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energies
Article
Household Sharing for Carbon and Energy
Reductions: The Case of EU Countries
Diana Ivanova * and Milena Büchs
School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK; M.M.Buchs@leeds.ac.uk
*Correspondence: d.ivanova@leeds.ac.uk
Received: 15 January 2020; Accepted: 20 February 2020; Published: 14 April 2020
Abstract:
As households get smaller worldwide, the extent of sharing within households reduces,
resulting in rising per capita energy use and greenhouse gas (GHG) emissions. This article examines
for the first time the differences in household economies of scale across EU countries as a way to
support reductions in energy use and GHG emissions, while considering differences in effects across
consumption domains and urban-rural typology. A country-comparative analysis is important to
facilitate the formulation of context-specific initiatives and policies for resource sharing. We find that
one-person households are most carbon- and energy-intensive per capita with an EU average of 9.2
tCO
2
eq/cap and 0.14 TJ/cap, and a total contribution of about 17% to the EU’s carbon and energy use.
Two-person households contribute about 31% to the EU carbon and energy footprint, while those of
five or more members add about 9%. The average carbon and energy footprints of an EU household
of five or more is about half that of a one-person average household, amounting to 4.6 tCO
2
eq/cap
and 0.07 TJ/cap. Household economies of scale vary substantially across consumption categories,
urban-rural typology and EU countries. Substantial household economies of scale are noted for home
energy, real estate services and miscellaneous services such as waste treatment and water supply; yet,
some of the weakest household economies of scale occur in high carbon domains such as transport.
Furthermore, Northern and Central European states are more likely to report strong household
economies of scale—particularly in sparsely populated areas—compared to Southern and Eastern
European countries. We discuss ways in which differences in household economies of scale may
be linked to social, political and climatic conditions. We also provide policy recommendations for
encouraging sharing within and between households as a contribution to climate change mitigation.
Keywords:
household size; household economies of scale; carbon footprint; energy footprint;
consumption; European Union; urban; rural; population density; climate change mitigation
1. Introduction
We need rapid and effective climate action to reduce global greenhouse gas (GHG) emissions and
avoid catastrophic climate change. Annual emissions must decrease to close to half of their 2010 levels
by 2030, and reach net-zero by 2050 to increase the probability of limiting temperature changes to
1.5
◦
C above preindustrial levels [
1
]. Yet there are some socio-demographic trends that may make it
more difficult to achieve this.
One such trend is the shrinking of household sizes globally. Together with the rise in global
emissions, the number of households has also been increasing, outpacing population growth. Several
studies have shown that there is a strong link between household size and per capita energy use and
GHG emissions [
2
–
6
] in both developed and developing countries [
7
]. When individuals live together,
there are “economies of scale”—people tend to share appliances, tools and equipment, cook together
and heat and cool common living spaces. These acts of sharing allow for the per capita energy use to
diminish with rising household size. Thus, as households get smaller, the extent of sharing within
Energies 2020,13, 1909; doi:10.3390/en13081909 www.mdpi.com/journal/energies
Energies 2020,13, 1909 2 of 28
households reduces, while the per capita energy use and emissions rise. Some domains, such as energy
consumption for heating, cooling and lighting, show substantially higher potential for household
economies of scale [
3
,
4
,
8
] compared to others such as transport, clothing, and services [
2
,
4
,
8
]. There
may also be social benefits associated with shared living and larger household sizes, as they tend to
counteract trends of isolation and loneliness and build stronger communities [
9
,
10
]. Furthermore,
recent research shows that members of grassroots initiatives, which may involve communal living such
as eco-villages and Transition towns, manage to reconcile lower carbon footprints and less materialistic
living with higher life satisfaction [
11
,
12
]. Recognizing the important role of household economies
of scale and their social and environmental implications, researchers have advocated policies and
initiatives that encourage larger households and sharing within and across households [13].
Yet, the majority of research evidence focusing on the role of household size for consumption-based
energy and GHG emissions is restricted to single country studies. A notable exception is a comparative
multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan
conducted by Lenzen and colleagues dating from 2006 [
14
]. There is a lack of up to date comparative
studies between countries [
5
], examining these trends in a broader context and discussing the potential
contextual differences across countries. An up-to-date country comparative perspective is important
from a policy perspective: is advice on supporting sharing within and across household equally valid
across all EU countries, or do these strategies need to vary and be adjusted to different contexts?
Furthermore, average household sizes differ between rural and urban areas and opportunities
to share may also vary with urban-rural context [
15
,
16
]. Yet, studies that examine the interaction
between household size and population density in a country-comparative setting are lacking. This
article addresses this gap, analyzing the role of household size and its interaction with urban-rural
typology across EU countries.
Our main finding is that household economies of scale vary substantially across consumption
categories, urban and rural typology and EU countries. High household economies of scale are noted
for home energy, real estate services, and miscellaneous services such as waste treatment and water
supply; yet, some of the weakest household economies of scale occur in high carbon domains such
as transport. Furthermore, Northern and Central European states are more likely to report strong
household economies of scale—particularly in sparsely populated areas—compared to Southern and
Eastern European countries. We discuss possible reasons for these patterns, as well as policy strategies
to encourage sharing within and between households to contribute to climate change mitigation.
1.1. Cross-Country Differences in Household Economies of Scale
There may be various factors explaining the potential differences in the household economies
of scale across EU countries. Some of these are related to the distribution of household size and
composition. Adding another member to a household is likely to reduce per capita energy use and
carbon footprints at a decreasing rate with rising household size; that is, increasing the household
size from one to two members may drastically reduce home energy use and the associated carbon
footprint, while a change from three to four members has been shown to produce a smaller effect
on average [
2
]. Furthermore, the household composition—e.g., the age and the gender of the new
household member—may also play an important role [3,5].
Several social, political and cultural factors are also likely to influence the effect that an additional
household member has on energy and carbon footprints. Widely reported long-term changes include
decreasing social trust, concern for others, conformity and religiosity, and increasing individualism,
gender egalitarianism, materialistic and extrinsic values [
17
,
18
], all of which may have implications
for household dynamics and sharing practices. Yet, following the global financial crisis, more recent
changes in values towards greater importance of conservation (security, tradition) and concerns for
close others (benevolence) have been noted in Europe [
17
]. As countries with extensive social nets report
lower value changes following the financial crisis [
17
], we discuss social welfare systems as an important
country-specific factor that influences the potential for sharing within and between households. Welfare
Energies 2020,13, 1909 3 of 28
regimes that promote individual independence, female participation in the labor force, and countries
with higher levels of secularization [
19
] may stand out with lower household sizes, which may also
affect the potential for household economies of scale. Differences in consumption patterns across
countries, stemming from differences in culture, social norms, geography and climate, infrastructural
and institutional contexts, may also explain some of the variation in household economies of scale.
While we cannot test these theories directly in our analysis, they offer potential explanations for
the country clustering of household economies of scale in our analysis.
1.2. Interaction between Household Size and Population Density
Urban areas are associated with high population and employment densities, compact and mixed
land uses, and high degrees of connectivity and accessibility [
20
–
22
]; as such they have higher potential
for collaborative consumption and sharing of resources between households, and more efficient uses
of infrastructure [
5
], the so-called “compact” or “density effect” hypothesis [
16
]. This is because urban
areas with narrower streets and smaller city blocks, compact and connected design, pleasant and safe
urban space and mixed land uses generally reduce travel distance and promote active travel (walking
and biking) and public transport [
5
,
20
,
22
]. Furthermore, urban dwellings are associated with smaller
sizes, a higher proportion of apartments and multi-family houses and the presence of district heating,
which are overall less carbon and energy intense per unit of area [
3
,
22
]. While there is strong evidence
for this density effect on per capita carbon and energy footprints in the European context, this is largely
compensated by higher income levels in cities [
23
]. Urban cores are generally preferred by more
affluent and younger adults with greater consumption opportunities and smaller household sizes
(and hence higher per capita carbon and energy footprints), while suburban areas benefit from larger
household sizes and economies-of-scale effects at the household level [
15
,
16
]. This clearly complicates
the established view that dense urban environments are more sustainable [5].
Furthermore, household economies of scale are likely to differ between rural and urban areas [
15
,
16
].
A recent study from the USA found household economies of scale to be about twice as large in rural
compared to urban contexts (up to 8% reduction in per capita carbon emissions when adding an
adult in rural contexts compared to 3% reduction in dense urban contexts) [
24
]. Lower household
economies of scale in urban areas have also been found in a European context [
25
]. An explanation for
this trend is that both household and urban economies of scale “are driven by proximity and realized
through sharing” [
24
]. Adding a member in a rural detached house will bring about higher savings
through sharing walls, living space and heating and cooling, compared to adding a member in a shared
apartment building, where walls are already shared between more households, living space is smaller
and common district heating may be present. Urban context is associated with proximity between
households and thus higher potential to share resources outside of the household, which may in turn
partially offset the household size effect. We explore differences in the household economies of scale
between urban and rural context through an interaction term between household size and population
density in the model.
1.3. This Study
In this article, we calculate the total and the average per capita EU carbon and energy footprints
for various household sizes. We examine the inter-country differences in household economies of scale
across 26 EU countries as a way to uncover sharing opportunities and support reductions in energy
use and GHG emissions. This analysis considers differences in effects across consumption domains, as
well as between rural and urban areas.
