Evaluating the Efficiency of Carbon Emissions Policies in a Large Emitter Developing Country

Abstract

This study compares the effects of three carbon emissions mitigation strategies – a carbon tax, a fuel tax and an emissions trading scheme (ETS) to combat the intended emissions target for Indonesia, a large emitting developing country. Although the fuel tax raises economic growth for this net oil importing economy, the carbon tax and ETS have less adverse effects on inflation, welfare loss, wage decline, and employment loss. Unlike the fuel tax, the carbon tax and ETS also promote substitution towards renewable energy, thereby contributing to future emissions mitigation. While the two policies show similar impacts, the carbon tax is the more practical choice in the short to medium term for developing countries with political economy constraints in their energy and transportation sectors, because it is simpler and can be implemented more swiftly than an ETS.

Key words: Carbon tax, fuel tax, emissions trading, computable general equilibrium model.

1. Introduction

Climate change is a global environmental externality that recognizes no borders. Because

greenhouse gases (GHGs) directly diffuse to the atmosphere and local climate change is

influenced by the global climate system, the incremental impact of an extra ton of GHGs on

climate change is independent of where it is emitted in the world (Stern, 2007). The

consequences of climate change vary across countries, and developing or low-income countries

who have historically contributed the least to the problem will be affected the most (Tol, 2009).

On the other hand, current trends show that GHG emissions from developing countries will

exceed those from developed countries within the first half of this century, thereby highlighting

the need for developing countries to contribute to efforts to mitigate future emissions (Chandler

et al., 2002).

Emissions mitigation is particularly urgent for large emitting developing countries but efforts to

do so are complicated due to the need for such economies to reduce emissions by a large amount

(and thereby cope with substantial reductions in the much needed economic growth for their

development) and to implement policies swiftly to reach their global commitment given by their

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intended nationally determined contributions (INDC) toward addressing climate change in the

2015 Paris Agreement.1 Thus this paper uses Indonesia as a case study given that it is the third

largest carbon emitter in the developing world after China and India and it has been projected

that Indonesia’s energy-related CO2 emissions will increase to over 800 million tons by 2035,

representing more than a two-fold increase over 25 years (Tharakan, 2015). The case study is

relevant due to the serious effort made by the Indonesian government through a 2011

Presidential Decree to set aside a Special Allocation Fund for Energy Efficieny to reduce GHGs

by 2020 and a commitment of up to 41% reduction in GHGs with international support

(Haryanto, 2015). But the 2016 International Energy Efficiency Scorecard2 which examines the

energy efficiency (the least-cost means of meeting new demand for energy) policies and

performance (measures energy use per unit of activity or service extracted) of 23 of the world’s

top energy-consuming countries has ranked Indonesia to be 18th. This low position highlights the

need for major improvements in Indonesia’s energy policies.

The literature has identified several tools for emissions mitigation such as the carbon tax,

emissions trading scheme (ETS), and fuel tax but the choice of which tools should be used to

achieve effective and efficient emissions mitigation is highly case specific. Stern and Noble

(2008) propose three basic criteria to assess emissions mitigation options: (i) Effectiveness,

which is achieving GHG emissions reduction of the required scale; (ii) Efficiency, which is

related to policies that can be implemented in the most cost-effective way with minimum adverse

effect on economic growth; and (iii) Equity, which addresses the fact that poor countries are

more vulnerable to climate change impacts and that wealthy countries are responsible for the past

emissions. For Indonesia, being a large emitter developing country highly endowed with non-

renewable energy resources and a player in the world energy market, the trade-off between

emissions mitigation and a substantial decline in economic growth needs to be carefully

managed to ensure sustainable economic growth.

1 The Paris Agreement adopted in December 2015 under the United Nations Framework Convention on Climate Change (UNFCC) aims to hold the increase in the global average temperature to well below 2ºC above pre-industrial levels and pursue efforts to limit it to 1.5ºC (UNFCC, 2015).2 See http://enertic.org/imgfiles/enerTIC/2016/Contenidos/20160-aceee-2016-international-energy-efficiency-scorecard.pdf

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To date, several studies (Datta 2010, Li et al. 2014; Spiller et al. 2014; Sterner 2012; Yan and

Crookes 2009) have found the fuel tax to be quite effective in reducing adverse environmental

effects in developing and developed countries. Others such as Alton et al. (2014), Calderón et al.

(2016), Chen and Nie (2016), Coxhead et al. (2013), Chandler et al. (2002), Long and Kim

(2014), and Vera and Sauma (2015) have considered carbon tax impacts on emissions in a range

of countries. Asafu-Adjaye and Mahadevan (2013) on the other hand compared the effects of a

domestic (within sectors in the economy) ETS and a fuel tax in Australia while Babiker et al.

(2004) and Troung (2010) considered the impact of an ETS operational within the EU. Truong

(2010) also compared a carbon tax with the ETS. As both these studies used different models and

welfare measures, their results were different in that Truong (2010) found welfare increases for

all but two EU countries while Babiker et al. (2004) reported welfare losses for all the EU

countries. For Indonesia in particular, Dartanto (2013) considered the reduction of fuel subsidies

on poverty while Nurdianto and Resosudarmo (2016), and Yusuf and Resosudarmo (2015)

examined the impact of a carbon tax principally on the distributional impacts on households in

terms of inflation, their real expenditure and income, and country-wide poverty and income

inequality.

This paper contributes to the existing literature in three ways. First, a comparative anlaysis of

three different tools – a carbon, tax, fuel tax and ETS are undertaken. The impacts of these tools

on several macroeconomic variables and the sectoral output and labour market outcomes of key

sectors are discussed and contrasted against the effectiveness and efficiency performance criteria

of emission controls. Second, two policy mix scenarios combining the carbon and fuel tax are

considered to shed light on the prospect of addressing adverse impacts and managing trade-offs

that may result from relying on a single policy. Third, unlike previous studies, the tax scenarios

are not based on an adhoc or random hypothetical basis but instead utilize Indomesia’s INDC

target. Thus the scenarios considered are directly useful to draw lessons for emissions control

policy.

The remainder of the paper is organized as follows. Section 2 reviews the relevant literature and

section 3 describes the modeling framework which is based on the Energy-Environmental

Version of the Global Trade Analysis Project (GTAP-E), the database used, and the simulated

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shocks for the analyses. The results of the study are presented and discussed in section 4 while

section 5 concludes.

