Decomposition of energy-related CO2 emissions in Australia: Challenges and policy implications

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Received date: 27 August 2014 Revised date: 5 December 2014 Accepted date: 11 December 2014 Please cite this article as: Shahiduzzaman, M., Layton, A., Alam, K., Decomposition of energy-related CO2 emissions in Australia: Challenges and policy implications. Economic Analysis and Policy (2015), http://dx.doi.org/10.1016/j.eap.2014.12.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1 2 3

Decomposition of energy-related CO2 emissions in Australia: Challenges and policy implications

4 5

Md Shahiduzzaman*

6

Australian Centre for Sustainable Business & Development

7

University of Southern Queensland, Australia

8

Email addresses: [email protected]

9 10

Allan Layton

11

School of Commerce

12

University of Southern Queensland, Australia

13 14 15

Khorshed Alam

16

School of Commerce

17

University of Southern Queensland, Australia

18 19 20 21

*

Corresponding author.

22 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Abstract: Changes in energy-related CO2 emissions aggregate intensity, total CO2 emissions and per-capita CO2 emissions in Australia are decomposed by using a Logarithmic Mean Divisia Index (LMDI) method for the period 1978-2010. Results indicate improvements in energy efficiency played a dominant role in the measured 17% reduction in CO2 emissions aggregate intensity in Australia over the period. Structural changes in the economy, such as changes in the relative importance of the services sector vis-a-vis manufacturing, have also played a major role in achieving this outcome. Results also suggest that, without these mitigating factors, income per capita and population effects could well have produced an increase in total emissions of more than 50% higher than actually occurred over the period. Perhaps most starkly, the results indicate that, without these mitigating factors, the growth in CO2 emissions per capita could have been over 150% higher than actually observed. Notwithstanding this, the study suggests that, for Australia to meet its Copenhagan commitment, the relative average per annum effectiveness of these mitigating factors during 2010–2020 probably needs to be almost three times what it was in the 2005-2010 period – a very daunting challenge indeed for Australia’s policymakers.

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I.

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Australia is one of the leading carbon dioxide (CO2) emitting countries in the world with

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per-capita CO2 emissions from fossil fuel use and cement production remaining above the

21

level of any other industrial and developed country (Olivier et al. 2012). From 1990 to 2010,

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the country’s total CO2 emissions increased by about 52% as compared to about 12%

23

increase for the overall OECD countries (OECD 2014). During that period of time,

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Australia’s per capita CO2 emissions grew by about 18% as compared to about a 10%

25

decrease in the United States (US), 18% decrease in the United Kingdom (UK), 0.6%

26

decrease in Canada and about 3% decrease for overall OECD.

Introduction

27 28

Australia’s CO2 emissions is a reflection of high energy consumption to support its

29

idiosyncratic economic activities – an unusual (for a developed country) reliance on the

30

extraction and export of raw materials – along with a very large land mass and often very

31

large distances for transporting goods to market or for export, as well as a heavy reliance on 2

1

coal for electricity generation (DCCEE 2010a, 2010b). Energy-related emissions contributed

2

about 91 percent of national CO2 emissions and 74 percent of national Greenhouse gases

3

(GHGs) emissions in Australia in 2009 (Department of Environment 2014).

4

Overall, there has been a steady decline in CO2 emissions growth in Australia over the period

5

of time. CO2 emissions from fuel combustion grew at an average rate of 1.5% in 2000s as

6

compared to 3.9% in 1970s (IEA 2014). On the other hand, gross domestic product (GDP)

7

grew at an average rate of 3.3% during 1970-2010, resulting in the sustained decline in

8

energy and CO2 emissions per unit of output (CO2 emissions intensity) (ABS 2014). This

9

decline in CO2 emissions intensity has been widespread and correlated with a decline in

10

energy intensity across different sectors of the economy including some end-use sectors such

11

as commercial, manufacturing, transport and residential (Sandu & Petchey 2009; Sandu &

12

Syed 2008; Shahiduzzaman & Alam 2013). Nonetheless, generation of electricity remains

13

predominantly dependent on the consumption of coal, representing about 51% of total CO2

14

emissions in the country in 2010 (IEA 2014).

15

Several developments have recently taken place in the policy area to reduce emissions. The

16

present recently-elected Coalition Government has introduced a Direct Action Plan as a

17

substitute for the Carbon Tax/Emissions Trading System introduced by the previous Labour

18

Government on July 01, 2012, but which was eliminated by the Federal Government in mid-

19

2014.

20

The data show a mixed picture regarding improvements in energy efficiency across

21

sectors/sub-sectors, especially since the 1980s. For example, the cumulative energy

22

efficiency effect in the ‘Agriculture’ sector has actually been slightly counterproductive over

23

the period, while changes in energy efficiency had a considerably stronger counterproductive

24

effect in the ‘Mining’ sector. In the aggregate ‘Manufacturing’ sector, the effect of energy 3

1

efficiency improvements was evidently quite beneficial in the first half of the 1980s, but has

2

remained relatively unchanged subsequently.

3

Whilst the Direct Action Policy of the current Coalition Government is a quite different

4

approach to the issue of emissions abatement compared to the previous Labour Government’s

5

carbon tax/price approach, both approaches had/have the aim of shifting the economy

6

towards a lower emissions mix of energy, improvements in energy efficiency, as well as to

7

encourage abatement activities across different sectors of the economy. Over time, a growing

8

population, industrial activities and prosperity have stimulated energy demand and supply,

9

and thus put upward pressure on CO2 emissions at a given level of energy mix. Going

10

forward, understanding the relative roles of these underlying factors is then an essential

11

ingredient to appropriate emissions reduction policy design in Australia.

12

The objective of this paper is therefore to provide a better understanding of the relative roles

13

of various proximate factors that have given rise to changes over time in energy-related CO2

14

emissions in Australia. In so doing, decomposition analyses of CO2 emissions – measured in

15

aggregate and per-capita bases, as well as a ratio to economic output are performed. The

16

decomposition framework employed derives from the so-called Logarithmic Mean Divisia

17

Index (LMDI) method.

18

Following this introduction, Section II presents a brief review of literature, Section III

19

outlines the methodology and data, Section IV discusses and illuminates the decomposition

20

results, Section V evaluates Australia’s current stated commitment to reducing emissions and

21

finally, Section V presents conclusions.

