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Economy-Wide Implications of a Carbon Tax in the Chilean

Electricity Generation Sector

Working paper

Carlos Benavides, Luis Gonzales, Manuel Díaz, Rodrigo Palma a, Gonzalo

García, Catalina Ravizza, Rodrigo Fuentes b

a Energy Centre, Faculty of Physical and Mathematical Sciences, University of Chile. Avenida Tupper 2007,

Santiago, Chile.

b Institute of Economics, Pontifical Catholic University of Chile.

Avenida Vicuña Mackenna 4860, Macul, Santiago, Chile

Abstract

This paper aims to analyse the economy-wide implications of a carbon tax applied on the Chilean

electricity generation sector. In order to reach this, both energy sectorial model and dynamic

stochastic generally equilibrium (DSGE) model have been used. An approach is proposed to

integrate results from the electricity generation sector and the DSGE model. Carbon taxes of 10,

20, 30, 40 and 50 US$/tCO2e are evaluated. We evaluate the carbon tax as energy prices shocks

on the path of gross domestic product (GDP). We suppose that these shocks are the result of

applying different values of the carbon tax in the electricity generation sector.

The results show that the effectiveness of this policy depends on some variables which are not

controlled by policy makers, for example, the non-conventional renewable energy investment cost

projection, price of natural gas, and feasibility of exploiting hydroelectric resource. For a carbon

tax of 20 US$/tCO2e, the average annual emission reduction should be between 1.1 and 9.1

million tCO2e. However, the price of the electricity (electricity generation level) should increase

between 8.3 and 9.6 US$/MWh, which is equivalent to a 7.4% and 8.5% with respect to currently

electricity price This shock decreases the annual GDP growth rate in a maximum value of 0.15%.

It means that the average yearly GDP growth rate will be 3.35% versus 3.5% of baseline scenario.

Economy-Wide Implications of a Carbon Tax in the Chilean Electricity Generation Sector

In addition, in this paper the carbon tax is compared to other policies: introduction of renewable

energy source and a sectorial cap. The results show that the same emission reduction can be gotten

with these policies but the macroeconomic impacts are lower.

Keywords: climate change, carbon tax, macroeconomic models, DSGE, generation expansion

planning, renewable energy

1. Introduction

Chile is a minor contributor to global GHG emissions (0.2%), however, its emissions

has grown by 232% over a 16-year period (1990-2006), according to the national GHG

inventory, see Figure 1 ( Ministry of Environment, 2011). These statistics also show

that the energy sector is responsible of the major amount. By 2011, the GHG emissions

from the energy sector reached 76.3 million ton CO2 eq., and electricity generation

sector reached 61.8 TWh (electricity consumption). Carbon intensity reached 0.49 kg

CO2/2005 USD (0.29 kg CO2/2005 USD PPP) (International Energy Agency, 2011).

Chile’s motivation to contribute to worldwide emissions reductions stems from

the United Nations Framework Convention on Climate Change (UNFCCC) and its

principle of common but differentiated responsibilities. The country intends to

contribute to achieving the ultimate objective of the Convention by undertaking

mitigation actions, as well as to take advantage of the potential environmental and social

benefits and improvements in the quality of growth that can be directly derived from

mitigation actions. In this line, “Chile will take nationally appropriate mitigation actions

to achieve a 20% deviation below the “Business as Usual” emissions growth trajectory

by 2020, as projected from year 2007 according to the commitment of Chile at

Copenhagen in 2010.

A more comprehensive analysis will help to exactly establish how Chile will

fulfil its commitment to achieve a 20 per cent reduction below the ‘business as usual’

emissions growth trajectory in year 2020. This paper aims to analyse the impact of a

2


specific economic instrument: a carbon tax applied to electricity generation sector. A

carbon tax would achieve reduce the GHG emission through two broad effects – a

demand effect, reducing energy demand due to higher prices in the electricity sector but

also in the whole economy, and a substitution effect, with switching from more to less

carbon intensive fuels. The second effect is analysed in this paper.

