Global Decarbonisation Patterns – Total and Transport CO2 ...

Global Decarbonisation Patterns –Total and Transport CO2 Intensity

1. Petri Tapio, Finland Futures Research Centre, University of Turku/Department of Environmental Sciences, University of Helsinki

2. Nufar Finel, Department of Environmental Sciences, University ofHelsinki

3. David Banister, Transport Studies Unit, University of Oxford

4. Jyrki Luukkanen, Finland Futures Research Centre, University ofTurku

5. Jarmo Vehmas, Finland Futures Research Centre, University ofTurku

6. Risto Willamo, Department of Environmental Sciences, University ofHelsinki

Working Paper N° 1056

October 2011

Transport Studies UnitSchool of Geography and the Environment

http://www.tsu.ox.ac.uk/

2

Global decarbonisation patterns – total andtransport CO2 intensity

October 2011

Petri Tapio1,2,*, Nufar Finel2, David Banister3, Jyrki Luukkanen4, JarmoVehmas4 & Risto Willamo2

1 Finland Futures Research Centre, University of Turku, Kankurintie 31a, FI-01260Vantaa, Finland, Internet www.ffrc.utu.fi, Research Group www.fidea.fi .2 Department of Environmental Sciences, University of Helsinki, E-Building P.O. Box27 (Latokartanonkaari 3), FI-00014 University of Helsinki, Finland.3 Department of Geography and the Environment, Oxford University, South Parks Road,Oxford, OX1 3QY, UK.4 Finland Futures Research Centre, University of Turku, Pinninkatu 47, 3rd floor, FI-33100 Tampere, Finland.*Corresponding author, [email protected], Phone +35840 555 2963.

Abstract: Global warming is now considered to be an established fact, and there is little

doubt about the contribution of human induced carbon dioxide emissions from fossil

fuels to the changing climate. Global CO2 emissions, including international aviation

and maritime transport, grew by 50% 1971-1990, and by a further 32% 1990-2006.

Carbon intensity of the economy, which can be measured by the carbon dioxide

emissions per unit Gross Domestic Product, has reduced over time globally: this is the

decarbonisation effect. In this paper, the world’s countries (116 individual countries and

five groups of countries) have been grouped through a cluster analysis based on trends

in total carbon intensity and transport carbon intensity of the economy (transport CO2

emissions/GDP). Various patterns have been found, some of which are unexpected.

Countries with very different GDP per capita levels have similar development patterns

in total carbon intensity or transport carbon intensity. It is concluded that economic

growth is neither the cause of nor the salvation for CO2 emissions growth, but more

subtle measures of emission control are needed for each country.

Key words: energy, transport, decarbonisation

Acknowledgement: This article is the co-product of the SIGHT and IFEE projects. The

Academy of Finland is gratefully acknowledged for funding.

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1. Introduction

Human induced global warming has developed from being a few climatologists’

dystopia to a plausible future scenario and (despite a few sceptics) a commonly agreed

fact. According to the Intergovernmental Panel on Climate Change (IPCC) Fourth

Assessment, there is no doubt that global average temperatures having risen and there is

only limited debate on whether human impact has contributed to the climate change.

The roles and shares of different greenhouse gas (GHG) emissions affecting radiative

forcing have been somewhat clarified since the First Assessment (Houghton et al.,

1990). Carbon dioxide (CO2) emissions from fossil fuel burning made up 57% of the

human induced GHG emissions in 2004 and this figure has grown throughout the years

(Rogner et al., 2007). As for transport, the share of CO2 is between 88-96% in the U.S.,

EU and Japan (Kahn Ribeiro et al., 2007).

While the First Assessment proclaimed that a 60-80% reduction of GHG emissions

from the 1990 level was needed to ‘stop’ climate change, climate stabilization scenarios

in the Fourth Assessment have given up the target. The six strictest stabilization

scenarios have the target of limiting global average surface temperature rise to

+2.0…2.4ºC from the pre-industrial era. This target would require the GHG emissions

to be reduced by 50-85% from the 2000 level (IPCC, 2007). The uncertainty is

considerable, since according to recent calculations a global 50% cut from 1990 values

(that is over 60% from 2005 values) would still leave a 16-51% probability of exceeding

the +2ºC warming target (Meinshausen et al., 2009). But the global emissions, including

international aviation and maritime transport, grew by 50% between 1970 and 1990, and

by a further 32% between 1990 and 2006, most of which occurred between 2000 and

2006 (CDIAC, 2009). According to International Maritime Organization (IMO 2010),

these figures underestimate the emissions of shipping.

When the growth of CO2 emissions from fossil fuel combustion is compared to

economic growth, a weak decoupling of trends can be recognised during 1971-2005. In

other words, the world economy grew faster than the CO2 emissions. This is the

decarbonisation effect (Nakićenović, 1996; Tapio et al., 2007; for other definitions see

Ausubel, 1995; Hoffert et al., 2002; Saikku et al., 2008; Sun and Meristö, 1999). When

emissions grow at a slower rate than the economy, one can speak of relative or weak

decarbonisation, but if the economy grows and emissions are reduced, the concept of

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absolute or strong decarbonisation is used, sometimes also referred to as delinking or

decoupling depending on the applied field (Ballingall et al., 2003; Banister and Stead,

2002; Tapio, 2005; Tapio et al., 2007; Vehmas et al., 2007; Wernick et al., 1996).

Some societal sectors have been less successful in restricting the growth of CO2

emissions than others. Transport is one of these, especially in Europe (IPCC, 2007;

Peake, 1994; Stead, 2001; Tapio et al., 2007). While the total CO2 emissions in the

EU27 countries were reduced by 0.3% between 1990 and 2005, the transport emissions

grew at least by 31% (IEA, 2007), probably more (IMO, 2010). This is not surprising

given the expansion and integration of the European Union and the globalization of the

economy that has led to increasing passenger and freight transport throughout the globe.

Transport engineering also plays a role through building capacity in the road, maritime

and air transport infrastructure, including efficient maintenance and logistics. Land-use

trends also have an impact on travel demand through the growth in urban sprawl and the

distance between different social functions, such as production, shopping, dwelling,

work and leisure time activities (Banister et al., 2000; Newman and Kenworthy, 2000).

In Europe, the growth of transport emissions and the growth of transport volume are

now seen as a major problem with a considerable concern at the EU level, as well as at

the national and urban levels (Banister et al., 2007).

2. Objective

These are general global trends. But if individual countries are examined, diverse

patterns are found. The purpose of this paper is to explore the different decarbonisation

patterns of the world’s countries. Countries with similar trends in total carbon intensity

(TIIN) and transport carbon intensity (TRIN) are grouped together. Total carbon

intensity is defined as total CO2 emissions per Gross Domestic Product in real terms

measured with purchasing power parities (GDPppp). Transport carbon intensity is

defined as transport CO2 emissions per GDPppp. Purchasing power parity is used in

order to reduce the effect of currency fluctuations and different price levels in different

countries. The carbon intensities are measured here in kilograms of CO2 per US$2000.