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II. A brief review of literature

4

1

The decomposition method has been used in a number of studies in recent years to identify

2

the fundamental determinants of changes in a country’s energy related CO2 emissions;

3

including, inter alia, Tol et al. (2009) for the US, Kaivo-oja and Luukkanen (2004) for

4

different European countries and Norway, Hatzigeorgiou et al . (2008) for Greece, Lise

5

(2006), Ipek Tunç et al. (2009) and Wietze (2006) for Turkey, Wu et al. (2005), Fan et al.

6

(2007), Ma and Stern (2008) and Zhang et al. (2009) for China, Paul and Bhattacharya (2004)

7

for India and De Freitas and Kaneko (2011) for Brazil.1 In an early study, Torvanger (1991)

8

applied the Divisia

9

emissions for nine OECD countries and found that the majority of the reduction in carbon

10

intensity in the sector was attributed to the reduction in energy intensity. A similar result was

11

found in Greening et al.(1998). Ang and Zhang (2000) have provided a comprehensive

12

survey of literature applying the index decomposition approach in energy and environmental

13

studies.

14

In Australia, the decomposition of CO2 emissions has received scant attention. The first such

15

analysis is believed to be by Cox et al. (1997) – a research report published under the

16

auspices of the Federal Government’s Australian Bureau of Agricultural and Resource

17

Economics (ABARE). Using the Arithmetic Mean Divisia Index (AMDI) method for the

18

period 1973-74 to 1994-95, the study found that energy efficiency played a dominant role in

19

reducing emissions over the period. Also using the AMDI method, Harris and Thorpe (2000)

20

and Tedesco and Thorpe (2003) extended the sample from 1973-74 to 1997-98 and from

21

1973-74 to 2000-01, respectively. However, there are theoretical and practical limitations

22

associated with the AMDI approach as it does not satisfy the factor reversal test and cannot

Index approach to decompose energy-related manufacturing CO2

1

Other studies such as Bloch et al. (2015) and Salim and Rafiq (2012) have used time series econometrics to explore relationship among GDP, energy consumption and emissions.

5

1

provide complete decomposition (Shahiduzzaman & Alam 2013). The LMDI method of

2

decomposition addresses these limitations. Ang (2004) has described LMDI as the “best”

3

decomposition method among various alternatives commonly used in the literature as it

4

overcomes the factor reversal test and provides a complete decomposition with no residual.

5

It appears that the only published article applying an LMDI decomposition method to

6

investigate the underlying contributing factors to CO2 emissions in Australia (along with

7

some other Asia Pacific Economic Cooperation countries) is that of Lee and Oh (2006) using

8

data for 1980-1998. As the data used were at the aggregate level it therefore failed to capture

9

the energy efficiency and structural change effects in the analysis.

10

Wood (2009) applied the Structural Decomposition Approach (SDA) of GHGs emissions for

11

the period 1976-2005. The difference between the Index Decomposition Approach (IDA) (as

12

exemplified by the LMDI approach) and SDA is that the latter uses an input-output model,

13

which can be applied to a given set of energy and production data at any level of aggregation.

14

These two methods have been developed independently in the literature and involve different

15

advantages and point of focus.2

16

The current study complements Wood’s by utilising an IDA method, more specifically, the

17

LMDI approach. In addition, the present paper adds data for the most recent years, and,

18

unlike previous studies applying the AMDI method, this study incorporates more data at the

19

sub-sectoral levels, thereby allowing a finer-grained picture of energy efficiency trends to

20

emerge. Finally, the study carries out separate decomposition analyses for aggregate CO2

2

Interested readers can consult Hoekstra and van den Bergh (2003) for a comparison between the IDA and SDA.

6

1

emissions, emissions as a ratio to output, and also on per-capita emissions, which has not

2

been done previously in any of the studies cited above in the Australian context.

3

III. Methodology and data

4

As already mentioned, the decomposition results in this study are derived by using a LMDI

5

approach. The LMDI decomposition approach is built upon the theoretical rigor of Divisia

6

aggregation. A major advantage of

7

decomposition results with no residuals (Ang 2004). As LMDI decomposition approach is

8

based on log values, one potential issue is the treatment of ‘zero’ values – as, for example,

9

would be the case if the consumption of a particular fuel type is not observed for one or more

10

periods in an economic sub-sector. As discussed by Ang and Liu (2007), this problem can be

11

handled by substituting a small positive number - for example, something between 10

12

and 10

13

approaches zero. Ang (2004) and Ang and Liu (2007) discuss the desirable properties of

14

LMDI decomposition approach in quite some detail.

15

The decomposition of CO2 emissions in Australia is performed in three forms: aggregate CO2

16

emissions, emissions as a ratio to output and per-capita emissions. The decomposition models

17

are described below.

18

Suppose that the aggregate CO2 emissions in a country are a function of varying levels of

19

economic output across various sectors and sub-sectors and the different compositions of

20

fuels used in those sectors and sub-sectors. We then denote by Ckm, the level of CO2

21

emissions at subsector k for fuel m used in that subsector, and write the identity:

22

C km =

the LMDI approach is that it provides complete

– for the zero values, with converging results in evidence as the small number

C km C k E k Qk Q j ⋅ ⋅ ⋅ ⋅ ⋅Q C k E k Qk Q j Q

(1.1) 7

1

Where: Q refers to aggregate output in the economy, Ck represents total CO2 emissions in

2

subsector k from the various types of fuels used in that subsector, Ek represents total energy

3

consumption in subsector k, and Qk and Qj denote output of subsector k and sector j,

4

respectively.

5

Before proceeding, it is perhaps worth noting that the decomposition (1.1) is clearly based on

6

a specific researcher-selected identity. This is true, and, indeed, the selected decomposition

7

identity used later to decompose total and per capita emissions is a little different to (1.1).

8

However, it would not be appropriate to consider (1.1) an arbitrary decomposition. On the

9

contrary, the variables on the right hand side are quite logically the proximate contributing

10

determinants of CO2 emissions – viz., fuel mix, CO2 intensity of energy used, energy

11

efficiency across industry sectors, sectoral and sub-sectoral structural composition of the

12

economy, and aggregate economic activity – resulting in a potentially very informative

13

decomposition of observed emissions.

14

CO2 emissions at sector j, Cj, is then the aggregation of the emissions from each fuel, m, used

15

across all sub-sectors in the sector,

16

C j = ∑∑ C km k

(1.2)

m

17

Therefore, aggregate CO2 emissions for the entire economy, C, is the sum of CO2 emissions

18

in the various sectors.

