topic 2-carbon footprint & sustainability david dingus

“Sustainable Development, Trade, and Environment”

Carbon Footprint as a Measure of Sustainability

David Dingus

Göttingen, 11403109

[email protected]

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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CARBON FOOTPRINT AS A MEASURE OF SUSTAINABILITY

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)


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

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• 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)


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)


“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)


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)


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)


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)


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)


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)


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)


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)


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

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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)


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)


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.

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Figure 21. UK Household GHG Emissions compared to Reduced Consumption Scenario (Druckman & Jackson 2010, p.1800)


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

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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.

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Appendix

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


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

topic 2-carbon footprint & sustainability david dingus

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