Decarbonisation is defined as a reduction in carbon intensity (total and/or transport)

over time (Tapio et al., 2007). The time frame used is 1971-2005, and the method used

is cluster analysis where countries with similar profiles of carbon intensity are grouped

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into clusters. Two runs are made, one for TIIN and the other for TRIN. Variation within

the clusters is then examined by the growth of GDP per capita and CO2 emissions per

capita for each country. In this explorative cluster analysis (see Jain et al. 1999) the

working hypothesis is that very different countries may have similar decarbonisation

patterns. In other words, the level of economic output does not determine the level of

carbon intensity.

The paper will proceed as follows. First, a review is made on the literature dealing with

the general relationship between the economy and environment. Next, the results of the

cluster analyses made by carbon intensities (total and transport) will be presented, and

this is followed by a section reporting the actual per capita GDP and CO2 emissions by

cluster. Finally, some policy conclusions for improving decarbonisation are made for

both the economy as a whole and for the transport sector.

3. Theoretical relation between the economy and environment

The relationships between economic growth, energy, transport, and environmental

issues are all subject to continuous debate. It dates back at least to Malthusian views of

population growth and food scarcity (Malthus, 1976). Based on British statistics

available in the late 18th century Malthus stated that geometric, exponential, J curve

growth of population is not compatible with linear growth of food supply. Malthus was

blamed for being pessimistic both from the right and the left – for example, by John

Stuart Mill (1976) who maintained that humans are capable of learning from their errors

and Friedrich Engels (1976) who emphasised the role of technical development in

increasing productivity, as well as the statistician Aldolphe Quetelet who used a wider

set of international population statistics (ref. Ariew, 2007).

The debate was fuelled by the Limits to Growth report by Meadows et al. (1972) who

used a computer model and stated that economic growth (using the term ‘capital’) and

population growth were the major drivers of resource scarcity and environmental

pollution. This study raised considerable debate over the following years with major

concerns raised by Oerlemans et al. (1972), Boyle (1973), Burke (1973) and, recently,

Turner (2008). Environmentally, the major critique was expressed by Oerlemans et al.

(1972, 252):

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“…who can say what the height of the 1970 global pollution level – which in the model is meant

to be a measure of the world-wide effect of environmental disruption caused by man – should be

in relation to the 1900 level?”

After a few years these studies were followed up by another report for the Club of

Rome, authored by Mesarovic and Pestel (1975), and the famous ‘Bad News’ debate

(Bad News…, 1980; Simon, 1980). The Bad News debate, also known as the Simon-

Ehrlich debate, began when Simon (1980) questioned the environmental concern by

presenting several time series data. The debate was analysed by Dunlap (1983). He

stated that using empirical evidence of the past to make assertions to the future carrying

capacity of the Earth is eventually a paradigmatic debate between ‘human

exemptionalism’ emphasising that humans are exempt from the laws of nature and the

‘ecological paradigm’ maintaining that, however intelligent, humans are under the

influence of natural laws. These paradigms lead to different interpretations of the same

facts.

However, forming alternative future scenarios by utilising the historical facts makes

more sense than starting from tabula rasa. Since the beginning of 1990’s, numerous

publications have elaborated the relationship between economic growth and the

environment by testing and reconsidering the so-called Environmental Kuznets Curve

(EKC) hypothesis, or the inverted U curve. According to the EKC hypothesis, at a low

level of GDP per capita, economic growth is the primary objective and the

environmental problems are of secondary importance, but when a certain level of GDP

per capita is achieved, there will be enough wealth to invest in environmental protection

resulting in mitigated problems (e.g. Ang 2006; Ayres, 2008; Munasinghe, 1996; Shafik

& Bandyopadhyay, 1992)1.

Empirical studies have shown that different environmental problems have a different

relationship to economic growth (Arrow et al., 1995; de Bruyn and Opschoor, 1997).

The EKC hypothesis seems to work rather well with impurities in production processes,

such as sulphur dioxide or particulate matter emissions to the air or phosphorous

emissions in water. It works less well regarding resources and emissions not regarded as

impurities but inherent in almost all economic activities. This includes CO2 emissions

and combined indices such as total material requirement (TMR), direct material flow or

1 The Scirus search engine found over twelve thousand hits with the ‘environmental Kuznetz curve’concept in April 2010.

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the ecological footprint (Behrens et al., 2007; deBruyn et al., 1998; Caviglia-Harris et

al., 2009; Guan et al., 2008; Turner, 2008; Vehmas et al., 2007). In many countries,

most notably the USA and Canada, the development of these pollutants (e.g. CO2) have

followed the N curve, where after a certain time, the easy technical solutions have been

implemented, and due to increasing material production and energy use the emission

curves turn up again (Galeotti et al., 2006). This development was already foreseen by

Georgescu-Roegen (1971). Furthermore, the results of EKC analyses tend to be strongly

affected by the case countries selected and the time period adopted (Ayres, 2008; Guan

et al., 2008; Stern, 2006; Vehmas et al., 2007; Zeng et al., 2008). In cross-sectional

studies comparing different nations on a single year, the relation is sometimes found

(see e.g. Ang & Liu 2006), but do countries follow the wealthier ones in their

development patterns over time?

GHG emissions have grown above the worst case average of the A1FI scenario of the

IPCC Special Report on Emissions Scenarios (SRES) (Schneider, 2009). Thus, the

debate between the environment and economic growth has re-emerged with the current

concern over climate change (Costanza, 2009; Fisher, 2007; Nordhaus, 2007).

Increasing doubts have also been expressed as to what GDP actually measures (Daly,

1996; Stiglitz et al., 2009). Is it a measure of welfare or only the volume of economic

production? Arguably it is the latter, but does it even measure the volume of economic

production correctly? These fundamental questions are acknowledged, but they are

beyond the scope of this paper.

The issue may be summed up by a variety of curve shapes (Fig. 1). One line of

discourse is the classic Malthusian problematic relationship between J curve and linear

upward curve, where the resolution is the S curve (Fig 1a). A second discourse is the

inverted U curve of the EKC hypothesis and the counterpart N curve. Even an L curve

has been found in Eastern European countries (Huang et al., 2008; Fig 1b). A steady

state approach promoted by Daly (1996) would represent a flat curve (Fig 1c).

Cornucopian thought would suggest a constant improvement due to economic growth, a

linear downward curve (Simon, 1980). Logically, each curve might have a low and

high as well as steeply and gently sloping or a rising variant (Fig 1d).

