The energy price equivalence of carbon taxes and emissions trading—Theory and evidence

Applied Energy 160 (2015) 164–171

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

The energy price equivalence of carbon taxes and emissions trading—Theory and evidence Fan-Ping Chiu a, Hsiao-I. Kuo b, Chi-Chung Chen c,⇑, Chia-Sheng Hsu d a

Department of Nephrology, Chang Gung Memorial Hospital, Keelung, Taiwan Department of Golden-Ager Industry Management, Chaoyang University of Technology, Taichung, Taiwan c Department of Applied Economics, National Chung-Hsing University, #250 Kuo-Kuang Road, Taichung, Taiwan d Department of Applied Economics, National Chung-Hsing University, Taichung, Taiwan b

h i g h l i g h t s
The price equivalence of carbon taxes and emissions trading from theoretical and empirical models are developed.
The theoretical findings show that the price effects of these two schemes depend on the market structures.
Energy prices under a carbon tax is lower than an issions trading in an imperfectly competitive market.
A case study from Taiwan gasoline market is applied here.

a r t i c l e

i n f o

Article history: Received 10 May 2015 Received in revised form 26 August 2015 Accepted 3 September 2015

Keywords: Carbon tax Emissions trading Conjectural variation Energy price

a b s t r a c t The main purpose of this study is to estimate the energy price equivalence of carbon taxes and emissions trading in an energy market. To this end, both the carbon tax and emissions trading systems are designed in the theoretical model, while alternative market structures are taken into consideration. The theoretical findings show that the economic effects of these two schemes on energy prices depend on the market structures. Energy prices are equivalent between these two schemes given the same amount of greenhouse gas emissions (GHGE) reduction when the market structure is characterized by perfect competition. However, energy prices will be lower when a carbon tax is introduced than when emissions trading is implemented in an imperfectly competitive market, which implies that the price effects of a carbon tax and emissions trading depend on the energy market structure. Such a theoretical basis is applied to the market for gasoline in Taiwan. The empirical results indicate that the gasoline prices under a carbon tax are lower than under emissions trading. This implies that the structure of the energy market needs to be examined when a country seeks to reduce its GHGE through the implementation of either a carbon tax or emissions trading. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction While carbon taxes and emissions trading are treated as the major competing climate change policy tools used to reduce greenhouse gas emissions (GHGE) [1–3], there has recently been much debate on their economic effects. A carbon tax is treated as an environmental tax that is levied on production activities/services that pollute environmental goods. The prices of products/services are increased and the demand for them is reduced after the ⇑ Corresponding author. Tel.: +886 4 2285 8137; fax: +886 4 2286 0255. E-mail addresses: [email protected] (F.-P. Chiu), [email protected] (H.-I. Kuo), [email protected], [email protected] (C.-C. Chen), victor9999951@ hotmail.com (C.-S. Hsu). http://dx.doi.org/10.1016/j.apenergy.2015.09.022 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.

addition of the carbon tax. Such carbon taxes are sometimes referred to as ‘‘price-based” policy instruments [4]. On the other hand, emissions trading imposes a total amount of carbon emissions but allows emission permits to be traded at different prices. Emissions trading is considered to be a ‘‘quantity-based” policy tool. The basic framework for emissions trading involves fixing a total emission level for all of players within a group with each player being able to buy or sell the right to an emission level at a particular price. In other words, the major function of the trading system is to reduce GHGE with lower abatement cost. Such a reduction through trading is more efficient. For instance, if the cost of reducing GHGE is lower in one location/sector than another location/sector, then the amount of the emissions reduction in a

