Environmental Impact Assessment of Household Consumption

Abstract

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http://onlinelibrary.wiley.com/doi/10.1111/jiec.12371/abstract

We analyze the environmental impact of household consumption in terms of the material, water, and land-use requirements, as well as greenhouse gas (GHG) emissions, associated with the production and use of products and services consumed by these households. Using the new EXIOBASE 2.2 multiregional input-output database, which describes the world economy at the detail of 43 countries, five rest-of-the-world regions, and 200 product sectors, we are able to trace the origin of the products consumed by households and represent global supply chains for 2007.

We highlight the importance of environmental pressure arising from households with their consumption contributing to more than 60% of global GHG emissions and between 50% and 80% of total land, material, and water use. The footprints are unevenly distributed across regions, with wealthier countries generating the most significant impacts per capita. Elasticities suggest a robust and significant relationship between households’ expenditure and their environmental impacts, driven by a rising demand of nonprimary consumption items. Mobility, shelter, and food are the most important consumption categories across the environmental footprints. Globally, food accounts for 48% and 70% of household impacts on land and water resources, respectively, with consumption of meat, dairy, and processed food rising fast with income. Shelter and mobility stand out with high carbon and material intensity, whereas the significance of services for footprints relates to the large amount of household expenditure associated with them.

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RESEARCH AND ANALYSIS
Environmental Impact Assessment
of Household Consumption
Diana Ivanova, Konstantin Stadler, Kjartan Steen-Olsen, Richard Wood, Gibran Vita,
Arnold Tukker, and Edgar G. Hertwich
Summary
We analyze the environmental impact of household consumption in terms of the material,
water, and land-use requirements, as well as greenhouse gas (GHG) emissions, associated
with the production and use of products and services consumed by these households.
Using the new EXIOBASE 2.2 multiregional input-output database, which describes the
world economy at the detail of 43 countries, five rest-of-the-world regions, and 200 prod-
uct sectors, we are able to trace the origin of the products consumed by households and
represent global supply chains for 2007. We highlight the importance of environmental
pressure arising from households with their consumption contributing to more than 60% of
global GHG emissions and between 50% and 80% of total land, material, and water use. The
footprints are unevenly distributed across regions, with wealthier countries generating the
most significant impacts per capita. Elasticities suggest a robust and significant relationship
between households’ expenditure and their environmental impacts, driven by a rising de-
mand of nonprimary consumption items. Mobility, shelter, and food are the most important
consumption categories across the environmental footprints. Globally, food accounts for
48% and 70% of household impacts on land and water resources, respectively, with con-
sumption of meat, dairy, and processed food rising fast with income. Shelter and mobility
stand out with high carbon and material intensity, whereas the significance of services for
footprints relates to the large amount of household expenditure associated with them.
Keywords :
environmentally extended
multiregional input-output
(EE-MRIO) analysis
expenditure elasticity
footprint analysis
household environmental impacts
industrial ecology
regression analysis
Supporting information is available
on the JIE Web site
Introduction
Scientists have investigated the resource use required to sup-
port household consumption in an effort to understand the re-
lationship between humans and nature (Wackernagel and Rees
1996; Fischer-Kowalski et al. 2014; Herendeen and Tanaka
1976). They have investigated the emissions caused by the pro-
duction, use, and disposal of products in final use to target efforts
to reduce environmental impacts and assess trade-offs (Dietz
et al. 2009; Hertwich 2011; Tukker et al. 2010). Traditionally,
the analysis of household environmental impacts was based on
national statistics and production systems, treating imported
goods as if they had been produced in the country where they
Address correspondence to: Diana Ivanova, Industrial Ecology Programme and Department of Energy and Process Engineering, Sem Sælands vei 7, Norwegian University of
Science and Technology (NTNU), NO-7491 Trondheim, Norway. Email: diana.n.ivanova@ntnu.no
© 2015 by Yale University
DOI: 10.1111/jiec.12371 Editor managing review: Kuishuang Feng
Volume 20, Number 3
are consumed (Lenzen et al. 2006; Hertwich 2011; Tukker and
Jansen 2006). The energy and emissions intensities of products
produced in different countries can be quite different, reflecting
a combination of differences in the structure and efficiency of
economies and in the product mix being produced. Including
the technology of important trade partners as proxies for imports
to Norway, Peters and Hertwich (2006) demonstrated a strik-
ing impact of technology differences: The foreign production
of products consumed by Norwegian households accounted for
13 million tonnes carbon dioxide (CO2), whereas using domes-
tic production as a proxy would give only 5 million tonnes.
Weber and Matthews (2008) found significant effects also for
the United States, which is less trade exposed. As a result, global
526 Journal of Industrial Ecology www.wileyonlinelibrary.com/journal/jie

RESEARCH AND ANALYSIS
multiregional input-output (MRIO) models were developed to
trace the environmental impacts associated with consumption.
Hertwich and Peters (2009) provided the first analysis of the
carbon footprint of different nations, identifying the role of
households, public consumption, and investments, and specify-
ing the role of different consumption categories as a function of
income. These calculations have been reproduced and updated
(Davis and Caldeira 2010) and extended to other pollutants,
materials, land use, and water consumption (Wiedmann et al.
