Measuring Sustainability Through Carbon Footprints

“Sustainable Development, Trade, and Environment”
Carbon Footprint as a Measure of Sustainability
David Dingus
Göttingen, 11403109
d.dingus@stud.uni-goettingen.de
June 5, 2015
Supervisors: Prof. Dr. Inmaculada Martinez-Zarzoso
&
Leoni-Eleni Oikonomikou

Table of Contents
Table of Figures………………………….…………………………………………………ii
Abbreviations……………..……………….……………………………………………….iii
Abstract…………………………………………………………………………………….iv
1. Introduction.………………………………………………………………….…………1
2. Technical Background.………………………………………………………….………2
2.1 Sustainable Development…………………………………………….…….………2
2.2 Carbon Footprint.…………………………………………………………….…….3
3. Methodology……………………………………………………………………………..4
3.1 Basic………………………………………………………………………………..4
3.2 Supply Chain……………………………………………………………………….4
3.3 Process Analysis……………………………………………………………………5
3.4 Input-Output Analysis………………………..…………………….………………6
3.5 WIOD & MRIO……………..…………….……………………………………….7
4. Evidence…………………………………………………………………………….……9
4.1 MRIO & Trade……………………………………………………………….…….9
4.2 MRIO & Socioeconomics…………………………………..…………………….12
4.3 Consumerism……………………………………………..………………………18
5. Conclusion………………………………………………………………………………19
References…………………………………………………………………………………..I
Appendix………………………………………………..…………………………………IV
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Table of Figures
Figure 1. Components of Sustainability……..…………………………………….………..2
Figure 2. Criteria of a Good Sustainability Indicator…………………………..….………..2
Figure 3. Criteria for Ranking a Sustainability Indicator………………………….………..3
Figure 4. Composition of GHG..……………….………………………………….………..3
Figure 5. Allocation of GHG Emissions…….……..………………….……………………3
Figure 6. Supply Chain for an iPod……….……………………………………….………..4
Figure 7. Supply Chain & Production Processes………………….……………….……….6
Figure 8. Expanding IO with Energy Analysis…………………………………….………..6
Figure 9. WIOD Table.…………………………………………………………….………..7
Figure 10. WIOD Table with MRIO………………………….……………..…….………..8
Figure 11. CF and Methods Scaled..……………………………………………..……..…..9
Figure 12. Consumption vs. Production Measures…………………………………….……9
Figure 13. Carbon Emission Trade Flows…………………………………………………10
Figure 14. Carbon Emission Trade Flows - Detailed…………………….…..……………11
Figure 15. Carbon Emission Sources to Income..………….………….…………………..13
Figure 16. UK Household CO2 Emission Sources..……….…………………….………..14
Figure 17. UK Household CO2 Emissions by Social Class..………………….…………..14
Figure 18. Chinese Household Emissions and Temperature.……………………….……..16
Figure 19. Chinese Household Emissions and Location…………………………………..16
Figure 20. Regression Analysis with Household Emissions………………………………17
Figure 21. UK Household GHG Emissions compared to RCS.………..…..…….………..19
Table A1. CF per-capita….…………………………………………….…………………..IV
Table A2. CF per-capita using MRIO..………..…………………………..………………..V 
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Table of Abbreviations
CO2 Carbon Dioxide Emissions
CF Carbon Footprint
EIO Environmental Input-Output Analysis
GHG Green House Gases
IO Input-Output analysis
LARA Local Area Resource Analysis
MRIO Multi-Regional Input-Output Analysis
PA Process Analysis
WIOD World Input-Output Database 
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Abstract
This paper presents evidence that a Carbon Footprint can be a valuable tool in
measuring sustainability. However, the effectiveness of the CF measure is highly dependent
upon the methods used in its calculation. Using existing literature we find empirical evidence
that underscores the importance of household level, consumption based, MRIO analysis as
providing the best measure. Using this method, the CF is able to identify the importance of
trade, socioeconomic, geographical, and consumerism as key areas of importance when trying
to identify current and future issues in sustainability.
Furthermore, while a CF cannot directly measure how these policy implications will
affect economic and social sustainability, it can help identify the key areas which will be
targeted and together with other measures could help identify and minimise losses.
Finally, no measure of sustainability is complete and the CF should always be
augmented with newest data and could further benefit from the inclusion of other GHG. 
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1. Introduction
For almost a decade, the world has been talking about climate change, how much of a
threat it really is, the policy implications and if they are feasible. The discussion went
mainstream in 2006 with the famous documentary: “An inconvenient Truth” that featured
former U.S. Vice President Al Gore. Gore presented global warming as a reality with
overwhelming scientific evidence. He highlighted the correlation between increasing amounts
of CO2 with other green house gases and global warming. He stressed that the public needs to
wake up and start taking action to curb our emissions (IMDB, 2006). In 2015, China released
its own version of an inconvenient truth titled: “Under the Dome”. It too features a
recognisable host: Chai Jing, a former state news anchor, who tells of the dangers from the
pollution in China’s cities (Gardner, 2015).
Regardless of the source, it has become clear that climate change has become a global
issue that will require broad actions with global effects. However, the question remains how
do we measure this threat and what are the policy implications? While there are a variety of
indicators to measure sustainability, carbon focused indicators seem to be the most popular.
Although every indicator has something valuable to contribute, this paper argues that a carbon
footprint can be a valuable measure of sustainability. Nonetheless, it is important to consider
how it is measured and interpreted in order to be a valuable measure of sustainability.
We will begin by defining sustainable development and a carbon footprint. Next, we
will discuss the methods used in measuring a carbon footprint. Then we will examine the
benefits of IO analysis using empirical evidence and identify tangible policy implications.
Finally, we will end with closing remarks on the limitations of a CF and how it could be
further improved. 
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2. Technical Background
2.1 Sustainable Development
In the Brutland report, sustainable development is defined as: “development that meets
the needs of the present generation without compromising the ability of future generations to
meet their own needs” (WCED, 1987). In Figure 1, the World Bank (2001) further emphasises
the 3 main components of sustainable development: social, economic, environment, where all
three components must fulfil the
criteria of sustainability to be
considered sustainable. Pursing
sustainability is a complex process
where focusing on one component
may often lead to the detriment of
another.
While this definition is clear, there remains the question of how to measure
sustainability, but first we must also consider how to assess these measures and what their
purpose should be. Bolin et al. (2001) argues that the purpose of assessing sustainability is to
provide policy makers with measures to guide their policy decisions given global and local
systems and both short and long-term implications. Moreover, Lundin (2003) suggest that
sustainable development indicators should include the summary in Figure 2 and furthermore
can be ranked based on the criteria in Figure 3. In this paper we will present empirical
evidence demonstrating how a CF indicator can fulfil these outlined requirements.
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Figure 1. 3 Components of Sustainability (World Bank 2001)
• Anticipate and assess conditions and trends
• Provide early warning information to prevent economic, social and
environmental damage
• Formulate strategies and communicate ideas
• Support decision-making
Figure 2. Criteria of a Good Sustainability Indicator (Dikshit et al. 2008, p. 192)