F.-P. Chiu et al. / Applied Energy 160 (2015) 164–171

cheaper location/sector means that the right can be sold to the location/sector with a higher cost through this trading system in order to reach the reduction goal. Therefore, the social welfare in both locations/sectors will be increased. There are some advantages and disadvantages of these two environmental tools. The biggest advantage of implementing emissions trading is to ensure that essential reductions in GHGE targets are met at the lowest possible cost. The other main advantage of this program is to provide the private sector with the flexibility required to reduce emissions while stimulating technological innovation and economic growth. Such a program has been implemented in many US states, in the EU, and in New Zealand and Australia. The advantage of implementing a carbon tax is to encourage the use of alternative sources of energy by making them costcompetitive with cheaper fuels. For instance, the imposition of a carbon tax on a cheaper fuel such as coal could raise the cost of producing electricity as compared with other cleaner power production activities. The other advantage of a carbon tax is to create a more stable carbon price than in the case of emissions trading since emissions trading sets a definite limit on emissions and not a definite limit on the price of carbon. Back to the empirical studies from literature reviews, we found that there are lots of studies related with the analysis of carbon tax on the reduction of greenhouse gas emission (GHGE). For instance, the effect of carbon tax on GHGE in a forest sector in Taiwan has been investigated by Chen et al. [5] and the empirical simulation results show that the significant reduction of carbon tax on wood product markets. Kahn and Franceschi [6] and Sumner et al. [7] have provided a very good review with possible policy consideration for this mitigation policy. On other hand, the effects of implementing a carbon tax on GHGE in a specific country have been examined by Callan et al. [8], Wang et al. [9], Fang et al. [10], Alton et al. [11], Vandyck and Regemorter [12], Liu and Lu [13]. Some of them have focused on developed countries such as Ireland and Belgium but some have paid attention on developing countries such as China and South Africa. Implementing a carbon tax in different countries may have different effects due to the alternative energy technology and marketing structures. This implies that such studies have not analyzed and compared with the effects of implementing a carbon by taking the marketing structure into the consideration. On other hand, the effects of implementing emission trading on the reduction of GHGE have been examined by Stevens and Rose [14], Linares et al. [15], Linares et al. [16], Kara et al. [17], Karali et al. [18]. Stevens and Rose [14] have provided a nice theoretical framework when implementing this emission trading system. Linares et al. [15], Linares et al. [16], and Kara et al. [17] have applied such mitigation scheme on power sector in different European countries while Karali et al. [18] have simulated this scheme on iron and steel sector in the US. Based on these literature reviews for carbon tax and emission trading, the effects of either carbon tax or emission trading on GHGE for different energy sectors in different countries have been investigated and analyzed clearly. However, the policy marker may like to select one of them as policy tool to mitigate GHGE since the cost of GHGE reduction needs to be minimized. Therefore, the economic outcomes including energy prices, gross domestic product (GDP), and welfare will be the criteria to compare when selecting these two policies [19–21]. For instance, countries or industrial sectors may select a lower energy price outcome when implementing a mitigation tool with the same GHGE reduction amount since the damage of lower energy price is much accepted. Therefore, the main contribution of this paper is to analyze and

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investigate the economic outcomes of these two policies in an energy market theoretically and empirically. Although both carbon taxes and emissions trading are implemented under a market mechanism, the economic effects on the prices and welfare of goods and services between these two systems will be different. The actually situations of implementing carbon taxes and emissions trading in different countries have been introduced and analyzed by Sugino et al. [22], Pollitt et al. [23], Jotzo and Löschel [24], Crossland et al. [25], Liu and Lu [13], WorldBank [26]. For instance, UK has participated in EU Emission Trading Scheme (ETS) since 2005 while a carbon price floor (CPF) to tax on fossil fuels used for power generation with US$ 15.75 per ton of CO2 emission was introduced in 2013. Japanese Voluntary Emission Trading Scheme (JVETS) was operated in 2005 to support the reduction of GHGE in Japan and a carbon tax with US$ 2 per ton of CO2 emission for all fossil fuels was also implemented in 2012. Mexico has introduced carbon tax on fossil fuels with US$3.5 per ton of CO2 emission in 2014, and subsequently announced an ETS for carbon emissions from energy sector. China prepares to launch a national ETS in 2016 to reduce carbon emissions and air pollutions. Such examples indicate that either a carbon tax or ETS is a practical policy tool which has been adopted by countries to reduce carbon emissions recently. Therefore, investigating the effects of such schemes on energy price has important policy implications for energy sector. Limpaitoon et al. [27] have pointed out that different kinds of pollution emissions trading will result in different results in different market structures. When a country attempts to reduce its emissions of greenhouse gases in an energy market using these policy tools, the lower the energy price, the better the economy. So the major purpose of this paper is to develop a theoretical model to compare the economic effects of carbon taxes and emissions trading in an energy market by considering alternative market structures. After that, such a theoretical model will be applied to the gasoline market in Taiwan and tested empirically. Therefore, the first contribution of this study is that we develop a theoretical model to compare the economic effects of carbon taxes and emissions trading in an energy market. The second contribution of this study is that we not only provide empirical evidence of Taiwan’s gasoline market to support the theoretical model, but also suggest useful policy implications to Taiwan’s gasoline market and other energy markets. A conjectural variation (CV) represents each firm’s (or player’s) strategy with respect to other players’ strategies on the quantity or price in a market. The CV method is comprehensively used in empirical analysis and could be applied to the estimation of the market structure in an imperfectly competitive market (see, for example, [28–30,27,31]). Therefore, such a CV approach is applied here to estimate the market structure of Taiwan’s gasoline market. The structure of this paper is as follows. The second section builds up a theoretical model to compare the economic effects of a carbon tax and emissions trading in an energy market, while the third section establishes the empirical models. The empirical results are shown in the fourth section, and finally the fifth section presents the conclusion and policy implications.