Figure 1: Chilean national inventory by sector

Carbon taxes have mostly been implemented in Scandinavian countries, Australia, and a

few other European countries. Finland (1990), Sweden (1991), Norway (1991) and

Denmark (1992) led the way in implementing a carbon tax (SBS, 2013).

Most previous studies have used computable general equilibrium (CGE) model to

analyses the economic implication of the mitigation actions.

In the LMTS process in South Africa (Vorster, Winkler, & Jooste, 2011)

(Winkler, 2010) a MARKAL modelling results considered the direct effects in the

energy sector only and economy-wide analysis was carried out using a CGE model. The

CGE analysis converted a given level of a CO2 tax to a comparative tax on coal, crude

oil or natural gas used as intermediate inputs in production processes. In (Mongelli,

3


Tassielli, & Notarnicola, 2009) an IO matrix is used to estimate the short-term effects of

a carbon tax in Italy, which include the percentage increase in prices and the increase in

the imports of commodities to substitute domestically produced ones as intermediate

input.

Sectorial model also have been used in previous studies. In (Zang, 2012) a fuzzy

mixed-integer energy planning model under carbon tax policy is developed. In

(Kainuma, Matsuoka, Morita, & Hibino, 1999) an end-use energy model is presented

for assessing policy options to reduce greenhouse gas emissions. This model evaluates

the effects of imposing a carbon tax on various carbon emitting technologies in order to

reduce CO2 emissions. In (Careri, Genesi, Marannino, & Montagna, 2011) the impact of

renewable energy source incentives and mitigation policies (feed-in tariffs, quota

obligation, emission trade, and carbon tax) are considered in the framework of the

generation planning problem to be solved by a generation company. Renewable energy

quota and emission limits result in a set of new constraints to be included in a traditional

generation expansion planning model. In (Chen, Kang, Xia, & Zhong, 2010) an

integrated power generation expansion planning model towards low-carbon economy is

proposed.

In this paper both energy sectorial model and DSGE have been implemented.

We have selected a DSGE model instead of a CGE model. DGSE and CGE modelling

have in common that they belong to the micro founded macroeconomic models of

general equilibrium, but they differ in two important issues regarding modelling results:

dynamics and uncertainty. DSGE models are strictly dynamic models, while CGE

models are comparatively static ones. “Dynamic” or recursively-dynamic CGE models

can only be achieved by considering perfect foresighted agents or myopic agents. The

dynamic characterization of the models allows for optimal decision rules that are not

4


policy invariant, and where time is directly considered. This characterization has gained

in terms of allowing analysing the paths of the variables behaviour instead of comparing

different equilibriums, and by analysing the reachability of them, relative to the static

characterization. The treatment of uncertainty in the DSGE models makes them get

ahead over CGE models, which are deterministic, because it allows a better fit of the

theoretical models with the data. These features are causing a shift from CGE to DSGE

modelling, becoming a valuable tool for assessing policy analysis, mechanisms analysis

and projections (Del Negro & Schorfheide, 2012).

In Chile there are two main independent power systems, the Central

Interconnected System (Sistema Interconectado Central, SIC), and the Norte Grande

Interconnected System (Sistema Interconectado del Norte Grande, SING). Figure 2 and

Figure 3 show the historical generation by source for the SIC and SING, respectively.

The hydroelectricity generation is one of the main source in the SIC, while in the SING

coal is one of the main energy sources.

Chile has a big potential to produce electricity with renewable energy sources,

such as hydroelectric (12,000 MW), solar (1,000,000 MW), wind (40,000 MW) and

geothermal (16,000 MW) sources (Energy Ministry, GIZ, 2014). However, some issues

have affected the development of this technology in Chile. In the case of solar and wind

energy, there is uncertainty related to the evolution of the investment cost in the future

and the access to long term contracts constitutes a barrier for project developments.