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2.2 Carbon Footprint
While there are many definitions of a carbon footprint we will focus on the definition
suggested by Minx & Wiedmann (2007, p.4): “The carbon footprint is a measure of the
exclusive total amount of carbon dioxide emissions that is directly and indirectly caused by an
activity or is accumulated over the live stages of a product.” Essentially, a carbon footprint
should not be based only on the carbon emissions from the production of a good, but also its
use, transport, and disposal, also referred to as a product life cycle.
As shown in Figure 4, Minx & Wiedmann (2007) further explain that while CO2 is
only one of several green house gases, it is the largest component, the one with the most
widely available data, and therefore the most practical. Ideally a measure of sustainability
based on GHG should include all gases within its makeup, but because of the many
limitations on data availability this would limit scalability, which is essential given the global
3
• What aspect of the sustainability does the indicator measure?
• What are the techniques/methods employed for construction of index like
quantitative/qualitative, subjective/objective, cardinal/ordinal, one-
dimensional/multidimensional.
• Does the indicator compare the sustainability measure across space or time
and in absolute or a relative manner?
• Does the indicator measure sustainability in terms of input or output?
• Clarity and simplicity in its content, purpose, method, comparative
application and focus.
• Data availability for the various indicators across time and space.
• Flexibility in the indicator for allowing change, purpose, method and
comparative application.
Figure 3. Criteria for Ranking a Sustainability Indicator (Dikshit et al. 2008, p. 195)
Figure 4. Composition of GHG
(Hertwich & Peters 2009, p. B)
Figure 5. Allocation of GHG Emissions
(Hertwich & Peters 2009, p. D)