2. Literature Review

The policies for GHG mitigation consist of a variety of economic instruments, ranging from

taxes and subsidies to income transfer schemes to quotas based on the carbon content of goods

(Wing, 2007). Among them, policies which are directly focused on energy induced emissions

aim to reduce the rate of non-renewable energy use, especially fossil fuels by reducing the

demand for energy and transport services which are generated from the above sources. The

literaure has also identified command-and-control measures such as environmental standards and

regulations where there is a mandated level of performance from polluters to take specific

actions that is enforced by law (Wang 2013). These could take the form of permissible pollution

or emission levels; technology-based standards that specify particular techniques or equipment

that firms must comply with inorder to address emissions; management-based standards that

require the implementation of particular management practice or industrial production process

(Harrison and Kostka, 2014).

The advantage of these standards is that they can be simple, direct and set on different bases for

different industries for the desired impacts but the disadvantge is that this requires significant

information gathering costs for planners and often a high degree of state capacity is required to

monitor and police the efforts (Kostka, 2016). Thus market-based instruments (MBIs) such as a

carbon tax and ETS are said to be more economically efficient and effective policies for carbon

emissions control (Nordhaus, 2014; Aldy and Stavins, 2012). It has been further argued that for

the effective mitigation of GHGs, the key requirement is a behavioral change. That is, both

producers and consumers need to change their energy source from fossil fuels to renewables over

time. This can be done with the aid of price signals to reflect the full cost of carbon in energy

sources.

One available tool for governments to use is the ETS which imposes a quantity cap on emissions

and generates a scarcity and this in turn creates a market determined price for emissions. Another

tool is the carbon tax which imposes a tax (price) on emissions. The tax increases the price of the

good or service and hence lowers the demand for it, which in turn reduces the quantity of

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emissions indirectly by the cutback in the production of that good or service (Andrew and

Kaidonis, 2011).

There are also other behavioral changes that will occur in the economy. Since carbon-intensive

goods will be associated with higher market prices and lower profits, market forces adjust in a

cost-effective way to minimize the emissions. The tax encourages conservation measures, energy

efficient investments, fuel and product switching, and alterations in the economy’s production

and consumption patterns. Moreover, the indirect effect through revenue recycling strengthens

the above impacts through changes in investment and consumption patterns (Baranzini et al.,

2000). Stiglitz (2016) argues that a carbon price that is high enough to reflect the social cost of

carbon emissions yields three types of benefits to local economies and the world. First, it helps

the world to achieve the agreed goal of limiting climate change. Second, it leads to larger

investments in local economies to mitigate global warming. Third, the revenues generated from

the carbon tax could be used to address other social and economic problems.

The carbon tax appeals to both economists and policy makers because of its ability, in theory, to

achieve a given level of emissions reduction at least cost to the society. The same effects can be

obtained through an ETS in principle by allocating emission permits to emitters and enabling

them to trade among themselves (Elkins and Baker, 2001). The literature shows that there is a

broader equivalence between an ETS and a carbon tax under a precise set of restrictive

assumptions (Farrow, 1995; Pezzey, 1992). However, the issue of which policy is more efficient

for carbon emissions control under a particular circumstance is a challenging one and the

evidence has so far been inconclusive in the literature. As noted above, the main difference

between an ETS and a carbon tax is whether a quantity or price adjustment is desired. With the

carbon tax, the price of carbon is fixed, and the amount of CO2 will be adjusted accordingly.

Conversely, with an ETS, it is the quantity of CO2 that is fixed, and the price of emissions

permits will be adjusted (Elkins and Baker, 2001).

Some studies suggest that the type of uncertainty must be taken into account when choosing the

optimal policy for different circumstances. As shown by Weitzman (1974), it is desirable to use a

carbon tax when there is uncertainty over the control cost function while it is preferable to fix the

quantity through an ETS when there is uncertainty about the damage function. Freebairn (2016)

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argues that a tax instrument with a comprehensive base, combined with effective revenue

recycling could be a suitable future policy for Australia, which is a large emitter developed

country. Babiker et al. (2004) showed that economic efficiency was a major issue for the ETS

under the Kyoto Protocol as it was found to be welfare decreasing in some EU countries because

of the pre-existing distortionary energy taxes.

Conversely, a small carbon tax on production contributes to the growth of social welfare in a

high emitter country like China (Chen and Nie, 2016). It is sometimes argued that an ETS is

desirable than a carbon tax because its outcome is more certain in achieving a particular target

(Pizer, 2006 and Elkins and Baker, 2001). However, this certainty is dependent on the structure

of the permit scheme and thus it is vital to have clear, enforceable, and well-mentioned emissions

target (ibid). It has been suggested that both of these instruments or multiple policies could be

combined to form a hybrid policy for an effective carbon emissions control (Lehmann, 2012;

Bennear and Stavins, 2007; Pizer, 2006; and Elkins and Baker, 2001). On the other hand, some

studies point out that although the combination of an ETS and a carbon tax is theoretically

possible, such a policy mix could be difficult to implement. This is due to the fact that

governments may be reluctant to implement unfamiliar or untested policy alternatives.

Furthermore, the inertia of existing instruments may make them difficult to displace (Sorrel and

Sijm, 2003).

The fiscal policies on transportation fuels could also play a vital role in mitigating GHG

emissions. This is mainly because of the rising demand for oil-based products which has been

driven by rapid growth in the transportation sectors in many economies. The literature shows that

fuel taxes (or removal of fuel subsidies) have a direct impact on fuel demand and oil imports and

hence on associated CO2 emissions. Morrow et al. (2010) argue that as the transportation sector

is the sector which consumes the majority of United States’ oil imports and produces a third of

the country’s total GHG emissions, a direct fuel tax would result in the greatest reductions in the

country’s CO2 emissions. The study also finds that an economy-wide carbon tax will

significantly reduce CO2 emissions in the electricity sector but will have a marginal impact on

emissions from the transportation sector. Estimating the past trends of energy demand, future

trends, and GHG emissions in China’s road transportation sector, Yan and Crookes (2009) find

the fuel tax to be one of the measures that would be most effective in reducing total energy

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demand, petroleum demand, and hence GHG emissions. Sterner (2012) shows significant

environmental effects associated with the fuel tax with country case studies in Europe and Japan,

while Spiller et al. (2014) and Li et al. (2014) provide some theoretical justification and

empirical evidence on the reduction of negative environmental externalities using fuel taxes.