2015; Kanemoto et al. 2014; Steen-Olsen et al. 2012). In re-
cent work, however, consumption is addressed more broadly,
not focusing on understanding households, but rather looking
at entire nations.
In this article, we analyze the environmental impact of
household consumption of different countries in terms of the
material, water, and land-use requirements, as well as green-
house gas (GHG) emissions, associated with the production
and use of products and services consumed by these households.
We do so using the newly established EXIOBASE 2.2 MRIO
database, which describes the world economy in 2007 consisting
of 43 countries, five rest-of-the-world (RoW) regions, and 200
product sectors (Wood et al. 2015). The land footprint reported
here is an unweighted land use as opposed to the productivity
weighting applied by Wackernagel and Rees (1996). The other
indicators are water footprint (Hoekstra 2003), carbon footprint
(Wiedmann and Minx 2008), and material footprint (Wied-
mann et al. 2010). The concept of footprint family has been
tested against criteria such as policy relevance, indicator cov-
erage, and their complementary properties (Galli et al. 2012).
The motivation behind the recent focus on national foot-
prints is the importance of emissions embodied in international
trade to climate policy (Wyckoff and Roop 1994; Munksgaard
and Pedersen 2001; Peters and Hertwich 2008). Time series of
national carbon footprints show that increasing imports from
developing countries have contributed significantly to the
continued rise of the national carbon footprint of developed
countries, even though many of these countries have managed
to stabilize and even reduce their territorial GHG emissions
(Kanemoto et al. 2014; Peters et al. 2011). Whereas the na-
tional focus is appropriate for national and international policy
making, an understanding of household footprints can provide
insights into the social determinants of environmental impacts
and can inform household actions directed toward reducing
footprints. Household consumption has a strong relation with
consumer behavior, lifestyles, and daily routines and a potential
resistance to change owing to social and cultural embeddedness
(Caeiro et al. 2012). Households have a relatively large degree
of control over their consumption, but they often lack accurate
and actionable information on how to improve their own
environmental performance (Gardner and Stern 2008), and
household footprint calculations can provide such information.
The novelty of our study is that it uses an integrated method-
ological framework across the set of footprint indicators to eval-
uate household environmental performance based on a database
with a higher level of environmental detail. This integrative ap-
proach allows us to assess and compare environmental impacts
of household consumption across indicators, regions, and con-
sumption categories directly and with lower uncertainty. It can
further be used to identify where mitigation of certain impacts,
for example, emission reductions, would come at the expense
of other impact categories, such as higher levels of water, land,
and material consumption (Tukker et al. 2013).
Methods and Data
Household environmental impacts are derived from an en-
vironmentally extended MRIO (EE-MRIO) model constructed
using the high-resolution EXIOBASE database (Wood et al.
2015). The core of the model is an input-output table represent-
ing the flow of goods and services throughout the global econ-
omy for the reference year 2007. All emissions and resources
required for the production of output are allocated to goods and
services purchased by final consumers (Hertwich 2011).
The analysis is based on version 2.2 of EXIOBASE, which
features a higher level of detail on environmentally relevant sec-
tors (e.g., agriculture, energy, and manufacturing) and environ-
mental extensions (e.g., emissions, resource use, and pollutants)
in comparison to other MRIO databases (Wood et al. 2015).
EXIOBASE has a major advantage in providing much greater
product disaggregation (200 product sectors) in an integrated
framework within the system of environmental-economic ac-
counting guidelines. It accommodates information about 43
countries, which, together, account for approximately 89% of
global gross domestic product (GDP) and between 80% and
90% of the trade flow by value within Europe (Stadler et al.
2014). The MRIO table is supplemented with information on
the environmental load intensities of economic sectors. Eco-
nomic accounts were coupled with data on resource use and
emissions sourced from databases with information on primary
resource extractions, emission factors and activity variables, and
agricultural and forestry activities (Food and Agriculture Orga-
nization of the United Nations Statistics Division, the Interna-
tional Energy Agency database, and so on) (Tukker et al. 2013).
The global warming potential (GWP) metric is used to con-
vert greenhouse gases (CO2, methane, nitrous oxide, and sulfur
hexafluoride) to equivalent amounts of CO2by weighting their
radiative properties for a time horizon of 100 years. Land use
reflects use of cropland, pasture land, and forest land. The ma-
terial footprint relocates the domestic extraction of raw mate-
rials (primary crops, crop residues, fodder crops, grazing, wood,
aquatic animals, metal ores, nonmetallic minerals, and fossil
fuels) from production to consumption in a mutually exclusive
way, including only materials that are directly used by an econ-
omy. Our water footprint indicator includes blue (fresh surface
and groundwater) water consumption embodied in agriculture
and livestock products, manufactured products, electricity, and
direct demand from households. The national environmental
footprint is calculated as a function of the footprint multiplier,
capturing the intensity of household purchases (e.g., amount of
GHG emissions per euro [EUR] of household expenditure), and
the product quantity demanded in monetary terms.