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relevance. Therefore, it would be more useful to focus on CO2 (Minx & Wiedmann, 2007).
For similar reasons, the literature tends to focus on CO2 at the household level given that most
CO2 emissions are somehow tied to households as seen in Figure 5.
3. Methodology
3.1 Basic
The basic approach to calculating CO2 emissions uses a system of national accounts to
determine CO2 at the national level. The CO2 aggregate can then be divided by the population
to determine a value of CO2 per capita, which allows for a cross-country comparison (Minx &
Wiedmann, 2007). Unfortunately this method incorrectly allocates all of the direct CO2 in a
country to its population and does not account for indirect emissions, which can lead to
incorrect policy implications, especially if there is a trade surplus or deficit.
3.2 Supply Chain
In order to correctly asses the CF of a product one would have to understand the
supply chain for that product. Dedrick, Kraemer, & Linden (2011) illustrate these
complexities with an iPod example show in Figure 6. The production of a single product
requires the production of various other products, which can be in different regions, with
different regional factors of production. Further complicating the matter is that this
complexity can begin even with a single component that makes up a larger project such as the
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Figure 6. Supply Chain for an iPod (Dedrick, Kraemer & Linden 2011)

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“Japanese” hard drive in the iPod, which was actually assembled in China from components
manufactured in China, Japan, the Philippines and others. In the literature one can observe the
use of PA and IO methods to account for these differences. They are sometimes augmented by
a hybrid approach, which seeks to utilise the advantages of both.
3.3 Process Analysis
PA is a bottom-up method to calculate CO2 production based on the lifecycle of a
product. It is detail oriented given that it is derived directly from the product itself, accounting
for the inputs and outputs used based on the manufacture, use and disposal of that specific
product. This allows for detailed analysis of the product and gives very specific implications
for handling the CO2 emissions of that particular product. However, the PA approach is not
necessarily scalable and in order to have macro-level implications, one would need to have all
of the data for each product, produced in every sector in an entire economy. This would be
quite costly and impractical. PA thus suffers from a ‘system boundary’ problem – only on-site,
most first-order, and some second-order impacts are considered (Lenzen, 2001). In summary,
PA is best for the analysis of the CF of individual products, which can provide detailed
information for specific products; however, it would be too expensive to compile the
necessary data for each product in an entire economy, and is thus limited in its ability to asses
the CF at the national and international level (Minx & Wiedmann, 2007).
3.4 Input-Output Analysis
IO analysis tries to capture the supply chain process of manufacturing where inputs
are used in one process to produce an output, which then becomes an input for another
process. In this case we will use EIO analysis to focus on the environmental impact of these
processes. EIO takes a macro approach to calculate the inputs and outputs used during the
lifecycle of a product. This is a very complex process and therefore assumes homogenous
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products, costs, and emissions to
reduce this complexity and can be
extended to capture trade by using
trade flows (Peters, 2010). Peters
(2010) also explains that using IO
analysis with trade flows can help
reduce the double counting of
emissions that other methods may
suffer from. Figure 7 depicts the processes to be captured by IO analysis. Minx et al. (2009)
provide a detailed background of input-output analysis first developed by Leontief in 1941,
and they explain how to apply it to carbon emissions. IO analysis begins with the basic
structure:
X = (I – A)-1Y
Here X is a vector for output, I is an identity matrix, A is the technical coefficient matrix, and
Y is the matrix for final demand (Serino, 2014). By including energy data we can then
calculate the carbon emissions produced by each process to use IO to calculate household
level carbon emissions as depicted
by Figure 8. The original formula
can then be transformed:
C = u’(I – A)-1y
Where C is the vector of final GHG
emissions, u is the vector of GHG
emissions intensity, I is an identity
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Figure 7. Supply Chain & Production Processes
(Benders, Kok, & Moll 2006, p.2748)
Figure 8. Expanding IO with Energy Analysis
(Benders, Kok, & Moll 2006, p.2749)

