Limited. All rights reserved.
Sustaining economic growth while also drastically reducing
emissions to the levels targeted by the Obama administration27
will depend upon large additional decreases in the energy
intensity of the US economy as well as radical decarbonization
of the energy sector (that is, very large changes in the fuel mix of
the energy sector away from fossil fuels and toward renewables
and/or nuclear energy). Although increased use of natural gas
by the energy sector has helped to keep US CO
2
emissions
from rising during the economic recovery of 2009–2013, our
decomposition analysis shows that decreases in the energy
intensity of the manufacturing, transport and service
sectors over the same period were even more important, and
that the largest decrease in emissions was due to decreased
consumption during the recession of 2007–2009. However,
the recovering economy is now urging emissions backup, it is
not clear whether decreases in energy intensity will continue,
and the overall climate benefits of increased gas use are in
question. Future reductions in US emissions will depend upon
policies (for example, the Environmental Protection Agency
Clean Power Plan) that can lock-in the recessionary emissions
reductions and ensure continued decarbonization of the US
energy system by deployment of more efficient and low-carbon
energy technologies28.
Methods
Index decomposition versus structural decomposition.Index decomposition
analysis (IDA) and SDA are two decomposition methods that have been frequently
used to calculate the contribution of different factors to the overall change in
carbon emissions and energy consumption. IDA is often used in studies that aim
to understand the drivers of energy use and emissions in a specific economic
sector, while SDA is used primarily by input–output practitioners whose research
focus on the changes in energy consumption and emissions of a whole economy,
for example, a country, a region, or the whole world29. Due to its simplicity,
transparency and lower data requirements, the IDA approach based on index
theory30–33 had been applied in numerous studies in the past34,35. However,
these advantages of the IDA approach may mean limitations for more detailed
in-depth analysis. For instance, lower data requirements also mean less detailed
decomposition of economic production structure34 because the IDA approach
cannot analyse the interdependency of different economic sectors35. Similarly,
IDA does not distinguish intermediate and final consumption, and thus cannot
capture indirect impacts of change in final consumption. In this study, we opt to
use SDA based on input–output analysis34. The SDA overcomes many of the static
features of input–output models, enabling the evaluation of changes over time in
economic structure, final demand components and categories. The SDA is capable
of distinguishing a range of production effects and final demand effects that the
IDA approach lacks35, and allows assessment of both direct and indirect effects
along the entire supply chain across upstream and downstream industries36.
Although the high level of data requirement by the SDA approach has been a
barrier in the past in light of the fact that many countries publish input–output
tables only once every 5 or more years, the recent development of global time series
input–output databases (for example, World Input–Output Database (WIOD)37
and The EOAR multi-region IO database38) and more regular publication of
economic-structure data in countries like the United States now make time series
SDA feasible. More detailed discussion on comparison of IDA and SDA
approaches and their methodological developments can be found in Hoekstra and
van den Bergh34 and Su and Ang29.
Structural decomposition analysis.SDA is a quantitative methodology based on
input–output modelling. SDA is a popular tool in assessing the contributions of
different factors and industry sectors to changes in energy use and CO
2
emissions
over time. The method has been applied to many different countries such as
Australia39, Denmark40,41, India42, Korea43, Netherlands44, the United States45 and
China15,35,46,47.
Input–output analysis is an accounting procedure that relies on national or
regional input–output tables. A country’s input–output tables show the flows of
goods and services and thus the interdependencies between suppliers and
consumers along the production chain across upstream and downstream industries
within an economy and between economies48. Environmental input–output
analysis illustrates the economy-wide environmental repercussions (here we use
CO
2
emissions as environmental indicator) triggered by economic activity, and can
be expressed mathematically as
CO2¼kIAðÞ
1yþHHdir ð1Þ
where CO
2
is the total economy-wide CO
2
emissions; kis a row vector of emission
coefficients (emissions per unit of economic output) in each economic sector; Iis
the identity matrix; Ais a matrix, and each column of Ashows input requirement
from each sector to produce one unit output of this column sector; yis a column
vector of final consumption; HH
dir
is a scalar of household direct emissions, for
example, heating and driving. We consider the production structure through
L¼(I–A) 1, which is the renowned Leontief inversion matrix. Changes in the
production structure thus refer to changing input requirements of each sector or, in
other words, industries using more or less intermediate inputs from each other. It
has been widely discussed that both emissions per unit of energy consumption
(fuel mix) and energy efficiency (energy consumption per unit of economic output)
are vital to the emission intensity of an economy49,50. Hence, we further
