Evaluación del Impacto de una BTA en la Economía China

7/29/2019 Evaluacin del Impacto de una BTA en la Economa China

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Impacts of border carbon adjustments on China's sectoral emissions:Simulations with a dynamic computable general equilibrium model

Qin BAO a, Ling TANG b, ZhongXiang ZHANG c,d,e,, Shouyang WANG a

a Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, Chinab School of Economics and Management, Beijing University of Chemical Technology, Beijing, Chinac Department of Public Economics, School of Economics, Fudan University, Shanghai, Chinad Center for Energy Economics and Strategy Studies, Fudan University, Shanghai, Chinae Institute of Policy and Management, Chinese Academy of Sciences, Beijing, China

a r t i c l e i n f o a b s t r a c t

Article history:

Received 6 December 2011

Received in revised form 14 November 2012

Accepted 18 November 2012

Available online 27 November 2012

Carbon-based border tax adjustments (BTAs) have recently been proposed by some OECD

(Organization for Economic Co-operation and Development) countries to level the carbon playing

field and target major emerging economies. This paper applies a multi-sector dynamic, comput-

ablegeneral equilibrium (CGE) model to estimate the impacts of the BTAs implemented by the US

and EU on China's sectoral carbon emissions. The results indicate that BTAs will decrease export

prices and transmit the effects to the whole economy, affecting sectoral output and demand from

both the supply side and demand side. On the supply side, sectors might move away from

exporting towards the domestic market, thereby increasing sectoral supply, while on the demand

side, the domestic income may be strikingly cut down due to the decrease in export price,

decreasing sectoral demand. Furthermore, such shrinkage of demandmay similarly reduce energy

prices, which would lead to an energy substitution effect and somewhat stimulate carbonemissions. Depending on the relative strength of the outputdemand effect and energy

substitution effect, sectoral carbon emissions and energy demands will vary across sectors, with

increases, decreases or shifts in different directions. These results suggest that an incentive

mechanism to encourage the widespread use of environment-friendly fuels and technologies will

be more effective than BTAs.

2012 Elsevier Inc. All rights reserved.

JEL class ification:

D58

F18

Q43

Q48Q52

Q54

Q56

Q58

Keywords:

Border carbon tax adjustments

Computable general equilibrium model

Carbon emissions

1. Introduction

As an essential part of post-Kyoto international climate negotiations, carbon-based border tax adjustments (BTAs) have beenproposed to level the playing field by the US, EU and other OECD (Organization for Economic Co-operation and Development)countries against countries without compatible emissions-reduction commitments, including China (Cosbey, 2008; Dong &Whalley, 2009a; Weber & Peters, 2009; Zhang, 2009, 2010b, 2010c, 2011). The US House of Representatives (2009) passed theAmerican Clean Energy and Security Act of 2009 (HR2998) on June 26, 2009, in which a carbon-based border-adjustmentprovision was proposed to protect the competitive advantages of American producers against their competitors in countrieswithout comparable emissions-reduction commitments. In the EU, the EC-commissioned High Level Group on Competitiveness,Energy and Environmental Policies proposed the BTA issue in its second report early in 2006. Moreover, BTAs have been

China Economic Review 24 (2013) 7794

Corresponding author at: School of Economics, Fudan University, 600 Guoquan Road, Shanghai 200433, China. Tel.: +86 21 65642734; fax: +86 21 65647719.

E-mail address: [email protected] (Z. Zhang).

1043-951X/$ see front matter 2012 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.chieco.2012.11.002

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recommended as useful policy tools to protect the competitiveness of domestic industries in the EU (Asselt & Biermann, 2007;Monjon & Quirion, 2010, 2011a; Zhang, 2012b) and Canada (Rivers, 2010).