In the case of hydroelectric source, environmental problems have faced some

projects. For example, the hydroelectric generation potential of the Aysén region has

been estimated in more than 7,000 MW. Two specific projects have been evaluated in

this zone: HidroAysen (2,750 MW) and Cuervo (640 MW). However, these projects

have had the opposition of several groups due to these will install in a pristine region of

5


Patagonia, known for glaciers and lakes. In addition, these projects require a

transmission line of more than 2,000 km to inject its energy to SIC power system. The

first project presented its environmental evaluation in year 2008, and was approved in

May of 2011. However an action complaint against environmental resolution was

presented which have to be solved by a Minister Committee. The final resolution of this

was extend for more than 3 years. Finally, the environmental evaluation was rejected by

the current Minister Committee. The environmental evaluation of the transmission line

has not been presented.

Figure 2: Electricity generation by sources, SIC 1999-2012.

Figure 3: Electricity generation by sources, SING 1999-2012.

6


To overcome the shortfall of Argentina natural gas, in Chile two LNG terminals were

build: Mejillones and Quintero. The first began to operate by 2009. However, currently

it is operating below its maximum capacity due to the high price of the LNG in

comparison to electricity generation form coal source (see Figure 3). In the case of

Quintero LNG terminal, it is operating full capacity. Four companies share the property

of this terminal: British Gas, ENAP, which is a state refiner, METROGAS, which is a

private gas distributor, and ENDESA, which is a one of the bigger private electricity

generation company in Chile. Apart of ENDESA, there other companies which have

natural gas power plant (for example Nehuenco [785 MW] and Nueva Renca [305

MW]), however, these do not have open access to the terminal. Sometimes

METROGAS or ENAP has sold gas some gas surplaces to these companies, but at a

high price. There are uncertainties about the access to the terminal to get gas at

competitive prices or the access using an own terminal.

To capture these uncertainties, a sensitivity analysis is proposed to manage the

following aspects: solar PV technology investment cost, projection for LNG prices, and

potential use of hydroelectric resource of extreme south of Chile. The baseline scenarios

depend on these uncertainties and, therefore, the effectiveness of the carbon tax too.

The main contributions of this paper are:

Analyse the impact of the carbon tax at the Chilean electricity sector using

both a sectorial model and a DSGE model.

Propose a novel approach to integrate results from the sectorial model and the

DSGE model.

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The paper is organized in four sections. In Section II, the methodological approach and

implementation is presented. Section III presents the results and analysis of the Chilean

case. Finally, in Section IV the main conclusions are summarized.

2. Methodology

2.1 Overview description

Figure 4 shows an overview of our approach to solve the problem. A generation

expansion planning model is developed. The models for industrial transport and CPR

sectors (commercial, public and residential) project the electricity demand which is an

input for the generation expansion planning model. In this iteration, the sectorial models

are run considering a base value for the GDP projection and considering a carbon tax

applied to the electricity generation sector. The electricity price got from the electricity

generation sector is an input for the DSGE model (price shock). This model could give

updated GDP projection according to the electricity price variation caused by the carbon

tax. In that scenario, the information would run from the macroeconomic model to the

sectorial models, and vice versa, until reaching the convergence for the GDP.

2.2 Energy sectorial model

Generation expansion planning model

The objective of this model is to determine the optimal mix of power generation to meet

projected demand. We formulate the problem as if planning was matched by a central or

state government. The objective function is to minimize the capital costs in new plants,

the variable cost related to fuel consumption, variable cost associated to non-fuel

consumption, and the cost of unserved energy. In addition, the objective function

includes a penalty for the GHG emission generated:

8


∑t

T

(∑i

N ∆t I ¿ P¿

(1+r )t ¿+∑i

N ∆t C ¿G¿

(1+r )t+∑

i

N ∆t f t Ei G¿

(1+r )t )¿ (1)

where the optimization problem variables P¿ (MW) and G¿(MWh) are the additional

installed capacity and the electricity generation, respectively. I ¿ is the capital cost

annuity (US$/MW), C ¿is the variable cost (fuel and non-fuel cost) (US$/MWh), Ei (ton

CO2/MWh) is the emission factor for each kind of technology, f t(US$/ton CO2) is the

carbon tax and r is the discount rate. The factor f t will take the values of 10, 20 and 30,

40, and US$/tCO2e between 2017 and 2030.