decompose the emission coefficients, kinto emission intensity (emissions per unit
of energy consumption) and energy intensity (energy consumption per unit of
output) k¼fE
ˆ, where fis a row vector of emissions per unit of energy use
(fuel mix) and E
ˆis the diagonalized matrix of energy use per unit of economic
output. To distinguish the contributions of different final demand components,
we further decompose yinto three components—average consumption structure,
per capita consumption volume and population: y¼y
s
y
v
p, where y
s
is a vector
of per capita consumption patterns; y
v
is a scalar of per capita consumption
volume; pis a scalar of population which could appear at the front or the back of
the input–output equation. Therefore, equation (1) can be transformed to:
CO2¼pf^
ELy
syvþHHdir ð2Þ
Over a given period of time, any changes in CO
2
emissions in a country can be
represented by equation (2), in which the seven factors of population, fuel mix,
energy intensity, production structure, consumption patterns and consumption
volume, plus household direct emissions, fully account for the changes in CO
2
emissions. A total difference of equation (2) generates equation (3)
DCO2¼Dpf^
ELy
syvþpDf^
ELy
syvþpfD^
ELy
syvþpf^
EDLysyv
þpf^
ELDysyvþpf^
ELy
sDyvþDHHdir
ð3Þ
where, Dis the difference operator. Equation (3) converts six multiplicative terms
in the first term of equation (2) into six additive terms. Each additive term in
equation (3) represents the contribution to a change in CO
2
emissions triggered by
a factor assuming all other factors are constant. For example, in the sixth term, Dy
v
is change in per capita consumption volume, and the term represents the change of
total CO
2
emission caused by a change in per capita consumption volume, with
population size, fuel mix, energy intensity, production structure and consumption
patterns staying constant.
In the SDA, it is possible to compare different terms relative to any time point
within a study period. However, there is no unique solution for the decomposition.
In this study, we use the average of all possible first-order decompositions
suggested by Dietzenbacher and Los51 and Seibel52 (see Supplementary Methods
and Supplementary Table 1 for a detailed discussion). We also simplify the
presented results by combining direct CO
2
emissions from households (for
example, natural gas heating in homes) with the emissions embodied in consumed
goods (that is, ‘consumption volume’).
The US input–output tables from 1997 to 2013 were collected from the Bureau
of Economic Analysis which is in make-use format11. We convert the make-use
table to symmetric input–output table following the method by Miller and Blair48
and then aggregated them into 35 economic sectors to match the energy and
emission data from the WIOD37.
The CO
2
emissions and energy data from 2010 to 2013 were collected from the
US Energy Information Administration (EIA)2. EIA only publishes energy and
emission data at aggregate sectoral level including manufacturing, electric power,
commercial and residential sectors. We disaggregated energy use of these four
sectors into 35 economic sectors according to the sectoral energy purchase
collected from Bureau of Economic Analysis11. Also, for data consistency, we scale
the energy and CO
2
emission data from WIOD to match the EIA data.
Our analysis focuses on US fossil fuel CO
2
emissions and does not include
emissions of non-CO
2
greenhouse gases such as methane. Incorporating methane
in the analysis would tend to reduce the climate benefit of gas via the fuel mix of
the power sector because of fugitive methane emissions, which may be
substantial23,53. Our analysis also focuses on CO
2
emissions produced in the
United States; emissions embodied in imports from other countries are not
included. This territorial perspective is consistent with the focus of prospective
policies, although some analysts have argued for consumption-based accounting as
a basis for climate policy54–56. For this reason, we also pay attention to how
changes in trade may have affected the factors of US production structure and
energy intensity by offshoring of energy-intensive manufacturing57.
Decomposing final demand.Because changes in the volume of goods and services
consumed were the single most important influence on US emissions between 1997
and 2013, we also analysed four separate components of final demand to assess the
trends in emissions related to each category as well as the important influences
on emissions in each case. Figure 4 shows the emissions associated with different
final demand (consumption) components: household consumption (Fig. 4a),
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8714
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governmental expenditure (Fig. 4b), capital formation (Fig. 4c) and exports
(Fig. 4d).
Between 2007 and 2013, emissions associated with household consumption
decreased by 11.0%, which was almost entirely driven by changes in fuel mix and
production structure, especially between 2009 and 2013, since consumption
volume was constant (Fig. 4a). Emissions associated with government expenditures
in the same time period decreased by 4.8%, and it was largely driven by changes in
energy intensity and production structure (Fig. 4b). In contrast, emissions related
to capital formation decreased by 24.4% between 2007 and 2013, primarily due to a
huge decline in the volume of capital investment (Fig. 4c, red curve). However,
changes in emissions related to exports between 2007 and 2013 were almost
entirely the result of changes in the volume of exports, with the other factors
cancelling each other out (Fig. 4d).