BTA measures are actually not new topics (Lockwood & Whalley, 2008), and the related policies mainly concentrate on two issues(Babiker & Rutherford, 2005; Dong & Whalley, 2009b; Kuik & Hofkes, 2010; Monjon & Quirion, 2010; Zhang, 2012b ). One issue isaddressing concerns regarding competitiveness, providing offsets for producers from participating regions that take on the emissions-reduction commitments against producers from non-participating regions with low carbon-abatement costs. Therefore, BTAs aredesigned to charge imported goods the equivalent of what the producers would have had to pay had the goods been produced in the

participating regions (Asselt & Brewer, 2010). The other issue is avoiding carbon leakage, i.e., that the carbon-emissions reductions inparticipating countries would increase emissions elsewhere as firms relocate (Babiker, 2005; Zhang, 2012b). BTAs are also believed toencourage more countries to participate in the global carbon emissions-reduction commitment (Droege, 2011). However, the legality ofBTAs has raised great concerns, and some people have arguedthat BTAs be considered WTO-consistent only if they are carefully designed(Bhagwati & Mavroidis, 2007; Houser, Bradley, Childs, Werksman, & Heilmayr, 2008; Zhang, 1998c, 2004, 2009, 2010b, 2010c; Zhang &Assuno, 2004).

A number of researchers have examined the impacts of BTAs and related policies. Most of the researchers have focused on theeffectiveness of BTAs for protecting competitiveness and avoiding carbon leakage. No general agreement has been found to date. On theone hand, some researchers have argued that BTAs would have positive effects on environmental improvements and acompetitive-disadvantage offset (Majocchi & Missaglia, 2002; Veenendaal & Manders, 2008). For example, Lessmann, Marschinski,and Edenhofer (2009) found the influences of carbon tariffs on international cooperation to be significantly positive. Ross, Fawcett, andClapp(2009)suggested BTAs an effective method forUS climate mitigation. Dissou and Eyland (2011)foundthat competitiveness wouldbe hindered by BTAs in Canada. Monjon and Quirion (2011b) discussed the leakage-avoiding effect of the EU's BTAs. Gros (2009) found

that BTAs would increase global welfare. Bhringer et al. (2010) studied the impacts of climate policies by the EU and US on the globaleconomy and environment, and the results suggested that the climate policies would not necessarily cause damage to the targeteddeveloping countries.

On the other hand, some studies have concluded that BTAs would be ineffective either to increase domestic competitiveness orto improve the global environment (Dong & Whalley, 2009a,b; Elliott et al., 2010; Weber & Peters, 2009). For example, Fischerand Fox (2009) suggested that BTAs would be beneficial for domestic production but not be effective to reduce global emissions.McKibbin and Wilcoxen (2009) found a modest effect of BTAs to reduce leakage and to defend against import-competingindustries without carbon costs. Kuik and Hofkes (2010) focused on the carbon leakage-avoidance effects of the EU EmissionsTrading System and suggested that BTAs might reduce the sectoral leakage rate of the iron and steel industry, but the overallleakage-reduction effect was modest.

While most of the existing studies focused on the effects of BTAs in developed countries, little attention has been paid todeveloping countries, especially China, the country that BTAs mainly target, either implicitly or explicitly. Most of the existingdiscussions about China are theoretical, and few numerical simulations have been carried out to extensively measure the

quantitative impacts of BTAs on China (Shi, Li, Zhou, Yuan, & Ma, 2010; Zhang, 2010a,b). Some numerical studies that did considerChina built global energy-economy models and just treated China as a nonspecific country with few detailed sectoral settings(Bhringer et al., 2010; Dong & Whalley, 2009a,b; McKibbin & Wilcoxen, 2009 ).

However, as a rapidly growing developing country, China has been one of the largest sources of carbon emissions, with itsshare in global CO2 emissions increasing rapidly from 5.7% in 1973 to 22.3% in 2008 (Fredrich & David, 2008; IEA, 2010). China'sshare in the global total final energy consumption has more than doubled over the past 30 years from 7.9% in 1973 to 16.4% in2008 (IEA, 2010). Furthermore, ever since 1978, China's economy has been growing rapidly, and this pattern is expected tocontinue in the near future. Such rapid development of the economy will inevitably increase China's energy demand and carbonemissions.