Ivanova et al., Environmental Impact Assessment of Household Consumption 527

RESEARCH AND ANALYSIS
Ta b l e 1 Environmental impact by final demand category
Carbon footprint Land footprint Material footprint Water footprint
Households 65 ±7% 70 ±11% 51 ±8% 81 ±7%
NPISH 1 ±1% 1 ±1% 1 ±1% 1 ±1%
Governments 7 ±3% 5 ±3% 7 ±3% 5 ±3%
Gross capital formation 24 ±7% 19 ±10% 37 ±9% 10 ±6%
Changes in inventories 3 ±2% 5 ±5% 4 ±3% 3 ±2%
Note: The mean and standard deviation estimates respond to the sample of 43 countries included in the EXIOBASE with the deviation caused by
the different distribution of final demand categories across countries. Environmentally relevant requirements are linked to final demand by households,
nonprofit institutions serving households (NPISH), governments, gross capital formation, and changes in inventories. Changes in inventories occurwhen
prices prevailing when goods are withdrawn differ from prices when production takes place (SNA 2008).
Following the convention of national accounts, final de-
mand is the estimate obtained by summing household, nonprofit
organization, and government spending as well as capital for-
mation and changes in inventories and valuables in a given year
(SNA 2008). In order to isolate the environmental impacts of
households, we only take into account household expenditure
across product sectors. This approach allows us to estimate the
magnitude of indirect GHG emissions and resource use embod-
ied in the global supply chains of household purchases subject
to certain limitations discussed later.
In addition, households generate environmental stress di-
rectly through their use of some products, for example, when
driving or using fuel to heat their homes. In EXIOBASE, house-
hold direct impacts are aggregated into a total for each region.
We distribute direct carbon emissions between personal trans-
port and residential fuel use following the GTAP 7 database
(Lee 2008). Using GTAP, we allocated CO2emissions from
coal, crude oil, and gas to housing (i.e., shelter) and those from
petroleum products to transport. Direct water use was allocated
to shelter under the consideration of previous observations (see
Vewin 2012; Vickers 2001; EEA 2001). Direct noncommercial
use of land and materials by households was neglected.
Finally, by applying the concept of expenditure elasticity, we
are able to evaluate changes in the environmental footprints re-
sulting from changes in household expenditure (Baiocchi et al.
2010; Kerkhof et al. 2009; Weber and Matthews 2008). House-
hold expenditure elasticity, ε, measures the percentage change
in the quantity of environmental impacts with respect to a
1% rise in the total household demand (measured in monetary
units) (equation 1):
εi=(∂fi/∂y)/(fi/y)(1)
where yrepresents per capita yearly expenditure and frepresents
per capita footprint for each of the footprint indicators i. Model
(1) can be transformed using natural logarithm transformation
resulting in a set of univariate regressions for each footprint
indicator, where aand ɛare constants and uis the error term
(equation 2):
ln fi=ai+εiln y+ui(2)
Results
Carbon Footprint
The global carbon footprint associated with household con-
sumption in 2007 was 22 gigatonnes (Gt) carbon dioxide equiv-
alent (CO2-eq) including direct impacts and impacts embodied
in household purchases. This amounts to 65% of the total
emissions generated that year. The average allocation of envi-
ronmental impacts across final demand categories is presented
in table 1. GHG emissions were unevenly distributed across
regions with households in four major economies, namely,
the United States, China, Japan, and Russia, contributing to
roughly half of the global impacts from household consumption.
Households in the United States alone contributed to a quarter
of global emissions, or 5.6 Gt CO2-eq. The most significant
contribution is from the consumption of energy-intensive
services and electricity produced from coal. The household
carbon footprint of the European Union (EU) amounted to
4.9 Gt CO2-eq.
On a per capita basis, carbon footprints of households vary
widely (figure 1). The United States contributes to 18.6 tonnes
(t) CO2per capita (CO2-eq/cap). The world average is 3.4 t
CO2-eq/cap, suggesting a 5.5-factor difference. In terms of the
total final demand, the United States contributes to 4.9 times
higher GHG emissions than the world average from a con-
sumption perspective and to only 3.9 times higher emissions
from a production perspective. Thus, the United States are a
net importer of GHG embodied in traded goods, largely owing
to household consumption, 74% of the country’s final demand.
A strong positive correlation between GDP per capita based
on purchasing power parity (PPP) and per capita carbon foot-
prints is signaled by the correlation coefficient of 0.87. Several
Western economies, such as Sweden (8.7 t CO2-eq/cap), France
(8.8 t CO2-eq/cap), and Japan (9.0 t CO2-eq/cap) stand out
with lower impacts than countries with similar income related
to the prevalence of nuclear and hydro power (EEA 2013).
Hence, a significant portion of household impacts from Sweden
and France are embodied in imports, 65% and 51%, respectively
(figure 2), owing to their higher carbon intensity compared to
domestic production.
The distribution of GHG emissions from household activity
on domestic goods and imports varies largely across countries.