References
1. IPCC. Fifth Assessment Report (AR5): Climate Change 2013 (2013).
2. EIA. U.S. Energy-Related Carbon Dioxide Emissions, 2013. U.S. Energy
Information Administration (2015).
3. Remarks by the President at the Morning Plenary Session of the
United Nations Climate Change Conference (2009; Available at https://
www.whitehouse.gov/the-press-office/remarks-president-morning-plenary-
session-united-nations-climate-change-conference.
4. EPA. Carbon pollution emission guidelines for existing stationary sources:
electric generating units. In: EPA-HQ-OAR-2013-0602 (2014).
5. Joskow, P. L. Natural gas: from shortages to abundance in the United States.
Am. Econ. Rev. 103, 338–343 (2013).
6. Kerr, R. Energy. Natural gas from shale bursts onto the scene. Science 328,
1624–1626 (2010).
7. Gold, R. Rise in U.S. Gas Production Fuels Unexpected Plunge in Emissions. In:
The Wall Street Journal (2013).
8. Shellenberger, M., Nordhaus, T., Trembath, A. & Luke, M. Coal Killer:
How Natural Gas Fuels the Clean Energy Revolution. In: The Breakthrough
(2013).
9. Hanger, J. Natural Gas is responsible for about 77% of carbon emission
reductions in 2012. In: John Hanger’s Facts of the Day (2012).
10. The Third National Climate Assessment. U.S. Global Change Research
Program (2014).
11. BEA. Input-Output Data. U.S. Bureau of Economic Analysis (2014).
12. EIA. International Energy Statistics. U.S. Energy Information Administration
(2014).
13. Afsah, S. & Salcito, K. Demand Reduction Slashes US CO2 Emissions in 2012.
CO2 Scorecard Group (2013).
14. Peters, G. P., Davis, S. J. & Andrew, R. A synthesis of carbon in international
trade. Biogeosciences 9, 3949–4023 (2012).
15. Peters, G. P., Weber, C. L., Guan, D. & Hubacek, K. China’s growing CO2
emissions: a race between increasing consumption and efficiency gains.
Environ. Sci. Technol. 41, 5939–5944 (2007).
16. de Haan, M. A structural decomposition analysis of pollution in the
Netherlands. Econ. Syst. Res. 13, 181–196 (2001).
17. McJeon, H. et al. Limited impact on decadal-scale climate change from
increased use of natural gas. Nature 514, 482–485 (2014).
18. Davis, S. J. & Shearer, C. Climate change: a crack in the natural-gas bridge.
Nature 514, 436–437 (2014).
19. EMF. Changing the game? Emissions and market implications of new natural
gas supplies. In: EMF Report. Energy Modeling Forum (2013).
20. Newell, R. G. & Raimi, D. Implications of shale gas development for climate
change. Environ. Sci. Technol. 48, 8360–8368 (2014).
21. Shearer, C., Bistline, J., Inman, M. & Davis, S. J. The effect of natural gas supply
on US renewable energy and CO
2
emissions. Environ. Res. Lett. 9, 094008
(2014).
22. Wigley, T. M. L. Coal to gas: the influence of methane leakage. Clim. Change
108, 601–608 (2011).
23. Brandt, A. R. et al. Methane leaks from North American natural gas systems.
Science 343, 733–735 (2014).
24. Ornetzeder, M., Hertwich, E. G., Hubacek, K., Korytarova, K. & Haas, W.
The environmental effect of car-free housing: a case in Vienna. Ecol. Econ. 65,
516–530 (2008).
25. Sirkin, H., Zinser, M. & Rose, J. How Cheap Natural Gas Benefits the Budgets
of U.S. Households. The Boston Consulting Group (2014).
26. Plumer, B. Is U.S.manufacturing making a comeback or is it just hype? In:
The Washington Post The Washington Post (2013).
27. Samuelsohn, D. & Friedman, L. Obama Announces 2020 Emissions Target,
Dec. 9 Copenhagen Visit. In: New York Times (2009).
28. Davis, S. J., Cao, L., Caldeira, K. & Hoffert, M. I. Rethinking Wedges. Environ.
Res. Lett. 8 (2013).
29. Su, B. & Ang, B. W. Structural decomposition analysis applied to energy and
emissions: some methodological developments. Energy Econ. 34, 177–188
(2012).
30. Hoekstra, R. & van den Bergh, JCJM Structural decomposition analysis
of physical flows in the economy. Environ. Resour. Econ. 23, 357–378
(2002).
31. Ang, B. W. & Liu, F. L. A new energy decomposition method: perfect in
decomposition and consistent in aggregation. Energy 26, 537–548 (2001).
32. Ang, B. W., Liu, F. L. & Chung, H.-S. A generalized fisher index approach to
energy decompostion analysis. Energy Econ. 26, 757–763 (2004).