The question is then raised regarding whether BTAs would lead China's industries to emit less carbon. Against thisbackground, this study aims to analyze the impacts of the BTAs implemented by the US and EU on China's sectoral carbonemissions by using a recursive dynamic computable general equilibrium (CGE) model. The CGE model may be the mostpopular modeling tool for the assessment of energy and environment policies globally ( Bhringer et al., 2010; Burniaux,

Chateau, & Duval, 2011; Hbler, 2011; Kuik & Hofkes, 2010; McFarland, Reilly, & Herzog, 2004; Rivers, 2010; Ross et al., 2009;Shoven & Whalley, 1972; Xu & Masui, 2009; Zhang, 1998a,b ). Compared with other policy-assessment methods, such aspartial equilibrium analysis and inputoutput (IO) analysis, the CGE method is able to reveal the comprehensiverelationships in the whole economy and conduct policy simulations under the assumption of general equilibrium. Moreover,detailed sectoral information, e.g., industrial prices and outputs, can be well characterized. China's CGE model has beenwidely used to analyze economy, energy and environmental policies (Fan, Liang, Wei, & Okada, 2007; He, Zhang, Yang, Wang,& Wang, 2010; Horridge & Wittwer, 2008;Liang, Fan, & Wei, 2007; Toh & Lin, 2005; Zhang,1995,1998a, 1998b). In this paper,a multi-sector CGE model including 7 energy sectors and 30 non-energy industrial sectors is developed and facilitates adetailed sectoral analysis. The model is calibrated based on data from the year 2007 and runs recursively to the year 2030. Inthe proposed model, a BTA module is specifically built to describe the border carbon tax imposed by the US and EU againstChina beginning in the year 2020.

The rest of the paper is organized as follows. The recursive dynamic CGE model for China is described in Section 2. Datadescriptions, model calibration and business-as-usual (BAU) simulation scenarios are presented in Section 3. Results about the

impacts of BTAs on China's industrial emissions and the further analysis are reported in Section 4. Robustness analyses of themodel are performed in Section 5. Section 6 provides some concluding remarks and policy implications.

78 Q. Bao et al. / China Economic Review 24 (2013) 7794


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2. The model

A recursive dynamic computable general equilibrium model is developed to evaluate the impacts of the BTAs imposed by theUS and EU on China's industrial carbon emissions.1 Our model is a modified version of the one proposed by Wu and Xuan (2002).In the model, industrial sectors are disaggregated into 7 energy sectors and 30 non-energy sectors based on the characteristics ofenergy intensity and export intensity, as shown in Table 1. The economic activities are categorized into four modules: production,international trade, income and expenditure as well as closures and dynamics. Additionally, a BTA module is designed to describe

the BTAs imposed by the US and EU. The framework of the model is illustrated in Fig. 1, and the details are discussed in thefollowing sub-sections.

2.1. Production module

The output Xi,t of sector i (i denotes both the energy sectors and non-energy sectors) in period t is captured by a constantelasticity of substitution (CES) function, with a nested structure of five levels designed to represent different substitutions amonga variety of inputs, as shown in the production module of Fig. 1. In the first layer, the fossil energy input FOSSILi,t of sector i inperiod t is composed of six types of fossil energy resources based on a CES function as described in Eq. (1).

FOSSILi;t f ossilf af ossilf;iFOSSIL Ff ossilf;i;tf

1=f1

where f=1/(1f) denotes the elasticity of substitution among different fossil energy resources, and af ossilf;i is the sharing

parameters withf ossilf af ossilf;i 1.Similarly, in the second layer, the energy input Energyi,tof sector i in period tis composed of the electricity ELEi,tand the fossil

energy composite FOSSILi,t. In the third layer, the energy composite Energyi,tand capital Ki,tcompose the capitalenergy input KEi,t,which is then combined into the capitalenergylabor input KELi,t with the labor input Li,t. All of these compositions follow CEStechnology. Meanwhile, the combined intermediate input TOTInti,t of each sector i in period t is composed of individualintermediate goods from different non-energy sectors using a Leontief function as described in Eq. (2).