Prior studies generally focus on a single country, while a comparative perspective is lacking.
A comparative perspective allows for a more robust discussion of the potential energy and GHG
emission cuts that could be achieved through within- and between-household sharing—and may help
formulate context-specific initiatives and policies for resource sharing on a regional and country level.
Energies 2020,13, 1909 4 of 28
Studies usually focus on either carbon or energy. In this study, we examine both in order to enable a
wider comparison.
2. Data and Methods
2.1. Databases
The Household Budget Surveys (HBS), harmonized and disseminated through Eurostat, collect
information about household consumption expenditure across EU countries. This study utilized data
from 2010, which is the latest available. Price coefficients were used to adjust household expenditure
to the reference year of 2010 and EUR/purchasing power standard (PPS) units, thus accounting
for price differences across countries and time (for the countries, which collected expenditure in a
different year) [
26
]. A detailed overview of the HBS accuracy (sampling and non-sampling errors),
timeliness, comparability and representativeness is provided elsewhere [
26
]. We transformed household
expenditure into per capita expenditure and proceeded with carbon and energy footprint calculations.
We calculated annual carbon and energy footprints on the household level, utilizing the
multiregional input-output database EXIOBASE (version 3.7) [
27
]. We applied the Global Warming
Potential (GWP100 [
28
]) metric to convert various GHGs (carbon dioxide, methane, nitrous oxide
and sulphur hexafluoride) to kilograms of CO
2
-equivalents per year (kgCO
2
eq). Annual energy
use was calculated using the net energy extension measures in terajoules (TJ). There is no double
counting with regards to the conversion from primary sources (derived directly from nature, e.g.,
coal) into secondary sources (coal-generated electricity, for instance) [
29
]. In this paper, we used the
terms “carbon footprints” and “GHG emissions”, as well as “energy footprints” and “energy use”
interchangeably. We expected that the two environmental indicators would depict similar trends in
terms of the effect of household size, as the majority of GHG emissions are related to energy use (e.g.,
burning of fossil fuels).
The EXIOBASE database covers high sectoral detail (200 products), 49 countries (including all
EU countries) and rest-of-the-world regions, and a wide range of environmental and social satellite
accounts [
27
,
30
]. We matched the HBSs household expenditure in 2010 with the environmental and
economic structure in EXIOBASE for the same year. For a detailed overview of the harmonization
steps between consumption from HBSs and the environmental intensities from EXIOBASE, see SM1
and elsewhere [4,31].
2.2. The Model
In order to examine inter-country differences in household size effects, we performed the regression
analysis for each EU country cseparately (see SM4 for a robustness check through a model including
all of the countries). We also performed the analysis on EU level. We applied the household weights
disseminated by Eurostat. The analysis is conducted on a per capita level for each household i, with
the following specified model:
Energies 2020, 13, x FOR PEER REVIEW 4 of 27
level. Studies usually focus on either carbon or energy. In this study, we examine both in order to 146
enable a wider comparison. 147
2. Data and Methods 148
2.1. Databases 149
The Household Budget Surveys (HBS), harmonized and disseminated through Eurostat, collect 150
information about household consumption expenditure across EU countries. This study utilized data 151
from 2010, which is the latest available. Price coefficients were used to adjust household expenditure 152
to the reference year of 2010 and EUR/purchasing power standard (PPS) units, thus accounting for 153
price differences across countries and time (for the countries, which collected expenditure in a 154
different year) [26]. A detailed overview of the HBS accuracy (sampling and non-sampling errors), 155
timeliness, comparability and representativeness is provided elsewhere [26]. We transformed 156
household expenditure into per capita expenditure and proceeded with carbon and energy footprint 157
calculations. 158
We calculated annual carbon and energy footprints on the household level, utilizing the 159
multiregional input-output database EXIOBASE (version 3.7) [27]. We applied the Global Warming 160
Potential (GWP100 [28]) metric to convert various GHGs (carbon dioxide, methane, nitrous oxide and 161
sulphur hexafluoride) to kilograms of CO2-equivalents per year (kgCO2eq). Annual energy use was 162
calculated using the net energy extension measures in terajoules (TJ). There is no double counting 163
with regards to the conversion from primary sources (derived directly from nature, e.g., coal) into 164
secondary sources (coal-generated electricity, for instance) [29]. In this paper, we used the terms 165
“carbon footprints” and “GHG emissions”, as well as “energy footprints” and “energy use” 166
interchangeably. We expected that the two environmental indicators would depict similar trends in 167
terms of the effect of household size, as the majority of GHG emissions are related to energy use (e.g., 168
burning of fossil fuels). 169
The EXIOBASE database covers high sectoral detail (200 products), 49 countries (including all 170
EU countries) and rest-of-the-world regions, and a wide range of environmental and social satellite 171
accounts [27,30]. We matched the HBSs household expenditure in 2010 with the environmental and 172
economic structure in EXIOBASE for the same year. For a detailed overview of the harmonization 173
steps between consumption from HBSs and the environmental intensities from EXIOBASE, see SM1 174
and elsewhere [4,31]. 175
2.2. The Model 176
In order to examine inter-country differences in household size effects, we performed the 177
regression analysis for each EU country c separately (see SM4 for a robustness check through a model 178
including all of the countries). We also performed the analysis on EU level. We applied the household 179
weights disseminated by Eurostat. The analysis is conducted on a per capita level for each household 180
i, with the following specified model: 181
ENVF stands for the estimated environmental footprint, namely the annual carbon or energy 182
footprint per capita measured in kgCO2eq and TJ, respectively, in logarithmic form. The log-183
transformation was done to achieve normally distributed regression residuals, which previously had 184
a positively skewed distribution. 185
LNINCOME measures the role of net disposable household income [32] (not equivalized) for the 186
environmental footprint. The income coefficient can be interpreted as income elasticity as both the 187
dependent and independent variables are measured in logarithmic form. As the Italian HBS does not 188
ENVF stands for the estimated environmental footprint, namely the annual carbon or energy
footprint per capita measured in kgCO
2
eq and TJ, respectively, in logarithmic form. The
log-transformation was done to achieve normally distributed regression residuals, which previously
had a positively skewed distribution.
LNINCOME measures the role of net disposable household income [
32
] (not equivalized) for the
environmental footprint. The income coefficient can be interpreted as income elasticity as both the
dependent and independent variables are measured in logarithmic form. As the Italian HBS does not
Energies 2020,13, 1909 5 of 28
include the income variable used for other countries, we employed the logarithm of total expenditure
instead as an independent variable, similar to other studies [14,24].
HHSIZE presents the number of household members. The term household refers to people with a
common use of an address, usually sharing space and practices [
9
]. In the HBSs, sharing common
accommodation and expenses was also central to the household definition.
The dummy variables for population density (DENSE and INTERMEDIATE) utilize the Eurostat’s
measure of the degree of urbanization [
33
], based on Local Administrative Units level 2 (LAU2). LAU
are low level administrative divisions below that of a province, region or state [
34
], where LAU2 is the
lowest consisting of municipalities or equivalent units in the 28 EU Member States (formerly NUTS 5
level) [
35
]. The degree of urbanization defined by Eurostat classifies LAU2 into sparsely, intermediate
and densely populated areas, using as a criterion the geographical contiguity in combination with the
population density in the different types of areas [
33
]. A map of the degree of urbanization in 2011 for
all of the EU and a detailed explanation of the undertaken steps for the LAU2 classification can be
found elsewhere [
33
]. In this article, variable DENSE takes the value of one for households that live in
areas with at least 500 inhabitants/km
2
, and zero otherwise (cities). INTERMEDIATE takes the value of
one for households that live in areas between 100 and 499 inhabitants/km
2
, and zero otherwise (towns
and suburbs). The base category SPARSE is associated with rural or sparsely populated areas with less
than 100 inhabitants/km2according to the HBS classification.
Similar to a prior study [
24
], we added an interaction term between household size and population
density (HHSIZE
×
DENSE) in order to explore the potential variability in household economies of scale
by urban-rural typology.
We also included spatial controls—a set of regional dummy variables (REGION)—aiming to
account for regional differences such as technological (e.g., energy efficiency or infrastructure, type of
dominant industries) as well as geographical and climatic context [
4
] (see SM1 for an overview of all
regions). The regional distribution is the first-level NUTS of the EU for most countries.
Prior work has discussed the selected variables in the model as key socio-demographic, economic
and geographical determinants of environmental footprints [
4
,
5
,
24
]. While additional factors such as
dwelling size and type, vehicle ownership, energy sources and prices [
3
,
21
] among others are important,
the HBSs do not collect such data. We also did not explore the role of household composition, while
prior studies found education, gender and age to have small and mixed effects [
2
–
5
,
36
]. For example,
females have been found to have lower carbon footprints associated with transport and food, and
higher energy use at home [
3
,
36
]. Single parent households (mostly headed by women) were found
to be more likely to experience fuel and energy poverty [
37
]. Age has been found to be positively
associated with energy use [
3
,
38
], although this effect may slow down or even change direction when
people reach their later years [
2
,
36
]. Education and social status may also redesign preferences towards
more or less emission- and energy-intensive consumption [2,4,39].