The above discussion illustrates that there is a variety of climate policies available and the

selection of the optimal single policy or a policy mix is not a straight forward exercise. Although

theoretical justifications are offered to rationalize a particular policy or multiple policies, other

factors could hinder the achievement of optimality by such policies. The literature points out that

when multiple policy instruments are operated in a world which is characterized by one or more

constraints in a general equilibrium system (referred to as the second best world), it prevents the

attainment of Pareto optimal conditions (Lipsey and Lancaster, 1956; Bennear and Stavins,

2007). Market failures, institutional capacity limitations, and most importantly, political

economy issues are some of the major constraints (Lehmann, 2012 and Bennear and Stavins,

2007). Past experiences in various countries have shown that the implementation of the carbon

tax and ETS tend to be hindered by political constraints resulting in sub-optimal results (e.g., see

Gawel et al., 2014; Del Río and Labandeira, 2009). For example, Jenkins (2014) showed that due

to political constraints, US carbon prices have remained as low as US$2-8 per ton of CO2. Hence

it is vital to consider both economic and political constraints in designing and selecting an

optimal policy for carbon emissions control.

With Indonesia in particular, Gunningham (2013) warns of the complex energy trillema

consisting of competing demands of energy security, the need for emissions control for climate

change mitigation and the need to address energy poverty. This is further complicated by the

existence of a distorted energy market given by the very high fuel subsidies and thus

substantially low gasoline and diesl prices compared to other countries (Gesellschaft für

Internationale Zusammenarbeit 2014). Although there have been some sporadic reductions in the

fuel subsidy over time (see CEIC Data 20173), using a partial equilibrium analysis, Luthfi and

Kaneko (2016) suggest that the Indonesian government should continue the ongoing reduction of

fuel subsidies to a point of complete removal in 2012 as that will reduce emissions by about 70

million tons (Mt) CO2. Daranto (2013) on the other hand used a computable general equilibrium 3 See http://www.ceicdata.com

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(CGE)-microsimulation model of Indonesia to simulate reductions in fuel subsidies and showed

that the savings accruing from phasing out the subsidies could be used to compensate low-

income households for energy price rises to effectively reduce poverty.

A reduction of the carbon tax of US$30 per ton of CO2 was however examined by Yusuf and

Resosudarmo (2015) using an Indonesian CGE model with a carbon emissions module while

Nurdianto and Resosudarmo (2016) used a multi-country CGE model called the Inter-Regional

System of Analysis for ASEAN and simulated a carbon tax of US$10 per ton of CO2. While the

former study found a contraction in GDP growth, the latter found an increase in GDP growth

although both studies conclude that the carbon tax is by and large progressive.

3. The Modelling Framework

3.1 The GTAP-E Model

Since the late 1970’s, the policy debate in western economies has gained much more interest on

energy policy evaluation. Among the various instruments available for these policy analyses,

CGE models have emerged and have been recognized as standard empirical tools especially for

ex-ante policy analysis (Holmoy, 2016). Since energy is an input in almost every economic

activity and there are limited substitutes for fossil fuels, energy policy effects are transmitted

through multiple markets resulting economy-wide effects (Wing, 2007). Energy policies such as

carbon taxes are market-based interventions, and the responses of the economic system for the

changes in these policies can be identified through CGE models (Taylor, 2016). A survey of

general equilibrium approaches for energy policy modeling has been conducted by Bergman

(1988). He finds that general equilibrium types of energy-economy models are both needed and

useful for two reasons. The first relates to the constant relation between energy consumption and

economic growth, which is difficult to justify on the basis of economic theory but needs to be

supported by empirical results. Second, the energy CGE models need to show not only how the

energy/GDP ratio varies with energy prices and policy measures, but also give a detailed picture

of the operation of some substitution mechanisms in the economy (ibid). Bhattacharyya (1996)

has surveyed the literature on general equilibrium models applied to energy studies, emphasizing

their unique features, evolution through time, and their limitations.

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Given the above advantages of the CGE approach, this study adopts it to capture economy-wide

effects associated with the energy and transport sector policy reforms evaluated in this study.

More specifically, we make use of the GTAP-E model developed by Burniax and Truong (2002)

and revised by McDougall and Golub (2007), together with the GTAP-E Database Version 9

(see Narayanan et al., 2015). The GTAP-E model introduces an energy-environmental dimension

to the standard GTAP model that enables the model to be used to analyze energy, GHG issues

and related policy issues.4

The standard GTAP model is a comparative-static, multisector, multiregional CGE model, which

considers perfect competition and constant returns to scale. The model is based on national or

regional input-output tables, and it fully tracks bilateral trade flows between all the countries in

the database. It uses the standard neoclassical assumption whereby consumers maximize utility

and firms maximize profits. The representative regional household’s expenditures are governed

by an aggregate Cobb-Douglas utility function, and it allocates constant budget shares of the

spending across three types of final demand, namely, private, government, and savings. Private

household preferences are represented using the non-homothetic constant difference elasticity

functional form. Unlike the standard GTAP model, the GTAP-E model has been reformulated by

adding a carbon tax to the demand function based on consumption of commodities (i.e. coal, oil,

oil products, and gas). Therefore, CO2 emission is a function of consumption.

Firms face a nested constant elasticity of substitution (CES) production function which uses

primary factor endowments (land, labor, capital, and natural resources) and intermediate inputs

to produce final goods. The household receives wages/rental rates from the firms in return to the

employment of factor endowments. Also, firms sell outputs to the other firms to be used as

intermediate inputs, to private households, government, and to the global market. The goods are

differentiated by their country of origin following the Armington assumption (Armington, 1969).

Therefore, the model fully tracks bilateral trade flows (Hertel and Tsigas, 1997). The production

structure of the GTAP-E model has been obtained by incorporating an explicit capital energy

composite input and a new endowment value-added nest formed by natural resources to the

standard GTAP model. The capital energy composite also follows the CES functional form. The

energy nest consists of multilevel structure of electric and non-electric energy, and the non-

4 See Burniax and Truong (2002) for the full documentation of the GTAP-E model.

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electric energy to coal and non-coal inputs based on Armington assumption (Figure A1 in the

Appendix details the capital energy composite structure). The model contains two global sectors,

a global bank and the other one related to the international transport activity.

Compared to the original GTAP-E model, the revised version has several advantages which are

useful for this study. First, CO2 emissions are calculated using a bottom-up approach, which was

not the method in the original model. This approach ensures that emissions are proportional to

the energy consumption of firms, households, and the government and are sourced from both

domestic and imported products. The carbon tax used in the revised model is a bloc level

variable which specifies both nominal and real rates and the relationship between them. The

assumed carbon and fuel tax policies used in the study will affect the prices and quantities of

energy and other commodities, resulting in changes in their consumption and production levels.

