Energy Economics 38 (2013) 32–41

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Energy Economics

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Implications of CO2 reduction policies for a high carbonemitting economy

John Asafu-Adjaye ⁎, Renuka MahadevanSchool of Economics, The University of Queensland, St Lucia, Q4072, Australia

⁎ Corresponding author at: School of Economics, The UnQ4072, Australia. Tel.: +61 7 33656539.

E-mail address: j.asafuadjaye@gmail.com (J. Asafu-A

0140-9883/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.eneco.2013.03.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 November 2012Received in revised form 4 March 2013Accepted 13 March 2013Available online 21 March 2013

JEL classification:Q4D5

Keywords:Carbon taxFuel taxEmission trading scheme

This paper uses a dynamic computable general equilibriummodel to compare themacroeconomic and sectoral im-pacts of three environmental policies in Australia— an emissions trading scheme (ETS), an ETS combinedwith tech-nological innovation in the renewable energy sector and a fuel tax as an alternative to the ETS. Overall, the impacts ofthe ETSwere not significantly adverse. Although the fuel tax had similar impacts to the ETS on keymacro-variablessuch as real GDP, employment, household consumption, exports and imports, it was however not effective com-pared to the latter in reducing emissions. Neither policy led to inflation growth ofmore than0.8% for any coalminingand non-mining Australian state. At the sectoral level, the GDP growth of energy-intensive industries such as coal,iron ore, steel and coal-powered electricity generators is adversely affected while electricity generators who usegas and renewable energy sources and the forestry sector gain. It was also found that a 10% technological changein the renewable energy sector over a decade did not significantly improve the outcome when coupled with theETS. Thus the Australian government's industry assistance to invest in low pollution technologies needs to bemore aggressive to meet current and future international emission abatement targets.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Although there is general consensus that global warming is a prob-lem, international agreement to address this problem has yet to be suc-cessful. Following the failed 2009 meeting at Copenhagen and othersthereafter, it has not been possible to produce a binding internationalagreement to replace the Kyoto Protocol in the period beyond 2012,when the original commitment period ended. While a global effort to-wards reducing greenhouse emissions is more preferable, in the face oflimited success in this effort, several developed countries have unilateral-ly implemented some form of carbon-related policies (be it a carbon taxor the emission trading scheme, ETS) on particular sectors or on anation-wide scheme. These countries include the UK, Finland, Italy,Germany, Denmark, Norway, Sweden, Switzerland, Ireland, NewZealand,and selected states in Canada (Alberta, Quebec, andBritish Columbia) andthe US (Colorado, California, and Maryland). This list now includesAustralia which is one of the largest per capita carbon emitters in the de-veloped world.

The Australia government's emission reduction policy wasimplemented on the 1st of July 2012, thus making this a timely andrelevant effort to analyse the situation. The policy comprises a fixedcarbon price in the first three years, followed by a flexible (marketdetermined) price ETS from 2015 onwards. After the first majorreview on climate change in Australia by Garnaut (2008), the initial an-

iversity of Queensland, St Lucia,

djaye).

rights reserved.

nouncement of a proposed emission reduction policy in 2009 had to beshelved due to lack of popular support. It was however revived in early2011 and attracted widespread discussion and debate.

It is interesting to note that, among the developed countries such asthe US and the EU, Australia was unique for not being adversely affectedby the 2008/09 global financial crisis and amidst the recent economicdebt plaguing some of the EU economies, Australia remains directly un-affected. Thus the growth trajectory pathwith the implementation of theETSmay not be similar to other countries. In addition, unlike other coun-tries with an emission reduction policy, Australia is currently benefittingfrom a resource boom, driven to a large extent by the demand for coaland iron ore by the world's largest energy consumer, China. This hasimplications for greenhouse gas (GHG) emissions, particularly carbon di-oxide (CO2)which is believed to be responsible for approximately half ofthe man-made contributions to the greenhouse effect (IPCC, 2007).

The objectives of this paper are threefold. First, the macroeconomicimpacts (such as GDP, inflation, employment, imports and exports ofvarious sectors) of the recently implemented ETS are analysed. It is as-sumed for modelling purposes that Australia implements its ETS inthe absence of an international emission trading agreement. Second,the situation is reassessed by incorporating technological innovationin the renewable energy sector as set out in the government's plans.This sector is expected to play a leading role in Australia's efforts to re-duce emissions and herewe consider the possible impacts as part of theETS. Third, the impact of an alternative policy such as a fuel tax is alsoconsidered and compared. This provides direction for similar researchin other economies in their quest to consider carbon reduction policies.

Table 1Australia's energy sources, production and CO2 emissions in 2009/2010.Sources: ABARE (2010a) and U.S. Department of Energy, Energy Information Administra-tion (1993).

Sources Production in PJ (%) CO2 (kg)/PJ CO2 emissions in mT

Coal 2229 (37.5%) 93.3 208.0Black coal 1488 (25.0%) 92.1 137.0Brown coal 741 (12.5%) 96.6 71.5Oil 2058 (34.6%) 71.5 147.1Natural gas 1372 (23.1%) 52.8 72.4Renewables 286 (4.8%) 0 0Total 5945 (100%) 636

Notes: PJ is the measurement unit for production in peta-joules.mT is the measurement unit for CO2 and stands for 1 mT/1000 kg.Percentage of total production is given in brackets.