We estimated the regression model based on household surveys from 25 EU countries (excluding
Sweden and the Netherlands due to lack of consumption data and Romania due to lack of population
density), with a total sample of 243,911 observations.
2.3. Limitations
Our analysis was affected by limitations regarding the representativeness, harmonization and
measurement errors of the HBSs. A detailed account of these limitations [
26
] and their implications
for the carbon and energy footprint calculations can be found elsewhere [
3
,
31
]. There may be higher
sampling error and inflated variation associated with infrequent purchases [
26
], for instance second
homes [40], personal vehicles, flights or furniture, and their associated environmental impacts.
There are some limitations regarding the environmental impact assessment. EXIOBASE offers
details of 200 products and services across 44 countries and five rest-of-the-world regions, and can
thus only distinguish the country-level carbon and energy intensities of largely heterogeneous product
groups. Particularly in the context of household dynamics, the product detail was insufficient to
Energies 2020,13, 1909 6 of 28
distinguish between consumption of items that are more likely to be shared within and between
households (e.g., use of shared appliances vs. individual equipment). Difficulties in allocating land
use change emissions to specific economic activities have been previously recognized [41,42].
Some products and services may also be purchased directly by households in some countries but
are provided through governmental spending in others. Focusing solely on household expenditure may
thus result in substantial variation in terms of spending on health, social work, education and transport
services, disregarding impacts associated with public provision, which affects comparative analysis [
43
].
As a result, our analysis may not capture well country differences in the between-household sharing
opportunities through the provision of public infrastructure.
Furthermore, as household carbon and energy footprints are based on monetary expenditure, there
are limitations due to potential price differences within products. Therefore, we likely overestimated
the environmental impact of expensive products (wealthier individuals) and underestimated the
impact of cheap products (and less wealthy individuals) [
44
]. In addition, we could not examine
the effect of “green consumerism” [
16
] on carbon and energy intensities, e.g., buying a fuel-efficient
car, opting for a green energy provider or a more expensive but energy efficient dwelling. Larger
households may also be more likely to purchase items in bulk and, thus, pay lower prices per item.
Prior work discusses the limitations associated with the monetary-based approach [2,31,44].
The HBS uses household size or type in the stratification criteria for most countries in order
to make the survey sampling more accurate [
26
,
32
]. Yet, there may be an under-representation of
less common household types such as intentional communities (e.g., eco-villages, co-housing). All
collective households such as elderly homes, boarding schools and others, where individual spending
cannot be distinguished from collective spending, have been excluded from the HBSs [26].
Furthermore, the population density variable and interaction effect are based on the LAU2
classification and as such it can only capture potential consumption and footprint differences between
cities, towns and suburbs and rural areas. We cannot capture differences in the between-household
sharing potential and opportunities on dwelling-, close community- or neighborhood levels.
3. Results
3.1. Descriptive Statistics and Bi-Variate Regressions
3.1.1. Household Size, Carbon and Energy Footprints
In per capita terms, one-person households have the highest average carbon and energy footprints
in the EU at 9.2tCO
2
eq/cap and 0.14 TJ/cap per year (Figure 1). They contribute 17-18% of the EU’s
total carbon and energy footprints, but constitute less than 13% of the EU population. Two-person
households are most numerous with 27% of the EU population. They also contribute the largest share
of the EU’s total carbon and energy footprints with 31-32%. The EU per capita average of carbon and
energy footprints for two-person households amounts to 8.4 tCO
2
eq/cap and 0.12 TJ/cap, respectively.
The largest households (>4 persons) contribute about 9-10% to total EU emissions and energy use and
represent 14% of the population. They have the lowest average carbon and energy footprints of 4.6
tCO2eq/cap and 0.07 TJ/cap, respectively (Figure 1).
Energies 2020,13, 1909 7 of 28
Energies 2020, 13, x FOR PEER REVIEW 7 of 27
(a)
(b)
Figure 1. Distribution of EU carbon (a) and energy (b) footprint shares by household size. The total
carbon and energy contribution can be split into two parts: the average carbon and energy footprints
per capita (y-axis) and the number of people within the household cohort in the EU (x-axis). The %-s
represent the share of total EU carbon and energy footprints by household sizes. Source: own
calculations based on country population from the World Bank for 2010.
Figure 2 depicts the relationship between average per capita carbon and energy footprints and
average household sizes across EU countries. The figure shows a negative trend across countries,
suggesting a substantial overlap between countries with high average carbon and energy footprints
and relatively low household sizes. The average household size in EU amounts to 2.4, varying
Figure 1.
Distribution of EU carbon (
a
) and energy (
b
) footprint shares by household size. The total
carbon and energy contribution can be split into two parts: the average carbon and energy footprints
per capita (y-axis) and the number of people within the household cohort in the EU (x-axis). The %-s
represent the share of total EU carbon and energy footprints by household sizes. Source: own
calculations based on country population from the World Bank for 2010.
Figure 2depicts the relationship between average per capita carbon and energy footprints and
average household sizes across EU countries. The figure shows a negative trend across countries,
suggesting a substantial overlap between countries with high average carbon and energy footprints
Energies 2020,13, 1909 8 of 28
and relatively low household sizes. The average household size in EU amounts to 2.4, varying between
2.2 and 2.9 across countries. The supplementary material (SM2) provides more detail about the
distribution of carbon and energy footprints, and household sizes across EU countries.
Energies 2020, 13, x FOR PEER REVIEW 8 of 27
between 2.2 and 2.9 across countries. The supplementary material (SM2) provides more detail about
the distribution of carbon and energy footprints, and household sizes across EU countries.
The countries with the highest per capita carbon and energy footprints in the EU include
Luxembourg, Greece (previously found to have one of the highest carbon footprints in the EU [4,43],
with a large vessel fleet in relation to its size, requiring a high use of fuel from bunkers [45]), Ireland,
Finland, United Kingdom, Belgium, Germany and Denmark, with carbon footprints between 14.1
and 9.1 tCO2eq/cap, and energy footprints between 0.2 and 0.13 TJ/cap (Figure 2, SM2). These are also
the countries with some of the lowest household sizes: Germany (2.0), Denmark and Finland (2.1),
Belgium and the United Kingdom (2.3). Finland and Denmark have the highest share of one-person
households from the total number of households at 40%, followed by Germany at 39%. These
observations broadly agree with the Eurostat statistics on household sizes (SM3).
The countries with the lowest per capita carbon and energy footprints include Romania, Croatia,
Hungary, Latvia, Poland, Bulgaria, Spain, Portugal and Slovakia, with carbon footprints between 3.6
and 6.2 tCO2eq/cap, and energy footprints between 0.05 and 0.09 TJ/cap. The countries with the
highest household sizes include Romania and Cyprus (2.9), Slovakia, Malta, Poland and Croatia (2.8),
and Spain (2.7). Romania, Malta and Spain have the lowest share of one-person households (19%)
from the total number of households.
Figure 3 shows average per capita carbon and energy footprints per household size across EU
countries. It confirms a drop in the environmental per capita impact with rising household size within
EU countries. While the slopes vary in steepness, we consistently confirm this trend for all EU
countries. For example, the average carbon footprint of Luxembourg ranges from 18.8 to 7.4
tCO2eq/cap for one-person and six-or-more persons households, respectively. Similarly, the per
capita energy footprint of the average one-person household in Luxembourg is 0.27 TJ/cap, while that
of an average six-or-more persons household amounts to 0.11 TJ/cap. According to Figure 3, the
spread of the average carbon and energy footprints across EU countries is much larger for smaller
household sizes compared to larger household sizes. Additionally, the absolute change in
environmental impacts with the addition of one more household member is decreasing in magnitude
with the rising household size.
(a)
Energies 2020, 13, x FOR PEER REVIEW 9 of 27
(b)
Figure 2. Association between average household size and average per capita carbon (a) and energy
(b) footprints in the EU. The carbon footprints are measured in tCO2eq/cap and energy footprints in
TJ/cap. Household weights provided by the HBS have been applied.
Figure 2.
Association between average household size and average per capita carbon (
a
) and energy
(
b
) footprints in the EU. The carbon footprints are measured in tCO
2
eq/cap and energy footprints in
TJ/cap. Household weights provided by the HBS have been applied.
The countries with the highest per capita carbon and energy footprints in the EU include
Luxembourg, Greece (previously found to have one of the highest carbon footprints in the EU [
4
,
43
],
with a large vessel fleet in relation to its size, requiring a high use of fuel from bunkers [
45
]), Ireland,
Finland, United Kingdom, Belgium, Germany and Denmark, with carbon footprints between 14.1 and
Energies 2020,13, 1909 9 of 28
9.1 tCO
2
eq/cap, and energy footprints between 0.2 and 0.13 TJ/cap (Figure 2, SM2). These are also the
countries with some of the lowest household sizes: Germany (2.0), Denmark and Finland (2.1), Belgium
and the United Kingdom (2.3). Finland and Denmark have the highest share of one-person households
from the total number of households at 40%, followed by Germany at 39%. These observations broadly
agree with the Eurostat statistics on household sizes (SM3).