TOTInti;t minInt1;i;t1;i

;Int2;i;t2;i

;;Int30;i;t30;i

!2

where Intj,i,t(j =1, 2, , 30) denotes the intermediate input of sector j (j denotes non-energy sectors only) to sector i in period t,and j,i is the inputoutput coefficient. In the last layer, the final output Xi,t of sector i in period t is composed of the totalintermediate input TOTInti,t and the capitalenergylabor composite KELi,t with CES technology as described in Eq. (3).

Xi;t a int;iTOTInti;tx akel;i kel;i;tKELi;t

x

1=x

3

where x=1/(1x) is the elasticity of substitution between the total intermediate input and the capitalenergylaborcomposite. aint,i and akel,i are the sharing parameters with aint,i+ akel,i=1. kel,i,t is the total factor-productivity coefficient thatcaptures the technology improvement.

The optimal production strategies are derived from the minimization of the production costs at the given prices.

2.2. International trade module

An Armington assumption (Armington, 1969) is used in the model such that the domestic goods and foreign goods are treatedas imperfect substitutes for each other. The total domestic demand Qi,t (or the total domestic output Xi,t) is composed of thedomestic goods Di,t and imports Mi,t (or exports Ei,t) using a CES function (or a constant elasticity of transformation [CET]

function), as shown in Eq. (4) (or Eq. (5)).

Qi;t am;iMi;tm adm;iDi;t

m 1=m

4

Xi;t ae;iEi;te ade;iDi;t

e

1=e5

where am,i and adm,i are the sharing parameters of imports and domestic goods, ae,i and ade,i are the shares of exports and domesticgoods, respectively, with am,i+ adm,i=1 and ae,i+ ade,i=1. im=1/(1m) is the Armington elasticity between the domesticgoods and imports, while ex=1/(e1) is the elasticity between the domestic goods and exports.

The optimal importing strategy is derived by minimizing the costs PMi,tMi,t+ PDi,tDi,tunder the constraint described by Eq. (4).Similarly, the optimal exporting strategy is derived by maximizing the sales PEi,tEi,t+PDi,tDi,tunder the constraint described by Eq.(5). PMi,t, PEi,t and PDi,t are the importing price, exporting price and domestic price of commodity i in period t, respectively.

1 The model documentation is available upon request.

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However, in this paper, the rebates by the US and EU are not considered because they are better addressed using a multi-country,global model. All other factors being equal, when the rebates for the US exports are included, the impacts of BTAs on China'seconomy will theoretically be larger. Under the accounting rules of the Intergovernmental Panel on Climate Change (IPCC), all ofthe greenhouse-gas emissions and removals are based on in-country production emissions (Davis & Caldeira, 2010; Zhang, 2011,2012a). Therefore, this study is based on this territorial-based emissions-accounting system, which would avoid double countingof carbon emissions. That is, China's exporters in each sector should be responsible only for the carbon emissions generatedduring their production, while the indirect emissions stemming from intermediate inputs that are produced by other sectors are

not considered. As recommended by the IPCC (2006), carbon emissions are calculated based on the use of fossil energy withcorresponding conversion factors, as described in Eq. (6).

Cei;t

X6f1

afbfcfFossilf;i;t

Xi;t6

where Cei,t denotes the carbon emissions per unit product of sector i in period t, and Fossilf,i,t denotes the demand for primaryenergy fof sector i in period t, where fincludes six fossil energy sources, i.e., the mining and washing of coal (M_C), extraction ofpetroleum (M_O), extraction of natural gas (M_G), processing of petroleum and nuclear fuel (OIL), processing of coke (COK) andproduction and supply of gas (GAS), as shown in Fig. 1. af, bfand cfare the conversion factor, the emissions factor and the fractionof oxidized carbon of energy f, respectively.