The production system in the revised version consists of more intermediate levels of nesting and

combinations of using capital with energy.

3.2 Aggregated Database and Shocks

This GTAP-E model is calibrated based on the GTAP 9 database and the extended energy

balances which are compiled by the International Energy Agency. The database consists of 140

regions, and for each region, CO2 emissions are distinguished by fuel type. We combined the 140

GTAP regions into 18 aggregates, and 57 GTAP commodity sectors into 10 aggregates as shown

in Table A1 in the Appendix. The base year economy of 2011 was used as it is the latest

available reference in the database.

The price homogeneous closure and an exogenous carbon tax rate variable were considered in

our simulations. Based on the carbon content of the commodities, the carbon tax is applied to

coal, oil, gas, and oil products. Conversely, based on consumption of both domestic and

imported oil products, the fuel tax was levied using its individual tax levers.

The INDC target specified by Indonesia in the Paris agreement was a 26% reduction of GHGs

against BAU (in 2010) by 2020 and a 29% reduction by 2030. Indonesia’s energy sector is

heavily dependent on non-renewable sources such as fossil fuels and natural gas (comprising

almost 95% of the national energy mix) that contribute significantly to GHG emissions

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(Mujiyanto and Tiess, 2013). The average growth of primary energy was around 7.7% per year

during the past four decades and the economy is estimated to grow at an annual average rate of

over 6% and with a population pf 307 million people by 2050, energy demand can be expected to

be high (Ibrahim et al., 2010). Thus addressing Indonesia’s future energy demand is an urgent

concern.

We used four policy instruments — carbon tax, fuel tax,5 policy mix of carbon and fuel taxes,

and an ETS — to achieve approximately 50% of Indonesia’s INDC target by the year 2030,

which represents a reduction of 56.4 Mt of CO2. We chose 50% and not the full target as

Indonesia’s emissions are also due to factors such as deforestation, peatland conversion, and

other land uses (CAIT Climate Data Explorer 2017). After running several trials of the above

instruments, we identified the levels of the carbon tax, fuel tax, and two options of policy mix

which are capable of achieving the set target of an approximately reducing emission by 15% to

be: a carbon tax of US$36/ton of CO2 (tCO2) or a fuel tax of 105% levied on oil products, or a

larger share of carbon tax (US$24/tCO2) combined with a smaller proportion of the fuel tax

(20%), or else a lower share of carbon tax (US$11/tCO2) combined with a larger share of the fuel

tax (50%). We also applied an emissions cap of 15% in the Indonesian carbon market to compare

the effects of an ETS with emissions mitigation taxes. All simulation scenarios are outlined in

Table 1.

[Table 1]

4. Results and Discussion

As seen in Figure 1, the total CO2 emissions in Indonesia in the base year is approximately 387

MtCO2. The consumption of oil products is the primary cause of emissions, accounting for

almost 204 MtCO2 (53%), followed by coal (108 MtCO2, 28%) and gas (75 MtCO2, 19%).

Approximately 22% of the country’s emissions come from coal-fired electricity plants. The most

highly consumed energy product in the private sector is oil products, of which approximately

42% of the emissions are due to private consumption of imported oil products. Approximately

48% of the emissions generated from firms’ consumption of oil products come from imported oil

products.

5 As the base year in the model is 2011, it was not possible to incorporate the reductions in fuel subsidy since 2013.

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[Figure 1]

4.1 Emissions Management under Mitigation Taxes and the ETS

For each policy option in Table 1, the emissions reduction from various energy sources and

energy price index are significantly different as shown in Table 2. The carbon tax and ETS show

the highest percentage reduction of CO2 emissions from coal sourced energy while the fuel tax

shows the maximum percentage reduction from oil products. This is because the carbon tax is

levied on all the energy sources (i.e. coal, oil, gas, and oil products) based on their carbon

content whereas the fuel tax is a consumption tax levied only on oil products.

[Table 2]

The changes in the energy price index under each scenario reflects how the mitigation taxes and

ETS will affect the country’s electricity sector, its energy mix, and energy substitution. Coal

being the energy source with the highest carbon content shows the greatest rise in prices with the

ETS (117%) and the carbon tax (113%). Given that oil products are the country’s most

consumed energy source, we observe the highest increase in price (114%) with the fuel tax. The

percentage increase in the prices of coal and oil products under the two policy mix strategies are

based on the relative proportions of carbon and fuel tax in the policy mix. For example, under the

policy mix strategy with the lower portion of a carbon tax and a higher share of fuel tax, we

observe a greater increase in the price of oil products (53%) compared to the percentage rise in

coal price (32%).

The changes in the prices of electricity are driven by the country’s energy mix. Most of

Indonesia’s electricity generation in 2013 was sourced from coal (44%), followed by oil products

(23%), gas (21%), hydropower (7%), and geothermal power (5%) (Tharakan, 2015). Our

simulation results indicate that the fuel tax is associated with the highest percentage increase in

electricity prices (59%). This is driven by the high share of oil products in the power generation

mix and the significant increase in the price of oil products associated with the fuel tax. Also, the

proportional reductions in demand for oil products in the generation mix is 35% with a

significant increase in demand for gas (47%) compared to coal (8%), resulting in increased

substitution towards gas-based electricity generation. Both the carbon tax and ETS increase

electricity prices by approximately 29%. Although the carbon tax and ETS result in coal price

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rises of 113% and 117%, respectively, we observe that the relative increase in the price of oil

products is nearly eleven times lower compared to the fuel tax despite the fact that coal

represents a greater share of power generation in the country. Amongst the policy options, we

find that the carbon tax and ETS result in the highest reductions in coal consumption

(approximately 36%) in the power sector and promote substitution towards oil-based generation.

4.2 Macroeconomic Impacts

The macroeconomic impacts of the selected mitigation tax scenarios and the ETS are compared

in Table 3. Compared to the base case, the fuel tax and the two policy mix have the most

favorable impacts with small gains in real GDP. The fuel tax results in a GDP increase of 0.29%

while the policy mix with a larger (smaller) share of carbon tax produces a 0.06% (0.34%)

increase. The GDP decomposition shows that there are small increments in consumption,

government expenditure, and exports while investment and imports decline with improvements

in net exports. On the other hand, the carbon tax and ETS reduce GDP (by 0.11-0.12%) in the

counterfactual scenario. The GDP decomposition shows that in both policies, reductions in

consumption, investment, and government expenditure outweigh the improvements in net

exports and hence a cost in terms of GDP.