The countries with the lowest per capita carbon and energy footprints include Romania, Croatia,
Hungary, Latvia, Poland, Bulgaria, Spain, Portugal and Slovakia, with carbon footprints between 3.6
and 6.2 tCO
2
eq/cap, and energy footprints between 0.05 and 0.09 TJ/cap. The countries with the highest
household sizes include Romania and Cyprus (2.9), Slovakia, Malta, Poland and Croatia (2.8), and
Spain (2.7). Romania, Malta and Spain have the lowest share of one-person households (19%) from the
total number of households.
Figure 3shows average per capita carbon and energy footprints per household size across EU
countries. It confirms a drop in the environmental per capita impact with rising household size within
EU countries. While the slopes vary in steepness, we consistently confirm this trend for all EU countries.
For example, the average carbon footprint of Luxembourg ranges from 18.8 to 7.4 tCO
2
eq/cap for
one-person and six-or-more persons households, respectively. Similarly, the per capita energy footprint
of the average one-person household in Luxembourg is 0.27 TJ/cap, while that of an average six-or-more
persons household amounts to 0.11 TJ/cap. According to Figure 3, the spread of the average carbon
and energy footprints across EU countries is much larger for smaller household sizes compared to
larger household sizes. Additionally, the absolute change in environmental impacts with the addition
of one more household member is decreasing in magnitude with the rising household size.
3.1.2. Household Size and Population Density
The countries with lower average household sizes—Belgium, Germany, the United Kingdom and
Finland—are also some of the most densely populated (Figure 4). At the same time, countries with
larger average household sizes are more sparsely populated—e.g., Slovakia, Croatia and Poland.
Notable exceptions are Malta (with high average household size and a predominantly urban
sample (92%) and Denmark (with low average household size and a largely rural sample, with as
much as 43% of the sample living in sparsely populated areas). Denmark has a long tradition of a
social-democratic welfare regime [
46
] with more liberal attitudes to family relationships and lower
levels of religiosity, which may explain the relatively lower household sizes at lower population
density. Compared to Western Europe, there is higher religious participation in Malta, attaching
great importance to teachings regarding family life, the morality of abortion, divorce and other
matters [
47
], which may explain the relatively large average household size. In addition, there may
also be geographical reasons for the relatively high population density, with Malta being a small island.
Similar to other studies [
23
], we find that population density is important for per capita carbon and
energy footprints (see SM2). Descriptive statistics should be interpreted with caution as they do not
control for the differences in income levels and other relevant factors, which tend to vary substantially
between urban and rural areas.
Energies 2020,13, 1909 10 of 28
Energies 2020, 13, x FOR PEER REVIEW 10 of 27
(a)
(b)
Figure 3. Mean per capita carbon (a) and energy (b) footprints by EU country by household size.
Households with household sizes >5 have been aggregated in the same group. The carbon footprints
are measured in tCO2eq/cap and energy footprints in TJ/cap. Household weights provided by the HBS
have been applied.
3.1.2. Household Size and Population Density
The countries with lower average household sizes—Belgium, Germany, the United Kingdom
and Finland—are also some of the most densely populated (Figure 4). At the same time, countries
with larger average household sizes are more sparsely populated—e.g., Slovakia, Croatia and Poland.
Notable exceptions are Malta (with high average household size and a predominantly urban
sample (92%) and Denmark (with low average household size and a largely rural sample, with as
much as 43% of the sample living in sparsely populated areas). Denmark has a long tradition of a
social-democratic welfare regime [46] with more liberal attitudes to family relationships and lower
Figure 3.
Mean per capita carbon (
a
) and energy (
b
) footprints by EU country by household size.
Households with household sizes >5 have been aggregated in the same group. The carbon footprints
are measured in tCO
2
eq/cap and energy footprints in TJ/cap. Household weights provided by the HBS
have been applied.
Energies 2020,13, 1909 11 of 28
Energies 2020, 13, x FOR PEER REVIEW 11 of 27
levels of religiosity, which may explain the relatively lower household sizes at lower population
density. Compared to Western Europe, there is higher religious participation in Malta, attaching great
importance to teachings regarding family life, the morality of abortion, divorce and other matters
[47], which may explain the relatively large average household size. In addition, there may also be
geographical reasons for the relatively high population density, with Malta being a small island.
(a)
(b)
Figure 4. Association between average household size and share of households living in densely (a)
and sparsely (b) populated areas in the EU. In cases where the shares do not add up to one, the
difference amounts to the share of households living in intermediately populated areas. Household
weights provided by the HBS have been applied.
Similar to other studies [23], we find that population density is important for per capita carbon
and energy footprints (see SM2). Descriptive statistics should be interpreted with caution as they do
not control for the differences in income levels and other relevant factors, which tend to vary
substantially between urban and rural areas.
3.1.3. Bi-Variate Regressions
Table 1 presents an overview of the standardized bi-variate Ordinary Least Squares (OLS)
regression coefficients and statistical significance between household size (as a dependent variable)
and urban-rural typology, carbon and energy footprints, and income per capita (as independent
variables) across EU countries. Table 1 confirms a strong negative relationship between household
size and per capita energy and carbon footprints within countries. The EU coefficients amount to
Figure 4.
Association between average household size and share of households living in densely
(
a
) and sparsely (
b
) populated areas in the EU. In cases where the shares do not add up to one, the
difference amounts to the share of households living in intermediately populated areas. Household
weights provided by the HBS have been applied.
3.1.3. Bi-Variate Regressions
Table 1presents an overview of the standardized bi-variate Ordinary Least Squares (OLS)
regression coefficients and statistical significance between household size (as a dependent variable) and
urban-rural typology, carbon and energy footprints, and income per capita (as independent variables)
across EU countries. Table 1confirms a strong negative relationship between household size and per
capita energy and carbon footprints within countries. The EU coefficients amount to
−
0.17 and
−
0.20
for carbon and energy footprints, respectively. Across countries, the coefficients vary between
−
0.11
(in Romania) and
−
0.39 (in Luxembourg) for carbon, and between
−
0.12 (in Romania) and
−
0.44 (in
Czech Republic) for energy.
Energies 2020,13, 1909 12 of 28
Table 1.
Standardized bi-variate regression coefficients (can be interpreted as pairwise correlation
coefficients) between household size and other variables by EU country.
Country Code Country Name Coefficients for Household Size (HHSIZE)
Densely
Populated
Sparsely
Populated
Carbon
Footprint
Energy
Footprint Income
EU European Union −0.040*** 0.042*** −0.170*** −0.196*** −0.208***
BE Belgium −0.102*** 0.011 −0.310*** −0.341*** −0.297***
BG Bulgaria −0.007 −0.025 −0.132*** −0.195*** −0.356***
CY Cyprus −0.072*** 0.01 −0.274*** −0.293*** −0.276***
CZ Czech Republic −0.083*** 0.054** −0.384*** −0.437*** −0.291***
DE Germany −0.179*** 0.114*** −0.169*** −0.182*** −0.196***
DK Denmark −0.114*** 0.087*** −0.162*** −0.228*** −0.154***
EE Estonia −0.009 −0.003 −0.196*** −0.241*** −0.239***
ES Spain −0.046*** 0.016* −0.191*** −0.217*** −0.416***
FI Finland −0.150*** 0.128*** −0.159*** −0.195*** −0.181***
FR France −0.082*** 0.101*** −0.229*** −0.294*** −0.242***
GB United Kingdom 0.017 −0.000 −0.139*** −0.141*** −0.161***
GR Greece −0.026 −0.008 −0.193*** −0.190*** −0.263***
HR Croatia −0.095*** 0.048** −0.156*** −0.200*** −0.326***
HU Hungary −0.147*** 0.118*** −0.282*** −0.264*** −0.415***
IE Ireland −0.052*** 0.082*** −0.261*** −0.236*** −0.261***
IT Italy −0.053*** 0.013* −0.273*** −0.259*** −
LT Lithuania −0.152*** 0.150*** −0.143*** −0.146*** −0.325***
LU Luxembourg −0.109*** 0.084*** −0.391*** −0.379*** −0.391***
LV Latvia −0.076*** 0.076*** −0.157*** −0.224*** −0.214***
MT Malta −0.001 − −0.253*** −0.245*** −0.240***
PL Poland −0.187*** 0.144*** −0.296*** −0.306*** −0.295***
PT Portugal −0.02 −0.044*** −0.127*** −0.134*** −0.243***
RO Romania − − −0.116*** −0.122*** −0.422***
SE Sweden −0.011 −0.002 −0.184*** −0.170*** −0.231***
SI Slovenia −0.079*** 0.061*** −0.277*** −0.316*** −0.179***
SK Slovakia −0.098*** 0.052*** −0.202*** −0.216*** −0.355***
Note: * p<0.05, ** p<0.01, *** p<0.001. The variables densely populated (DENSE) and sparsely populated
(SPARSE) are dummies. In the context of this table, HHSIZE can be interpreted as a dependent variable, and the rest
of the variables—as independent variables. Household weights provided by the HBS have been applied.
Furthermore, densely populated contexts (cities) are associated with smaller household sizes,
and sparsely populated rural contexts – with larger household sizes in most EU countries (Table 1).