Two methods are widely used to calculate the emissions embodied in trade: one is based on producing countries' carbon

emissions, and the other is based on BTA-collecting countries' carbon emissions. However, neither of the methods is ideal becausesimply assuming that either all of the imports produced using Chinese domestic technologies or all of the Chinese exportsproduced using BTA-collecting countries' technologies may overestimate or underestimate China's CO2 emissions embedded intrade because this approach fails to capture potentially large national differences in technology levels across trading partners(Zhang, 2012a). In this paper, because we use a single country CGE model and due to a lack of proper data, we focus on the formerassumption to make the BTA policy transmission more clear. Because sectoral carbon emissions are from burning fossil fuels, wecalculate both the direct carbon emissions and total carbon emission in each sector. Sectoral direct carbon emissions (CO2) arefrom primary energy use, while total carbon emissions (TOTCO2) are sectoral carbon emissions from both primary and secondenergy use. The latter includes the indirect emissions from the fossil-fuel use to generate the electricity used in this sector.

The carbon border tax is then calculated by multiplying the sectoral embodied carbon emissions by the tax level. When theBTAs are imposed, each production sector in China will have to pay an additional carbon-emissions cost for its commoditiesexported to the foreign regions that impose the BTAs. China's exporting prices to the regions with BTAs are described in Eq. (7),while those to other regions are shown in Eq. (8).

1esubi;t

PXEs;i;t btas;tCei;t PWEs;i;t 7

1esubi;t

PXEs;i;t PWEs;i;t 8

where PXEs,i,t is the export price of commodity i to the region s in period t, esubi,t is the export rebate rate of commodity i in periodtprovided by China's government, PWEs,i,t is the adjusted world-export price of commodity i in period t, and btas,t is the carbonborder tax rate of the region s in period t. Hereby, s denotes the US and EU in Eq. (7) and denotes JAP and ROW in Eq. (8).

As mentioned above in the international trade module, the large-country assumption is used to depict the export behaviors ofChina's enterprises. Under this assumption, since China plays a significant role in the international trade market, its willingness totrade has much power in driving world export and import price settings. Therefore, China's export prices faced by domesticproducers will be influenced by both BTAs and their willingness to trade.

2.4. Income and expenditure module

The income and expenditure of different agents are illustrated in Fig. 1. There are four types of agents, including enterprises,households, the national government and foreign countries.

Enterprises gain their income from returns of capital and government transfers. After paying income tax to the governmentand transferring some of the income to households, enterprises make their savings.

Households gain their income from labor income, returns of capital and transfers by the government, enterprises and foreigncountries. After paying for income tax, they gain disposable income, which can be consumed or saved.

The national government gains income from various taxes, including indirect tax from production sectors, tariffs againstimports and income taxes from enterprises and households, and expends this income through transfers and consumption orleaves it as savings.

Foreign countries gain their income from capital investments in China and exports to China. Meanwhile, they have to pay for

their imports from China. The net earnings after paying for transfers to China's households and government are the savings offoreign countries. The carbon border tax collected by foreign regions with BTAs will be added to their earnings.



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2.5. Dynamics and closure module

The dynamics of the model are drivenby the combination of total factortechnological progress, labor andcapital. The technologicalprogressis indicated by the totalfactor productivity coefficientkel,i,t, as mentioned above in Eq. (3). In general equilibrium, commoditymarkets and factor markets are cleared. Specifically, in the labor market, it is assumed that in the long run, wages are endogenouslydetermined, while the total supply of labor force is exogenous with a population constraint. The growth of the labor force isexogenously designed, as described in Eq. (9), and the sectoral labor force is determined endogenously.

Lt Lt1 1 grlt 9

where Lt and Lt1 are the total labor force in periods tand t1, respectively. grlt is the growth rate of labor force in period t.In the capital market, the rate of return is assumed to be determined by monetary policy endogenously in the long run, while

the capital accumulation is determined as shown in Eq. (10). The sectoral capital is determined endogenously

Kt 1 Kt1 It 10

where Kt and Kt1 are the total capital stock in periods tand t1, respectively. It is the total investment in period t, and is thecapital depreciation rate.