2. Theoretical model Suppose that there exist n firms that can produce gasoline accompanied by carbon dioxide emissions and that the inverse P demand function in the gasoline market is P ¼ f ðQ Þ; Q ¼ ni¼1 qi , where qi represents the quantity of gasoline of the ith firm. The following analysis will focus on a comparison of energy prices

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between a carbon tax and emissions trading in the domestic gasoline market. 2.1. Firm behavior under a carbon tax and emissions trading 2.1.1. Case for carbon tax Suppose that the government implements a carbon tax (t) in a gasoline sector in order to reduce GHGE. The profit function for the ith firm under the carbon tax is

pi ¼ f ðQÞqi  C i ðqi Þ  t  GHGi  qi

ð1Þ

where C i ðqi Þ is the cost function for producing gasoline for firm i and GHGi is the amount of carbon emissions per unit of gasoline production. The first-order condition is

@ pi 0 ¼ P T þ qi 1 þ ki f ðQ Þ  MC i  t  GHGi ¼ 0; @qi 

where kti ¼

 t

Pn

@qi0 i¼1;i–i0 @qi

8i

ð2Þ

is a conjectural variation term for firm i

under a carbon tax which refers to how the quantity produced by firm i changes when other firms change their production quantity. MC i is the marginal cost, and P T is the gasoline price under the carbon tax system. 2.1.2. Case for emissions trading If a government implements an emissions trading system in the gasoline sector, the summation of the reduction in GHGE for all firms will be fixed at a particular amount. So the optimization problem for firm i is

Max

pi ¼ f ðQ Þqi  C i ðqi Þ  PC  ETqi

s:t:

n n X X GHGi  qi  ETqi 6 ETRATIO  TOTGHG

qi ;ETqi

i¼1

ð3Þ

i¼1

where ETqi is the emissions trading quantity of the ith firm buying from the gasoline industry and P C is the emissions trading price. TOTGHG is a fixed amount of total emissions before implementing this emissions trading system while ETRATIO is the ratio of total emissions. The equilibrium conditions are

  0 @ pi ¼ P ET þ qi 1 þ kET f ðQÞ  MC i  l  GHGi ¼ 0; i @qi @ pi ¼ Pc þ l ¼ 0; @ETqi

8i

8i

ð4Þ

ð5Þ

where kET i is a conjectural variation term for firm i under the emissions trading system, and l is the shadow price when the fixed amount is binding and is represented as the implicit carbon tax. PET is the gasoline price under emissions trading. The equilibrium conditions for Eqs. (4) and (5) may be summarized as shown in Eq. (6).

  0 @ pi ¼ P ET þ qi 1 þ kET f ðQÞ  MC i  Pc  GHGi ¼ 0; i @qi

8i

ð6Þ

The equivalence of the carbon tax and emissions trading on gasoline prices may be found by comparing Eqs. (2) and (6) which will depend on the market structures. A perfectly competitive market and an imperfectly competitive market will be discussed here.

2.2. Economic effects under alternative market structures 2.2.1. Case for perfectly competitive market The value of conjectural variation will be negative one under a perfectly competitive market for both the carbon tax and emissions trading systems. Therefore, Eqs. (2) and (6) will be modified as follows:

@ pi ¼ PT  MC i  t  GHGi ¼ 0; @qi @ pi ¼ PET  MC i  P c  GHGi ¼ 0; @qi

8i

8i

If the carbon tax (t) equals the emissions trading price (PC ), then the gasoline price is equivalent (i.e., P T ¼ P ET ). However, the gasoline price under the carbon tax scheme (PT ) will be higher than that under the emissions trading scheme (P ET ) if the carbon tax is higher than the emissions trading price and vice versa. 2.2.2. Case for imperfectly competitive market Eqs. (2) and (6) also show that the price equivalence of the carbon tax and emissions trading would hold if a player were to act as a Cournot-competitor (i.e., a zero conjectural variation) under both systems. Such an equivalence comparison is similar to the case of a perfectly competitive market. However, the price of gasoline under a carbon tax scheme is lower than under an emissions trading system if the player behaves more competitively than it would in the Cournot case (i.e., a negative conjectural variation). If the conjectural variation term is positive, which implies that the player acts more collusively than in the Cournot case, then the gasoline price under a carbon tax will be higher than under emissions trading. Such theoretical findings are summarized in Table 1. Based on the brief summary provided in Table 1, the energy price under these two schemes depends on the market structure. Therefore, we will provide the Taiwan gasoline market as an example to evaluate the economic impacts of these two schemes empirically. To apply such theoretical findings to the real practice of emission trading and carbon tax systems, the case of UK and Japan will be illustrated here. UK has participated in EU Emission Trading Scheme (ETS) since 2005 while the carbon price floor (CPF) is introduced as a tax on fossil fuels used to power generation with US $15.75 per ton of CO2 emission since 2013. Similarly, the Voluntary Emission Trading Scheme in Japan was operated in 2005 to support the reduction of GHGE. In 2012, carbon tax for all fossil fuels was also introduced in Japan while the tax rate is equal to US$2 per ton of CO2 emission. Based on the theoretical findings, we suggest both governments need to measure the marketing structure for power sector or fossil fuel markets first if the emission trading system is applied into these two markets. Once the marketing structure is confirmed, the mitigation policy tools from carbon tax or emission trading could be selected one instead of implementing them simultaneously in order to have a better social welfare with lower energy prices.