The lowest regression coefficients between household size and densely populated context are found
in Poland (
−
0.19) and Germany (
−
0.18). The opposite is true for sparsely populated areas, with the
highest significant coefficient between household size and sparsely populated context found in Poland
(0.14). Portugal shows an exceptional trend, being the only country with a negative and significant
coefficient for household size and sparsely populated context.
While we note substantial inter-country differences, household dynamics should clearly be
analyzed controlling for other socio-demographic trends (such as income and population density) [
16
].
For example, the analysis confirms a strong negative relationship between income and household
size across all EU countries—suggesting an association between lower household sizes and higher
incomes—with a coefficient amounting to −0.21 for the EU.
3.2. Household Economies of Scale for Total Carbon and Energy Footprints
Figure 5portrays results from a multi-variate OLS regression on the role of HHSIZE for per capita
carbon and energy footprints in logarithmic form (dependent variables). There are additional variables
in the models such as income, urban-rural typology and geographical region (see the Data and Methods
section for the model specification). The figure shows two model specifications, including (in blue)
and excluding (in red) the HHSIZE×DENSE interaction terms.
Energies 2020,13, 1909 13 of 28
Energies 2020, 13, x FOR PEER REVIEW 13 of 27
and Methods section for the model specification). The figure shows two model specifications,
including (in blue) and excluding (in red) the HHSIZE×DENSE interaction terms.
(a)
(b)
Figure 5. Household size effect across countries with dependent variables—the log of carbon
footprints per capita (a) and energy footprint per capita (b). The blue coefficients depict the HHSIZE
coefficient acquired from the model including interaction effect (HHSIZE×DENSE) and the red
coefficient—the HHSIZE coefficient from the model without any interaction term. All models control
for income, rural-urban typology and region. See Data and Methods for the model specification.
Household weights provided by the HBS have been applied.
Figure 5 shows considerable variation across countries: while most countries display strong and
moderate household economies of scale, there are also countries with no household economies of
scale, or even with positive HHSIZE effects. Most countries (15 out of 25) in the EU sample show a
negative and significant HHSIZE effect, which is in line with our initial hypothesis.
Figure 5.
Household size effect across countries with dependent variables—the log of carbon footprints
per capita (
a
) and energy footprint per capita (
b
). The blue coefficients depict the HHSIZE coefficient
acquired from the model including interaction effect (HHSIZE
×
DENSE) and the red coefficient—the
HHSIZE coefficient from the model without any interaction term. All models control for income,
rural-urban typology and region. See Data and Methods for the model specification. Household
weights provided by the HBS have been applied.
Figure 5shows considerable variation across countries: while most countries display strong and
moderate household economies of scale, there are also countries with no household economies of scale,
or even with positive HHSIZE effects. Most countries (15 out of 25) in the EU sample show a negative
and significant HHSIZE effect, which is in line with our initial hypothesis.
Energies 2020,13, 1909 14 of 28
An increase in the EU household size by one member brings about a 5%-reduction in the carbon
footprint and 7%-reduction in the energy footprint (Figure 5, in blue). The countries with the strongest
household economies of scale include Luxembourg, Slovenia and Czech Republic, described by negative
and significant HHSIZE at the 5% level coefficients, ranging from
−
0.11 to
−
0.13. The coefficients
suggest that an increase in household size by one member decreases the per capita carbon and energy
footprint by up to 12% (taking the exponent of the coefficient). Other countries—such as Belgium,
Germany, Finland, France and the United Kingdom—are characterized by moderate household
economies of scale. Their HHSIZE effects vary between
−
0.03 and
−
0.10, suggesting that an increase in
the household size by one member reduces per capita carbon and energy footprints by 3–10%.
However, Figure 5also points to countries—such as Cyprus and Lithuania—with no visible
household economies of scale for the total carbon and energy footprint per capita. Against our initial
hypothesis, several countries even stand out with positive and significant HHSIZE coefficients such as
Spain, Italy, Greece, Portugal and Croatia. There are no significant differences between the HHSIZE
coefficients for carbon and energy footprints in most countries (see SM Figure S9), suggesting similar
economies of scale for energy and emissions.
The 95% confidence intervals of the HHSIZE coefficients in blue and red are also largely overlapping
across EU countries, meaning that there is no significant difference of the HHSIZE effect magnitude
regardless of whether or not the interaction term is included.
The following two sections explore these inter-country differences (1) for different consumption
domains; and (2) in their interaction with population density. We consider contextual differences
between countries to discuss these results in the Discussion section.
3.3. Household Economies of Scale by Consumption Categories
Figure 6provides an overview of the HHSIZE regression coefficients across the various
consumption categories with the logarithm of the carbon footprint by consumption category as
a dependent variable. We note substantial differences between EU countries within each consumption
category, both in terms of household economies of scale and carbon contribution. Figure 7shows
the HHSIZE regression coefficients and their 95% confidence intervals across the EU countries.
A detailed overview of the sectors included in each consumption category can be found in the
supplementary material.
The coefficient ranges highlight the differences of the magnitude of household economies of scale
and point to some of the products and services associated with higher sharing rates compared to others.
For example, the strongest household economies of scale are noted for housing categories such as rents
and mortgages, electricity and household services. These housing categories have median carbon
shares of 5%, 8% and 4%, respectively (Figure 6). At the same time, some of the weakest household
economies of scale are noted in the transport domain, which is also characterized with the highest
median carbon share of 25%.
Energies 2020,13, 1909 15 of 28
Energies 2020, 13, x FOR PEER REVIEW 14 of 27
An increase in the EU household size by one member brings about a 5%-reduction in the carbon
footprint and 7%-reduction in the energy footprint (Figure 5, in blue). The countries with the
strongest household economies of scale include Luxembourg, Slovenia and Czech Republic,
described by negative and significant HHSIZE at the 5% level coefficients, ranging from −0.11 to −0.13.
The coefficients suggest that an increase in household size by one member decreases the per capita
carbon and energy footprint by up to 12% (taking the exponent of the coefficient). Other countries—
such as Belgium, Germany, Finland, France and the United Kingdom—are characterized by moderate
household economies of scale. Their HHSIZE effects vary between −0.03 and −0.10, suggesting that
an increase in the household size by one member reduces per capita carbon and energy footprints by
3–10%.
However, Figure 5 also points to countries—such as Cyprus and Lithuania—with no visible
household economies of scale for the total carbon and energy footprint per capita. Against our initial
hypothesis, several countries even stand out with positive and significant HHSIZE coefficients such
as Spain, Italy, Greece, Portugal and Croatia. There are no significant differences between the
HHSIZE coefficients for carbon and energy footprints in most countries (see SM Figure S9),
suggesting similar economies of scale for energy and emissions.
The 95% confidence intervals of the HHSIZE coefficients in blue and red are also largely
overlapping across EU countries, meaning that there is no significant difference of the HHSIZE effect
magnitude regardless of whether or not the interaction term is included.
The following two sections explore these inter-country differences (1) for different consumption
domains; and (2) in their interaction with population density. We consider contextual differences
between countries to discuss these results in the Discussion section.
3.3. Household Economies of Scale by Consumption Categories
Figure 6 provides an overview of the HHSIZE regression coefficients across the various
consumption categories with the logarithm of the carbon footprint by consumption category as a
dependent variable. We note substantial differences between EU countries within each consumption
category, both in terms of household economies of scale and carbon contribution. Figure 7 shows the
HHSIZE regression coefficients and their 95% confidence intervals across the EU countries. A detailed
overview of the sectors included in each consumption category can be found in the supplementary
material.
(a)
Actual and imputed rent Electricity
Household services (e.g. waste, water supply, insurance) Food
Gas, liquid and solid fuels Other services and manufactured products
Appliances, equipment and furniture Transport
Energies 2020, 13, x FOR PEER REVIEW 15 of 27
(b)
Figure 6. A summary of the HHSIZE regression coefficients of EU countries (displayed on Figure 7)
with the logarithm of the per capita carbon footprint by consumption category as dependent variables
(a) and the proportion of the individual consumption categories of the overall carbon footprint of EU
countries (b). The categories are ordered by the median HHSIZE effect depicting the importance of
the household economies of scale from the strongest to the weakest.
The coefficient ranges highlight the differences of the magnitude of household economies of
scale and point to some of the products and services associated with higher sharing rates compared
to others. For example, the strongest household economies of scale are noted for housing categories
such as rents and mortgages, electricity and household services. These housing categories have
median carbon shares of 5%, 8% and 4%, respectively (Figure 6). At the same time, some of the
weakest household economies of scale are noted in the transport domain, which is also characterized
with the highest median carbon share of 25%.
3.3.1. Housing
Substantial household economies of scale are noted for home- and housing-related categories,
particularly housing rent or real estate services (Figure 7(a)), electricity (Figure 7(b)) and household
services such as waste treatment, water supply and insurance (Figure 7(c)). The HHSIZE effect
associated with rents and mortgages vary between −0.08 (for Estonia) and −0.47 (for the United
Kingdom) (the category includes development of building projects, management and support
services). This means that an increase in the household size by one member is associated with an 8–
37% reduction (taking the exponent of the coefficient) in the carbon footprint associated with real
estate services. With regards to electricity, negative and significant HHSIZE coefficients between
−0.05 for Estonia and −0.23 for the United Kingdom and Slovenia are noted; this suggests a 3-21%
reduction in the related per capita carbon footprint with an additional household member. Cyprus
and Sweden stand out with insignificant HHSIZE effects (the Swedish HBSs offered a lower level of
consumption detail aggregating all home-related energy consumption). Similarly, strong household
economies of scale are noted in terms of household services with the largest (negative) coefficients
noted for Slovakia (−0.28), Lithuania and Estonia (−0.25). That is, the increase of household size by
one member results in a reduction of the household services emissions by as much as 24%.