33. Ang, B. W. & Zhang, F. Q. A survey of index decomposition analysis in energy
and environmental studies. Energy 25, 1149–1176 (2000).
34. Hoekstra, R. & van den Bergh, J. C. J. M. Comparing structural decomposition
analysis and index. Energy Econ. 25, 39–64 (2003).
35. Feng, K., Siu, Y. L., Guan, D. & Hubacek, K. Analyzing drivers of regional
carbon dioxide emissions for China. J. Ind. Ecol. 16, 600–611 (2012).
36. Miller, R. E. & Blair, P. D. in Input-Output Analysis: Foundations and
Extensions 2nd edn (Cambridge Univ. Press, 2009).
37. WIOD. World Input-Output Database. the 7th Framework Programme, the
European Commission (2012).
38. EORA The Eora MRIO Database (2012).
39. Wood, R. Structural decomposition analysis of Australia’s greenhouse gas
emissions. Energy Policy 37, 4943–4948 (2009).
40. Rormose, P. & Olsen, T. Structural Decomposition Analysis of Air Emissions in
Denmark 1980-2002. In: 15th International Conference on Input-Output
Techniques. International Input-output Association (2005).
41. Wier, M. Sources of changes in emissions from energy: a structural
decomposition analysis. Econ. Syst. Res. 10, 99–112 (1998).
42. Mukhopadhyay, K. & Chakraborty, D. India’s energy consumption changes
during 1973/74 to 1991/92. Econ. Syst. Res. 11, 423–438 (1999).
43. Lim, H.-J., Yoo, S.-H. & Kwak, S.-J. Industrial CO2 emissions from energy use in
Korea: a structural decomposition analysis. Energy Policy 37, 686–698 (2009).
44. Wu, L., Kaneko, S. & Matsuoka, S. Driving forces behind the stagnancy of
China’s energy-related CO2 emissions from 1996 to 1999: the relative
importance of structural change, intensity change and scale change. Energy
Policy 33, 319–335 (2005).
45. Casler, S. D. & Rose, A. Carbon dioxide emissions in the U.S. economy.
Environ. Resour. Econ. 11, 349–363 (1998).
46. Guan, D., Hubacek, K., Weber, C. L., Peters, G. P. & Reiner, D. M. The
drivers of Chinese CO2 emissions from 1980 to 2030. Global Environ. Change
18, 626–634 (2008).
47. Guan, D., Peters, G. P., Weber, C. L. & Hubacek, K. Journey to world top
emitter: an analysis of the driving forces of China’s recent CO
2
emissions surge.
Geophys. Res. Lett. 36, 1–5 (2009).
48. Miller, R. E. & Blair, P. D. Input-Output Analysis: Foundations and Extensions
2nd edn. (Cambridge Univ. Press, 2009).
49. Rosa, E. A. & Dietz, T. Human drivers of national greenhouse-gas emissions.
Nat. Clim. Change 2, 581–586 (2012).
50. Li, W. & Ou, Q.-X. Decomposition of China’s carbon emissions intensity from
1995 to 2010: an Extended Kaya Identity. Math. Prob. Eng. 2013, 7 (2013).
51. Dietzenbacher, E. & Los, B. Structural decomposition techniques: sense and
sensitivity. Econ. Syst. Res. 10, 307–323 (1998).
52. Seibel, S. Decomposition Analysis of Carbon Dioxide Emission Changes in
Germany - Conceptual Framework and Empirical Results. European
Commission, Working papers and studies (2003).
53. Howarth, R., Santoro, R. & Ingraffea, A. Methane and the greenhouse-gas
footprint of natural gas from shale formations. Clim. Change 106, 679–690
(2011).
54. Helm, D. Forget Kyoto: Putting a Tax on Carbon Consumption. Yale
Environment 360 (2012).
55. Peters, G. P. & Hertwich, E. G. CO2 embodied in international trade with
implications for global climate policy. Environ. Sci. Technol. 42, 1401–1407
(2008).
56. Bo
¨hringer, C., Carbone, J. & Rutherford, T. Embodied Carbon Tariffs. National
Bureau of Economic Research (2011).
57. Davis, S. J. & Caldeira, K. Consumption-based accounting of CO
2
emissions.
Proc. Natl Acad. Sci. USA 107, 5687–5692 (2010).
Acknowledgements
We thank Christine Shearer for helpful comments on an earlier version of the
manuscript.
Author contributions
K.F. and K.H. designed the research. K.F. and L.S. prepared the data. K.F., S.J.D., L.S. and
K.H. conducted the analysis; S.J.D. prepared the figures. K.F., S.J.D., L.S. and K.H. wrote
the paper.
Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
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