Electricity35%

Stationary energy excluding electricityTransport

16%

Fugitive emissions

8%

Industrial processes

6%

Waste3%

Agricultural15%

33J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

Many of the previous studies on the impact of an emission reductionpolicy such as a carbon tax (Bureau, 2011; Datta, 2010; Floros andVlachou, 2005; Lund, 2007; Proost and Van Regemorter, 1992 amongothers) were partial equilibrium models which are a snapshot analysisof the impacts and have the following disadvantages. First, they are un-able to consider interactions among different sectors in the economyand ignoring such secondary feedback effects and macroeconomicadjustments provides inaccurate analyses of the true impacts. Second,they are based on traditional macroeconomic analyses that consideraggregate macroeconomic variables but they mask the impacts on adisaggregated level such as the different sectors of the economywhichmay be of particular importance for policy. Third, they are a snap-shot analysis and do not differentiate between short and long run im-pacts which can have an important bearing on policy implications.

Computable general equilibrium (CGE) analysis on the other handovercomes the above problems in the partial equilibrium models. How-ever, to date, several studies (Bohringer and Rutherford, 1997; Bye andNyborg, 2003; Dissou, 2005; Kiuila and Markandya, 2009; Scrimgeouret al., 2005; Yusuf and Resosudarmo, 2007) have used CGE models toanalyse the carbon tax issue. Many of the earlier CGE studies were staticin nature and therefore were unable to trace the effects continuouslyover time.More recentmodels (reviewedbelow) used to analyse climatepolicy in Australia have been dynamic. Such models, including theMonash Multi-Regional Forecasting Model, (MMRF) applied in thisstudy, overcome this limitation to provide a better insight to the changesand adjustments required to smoothen the adverse impacts on the econ-omy. Another advantage of the dynamic approach is that it enables anal-yses that consider the implementation of different policies in specifictime periods or the incorporation of a combination of various policiesin different years. This aspect of time dimension is a crucial element ofa policy package that can be realistically analysed in line with the timeframe in place for implementation.

Adams et al. (2000) used an earlier version of the MMRF (calledMMRF-GREEN) to estimate the impacts of a carbon price of $44.33 perton CO2 required to reach Australia's Kyoto target. More recently, theAustralian Federal Treasury (Commonwealth of Australia, 2011a) hasused the GTEM model1 (a dynamic global CGE model) and the MMRFto analyse the implications of an ETS in Australia, while the Garnaut Re-view (Garnaut, 2008) also used the MMRFmodel in its investigation onclimate change. McKibbin et al. (2011a) used the G-cubedmodel whichis a dynamicmulti-country CGEmodel to analyse the implications of theCopenhagen Accord. The results show that, for Australia, CO2 emissionsdecline by 35% and real GDP declines by about 6% in 2020 relative toBAU.McKibbin et al. (2011b) have also used the G-cubedmodel to com-pare two carbon mitigation policies in the US — a carbon tax on fossilfuels in the energy sector and a tax credit for energy efficient householdcapital.

While these studies make a contribution to the climate change de-bate in Australia, some of them are commissioned reports and not pub-lished in peer-reviewed journals. Both the GTEM and G-cubed modelsare multi-country models with highly aggregated industrial sectorsand therefore do not provide as much sectoral detail as the MMRFmodel. Here, we revisit the carbon emission reduction policy by exam-ining alternative scenarios not considered by the above studies and wedraw some general implications for other countries considering an ETS.Specifically, we consider a fuel tax as an alternative policy to the ETS, aswell as modelling the role of technological change in a climate mitiga-tion policy. This is an important contribution because in Australia(and elsewhere), there is a lot of opposition to the ETS and it is thereforeof policy relevance to investigate whether there are alternative policiesthat could achieve the same environmental goal.

The paper is organized as follows. The next section presents anoverview of the energy mix, production and intensity in Australia.

1 The GTEMmodel has 13 regions (including Australia, the US, China and India) eachwith 19 industrial sectors and a representative household.

The third section discusses the current carbon policy targets of theAustralian government that form the basis of the three carbon policyscenarios that are simulated for analysis. The fourth section sets outthe theoretical model and data used while section five presents andanalyses the empirical results. The last section concludes with somepolicy implications.

2. The role of carbon in the Australian economy

The energy industry in Australia is a significant contributor to theeconomy as Australia's net energy exports are equivalent to morethan two-thirds of its domestic energy production. Energy exportsaccounted for 33% of Australia's total exports of goods and services in2008–09 and since 1988/89, the value of Australia's energy exports(in 2008–09 Australian dollars) has increased at an average rate of10% per annum (ABARE, 2011a, 2011b).

Table 1 profiles the energymix and the associated emissions in Aus-tralia. It can be seen that coal and natural gas are the major fuels pro-duced by Australia. Although oil is used to produce energy, this ismostly imported. Coal is the dominant fuel accounting for close to 40%of total energy production and over 80% of the electricity generated inAustralia uses coal as an energy source. Renewables on the other handonly represent about 5% of total energy production.