19

C = ∑C j

(1.3)

j

20 21

From (1.1) through (1.3) and dividing both sides of equation by Q, we can write

C C E Q Qj C = ∑∑∑ km ⋅ k ⋅ k ⋅ k ⋅ Q C k E k Qk Q j Q j k m

(1.4)

8

C Q

1

Where

denotes the “aggregate CO2 intensity” of the economy. By relabelling each

2

component, Equation (1.4) can then be re-written as

3

C I = ∑∑∑ C I fm ⋅ C I eme ⋅ C I int ⋅ C I sk ⋅ C I sj j

k

(1.5)

m

4

Where: C I represents the aggregate CO2 intensity of the economy, with the right hand side

5

(RHS) variables representing the various contributing determinants of that aggregate CO2

6

intensity; viz., CIfm representing the contribution coming from the different fuel mixes used

7

across the economy’s various sub-sectors, CIeme representing the contribution coming from

8

the carbon intensity of energy used in the different sub-sectors, CIint representing the

9

contribution coming from the energy intensity of each sub-sector – sometimes called the real

10

intensity effect, CIsk representing the contribution coming from the economy’s sub-sectoral

11

structural composition, and with CIsj

12

economy’s sectoral level structural composition.

13

Differentiating equation (1.5) with respect to time results,

representing the contribution coming from the

C I = ∑∑∑C I fm ⋅ CI eme ⋅ CI int ⋅ CI sk ⋅ CI sj + ∑∑∑CI fm ⋅ C I eme ⋅ CI int ⋅ CI sk ⋅ CI sj j

14

k

m

+ ∑∑∑C j

k

I

j

fm

⋅C

I

eme

⋅ C

I

int

⋅C

I

sk

m

k

m

⋅ C sj + +∑∑∑CI fm ⋅ CI eme ⋅ CI int ⋅ C I sk ⋅ CI sj I

j

k

(1.6)

m

+ ∑∑∑CI fm ⋅ CI eme ⋅ CI int ⋅ CI sk ⋅ C I sj j

15

k

m

Writing equation (1.6) in terms of growth rates and integrating,

ΔCI = ∫ ∑∑∑g fm ⋅ ω jkm.dt + ∫ ∑∑∑geme ⋅ ω jkm ⋅ dt + ∫ ∑∑∑gint ⋅ ω jkm ⋅ dt t

0

16

j

k

0

m

t

j

k

0

m

+ ∫ ∑∑∑gsk ⋅ ω jkm.dt + ∫ ∑∑∑gsj ⋅ ω jkm.dt t

0

17

t

t

j

where, ω jkm =

k

m

0

j

k

m

C km C k E k Qk Q j ⋅ . ⋅ ⋅ . C k E k Qk Q j Q 9

j

k

m

(1.7)

1

Equation (1.7) is then solved using logarithmic mean as a weight function (Sato 1976; Vartia

2

1976). This approach results in the additive LMDI specification for changes in the aggregate

3

CO2 intensity of the economy, denoted as: ΔC I = ΔC I fm + ΔC I eme + ΔC I int + ΔC I sk + ΔC I sj

4

(1.8)

5

Where: Δ C I is the change of aggregate CO2 intensity of the economy from one period to

6

another, with the RHS variables representing the various contributing determinants of that

7

change in aggregate CO2 intensity; viz., ΔC I fm representing the contribution coming from

8

changes in the fuel mixes used across the economy’s various sub-sectors, Δ C I eme

9

representing the contribution coming from changes in the carbon intensity of energy used in

10

the different sub-sectors, ΔC I int representing the contribution coming from changes in the

11

energy intensity of each sub-sector – ie., changes in real intensity, Δ C I sk representing the

12

contribution coming from changes in the economy’s sub-sectoral structural composition, and

13

with ΔC I sj representing the contribution coming from changes in the economy’s sectoral

14

level structural composition.

15

Decomposition model for total and per capita CO2 emissions

16

The decomposition of CO2 emissions is often implemented by applying the so called Kaya

17

identity (Kaya 1990):

18

C=

19

where: in addition to the aggregate level carbon intensity of energy use (C/E) and the

20

aggregate level energy intensity (E/Q) in the economy, Q/P and P denote per-capita real

21

income and population, respectively. The last terms in the decomposition model are included

C E Q ⋅ ⋅ ⋅P E Q P

(1.9)

10

1

in the decomposition in order to capture wealth and population effects on aggregate CO2

2

emissions in the economy.

3

Following a similar model derivation as outlined in 3.1 and using a variant of the Kaya

4

identity, Equation (2.1) can be written as:

5

C = ∑∑ j

k

C k E k Qk Q j Q ⋅ ⋅ ⋅ ⋅ ⋅P E k Qk Q j Q P

(2.0)

6

The additive LMDI model for aggregate CO2 emissions can then be expressed as:

7

ΔC = ΔC eme + ΔC int + ΔC sk + ΔC sj + ΔC wealth + ΔC pop

8

Where ΔC represents changes in aggregate CO2 emissions in the economy from one period to

9

another, with the RHS variables representing the various contributing determinants. The first

10

four are as defined earlier, with ΔCwealth (changes in GDP per capita) and ΔC pop (changes in

11

population) representing the contributions to changes in CO2 emissions arising from changes

12

in wealth and population, respectively.

13

Similarly, the decomposition model for per-capita CO2 emission takes the following form:

14

C E Q Qj Q C = ∑∑ k ⋅ k ⋅ k ⋅ ⋅ P Qk Q j Q P j k Ek

15

The additive decomposition model for per-capita CO2 emissions then becomes:

16

ΔC P = ΔC P eme + ΔC P int + ΔC P sk + ΔC P sj + ΔC weath

17

where ΔC P denotes the change in per-capita CO2 emissions in the economy from one period

18

to another, and the right hand side variables denote the contributions to the changed per

19

capita emissions coming from changes in the various determinants as previously explained

20

above.