The problem is subject to the following constraints:

Energy balance between the electricity generations and load. A multi-nodal

formulation is used to represent the interconnection between SIC and SING.

Upper and lower bounds to appropriate limits to the electricity generations.

Maximum feasible amount of investment for each kind of technology that

could happen in a year.

Maximum feasible amount of investment for each kind of technology for the

total period. Potential capacity for hydroelectric run-of-river power plants

2014-2015 is set according potential projects which are under evaluation. In

the case of geothermal energy, a conservative expectative is assumed in

comparison with previous works in Chile (Comité Técnico de la Plataforma

Escenarios Energéticos 2030, 2013).

Quota obligation to renewable energy generation according to new renewable

energy law (20% by 2025).

Other assumptions:

9


Technical parameters of the electricity generation plants were obtained from

ISOs web pages (CDEC-SIC) and (CDEC-SING).

Projects which are been building were collected from reports published by the

national regulatory institution (National Energy Comission, 2013) (National

Energy Comission, 2013a).

Hydroelectric-dam plants can regulate it generation during every stage. On the

contrary, hydraulic-run of river and small hydroelectric plants are not able to

regulate.

Minimum technical power is considered for coal and LNG power plants. This

constraint avoids starts up and shut down between blocks in a same stage.

Investment costs for the first year were obtained from (National Energy

Comission, 2013) (National Energy Comission, 2013a) (National Energy

Comission, 2014) (National Energy Comission, 2014 a). The projection is based

on growth rate used in (Comité Técnico de la Plataforma Escenarios Energéticos

2030, 2013).

The interconnection between SIC and SING power systems is simulated by year

2019.

Baseline scenario does not consider nuclear energy.

For the below example, a decrease of the GDP induced by a carbon tax applied in the

electricity sector will cause an increase of the energy prices.

10


Figure 4: General description of the Chilean approach.

2.3 Macroeconomic model

A DSGE model for the Chilean economy is used for this exercise. This model was

developed by Medina and Soto at (Medina & Soto, 2006). The Figure 5 represents the

structure of the main agents involved in this model. There is a continuum of households

and different types of firms in the economy. Households live infinitely, take decisions

on consumption and savings, and set wages in a staggered way. There is a set of firms

that produce differentiated varieties of tradable intermediate goods using labour and

capital. They have monopoly power over the varieties they produce and set prices in a

staggered way. Another set of firms are importers that distribute domestically different

varieties of foreign intermediate varieties. These firms have monopoly power over the

varieties they distribute, and also set prices in a staggered fashion. There is a third single

firm that produces a commodity good which is completely exported abroad. This firm

has no market power: it the international price of the commodity good as given.

Production by this firm is exogenously determined and requires no inputs. Its revenues

are owned by the government and by foreign investors.

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Domestic and foreign intermediate varieties are used to assemble two final

goods: home and foreign goods. These two final goods are combined into a bundle

consumed by household, another bundle consumed by the government and a third

bundle that corresponds to new capital goods that are accumulated to increase the

capital stock.

Figure 5: General description of DSGE model

Adapting DSGE following (Medina & Soto, 2007) model for the Chilean economy

we calibrate the performance of the price of energy by using the historical data of

National Energy Balance from 1990 to 2012. A composite of energy is generated as:

Et=∑ α j E j

(2)

In the last equation Et is the total energy resulting from the sum of each “j” energy

source E j weighted by of their relative prices (α j). One of these energy sources is the

electricity. With Et we will estimate with Bayesian approach the elasticity of

substitution in order to update the current elasticity of the DSGE model (Medina &

Soto, 2007) and obtain the parameters of the model.

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Moreover, the share of the energy by the total output of the economy would be

modified with the productivity according the indirect productivity of the expenditure of

energy by output Et /GDP.