Table 1 The equivalence of carbon tax and emissions trading. Explicit and implicit tax

Conjectural variation

Electricity price

t = l = Pc

kti ¼ kET i ¼ 1 or

PT = PET

t P l ¼ Pc t 6 l ¼ Pc t = l = Pc

kti ¼ kET i ¼ 0 kti ¼ kET i ¼ 1 or

P T P P ET

kti ¼ kET i ¼ 0

P T 6 P ET

kti P kET i (if both are negative)

P T 6 P ET

kti P kET i (if both are positive)

P T P P ET

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3. Empirical models There are two major gasoline producers in the Taiwan oil market, one being the CPC Corporation and the other the Formosa Petrochemical Corporation. The domestic gasoline prices are determined by these two firms and the market prices are always the same due to their price strategies. This implies that the Taiwan gasoline market is either a monopolistic market or an oligopolistic market. There is no existing study on the market structure in the Taiwan gasoline market, and such an estimation of the market structure will provide useful information when levying a carbon tax or implementing emissions trading. This section will demonstrate how the market structure is estimated using the conjectural variation approach. 3.1. An inverse demand function It is necessary to estimate the inverse demand function before estimating the structure of the gasoline market in Taiwan. The inverse demand function for gasoline products is shown as follows:

Pm ¼ c0 þ c1 Q d þ c2 Poil þ c3 gas þ c4 coal þ e

ð7Þ

where Pm is the retail price of gasoline by NT dollars per kg, Q d is the quantity of gasoline demanded by 1000 l, P oil is the crude oil price by NT dollars per kg, gas is the gas consumption by metric ton to represent the substitution factor, coal is the coal consumption by 1000 metric ton and e is an error term, and c0 ; c1 ; c2 :c3 ; c4 are parameters to be estimated. To judge whether an imperfectly competitive market exists or not, the inverse demand function in Eq. (7) has to include the interactive effect of the gasoline quantities and the gas consumption [32]. The market price would be different if the slope were not fixed. Therefore, Eq. (7) could be modified as in Eq. (8):

Pm ¼ c0 þ c1 Q d þ c2 oil þ c3 gas þ c4 coal þ c5 ðgas  Q d Þ þ e

ð8Þ

The slope of the inverse demand function in Eq. (8) is ðc1 þ c5 gasÞ, which shows that it is not fixed when the exogenous variables (such as gas consumption) change. This indicates that a shift in or rotation of the inverse demand function will occur when the exogenous variables change. If there is a price difference (i.e., the difference between the market price and the marginal cost) in the gasoline market, the shift or rotation in the inverse demand curve will enlarge the price gap. The more that it deviates from the perfectly competitive market, the greater that the price difference will be Seldon et al. [33]. 3.2. The inverse supply function and market structure The market structure can be estimated by using the inverse demand and supply functions simultaneously. In order to deduce the supply function for gasoline, Eq. (6) could be modified as in Eq. (9):

Pw ½1 þ qi =Q s  ð1 þ ki Þg ¼ MC i

ð9Þ

where Pw is the wholesale gasoline price that firms received and qi is the gasoline production quantity for firm i. Q s is the total supply quantity in the Taiwan gasoline market, while MC i is the marginal cost of firm i and g is the demand flexibility. After multiplying the market share of the ith gasoline firm (Si) on both sides of Eq. (9) and adding the summation for all firms, Eq. (10) is obtained. 2 2 X X Si Pw ½1 þ Si ð1 þ ki Þg ¼ Si MC i i¼1

i¼1

ð10Þ

167

Eq. (11) can be derived from Eq. (10) based on the assumptions of P2 P2 2 i¼1 Si ð1 þ ki Þ ¼ k and i¼1 Si MC i ¼ MC that indicate that the industry’s marginal cost for gasoline (i.e., MC) is the summation of each firm’s marginal cost using the market share as a weight.