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matrix, A is the matrix of technology coefficients, (I – A)-1 is the Leontief Inverse matrix, and
y is the diagonalised vector of final demand (Druckman & Jackson, 2010).
Under the consumption method, household expenditure is used as a proxy for
consumption to determine the national level of carbon emissions. Where the economic IO
data is combined with energy data for the country, which gives us IO energy analysis. This
can then be matched up with each sector in the economy and the sectoral makeup for the
country. This provides us with the level of carbon emissions within the country, and we can
then augment this data with household level expenditure to determine how much of those
emissions are consumed in country and how much are exported.
3.5 WIOD & MRIO
Recently, the WIOD has released IO tables for CO2 emissions, where they have
already augmented their IO tables with energy data for each sector. They arrive at a table
shown in Figure 9. WIOD tables can be calculated using production or consumption based
data. The consumption based method uses the following formula:
Econs = Ed + Eimp + EH
Where the emissions from consumption are equal to the emissions from demand + emissions
from imports and the emissions coming directly from household consumption. On the other
hand, emissions based on production uses the following formula:
Eprod = Ed + Eexp + EH
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Figure 9. WIOD Table (Erumban, et al. 2010)

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Where the emissions from production are equal to the emissions from demand + the emissions
from exports and the emissions from household. These formulas were taken from Boitier
(2012).
Although we can see how exports and imports are accounted for, WIOD tables can
miss out on some of the complexities illustrated in the iPod example, where intermediary
processes may be carried out abroad and not captured in a country’s WIOD table. IO tables
can be extended using multiple regions to better account for the complex intermediary
processes that may occur in multiple regions as shown in Figure 10.
Overall MRIO analysis is beneficial because unlike PA, it is easily scalable to the
national level since it looks at total consumption/production and assumes homogenous
products, costs and emissions. As a result, input-output analysis cannot lead to any
implications for specific products or consumption at the micro level, but instead can be used
to derive global indicators.
Many propose that a MRIO-hybrid approach would be the best compromise, as it
attempts to combine the advantages of both PA and MRIO and limit the disadvantages of
these approaches as depicted in Figure 11. Of course it is much more difficult to calculate and
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Figure 10. WIOD Table with MRIO (Erumban, et al. 2010)

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may not be possible due to data limitations. Furthermore, although there are many GHG, the
data is best available for CO2 emissions, and even then, WIOD was only able to calculate IO
tables for 35 countries. Data availability remains a severe limitation, and thus far CO2 has
been the most complete option, and itself is still a work in progress.
4. Evidence
4.1 MRIO and Trade
In the literature it is often argued that consumption based measures that use household
expenditure data provide a clearer and more accurate picture of carbon emissions and their
sources. In Figure 12, Boitier (2012) uses the WIOD tables with the MRIO extension to
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Figure 11. CF and Methods Scaled (Peters 2010, p.246)
Figure 12. Consumption vs. Production Measures (Boitier 2012, p.7)

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compare consumption and production methods. Most importantly, he finds that the OECD and
rich countries produce less CO2 than the BRIC under the production based method. However,
using the consumption based method the relationship switches, where the OECD produces
more CO2 than the BRIC.
Hertwich & Peters (2009) compare 73 countries using the GTAP database, which also
includes other GHG. They note that CO2 is by far the largest contributor to GHG as seen in
Figure 4, yet they argue that each GHG behalves differently and there may be other policy
implications that CO2 alone may miss. Furthermore, they highlight the data limitations using a
comprehensive set of GHG and explain that they could only examine a limited number of
countries, and even then many sectors could not be calculated and were omitted.
Nonetheless, comparing CO2 per-capita found on Table A1 in the appendix we see that
the CF of every country is larger using MRIO as seen in Table A2. More important are the
differences for China and the U.S. Under the basic method of CO2 per capita, China has 2.7
tons per capita for 2001, while using the MRIO method that number increases to 3.1 tons per
capita. On the other hand the U.S. has a basic number of 19.3 tons per capita, but using MRIO
they find 28.6 tons per capita. They argue that a lot of the difference is due to trade, as a
country such as China exports many products; they are also exporting their emissions. On the
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Figure 13. Carbon Emission Trade Flows (Caldeira & Davis 2009, p.5688)

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other end, is the U.S. which imports many products from abroad and thus imports CO2 from
China, underscoring the importance of understanding product supply chains when trying to
calculate the CF for a country (Hertwich & Peters, 2009).
Caldeira & Davis (2009) find a correlation between trade and CO2 emissions, shown
in Figures 13 & 14 using MRIO analysis. Here we can clearly see that the U.S. is the largest
importer of CO2 and China the largest exporter. Mathews & Weber (2008) explain that it is not
only the amount of products that determine CO2 emissions, but also where. They find that
Germany has the most efficient production supply chains in terms of CO2 emissions, while
China has the least efficient. In other words, using MRIO can help more precisely identify
carbon emissions not just based on trade flows, but also based on the technologies or lack
thereof in the production process. These differences are vital when determining effective
policy implications, as they could yield vastly different results, and underestimate the true
amount of CO2 and their sources.
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Figure 14. Carbon Emission Trade Flows - Detailed (Caldeira & Davis 2009, p.5689)