The electricity price got from the electricity generation sector for these different

scenarios is an input for the DSGE model. After changing energy prices the economy

must move to a new long-run equilibrium. The resulting steady state will show a shift in

the growth rates during the convergence period. The following expression is used to

calculate the energy price difference is:

∆ Price( USMWh )=∆ CAPEX +∆ OPEX+TAX

∑t=tini

T

Gt /(1+r)tini−t (3)

Where ∆ CAPEX corresponds to the present value of the variation of the capital

expenditure in power plants in comparison with the baseline scenario, i.e., the case

without carbon tax; ∆ OPEX represents the variation of the operation expenditure, TAX

is the carbon tax that the generation companies should pay due to their GHG emissions,

Gt is the total electricity generation in the year t, tini is the starting year for the carbon tax

application. ∆ CAPEX , ∆ OPEX , and TAX are outputs of the electricity generation

model.

Upon reaching the new steady state the economy will grow at similar rates

before imposing the tax because of the improvements in productivity and population

growth, but this growth will be based on a lower level due to lower growth rates in the

transition period.

Regarding the implementation of the proposed analysis framework, the

generation expansion planning model was programmed in MATHPROG using a GLPK

13


distribution and solved by CPLEX MIP solver. The DSGE model was programmed

using DYNARE and MATLAB (Cerda, 2010). The MIP problems were solved with a

gap below 0.03%.

3. Case study

The impacts of carbon tax on the Chilean electricity generation sector are

evaluated considering five values of carbon tax scenarios: 10, 20, 30, 40, and 50

US$/tCO2e. The carbon tax is applied between 2017 and 2030 to both SING and SIC

power systems.

For this simulation exercise we did several assumptions. An annual exogenous

growth rate of 1.5% and 1.0% for productivity and employment is assumed. For every

scenario we assume that the convergence time of electricity prices to the new

equilibrium is 12 quarters and gradual increases will be linear. The currently price of the

original steady state energy is 105 USD/MWh; it is estimated as the average between

January 2007 and February 2013.

A sensitivity analysis is performed for 3 main parameters which are inputs for

the optimization problem: projection of investment cost in solar PV technology,

projection for LNG prices, and potential use of hydroelectric resource of extreme south

of Chile, see Table I. Considering these uncertainties 4 scenarios are build (see Table

II).

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TABLE I. Sensitivity analyses considering three sources of uncertainties

Solar Investment

Cost

LNG Prices Situation of hydroelectric

resources at extreme south of

Chile

Case 1: Base situation Case 1: Base situation Case 1: No exploitation of

additional hydroelectric resources.

Case 2: More

optimistic projection

of solar investment

cost, see Annex.

Case 2: More optimistic

LNG prices projection for

power plants which have

not open access to LNG

terminal, see Annex

Case 2: An additional potential of

2,750 MW of hydroelectric source

is considered.

TABLE II. Evaluated scenarios

Scenario Solar

Investment

Cost

LNG

Prices

Exploit the hydroelectric resource of

extreme south of Chile

1 Case 1 Case 1 Case 1




3.1 Electricity generation sector results

The Figure 6 shows the GHG trajectory for the scenarios defined in Table II. These

scenarios are called baseline scenarios (without carbon tax). The carbon tax is applied in

every of these four scenario. Figure 7, Figure 8, Figure 9, and Figure 10 show the total

15


emissions (SING and SIC) for the baseline scenarios, in comparison to different carbon

tax values.

Figure 6: GHG trajectory for different baseline scenarios

In addition, different indicators are proposed to compare the impact on the

carbon tax on the electricity generation sector in comparison to the baseline scenario:

OPEX, carbon tax, CAPEX, emission reduction. These values are expressed in present

value and as variation with respect baseline scenario. Also the abatement cost and

average increase of the electricity price in a national level are reported, see Table III.

Then the electricity price rise is evaluated as shock in the DSGE model.

Figure 7: Emissions for baseline scenario #1 in comparison to different scenarios of

carbon tax.

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carbon tax.


carbon tax.

Figure 10 Emissions for baseline scenario #4 in comparison to different scenarios of

carbon tax.

17


TABLE III. Indicators associated to the application of carbon tax on the Chilean

electricity generation sector.