(2.1)

(2.2)

11

(2.3)

1

Data

2

While the application of the decomposition method involves the use of CO2 emissions data at

3

lower levels of aggregation (e.g., sub-sectoral and sectoral), emissions data at such

4

disaggregated levels is not readily available in Australia. It is therefore necessary to estimate

5

the data at the lower levels of aggregation in such a way that they sum to the sectoral level

6

(for the sub-sectors) and the national emissions data (for the sectors). The level of CO2

7

emissions in the kth sub-sector is estimated based on the consumption of the various fossil

8

fuels in the sector, carbon emissions factors, and the fractions oxidised as follows:

9

Ckt = ∑Ekmtem (1− smt )omw

(2.4)

m

10

where Ckt is the CO2 emissions from a sub-sector k at time t. Ekmt is the consumption of fuel

11

m in sub-sector k in time t, em is the carbon emissions factor of fuel m, smt is the fraction of

12

the mth fuel that is not oxidized as raw materials in year t, om is the fraction of carbon oxidized

13

based on fuel type m, and w is the molecular weight ratio of CO2 to carbon (44/12). CO2

14

emissions in this study therefore constitute the emissions arising from combustion of fuel for

15

energy purposes and combustion of fuel for transportation. The fuel vectors included in the

16

study are coal, petroleum, natural gas, and others (included here are wood, wood waste,

17

bagasse).

18

Unadjusted CO2 emission coefficients (em.w) for full combustion are taken from Tedesco and

19

Thorpe (2003). Because total energy consumption is computed as the total quantity (in energy

20

units) of primary and derived fuels consumed minus the quantity of derived fuels produced,

21

smt is assumed to be zero. Following IPCC (1997), the default values of the rates of oxidised

22

carbon are assumed to be 98% for coal, 99% for oil products and 99.5% for natural gas. Fig.

23

1 shows the trends of national CO2 emissions estimated in this study (dotted line) compared 12

1

to the Government’s national inventory total (Department of Environment 2014) from the

2

reference year of 1990.

3

Figure 1. CO2 emissions energy sources: National inventory vs. estimation in this study

1.7

Index 1990=1

1.5 1.3 1.1 0.9 0.7 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0.5

4

National Inventory

Our estimation

5

Sources: DCCEE (2014) and author’s estimation.

6

The decomposition analysis is performed by considering data for eight sectors and 14 sub-

7

sectors for a period of 1978-2010. Further details on the sectors and sub-sectors considered in

8

this study can be found in Shahiduzzaman and Alam (2013). Annual data for energy

9

consumption for sectors/subsectors are collected online from ABARES (2011) (Table F,

10

Australian energy consumption, by industry and fuel type) and data for Industry Gross Value

11

Added are collected from ABS (Table 33, Cat no 5206.0, Australian National Accounts:

12

National Income, Expenditure and Product). Energy consumption data are in petajoule and

13

Industry Gross Value Added data are in millions of Australian dollars in Chain volume

14

measures (reference year 2008-09). The population data used are Australian resident

15

population in thousands and compiled from the ABS (2013) and ABS (2010). Figure 1

13

1

shows that our estimated aggregate national CO2 emissions data match quite well with that of

2

the Government’s national inventory.3

3

IV.

4

Table 1 presents the decomposition results for the changes in total or aggregate CO2

5

emissions (Equation 2.1).4 As shown in the bottom row of the table, aggregate CO2 emissions

6

increased by 206 Mt-CO2 per year between 1979 and 2010. Over the period of time, the

7

wealth effect (GDP per capita) and scale effect (population) have been the dominant factors

8

in increasing emissions, whilst sectoral structural change and sub-sectoral energy intensity

9

(energy efficiency) have been the dominant factors in reducing emissions.

Decomposition results

10

Interestingly, without any counterbalancing forces, the decomposition analysis suggests

11

that income per capita and population effects could well have produced an increase in total

12

emissions more than 50% higher than actually occurred over the period ([188.4+133.8]/205.7

13

= 1.565). On the other hand, energy efficiency improvements, structural changes in the

14

economy away from relatively more energy intensive activities, and the use of relatively less

15

CO2 intensive energy all apparently acted as mitigating forces to produce the actual observed

16

increase in CO2 emissions. Structural changes in the economy contributed about 36% to these

17

counterbalancing forces while energy efficiency improvements and movements towards

18

relatively less CO2 intensive energy sources contributed the other 64%. The role of energy

19

efficiency improvements appears particularly significant during 2006-2010. During this

3

The figure is presented from 1990 as the National inventory data for CO2 emissions is only available from that year. 4

The year-to-year decomposition results for total CO2 emissions are not presented here to conserve space, but can be made available from the corresponding author upon request.

14

1

period, energy efficiency improvements apparently contributed just over 80% of the

2

counterbalancing effect to the wealth and population effects.

3 4

Table 1. Decomposition of changes in total CO2 emissions per year (Mt – millions of tonnes)

5

ΔC = ΔCemf + ΔCint + ΔCstrss + ΔCstrs + ΔCwealth + ΔCpop

1979-1986 1987-1992 1993-1996 1997-2000 2001-2005 2006-2010 1979-2010

CO2 intensity of energy use -10.46 -0.43 -2.15 -3.14 -3.74 2.59 -17.33

Sub-sectoral energy intensity Structural Structural GDP Total (energy change: change: per CO2 efficiency) sub-sector sector capita Population emissions -26.69 6.60 1.68 31.27 24.17 26.58 -7.35 12.15 -10.59 28.28 21.70 43.76 -6.11 4.09 -11.92 33.87 13.36 31.14 5.13 2.82 -24.50 39.89 14.60 34.81 -1.69 6.07 -21.96 35.08 22.80 36.56 -19.65 11.15 -18.42 20.01 37.20 32.87 -56.35 42.88 -85.71 188.40 133.83 205.72

6

Notes: Figures represent the impact of the six factors included in the decomposition of changes in

7

total CO2 emissions over the periods in question. Negative numbers represent a negative contribution

8

to changes in total CO2 emissions (ie., emissions are calculated to have been reduced by that factor).

9

The opposite interpretation applies to positive figures. The horizontal addition of the decomposition

10

factors at a particular point in time is equal to the variable decomposed- total CO2 emissions in this

11

case.

12 13

Despite this quite strong contribution to reducing CO2 emissions from structural changes in

14

the economy, energy efficiency improvements, and switching to less CO2 intensive fuels over

15

the last 30 years, the current trajectory of total CO2 emissions is such that the government’s

16

commitment to reducing carbon pollution to a level at least five percent lower than that in

17

2000 by the year 2020 will seemingly still be very difficult to achieve. Real GDP in Australia

18

grew steadily since the mid-1980s and grew at a rate of 1.4 percent even in 2009 despite the

19

Global Financial Crisis, when almost all advanced countries experienced a negative growth

15

1

rate (IMF 2011).5 According to the IMF (2011) estimates, population growth is likely to

2

remain steady at about 1.3 percent during the current decade, a growth rate similar to that

3

during the 2000-10 decade. It is therefore quite certain that the core driving forces of energy

4

demand (population and economic growth) will continue to remain stable and strong over the

5

period out to 2020 (Garnaut 2011).