[Table 3]

In general, the mitigation taxes and ETS result in improvements in the trade balance more so for

fuel tax and high proportions of taxation on oil products in the policy mix options. This is mainly

due to the larger reductions in oil imports and improvements in the trade balance in other

industries and services which are not energy intensive. With the fuel tax, the trade balance

improves in the counterfactual scenario by US$26,881 million, of which the highest

contributions are from other industries and services (58%) and oil (49%) (see Table 4). The

carbon tax (ETS) improves the economy’s trade balance by US$7,160 million (US$7,392

million) with 72% of the share coming from other industries and services and 22% from oil. The

policy mix with a larger proportion of fuel tax (carbon tax) delivers a trade balance of

US$16,120 million (US$10,265 million) with major shares of import reductions and trade

improvements in the same two sectors mentioned above.

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These results indicate that taxation on oil products using the fuel tax or policy mix (with higher

share of fuel tax) improves net exports and hence the trade balance substantially more than the

respective carbon tax and ETS. The country’s energy mix is dominated by oil. For example,

Indonesia was ranked 20th in the world as an oil producer, contributing 1.2% of total global

production in 2010 (BP Statistical Review of World Energy, 2011). However, production

declined by 33% over the period 2000 to 2009 turning Indonesia from an oil exporter to a net

importer in 2004. Rising oil prices and lack of refinery capacity also contributed to oil imports

(Mujiyanto and Tiess, 2013). Our results suggest that a tax policy or ETS could result in savings

from oil imports and at the same time drive the country towards a low-carbon economy. The

carbon tax and ETS showed almost identical impacts on net exports and the trade balance and

could be appropriate policies in a country whose future energy mix would be dominated by coal.

it has been shown that these approaches reduce CO2 emissions generated from all the energy

sources whilst encouraging substitution towards renewable energy sources.

[Table 4]

The electricity generation and the energy-intensive industries use substantial amounts of

domestic coal and gas. On the other hand, the transportation sector uses significant amounts of

domestic and imported oil products. The taxation on energy commodities and ETS are a cost to

producers and hence affect their profits. Firms pass this burden to consumers through increased

prices of goods, which is then reflected in the economy by an increase in the consumer price

index (CPI). The fuel tax results in the highest increase in the CPI (4.6%), followed by the policy

mix with a larger proportion of fuel tax (2.27%) and the policy mix with a smaller share of fuel

tax (1.02%). The carbon tax and ETS have a lower inflationary effect in the economy (0.49%-

0.51%). The rise in CPI can be explained by the sectoral changes in prices shown in Table 4. The

fuel tax results in the highest increase in the price of energy goods, oil products (20%), electricity

price (62%), transport sector prices (28%), and energy-intensive industries (4%). The carbon tax

and ETS are associated with the least increase in the prices of the above sectors and hence the

least increase in the CPI.

The carbon tax and ETS are more attractive and cost-effective market-based instruments given

their revenue generating capacity and hence the ability to compensate losers. The results indicate

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that the carbon tax and ETS could generate revenues of US$1396 million and US$1445 million,

respectively. These revenues are equivalent to approximately 0.16%-0.17% of Indonesia’s 2011

GDP. The policy mix option with a larger share of a carbon tax also could generate US$933

million in revenue. The revenues from the fuel tax and the policy mix with a greater share of fuel

tax are accounted for under the other indirect taxes, and they are far below those from the carbon

tax and ETS.

The welfare changes for each scenario are also reported in Table 3. Welfare in the model is a

money-metric measure of total household income at constant prices, represented by the

equivalent variation (EV) expressed in millions of US$ in constant 2011 prices. In general,

Indonesia experiences a net welfare loss with both tax and ETS policies. This is similar to

Troung’s (2010) welfare result for the carbon tax and ETS for the EU. In all the scenarios for

Indonesia, the losses in allocative efficiency and terms of trade effects outweigh the gains in

output change effects. The carbon tax and ETS are associated with the least welfare deterioration

(US$1586-US$1596 million) compared to the fuel tax (US$14797 million) and policy mix

options (US$2274-US$5249 million). The allocative inefficiency is a result of the movement of

inputs from high marginal value product sectors to low marginal value product sectors (Huff and

Hertel, 2000). For example, the substitution away from cheaper inputs which are now being

taxed to the expensive alternative inputs cause increases the cost of production and hence the

lowered marginal value product in such sectors.

As noted by Babiker et al. (2004), the loss in welfare associated with carbon taxation may be

primarily due to the existence of distortionary energy taxes. In the case of the ETS, the general

proposition is that emissions trading may be welfare decreasing when primary gains from trading

are outweighed by the secondary costs which are related to pre-existing distortions and market

imperfections (ibid). The pre-existing distortionary taxes cause efficiency losses and hence

affect welfare. Also, pricing fuels lower than its cost is inefficient as it leads to overconsumption

(Davis, 2013). The massive consumer fuel subsidies that existed in Indonesia before 2015

contributed to significant economic distortions and market imperfections (Winters and Cawvey,

2015). Statistics indicate that the country spent US$17.7 billion on energy subsidies in 2012,

which was approximately 17% of total government expenditure (Ministry of Finance, 2012). The

15

current fuel subsidies in Indonesia required the proposed fuel tax to be higher in this study (i.e.

105%) than it would be otherwise.

4.3 Sectoral Impacts

As seen in Table 4, sectoral outputs are determined by the emissions intensity of different

industries. Production processes with higher emissions intensity such as electricity, gas, oil

refinery, energy intensive industries, and transport are the sectors whose outputs decline and

prices rise the most. These effects are more pronounced with the fuel tax, especially in sectors

like electricity, energy intensive industries, and transport where the most used energy source is

oil products. Given the relatively low electrification ratio in Indonesia6 compared to other

developing countries, households who have access to electricity are more adversely affected by

the fuel tax compared to the other policy options. Also, poor households who already have

access to electricity but consume a lot due to larger family sizes would be worse off as a result of

taxation. Since the fuel tax is levied only on oil products and hence it enables substitution

towards gas and coal, outputs of these sectors increase by 17% and 16% and respectively. With

the fuel tax, the price of coal declines by 4% given the higher price inelasticity of coal compared

gas. This would encourage more use of coal than gas in Indonesia.