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Brueckner et al. (2010) explain that the Kyoto protocol has focused mainly on
production based methods to calculate CO2 emissions. This has thus allowed rich countries to
export their emissions on the less developed countries. Brueckner et al. (2010) therefore
compare production and consumption based methods under MRIO and found similar results,
concluding that the U.S. and the EU have the highest leakage, and China as the largest
exporter of CO2. Using their results, Brueckner et al. (2010) criticise the Kyoto protocol
because it only encourages individual countries and regions to decrease their emissions, often
transferring them to poorer countries. They therefore recommend that the Kyoto protocol
should be updated using the CF with MRIO analysis to arrive at real global solutions.
Nonetheless, they highlight the limits of CO2 as a measure of sustainability, that has
something to contribute, but one that should be augmented by all GHG and other resources
such as water (Brueckner et al. 2010).
4.2 MRIO and Socioeconomics
In their analysis Hertwich and Peters (2009) also found large structural differences in
economies and CO2 emissions. Referring to Table A2, we see that in Zambia and Bangladesh
more than 50% of their CO2 emissions come from food. On the other end is Luxembourg
where 50% of their CO2 emissions come from mobility. In general we can see a transition
where as a country becomes richer, food becomes less of a contributor to carbon emissions,
while shelter and mobility become increasing more important. Finally, there are also
advanced, highly urbanised areas such as Hong Kong, were most of their carbon emissions
come from clothing and manufactured products, highlighting the contribution of consumerism
on CO2 emissions.
It is important to note that this does not mean that rich countries have fewer emissions
from food, but that food makes up a smaller proportion as they consume more, causing CO2
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emissions rise, as food is not a normal good. The relationship between food (in terms of
GHG) and income are highlighted in Figure 15. Similar to Bennet’s law, we can observe an
almost horizontal line, meaning that as a country develops and becomes richer the amount of
food they consume will increase only slightly. Conversely, all other categories have a much
more positive relationship with income. We can infer that as countries develop and become
richer, CO2 will continue to increase, but will be driven by non-food sources and thus provide
us with more focused policy implications.
Druckman & Jackson (2008) use quasi-MRIO analysis to calculate the CF in the U.K.
In Figure 14 we can see their results, which show Recreation & Leisure to be the biggest
sources of UK carbon emissions. They underscore the strong preferences for UK households
to travel abroad. Druckman & Jackson (2009) add that when UK households were asked what
they would do with the extra savings from installing insulation in their home, thereby
reducing their heating cost and reducing their carbon footprint, most respondents answered
that they would use the extra savings to fly somewhere on vacation. In effect, the act of
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Figure 15. Carbon Emission Sources to Income (Hertwich & Peters 2009, p. E)

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reducing one’s CF in one area, can be overwhelmed by their consequential choices. This
rebound effect underscores the importance of understanding preferences and consequences of
policy actions (Druckman & Jackson, 2009).
Druckman & Jackson (2009) also examine CO2 emissions by social class by
modifying MRIO with LARA in order to split up the effects between different sub-groups.
LARA is able to differentiate between socioeconomic groups by estimating expenditure,
resource use, and emissions by identifying areas
with similar characteristics and measuring their
differences compared to other areas. This
expenditure difference can then be used to
argument MRIO tables. In Figure 17 we can see
their results and how personal flights correspond
with income in which case those in the
countryside and prospering suburbs not only fly
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Figure 16. UK Household CO2 Emission Sources (Druckman & Jackson 2009, p. 2075)
Figure 17. UK Household CO2 Emissions by Social Class
(Druckman & Jackson 2009, p. 2074)