528 Journal of Industrial Ecology

RESEARCH AND ANALYSIS
Countries Carbon Footprint
(tCO2-eq)
Land Footprint (1000 m
2
) Material Footprint (t) Water Footprint (m
3
)
2094.910.03.4World average
29817.418.111.3Austria
49217.828.112.2Belgium
1828.16.95.4Bulgaria
27812.49.210.9Cyprus
17411.89.29.4Czech Republic
34716.020.011.9Germany
45316.820.912.2Denmark
25815.620.910.9Estonia
56114.221.08.1Spain
30417.927.413.6Finland
39614.222.38.8France
70018.326.913.4Greece
1947.38.25.9Hungary
29717.122.112.9Ireland
40713.619.19.6Italy
1809.112.56.5Lithuania
81627.644.418.5Luxembourg
18110.822.96.2Latvia
62814.814.99.2Malta
57517.235.511.8Netherlands
13010.39.27.8Poland
50911.518.06.8Portugal
32512.29.44.6Romania
32215.718.88.7Sweden
26213.420.210.1Slovenia
28711.914.58.3Slovakia
45617.921.913.3United Kingdom
65118.423.018.6United States
2909.211.29.0Japan
1303.15.41.8China
51018.140.614.6Canada
34010.413.88.7South Korea
1598.222.01.8Brazil
2612.02.10.8India
2775.916.63.8Mexico
3319.369.67.6Russia
660
26.3160.817.7Australia
39615.726.511.3Switzerland
3887.713.04.7Turkey
3087.79.28.6Taiwan
47418.637.210.3Norway
81.52.72.61.3Indonesia
1656.621.55.5South Africa
Figure 1 Environmental footprints of household consumption across countries. The figure includes the world average and 43 selected
countries from EXIOBASE, ordered alphabetically by country codes. The world average includes all 43 countries and the five
rest-of-the-world regions.
Ivanova et al., Environmental Impact Assessment of Household Consumption 529

RESEARCH AND ANALYSIS
0% 20% 40% 60% 80% 100%
Switzerland
Sweden
France
Norway
Netherlands
Germany
United Kingdom
Greece
South Korea
Canada
Turkey
Japan
Mexico
Poland
Taiwan
Australia
Brazil
Indonesia
Russia
United States
India
South Africa
China
Carbon footprint
Indirect domestic Indirect domestic Indirect domesticIndirect foreign Direct Indirect domestic Indirect foreign Direct
0% 20% 40% 60% 80% 100%
Netherlands
Taiwan
South Korea
Germany
Japan
Switzerland
United Kingdom
France
Greece
Norway
Sweden
Poland
Turkey
United States
Canada
Mexico
China
Indonesia
India
South Africa
Brazil
Australia
Russia
Land footprint
Indirect foreign
0% 20% 40% 60% 80% 100%
Taiwan
Netherlands
South Korea
Japan
Switzerland
United Kingdom
Germany
Norway
France
Sweden
Greece
Canada
Turkey
Russia
United States
Australia
Mexico
Poland
South Africa
Indonesia
Brazil
India
China
Material footprint
Indirect foreign
0% 20% 40% 60% 80% 100%
Netherlands
South Korea
Switzerland
Japan
United Kingdom
Germany
Taiwan
Norway
Canada
Sweden
Poland
France
Russia
Australia
Mexico
South Africa
Indonesia
Greece
Turkey
United States
Brazil
China
India
Water footprint
Figure 2 Indirect versus direct environmental impacts of household consumption across 23 selected countries. The figure separates
household consumption footprint on direct (pressures that are emitted directly by consumption activities), indirect domestic (embodied in
domestically produced products and services), and indirect foreign (embodied in impor ted products and services) across selected countries
available in EXIOBASE. Households are not accountable for direct environmental impacts in relation to land and material use in EXIOBASE.
Countries have been ordered by their share of indirect domestic impacts. Check the supporting information available on the Web for an
overview of all 43 countries.
Luxembourg stands out with a low share of domestic indirect
emissions, approximately 1.4 t CO2-eq/cap in 2007 or 8% of the
country’s carbon footprint (figure 2). China, on the other hand,
relies strongly on domestic production to satisfy local household
demand, with indirect domestic impacts accounting for 92% of
the country’s total footprint.
Households emitted 4.4 Gt CO2-eq directly through activ-
ities involving combustion of fuel amounting to roughly 20%
of global GHG emissions from household activity. On aver-
age, direct carbon emissions originate from the use of transport
(73%) and household fuel (27%). The share of direct GHG
emissions is largest for households in France and Belgium, more
than 28%. The larger fraction of carbon impacts occurs in the
form of emissions embodied in purchases, as opposed to direct
impacts. On a global scale, GHG emissions embodied in house-
hold purchases are driven by consumption of services (27%),
shelter (25%), manufactured products (17%), mobility (15%),
and food (13%).
Figure 3 presents an analysis of the carbon intensity of dif-
ferent consumption categories of EU households. Mobility has
the highest amount of emissions per unit of household expen-
diture within the EU, 3.4 kg CO2-eq/EUR (figure 3). Through
driving own vehicles, EU households emit roughly half of the
GHG emissions related to mobility directly, a total of 0.7 Gt
CO2-eq. The remaining mobility-related emissions are indirect
emissions, in particular, consumption of gasoline and diesel (0.4
Gt CO2-eq) and motor vehicles (0.2 Gt CO2-eq). Shelter is
similarly significant for the carbon footprint of EU households,
comprising 26% of their impacts. This consumption category
has a lower carbon intensity, 0.9 kg CO2-eq/EUR, though it is
associated with a higher share of household expenditure. Of of
the six consumption categories, services are least carbon inten-
sive; however, 45% of household expenditure is directed toward
consumption of services, hence, the category’s contribution of
17% from the total carbon impacts within EU.