Fig. 1 shows Australia's total greenhouse emissions (excluding landuse and forestry)measured in carbondioxide equivalents. Electricity gen-eration is the highest at 35% followed by stationary energy (emissionsfrom fuels consumed in themanufacturing, construction and commercialsectors, and other sources like domestic heating), agriculture at 16% andtransport at 15%.

As a country, Australia's share of global GHG emissions is 1.47% andthis is lower than Italy, Canada, Germany, the UK, the USA and China(World Resources Institute, 2010). But at 27.5 mT of GHG emissions

17%

Fig. 1. Australia's greenhouse gas emissions by emission process.Source: DCCEE (2011a).

0.004

0.005

0.005

0.006

0.006

0.007

0.007

Fig. 2. Australia's energy intensity (PJ/A$ million).Source: ABARE (2011b) and ABS (2011).

34 J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

per capita, Australia is a worse polluter than other developed countriessuch as the USA, Canada, Ireland, Japan, New Zealand and Luxembourg(ibid). Nevertheless, Australia's energy intensity (defined as energyconsumption per unit of GDP) has been steadily declining as seen inFig. 2. According to ABARE (2011a), this could be due to two key factors.

First, technological improvement and fuel switching have led togains in fuel efficiency. Second, there has been more rapid growth inless energy intensive sectors (e.g. the service sector) compared tomod-erate growth in the energy intensive sectors (e.g. manufacturing). How-ever, the trend in energy intensity is not uniform in all states andterritories. For instance, Queensland and Western Australia have beenexperiencing a mining boom in recent years, which has translated intohigher energy intensities compared to the other states and territories.

3 Trade exposed industries are those with a ratio of the value of imports and exportsto the value of domestic production greater than 10% in any of the years 2004–2008.

4 For details, see DCCEE (2011b).5 Technological change is defined here as exogenously specified innovations or pro-

3. Australia's carbon policy framework

On 4th of May 2009, the then minister for Climate Change andWater, announced new targets for Australia's carbon pollution below2000 levels which were anchored under the Cancun agreement(Wong, 2010). These targets were: (i) an unconditional commitmentof 5% reduction of CO2-e (carbon dioxide equivalents orGHGemissions)from 2000 levels by 2020 (ii) a reduction of 15% from 2000 levels by2020 if major developing economies commit to substantial emission re-straints and advanced economies take on commitments comparable toAustralia's (iii) a reduction of 25% from 2000 levels by 2020 if theworldagrees to stabilize levels of CO2-e of 450 ppm or lower.

In light of the government's commitment under the Copenhagen Ac-cord, Table 2 presents estimates of Australia's ‘abatement challenge’,that is, the quantity of abatement for which additional policies are re-quired to generate in order to hit the 2020 target. It can be seen thatto achieve the 5% target (against 2000 levels), an additional 160 metrictons (mT) of CO2-e must be abated in 2020. To achieve the 15% target,an additional 216 mT CO2-e should be abated in 2020. Finally, toachieve the 25% reduction target, an additional 272 mT CO2-e shouldbe abated in 2020. In the absence of any action to abate GHG emissionsunder a BAU scenario, emissions are projected to reach 690 mTCO2-e in2020 or 24% above 2000 levels (ABARE, 2010b).

Since 2009, a number of policies (both voluntary and compulsory)2

have been implemented by the government to reduce emission levels.However, the most important move has been the Australian govern-ment's legislation of a carbon price of A$23 per ton CO2-e on key pol-luters commencing in July 2012 with fixed price increases for threeyears before moving to an ETS in 2015.

The Australian government is nevertheless intending to use/recyclethe revenue earned from the ETS (see MPCCC, 2011 for details) to

2 See http://www.climatechange.gov.au/en/government/initiatives.aspx for details.

i) increase the income tax threshold for consumers and to compensatelow income families andpensioners to copewith price increases, ii) givesubsidies to emissions-intensive trade-exposed3 industries (EITI) tomanage the impacts on business so as to maintain the competitivenessand economic sustainability, iii) and provide grants to encourage in-vestment in renewable technologies.

While there is a wide range of assistance schemes provided by thegovernment to industries,4 not all of them can be modelled due to thedifficulty of predicting investment and behaviour of industries. Herewe consider the effects of technological change brought about by thegovernment's clean energy policies. The flagship of this strategy is theRenewable Energy Target that will ensure that 20% of Australia's elec-tricity comes from renewable sources by 2020. Currently, renewableenergy contributes 9.64% to total electricity generation (DCCEE, 2011a).

In Australia, the key drivers in efforts to reach the Renewable EnergyTarget are photovoltaics and wind power. The latter, in particular,presents fewer risks on contributing to the grid due to better powergeneration and less down time. However, both technologies haveshown cost reductions over time as a result of technological improve-ments as well as economies of scale (Hearps et al., 2011). The Interna-tional Energy Agency (IEA, 2010) predicts that the 2010 capital costestimate of $1725/kW for wind power will decline to $1420/kW by2030, a reduction of about 18%. On the other hand, the Global WindEnergy Council (GWEC, 2010) predicts that the current capital cost of$1890/kW will decline to $1590/kW by 2030, which is a reduction of16%.

To simulate the above scenario, we assume that predicted cost re-ductions in renewable energy capital costs will be proportional to therate of technological change in the industry.5 On the basis of the IEAand GWEC findings, we conservatively assume that the capital cost ofrenewable energy generation will decline by 10% between 2013 and2023 (i.e. a decline of 1% per annum).