Given the various paradigmatic interpretations, a period of 35 years is examined from a

global perspective using linear scales. The results of two comparable relationships

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between economic output (GDP) and environmental harm (total CO2 emissions and

transport CO2 emissions are interpreted by using the illustrations of Fig. 1, and the

absolute levels of carbon intensity are also analysed.

En

vir

on

me

nta

lha

rm

GDP per cap

J curve

S curve

a)

En

vir

on

me

nta

lha

rm

GDP per cap

N curve

L curve

EKC curve

b)

En

viro

nm

en

talh

arm

GDP per cap

Flat curve high

Flat curvemedium

Flat curve low

c)

En

viro

nm

en

talh

arm

GDP per cap

Linear upwardcurve fast

Linear upwardcurve slow

Linear downwardcurve slow

Linear downwardcurve fast

d)

Figure 1. Alternative relationships between economic growth and environmental harm. a) Limits

to Growth debate; b) Environmental Kuznets curve and empirical findings challenging

conventional wisdom; c) Steady State approaches emphasizing the absolute volume of

environmental harm; d) Approaches emphasising the speed of growth (or decrease) of

environmental harm. A summary of the following references: (de Bryun et al., 1998; Fonkych

and Lempert, 2005; Huang et al., 2008; Meadows et al., 1972; Shafik and Bandyopadhyay,

1992; Song et. al, 2008; Tapio et al., 2007).

4. Material and methods

4.1 Geographic, temporal and structural scope of the data

The data for the decarbonisation analysis are from the International Energy Agency

(IEA, 2007). For the period of 1971-2005, the research material contains 116 individual

countries and five groups of countries – Former USSR (15 current countries), Former

Yugoslavia (5), Other Africa (37), Other Asia (17) and Other Latin America (17).

Fifteen current countries are left out of the analysis as there were no data at all, and four

because there were no data for the whole period (see Appendix B).

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Total CO2 emissions used in the study include the aggregated standard IPCC “sectoral

approach” emissions plus emissions from international aviation and maritime transport.

The original transport emissions data include the categories of “transport” including

road transport and “other transport” including rail transport, domestic aviation and

waterborne transport. International aviation and maritime transport are reported as

separate categories not included in “transport”. These were summed up to form

transport CO2 emissions used in the study. Pipeline transport (of oil, gas and water) is

excluded from transport emissions but is included in total emissions.

Adding international aviation and maritime transport gives a non-conventional

perspective to the analysis, and this provides an important new source of data for

analysis. According to Eyring et al. (2009) and Lee et al. (2009), these covered up 1.9%

(aviation) and 2.7% (maritime) of the total CO2 emissions in the World in 2007. The

radiative forcing effect by aviation is higher due to other GHG emissions and contrails,

whereas it might be significantly lower by shipping in the short term due to the cooling

effect of high particle and sulphur oxides emissions from impure burning processes

(Eyring et al., 2009; Lee et al., 2009; IMO, 2010). For total emissions, the IPCC

sectoral approach has also been used with the international transport emissions added to

it. Some criticism has been posed towards the IEA data since it gives clearly and

systematically lower fuel consumption numbers to maritime transport than activity

based studies (IMO, 2010, 26). This paper is concerned about the trends in total

transport CO2 emissions and as the IEA data is consistent and transparently

documented, it is possible to carry out this international comparison over a considerable

time period. Nevertheless, it is important to keep in mind the various uncertainty in the

statistical systems in different countries.

International aviation and maritime emissions are allocated to countries where the ships

and planes are bunkered. This approach can be criticized because it does not take into

account the responsibilities of the consumers and multinational firms, who often come

from the wealthier countries – thus externalizing their emissions to developing

countries. However, this question is not only inherent to the transport emissions but also

to the total emissions, because many countries buy electricity from other countries and

therefore emissions are allocated to the seller rather than the consumer of energy.

Furthermore, there are also embedded emissions related to all exports and imports of

goods, covering over 20% of total CO2 emissions in 2001 (Peters and Hertwich, 2007).

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Allocating transport emissions is rather complicated as a ship typically buys fuel en

route to somewhere else than where it is bunkered. Dominant fuel seller countries keep

the fuel price low and attract ships with this product. The same phenomenon may be

observed in road transport near country borders. The product measured by this method

is not the good transported, nor transport service itself but energy sold to the vehicle.

Another apparent problem is that the allocation does not take into account the place

where emissions are released. This is crucial in measuring the emissions affecting health

and acidification (Eyring et al., 2009). Regarding CO2 this fact is less dramatic since it

spreads throughout the globe.

4.2 Cluster analysis

The decarbonisation patterns are explored with a hierarchical cluster analysis using the

Furthest Neighbour algorithm in the SPSS 15.1 software (see e.g. Everitt et al., 2001).

There are two runs, one on total carbon intensity (TIIN) and another on transport carbon

intensity (TRIN). The clustered entities are the countries (or groups of countries), and

the 35-year time series data are organized as thirty-five variables in each run. No

variable standardization is needed since all variables are on the same scale. The normal

Euclidean measure of dissimilarity has been used where the units of carbon intensity are

expressed as kilograms of CO2 per US$2000.

Cluster analysis summarises the annual differences in the carbon intensity between pairs

of countries. The distance of one case (in this case a country) to be grouped to the other

cases already grouped can be measured by various rules, for example, the distance to the

nearest neighbour case of the already established groups, the average distance to all

members of the established groups or to the furthest neighbour of each group (Jain et al.

1999; Everitt et al. 2001). Furthest Neighbour was chosen in this study for four reasons:

1) Complex statistical methods were not needed since the sample consists of almost all

countries in the world. 2) The principle of the grouping is simple and clear. 3) Furthest

Neighbour has performed reasonably well in comparative studies (Milligan 1996). 4) A

considerable alternative, the Ward method, would require the use of squared Euclidean

measure that would emphasize large differences in few calendar years rather than

smaller systematic differences throughout the period.

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Furthest Neighbour belongs to agglomerative cluster analyses, in which, at first, all

cases are treated as single clusters. The algorithm first clusters together the closest cases

and then compares the distance of each case to the furthest neighbour of the already

grouped clusters (e.g. Tan et al. 2006).

Cluster analysis does not determine the number of clusters, but displays the summed

differences between cases (i.e. the countries), and the order in which the countries

should be clustered together. The number of outliers is minimized, but the variation of

the patterns is identified. Larger clusters are divided into sub-groups whereas smaller

clusters are not further divided for the purpose of illustration. The smaller clusters with

rather high differences also have high intensities, that is the shapes of the curves are

close to each other although there is some difference in their position.