6

Decomposition of per-capita CO2 emissions

7

The decomposition results of changes in per-capita CO2 emissions grouped into selected

8

periods are presented in Table 2.6 As seen in the table, the net increase in per-capita CO2

9

emissions over 1979-2010 has been 4.05 tonnes per year. Without any counterbalancing

10

effects, the decomposition analysis suggests that growth in per capita income would have

11

produced a per capita increase in emissions of 10.44 tonnes or over 150% higher than

12

actually observed. This probably brings into much clearer relief (than the previous analysis of

13

total CO2 emissions) the extent to which Australia’s recent activities – policy and otherwise –

14

have been impacting positively on the country’s CO2 emissions challenge. The

15

counterbalancing factors of structural change in the economy, changes in the carbon intensity

16

of energy used, and energy efficiency improvements all combined to produce the much lower

17

actual per capita emissions observed.

18

An important result that can be seen from Table 2 is that the growth path of per-capita CO2

19

emissions slowed significantly in the post- 2000 period. Indeed, during 2006-2010, per-capita

20

CO2 emissions actually declined compared with the 2001-2005 per capita emissions level,

5

The only exception is Israel and Korea, which experienced real GDP growth rate of 0.8 and 0.3 respectively in 2009. 6

The complete year-to-year decomposition of the changes in per-capita CO2 emissions are not presented to conserve space, but can be made available from the corresponding author upon request.

16

1

and, from the year-to-year changes, we found this overall decline was the result of year-on-

2

year declines in three of the five years, including the most recent year of 2010 (results are not

3

reported here). Whilst the impact of the Global Financial Crisis (GFC) quite probably played

4

some part in the 2006-2010 per capita emissions decrease, it should be remembered that,

5

unlike most other developed countries, Australia did not go into recession at any time during

6

the 2007 - 2010 period (and not through till 2014 either).

7

Thus, we believe there are some grounds for a degree of optimism to be drawn from these per

8

capita emissions results. For example, it is of some significance that in the 2006–2010 period,

9

the per capita income effect, which had contributed so robustly to per capita emissions

10

growth since the late 1970s, exhibited a much smaller contribution during that period.

11 12 13

Table 2. Decomposition results for changes in per-capita CO2 emissions (tonnes) per year: cumulated over selected periods

ΔC P = ΔC P eme + ΔC P int + ΔC P sk + ΔC P sj + ΔCweath

1979-1986 1987-1992 1993-1996 1997-2000 2001-2005 2006-2010 1979-2010

CO2 intensity of energy use -0.689 -0.028 -0.121 -0.165 -0.190 0.116 -1.077

Sub-sectoral energy intensity Structural Structural Per (energy change: change: capita GDP per efficiency) sub-sector emission sector capita -1.746 0.432 0.124 2.038 0.159 -0.430 0.721 -0.638 1.700 1.325 -0.340 0.227 -0.664 1.880 0.982 0.278 0.149 -1.301 2.118 1.078 -0.078 0.296 -1.097 1.762 0.692 -0.922 0.541 -0.871 0.947 -0.190 -3.238 2.367 -4.447 10.443 4.047

14

Note: The horizontal addition of the decomposition factors at a particular point in time is equal to the

15

variable decomposed- per capita CO2 emissions in this case.

16

Decomposition of aggregate CO2 intensity

17

By performing the complete decomposition of changes in Australia’s CO2 emissions

18

aggregate intensity over time, indices of the various proximate contributing determinants to

19

such changes can be calculated and are summarised for different sub-periods in Table 3. As 17

1

can be seen in the table, changes in sub-sectoral energy efficiency played a sustained role in

2

reducing CO2 emissions intensity until the middle of the 1990s. Then changes in sub-sectoral

3

energy efficiency actually provided a negative impetus to reducing aggregate CO2 emissions

4

intensity for a few years in the second half of 1990s.

5 6 7

Table 3. Decomposition results for changes in aggregate CO2 intensity for different subperiods ΔC I = ΔC I fm + ΔC I eme + ΔC I int + ΔC I sk + ΔC I sj

1979-1986 1987-1992 1993-1996 1997-2000 2001-2005 2006-2010 1979-2010

Fuel mix effect -.000070 -.000001 .000001 -.000003 -.000045 -.000010 -.000129

CO2 intensity of energy use -.023832 -.000935 -.003396 -.003886 -.004147 .002233 -.033964

Subsectoral energy intensity Structural Structural Aggregate (energy effect effect intensity efficiency) subsector sector CO2 -.060195 .014714 .004899 -.064485 -.012515 .021614 -.019582 -.011419 -.009223 .006130 -.018032 -.024520 .007015 .003525 -.031086 -.024434 -.001404 .005983 -.023201 -.022814 -.018139 .010844 -.017254 -.022326 -.094460 .062809 -.104256 -.170000

8

Note: The figures in the table are to be interpreted as percent changes in aggregate CO2 emissions

9

intensity. For example, -.170000 should be interpreted as a 17% reduction in aggregate CO2

10

emissions intensity from 1979 to2010. The horizontal addition of the determining factors’

11

contributions sum to the relevant measured change in aggregate CO2 emissions intensity.

12

Also worthy of note is the apparent role played by changes in the structural composition of

13

the economy. Since the late 1980s there appears to have been a relatively strong and

14

persistent contribution to the observed reduction in emissions intensity from structural

15

composition changes at the sectoral level (rather than at the sub-sectoral level). We

16

conjecture that the major underlying factor explaining this contribution has been the change

17

in the relative significance of the Manufacturing sector vis-à-vis the Services sector in the

18

Australian economy over the 30 year period.

18

1

Finally, whilst of a lesser effect, there has also been a steady, but mostly small positive

2

contribution to reducing aggregate CO2 intensity coming from changes in the CO2 intensity of

3

energy used across sub-sectors. Fuel mix effects appear to have been negligible across the

4

period.