Pw ð1 þ kgÞ ¼ MC

ð11Þ

Eq. (11) shows that the gasoline market is a perfectly competitive market if the conjectural variations for all firms are 1 (i.e., ki ¼ 1 and k ¼ 0). If the firm behaves as a Cournot–Nash competitor (i.e., ki ¼ 0 and k ¼ 1), then the market equilibrium will be a Cournot–Nash equilibrium. If the conjectural variation item for oil firms ranges from 1 to 0, then the market will be characterized by imperfect competition. The industrial marginal cost function is generally denoted as a function of the quantity of gasoline (Q s ), staff (N), and factor endowments as shown in Eq. (12):

MC ¼ b0 þ b1 Q s þ b2 N þ b3 C B þ b4 C o þ u

ð12Þ

where N is the number of staff, C B is the business cost by NT dollars, C O is the operating cost by NT dollars, and u is an error term. The inverse demand elasticity can be derived from Eq. (8) as shown in Eq. (13):

e ¼ ðc1 þ c5 gasÞðQ s =Pw Þ

ð13Þ

where gas; Q S ; P w represents the means of the variables for gas; Q s ; Pw , respectively. After incorporating Eq. (12) and (13) into (11), the supply curve is as shown in Eq. (14):

Pw ¼ b0 þ b1 Q s þ b2 N þ b3 C B þ b4 C O  k  ðc1 þ c5 gasÞQ S

ð14Þ

The market behavior in relation to the gasoline market can be extrapolated through the k estimator in Eq. (14). 4. Data set and empirical results for the market structure The empirical estimation procedures are divided into three parts in this paper. The first one involves estimating the market structure for domestic gasoline in Taiwan where the inverse demand function in Eq. (8) will be estimated first followed by the inverse supply function in Eq. (14). The market structure will be ascertained based on the estimation outcomes from Eq. (14). The second one has to do with simulating the economic impacts of implementing the carbon tax and emissions trading systems on the energy market under both perfect and imperfect competition. Finally, we discuss the price equivalence between the carbon tax and emissions trading schemes under alternative market structures. 4.1. Data sets In order to estimate the structure of the gasoline market in Taiwan, data from both the demand and supply sides are needed. Since the gasoline market in Taiwan was not privatized prior to December 2001, the monthly data used in this study are collected from January 2002 to June 2013. For the demand side, the retail prices of 95 octane gasoline during this period are considered as proxy variables for the gasoline price since 95 octane gasoline occupies at least 90% of the market quantity. The quantity of gasoline demanded is the quantity of monthly domestic demand, and both the data for price and quantity are obtained from the Taiwan Bureau of Energy. Other variables including the international crude oil price, and the quantities of gas and coal demanded in Taiwan are also obtained from the Taiwan Bureau of Energy.

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Table 2 Descriptive statistics for demand and supply data. Variables

Unit

Mean

Min

Max

Retail Market price (Pm) Wholesale price (Pw) Crude oil price (Poil) Demand Quantity (Qd) Quantity (Qs) Coal Gas Number of employees (N) Business cost (CB) Operating cost (Co)

NT$ per liter NT$ per liter US$ per barrel Million Liter Million Liter Million ton Million m3 Person Million NT$ Million NT$

27.21 24.29 102.71 3430 1330 5.024 140 9470 54.1 52.3

18.2 16.717 30.10 2670 1640 3.26 102 3026 12.8 12.2

36.1 31.38 204.48 4140 2720 5.920 217 15977 96.9 95.0

For the supply side, data on both the supply price and quantity are obtained from the Taiwan Bureau of Energy. In addition, the data on the number of employees (or staff), business costs and operating costs are collected from CPC Corporation and Formosa Petrochemical Corporation. These data sets are obtained, respectively, from the annual reports of CPC Corporation and Formosa Petrochemical Corporation, respectively. All prices are measured in NT dollars (i.e., 1 USD = 30 NTD). The descriptive statistics for these variables are presented in Table 2. Table 2 illustrates that the average wholesale price per month was approximately NT$24 per liter, which is lower than the average retail market price of NT$27 per liter. This implies that consumers pay more to purchase gasoline than the producers. The mean for the quantity of gasoline demanded is 3430 million liters per month, while the quantity of gasoline supplied is about 1330 million liters per month per firm. In terms of energy alternatives, much less coal and gas are consumed on average per month than gasoline. The average number of employees at gasoline companies is about 9470, and average business costs are higher than operational costs. In addition, we employ the West Texas Intermediate (WTI) oil price to represent the crude oil price and its average is about US$102 per barrel. Our data comprise timeseries data covering the period from 2002 to 2013 for the two major gasoline companies (e.g., the CPC Corporation and Formosa Petrochemical Corporation). Therefore, the panel model with random effects is applied to estimate the market structure of the gasoline market.