Land Footprint
Almost 65 million square kilometers (km2) of global land use
was required to meet household demand in 2007. As a result,
roughly 70% of the global land use was embodied in house-
hold purchases, with the ratio reaching up to 94% for Russia
and South Africa. Other countries with developed resource-
intensive forestry sectors, such as Canada and Finland, rely
strongly on wood products for domestic construction and in-
vestments, hence, their lower relative importance of households
(figure 2).
Together, the purchases of households in Russia, China, the
United States, Brazil, and Australia account for more than 48%
(31 million km2) of total land resources embodied in house-
hold consumption in 2007. The EU contributed 15% (9.6 mil-
lion km2). GDP correlates weakly with household land require-
ments, with a correlation coefficient of 0.38. Australia has the
most extensive per capita land footprint, 0.16 km2/cap, more
than 16 times higher than the global average of 0.01 km2/cap.
Russia has the second largest impact per capita at 0.07 km2/cap.
The two countries are also the ones with the highest share of
household impacts embodied in domestic production, equiv-
alent to more than 93% of land use. Australian land use is
embodied in household purchases of food products, whereas
shelter requirements dominate the land footprint of Russian
households. Other forestry-rich countries, such as Norway and
530 Journal of Industrial Ecology

RESEARCH AND ANALYSIS
27.0%
16.6% 26.2% 3.5%
9.5%
17.2%
0
0.5
1
1.5
2
2.5
3
3.5
4
EXPENDITURE PER CAPITA (EUR)
Mobility
Manufactured Products
Shelter
Clothing and Footware
Food
Services
05000 10000
51.1%
4.3%
13.4%
12.8%
16.4% 2.0%
0
1
2
3
4
5
6
7
8
EXPENDITURE PER CAPITA (EUR)
Food
Clothing and Footware
Manufactured Products
Shelter
Services
Mobility
05000 10000
26.0%
20.3% 9.5% 4.7%
19.7%
19.7%
0
0.375
0.75
1.125
1.5
1.875
2.25
2.625
3
EXPENDITURE PER CAPITA (EUR)
Food
Manufactured Products
Mobility
Clothing and Footware
Shelter
Services
05000 10000
60.7%
5.0%
10.7%
18.5% 2.0% 3.1%
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
EXPENDITURE PER CAPITA (EUR)
Food
Clothing and Footware
Manufactured Products
Services
Mobility
Shelter
05000 10000
MATERIAL FOOT PRINT MULTIPLIER (KG/EUR)
Figure 3 Contribution of consumption categories to the carbon, land, material, and water footprint of EU households. The contribution
of consumption categories to the total environmental footprints can be split into two parts: the quantity of products within the category
bought, measured by expenditure per capita in EUR, and the footprint intensities measured by footprint multipliers—the environmental
impact per EUR of expenditure in the category. Consumption categories in the legend have been ordered by their environmental intensity
(by magnitude of multipliers). The footprint multipliers are measured in kg CO2-eq/EUR for carbon, m2/EUR for land, kg/EUR for material,
and m3/EUR for water. The percentage labels describe the share of a category’s footprint from the total footprint of household consumption
within EU. The lighter shaded parts of “Shelter” and “Mobility” columns denote direct GHG emissions and water use by households. Check
the supporting information on the Web for a breakdown of countries’ footprint across consumption categories. EU =European Union;
EUR =euro; kg CO2-eq =kilograms carbon dioxide equivalent; m2=square meters; m3=cubic meters; GHG =greenhouse gas.
Finland, similarly have a significant portion of land use linked
to purchases of wood and other forestry products.
Smaller EU economies face geographical restrictions and
limited resources encouraging them to satisfy a larger share of
household demand through imports from the developing world.
As depicted in figure 2, households in Luxembourg, the Nether-
lands, and Belgium give rise to the highest land footprint per
capita within the EU, although only a negligible fraction of that
reflects domestic land use, between 1% and 3%. A significant
share of the land impacts of the countries is owing to imports
of crops and seeds from Brazil, China, and the United States.
The Netherlands, for example, relies strongly on imported feed
for its developed livestock industry (Tukker et al. 2014).
On a global scale, 46% of the land use occurs to meet
household demand for food, followed by shelter and services,
contributing 26% and 15%, respectively. Food has the
largest land multiplier within the EU, with 7.2 square meters
(m2)/EUR spent on food. Household consumption of nonclassi-
fied food items entails a significant fraction of resource use across
EU countries, around 2.6 km2. Clothing is the second most
land-intensive of consumption categories (figure 3), though it is
associated with only 4% of the land use by EU households. Mo-
bility is the least land-intensive consumption category requiring
0.5 m2/EUR. Area covered by roads and other transportation
infrastructure are not included in the estimate.
Material Footprint
The global material footprint of households amounted to 32
Gt in 2007, which is equivalent to 48% of the total raw ma-
terials that were extracted and used that year (table 1). Two
fifths of the total material use fulfills consumption requirements
of households in the United States, China, India, and Brazil.