The output deterioration is minimum with the carbon tax and ETS in the energy-intensive

industries, transport, and oil products. Although the least production decline in the electricity

sector is achieved with the policy mix strategies, the carbon tax, and ETS are associated with the

least price rises in those sectors. Also, the coal and oil industries experience positive output

changes and price decline with the carbon tax and ETS. This is because the price elasticity of

demand is high enough to cause significant reductions in demand with the taxation and ETS

scenarios. Also, even if the carbon tax is levied on all the energy sources, the country needs to

fulfill its energy demand from the available next-best options. Therefore, the rising coal and oil

outputs may be due to t substitution towards the cheapest energy sources available. The literature

suggests that in the absence of market-distorting fuel subsidies, higher fuel prices will be an

6 Improving electrification rate has been prioritised in Indonesia’s National Energy Policy as the electrification ratio was 80.5% in 2013 (IEA 2015). However, this figure has been reported to be less than 65% in several parts of the country (ibid).

16

incentive for renewable energy development (Aklin and Urpelainen, 2013). Indonesia has

already explored renewable sources such as geothermal resources for electricity production in

1974. However, such resources have not come into operation as fast as coal-fired plants. Given

the slow pace of renewable energy development, especially geothermal development for

electricity generation, the Indonesian government is more likely to build more coal-fired plants

to fulfill rising energy demand in the future (Winters and Cawvey, 2015).

Sectors such as other industries and services, oil, oil products, coal, and gas experience

improvements in their trade balance in response to all the tax options and the ETS. The

agriculture and forestry sectors also see improvements in their trade balance. In general, the fuel

tax policy produces more favorable outcomes compared to the other policy options due to the

greater exports expansion and significant imports contraction in these sectors. Given that the

country is a net oil importer and oil is the most widely used energy source in most industries, the

impact of the fuel tax on imports, exports, and therefore the trade balance is higher compared to

the other policy options. Oil and oil products imports decline by 62% and 20%, respectively,

while the respective exports increase by 96% and 52% respectively in response to the fuel tax.

Therefore, it would appear that the fuel tax could promote Indonesia’s status as a net exporter.

The sectors adversely by the tax policies and the ETS are electricity, energy intensive industries,

and transport. Exports of these sectors are seen to decline and imports increase resulting in

negative trade balances. The results further indicate that these adverse effects are more

significant with the fuel tax than the other policy options. For example, exports of energy

intensive industries decline by 19% with fuel tax compared to around 4% with the carbon tax and

ETS. By and large, all the energy-intensive industries are affected less adversely compared to the

fuel tax.

4.4 Employment Impacts

The tax policies and the ETS have a negative impact on both skilled and unskilled wages as seen

in Table 5. The wage reduction is threefold with the fuel tax compared to the carbon tax and

ETS. In line with the reduction in wages, there is a negative effect on both skilled and unskilled

employment in the counterfactual scenario. The worst affected sector in terms of employment is

the oil products sector, which is more adversely affected by the fuel tax than the other policy

17

options. This is because oil products is the sector which is most highly exposed to the tax

incidence. The reduction in household income as a result of the decline in employment and

wages also contributes to the welfare losses indicated in Table 3.

[Table 5]

Table 5 also shows that the electricity and transport sectors experience a dramatic increase in

employment despite the demand for their products declining due to the increases in prices. One

explanation for this observation is that these sectors produce essential products and services that

have no close substitutes which therefore make it difficult for consumers to switch consumption.

For example, in the case of electricity consumers may switch to energy efficient appliances

which could result in decline in the demand for electricity. Another reason is that as these

products and services are price inelastic, even if the demand decreases, the overall GDP value

will rise as a result of the increase in price which outweighs the contraction in demand.

Furthermore, more labor may be absorbed into these sectors to substitute the production process

with expensive carbon intensive inputs. Overall, the least adverse effects on employment is given

by the carbon tax and ETS.

5. Conclusions

This paper analysed and compared the efficiency of carbon emissions control, macroeconomic,

sectoral, and employment effects of five potential emissions reduction policies in Indonesia to

reach its INDC targets. We employed the GTAP-E model to simulate a carbon tax, a fuel tax,

two policy mix strategies with different proportions of carbon and fuel tax, and an ETS to

examine the economy-wide effects in the counterfactual scenario.

Our results indicated that the MBIs of the carbon tax and the ETS produce similar results as was

found by Truong (2010) for the EU. There was also a net welfare loss associated with these

MBIs which was the case for the EU countries in Babiker et al’s (2004) study. Unlike these

policies, the fuel tax for Indonesia however performs well in terms of output expansion but the

carbon tax and ETS result in higher revenue generating effects, lower inflation, and have less

adverse effects in terms of welfare loss, decline in wages and employment loss. Although the

fuel tax is the more cost-effective in terms of the increase in GDP, considering the other

18

economy-wide effects, we find the MBIs to be the best option for emissions mitigation in

Indonesia. Since the MBIs are levied on all the energy sources, they have greater flexibility to

allow future electricity prices to adjust and they also promote substitution towards renewable

energy, thereby contributing to future emissions mitigation. However, the fuel tax was found to

result in the greatest improvement in the trade balance through import contraction but this is due

to Indonesia being a net energy importer.

The choice of which MBI is more effective for emissions control is not straight forward. It has

been argued that the carbon tax could be more stable than the ETS due to the high volatility of

the carbon price experienced in the EU, the largest emissions trading market in the world. To

date, the only operating ETS successful in reducing emissions has been the US Acid Rain

Program (Andrew and Kaidonis, 2011). Indonesia is a large emitter developing country with a

distorted energy market and a high level of political economy constraints, especially in the

energy and transportation sectors (Kaneko and Kawanishi (eds. 2016). Hence it is highly

unlikely that in the short to medium term it will have in place the proper institutions required to

participate in an ETS. The carbon tax therefore remains as the more realistic choice for Indonesia

because it is much simpler and can be implemented much quicker than an ETS. Admittedly,

sound governance at both national and regional level is required for the carbon tax to be effective

given concerns of governance in Indoneisa (Gunningham 2013).

As is the case in any study, this study has some limitations which can be addressed in future

research. First, the actual trend of emissions can be lower or higher than the BAU projections

depending on the other emissions control policies and practices implemented in the country. As

the emissions reduction targets and adopted tax rates are based on BAU projections, an under or

overestimated BAU projection will affect the robustness of the simulation results. Second, as the

study has identified the carbon tax as the best policy option, the impacts of revenue recycling

should be incorporated to design a comprehensive carbon tax policy and assess the options more

holistically. Revenue recycling options that can be considered in future research include

investments in renewable energy exploitation, generation, and adoption (e.g., geothermal energy

as Indonesia owns substantial geothermal resources) and technological change in the electricity

generation mix and transportation sectors. Third, a policy mix strategy combining the carbon tax

19

and the ETS could be a better option in the second best world which has not been considered in

this study.