4.2. Estimation outcomes for inverse demand and supply functions In order to investigate the conjectural variations of the gasoline market in Taiwan, the first step is to apply data sets from Table 2 into Eq. (8) to estimate the inverse gasoline demand function as presented in the first column of Table 3. The results of the

estimation show that the coefficient of this intercept variable (gas  Q d ) is statistically significant, which implies that an imperfectly competitive market exists in the Taiwan gasoline market. The price elasticity is about 0.146, which indicates that the quantity of gasoline demanded will increase by 0.146% as the result of a one percent increase in the gasoline price. The estimation results for the inverse supply function from Eq. (14) are shown in the last column of Table 3. They show that the coefficient of quantity has a positive sign, which is consistent with the concept in economic theory, where there is a positive correlation between the quantities and prices for the gasoline supply function. The estimated results for the conjectural variation (k) in Table 3 are close to 0.49 at the 1% level of statistical significance, which shows that the Taiwan gasoline market is an imperfect market (or a collusive market). Such an estimation outcome is consistent with the pricing behavior of these two petroleum firms during the last decade in Taiwan. The empirical outcomes from Table 3 also show that the price mark-up (i.e., the retail market price minus the marginal cost) is about 0.54% higher than the marginal cost, which explains why these two gasoline firms have generated more profit than in a competitive market. 5. Simulations of the carbon tax and emissions trading Carbon taxes and emissions trading are the major climate change competing policy tools which have been used to reduce greenhouse gas emissions (GHGE). However, there has recently been much debate on the economic effects of these tools, especially on energy prices. A carbon tax is treated as one of a number of environmental taxes that are levied on production activities/ services that pollute environmental goods. The price of gasoline increases as the demand for it decreases after the implementation of the carbon tax. Emissions trading may also have such a function. 5.1. Price equivalence of the carbon tax and emissions trading In this subsection we examine the experiences of Taiwan in levying carbon taxes to hypothesize four possible carbon tax regimes ranging from US$20 to US$50 per ton of CO2 in the Taiwan gasoline market. Empirically, these carbon taxes are added to the empirical demand function in the Taiwan gasoline market as shown in Table 3 before recalculating the equilibrium price and quantity. The simulation results for the carbon tax are shown in the first row of Table 4. Table 4 shows the simulation results under different carbon taxes for the equilibrium prices, quantity, welfare and the reduction in CO2 emissions given an imperfectly competitive gasoline market in Taiwan. If the carbon tax is implemented with US$20

Table 3 Estimation results for inverse demand and supply functions. Inverse demand function Dependent variable: Pm

Inverse supply function Dependent variable: Pw

Variables

Coefficient

Variables

Coefficient

Qd Poil Gas Coal Qd ⁄ gas Constant

6.45e06*** (2.16e06) 0.0907776*** (0.0041487) 0.0013985*** (0.0002986) 0.0002266*** (0.0000534) 5.20e08*** (1.52e08) 17.87935** (7.202554)

Qs Market power (k) N CB Co Constant

2.51e10 (1.64e09) 0.4941479* (0.319) 0.0003337 (0.0003339) 1.53e06** (6.30e07) 1.38e06** (6.48e07) 15.58812*** (1.064007)

Number of observations: 139 Note: (1) Standard errors are in parentheses. * Indicates p-value <0.1. ** Indicates p-value <0.5. *** Indicates p-value <0.05.

Number of observations: 22

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F.-P. Chiu et al. / Applied Energy 160 (2015) 164–171 Table 4 Price equivalence for carbon tax and emissions trading in a perfectly competitive market.

Carbon tax Price(PT in US$/liter) Quantity in million liters Social Welfare in million US$ Reduction in CO2 emissions (ton)

0 (US$/ton)

20 (US$/ton)

30 (US$/ton)

40 (US$/ton)

50 (US$/ton)

0.95 1805.50 116.03 0

0.97 (2.11) 1169.78 (35.21) 101.67 (12.38) 1,436,402

0.98 (3.16) 852.19 (52.80) 83.71 (27.85) 2,155,132

0.99 (4.21) 534.61 (70.39) 58.57 (49.52) 2,873,862

1.00 (5.26) 218.89 (87.87) 26.44 (77.21) 3,588,358

0.10 48.76

1.01 35.52

1.03 22.28

1.05 9.12

Emissions trading Price (PET in US$/liter) Social Welfare in million US$

Note: Numbers in parentheses represent the percentage change with respect to no carbon tax.