5

To sum up, over the 30 plus year period of the study, aggregate energy-related CO2 emissions

6

intensity in the Australian economy reduced by about 17%. The dominant factor in reducing

7

CO2 aggregate intensity until the middle of the 1990s was increased energy efficiency (or

8

decrease in real intensity) in the economy with changes in the structural composition of the

9

economy at the sectoral level also playing a continuing role since the late 1980s. Increased

10

energy efficiency again played a significant role in reducing CO2 aggregate intensity in the

11

most recent five year period from 2006 – 2010. However, quite surprisingly, in this most

12

recent period – and unlike all previous periods in the sample - CO2 intensity of energy used

13

actually had an apparent positive decomposed contribution to measured aggregate CO2

14

emissions intensity. This may be an indication that, if a continued reduction in aggregate

15

CO2 emissions intensity is desired, more action needs to be taken on the policy front to shift

16

towards lower CO2 intensive fuels in Australia.

17

In Appendix 1 we have also provided derived data for the apparent contributions to the

18

reduction in CO2 emissions aggregate intensity from energy efficiency improvements in

19

different end-use sectors/sub-sectors of the economy. The data show a mixed picture

20

regarding improvements in energy efficiency across sectors/sub-sectors, especially since the

21

1980s. For example, the cumulated energy efficiency effect in the ‘Agriculture’ sector has

22

actually been slightly counterproductive over the sample period, while changes in energy

23

efficiency had a considerably stronger counterproductive effect in the ‘Mining’ sector. In the

24

aggregate ‘Manufacturing’ sector the effect of energy efficiency improvements was evidently 19

1

quite beneficial in the first half of the 1980s, but has remained relatively unchanged

2

subsequently.

3

In terms of manufacturing sub-sectors, the energy efficiency improvement picture for the

4

‘Metal’ sub-sector is quite similar to that for the overall manufacturing sector due to the fact

5

that it accounts for a high proportion of emissions for the overall sector (about 50 percent of

6

total CO2 emissions in the ‘Manufacturing’ sector). Energy efficiency improvements in most

7

sub-sectors in the ‘Manufacturing’ sector appear to have had a beneficial impact on the

8

reduction of CO2 emissions aggregate intensity. Similarly, energy efficiency improvements in

9

the ‘Construction’, ‘Commercial and services’, ‘Residential’ and, most especially, in

10

‘Transport’ appear to have contributed favourably to reducing CO2 emissions aggregate

11

intensity in Australia over last three decades.

12 13

V.

14

The existing policies of emissions reduction in Australia are in line with the government’s

15

broad objectives of reducing total GHG emissions as part of its national and international

16

commitments. As a signatory to the Kyoto protocol, Australia had a target of limiting total

17

GHGs emissions to an average of 108 percent of 1990 levels between 2008 and 2012 (United

18

Nations 1998). The county has now successfully met the 2012 target and undertaken a new

19

commitment to reduce emissions to five percent below 2000 levels by 2020 (United Nations

20

1998)

21

As seen from Table 4, the major contributions to achieving this GHGs emission reduction

22

have come from land-use and land-use change (79%) and forestry and waste services (17%),

23

with a minor contribution from agriculture (1%). On the other hand, GHGs emissions from

24

energy related sources and industrial processes are estimated to have increased by 46 percent

Australia’s commitment to reducing emissions

20

1

and 27 percent respectively between 1990 and the Kyoto period average (2008-2012) (Table

2

4). It is also to be noted that CO2 emissions from energy related sources consisted of about 74

3

percent of total GHGs emissions in 2009. Therefore, while Australia may well meet the

4

Kyoto target successfully, the future challenge remains to curb CO2 emissions from energy-

5

related sources as part of the recent pledges as described below.

6

Table 4. Actual and projected GHGs emissions as compared to 1990 level Actual emissions (Mt-CO2e)

Energy Industrial process Agriculture Waste Land use change and forestry Total GHGs emissions

1990

2008

2009

289 24 87 18 132 550

418 31 88 14 29 580

417 30 85 14 19 565

Projected emissions (Mt-CO2e) % of Kyoto period average 1990 level 422 31 86 15 28 582

146 127 99 83 21 106

7

Source: DCCEE (2010c; Department of Environment 2014).

8

Beyond the Kyoto period, Australia has pledged to attain a target of 5 percent unconditional

9

reduction of GHGs emissions by 2020 relative to the 2000 level (DCCEE 2011a), with up to

10

a 15 to 25 percent conditional reduction subject to demonstrated significant action by others.

11

The commitment was formally submitted to the Copenhagen Accord in January 2010.

12

However, the recent projection from DCCEE (2010c) suggests that, based on domestic policy

13

settings in place at that time, there may well be a 24% projected increase in emissions by

14

2020 from the 2000 level rather than a decline (see Table 5 below)!

15

Furthermore, emissions from energy-related sources are expected to increase 38 percent

16

between 2000 and 2020, adding another 137 Mt-CO2e from this source into the atmosphere

17

by 2020 compared with what was emitted in 2000. Therefore, assuming the DCCEE’s current

18

projections out to 2020 are reliable to a reasonable order of magnitude, on a prima facie 21

1

basis, the unconditional reduction of emissions by 5 percent by 2020 from the 2000 level will

2

really be quite a daunting task for Australia. This issue is discussed further below and also

3

again in the Conclusions section below.

4

Table 5. Actual and projected GHGs emissions as compared to 2000 level Actual emissions (Mt-CO2e)

Projected emissions (Mt-CO2e) % of

2000

2008

2009

2020

2000 level

Energy

361

418

417

498

138

Industrial process

26

31

30

40

154

Agriculture

94

88

85

94

100

Waste

15

14

14

16

107

Deforestation and Forestry

62

29

19

42

68

Total GHGs emissions

558

580

565

690

124

5

Source: DCCEE (2010c; Department of Environment 2014)

6

Further assessment based on the decomposition results

7

Based on the decomposition results for total CO2 emissions, a further assessment for the 2020

8

target can be made. We note from Table 5 that the government is assuming a 19% growth in

9

energy-related Mt CO2-e emissions from 2009 out to 2020 (ie., 498/417), or about 1.6% per

10

year over the 11 year period.

11

The base data for the year 2000 for total energy-related CO2 emissions used in our study is

12

339 (Million Mt) – refer to Appendix 2-and, as can be calculated from the changes data

13

provided in Table 1, total CO2 emissions grew by 20% from 2000–2010 or about 1.8% per

14

year over this 10 year period. Thus, whilst we are unaware of the detailed assumptions

15

underlying the government’s projections from 2009–2020, the projected 19% increase seems

16

approximately consistent with what actually occurred in the 2000–2010 period. Indeed, in

22

1

the most recent five year period from 2005–2010, the growth in total emissions was in fact

2

1.6% per year, the same as implied by the DCCEE’s projections out to 2020.