5. Results

Based on the cluster analysis of total carbon intensity (TIIN), the countries can be

presented in eleven clusters and four outliers (Fig. 2). As for transport carbon intensity

(TRIN), ten clusters and five outliers can be formed (Fig. 3). The clusters are described

in terms of the absolute level of carbon intensity – very high, high, rather high, rather

low, low, and very low. This tells about the eco-efficiency (Schmidt-Bleek, 2000) of the

economy, or to put it simply: how many kilograms of CO2 need to be emitted in order to

earn one dollar in the countries in a cluster on average? In addition, the interest here is

also in the trend whether the carbon intensity of a cluster is increasing (‘worsening’),

decreasing (‘progressing’) or representing a flat line (‘constant’ or ‘fluctuating’).

5.1 Patterns of total carbon intensity

Clusters 6 and 7 represent the lowest lines for total carbon intensity (TIIN), around

200g/$ and 300g/$, respectively (Fig. 2). There was limited decrease or increase in

carbon intensity between 1971 and 2005. Cluster 6 consists mostly of developing

countries and is perhaps the most coherent cluster in terms of the development of

economic output per capita (see Table 1). Cluster 7, in turn, includes a variety of

(mainly West) African, Latin American and South Asian countries ranging from

developing countries to emerging economies and even the mature European economy of

Switzerland.

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The next lowest levels are in Clusters 1 and 2, but whereas the countries in Cluster 1

achieved a reduction from 500g/$ to below 400g/$, the carbon intensity of Cluster 2

increased from some 300 to 400g/$. Cluster 1 includes mature economies from Europe

and countries of various stages of development from Latin America, Eastern Asia and

Africa. Cluster 2 has a notable share of Mediterranean countries as well as developing

or emerging economies in South-Eastern Asia and Latin America.

The shapes of the lines of Cluster 3 (increase) and 4 (decrease) were similar to those of

Clusters 1 and 2, but steeper and at a higher carbon intensity level (500g/$ on average).

Cluster 3 is a small group of Islamic countries – two oil producers, Brunei and Iran, and

Jordan. Cluster 4 consists of a few mature Northern European economies, two

Mediterranean islands and two industrial East-Asian countries.

Cluster 5 has an almost identical downward shape with that of Cluster 4 but, again, at a

higher level, now from 1.0 to 0.5kg/$. It consists of fossil fuel intensive North

American and Central European countries. Cluster 8 represents a very steep reduction

from 2.5 to 0.8kg/$, but is still rather high compared to the other clusters. The members

of this cluster come distinctively from Eastern Europe and have similar profile to the

emerging economies of China and Jamaica, as well as the highly mature economies of

Luxembourg and Singapore.

The carbon intensity in the countries of Cluster 9, on average, increased but slowly,

starting from figures almost triple to those of Cluster 3 in 1970, but both clusters ended

up at the same level by 2005. The very high intensity of Cluster 10 had a peak of

1.7kg/$ in 1995, rising before that and lowering afterwards. Both clusters 9 and 10

include almost completely oil producers since Lebanon is the only country not having

produced oil between 1970-2005.

Cluster 11 has the highest carbon intensity levels, but also a considerable development

from 5 to 2 kg/$. This small cluster consists of only two countries – Bahrain and North

Korea. In addition to the clusters, there were five outliers from North Africa and Middle

East which had patterns of their own.

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Figure 2. Total carbon intensity of the world 1971-2005 – eleven clusters and four outliers (units

kg CO2/US$2000). Cluster averages indicated with thick line. Country codes in Appendix B.

T o tal carbo n intensity

C luster 1

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 1: AT, HK, KE, SE, CL, IS, NO, VN,

CU, IT, PA, ZM , FR, JP, SN


C luster 3

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 3 : BN, IR, JO


C luster 5

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 5 : BE, CA, DE, HU, NL, US, Other

Latin America


C luster 7

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 7 : AO, AR, BR, CO, CG, CI, DO,

GA, M A, M Z, M M , PE, PH, PT, CH, TN,

UY, Other Asia


C luster 9

0

0 .5

1

1.5

2

2 .5

1970 1975 1980 1985 1990 1995 2000 2005

C luster 9 : AU, LB, LY, GA, SA, ZA, SY,

VE, ForYU


C luster 11

0

1

2

3

4

5

6

7

1970 1975 1980 1985 1990 1995 2000 2005

C luster 11: BH, KP


C luster 2

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 2 : DZ, BO, EC, EG, GR, IN, ID, IL,

M Y, M X, NZ, NG, PK, ES, TH, TR, ZW


C luster 4

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 4 : TP, CY, DK, FI, IE, KR, M T, GB


C luster 6

0

0.2

0.4

0.6

0.8

1

1.2

1970 1975 1980 1985 1990 1995 2000 2005

C luster 6 : BD, BJ, CM , CD, CR, SV, ET,

GH, GT, HT, HN, NP, NI, PY, LK, SD, TZ,

TG, Other Africa


C luster 8

0

0 .5

1

1.5

2

2 .5

1970 1975 1980 1985 1990 1995 2000 2005

C luster 8 : BG, CN, JM , LU, PL, RO, SG,

SK, YE


C luster 10

0

0 .5

1

1.5

2

2 .5

1970 1975 1980 1985 1990 1995 2000 2005

C luster 10 : ForUSSR, KW, TT, AE


Out lie rs

0

1

2

3

4

5

6

7

1970 1975 1980 1985 1990 1995 2000 2005

Outliers : AL, GI, IQ, OM

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5.2 Patterns of transport carbon intensity

The second cluster analysis is performed on transport carbon intensity (TRIN). Here, the

lowest intensity levels are within Clusters 1 and 2, consisting mainly of the low

intensity countries of the TIIN Clusters 6 and 7 (Fig 3; Table 1). The lines are flat and

stayed below 50g/$ in Cluster 1 and below 100 g/$ in Cluster 2 throughout the period.

Cluster 1 is a rather small group of six very low GDP countries. Cluster 2 instead is a

large and diverse group of 40 countries ranging from South and Central European

mature economies to Latin American and Asian developing and emerging economies.

The next lowest intensities are within Clusters 3, 4 and 5, where Cluster 3 achieved a

reduction below 100g/$ whereas Cluster 4 intensity increased above this line and

Cluster 5 stayed a little above it throughout the period. Cluster 3 consists of many

countries that have undergone or are in the process of transition towards market

economy. Some restriction to mobility is perhaps still present. Cluster 4 countries do not

seem to have a common denominator. Cluster 5 is the second largest cluster including

mainly mature European economies but also emerging Latin American, Asian and

African economies.