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the most, but also drive the most, and consume the most direct domestic energy. Druckman &
Jackson (2009) highlight their results for those living in the city, citing them as the most
efficient, noting that those constrained by circumstances are not able to meet their needs and
preferences, and are actually less efficient that those living in the city. This has very strong
policy implications for promoting city living, including the importance of access to public
transport and decreasing the need for a car, and the efficiencies of living in smaller and more
efficient dwellings.
Regardless of social class, Druckman & Jackson (2009) find that the majority of CO2
emissions across all cohorts is embedded in goods and services, and that a large portion of
CO2 emissions come from consumerism and a desire to “keep up with the Joneses.” Moreover
they argue that attempts to curb CO2 emissions have been negated by the rebound effect and
the “offshoring” of emissions through the purchase of imported goods and services. In fact,
approximately 40% of emissions from the UK occurred outside of its borders in 2004
(Pauwelyn & Sindico, 2008). Using MRIO we can better account for these emissions and
measure the progress of CO2 emissions.
The importance of MRIO is further emphasised when one considers the policy
implications these results have for developing countries. As developing countries become
richer and the social structure of the country changes, the makeup of CO2 emissions will
change as well. If used correctly, MRIO can provide insight into what CO2 emissions may
look like as a country continues to develop, especially as it engages in more trade and
consumes more goods and services.
Policy implications in a developing country are particularly important for the world’s
most populous nation, where half-a-billion people have been pulled out of poverty, there is a
rapidly growing middle class, and international travel is becoming more common (Gleaser et
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al., 2009). Using household consumption data from 74 major Chinese cities, Glaeser et al.
(2009) examine the effects of development on CO2 emissions. They find that even the dirtiest
city in China, Daqing, produces one-fifth of the CO2 of America’s cleanest city: San Diego.
Considering that these results only consider CO2emissions consumed in the city and not those
exported, and also that this is based on per-capita, not absolute values, the results are in line
with other MRIO analyses.
Glaseser et al. (2009) however also consider the effects of geographical location in
addition to the changing socio-economic
structure of China. They note that the most
polluting factor in a Chinese city is heating,
which is determined by the geographic location.
They find that in general the farther north and
the colder a city is, the more heating is
consumed and the more CO2 is produced. In
16
Figure 18. Chinese Household Emissions and Temperature (Glaeser et at. 2009, p.46)
Figure 19. Chinese Household Emissions and Location
(Glaeser et at. 2009, p.45)

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Figure 18 we can see the correlation they observe between cold weather and CO2 emissions.
Figure 19 depicts the same results geographically, and while there are variances, there is a
general correlation with more northern climates being more polluting, although other factors
such as infrastructure and income also play a contributing factor. Glaeser et al. (2009) explain
that heat is often provided as a human right in northern China, free of charge, and cannot be
adjusted directly. Therefore, there is very little increase in heat use as income increases and
the CO2 emissions have very little variance between socioeconomic cohorts.
In their regression analysis show in Figure 20, Glaesar et al. (2009) regress
transportation variables on income. Interestingly we see that as income increases, taxi and bus
use decrease, and while car use increases, rail increases substantially. This has key policy
implications, given that rail is the cleanest form of transit among this group and becomes
substantially more popular as income increases.
Glaesar et al. (2009) note that China still has a ways to go until its people achieve the
wealth of the U.S., but the key question is how middle-class and wealthy Chinese will behave
with their new found wealth: will they become like Americans or rich Chinese? They argue
that their results suggest that as the socio-economic structure of China changes and more
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Figure 20. Regression Analysis with Household Emissions (Glaeser et at. 2009, p.43)