20

Table 1 Simulation Scenarios To Meet the INDC Emissions Target

Policy Scenario Scenario Description

Carbon Tax CTax36 Carbon tax of US$36/tCO2

Fuel Tax FTax105 Fuel tax of 105% on oil products

Policy MixCF24_20 Carbon tax of US$24/tCO2 and fuel tax of 20% on oil products

CF11_50 Carbon tax of US$11/tCO2 and fuel tax of 50% on oil products

Emissions trading scheme ETS Indonesia reduce emission levels by 15%

21

Figure 1 Base Year CO2 Emissions

Coal Gas Oil-products Total0

50

100

150

200

250

300

350

400

450

107.87

75.25

203.97

387.09

CO2

Emis

sion

s (M

tCO

2)

Note: The CO2 emissions from oil for Indonesia at 0.0024 is negligible and hence not reported.

Source: GTAP-E Database Version 9. 

22

Table 2 Impact on CO2 Emissions and Energy Prices

Indonesia Carbon Tax Fuel Tax Policy Mix ETSCTax36 FTax105 CF_24_20 CF11_50

CO2 Emissions (% Change)

Coal -33.71 4.71 -25.88 -13.05 -34.4Oil -13.68 33.3 -5.43 8.83 -13.99

Gas -13.19 15.88 -7.9 1.47 -13.59Oil Products -5.1 -35.79 -11.41 -21.61 -5.26

Total -15 -15 -15 -15 -15

CO2 Emissions Abatement (MtCO2)

Coal -36.36 5.08 -27.92 -14.08 -37.11Oil 0.00 0.00 0.00 0.00 0.00

Gas -9.93 11.95 -5.94 1.11 -10.23Oil Products -10.40 -73.00 -23.27 -44.08 -10.73

Total -57 -56 -57 -57 -58

Energy Price Index

(% Change)

Coal 112.82 -5.78 74.2 31.69 116.83Oil -0.77 -7.6 -2.44 -4.55 -0.79

Gas 25.97 -2.76 16.92 6.68 26.9Oil Products 10.49 113.58 24.33 52.72 10.81

Electricity 28.55 59.05 30.61 37.76 29.48

Source: Model simulations

23

Table 3 Macroeconomic Impacts

 Carbon Tax Fuel Tax Policy Mix ETS

CTax36 FTax105 CF24_20 CF11_50

GDP (% Change) -0.12 0.29 0.06 0.34 -0.11 Private Consumption -0.05 0.60 0.18 0.54 -0.04 Investment -2.86 -10.06 -3.90 -5.92 -2.94 Government Expenditure -0.10 0.33 0.10 0.40 -0.09 Exports 1.63 5.52 2.45 3.32 1.71 Imports -1.90 -7.75 -2.61 -4.64 -1.93

Consumer Price Index (CPI) (% Change) 0.49 4.6 1.02 2.27 0.51

Trade Balance (US$million) 7160.2 26881.4 10264.63 16119.68 7392.15

Tax Revenues (US$million) Carbon Tax/Trading Revenue 1395.6 - 5.8 431 1445.2 Other Tax Revenue (Including fuel tax) -2.6 19.6 933.3 18.9 -2.1Welfare (EV measured in US$ million) -1595.64 -14796.76 -2274.15 -5248.91 -1585.91 Allocative Efficiency Effects -1070.11 -14041.07 -1727.76 -4755.2 -1118.1 Terms of Trade Effects -574.76 -993.52 -612.53 -600.01 -519.84 Output Change Effect 220.83 176.3 190.74 161.52 227.76

Source: Model simulations

24

Table 4 Sectoral Effects

Indicator Policy Instrument

Agr

icul

ture

Fore

stry

Coa

l

Oil

Gas

Oil

prod

ucts

Elec

trici

ty

Ener

gy In

tens

ive

Indu

strie

s

Tran

spor

t

Oth

er In

dust

ries

and

Serv

ices

Output (% Change)

Carbon Tax Ctax_36 -0.01 -0.28 1.88 0.08 -3.7 -5.51 -15.38 -2.3 -1.95 -0.26Fuel Tax Ftax_105 -0.8 -2.4 15.75 -1.91 16.75 -50.56 -17.08 -10.85 -11.97 -2.24

Policy Mix 1 CF24_20 -0.1 -0.53 3.99 -1.02 0.38 -18.09 -14.43 -3.63 -3.72 -0.48Policy Mix 2 CF11_50 -0.33 -1.13 7.88 -1.69 6.6 -31.28 -13.94 -6.21 -6.89 -1.02

ETS -0.01 -0.29 2.11 0.09 -3.81 -5.74 -15.79 -2.44 -2.03 -0.27

Price (% Change)

Carbon Tax Ctax_36 -0.65 -1.29 -1.43 -1 -0.16 0.83 28.56 0.84 3.28 -0.57Fuel Tax Ftax_105 -2.72 -3.85 -3.91 -7.24 -0.83 19.79 62.21 4.13 28.07 -1.46

Policy Mix 1 CF24_20 -0.86 -1.61 -1.77 -2.83 -0.31 3.2 30.96 1.35 7.1 -0.69Policy Mix 2 CF11_50 -1.4 -2.31 -2.43 -4.72 -0.51 7.93 39 2.32 14.52 -0.91

ETS -0.67 -1.31 -1.45 -1.01 -0.14 0.89 29.51 0.87 3.38 -0.58

Contribution to the Trade Balance (US$million)

Carbon Tax Ctax_36 269.94 8.69 1311.19 1559.45 447.39 1195.15 -0.49 -2229.2 -585.44 5183.51Fuel Tax Ftax_105 1228.88 27.8 3749.99 13146.6 2401.99 4848.58 -1.22 -10067.7 -4169.65 15716.13

Policy Mix 1 CF24_20 369.44 11.03 1638.27 4732 885.56 821.25 -0.54 -3555.02 -1237 6599.64Policy Mix 2 CF11_50 622.73 16.18 2277.42 8118.07 1424.44 2690.2 -0.72 -5967.64 -2380.98 9319.98

ETS 275.19 9.01 1404.92 1629.15 466.17 1243.89 -0.5 -2368.15 -608.99 5341.46

Exports (% Change)

Carbon Tax Ctax_36 2.89 5.92 5.86 9.56 3.81 -3.45 -75.49 -4.28 -11.36 3.68Fuel Tax Ftax_105 12.78 19.11 16.98 95.55 19.99 -51.98 -93.34 -19.19 -60.31 9.94