The development of Clusters 6, 7 and 8 follows a similar shape to Clusters 3, 4 and 5,

respectively, but on a clearly higher level at over 200g/$. Further, the reduction of

transport carbon intensity in Cluster 6 was more rapid than that of the respective Cluster

3, especially during 1971-1985, almost levelling off after that. Cluster 6 includes the

fossil fuel intensive North American countries, Canada and the U.S., but also three

African countries and the aggregated group of ‘Other Latin American’ countries that

followed a similar development in transport carbon intensity despite the large

differences in economy and society at large. The small Cluster 7 consists of fossil fuel

intensive countries and important international transport nodes. Cluster 8 includes oil

producers and countries having been isolated due to geographic of political/military

grounds.

There were two small hyper transport carbon intensive Clusters 9 and 10, where Cluster

9 (Arab Emirates and Iraq) intensity boomed from below 50g/$ to over 1.5kg/$ during

the period and Cluster 10 (Singapore and Yemen) intensity was halved from over 1kg/$

level. These changes in intensity were due mainly to significant changes in the GDP of

15

the countries. In addition, six countries were outliers and were not grouped to any of the

clusters.

Figure 3. Transport carbon intensity of the world 1971-2005 – ten clusters and five outliers (y-

axis unit kg CO2/US$2000). Cluster averages indicated with thick line. Country codes in Appendix

B.

T ranspo rt carbo n intens ity

C luster 1

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 1: BD, CD, ET, HT, M A, NP


C luster 3

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 3: AL, BJ, CN, CI, CU, KP, PL, LK,

SD, ZW


C luster 5

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C lust er 5: ForUSSR, AO, BE, BO, BG, CL, DK,

EG, FI, GA, HK, IS, IE, M Y, M X, NZ, NO, PA, ZA,

ES, SE, TH, GB, ZM , ForYU


C luster 7

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 7: AU, EC, LU, NL, NG


C luster 9 (N o te scale)

0

0.5

1

1.5

1970 1975 1980 1985 1990 1995 2000 2005

C luster 9: IQ, AE


C luster 2

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C lust er 2 : DZ, AR, AT, BR, CM , TP, CO, CR, CZ,

DO, SV, FR, DE, GH, GT, HN, HU, IN, ID, IL, IT, JP,

M Z, M M , NI, PK, PE, PH, PT, RO, SK, CH, TZ, TG,

TN, TR, UY, VN, OthAfr, OthAsia

T ranspo rt carbo n intensity

C luster 6

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 6: CA, CG, KE, M T, SN, US, Other

Latin America


C luster 8

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 8: CY, JM , JO, KW, LB, SY, VE


C luster 10 (N o te scale)

0

0.5

1

1.5

1970 1975 1980 1985 1990 1995 2000 2005

C luster 10: SG, YE


C luster 4

0

0.1

0.2

0.3

0.4

1970 1975 1980 1985 1990 1995 2000 2005

C luster 4: BN, GR, IR, KR, PY, QA

T ranspo rt carbo n intensity

Out liers

(N o te sca le)

0

0.5

1

1.5

2

2.5

1970 1975 1980 1985 1990 1995 2000 2005

Outliers: BH, LY, OM , SA, TT.

GI excluded (min 3,2, max 6.1).

16

5.3 The similar are different

The TIIN and TRIN Clusters and are compared in Table 1. The table shows the

countries located in each Cluster. There are many countries that stay together in both

runs, but every cluster except TRIN Cluster 10 was divided between the runs. This

indicates that the TIIN and TRIN live their own lives and have partly separate driving

forces. As for TIIN, the multitude of available energy sources in different parts of the

world seems to be the cause rather than the level of energy use. As for TRIN, the level

of mobility, modal split and unit emissions merely seem to result in the differences

since transport is almost exclusively dependent on oil.

The patterns show that the carbon intensity of world’s countries has developed in a

variety of ways between 1971 and 2005. It is notable, that many low intensity countries

have produced a flat line. The higher the intensity, the more likely it has changed,

increased or decreased, over time.

The countries grouped in the same cluster are similar in terms of their carbon intensity

(total or transport) as calculated through a series of ratios. But how similar are they in

other terms? As can be seen in Figure 2, Figure 3 and Table 1, some rather similar

countries seem to stick together, such as the UK, Ireland, Denmark and Finland; or the

U.S. and Canada; or Sweden, Iceland and Norway. But also very different countries

may end up in the same clusters, such as the latter Nordic group with Chile, Hong

Kong, Panama and Zambia, or the U.S. and Canada with the group of Other Latin

America.

Looking at TRIN alone, the U.S. and Canada have developed in a similar way to Congo,

Kenya, Malta and Senegal. The two Nordic clusters are merged together, and also

include Angola, Belgium, Bolivia, Bulgaria, Egypt, Former USSR, Former Yugoslavia,

Gabon, Malaysia, Mexico, New Zealand, South Africa, Spain and Thailand. This is a

considerable variety of countries in the geographic, economic, social and cultural sense.

17

Table 1: Countries located in cross-tabulated total carbon intensity (TIIN) clusters and transportcarbon intensity (TRIN) clusters using data from 1971-2005TIIN1971-2005Clusters

TRIN 1971-2005 Clusters To-tal

TRIN_IVery lowintensityFlat line

TRIN_IILowintensityFlat line

TRIN_IIIMediumintensityDecrease

TRIN_IVMediumintensityIncrease

TRIN_VMediumintensityFlat line

TRIN_ VIHighintensityDecrease

TRIN_VIIHighintensityIncrease

TRIN_VIIIHighintensityFlat line

TRIN_IXVery highintensityIncrease

TRIN_XVery highintensityDecrease

TRIN_OVariouspatterns

TIIN_ILowintensityDecrease

AustriaItalyFranceJapanVietnam

Cuba

NorwaySwedenChileZambiaPanamaIcelandHongKong

KenyaSenegal

15

TIIN_IILowintensityIncrease

IndiaPakistanIndonesiaAlgeriaTurkeyIsrael

Zimbabwe Greece

SpainThailandBoliviaMexicoEgyptMalaysiaN.Zealand

EcuadorNigeria

17

TIIN_IIIMediumintensityIncrease

BruneiIran

Jordan

3

TIIN_IVMediumintensityDecrease

Taiwan Korea

IrelandDenmarkFinlandUK

Malta Cypros

8

TIIN_VHighintensityDecrease

HungaryGermany

BelgiumCanadaUSAOther Lat.Am.

Nether-lands

7

TIIN_VIVery lowintensityFlat line

BangladeshNepalDemCongoEthiopiaHaiti

El SalvadorTogoOthAfricaGuatemalaNicaraguaHondurasCameroonGhanaTanzaniaCosta Rica

BeninSri LankaSudan

Paraguay

19

TIIN_VIILowintensityFlat line

Morocco

PhilippinesMyanmarMozambiqueTunisiaPeruOthAsiaColombiaArgentinaBrazilPortugalUruguayDominicanRSwitzerland

Côte d'IvoireAngolaGabon

Congo

18

TIIN_VIIIVery highintensityDecrease

Czech Rep.RomaniaSlovakRep.