3

Given that one can infer the 2020 DCCEE projection is consistent with the observed increase

4

in emissions from 2005 to 2010, one could go one step further and use the decomposition

5

analysis for this same period to gain insight into the size of the challenge facing Australia and

6

its policymakers in seeking to meet the Copenhagan Accord 2020 target commitment. To do

7

so we construct the following scenario.

8

We assume real GDP growth of, on average, 3% per annum from 2010 to 2020 (which is

9

consistent with 1.5% per annum population growth and 1.5% per capita income growth per

10

annum), so that, cet par (in regard to the sectoral composition of the economy, energy

11

efficiency across the economy, and patterns of use of different energy sources in the economy

12

in 2010), and without any additional mitigating forces at all, this important push factor would

13

see emissions grow by something like 34% from 2010 to 2020. In our study, the 2010

14

emissions figure was 408 Million Mt which, with 34% growth, would see 2020 emissions of

15

547 Million Mt, or an increase of 139 Million Mt over 2010.

16

However, 2000 emissions were 339 Million Mt. Even assuming a growth of 10% over 2000

17

by 2020 in energy-related emissions – rather than a 5% reduction (as per Copenhagan) –

18

2020 energy-related emissions would need to be around 373Million Mt, or some 174 Million

19

Mt less than the 547 Million Mt based on GDP growth, and also some 36 Million Mt less

20

than the 2010 emissions of 408 Million Mt!7 This means the mitigating factors of energy

21

efficiency improvements, continuing movement towards less CO2-intensive energy sources, 7

From Table 5, depending on what happens in the other listed areas, a 10% rise from 2000 in energy-related emissions could well be roughly consistent with an overall 5% reduction in emissions. Of course, we are only using the 10% increase in energy-related emissions as an illustration of what is seemingly required. It is hard to imagine a growth in energy-related emissions of more than 10% from 2000 to 2020 being consistent with a 5% overall reduction in emissions. Anything less than 10% growth would just make the task even greater than that outlined in the text.

23

1

changes in fuel mix, and continued sectoral composition changes would need to produce the

2

required 174 Million Mt reduction. What is the size of this task?

3

From Table 1 we see that, in the 2005 – 2010 period, the decomposition analysis suggests

4

that income per capita and population factors alone would have produced 57 Million Mt

5

increased emissions. The actual increase was 33 Million Mt, so the analysis suggests the

6

above-listed mitigating factors accounted for 24 Million Mt of emissions which would

7

otherwise have occurred. Of the 57 Million Mt which could have occurred, this amounts to a

8

42% mitigation. Turning to the 2010 – 2020 task, the required mitigation of 174 Million Mt

9

in 2020 is 125% of the implied increase from 2010 of 139 Million Mt from economic growth

10

alone. In other words, another way of thinking about the task ahead is that the listed

11

mitigating factors will have to exert almost three times the average annual impact which they

12

had in the 2005 – 2010 period.

13

Presumably, an important element in achieving what is required needs to be government

14

actions on the policy front.

15

On this, the Labor Government in Australia passed legislation in 2011 to introduce a carbon

16

price to be implemented from 1 July 2012 (DCCEE 2011a). The price of carbon was to be

17

fixed for a three year period, starting at $23 per tonne (with subsequent defined annual

18

increases) – thereby amounting to a straight tax on emissions over this period - before

19

moving to a full market-based emissions trading system from 1 July 2015. The Labor

20

Government subsequently moved to introduce the full market based trading system a year

21

earlier than originally envisaged, viz., from July 1, 2014. This was no doubt on account of the

22

Australian “price” being so much higher than the market-based international economic price;

23

in the EU, for example, it was less than $10 per tonne in 2013, this probably being due, at

How might this formidable task be accomplished?

24

1

least in part, to the continuing lethargic economic activity in Europe right through the 2010 –

2

2013 period.

3

The stated purpose of this policy initiative was to foster a significant transformation in

4

electricity generation and other carbon intensive sectors of the economy that are currently

5

lagging behind in terms of energy efficiency improvement. The government also set a

6

Renewable Energy Target (RET) with the aim of bringing about a significant shift in

7

electricity generation towards generation from renewable energy technologies. The RET has

8

the aim that about 20 percent of electricity generation will come from renewable energy

9

sources by 2020 (DCCEE 2011b).

10

It was expected that the introduction of carbon taxing/pricing in conjunction with the RET

11

would significantly boost energy efficiency and reduce carbon intensity of energy use to

12

achieve a low emissions future.8 In September, 2013, the Federal Government in Australia

13

changed and a Coalition Government was elected with a claimed mandate to repeal the

14

carbon tax/price. At the time of writing it was early in the new government’s term so it was

15

unclear whether the repeal legislation Bill would pass through the Senate. Whilst the new

16

Government’s stated intention is to remain true to the Copenhagan Accord commitment, it

17

has stated that it would achieve the desired outcome through its so-called Direct Action Plan

18

aimed at inducing industry to voluntarily reduce emissions, catalysed by suitably designed

19

incentive schemes provided by the government.

20

Most recently (as at June 2014), the Australian government was awaiting the outcome of an

21

independent review of the effectiveness of the RET which, when the legislation was

22

originally introduced, required a review in early 2014. The Review is due to be handed to the

23

government by mid-2014. Interestingly, internationally, the US government announced in 8

See Valentine (2010) for a critical evaluation of Australia’s renewable energy target.

25

1

June, 2014, that it would impose a set of legislated rules on power generators across the US

2

so that CO2 emissions from power generation would be cut by 30% from 2005 levels by

3

2030. Each state would have its own separate target and generators would have a flexible set

4

of options to use to achieve their state’s required target. States will have till June 30, 2016 to

5

submit their plans for achieving their state’s required target to the Federal government’s

6

Environmental Protection Agency.

7

Irrespective of the policy approach ultimately followed by Australian Government

8

policymakers, what is seemingly abundantly clear from the aforegoing discussion is that the

9

task of ramping up the effectiveness of the mitigating factors - through whatever might be the

10

set of politically determined preferred policy approaches - in order to have a realistic prospect

11

of meeting the Copenhagan Accord 2020 commitment is a challenge of quite daunting

12

proportions. Notwithstanding this, as detailed in section IV above, Australia has nonetheless

13

been making some quite impressive progress on limiting emissions growth when the data are

14

analysed on a per capita basis.