per ton for CO2 emissions, the market price will be increased from US$0.95 to US$0.97, reflecting a 2.11% increase in price. The quantity of gasoline is reduced by about 35.21% due to the higher demand elasticity; therefore, with a carbon tax, gasoline prices will increase while the consumption of gasoline will decrease significantly. The reduction in CO2 emissions will reach as high as 14,736,402 tons when a US$20 per ton carbon tax is implemented in the Taiwan gasoline market, and the reduction in social welfare will be US$101.67 million (i.e., a 12.38% reduction). It should be noted that the social welfare includes the producer’s surplus and consumer’s surplus plus government tax revenue from the implementation of the carbon tax. If the carbon tax is increased to US$30 per ton, the increase in the equilibrium price will be 3.16% and will result in a 52% reduction in gasoline consumption. The reduction in CO2 emissions is about 2,155,132 tons. A similar situation is also found for a US $40 and US$50 per ton carbon tax. The current total CO2 emissions in Taiwan amount to about 0.25 billion tons and the Taiwan government has set a goal to reduce CO2 emissions by 20% (i.e., 50 million tons) by 2020. We found that the reduction in CO2 emissions due to the carbon tax in the gasoline market could contribute about 4.3% to support the goal of a reduction in CO2 emissions by 2020, which implies that the effects of a carbon tax on the reduction in CO2 is highly significant. The main purpose of this paper is to investigate the equivalence of carbon tax and emissions trading in the energy market. In an imperfectly competitive market, for example, if a US$20 per ton carbon tax is implemented, the equilibrium price is US$0.97 (PT ), while the reduction in CO2 emissions is 1,436,402 tons as shown in the upper row of Table 4. In this study, the equilibrium price for the emissions trading system (i.e., P ET ) could be estimated given the same amount by which the CO2 emissions are reduced (e.g., 1,436,402 tons). The calculation is based on Eq. (6) and the estimated results are shown at the bottom of Table 4. The empirical results show that the gasoline price for an emissions trading system in an imperfectly competitive market is US$0.10, which is

higher than the price when implementing a US$20 carbon tax (i.e., US$0.97). Similarly, the prices under an emissions trading system given the same reduction in CO2 emissions are higher than those prices under a carbon tax, which indicates that the carbon tax system is a better tool for reducing CO2 emissions in the Taiwan gasoline market since the increase in the energy price is much lower than it would be under the emissions trading system. Therefore, the economy could continue to grow with a lower energy price and the CO2 emissions could be reduced when implementing such a carbon tax. 5.2. Simulation results under alternative market structures In addition, this paper further simulates the economic effects of different levels of carbon taxes under alternative market structures. In other words, an imperfectly competitive market (i.e., CV = 0) and a perfectly competitive market (i.e., CV = 1) will be simulated here. The same energy policy may give rise to different economic effects including price and quantity when market structures are different. The empirical results for the simulation are shown in Table 5. Table 5 indicates that the gasoline prices for different carbon taxes under a perfectly competitive market are lower than those for an imperfectly competitive market. Therefore, the equilibrium quantity, social welfare, and the reduction in CO2 emissions under these two alternative market structures are all different. For instance, without any implementation of the carbon tax, the gasoline price under an imperfectly competitive market is around US$0.95 per liter as shown in the upper row in Table 5, which is higher than in a perfectly competitive market (i.e., US$0.90) as shown in the bottom row of Table 5. Therefore, the equilibrium quantity under an imperfectly competitive market (i.e., 1805.50 million liters) is lower than under a perfectly competitive market (i.e., 3608.98 million liters), which causes social welfare in an imperfectly competitive market to be lower than in a perfectly competitive market.

Table 5 Simulations for the carbon tax under alternative market structures. Carbon tax

0 (US$/ton)

20 (US$/ton)

30 (US$/ton)

40 (US$/ton)

50 (US$/ton)

Price ðP Timp in US$=literÞ

0.95

0.97

0.98

0.99

1.00

Quantity in million liters Social Welfare in million US$ Reduction in CO2 emissions (ton)

1805.50 116.03 0

1169.78 101.67 1,436,402

852.19 83.71 2,155,132

534.61 58.57 2,873,862

218.89 26.44 3,588,358

Price ðP Tp in US$=literÞ

0.90

0.94

0.96

0.97

0.99

Quantity in million liter Social welfare in million US$ Reduction in CO2 emissions (ton)

3608.98 232.06 0

2339.56 203.35 2,872,803

1704.39 167.43 4,310,263

1069.22 117.13 5,747,724

434.05 52.46 7,185,184

Imperfectly competitive market (i.e., CV = 0)

Perfectly competitive market (i.e., CV = 1)