ChinaPoland

Bulgaria Luxembourg JamaicaSingaporeYemen

10

TIIN_IXHighintensityIncrease

QatarForYugosl.South Africa

AustraliaSyriaVenezuelaLebanon

SaudiArabiaLibya

9

TIIN_XVery highintensityFluctuating

ForUSSR KuwaitU. ArabEmirates

Trinidad&Tobago

4

TIIN_XIHyperintensityDecrease

Dem P.Repof Korea

Bahrain

2

TIIN_OVariousPatterns

Albania IraqOmanGibraltar

4

Total 6 40 10 6 25 7 5 7 2 2 6 116

TIIN_I means Cluster I in the analysis of total carbon intensity. Respectively, TIIN_II means Cluster II in the analysis oftotal carbon intensity and so on. TIIN_O and TRIN_O mean Outliers.

18

In order to get a picture of the differences between the countries with similar

development patterns of carbon intensity, the GDP per capita and CO2 emissions per

capita values (average and standard deviation) in each TIIN cluster are presented in

Table 2 Figures 4 and 5 visualize the development. Although there is a low variation

within clusters in carbon intensity, the variety of the components (GDP per capita and

emissions per capita) is impressive, with standard deviation (SD) in GDP per capita

ranging from 39% to 156% percent of the cluster means in 1971, 30% to 115% in 1990,

and from 23% to 119% in 2005. Only TIIN Cluster 5 has SD values constantly below

60% of the average value, but this is still considerable, as can be seen in Figure 3.

Another rather coherent group is TIIN Cluster 4. Similar findings concern total CO2

emissions per capita, where the SD measured as per cent from the mean value varies

between 45% and 127% in 1971; 26% and 115% in 1990; and 15% and 115% in 2005.

As for transport CO2 emissions per capita, the SD values are between 14% and 139% in

1971; between 38% and 137% in 1990; and, between 31% and 139% in 2005. Again,

TIIN Clusters 4 and 5 are the only groups having SD values below 60% of the cluster

average.

Table 2. The similar are different. Average per capita values and standard deviation (SD) in per

cent for GDP, total CO2 emissions and transport CO2 emissions within the TIIN clusters

Group

Year 1971 1990 2005 1971 1990 2005 1971 1990 2005Cluster 1 Mean value 8.28 13.83 18.28 4.40 4.52 5.28 1.16 1.50 2.01

SD in % 73 76 77 79 77 76 69 82 85

Cluster 2 Mean value 4.51 6.48 8.74 1.51 2.70 3.63 0.50 0.87 1.15

SD in % 107 99 99 114 96 91 124 108 108


SD in % 109 106 82 53 89 72 14 65 57


SD in % 64 30 23 68 26 15 56 38 31


SD in % 39 36 36 45 42 44 60 60 52


SD in % 66 67 78 80 81 85 77 89 108


SD in % 112 115 104 118 115 105 94 121 110


SD in % 95 101 105 127 84 99 123 173 171


SD in % 156 81 92 83 92 107 105 71 85


SD in % 84 69 47 90 79 49 118 137 89


SD in % 117 90 119 104 91 111 139 129 139

Outliers Mean value 6.35 7.78 10.28 7.75 15.88 38.22 6.52 13.04 33.96

SD in % 49 78 91 130 155 173 146 186 192

World average 4.62 6.30 8.49 3.90 4.13 4.37 0.90 1.00 1.13

GDPppp per cap (103US$2000) Total CO2 per cap (t) Transport CO2 per cap (t)

19

Figure 4. Similar development of carbon intensity is not necessarily determined by the level of

economic output. Total CO2 emissions per capita (y-axis) and GDPppp per capita (x-axis) in the

TIIN clusters 1971-2005. Each line presents the development in one country. CO2 emissions

are measured in tonnes of CO2, GDP measured in thousand US$2000 using purchasing power

parities.

TIIN Cluster 1

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: AT, CU, FR, HK, IS, IT, JP, KE, NO, PA, SN, VN, ZM

TIIN cluster 2

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: DZ, BO, EC, EG, GR, IN, ID, IL, MY, MX, NZ, NG,

PK, ES, TH, TR

TIIN Cluster 3

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: BN, IR, JO

TIIN Cluster 4

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: TP, CY, DK, FI, IE, KR, MT, GB

TIIN Cluster 5

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: BE, CA, DE, HU, NL, US, Other Latin America

TIIN Cluster 6

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: BD, BJ, CM, CD, CR, SV, ET, GH, GT, HT, HN, NP,

NI, PY, LK, SD, TZ, TG, Other Africa

TIIN Cluster 7

0

5

10

15

20

25

0 5 10 15 20 25 30 35 40 45

Countries: AO, AR, BR, CO, CG, CI, DO, GA, MA, MZ, MM, PE,

PH, PT, CH, TN, UY, Other Asia

TIIN Cluster 8 Note scales!

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50 60

Countries: BG, CN, CZ, JM, LU, PL, RO, SG, SK, YE

TIIN Cluster 9 Note scales!

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120

Countries: AU, LB, LY, QA, SA, ZA, SY, VE, Former Yug

TIIN Cluster 10

Note scales!

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50 60

Countries: KW, TT, AE, Former USSR

TIIN Cluster 11

Note y-axis scale!

0

5

10

15

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35 40 45

Countries: BH, KP

TIIN outliers

Note y-axis scale!

0

20

40

60

80

100

120

0 5 10 15 20 25 30 35 40 45

Countries: AL, GI, IQ, OM

20

Figure 5. Transport carbon intensity clusters in terms of transport CO2 emissions per capita (y-

axis) and GDPppp per capita (x-axis) 1971-2005. Each line presents the development in one

country. Transport CO2 emissions are measured in tonnes of CO2, GDP measured in thousand

US$2000 using purchasing power parities.