In 2007, households from the United States alone contributed
to the largest material footprint in absolute values, 5.5 Gt or
17% of the global material impacts. More than one quarter of
this amount was used to enable local consumption of products
of meat cattle, processed food, and hotel and restaurant ser-
vices. In comparison, the EU has a household material footprint
of 7.1 Gt.
On a per capita basis, Luxembourg and Australia stand out
with high levels of material footprint, 27.6 and 26.3 t/cap, re-
spectively. Other developed economies show similar levels of
household material impacts, hence, the correlation of 0.87 be-
tween national GDP and material footprint. In comparison,
the global average amounts to 4.9 t/cap. Across the selected 43
countries, India has the lowest value of material impacts per
capita, 2 t/cap.
Forty percent of global household material impacts (13 Gt)
can be linked to internationally traded commodities across the
43 countries and five RoW regions. For Luxembourg, only 2%
of the material footprint results from domestic extraction of
materials, equivalent to a total of 0.2 million tonnes (Mt) in
2007. The rest of the material footprint, a total of 13 Mt, can
be linked to household consumption of foreign products with
highest environmental impacts embodied in imports of raw
materials (e.g., crude oil, sand, and clay) from Russia and China
and chemicals (e.g., fertilizers) from India.
In the case of Australia, 58% of households’ total material
footprint in 2007, around 322 Mt, is linked to extraction of raw
materials from the domestic natural environment. The mate-
rial footprint embodied in imports is dominated by industrial
Ivanova et al., Environmental Impact Assessment of Household Consumption 531

RESEARCH AND ANALYSIS
materials (e.g., sand and clay, and coal) from China. Norway
has the third largest material footprint per capita. More than
three fourths of the embodied material impacts relate to for-
eign extraction, especially imports from China and Russia. The
country is a net exporter of materials such as stone and crude
oil.
Globally, 36% of the material footprint arising from house-
hold activity can be attributed to food consumption, followed
by services (23%) and manufactured products (17%). A com-
parison of the material intensities of consumption categories
in the EU (figure 3) shows food to have the highest material
multiplier, with 2.8 kilograms (kg) of extracted materials em-
bodied per EUR. More than 11% of EU households’ material
footprint is embodied in consumption of processed food and
dairy products (0.8 Gt). Consumption of manufactured prod-
ucts is the second largest contributor to the material footprint
of EU households, with 20% (1.8 kg/EUR).
Water Footprint
In 2007, the global blue water footprint associated with
household consumption is 1,386 cubic kilometers (km3). Thus,
households accounted for 81% of the total use of fresh water re-
sources, followed by fixed capital formation (10%) and demand
from governments (5%).
A total of 670 km3or 48% of global water impacts is em-
bodied in household activity from India, the United States, and
China. The per capita footprint is smallest in Indonesia, with
82 m3/cap, and largest in Luxembourg, with 820 m3/cap, with a
global average of 210 m3/cap. Again, the GDP level correlates
positively with household freshwater use, with a coefficient of
0.74. Our choice of environmental indicator, however, could
potentially influence the findings. Blue water consumption does
not take into account the variation of crop water needs owing
to the climate with dry warm climates, such as Spain, requiring
much irrigation (Steen-Olsen et al. 2012).
On average, less than 5% of total household water footprint
is in the form of direct consumption of water resources. Russia,
Canada, the United States, and Norway stand out among the
countries, with the largest per capita direct water use by house-
holds ranging between 28 and 25 m3/cap. With regard to water
use embodied in global supply chains, consumption of agricul-
ture and livestock products required a total of 975 km3of water
resources, or 74% of the indirect water footprint. The second
largest contributor to blue water footprint is the services sector
demanding approximately 18% of global household footprint.
South Korea has the largest contribution of services to water
use: 34% of the total footprint or 5.3 km3. Hotel and restau-
rant services have the highest water intensity. Water footprint
multipliers are ranked similar to the land footprint multipliers
within the EU, with food being most intensive (0.17 m3/EUR),
followed by clothing (0.05 m3/EUR) and manufactured prod-
ucts (0.02 m3/EUR).
Approximately 27% of household water footprint was em-
bodied in imports (370 km3). Luxembourg has the highest frac-
tion of impacts embodied in imports amounting to 99% with
high importance of food imports, such as seeds, grains, veg-
etables, and fruits. Greece has the second highest per capita
footprint, approximately 700 m3, which is relatively equally dis-
tributed across domestic extraction (57%) and imports (41%)
of indirect water resources, with the latter largely linked to
food and agricultural products. Emerging economies such as
the Brazil, Russia, India and China (BRIC) countries, are self-
sustained when it comes to their water consumption.
The environmental impacts of household consumption are
strongly correlated with consumer expenditure as listed in
table 2. The expenditure elasticity of carbon is positive and
significant at the 1% level, meaning that as household income
levels rise, the carbon footprint increases by 66% for each dou-
bling of household spending. The land and water footprint
further have a positive statistical association with household
expenditure, though differences in the expenditure variable ex-
plain a much lower fraction of the variation of the resource use
across countries. Elasticity of food and shelter have the lowest
R2, likely reflecting the relevance of other determinants of land
and water use such as natural resource availability and other
geographical conditions (Hertwich and Peters 2009). A fur-
ther breakdown of expenditure elasticities on a sectoral level
shows that environmental impacts from staple food purchases
(e.g., wheat, cereal grains, seeds, and nonclassified crops) do
not increase significantly with household expenditure, unlike
the footprint of dairy and meat products.