In our last simulation, we consider a fuel tax as an alternative to theETS. It is generally agreed in the environmental externality literaturethat market-based instruments (MBIs) such as taxes and tradablepollution permits are more effective in achieving environmental objec-tives than ‘command-and-control’ (CAC) methods such as regulationsor emission standards (Baumol and Oates, 1988; Ekins and Barker,2001). However, there is no general consensus on the effectiveness ofalternative MBIs. Studies such as Barker and Rosendahl (2000) haveargued that carbon taxes are more efficient than fuel or energy taxes.

cesses in the renewable energy sector that allow firms to lower their per unit genera-tion costs.

Table 2Australia's abatement challenge (mT CO2-e) in 2020.Source: DCCE (2010).

Target 2000 2020 Abatementchallenge

Reduction frombaseline (%)

Baseline emissions 589 690−5% target 530 160 23−15% target 474 216 31−25% target 418 272 39

35J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

However, a counter argument is that a carbon tax may have adversedistributional effects in the form of a reduction of the competitivenessof energy-intensive industries and the welfare of poorer households.Also a carbon tax could result in double taxation (i.e. taxation of both in-termediate inputs and final outputs) and the efficiency benefits of a car-bon tax may not be realised if other distortionary taxes (e.g. labour orpayroll taxes) or subsidies exist.

Since 1st of July 2006, the Australian government has implementedmajor reforms to the fuel excise system (Commonwealth of Australia,2004). For administrative simplicity, a banded fuel excise system wasadopted, with differing rates for high (38.143 cpl (cents/litre), medium(25 cpl) and low (17 cpl)) energy fuels. High-energy content fuels in-clude petrol and diesel, mid-energy content fuels include liquefied pe-troleum gas, liquefied natural gas and ethanol, while low-energycontent fuels include methanol. From 2012 to 2015, annual increasesreflecting a carbon tax charge will be applied to these rates. To simulatethis scenario, we applied increases to the current fuel tax rates whichwill help Australia reach its 5% target under the Copenhagen Accord.

3.1. Carbon policy simulations

Based on the above discussion, the following scenarios are analysed.

3.1.1. Scenario 1Based on the government's inflation-indexed emission reduction

policy, the following prices ($/t CO2-e) were imposed: A$23 in 2012, A$24.15 in 2013, and A$25.40 in 2014, with the price being determinedby the ETS from 2015 onwards. In this simulation, the additional reve-nue from the tax is returned to the representative household in theform of a lump sum transfer, thus maintaining revenue neutrality inthe government's budget.6

3.1.2. Scenario 2Technological change in renewable energy electricity generation is

modelled together with above scenario. Here we increase technologicalchange by 1% per annum between 2013 and 2023.

3.1.3. Scenario 3Fuel tax as an alternative to the ETS.We apply the following percent-

age increases to the existing rates: 23.7% in 2012; 26.4% in 2013; and29.7% in 20147; with the rates increasing by 2% per annum from 2015onwards.

6 This is not the same as modelling the government's policy of compensating low-income households because the model in this study has only one representative house-hold and therefore it would not be possible to identify the poor households.

7 The fuel tax rates were determined by using the model to predict CO2 emission re-ductions at different fuel tax rates for high, medium and low energy fuels. The ratesshown here are those that gave emission reductions close to the ETS over the simula-tion period.

4. Theoretical model and data

4.1. Overall model structure and dynamics

The dynamic CGEmodel used is theMMRF adapted fromAdams et al.(2011). The MMRF model distinguishes eight regions of Australia (sixstates and two territories), and the version used in this analysis accountsfor 58 industries producing 63 commodities. It also recognises GHGsfrom each of the 58 industries and eight regions. There are five agentsin the model: industries, capital creators, households, governments andforeigners. Each region in the MMRF model has a single household anda regional government, and there is also a federal government. Themodel allows interstate movements of commodities and factors of pro-duction (particularly labour) between the eight regions.

The model assumes a perfectly competitive market in which pro-ducers choose their inputs to minimise the cost of producing anygiven level of output subject to a given production technology. Substitu-tion is allowed between intermediate inputs and between primary fac-tors of production, capital, labour and land. Consumers choose goodsand services tomaximise utility subject to their budget constraints. Sub-stitution is allowed between commodities and between sources ofcommodities.

Themodel allows for inter-fuel substitution in electricity generation,as well as mechanisms for the endogenous take-up of abatement mea-sures in response to GHG policy measures. The MMRF model incorpo-rates most of the dynamic features of the MONASH model (Dixon andRimmer, 2001). One key driver of the dynamics is the specification ofthe process of physical capital accumulation. It is assumed that invest-ment in year t becomes operational in year t + 1. That is,

Kj;q t þ 1ð Þ ¼ 1–Dj;q

� �� Kj;q tð Þ þ Yj;q tð Þ ð1Þ

where

Kj,q(t) is the quantity of capital available in industry j in region q atthe start of year t;

Yj,q(t) is the quantity of new capital created for industry j in regionq in year t; and

Dj,q is the rate of depreciation in industry j.