While similar ranges of the household economies of scale are noted for electricity and housing
fuels (Figure 6), the strong positive outliers in terms of HHSIZE effects lower the median household
economies of scale for housing fuels. We found negative and significant HHSIZE coefficients varying
between −0.17 (for Czech Republic) and −0.04 (for Germany and Slovenia) across most EU countries
(Figure 7(e)). The positive and significant effects—especially for Malta and Cyprus—could
potentially be explained by product allocation inconsistencies of fuel use from marine bunkers [45]
Figure 6.
A summary of the HHSIZE regression coefficients of EU countries (displayed on Figure 7)
with the logarithm of the per capita carbon footprint by consumption category as dependent variables
(
a
) and the proportion of the individual consumption categories of the overall carbon footprint of EU
countries (
b
). The categories are ordered by the median HHSIZE effect depicting the importance of the
household economies of scale from the strongest to the weakest.
Energies 2020,13, 1909 16 of 28
Energies 2020, 13, x FOR PEER REVIEW 16 of 27
(where we do not expect household economies of scale) being inaccurately allocated to household
fuels in the national accounts.
Figure 7. Cont.
Energies 2020,13, 1909 17 of 28
Energies 2020, 13, x FOR PEER REVIEW 17 of 27
Figure 7. Cont.
Energies 2020,13, 1909 18 of 28
Energies 2020, 13, x FOR PEER REVIEW 18 of 27
Figure 7. Regression coefficients for household size effects on the logarithm of the per capita annual
carbon footprint by consumption category. Categories: (a) Actual and imputed rent; (b) Electricity; (c)
Household services, e.g., waste treatment, water supply, insurance; (d) Food; (e) Gas, liquid and solid
fuels; (f) Other services and manufactured products; (g) Appliances, equipment and furniture; and
(h) Transport. The categories are ordered by the median HHSIZE effect depicting the importance of
the household economies of scale from the strongest to the weakest. Household weights provided by
the HBS have been applied.
The strong household economies of scale in the household domain are in line with prior claims
that household size is one of the largest determinants of domestic energy consumption [48] and
shelter carbon footprints [4]. They result from the sharing of space and embodied energy in buildings,
energy for heating, cooling, lighting and shared appliances and activities [9].
3.3.2. Food
Food-related economies of scale in larger households may occur when household members
prepare (e.g., when sharing food ingredients) and manage food together (e.g., when they better
manage food waste [49], which we were not able to test in this study). Furthermore, larger households
may be more likely to buy food in larger quantities, which may cost less per unit [50]. While this may
allow for a reduction in embodied emissions, e.g., through reduced packaging, in our model we were
unable to capture any differences in carbon intensities within food products. As we applied
monetary-based carbon intensities, any reduction in food spending due to lower price is reflected in
our model in lower carbon footprints, which may be misleading in cases of large price variation
within products. Finally, there may be other carbon reduction potential associated with the use of
common utensils, appliances for cooking and storing food and shared shopping for larger
households. These effects are included in the estimates for housing and transport in our analysis.
Figure 7(d) denotes significant negative coefficients between −0.20 (for Slovenia) and −0.05 (for
Denmark, Spain and Greece), suggesting that an increase in the household size with one member
leads to a decrease in the food-related carbon footprint by 5–18%.
3.3.3. Equipment, Transport and Other Consumption
While we expected substantial household economies of scale for shared appliances, equipment
and furniture, we find that most EU countries report positive HHSIZE regression coefficients (Figure
7(g)). A potential explanation of this result is that while some appliances, machinery and furniture
are shared within households, the sectoral detail of EXIOBASE does not allow us to distinguish
between typically shared and individually-used items. Furthermore, this category only includes the
purchase of items (and hence their embodied carbon footprint), while the direct emissions associated
with the use phase is included in the analysis of electricity and housing fuels. Notable exceptions
with moderate household economies of scale for home appliances and equipment include
Luxembourg and Slovenia with regression coefficients of −0.09 and −0.05, respectively.
Figure 7.
Regression coefficients for household size effects on the logarithm of the per capita annual
carbon footprint by consumption category. Categories: (
a
) Actual and imputed rent; (
b
) Electricity;
(
c
) Household services, e.g., waste treatment, water supply, insurance; (
d
) Food; (
e
) Gas, liquid and
solid fuels; (
f
) Other services and manufactured products; (
g
) Appliances, equipment and furniture;
and (h) Transport. The categories are ordered by the median HHSIZE effect depicting the importance
of the household economies of scale from the strongest to the weakest. Household weights provided
by the HBS have been applied.
3.3.1. Housing
Substantial household economies of scale are noted for home- and housing-related categories,
particularly housing rent or real estate services (Figure 7(a)), electricity (Figure 7(b)) and household
services such as waste treatment, water supply and insurance (Figure 7(c)). The HHSIZE effect
associated with rents and mortgages vary between
−
0.08 (for Estonia) and
−
0.47 (for the United
Kingdom) (the category includes development of building projects, management and support services).
This means that an increase in the household size by one member is associated with an 8–37% reduction
(taking the exponent of the coefficient) in the carbon footprint associated with real estate services.
With regards to electricity, negative and significant HHSIZE coefficients between
−
0.05 for Estonia
and
−
0.23 for the United Kingdom and Slovenia are noted; this suggests a 3-21% reduction in the
related per capita carbon footprint with an additional household member. Cyprus and Sweden stand
out with insignificant HHSIZE effects (the Swedish HBSs offered a lower level of consumption detail
aggregating all home-related energy consumption). Similarly, strong household economies of scale
are noted in terms of household services with the largest (negative) coefficients noted for Slovakia
(
−
0.28), Lithuania and Estonia (
−
0.25). That is, the increase of household size by one member results in
a reduction of the household services emissions by as much as 24%.
While similar ranges of the household economies of scale are noted for electricity and housing
fuels (Figure 6), the strong positive outliers in terms of HHSIZE effects lower the median household
economies of scale for housing fuels. We found negative and significant HHSIZE coefficients varying
between
−
0.17 (for Czech Republic) and
−
0.04 (for Germany and Slovenia) across most EU countries
(Figure 7(e)). The positive and significant effects—especially for Malta and Cyprus—could potentially
be explained by product allocation inconsistencies of fuel use from marine bunkers [
45
] (where we
do not expect household economies of scale) being inaccurately allocated to household fuels in the
national accounts.
The strong household economies of scale in the household domain are in line with prior claims
that household size is one of the largest determinants of domestic energy consumption [
48
] and shelter
carbon footprints [
4
]. They result from the sharing of space and embodied energy in buildings, energy
for heating, cooling, lighting and shared appliances and activities [9].
Energies 2020,13, 1909 19 of 28
3.3.2. Food
Food-related economies of scale in larger households may occur when household members
prepare (e.g., when sharing food ingredients) and manage food together (e.g., when they better manage
food waste [
49
], which we were not able to test in this study). Furthermore, larger households may be
more likely to buy food in larger quantities, which may cost less per unit [
50
]. While this may allow for
a reduction in embodied emissions, e.g., through reduced packaging, in our model we were unable to
capture any differences in carbon intensities within food products. As we applied monetary-based
carbon intensities, any reduction in food spending due to lower price is reflected in our model in lower
carbon footprints, which may be misleading in cases of large price variation within products. Finally,
there may be other carbon reduction potential associated with the use of common utensils, appliances
for cooking and storing food and shared shopping for larger households. These effects are included in
the estimates for housing and transport in our analysis.
Figure 7(d) denotes significant negative coefficients between
−
0.20 (for Slovenia) and
−
0.05 (for
Denmark, Spain and Greece), suggesting that an increase in the household size with one member leads
to a decrease in the food-related carbon footprint by 5–18%.
3.3.3. Equipment, Transport and Other Consumption
While we expected substantial household economies of scale for shared appliances, equipment and
furniture, we find that most EU countries report positive HHSIZE regression coefficients (Figure 7(g)).
A potential explanation of this result is that while some appliances, machinery and furniture are
shared within households, the sectoral detail of EXIOBASE does not allow us to distinguish between
typically shared and individually-used items. Furthermore, this category only includes the purchase of
items (and hence their embodied carbon footprint), while the direct emissions associated with the use
phase is included in the analysis of electricity and housing fuels. Notable exceptions with moderate
household economies of scale for home appliances and equipment include Luxembourg and Slovenia
with regression coefficients of −0.09 and −0.05, respectively.
We did not find consistent household economies of scale for transport—with positive or
insignificant coefficients for all EU countries (Figure 7(h)). Larger households have potential to
stabilize car ownership [
51
,
52
], where additional household members do not require additional number
of cars. Prior longitudinal analysis of French car sharing practices shows that while household car
sharing is a regular practice concerning almost half of the French car fleet, this trend is decreasing [
53
].