TRIN Cluster 1

Note low er scales

0

0.5

1

1.5

2

2.5

3

3.5

4

0 5 10 15 20

Countries: BD, CD, ET, HT, MA, NP

TRIN Cluster 2

0

1

2

3

4

5

6

7

8

0 10 20 30 40

Countries: DZ,AR,AT,BR,CM,TP,CO,CR,CZ,DO,SV,FR,DE,GH,GT,HN,HU, IN, ID, IL, IT,JP,MZ,MM,NI,PK,PE,PH,PT,RO,SK,CH,TZ,TG,TN,TR,UY,VN,Other Africa,Other Asia

TRIN Cluster 3

0

1

2

3

4

5

6

7

8

0 10 20 30 40

Countries: AL, BJ, CN, CI, CU, KP, PL, LK, SD, ZW

TRIN Cluster 4

Note higher x-axis scale

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100 120

Countries: BN, GR, IR, KR, PY, QA

TRIN Cluster 5

0

1

2

3

4

5

6

7

8

0 10 20 30 40

Countries: AO, BE, BG, CL, DK, EG, FI, GA, HK, IS, IE, MY,

MX, NZ, NO, PA, ZA, ES, SE, TH, GB, ZM

TRIN Cluster 6

0

1

2

3

4

5

6

7

8

0 10 20 30 40

Countries: CA, CG, KE, MT, SN, US, Other Latin America

TRIN Cluster 7

Note higher scales

0

5

10

15

20

25

0 10 20 30 40 50 60

Countries: AU, EC, LU, NL, NG

TRIN Cluster 8

Note higher scales

0

5

10

15

20

25

0 10 20 30 40 50 60

Countries: CY, JM, JO, KW, LB, SY, VE

TRIN Cluster 9

Note higher scales

0

5

10

15

20

25

0 10 20 30 40 50 60

Countries: IQ, AE

TRIN Cluster 10

Note higher scales

0

5

10

15

20

25

0 10 20 30 40 50 60

Countries: SG, YE

TRIN Outliers

Note higher scales

0

5

10

15

20

25

0 10 20 30 40 50 60

Countries: BH, LY, OM, SA, TT. GI excluded from the graph -

GDP21 700 $, CO2 132 tn/cap in 2005.

21

6. Discussion

6.1 Interpretation of the results

The theoretical section of this paper has considered the different perspectives and

previous empirical findings on the relationships between economic growth and the

environment. The empirical relationships in this study have been described in terms of

CO2 emissions from burning fossil fuels in 116 individual countries and five country

groups. Emissions from the transport sector have also been addressed. What then are the

lessons learnt from the analyses above?

There is no single relationship between economic growth and CO2 emissions, but a

variety of relationships. Inverted U curve shapes has been established in TIIN Clusters

1 and 11 (perhaps also 4) and perhaps TRIN Cluster 3. The N curve is present in TIIN

Cluster 5 and TRIN Clusters 6 and 8. Disturbingly many clusters followed the Linear

upward curve: TIIN Clusters 2 and 7 and especially TRIN Clusters 2, (3,) 5, 7, 8 and

10. The Flat curve could be found in TIIN Cluster 6 and TRIN Cluster 1. There are also

more exotic shapes, such as the Z curve that apply to TIIN Clusters 8 (excluding

Singapore) and 9. Some shapes are virtually non-identifiable: TIIN Clusters 3 (a T

curve?) and 10 as well as TRIN Clusters 4 and 9. There were no J curve clusters, but

some countries embedded in Linear upward curve clusters followed the J curve – in

TIIN at least Chinese Taipei, Malaysia and Portugal; and in TRIN at least Belgium,

Hong Kong, Luxembourg, Portugal and Singapore.

The relationship is not determined by the level of GDP per capita (see also Narayan &

Narayan 2010). For example, TIIN Cluster 5 countries present the N curve but while the

upward turning point was about $22,200 in the USA, it was about $18,600 in the

Netherlands and about $9,900 in Hungary. TIIN Cluster 4 countries might be an

example of the inverted U curve, but here, for example, Denmark peaked at $26,100,

whereas Malta peaked at $13,300. The explanatory and predictive power of the EKC

and the N curve hypotheses must be seriously questioned.

Emissions from the transport sector have special characteristics (Table 3). Transport is

stubbornly dependent on oil products, as alternative fuels are less competitive in terms

of price, energy density, ease of handling and the ubiquity of the supporting

22

infrastructure. Emissions seem to have either grown fast or grown slowly at the best.

Making transport systems more sustainable is a major challenge for the coming decades.

Table 3. The logically possible decarbonization patterns (total and transport) and the clusters of

actual development 1971-2005. Hyperintensive clusters indicated with bold font.

6.2 Policy conclusions

Including international transport emissions in the decarbonisation analysis produces a

picture that challenges conventional wisdom. Countries having international transport

nodes, such as the Netherlands, seem less sustainable than in analysis where

international transport is omitted. There is no unique, self-evident way of allocating

international emissions to various countries. However, since transport is a service in the

economy, one solid way to treat its emissions - as with any other embedded emissions -

would be to allocate it to the producer which in practice means the country selling the

fuel. The same principle is used in electricity sales as well as in producing goods. This

Total carbon intensity Transport carbon intensity

High intensity

Decreasing Cluster 5 Cluster 6

Increasing Cluster 9 Cluster 7

Constant

Fluctuating Cluster 8

Mediumintensity

Increasing Cluster 3 Cluster 4

Decreasing Cluster 4 Cluster 3

Constant Cluster 5

Fluctuating

Low intensity Increasing Cluster 2

Decreasing Cluster 1

Constant Cluster 7 Cluster 2

Fluctuating

Very lowintensity

Increasing

Decreasing

Constant Cluster 6 Cluster 1

Fluctuating

Very highintensity

Increasing Cluster 9

Decreasing Clusters 8 and 11 Cluster 10

Constant

Fluctuating Cluster 10

Decarbonization profile

23

might have an effect on fuel prices, and help reduce the distortions caused in many

countries by the extensive use of fuel subsidies for transport.

It is concluded that the GDP per capita level determines neither the total carbon

intensity nor the transport carbon intensity. Therefore, the realistic debate is not between

the pro-growth and anti-growth arguments but more subtle strategies and means of

emissions reduction are needed (see e.g. Pacala and Socolow, 2004; Jollands et al.,

2010). Now, that the economy is in recession and considerable governmental funds are

used in various market interventions, it is important to emphasize climate friendly

measures for recovery instead of getting back to business as usual. If progress in

reducing carbon dependency is not determined by the level of the GDP, then this is

good news.

Increasing eco-efficiency of the economy results in a reduction of carbon intensity. This

is an important goal for effective use of resources, since carbon dioxide is not an

impurity in the burning process, but an indicator of fossil fuel use as well. Efficient use

of resources is a relevant policy goal independent of the level of economic output (see

e.g. Darmstadter, 2001). This is why carbon intensity, rather than carbon dioxide

emissions per capita was used as the basic unit of measurement in this study.

6.3 Directions for further research

The analysis of this paper is based on aggregate emissions. It can be argued that

aggregated carbon intensity does not reveal the reasons behind the results – very

different countries may look like similar in terms of aggregated carbon intensity of the

economy. For this reason, further research should pay attention to the following six

suggestions.