Investment in industry j in region q in year t is explained by thefollowing mechanism:

Kj;q t þ 1ð Þ=Kj;q tð Þh i

–1 ¼ Ft j;q ERORj;q tð Þh i

ð2Þ

where ERORj,q(t) is the expected rate of return on investment in industry jin region q in year t, and Ftj,q[] is an increasing function of the expectedrate of return with a finite slope. Given a starting value for capital att = 0, Eqs (1) and (2) can be used to trace the time paths of industry cap-ital stocks. Further details on the model specification can be found inAdams et al. (2011).

The model is recursive dynamic in the sense that each period issolved as a static equilibrium problem given the stock of capital and in-vestment. The results for a particular period are used to update the da-tabase to form the basis for the next simulation and so on. The model'sequations are linearised and solved using Version 9 of GEMPACK(Harrison and Pearson, 1996).

4.2. Modelling emissions

Emissions in the database are broken down according to emittingagent, emitting state or territory and emitting activity. Most of the emit-ting activities relate to the burning of fuels such as coal, natural gas, orpetroleumproducts. This category of emissions is referred to as ‘activity’and covers emissions such as fugitives and agricultural emissions not

Table3

Base

case

projection

sof

grow

th,2

012–

2030

.

Variable

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

Nationa

lpop

ulation

grow

tha

1.40

1.38

1.38

1.36

1.35

1.34

1.33

1.32

1.21

1.20

1.19

1.17

1.15

1.13

1.11

1.09

1.07

1.05

1.02

Impo

rtsa

4.60

4.41

3.78

2.61

3.00

2.84

2.64

2.36

2.95

2.91

2.90

2.85

2.83

2.79

2.77

2.78

2.69

2.70

2.70

Agg

rega

tereal

inve

stmen

tb4.08

4.16

3.44

2.14

2.36

2.41

2.19

1.39

2.20

2.05

2.19

2.11

2.09

1.99

2.04

2.02

1.92

1.95

1.97

Nationa

lrea

lho

useh

old

cons

umptionb

2.82

3.57

3.01

2.41

2.97

2.83

2.83

3.22

2.78

2.78

2.63

2.61

2.60

2.60

2.55

2.51

2.53

2.49

2.46

Tourism

c7.72

0.14

2.79

7.24

6.71

9.82

8.65

7.34

5.26

5.37

5.23

5.05

4.96

4.88

4.78

4.75

4.34

4.52

4.45

Expo

rtvo

lumes

d4.51

4.66

3.86

2.58

2.90

2.75

2.71

2.36

2.95

2.91

2.90

2.85

2.83

2.79

2.78

2.78

2.69

2.70

2.70

Nom

inal

exch

ange

rate

b

−1.11

−3.53

−0.92

−1.23

−1.34

1.16

−0.68

−1.06

−0.38

−0.33

−0.25

−0.29

−0.28

−0.30

−0.26

−0.24

−0.33

−0.24

−0.22

Real

GDPe

3.26

3.94

3.24

2.30

2.73

2.65

2.62

2.58

2.54

2.50

2.43

2.39

2.38

2.35

2.33

2.30

2.28

2.27

2.26

Percen

tage

chan

gein

emission

s(K

t)f

1.95

3.63

2.82

1.06

0.31

2.42

0.24

2.94

0.27

−1.19

1.54

1.00

2.86

1.39

−0.19

1.51

1.25

2.27

1.59

Sources:

aAus

tralianBu

reau

ofStatistics.

bAccessEc

onom

ics.

cTo

urism

ForecastingCo

uncil.

dAus

tralianBu

reau

ofAgriculturala

ndRe

source

Econ

omics.

eAus

tralianFe

deralT

reasury.

fMcLen

nanMag

asan

ikAssoc

iates(2

010)

.

36 J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

related to fuel burning. There are several types of GHG emitting fuelssuch as coal, natural gas, gasoline, diesel, liquefied petroleum gas andother fuels. Fuel-burning emissions are modelled as proportional tofuel usage and price-induced substitution effects generate emission re-duction. Six types of electricity generators are recognized in themodel'sdatabase — coal, natural gas, oil, nuclear, hydro and other. Electricitygenerated from renewable energy (solar and wind) falls under the‘other’ category.

4.3. Base case projection

To assess the impacts of the environmental policies, we first usethe MMRF to produce a base case projection, which is defined as thegrowth path the economy would take without the policy (i.e. underBAU conditions). Following that, we undertake a second (deviation)projection with the policy change in place. The impacts of the policychange can then be measured by the differences between the devia-tion and BAU projections. The model's base year is 2005 and for theperiod 2006–11, model growth rates are based on data obtainedfrom published sources. The base case projections for key macroeco-nomic variables and emissions for use in the model parameters areshown in Table 3.

The variables that are forecast in the base case projections are setexogenous and shocked according to their projected values. In the de-viation simulation, these variables revert to being endogenous. Addi-tional shocks are then added in the deviation simulation as discussedin Section 3.1 to simulate the impacts of the three policy scenarios.

5. Empirical results

The simulation results are presented in three stages. We first con-sider the impacts of the policies on key macroeconomic variables. Fol-lowing that, we present results for the impacts on selected industriesand the impacts of the policies on emissions. At each stage we alsocompare the effects of the three policies.