Their analysis further highlighted gender differences in terms of car sharing within households, with
a higher proportion of main users being male and a higher proportion of secondary users being
female [53].
However, our analysis suggests that the benefits of shared travel within the household are not
realized in many countries in Europe (Figure 7(h)). The lack of household economies of scale with
regards to personal vehicles and equipment (SM4) suggests that additional household members may
also activate a need for another household car, e.g., following a partnership formation [
54
]. There may
also be offsetting effects such as using the car more intensively or having a larger car in single-car
households [
55
]. Furthermore, no household economies of scale were noted for other transport modes
such as air travel, for which there is a growing demand with rising incomes in Europe [31].
Finally, we did not observe substantial household economies of scale with regards to other services
and manufactured products (Figure 7(f)). There may also be additional factors that strongly correlate
with household size (e.g., demographic, social, cultural and economic characteristics) that we could
not include in our model due to lacking data, which may explain the variation in coefficients.
3.4. Household Size and Population Density Interaction
In this section, we discuss the magnitude and significance of interaction effects depicted in
Figure 8(HHSIZE
×
DENSE) across EU countries (the model in blue in Figure 5, controlling for income,
Energies 2020,13, 1909 20 of 28
household size, population density and region). The majority of EU countries show insignificant
interaction coefficients, suggesting no significant differences in the HHSIZE effect between densely and
sparsely populated areas in the EU.
Energies 2020, 13, x FOR PEER REVIEW 20 of 27
(a)
(b)
Figure 8. Interaction effect between household size and population density (HHSIZE×DENSE) across
countries. Dependent variables—the log of per capita carbon (a) and energy (b) footprints. We
excluded the HHSIZE×INTERMEDIATE interaction term as no significant differences of the
household size effects between intermediately and sparsely populated areas were noted. See Data
and Methods for the model specification. Household weights provided by the HBS have been applied.
4. Discussion and Conclusions
4.1. Household Dynamics within the EU
One-person households are the most carbon and energy intensive in per capita terms,
contributing to 17-18% of the EU total carbon and energy footprint. The per capita carbon and energy
footprint of a one-member household is about twice that of a five- or more person household in the
EU. The share of those living in one-person households varies from 40% in Finland and Denmark to
19% in Spain, Malta and Romania, with an EU average of 31% from the total number of households.
Figure 8.
Interaction effect between household size and population density (HHSIZE
×
DENSE) across
countries. Dependent variables—the log of per capita carbon (
a
) and energy (
b
) footprints. We excluded
the HHSIZE
×
INTERMEDIATE interaction term as no significant differences of the household size
effects between intermediately and sparsely populated areas were noted. See Data and Methods for the
model specification. Household weights provided by the HBS have been applied.
However, several countries such as Czech Republic and Germany demonstrate negative HHSIZE
effects and positive interaction effects (HHSIZE
×
DENSE), both of which are significant at the 5%
level. This result suggests that adding another household member in a sparsely populated (rural)
environment is associated with larger household economies of scale compared to doing so in a densely
Energies 2020,13, 1909 21 of 28
populated (urban) environment. While adding a household member to a rural household reduces per
capita energy footprints by 7–11%, adding a household member to a dense urban household reduces
them by 4–7% (Figure 5, Figure 8). The lower household economies of scales in densely populated
environments are noted particularly for electricity, housing fuels, appliances, equipment and furniture,
and food. This is plausible because these types of environmental impacts tend to be higher in rural
areas in these countries, compared to urban areas, so that greater household sizes can reduce these
impacts more in rural areas.
We find negative interaction effects for other countries, particularly for Greece, Estonia, Cyprus
and Croatia, suggesting that households in densely populated regions encounter higher household
economies of scale in these countries, compared to sparsely populated areas. The analysis of
consumption categories suggests that these negative interaction effects are primarily associated with
consumption of household services (e.g. water and waste), other services and manufactured products.
These negative interaction effects may contradict our previous hypothesis that the interaction
between household and urban economies of scale leads to higher household economies of scale in rural
and sparsely populated areas [
24
,
25
]. However, these are all countries where per capita environmental
footprints tend to be higher in urban compared to rural areas (SM2), so it is plausible that adding a
household member in urban areas leads to greater reductions of per capita environmental footprints
there compared to rural areas.
4. Discussion and Conclusions
4.1. Household Dynamics within the EU
One-person households are the most carbon and energy intensive in per capita terms, contributing
to 17-18% of the EU total carbon and energy footprint. The per capita carbon and energy footprint of a
one-member household is about twice that of a five- or more person household in the EU. The share
of those living in one-person households varies from 40% in Finland and Denmark to 19% in Spain,
Malta and Romania, with an EU average of 31% from the total number of households.
We note substantial differences in household sizes across various EU countries as well as the
role of household size for per capita carbon and energy impacts. Adding an additional household
member results in a carbon and energy reduction of above 10% on average in some EU countries.
This result confirms that shrinking household sizes across the EU and globally are of key concern
for climate change mitigation. They should thus be adequately considered in modelling work, e.g.,
prospective scenarios of socio-demographic trends and their influence on carbon and energy footprints
and pathways to meet carbon targets. Household dynamics should also be regarded in the context of
mitigation solutions and experimentation with alternative household formations.
Substantial differences in the household economies of scale are noted for various consumption
domains, with a higher potential in housing-related items such as electricity use (up to 21% reduction
with an additional household member), real estate services (up to 37%) and household services such as
waste collection and water supply (up to 24%). Food and fuel consumption show moderate household
economies of scale with up to 18% reduction of the carbon footprint with an additional household
member in some EU countries. We note lower or no household economies of scale in other domains of
consumption (e.g., transport, manufactured products and services), where an increase in the household
size likely corresponds to an increase in consumption needs (e.g., second household vehicle, more
clothing, educational or health services with an additional household member).
Furthermore, the majority of EU countries have comparable household economies of scale between
urban and rural areas (insignificant interaction term). Other countries such as Czech Republic and
Germany report higher household economies of scale in sparsely populated areas, in line with prior
evidence [
24
] (positive interaction term). We also found a negative interaction effect between household
size and population density for a third group of countries, which counters our original hypothesis; yet,
Energies 2020,13, 1909 22 of 28
these are countries in which per capita emissions and energy use tend to be higher in urban compared
to rural areas, unlike most other EU countries.
4.2. Country Clusters and Contextual Factors
Table 2summarizes our observations regarding the household economies of scale by various
consumption domains and the interaction with population density. Two clusters of countries
emerge—one with strong or moderate household economies of scale, and one with lower or no
household economies of scale.
Table 2.
A summary of country clusters with regards to household economies of scale and other
contextual differences.
Country Clusters Example
Countries
Mean
Household
Size and T-test
Household Economies of Scale by
Consumption Domains
Interaction with
Population Density
1
Countries with
high/moderate/low
household economies of
scale
LU, SI, CZ, BE,
DE, FI, FR, GB,
MT, DK, HU,
IE, LV, PL, SE,
SK
2.54 (0.003)
Strong household economies of scale for
actual and imputed rent (GB, CZ, DK, SE),
electricity (GB, BE, CZ, DK, FR, SI),
household services (SK, LV), food (MT, SI,
LU), housing fuels (CZ, HU), other goods
and services (MT, LU, LV), appliances and
equipment (LU, SI);
Higher household
economies of scale in
rural areas compared to
urban areas (DE, CZ)
2
Countries with no
household economies of
scale/Countries with
positive HHSIZE effect
CY, LT, EE, ES,
IT, GR, PT, HR,
BG
2.64 (0.005)
Some of the lowest household economies of
scale (or positive coefficients) for actual and
imputed rent (EE, CY), electricity (CY),
household services (GR), food (PT, ES, GR),
housing fuels (CY), other goods and services
(EE, ES, LT, IT), appliances and equipment
(EE, BG, GR, LT, IT) and transport (GR, BG);
Higher household
economies of scale in
urban areas (GR, EE, CY,
HR), relatively low share
of urban population and
higher environmental
impacts in urban areas.
Difference ***
Note: One-sided two-sample unweighted t-test is performed in order to compare the average household sizes
between the country clusters under the following hypotheses: H
0
:
µcluster2 −µcluster1
=0, H
A:µcluster2 −µcluster1
>0.
We estimated separate variances to control for significant differences in sample sizes between the country clusters.
Standard errors are presented in parenthesis. T-test significance levels: * p<0.1, ** p<0.05, *** p<0.01.
The first cluster—with high and moderate household economies of scale—consists of
predominantly Northern and Central European countries. An increase in the household size by
one member results in a reduction of the total carbon and energy footprint by 3–13% (Figure 5). This
cluster is characterized by strong welfare regimes that promote individual independence and female
labor market participation [
19
]—which may explain the lower household sizes in these countries. The
cluster includes Belgium, Denmark, Sweden, Finland, France, Germany and the United Kingdom,
which are similar in terms of socio-demographic context [
56
]. The small countries of Malta and
Luxembourg are exceptions in terms of welfare regime [
47
,
56
]; the regression coefficients of Malta
in particular are characterized by relatively high error ranges across most consumption categories,
and results should thus be interpreted with caution. Finally, the Czech Republic, Poland, Slovakia,
Slovenia and Hungary (and Croatia, which is allocated to the second cluster in terms of household
economies of scale in our analysis) are characterized by the Central Europe welfare model, associated
with lower income inequality, lower rates of unemployment, higher labor market flexibility and higher
social contributions and government expenditure as a share of the Gross Domestic Product compared
to the Eastern European countries in the second cluster [56].