The share of emissions and resources use for production of
nonprimary consumption items, such as some services, man-
ufactured products, and clothing consumed by households, is
much smaller in emerging economies and strongly driven by
rising levels of disposable income and expenditure. This is re-
flected in the higher elasticities coefficients of those consump-
tion categories across the footprint indicators (table 2).
Discussion and Conclusions
This study provides a comprehensive insight into the global
environmental impacts by households. It highlights the sig-
nificance of environmental pressure arising from households,
with their consumption giving rise to more than 60% of global
GHG emissions and between 50% and 80% of total resource
use. A significant portion of the emissions and resource use are
embodied in internationally traded commodities.
The regression analysis introduces household expenditure
as a positive and highly significant determinant of household
environmental impacts, with an elasticity coefficient varying
between 0.40 for water and 0.66 for carbon. National income
is also positively correlated with the footprints, which is con-
sistent with our expectations that higher disposable income of
households reflects more purchases of products, hence, higher
levels of embodied impacts. The correlation coefficient de-
scribing the relationship between GDP and household land use
is smaller than the coefficients regarding the other footprints,
suggesting the importance of other factors for the variation of
land use. For example, previous studies have investigated the
532 Journal of Industrial Ecology

RESEARCH AND ANALYSIS
Ta b l e 2 Elasticity of footprints with respect to total household expenditure, by footprint and consumption categor y
Carbon footprint Land footprint Material footprint Water footprint
ɛR2ɛR2ɛR2ɛR2
Total 0.66*** 0.83 0.56*** 0.49 0.54*** 0.85 0.40*** 0.54
Direct impact
Shelter 0.70*0.08 — — — — 0.20*0.07
Mobility 0.80*** 0.83 — — — — — —
Indirect impact
Shelter 0.58*** 0.44 0.45** 0.20 0.73*** 0.54 0.75*** 0.60
Food 0.41*** 0.62 0.49*** 0.41 0.29*** 0.46 0.30*** 0.35
Clothing 0.58*** 0.63 0.76*** 0.65 0.63*** 0.62 0.67*** 0.62
Mobility 0.77*** 0.79 0.80*** 0.68 0.76*** 0.81 0.54*** 0.38
Manufactured products 0.75*** 0.86 0.88*** 0.69 0.75*** 0.87 0.72*** 0.77
Services 0.75*** 0.81 0.91*** 0.69 0.71*** 0.81 0.69*** 0.51
Note: Expenditure elasticity of consumption measures the effect of changes in per capita expenditure on the environmental footprints. The “Total”
row shows the estimated coefficients when using the total per capita footprints as dependent variables that are regressed on household expenditure per
capita. To compare coefficients across consumption categories, additional regressions are run separately where dependent variables are the environmental
footprints of the different categories. The land and material footprints are associated with no direct impacts by households. The symbols *, **, and ***
denote significance levels, α, of 10%, 5%, and 1%, respectively.
influence of resource availability on the national land-use
footprints (Wiedmann et al. 2015; Weinzettel et al. 2013). We
also find the largest consumers of land, Russia (forest land) and
Australia (arable and pasture land), to have the highest share of
domestic land impacts, suggesting that households tend to con-
sume more resources when they are readily available. It should
be noted that our land indicator currently does not capture any
potential differences in the land’s fertility across countries; how
the choice of indicator affects results should be investigated
further.
We confirm earlier conclusions about mobility, shelter,
and food being the most important consumption categories
(Hertwich and Peters 2009). Though their environmental
relevance varies across footprint indicators, the three cate-
gories consistently make up between 55% and 65% of the
total impacts. Food has the highest land, material, and water
multipliers, hence, switching a EUR of expenditure from food
to clothing in the EU, for example, results in a reduction of
5.1 m2of land resources, 1.0 kg of extracted materials and
0.1 m3of fresh water. At the same time, any redirecting
expenditure from the food category to any other services would
cause increases in GHG emissions. This brings attention to an
important implication for any policy targeting reductions of
household footprints in absolute terms, particularly, what is the
environmental opportunity cost of reducing impacts in a certain
category. Conversely, a redirection of household expenditure
toward less resource-intensive services is more straightforward
given that it results in impact reduction across all footprint
indicators. Nevertheless, one should always regard a certain
degree of nonsubstitutability of consumption items and
categories in the redesign of household expenditure patterns.
Further, GHG emissions and resource use from food con-
sumption rise with income, though at a lower rate than non-
primary consumption categories. The result is mainly driven by
rising importance of dairy and meat products, processed food,
and tobacco products at high household expenditure. The large
footprints of nonclassified food items necessitate further inves-
tigation.
Mobility has the largest carbon footprint in the EU, with
household impact roughly evenly distributed between direct
tailpipe emissions from driving private cars and emissions em-
bodied in purchases of fuel, transport services, and vehicles.