5.1. Macroeconomic impacts

In the BAU scenario, economic growth is projected to decline froma rate of 3.3% in 2012 to 2.3% in 2030, which is equivalent to an aver-age growth rate of 2.6% per annum over the simulation period (seeFig. 3). With the ETS in place (Scenario 1), the economy grows at arate of 3.1% in 2012 and declines to 2.2% by 2030, which is an averageof 2.5% per annum. The policy therefore reduces average real GDPgrowth rate by about 0.1 percentage points per annum relative tothe BAU scenario.

The decline in GDP growth can be attributed to a number of fac-tors. Firstly, from the production side, the ETS increases the unitprice of investment, thus reducing the rates of return and investmentin factor inputs such as capital stock and labour, leading to a fall in ag-gregate employment (see Fig. 5). From the consumption side, the ETSraises the price of fuel, as well as the prices of commodities producedwith energy-intensive inputs. Thus in the short term (2012 ̶ 15) theprice level rises relative to BAU (Fig. 7) and real household consump-tion falls (Fig. 4). Taken together, the fall in real household consump-tion and real investment, coupled with a reduction in net exportsleads to a decline in real GDP relative to the base case (Fig. 3). Al-though real household consumption growth increases between2015 and 2020, investment growth is always below BAU and hencereal GDP growth is lower relative to BAU over the forecast period.

In the technological change scenario (Scenario 2), economicgrowth picks up after 2015. The average growth rate for Scenario 2is 2.7% over the period 2012–30, which slightly exceeds the first sce-nario and that of BAU. Fig. 4 shows that the ETS reduces real house-hold consumption by 0.1 percentage points per annum on averagerelative to BAU over the forecast period. The implementation of the

2.00

2.20

2.40

2.60

2.80

3.00

3.20

3.40

Scenario 1: carbon tax Scenario 2: tech change

Scenario 3: fuel tax BAU

Fig. 3. Impacts on real GDP growth (%).

0.6

0.8

1

1.2

1.4

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BAU Scenario 1: carbon tax

Scenario 2: tech change Scenario 3: fuel tax

Fig. 5. Impacts on aggregate employment (%).

37J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

ETS coupled with investment in renewable energy results in slightlyimproved growth in real household consumption and moderatesthe electricity price rises to some extent. The fuel tax (Scenario 3) im-pacts on real GDP and household consumption on the other hand aresimilar to that of the ETS.

The impacts of the policies on aggregate employment are com-pared in Fig. 5. In the short run (2012–15), the ETS reduces thegrowth rate of aggregate employment below BAU. However, employ-ment growth in the medium term (up to 2020) is higher than BAUand is marginally below BAU by 2030. Investment growth in the

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Scenario 2: tech change Scenario 3: fuel tax

Fig. 4. Aggregate real household consumption (%).

case of Scenario 2 (not shown) is higher than Scenario 1 and thereforeaggregate employment growth after 2015 is higher and is above BAUover the simulation period. The relatively positive impact of Scenario2 on employment and real household consumption compared to theother two scenarios underlines the importance of ensuring that com-plimentary policies accompany efforts by the government to mitigateemissions. The fuel tax once again has similar effects to the ETS.

Next, we analyse the implications of the policies on Gross StateProduct (GSP). Here we consider the key mining states of Queensland,New South Wales, and Western Australia, as well as the non-miningstate of Tasmania. As expected, the ETS has an adverse impact on theGSP of the mining states (see Fig. 6). On the other hand, it has a benefi-cial impact on Tasmania's GSP due to the fact that its major source ofenergy is hydroelectricity which has no GHG emissions. However, inScenario 2 (ETS plus technological innovation in renewable energy),we find that average annual GSP growth in all states is higher than in

0.00

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Queensland West Australia Tasmania

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Fig. 6. Impacts on average annual real gross state product growth (%).

2.802.852.902.953.003.053.103.15

BAUScenario 1:carbon tax Scenario 2:

tech change Scenario 3:fuel tax

2.802.852.902.953.003.05b

a

38 J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

the base case. The GSP impacts for Scenario 3 (fuel tax) are again similarto the ETS.

Fig. 7 presents the impacts of the three policies on the generalprice level in the selected Australian states. The immediate effect ofthe ETS is an increase in the product prices of emissions-intensive in-dustries such as coal-fired electricity plants and transport. The extentto which the tax is passed on to consumers depends on the energysubstitution possibilities within a given industry and the price elastic-ity of demand for the product. Our results indicate that prices risefastest relative to BAU during the fixed-price period of the tax, withthe steepest rises occurring in the key mining states of Queenslandand Western Australia. For selected states in Fig. 7, the price levelrises by 0.3% on average relative to BAU for the period 2012–14. Pricestend to stabilize after four years. Over the simulation period, pricesfall slightly below BAU in Queensland due to easier substitution inemissions-intensive industries. Prices rise fastest in Tasmania despiteit being the state where emissions decline the fastest (see Fig. 8). Thissuggests that the price rise in Tasmania could be related to otherfactors.

a

b

c

Fig. 7. a. Impacts on inflation (%) — Scenario 1. b. Impacts on inflation (%) — Scenario 2.c. Impacts on inflation (%) — Scenario 3.