The second cluster—with lower or no household economies of scale—consists of predominantly
Southern and historically Catholic countries as well as some Eastern European states. An increase
in the household size by one member does not change the total per capita carbon and energy
footprint, or even increases in the per capita environmental impact (Figure 5). These countries already
have higher household sizes, and emphasize the role of the family for mutual support or are more
“collectivistic”. Greece, Spain, Italy, Cyprus and Portugal stand out from other EU countries in
terms of their welfare regimes previously described as the Mediterranean welfare model [
56
] with
stronger influence of Catholicism and traditional family values [
57
–
59
]. The Eastern European welfare
model—including Lithuania, Estonia and Bulgaria (and also Latvia, which is included in the first
Energies 2020,13, 1909 23 of 28
cluster in our analysis)—is associated with strong nuclear family institutions, low social protection
expenditure primarily on old-age pensions, high income inequality, rigid and discriminatory labor
markets and lower government capacity for generous social policies [
56
,
60
]. This might also contribute
to higher family dependency for financial and welfare support, and hence higher household sizes. The
reliance on extended family for assurance against risks of ill health, unemployment or poverty could
be reduced with higher standards of living and the provision of stronger social-security systems in
these countries [61].
These clusters show significant differences in terms of the average household sizes, with the second
cluster denoting a significantly higher household size (Table 2, Figure 2). Considering the decreasing
rate of household economies of scale with rising household sizes, this may partly explain the lower
household economies of scale in these countries—where there already is a lot of within-household
sharing, thus, there is less to gain by adding a household member.
Heating degree days are positively correlated with the housing-related energy use (and carbon
footprints), with dwellings in colder regions requiring more energy to heat over the year [
4
]. This effect
is partly mediated by stricter building standards in northern European countries, which reduces the
amount of energy for heating per heating degree day [
62
]. Nevertheless, colder countries are likely to
report higher household economies of scale particularly due to the high importance of home energy
for the overall household economies of scale, which is also in line with the country clustering. This
might also explain why we find significant positive HHSIZE effects in countries such as Spain, Italy,
Greece, Portugal and Croatia. Not only are these countries with relatively large average household
sizes already (and hence less scope for further within-household sharing), but there is also less of
a requirement for heating, which is associated with some of the strongest capacity for household
economies of scale.
The positive HHSIZE coefficients for some of the categories, where we expect relatively low
possibilities for sharing is likely driven by other socio-demographic, infrastructural and economic
factors that vary with household size, that we cannot explicitly control for in our model because they
are not captured in the HBSs.
4.3. Policy Recommendations
Targeting the trend towards smaller households and under-occupation of homes in the EU and
globally is a key option to reduce per capita carbon and energy contributions, with a higher mitigation
potential compared to efficiency improvements such as upgrading the thermal insulation or more
efficient appliances [
42
,
63
]. Understanding needs and expectations about personal space as well as
changing social norms [
18
] are key for the upscaling of “downsizer homes” [
63
] and other alternatives
to encourage within household sharing. Household sharing has an important gender dimension [
53
]
as well; sharing may support the depersonalization of objects allowing for them to be managed and
used jointly, thus encouraging even more (and more gender equal) sharing [53].
Yet, the trend of smaller households results from a myriad of processes, some of which cannot
be reversed (e.g., falling birth rates or liberation from norms), or which we consider valuable for
other reasons (e.g., female emancipation, financial independence or residential autonomy) [
48
,
61
].
For example, higher divorce rates worldwide may result in an increase of energy use and GHG
emissions per capita [
13
]; however, the freedom to divorce is also a matter of human rights and social
justice. This makes it crucial for policy interventions to realize the complexity of household dynamics
and the inter-connections with social and environmental wellbeing.
Proximate causes of the reduction in household sizes worldwide include lower fertility rates,
higher divorce rates and a decline in the frequency of multi-generational families with increasing
non-family provision of care among others [
61
,
64
]. There is some evidence that the trend of decreasing
fertility rates and increasing divorce rates is reversing since the early 2000s [
65
,
66
], which may also
stabilize or even reverse the trend of smaller households. This suggests that the trend towards smaller
families over the past half century did not result from a lasting change of family preferences, but rather
Energies 2020,13, 1909 24 of 28
from a change in women’s roles and labor market participation when institutions and partnerships
had not yet adapted [
65
]. To successfully promote parenthood and female labor force participation,
there is a need for a strong investment in childcare services, flexible workplace support and other
family support [
65
,
66
]. Such policies may help reconcile work and family responsibilities and promote
gender equity [65].
Additional social and psychological factors that may have influenced the reduction in household
sizes include liberation from strict norms, less religiosity and increased importance of individual
autonomy, self-actualization and privacy [
48
,
61
]. Support and increased visibility [
67
] for alternative
household types—such as intentional communal living—may encourage larger households, which
share lifestyles, cultural elements and common sense of purpose. Such alternative forms of living may
thus be less challenging in terms of these social and psychological factors [
12
], compared to traditional
family living. Yet alternative living arrangements may also be associated with difficulties in negotiating
common and personal items, space and time [
9
]. Partnerships between policymakers and sharing
initiatives may help tackle such difficulties by alleviating structural and institutional constraints and
reducing social distance (e.g., by fostering care for the community) and geographical distance (e.g.,
by improving connectivity), which impede sharing [
9
,
11
]. Sharing emerges as an opportunity to act
collectively on growing social, political and environmental awareness and steadily transforms social
norms and routines [68].
The complexity of household dynamics and the low household economies of scale in high-carbon
consumption domains such as transport encourage the consideration of additional ways to share
resources between households as well. For example, while sharing a car may reduce the energy use
and emissions associated with travel within the household (particularly in car-dependent areas outside
urban cores [
22
]), in the presence of an excellent public transport system, the mitigation potential may
actually be higher through sharing between households. Further research on household sharing in the
context of public infrastructure and sharing initiatives at a higher spatial resolution—in both urban
and rural context—is needed to explore the carbon mitigation potentials associated with sharing. Such
wider sharing practices for de-carbonization and low energy demand require the provision of social
and technological infrastructure such as investment in public spaces, green areas, mass transportation
and new forms of peer-to-peer sharing [
9
,
24
]. The establishment of collective systems (e.g., universal
basic services [
69
])—as opposed to highly individualized energy service delivery—also enables more
resilient societies and prevents future emission lock-in [70].
In this paper, we explore possible impacts of household dynamics on per capita emissions, and
examine difference in within household economies of scale across EU countries. Our main finding
is that household economies of scale vary substantially across consumption categories, urban and
rural typology and EU countries. We identify potential explanations associated with the sharing
potential of various products and services, contextual differences in terms of social and cultural norms,
geographic context, infrastructural and political context. Targeting trends towards smaller households
and under-occupation of homes and encouraging sharing offers substantial potential to mitigate climate
change with already available technologies and infrastructure.
Supplementary Materials:
The following are available online at http://www.mdpi.com/1996-1073/13/8/1909/s1,
SM1: Household Budget Surveys, SM2: Descriptive statistics by countries, SM3: Eurostat statistics, SM4:
Total carbon and energy footprint determinants, Supplementary spreadsheet including the overview of
consumption categories.
Author Contributions:
Conceptualization, D.I. and M.B.; Formal analysis, D.I.; Funding acquisition, D.I. and
M.B.; Investigation, D.I. and M.B.; Methodology, D.I. and M.B.; Software, D.I.; Supervision, M.B.; Visualization,
D.I.; Writing—original draft, D.I.; Writing—review and editing, M.B. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the European Union’s Horizon 2020 research and innovation program
under Marie Sklodowska-Curie grant agreement, grant number 840454. The authors also received support from
the UK Research Councils under the Centre for Research on Energy Demand Solutions.
Energies 2020,13, 1909 25 of 28
Acknowledgments:
We thank the whole EXIOBASE team for the effort to build the database and make it available
for other researchers to use. In particular, we would like to thank Richard Wood for his assistance in the early stages
of the environmental footprint analysis and Arkaitz Usubiaga-Liaño for his effort in compiling and communication
the energy extensions. We would also like to thank Sylke Schnepf and two anonymous reviewers for their
valuable feedback.
Conflicts of Interest: The authors declare no conflict of interest.
Data Statement:
The data associated with this paper is available from University of Leeds at https://doi.org/10.
5518/785. The dataset includes the per capita carbon and energy footprint calculations (generated by the authors of
this study) together with household and country IDs from the HBS dataset disseminated by Eurostat. Please use
the following data citation when referring to the dataset [
71
]: Diana Ivanova and Milena Büchs (2020): Carbon and
energy footprints of European households (EU HBS) University of Leeds. [Dataset]. https://doi.org/10.5518/785.
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