Indicators Scenario #1 Scenario #2

Tax (US$/tCO2) 10 20 30 40 50 10 20 30 40 50

∆ OPEX (M US$) -151.9 -414.4 -825.8 -1,773.6 -2,573.9 -755.7 -1,716.3-

2,374.6-2,827.2 -3,310.8

INCOME TAX (M US$) 2,074.64,091.

95,955.7 7,080.2 8,318.8 1,902.7 3,378.7 4,869 6,190.1 7,308.9

∆ CAPEX (M US$) 266.3 869.3 1,817.3 4,912.9 6,617.6 1561 3,237.9 4,267.3 5,115.9 6,253.6

Total ∆ Emission (M tCO2e) -20.6 -29.7 -51.0 -134.2 -168.1 -50.2 -126.9 -151.6 -172.4 -196.7

Average ∆ Annual Emission

(M tCO2e)-1.5 -2.1 -3.6 -9.6 -12.0 -3.6 -9.1 -10.8 -12.3 -14.0

Abatement Cost (US$/tCO2) 276.4 426.7 416.4 267 253.5 215.5 144.3 165.1 176.5 180.9

Increase of Electricity Price

(US$/MWh)4.3 8.9 13.7 20.1 24.3 5.3 9.6 13.3 16.7 20.2

Indicators Scenario #3 Scenario #4

Tax (US$/tCO2) 10 20 30 40 50 10 20 30 40 50

∆ OPEX (M US$)-

239.8-424.9 -760.4

-

1,206.9-2,113.8 -169.7 -413.7 -863.2

-

1,921.2-2,413.2

INCOME TAX (M US$) 1,904 3,757.4 5,511.1 6,886.3 8,139.5 2,096.3 4,124.6 5,923.1 7,006.1 8,451.7

∆ CAPEX (M US$) 321.9 887.2 1,570.9 2,963.9 1,570.9 324.7 877.8 2,093.2 5317.4 6,497.6

Total ∆ Emission (M tCO2e) -11.5 -20.1 -33.5 -71.9 -102.7 -4.2 -15.7 -43.4 -130.1 -149.7


(M tCO2e)-0.8 -1.4 -2.4 -5.1 -7.3 -0.3 -1.1 -3.1 -9.3 -10.7

Abatement Cost (US$/tCO2) 436.1 590.8 560.9 377.9 235.7 1,498.8 928.8 520.3 288.8 297.5

Increase of Electricity Price

(US$/MWh)3.9 8.3 12.4 17.0 21.3 4.4 9.0 14.1 20.5 24.6

As the carbon taxes go up, the electricity sector’s carbon emissions come down. The

operational costs also come down, while the total investment in new power plants

increases and the cost of electricity rises, relative to a business-as-usual scenario. The

total investment increases due the fact that more renewable energy sources are

introduced which have higher investment cost (US$/kW) than coal plants. The highest

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emission reduction happens in the scenario 2. This is because the solar investment cost

projection is more optimistic, and the carbon tax promotes the introduction of new non-

conventional renewable energy source happens earlier in comparison to scenario 1. On

the contrary, the impact on the carbon tax is low in the scenario 4. This is because this

baseline scenario includes more hydroelectric sources than scenario 2, and then there is

less thermoelectric generation with coal sources. In scenario 3, there is more electricity

generation with LNG source in comparison to scenario 1 (and less with coal

generation). This is because the impact on carbon tax is lower than scenario1. These

results show that the effectiveness of the carbon tax depends on certain variables which

are not controlled by policy makers.

For example, the Table IV shows that a carbon tax of 10-20 US$/tCO2e could

not have a big impact in terms to reduce GHG emissions (the minimum values are 0.3

and 1.1 million tCO2e, respectively).

TABLE IV. Emission reduction and electricity price rise range.