First, a structural decomposition of CO2 emissions from different sectors such as

transport, industry, agriculture, services etc. of the economy should be carried out,

identifying how the change in emissions or emission intensity has changed due to

changes in economic activity and carbon intensity of each sector, and due to change in

the shares of different sectors in the whole economy. There is a large literature of this

kind decomposition analysis which has been applied to energy consumption and CO2

24

emissions and corresponding intensities as well. Recommendations for the use of

different decomposition techniques in these fields have been made e.g. by Ang (2004).

Second, detailed structural decomposition of emissions by fuel sources and transport

mode should be made, and this includes the increases in maritime and aviation where

considerable future growth is expected. Earlier studies exist on total emissions (e.g. Sun,

2004) or energy consumption (e.g. Ang, 1994), but the same analysis could be repeated

using traffic mode specific emissions in order to gain a more in-depth view. For

example, does road transport really dictate all the results or might some weak signals be

found in trends in the other modes?

Third, a Kaya identity or IPAT and ImPACT analysis should be performed in order to

find out the contribution of population, GDP per capita, Energy use per GDP and CO2

emissions per energy use to the emissions (see eg. Ehrlich & Holdren 1971; Raupach et

al. 2007; Ausubel & Waggoner 2008; Saikku et al. 2008). Each factor could be

incorporated in a cluster analysis. As for transport, this is a very challenging task, since

transport volumes would be an equivalent to energy use, but there is no international

data on transport volumes. Eurostat has data on transport volumes for EU15 countries

from 1970 onwards, but for other countries, available data is either scattered, scarce, or

incompatible. Road and some rail transport volume statistics may be found from

International Road Federation (IRF), but domestic waterborne modes, domestic

aviation, bicycling and walking are excluded.

Fourth, for a sensitivity analysis, other CO2 emission data could be used to compare

results based on various data sources, for example comparing IMO and IEA data on

maritime CO2 emissions or CDIAC and IEA data on total emissions.

Fifth, as time passes by, the analysis could be updated with the most recent data sets.

The period of 2005-2010 might give a very different view due to the global economic

recession, and the increasing role that the large countries of Asia are having on global

travel patterns, the use of energy and CO2 emissions levels.

Sixth, the policies of the best performing countries in the best practice clusters should

be analysed by qualitative policy analysis. These countries are characterised by low

and/or decreasing carbon intensity combined with high GDP per capita: Switzerland,

25

Sweden and Norway in total carbon intensity and Japan, Germany and Switzerland in

transport carbon intensity. A qualitative analysis on policy documents, interviews with

key informants in the administration, supplemented by geographic information and a

wider set of statistics would contribute to the understanding of success stories. Wealthy

countries having high carbon intensity could be analysed in this comparison with the

‘best of the best’.

Appendix A: Clustering process

The process of clustering the countries is presented in the hierachical trees, or

dendrograms. Fig.6 presents the grouping of the TIIN clusters and Fig. 7 the grouping

of the TRIN clusters. Each cluster is indicated with a specific colour.

26

Figure 6. TIIN dendrogram – each cluster is indicated with a colour. Outliers in black ink.

27

Figure 7. TRIN dendrogram – each cluster is indicated with a colour. Outliers in black ink.

28

Appendix B: Country codes used in graphs.

Country Code Country Code Country Code

Albania AL Greece GR Panama PA

Algeria DZ Guatemala GT Paraguay PY

Angola AO Haiti HT Peru PE

Argentina AR Honduras HN Philippines PH

Australia AU Hong Kong (China) HK Poland PL

Austria AT Hungary HU Portugal PT

Bahrain BH Iceland IS Qatar QA

Bangladesh BD India IN Romania RO

Belgium BE Indonesia ID Saudi Arabia SA

Benin BJ Islamic Republic of Iran IR Senegal SN

Bolivia BO Iraq IQ Singapore SG

Brazil BR Ireland IE Slovak Republic SK

Brunei Darussalam BN Israel IL South Africa ZA

Bulgaria BG Italy IT Spain ES

Cameroon CM Jamaica JM Sri Lanka LK

Canada CA Japan JP Sudan SD

Chile CL Jordan JO Sweden SE

China CN Kenya KE Switzerland CH

Chinese Taipei TP Dem. People's Republic of Korea KP Syria SY

Colombia CO Korea KR United Republic of Tanzania TZ

Congo CG Kuwait KW Thailand TH

Democratic Republic of Congo CD Lebanon LB Togo TG

Costa Rica CR Libya LY Trinidad and Tobago TT

Côte d'Ivoire CI Luxembourg LU Tunisia TN

Cuba CU Malaysia MY Turkey TR

Cyprus CY Malta MT United Arab Emirates AE

Czech Republic CZ Mexico MX United Kingdom GB

Denmark DK Morocco MA United States US

Dominican Republic DO Mozambique MZ Uruguay UY

Ecuador EC Myanmar MM Venezuela VE

Egypt EG Namibia NA Vietnam VN

El Salvador SV Nepal NP Yemen YE

Ethiopia ET Netherlands NL Zambia ZM

Finland FI New Zealand NZ Zimbabwe ZW

France FR Nicaragua NI Other Africa OthAfra

Gabon GA Nigeria NG Other Latin America OthLAmb

Germany DE Norway NO Other Asia OthAsiac

Ghana GH Oman OM Former Yugoslavia ForYugd

Gibraltar GI Pakistan PK Former USSR ForUSSRe

aBurkina-Faso, Burundi, Cape Verde, Central African Republic, Chad, Comoros, Djibouti, EquatorialGuinea, Gambia, Guinea, Guinea-Bissau, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania,Mauritius, Niger, Reunion, Rwanda, Sao Tome and Principe, Seychelles, Sierra Leone, Somalia,Swaziland & Uganda.bAntigua and Barbuda, Aruba, Bahamas, Barbados, Belize, Bermuda, Dominica, French Guyana,Grenada, Guadeloupe, Guyana, Martinique, St. Kitts and Nevis, Anguilla, Saint Lucia, St. Vincent andGrenadines & Surinam.cAfghanistan, Bhutan, Cambodia, Fiji, French Polynesia, Kiribati, Laos, Macau, Maldives, NewCaledonia, Papua New Guinea, Samoa, Solomon Islands, Tonga & Vanuatu.dBosnia-Herzegovina, Croatia, Former Yugoslav Republic of Macedonia, Serbia/Montenegro & Slovenia.eArmenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Republic ofMoldova, Russia, Tajikistan, Turkmenistan, Ukraine & Uzbekistan.NB! The following countries are excluded due to incomplete or lack of data: Anguilla, Botswana, BritishVirgin Islands, Cambodia, Cayman Islands, Christmas Island, Cook Islands, Eritrea, Falkland Islands,Liechtenstein, Mongolia, Montserrat, Namibia, Nauru, Netherlands Antilles, Niue, Palau, Saint Helena,Saint Pierre-Miquelon, Turks and Caicos Islands & Western Sahara.

29

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