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people become middle-class and richer, they will produce less CO2 than a similar person
would in the United States. This is because of the massive investment in infrastructure and
how relatively young and more efficient China’s cities are in comparison to the U.S. (Gleaser
et al., 2009).
However, they also point out that the continued growth in China’s northern most cities
will cause the largest increase in CO2 emissions, more than just having becoming wealthier,
due to the large influence and importance of heating in the winter.
Finally, their regression does not include a complete MRIO analysis, and it would be
highly beneficial to determine how much CO2 emissions will rise due to increased
consumption from manufactured products. We have already seen that Hong Kong, a culturally
similar area, emits most of its CO2 from consumerism (Hertwich and Peters, 2008).
Nonetheless, CO2 emissions based on household consumption are able to paint a clear picture
for policy that focuses on infrastructure investment, efficient housing, and the promotion of
city living, especially in mild climates. Furthermore, these results stress the benefits of a
Hybrid approach, where heterogeneity in a country can be accounted for, which is key in
China where highly-variable local factors can have a significant effect on the national level
analysis.
4.3 Consumerism
Closely tied with socio-economic status is the issue of consumerism. Janda &
Trocchia (2002) present an argument that developed countries have a case of mass over-
consumption, where many goods purchased are not needed as evidenced by the plethora of
unused items that can be had on websites such as eBay. Douglas (2006) explains that in
today’s societies people need goods to socialise and participate in society, because it helps
establish our social status. Pickett & Wilkinson (2009) present evidence that this pressure to
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possess material goods creates a need for “keeping up with the Joneses” and only becomes
worse as a country becomes more unequal. Evidence shows that this materialism doesn’t
actually lead to happiness and is in fact a zero-sum game, because when one person purchases
a good they receive very little welfare gain, but those around them loose much more welfare
as they feel left-out (Hirsch, 1977).
Given the amount of CO2 emitted by these material goods, Druckman & Jackson
(2008) use household consumption data to determine how much CO2 is produced by UK
consumption, furthermore they look if the policy implication from their results are feasible.
They find that by making lifestyle changes that UK households could reduce their CF by 33%
compared to the 1990 level as seen in Figure 21. They determine that the biggest reductions
could be achieved in Electricity & Gas by making better choices and using more efficient
appliances and public transport. They also find that consumption of Goods & Services, and
Restaurants & Hotels could be reduced.
19
Figure 21. UK Household GHG Emissions compared to Reduced Consumption Scenario
(Druckman & Jackson 2010, p.1800)

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They conclude that the largest contributor, is consumerism: the high consumption of
material goods, and argue that this problem will only get worse in the future, as developing
countries grow and consume more material goods (Druckman & Jackson, 2008). Using a
consumption based CF measure allows for the impact of this consumption to be captured.
This is particularly important because many of these material goods are traded, and using
production data would fail to capture the emissions and underestimate the CF.
While lifestyle changes are difficult to implement, they cite evidence from a UK
household survey conducted by Hamilton (2003) that suggests that a significant portion of
UK households would be willing to accept a reduction of purchased goods of around 40%.
Although their results are clear, it still remains questionable whether or not these implications
are feasible; however, the consumption based CF indicator was able to provide clear areas in
the economy to focus on, whether or not that is by reducing consumption, or by using
technology to reduce those emissions. Most importantly their study avoids the problem of
exporting carbon on other countries and highlights future problems developing countries will
face as they develop a strong consumer base.
5. Conclusion
Using MRIO to calculate a CF allows us to capture the complex supply chains and trade
flows stemming from modern day consumption patterns. Most importantly this provides
strong policy implications that help to capture the exporting of CO2 on less developed
countries with “dirtier” supply chains. Empirical results using the CF have demonstrated the
importance of trade flows, socioeconomic factors, consumerism, geographical factors, and
efficient housing and public infrastructure.
While no indicator alone provides a complete picture, the literature has demonstrated
that using a CF as a measure of sustainability has given clear policy implications. It is
20

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however important to consider the exact method used to calculate the CF, as those using
consumption based data and account for trade, provide the most accurate picture. In general
MRIO analysis is the most popular method in the literature and can provide clear indicators of
current trends, what to consider in the future, and help mitigate the problem of externalising
emissions. The CF measure also has readily available data and is thus able to be used to
calculate IO tables for a variety of countries.
Of course this model has shortcomings and should be constantly improved. For
example, switching to a Hybrid approach would allow for a more detailed analysis to be
included and account for regional differences, which is vital in countries with diverse regions
such as the United States and China. Additionally, the CF could be further augmented to
include other GHG; however, one must carefully consider the costs against the marginal
gains. Nonetheless, it would provide an even more comprehensive understanding.
Furthermore, because these methods rely heavily on understanding the organisational
structure of industries, this information should always be updated to reflect new changes and
information.
Finally, using CF as a measure of sustainability has its limitations as it does not directly
consider the costs to society or the economy. However, the model does provide clear areas to
target, and this information could then be combined with other measures such as cost-benefit
analysis to estimate these social and economic costs, allowing policies to be implemented that
consider and minimise any loss. Overall, given the amount of data available, a CF can be a
valuable tool to measure sustainability using consumption based IO analysis and can be
further improved upon as additional and more detailed data becomes available.
21

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Appendix
IV
Table A1. CF per-capita (World Bank 2015)

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V
Table A2. CF per-capita using MRIO (Hertwich & Peters 2009, p.C)

Measuring Sustainability Through Carbon Footprints

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