2.75BAU Scenario 1:

carbon taxScenario 2:tech change

Scenario 3:fuel tax

Fig. 8. a. Aggregate export growth (%). b. Aggregate import growth (%).

Fig. 8 shows the impacts of the policies on aggregate exports andimports. Over the simulation period, the ETS (Scenario 1) causes aslight reduction in aggregate exports on average compared to BAU(Fig. 8). Implementation of the ETS coupled with technological inno-vation in the renewable sector (Scenario 2) raises aggregate exports

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ElecGa s ElecOthe r Forestry

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Scenario 1

Scenario 2 Scenario 3

b

a

Fig. 9. a. Losers — average GDP growth (%). b. Winners — average GDP growth (%).

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Scenario 1 Scenario 2 Scenario3

Fig. 10. Annual average change in emissions (%).

8 In general, the difference between the ETS and fuel tax is also due to the fact thatthe latter tends not to differentiate between levels of CO2 emissions. Thus to achievethe same emission level, the energy tax would need to be much higher.

39J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

above BAU. The ETS causes imports to contract relative to BAU butcoupled with technological change, imports increased compared tothe ETS alone but are still below BAU. The fuel tax (Scenario 3) how-ever results in the smallest increase in import growth.

5.2. Sectoral impacts

Fig. 9 shows the impacts of the policies on selected industries. TheETS has both negative and positive impacts at the sectoral level. Ascan be expected, energy-intensive industries are severely affectedby the policy. For example, for the coal industry, average growth is1.0% per annum less than in BAU (Fig. 9). This is due to the fairlyhigh level of fugitive emissions associated with coal mining. In addi-tion, faced with high energy costs that the coal sector is unable topass on to foreign buyers, this industry suffers a loss in exports. Theiron ore and steel industries also suffer contractions in output for sim-ilar reasons. Other mining industries (not shown here) that are se-verely affected by the ETS are the gas, the non-iron ore and othermining sectors, which decline by 0.6%, 0.2% and 0.6% per annum re-spectively. These averages however mask the impacts on individualstates. For example, in Western Australia, the gas sector contractsby 1.3% per annum on average while the coal industry in Queenslandcontracts by about 1.1% per annum on average.

In Scenario 2, where the ETS is implemented with investment in re-newable energy technology, we observe that the contraction in the coalindustry is much less with a fall in the average GDP growth of about 1%per annum relative to BAU. However, the impacts on the iron ore andsteel industries are not much different from Scenario 1, possibly be-cause ofmore constraints in substituting energy sources. A fuel tax (Sce-nario 3) instead of an ETS has a less adverse impact on the coal industrybut no significant impacts on the steel and iron ore industries.

Three industries are standout winners of the ETS. These are electric-ity generators who use gas and other energy sources (e.g. renewableenergy) and the forestry sector (Fig. 9b). These electricity generatorsare low-emission industries for whom the ETS causes substitution intheir favour at the expense of the high emission coal generators. As a re-sult, gas-generated electricity's GDP grows at an average of about 9%perannum on average, while electricity generated from other energysources grows at about 5% per annum on average. The forestryindustry's GDP increases because as a net sink, the ETS serves as a sub-sidy for this industry. The technological innovation and fuel tax policieshave similar impacts on the growth rates of the three industries.

5.3. Impacts on emissions

Fig. 10 indicates that the ETS is effective in lowering emissions in allthe states relative to BAU. Tasmania emerges as the state inwhich emis-sions decline the fastest, followed byNewSouthWales, andQueenslandWest Australia in that order. Relative to the base case, these statesswitch frombeing net GHGemitters to net GHG sinks. The rates of emis-sion decline for Queensland andWestern Australia (not shown) are rel-atively slow but remain below BAU at the end of the simulation period.In all the states, the largest contributions to aggregate reduction inemissions come from mining, agriculture and electricity generation.The results for Scenario 2 produce more drastic reductions in emissionsand all the states becomenet GHG sinks by the end of the simulation pe-riod. Replacing the ETSwith the fuel tax (Scenario 3) reduces emissionsin all states relative to BAU. However, the percentage reductions are lessthan in Scenarios 1 and 2. This suggests that the ETS is more effectivethan the fuel tax in reducing emissions.

5.4. Comparison with previous CGE carbon tax studies and policyimplications

In relation to themacroeconomic impacts, our results are fairly con-sistent with previous Australian studies. Adam et al.'s (2000) study

found that the imposition of a carbon price of $44.33 per ton in 2004–05 reduces real GDP in 2011–12 by about 0.6% relative to its base case.In the Treasury study, a carbon price of $23 per ton CO2-e reduces realGDP by 0.3% relative to base case by 2020 (Commonwealth ofAustralia, 2011a), while the GTEM model estimates Australia's GDP re-duction to be between 0.7% and 2.7% by 2020 for a carbon price of be-tween $20–$30 per ton CO2-e (Ahammad et al., 2006).