Policy Mix 1 CF24_20 3.86 7.51 7.33 29.95 7.4 -12.08 -77.92 -6.79 -22.64 4.52Policy Mix 2 CF11_50 6.39 11.01 10.22 54.46 11.86 -26.77 -84.19 -11.39 -39.8 6.06

ETS 2.95 6.13 6.19 10.03 3.94 -2.69 -76.56 -4.48 -11.78 3.79

Imports (% Change)

Carbon Tax Ctax_36 -1.66 -3.43 18.59 -8.84 157.03 -4.31 69.57 1.06 4.64 -2.24Fuel Tax Ftax_105 -7.71 -11.18 -15.47 -62.07 -6.24 -19.52 214.79 4.6 42.74 -7.69

Policy Mix 1 CF24_20 -2.3 -4.4 13.01 -26.13 92.02 -3.39 80.36 1.7 10.45 -2.97Policy Mix 2 CF11_50 -3.93 -6.56 2.63 -42.17 34.37 -10.62 113.53 2.82 21.81 -4.43

ETS -1.68 -3.5 18.76 -9.17 164.28 -4.32 72.34 1.24 4.86 -2.32

Source: Model simulations

25

Table 5 Labor Market Effects (Percentage change)

  Industry

Carbon Tax Fuel Tax Policy Mix Policy Mix ETSCTax36 FTax105 CF24_20 CF11_50

Skilled Unskilled Skilled Unskilled Skilled Unskilled Skilled Unskilled Skille

d Unskilled

Average Wages -1 -0.99 -3.55 -3.65 -1.35 -1.37 -2.05 -2.1 -1.02 -1.02

Employment in Various Industries               Agriculture 0.06 0.06 -0.71 -0.69 -0.01 0 -0.22 -0.21 0.06 0.06  Forestry -0.37 -0.38 -2.59 -2.57 -0.64 -0.63 -1.26 -1.25 -0.39 -0.39  Coal -0.78 -0.82 8.38 8.84 0.71 0.75 3.36 3.56 -0.55 -0.58  Oil 0.03 0.03 -3.91 -3.87 -1.76 -1.75 -3.03 -3.02 0.04 0.04  Gas -2.96 -2.97 19.05 19.17 1.23 1.24 7.81 7.86 -3.04 -3.05  Oil products -3.29 -3.3 -34.98 -34.9 -13.29 -13.28 -22.32 -22.27 -3.44 -3.45  Electricity 18.41 18.4 61.46 61.68 23.16 23.18 34.89 34.97 18.99 18.97  Energy Intensive Industries -0.2 -0.21 -3.77 -3.67 -0.78 -0.76 -1.91 -1.87 -0.28 -0.29

Transport 8.58 8.56 71.06 71.36 17.45 17.47 35.39 35.5 8.82 8.81  Other Industries and Services -0.32 -0.34 -2.77 -2.63 -0.66 -0.64 -1.35 -1.29 -0.33 -0.34

Source: Model simulations

26

Appendix

Figure A1 The GTAP-E Model Capital-Energy Composite Structure

Source: Burniaux and Truong (2002).

27

Table A1 Regional and Sectoral Aggregation

Aggregated Regions Countries Included Aggregated

Sectors Commodities Included

1. Oceania Australia, New Zealand, Rest of Oceania 1. Coal Coal mining

2. Cambodia 2. Oil Crude oil

3. Indonesia 3. Gas Gas manufacture, distribution

4. Laos 4. Oil products Petroleum, coal products

5. Malaysia 5. Electricity Electricity

6. Philippines 6. Forestry Forestry

7. Singapore 7. Transport Transport nec, Sea transport, Air transport

8. Thailand

9. Vietnam

10. Rest of South East Asia

11. East AsiaChina, Hong Kong, Japan, Korea, Mongolia, Taiwan, Brunei Darussalam, Rest of East Asia

8. Agriculture Paddy rice, Wheat, Cereal grains nec1, Vegetables, fruits, nuts, Plant based fiber, Crops nec1 Vegetables, fruits, nuts, Plant based fiber, Crops nec, Oil seeds, Sugar cane, sugar beet, Plant-based fibers, Cattle, Sheep, Goats, Horses, Animal products, Raw milk, Wool, Silk-worm cocoons, Meat: Cattle, Sheep, Goats, Horses, Fishing

12. South Asia Bangladesh, India, Nepal, Pakistan, Sri Lanka, Rest of South Asia

9. Energy intensive industries Chemical, rubber, plastic products, Mineral

products nec, Ferrous metals, Metals nec.

13. North America

Canada, United States of America, Mexico, Rest of North America 10. Other industries

and servicesMeat: cattle, sheep, goat, horse. Meat products nec, Vegetable oils and fats, Dairy products, Processed rice, Sugar, Food products nec, Beverages and Tobacco products, Textiles, Wearing apparel, Leather products, Wood products, Paper products, publishing, Metal products, Motor vehicles and parts, Transport equipment nec, Electronic equipment, Machinery and equipment nec, Manufactures nec, Water, Construction, Trade Transport nec, Sea transport, Air transport, Communication, Financial services nec, Insurance, Business services nec, Recreation and other services, Pubadministration/Defense/Health/Education, Dwellings.

14. Latin America

Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela, Rest of South America, Costa Rica, Guatemala, Honduras, Nicaragua, Panama, El Salvador, Rest of Central America, Dominican Republic, Jamaica, Puerto Rica, Trinidad and Tobago, Caribbean.

15. European Union 25

Austria, Belgium, Cyprus, Czech Republic, Denmark, Germany, Estonia, Finland, France, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherland, Poland, Portugal, Slovenia, Slovakia, Spain, Sweden, United Kingdom

16. Sub-Saharan Africa

Benin, Burkina, Faso, Cameroon, Cote d' lvoire, Ghana, Guinea, Nigeria, Senegal, Togo, Rest of Western Africa, South Central Africa, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe, Rest of Eastern Africa, Botswana, Namibia, South Africa, Rest of South African Customs

17. Middle East and North Africa

Egypt, Iran, Morocco, Tunisia, Turkey, Rest of North Africa, Rest of Western Asia.

18. Rest of the World

Switzerland, Norway, Rest of EFTA, Albania, Bulgaria, Belarus, Croatia, Romania, Russian Federation, Ukraine, Rest of Eastern Europe, Rest of Europe, Kazakhstan, Kyrgyzstan, Rest of Former Soviet Union, Armenia, Azerbaijan, Georgia.

   

Note: 1stands for not elsewhere classified.

28

Source: Authors’ aggregation using GTAP database Version 9.

29

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