Carbon tax

(US$/tCO2e)

Average annual emission

reduction Range (million

tCO2e)

Increase of Electricity

Price Range (US$/MWh)

10 [0.3, 3.6] [3.9, 4.4]

20 [1.1, 9.1] [8.3, 9.6]

30 [2.4,10.8] [12.4,14.1]

40 [5.1,12.3] [16.7,20.5]

50 [7.3,14.0] [20.2,24.6]

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3.2 Macroeconomic results

The effect of taxing CO2 emissions in the electricity sector on the path of gross

domestic product (GDP) is evaluated. Figure 11, Figure 12, Figure 13, and Figure 14

show the results for the four scenarios evaluated which are compared with respect to the

baseline scenario. These figures show the annual GDP reduction (percentage over

baseline deviations) which varies between 1 and 6%.The highest GDP reduction

happens between 2021 and 2025.

It is important to note this analysis is not considering recycling of the results (tax

collection is not reinvested at the economy) so the cases presented below can be

considered as the worst case (maximum negative effect) of this instruments over the

GDP path.

Figure 11: Effect on the level of GDP (% over baseline deviations). Scenario #1.

20




21



The Table V shows the equivalent average annual GDP growth observed after

the carbon tax is applied (for the period 2017-2030). We can see the most important

effect over the GDP growth is observed at the Scenarios 1 and 2 for the tax value of 50

US$/tCO2e, where the average yearly GDP growth rate will be 3.11% versus 3.5% of

baseline scenario (reduction of 0.4%).

TABLE V. Effect on GDP growth for different tax levels and scenarios

Carbon tax

(US$/tCO2e)

Scenario 1 Scenario 2 Scenario 3 Scenario 4

GDP

growth

(%)

Reductio

n (%)

GDP

growth

(%)

Reductio

n (%)

GDP

growth

(%)

Reductio

n (%)

GDP

growth

(%)

Reductio

n (%)

10 3.43% 0.1% 3.42% 0.1% 3.44% 0.1% 3.43% 0.1%

20 3.36% 0.1% 3.35% 0.2% 3.37% 0.2% 3.36% 0.2%

30 3.28% 0.2% 3.29% 0.2% 3.30% 0.2% 3.28% 0.2%

40 3.18% 0.3% 3.23% 0.3% 3.23% 0.3% 3.17% 0.3%

50 3.11% 0.4% 3.18% 0.3% 3.16% 0.3% 3.11% 0.3%

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3.3 Comparison to other policies

We compare the carbon tax with other policies that stockholders can apply in order to

reduce GHG emissions: introduction of renewable energy source and a sectorial cap.

Currently Chile has a non-conventional renewable energy (NCRE) law, based on a

quota system, which states that the 20% of energy sales have to be provided by NCRE

source by 2025. We evaluated to increase this percentage to 25% by 2030 (25/30) and

30% by 2030 (30/30). The Table IV shows the results for the Scenario 1 of Table II.

TABLE VI. Evaluation of a change to currently non-conventional renewable

energy law.

Indicator 25/30 30/30

∆ OPEX (M US$) -34.8 -225.2

∆ INCOME TAX (M US$) 0.0 0.0

∆ CAPEX (M US$) 550.5 1431.3

Total ∆ Emission (M tCO2e) -19.9 -33.3

Average ∆Annual Emission (M tCO2e) -1.4 -2.4

Abatement Cost (US$/tCO2) 99.5 150.7

∆ Electricity Price (US$/MWh) 1.0 2.4

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The emission reduction of the 25/30 case is similar to the emission reduction of 10

US$/tCO2e carbon tax case, and the emission reduction of the 30/30 case is similar to

the emission reduction of 20 US$/tCO2e carbon tax case. However, electricity price

increase is lower in both cases. Therefore, the impact on GDP will be lower.

Additionally, a sectorial cap in the electricity generation sector is evaluated. The

results are shown in Table V. The simulations were done considering an emission cap

equals to the emission trajectory when the carbon tax is applied. The cap is introduced

in the optimization problem as a constraint. The emission reduction is the same,

however, the result shows the electricity price increase is lower than the carbon tax

cases; therefore, the impact on GDP will be lower.

TABLE VII. Indicators associated to the application of sectorial cap on the

Chilean electricity generation sector.