With regard to comparison of the efficiency of alternative policy in-struments for mitigating CO2 emissions, our results share some similari-ties with static CGE model studies conducted for other countries.Wissema and Dellink (2007) compared two environmental taxes for Ire-land— a carbon tax and a uniform energy tax. They found the carbon taxto bemore efficient in achieving a target of 25.8% reduction inCO2-e emis-sions. Scrimgeour et al. (2005) also compared the relative efficiency ofthree environmental taxes in New Zealand — an energy tax on all fossilfuels, a carbon tax and a fuel tax on petroleum products. Again it wasfound that the carbon tax is likely to achieve greater reductions in emis-sions and fossil fuel consumption than either the energy or fuel tax. Final-ly, Devarajan et al. (2009) compared three alternative policies to reduceemissions in South Africa. These were a carbon tax, a sale tax onenergy-intensive sectors and a sale tax on energy inputs. The carbon taxwas found to be the most economically efficient among the three taxes.

On the basis of our findings we can draw a number of general im-plications for other countries contemplating the implementation of aclimate change mitigation policy. The first is that an ETS is more effi-cient than a fuel tax in achieving a given environmental target. Thisconclusion is supported by other studies reviewed above. Whereasboth an ETS and energy tax provide incentives to reduce energy usecompared to CACs, the ETS is more economically efficient than the en-ergy tax because it induces users to substitute emissions-intensivefuels for energy sources with low or zero carbon intensity. This hap-pens because the price on carbon changes the prices of differentfuels to reflect their impacts in terms of CO2 emissions.8 Althoughnot specifically investigated in this study, a carbon tax has beenfound by other studies (e.g., Scrimgeour et al., 2005) to be more re-gressive by placing a greater burden on low-income households.Thus, it is of outmost importance for governments contemplating anemission mitigation policy to devise well-targeted assistance pro-grams to assist poor households. Finally, our results show that to en-hance economic output and achieve a greater reduction in emissions,

40 J. Asafu-Adjaye, R. Mahadevan / Energy Economics 38 (2013) 32–41

a carbon mitigation policy must be complemented with a strategy toaccelerate technological change in the renewable energy sector.Given the current high costs and long-term nature of the financialbenefits, it is unlikely that firms will invest in this area of their ownaccord. However, in view of the positive externalities involved,there is a strong case for public investment in the form of subsidiesor tax incentives to assist industries to transition to less polluting en-ergy sources.

6. Conclusion

In this study we have considered the macroeconomic and sectoralimpacts of three environmental policies to reduce Australia's CO2

emissions using a dynamic CGE model of the Australian economy.The ETS causes real GDP to decline at an average of 0.1% per annumrelative to BAU. It also has an adverse impact on welfare proxied byreal household consumption. This is due to a loss in employmentand small rises in consumer prices. Although the ETS coupled withtechnological innovation in the renewable sector did not providemuch improvement, it significantly improved aggregate employmentin the medium to long term compared to the other policies.

At the industry level, the GDP growth of energy-intensive indus-tries such as coal, iron ore, steel and coal-powered electricity genera-tion is severely affected by the policies. The winners from the ETS areelectricity generators who use gas and renewable energy sourcesand the forestry sector. The impacts from Scenarios 2 and 3 arenot much different. The ETS adversely affects the GDP growth ofmineral-dependent states such as Queensland and Western Australiaalthough it is effective in lowering emissions in all states. Our resultsshowed the ETS to be more efficient at reducing CO2 emission thanthe fuel tax.

Our study simulated a 10% technological change in the renewableenergy sector over a ten year period but this only slightly improvedresults of the standalone ETS strategy. Hence for more significant im-provements, public policy towards incentives for technological changein this sector must be seriously considered. Under the government'sClean Energy Future policy (DCCEE, 2011b), A$800 million has been al-located to assist manufacturers who invest in low pollution technolo-gies but this assistance is earmarked for industries that use more than300 MW h of electricity of 5 TJ of natural gas a year. There is no assis-tance to firms to invest in renewable energy sources such as wind orsolar energy which our results have shown to be important. Given thecurrent high costs of renewable energy and the obvious environmentalbenefits, there is strong justification for governmentfinancial assistancein this sector.

The Clean Energy Future policy document also includes a $1.3 billionCoal Sector Jobs Package to assist coal mines to implement carbonabatement technologies. Our results indicate that in the key miningstates where there is a fall in GDP as a result of the ETS, this may needto be complemented with structural adjustment assistance to commu-nities and businesses.

In the long run, there is however a need for the government totake a comprehensive look at existing distortionary state and federaltaxes such as income tax, savings tax, property tax and indirect taxeswhich may negate the efficiency gains of the ETS (Barker andRosendahl, 2000). In fact, the Australian government has acknowl-edged the shortcoming of the current taxation system in a policydocument, A Tax Plan for Our Future and is planning a reform of busi-ness, environmental and social taxes (Commonwealth of Australia,2011b).

To conclude, it is worth considering the limitations of the studyand how the results could be extended in future research. Themodel is based on neoclassical assumptions such as perfectly compet-itive markets and constant returns to scale. While these assumptionsmake the model easier to solve, it is well known that they may notapply in practice. Future research could consider extensions such as

imperfect competition and increasing returns to scale. Furthermore,the current model could be further enriched to analyse severalschemes that will be in place at some stage under the Clean Energy Fi-nance Corporation, the Australian Renewable Energy Agency, theClean Technology Program, the Steel Transformation Plan, the CarbonFarming Futures Fund and the Biodiversity Fund. As the details ofthese plans have yet to be finalised, presently, investment and behav-iour in response to these plans are difficult to predict and hencemodel.

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