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When a carbon tax is implemented, a similar situation also arises. In other words, gasoline prices with different levels of carbon taxes in an imperfectly competitive market are all higher than those in a perfectly competitive market, while the quantity of gasoline and social welfare has the reverse effects. The most importing finding in Table 5 is that the CO2 reduction due to the implementation of the carbon tax in a perfectly competitive market is higher than in an imperfectly competitive market. Therefore, a perfectly competitive energy market will result in higher social welfare with a more significant contribution in terms of a CO2 reduction. 6. Conclusion and policy implications Climate change has become an important issue in the world due to the increasing greenhouse gas emissions (GHGE). The report compiled by the IPCC [34] has shown that the global surface temperature has increased by 0.56–0.92 °C in the last century. Therefore, 192 countries have signed the agreement to reduce GHGE as shown in the United Nations Framework Convention on Climate Change (UNFCCC). Faced with the global warming problem, there are two possible methods that can be used to control greenhouse gas emissions. The first method includes energy conservation, increases in energy efficiency, the use of renewable energy, the issuance of discharge permits, and a carbon tax, all of which are designed to reduce emissions of greenhouse gases, while the second method is to employ land and marine species to sequestrate carbon [5]. However, the reduction in GHGE using different methods may lead to different economic outcomes in energy markets. For instance, the energy price will be different when a carbon tax or emissions trading is implemented. Carbon taxes and emissions trading have been treated as the major climate change competing policy tools to reduce greenhouse gas emissions, but recently there has still been much debate on the economic effects. Therefore, the major contribution of this paper is to develop a theoretical model to compare these two systems and then to conduct an empirical study using the Taiwan gasoline market as an example. To compare these two systems, the same amount of GHGE reduction is assumed. The theoretical findings indicate that energy price equivalence exists for the carbon tax and emissions trading if the energy market is a perfectly competitive market. In addition, the empirical results also show that the gasoline price under a carbon tax and under an emission trading system will be the same if the Taiwan’s gasoline market is a perfectly competitive market. In other words, the economic impacts on the energy price and quantity will be the same when implementing either a carbon tax or emissions trading scheme given the same reduction in GHGE. However, the energy price under a carbon tax scheme is lower than under an emissions trading system if the energy market is close to being characterized by a monopoly or a collusive market. Such theoretical and empirical findings show that the economic effects of a carbon tax and emissions trading depend on the structure of the energy market. Therefore, the use of the Taiwan gasoline market is applied to evaluate the economic impacts of these two schemes empirically. To examine the economic effects of a carbon tax and emissions trading on Taiwan’s gasoline market, both the inverse demand and supply functions need first of all to be estimated. A conjectural variation approach is applied here to test the structure of the gasoline market in Taiwan. The empirical results show that the Taiwan gasoline market is close to being a collusive market (k ¼ 0:49), which is consistent with the observation since there are only two companies (e.g., CPC and Formosa Petrochemical Corporation) supplying gasoline in Taiwan. Subsequently, the carbon tax and the emissions trading systems are simulated in Taiwan’s gasoline

market. The empirical outcomes indicate that the prices under the carbon tax system given the same reduction in CO2 emissions are lower than those prices under the emissions trading system. For instance, if the carbon tax is implemented with US$20 per ton of CO2 emissions, the gasoline price will be US$0.97. However, the gasoline price for an emission trading system is US$0.10 under the same amount reduction of CO2 emissions which is higher than that of carbon tax. It indicates that the carbon tax system is a better tool for reducing CO2 emissions in Taiwan’s gasoline market since the increase in the energy price is much lower than under the emissions trading system. Therefore, the economy will not be adversely affected when implementing a reduction in GHGE. In addition, we also suggest that country with the existing carbon tax or emission trading scheme should constructively make the energy market structure to be more competitive. For instance, there is a deregulated and competitive electricity market in UK, therefore, social welfare with lower electricity price when implementing a carbon tax and ETS would be better than those in imperfectly competitive electricity market. Furthermore, if carob tax or emission trading scheme will be the possible tools to mitigate GHGE for a country wants to implement in the future, the energy marketing structure needs to be measured before selecting these tools. For instance, China government prepare to introduce national emission trading scheme in 2016, however, both the gasoline market and electricity market are imperfectly competitive market. According to the theoretical findings, we suggest that China government could firstly implement a carbon tax in 2016 and finally open the energy market in the future in order to get better economic and environmental effects. Based on the theoretical and empirical findings in this paper, the most important implication is that the structure of the energy market such as that of the oil or electricity market needs to be examined when a country aims to reduce its GHGE by implementing a carbon tax or emissions trading scheme. Different market structures with the implementation of a carbon tax or emissions trading will result in alternative economic effects on energy prices and quantities. However, we find that a perfectly competitive energy market structure always has better economic and environmental outcomes than an imperfectly competitive energy market structure.

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