Indicator Cap 1 Cap 2 Cap 2 Cap 3 Cap 4

∆ OPEX (M US$) -156.3 -418.5 -812.0 -1785.8 -2593.3

∆ INCOME TAX

(M US$)

0.0 0.0 0.0 0.0 0.0

∆ CAPEX (M US$) 248.4 879.4 1816.2 4928.6 6642.2

∆ Emission

(M tCO2e)

-20.8 -29.7 -51.1 -134.3 -168.3


(M tCO2e)

-1.5 -2.1 -3.7 -9.6 -12.0

Abatement Cost (US$/tCO2) 11.5 43.0 60.0 81.9 82.8

∆ Electricity Price

(US$/MWh)

0.2 0.9 2.0 6.2 8.0

24


Where:

Cap1: the emission cap is equal to the emission reduction that we got when the

carbon tax of 10 US$/tCO2 is applied (scenario 1)









The effect of NCRE law modifications and sectorial cap over the path of gross domestic

product (GDP) with respect to the baseline scenario is showed in the Figure 15. The

highest GDP reduction happens between 2021 and 2025. This reduction (percentage

over baseline deviations) varies between 0.05 and 2%.

25


Figure 15: Effect of NCRE law modifications and sectorial caps on the level of GDP (%

over baseline deviations).

4. Conclusions

The economy-wide implications of a carbon tax applied in the Chilean electricity

market were successfully evaluated. A novel approach is proposed to integrate results

from the electricity generation model and the DSGE model. The results show that the

effectiveness of this policy depends on some variables which are not controlled by

policy maker such as non-conventional renewable energies investment cost projection,

price of LNG, and exploit of hydroelectric resources. For example, in a scenario with

carbon tax of 20 US$, the annual average emission reduction should be between 1.1 and

9.1 M tCO2e. However, the price of the electricity (electricity generation level) should

increase between 8.3 and 9.6 US$/MWh, which is equivalent to a 7.4% and 8.5% with

respect to currently electricity price. This shock decreases the annual GDP growth rate

in a maximum value of 0.15%. It means that the average yearly GDP growth rate will be

3.35% versus 3.5% of baseline scenario.

On the other hand, alternative policies were evaluated getting interesting results.

The results show that the same emission reduction can be achieved with these policies

with lower macroeconomic impacts. Nevertheless, it is important to note that in the case

of carbon tax recycling is not considered in the macroeconomic evaluation.

ANNEX

Figure 16 shows the investment costs in the power generation sector that were

considered for the preliminary calibration for the SIC and the SING.

26


Figure 17 shows the LNG prices projection considered in this paper.

Figure 16: Investment cost (US$/kW)

Figure 17: LNG prices projection. LNG 1 SIC and LNG 1 SING are the prices of LNG

for plants which have open access to the LNG terminal in SIC and SING, respectively.

LNG 2 SIC and LNG 2 SING is the price for plant which have not open access to LNG

terminal.

ACKNOWLEDGMENTS

The paper was supported by Climate and Development Knowledge Network (CDKN)

and MAPS Programme.

27


Disclaimer

This document is an output from a project funded by the UK Department for

International Development (DFID) and the Netherlands Directorate-General for

International Cooperation (DGIS) for the benefit of developing countries. However, the

views expressed and information contained in it are not necessarily those of or endorsed

by DFID or DGIS, who can accept no responsibility for such views or information or

for any reliance placed on them. This publication has been prepared for general

guidance on matters of interest only, and does not constitute professional advice. You

should not act upon the information contained in this publication without obtaining

specific professional advice. No representation or warranty (express or implied) is given

as to the accuracy or completeness of the information contained in this publication, and,

to the extent permitted by law, the entities managing the delivery of the Climate and

Development Knowledge Network do not accept or assume any liability, responsibility

or duty of care for any consequences of you or anyone else acting, or refraining to act,

in reliance on the information contained in this publication or for any decision based on

it. Management of the delivery of CDKN is undertaken by PricewaterhouseCoopers

LLP, and an alliance of organisations including Fundación Futuro Latinoamericano,

INTRAC, LEAD International, the Overseas Development Institute, and

SouthSouthNorth.

Copyright © 2014, Climate and Development Knowledge Network. All rights reserved

28


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31

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