The magnitude of direct emissions is also strongly determined
by total household expenditure, with a doubling of the total
expenditures resulting in an 80% rise of direct transport emis-
sions. The results draw attention to potential limitations of
policy measures to reduce GHG emissions related to trans-
portation. For starters, if the sole effect of rising purchasing
power on mobility demand was to switch to more fuel-efficient
vehicles, we would have found a negative coefficient on di-
rect emissions. Instead, our results can be explained by other
effects taking place as well. For instance, low-income house-
holds are generally characterized by lower car ownership rates;
hence, they are more likely to resort to low-carbon alternatives,
such as public transportation and cycling (Steen-Olsen et al.
2016). Further, the more efficient use of own vehicles poten-
tially gives rise to rebound effects that could be direct (driv-
ing more owing to increased affordability of fuel) and indirect
(switching purchasing power to other goods). Nevertheless, we
show that mobility has the largest carbon multiplier in the EU
context, according to which a redirection of 1 euro of house-
hold expenditure to the second most carbon-intensive category,
manufactured products, would result in a carbon cut of 2.4 kg
CO2-eq. This is rather encouraging for residential GHG mit-
igation programs, especially in areas with high motor vehicle
emissions.
In 2007, shelter, more particularly, the consumption of elec-
tricity, wood products, housing fuel, and real estate services,
Ivanova et al., Environmental Impact Assessment of Household Consumption 533

RESEARCH AND ANALYSIS
contributed to 26% of the carbon, 13% of the land, and 20%
of the material footprints within the EU, with average impact
intensity relative to other consumption categories. Statistical
analyses indicate insignificant elasticity coefficients on direct
shelter impacts and significant, though smaller, coefficients on
indirect ones. As a basic need, shelter is relatively more impor-
tant at low income; in contrast, we expect the importance of
nonprimary categories to increase at a higher rate with rising
consumer purchasing power.
Our model assigns environmental impacts according to
household expenditure on products and services; hence, the
model potentially leaves out relevant consumption financed by
the government and investment. The implications are twofold.
First, potential differences might occur across countries in terms
of which goods and services households cover directly, thus,
imposing limitations to the comparative analysis. For exam-
ple, the sector of health and social work services has the sec-
ond largest carbon footprint out of all industrial sectors in the
United States, whereas its lower relative importance in other
countries relates to health-related expenditures often being cov-
ered by governments or employers (Hertwich 2011; Ferguson
and MacLean 2011; Weber and Matthews 2008). Second, the
model falls short when it comes to endogenizing capital, such as
residential buildings and road infrastructure used by household
and underestimates household impacts related to shelter and
mobility. Actual and imputed rent is included in the calcula-
tion of environmental impacts from real estate services, which
contributed to 4% of global carbon and material footprints by
households in 2007.
A significant fraction of household footprints in the
developed world depends on impacts embodied in imports
from poorer countries. This limits developed countries’ ability
to decouple impacts from wealth (and expenditure) through
technology and efficiency improvements. Our study further
has some limitations regarding the macro-level expenditure
elasticities. The cross-country regression results need to
be interpreted cautiously in the absence of corresponding
expenditure elasticities at the micro level (Baiocchi et al. 2010;
Hubacek et al. 2014). For example, we have no way of observing
potential changes in the expenditure-footprint relationship
across countries and socioeconomic groups. Further, previous
studies have signaled for the benefits of spatial aggregation in
relation to the calculation of environmental impacts embodied
in trade (Su and Ang 2010).
Our study provides a comprehensive insight about the en-
vironmental consequences of household purchasing decisions
and informs mitigation strategies about the consumption cat-
egories with the highest environmental relevance. This work
goes beyond presenting a snapshot of household emissions and
resource use and provides a different perspective on footprint
determinants and strategies for environmentally driven reallo-
cation of household spending. Ultimately, a behavioral change
may have a significant potential to balance economic growth
with environmental performance.
Acknowledgments
This work is part of the GLAMURS project financed by
the European Union’s seventh framework program (contract
613420). EXIOBASE 2.2 was constructed in the CREEA
project, financed by the same program (contract 265134).
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About the Authors
Diana Ivanova is a Ph.D. candidate, Konstantin Stadler
is a senior researcher, Kjartan Steen-Olsen is a researcher,
Richard Wood is a senior researcher, and Gibran Vita is
a researcher, all at the Industrial Ecology Programme at the
Norwegian University of Science and Technology (NTNU) in
Trondheim, Norway. Arnold Tukker is a professor and the
director of the Center for Environmental Science of Leiden
University in Leiden, the Netherlands. Edgar Hertwich was
the director of the Industrial Ecology Programme and a Pro-
fessor at the Department of Energy and Process Engineering at
NTNU at the time this article was written. He is now a pro-
fessor in the School of Forestry and Environmental Studies at
Yale University, New Haven, CT, USA.
Supporting Information
Additional Supporting Information may be found in the online version of this article at the publisher’s web site:
Supporting Information S1: This supporting information provides information about household environmental footprints
including total and per capita absolute values across countries and RoW regions and consumption categories; information
about total household expenditure, population, and national GDP (purchasing power parity; PPP); version of figure 2
depicting all 43 countries; and further description of the database.
536 Journal of Industrial Ecology