15

VI. Conclusions

16

In this paper, changes in CO2 emissions aggregate intensity, total CO2 emissions and per-

17

capita CO2 emissions in Australia for the period 1979-2010 are decomposed by using a

18

LMDI decomposition approach. CO2 emissions for different sectors and sub-sectors are

19

estimated from the fuel consumption data. The various decomposition analyses on CO2

20

emissions allow the relative contributions of the proximate causes of emissions growth in

21

Australia over the last three decades to be measured.

22

The results indicate improvements in energy efficiency played a dominant role in the

23

measured 17% reduction in CO2 emissions aggregate intensity in Australia over the period in 26

1

question, along with a movement towards the use of relatively less CO2 intensive energy.

2

Structural changes in the economy, such as changes in the relative importance of the services

3

sector vis-à-vis manufacturing, have also played a major role over the period. Results also

4

suggest that, without these mitigating factors, income per capita and population effects could

5

well have produced more than 50% more in total emissions over the period than actually

6

occurred. Perhaps most starkly, the results indicate that, without these mitigating factors, CO2

7

emissions per capita could have been more than 150% higher than actually observed.

8

Notwithstanding this, the study nonetheless suggests that, for Australia to meet its

9

Copenhagan commitment, the relative average annual effectiveness of these mitigating

10

factors during 2010–2020 probably needs to be almost three times what it was in the 2005-

11

2010 period – a very daunting challenge indeed for Australia’s policymakers.

12

In fact, one could argue that the favourable effects of structural change in the economy may

13

slow in forthcoming years. If this proves to be the case, energy efficiency will have to play an

14

even more profound role in reducing emissions in the future. In regard to this, there are in

15

evidence differences in the extent to which increased energy efficiency has reduced CO2

16

emissions across different end-use sectors of the economy.

17

The data seem to suggest that some sectors such as ‘Agriculture’ and ‘Mining’ could

18

contribute relatively more in improving energy efficiency. In Manufacturing, the Metals

19

subsector appears to be making a modest contribution. On the other hand, ‘Construction’,

20

‘Commercial and services’, ‘Transport’ and ‘Residential’ sectors have achieved sustained

21

declines in CO2 emissions intensity from improvements in energy efficiency. In policy terms

22

then, it is therefore necessary to focus on sectors/sub-sectors where the effect of energy

23

efficiency improvement is lagging behind in reducing emissions. 27

1

The decomposition results indicate that the future path of emissions reduction is very

2

challenging from the policy perspective. Scale effects, i.e., the per capita income effect and

3

the population effect, have been the dominant factors in increasing emissions, although the

4

role of the income effect has apparently slowed in the most recent period. And, of course, as

5

noted above, significant gains in emissions reduction were achieved by the improvement of

6

energy efficiency and the reduction in carbon intensity of energy use. Very importantly,

7

following from the initial per capita results described here, future research could productively

8

investigate whether a significant turning point has truly occurred in per-capita CO2 emissions

9

in Australia and, if so, what are the main explanatory factors.

10 11

28

Appendix 1 . Changes in CO2 emissions aggregate intensity due to changes in energy efficiency (real intensity) in selected economic sectors/sub-sectors over the period 1978-2010: Changes over selected sub-period (a negative indicates a beneficial contribution to reducing aggregate emissions intensity)

1979-1986 1987-1992 1993-1996 1997-2000 2001-2005 2006-2010 1979-2010

AGRI MIN MFG F&B TEXT WOOD PETR 0.00056 -0.00021 -0.01963 -0.00017 -0.00017 -0.00030 -0.00622 -0.00030 0.00230 0.00113 -0.00084 0.00005 0.00030 -0.00057 -0.00010 0.00157 -0.00101 0.00091 -0.00005 0.00013 -0.00045 -0.00053 0.00077 -0.00144 -0.00036 -0.00013 0.00004 -0.00128 0.00129 0.00071 -0.00123 -0.00006 0.00010 0.00012 -0.00057 -0.00079 0.00466 0.00102 -0.00160 0.00005 0.00045 0.00596 0.00013 0.00980 -0.02115 -0.00211 -0.00015 0.00074 -0.00312

AGRI- Agriculture, forestry and fishing; MIN- Mining MFG - Manufacturing F&B - Food, beverage and tobacco products TEXT - Textile, clothing, footwear and leather WOOD- Wood, paper and printing PETR- Petroleum, coal, chemical and associated products

MET MACH ELEC CONS SER TRANS RESI 1979-1986 -0.01635 -0.00004 -0.01233 -0.00108 0.00006 -0.01636 -0.00239 1987-1992 0.00018 0.00005 -0.00415 0.00006 0.00024 -0.00232 -0.00047 1993-1996 -0.00123 -0.00009 0.00124 -0.00113 -0.00012 -0.00545 -0.00032 1997-2000 0.00266 -0.00004 0.01744 -0.00092 -0.00037 -0.00570 -0.00105 2001-2005 -0.00074 -0.00015 0.02040 -0.00047 -0.00031 -0.01468 -0.00135 2006-2010 -0.00453 -0.00003 -0.01337 -0.00053 -0.00003 -0.00636 -0.00044 1979-2010 -0.02001 -0.00029 0.00923 -0.00407 -0.00053 -0.05086 -0.00601 MET- Metal products MACH- Machinery and equipment ELEC- Electricity generation and supply CONS - Construction SER- Commercial and services TRANS- Transport, postal and warehousing RESI - Ownership of dwelling

29

Appendix 2. Annual Totals for energy-related CO2 emissions: 1978 – 2010 (thousands of metric tonnes)

Year Emissions 1978 202695.832 1979 207039.912 1980 213178.684 1981 211788.858 1982 218815.038 1983 211059.836 1984 218070.512 1985 229090.263 1986 229276.54 1987 238722.088 1988 245555.309 1989 260968.482 1990 268296.621 1991 270331.091 1992 273032.928 1993 277164.335 1994 282449.427

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

30

Emissions 294229.157 304174.062 311834.382 324721.645 333812.046 338985.359 345649.492 353022.995 359201.554 370140.364 375545.578 389585.555 390359.485 391343.537 406648.872 408418.066

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