The closure part of the model includes three aspects. First, foreign savings are assumed to be endogenous, while the exchangerate is assumed to be exogenous. Second, government surplus or deficit is assumed to be endogenous, while the various tax ratesare assumed to be exogenous. Third, the Neoclassical closure is applied in the model, i.e., the total investment equals the totalsavings. Therefore, the investment is determined using Eq. (11).

It St 11

where St is the total savings in period t.

Table 2

Stylized facts of 37 sectors in the year 2007.

Ratio of

export (%)

Share of export to

the US and EU (%)

Energy intensity

(tons of coal equivalent

per ten thousand RMB yuan)

Direct carbon

emissions (ton)

Total carbon

emissions (ton)

Carbon intensity

(tons of carbon emissions

per ten thousand RMB yuan)

AGR 1.36 26.48 0.07 2964.55 3642.64 0.07

CEM 2.70 32.61 0.36 6253.05 7812.08 0.58

CMF 6.13 16.50 0.05 169.72 367.10 0.09

CNS 0.67 8.35 0.04 1725.94 2949.88 0.05

COK* 8.23 13.93 0.48 1656.80 1964.56 0.69

CUM 69.20 29.36 0.01 25.21 84.02 0.02EEQ 42.67 37.73 0.01 351.22 1386.42 0.02

ELEa 0.21 8.35 1.08 44857.77 16710.45 0.55

EQP 15.24 48.36 0.02 988.58 2328.74 0.06

FOD 5.22 27.75 0.03 1400.83 2055.46 0.06

FUR 32.90 50.11 0.01 203.58 362.96 0.02

GAS* 0.00 0.44 495.40 522.73 0.48

GLS 14.94 38.56 0.36 1428.16 1664.05 0.50

M_C* 2.62 13.93 0.31 3700.49 4591.72 0.52

M_G* 2.08 13.93 0.20 309.16 562.89 0.26

M_O* 2.08 13.93 0.20 894.26 1628.22 0.26

MCM 9.96 39.20 0.03 257.67 528.72 0.08

MET 20.98 45.36 0.02 291.20 1497.15 0.09

MFM 0.02 24.65 0.05 165.94 1025.95 0.30

MIN 4.18 43.83 0.07 259.94 631.26 0.18

MNF 3.44 39.62 0.03 73.17 439.72 0.19

NFR 7.58 39.62 0.06 1192.25 3054.43 0.16NMM 13.62 37.13 0.30 1630.81 1960.11 0.43

OIL* 3.10 13.93 0.41 4811.60 5216.14 0.31

OSR 6.41 8.35 0.03 3672.45 6908.11 0.05

OTM 13.07 35.95 0.01 166.82 363.47 0.04

PAP 3.98 31.86 0.09 891.51 1279.80 0.16

PRT 31.24 60.72 0.01 59.15 167.53 0.03

RCM 10.74 29.79 0.28 8997.67 12225.11 0.38

RUB 17.43 41.24 0.02 378.40 1015.89 0.06

STL 9.56 24.65 0.50 22258.98 24634.40 0.64

TEX 34.06 34.68 0.03 945.36 1784.55 0.07

TOB 0.81 20.22 0.02 57.31 72.97 0.03

TRM 10.47 38.38 0.02 505.09 1044.56 0.03

TRP 10.75 8.35 0.40 10569.69 11419.31 0.30

WOD 23.19 60.34 0.02 215.56 580.11 0.06

WTR 0.00 0.02 20.48 362.55 0.33

* Energy sectors (commodities).

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3. Data, calibration and scenarios

3.1. Data sources

A social accounting matrix (SAM) provides a uniform database for the CGE model, reflecting the detailed economic activities ofthe whole economy. In this study, the SAM is built based mainly on China's national inputoutput (IO) data from 2007. Inaddition, other data are derived from such sources as the National Bureau of Statistics of P.R. China (2009a, 2009b), GeneralAdministration of Customs of P.R. China (2009) and Almanac of China's Finance and Banking Editorial Board (2009).

Table 2 presents some stylized facts for 37 sectors in the baseline year 2007. The ratio of export is defined as the ratio of sectoralexport to sectoral output. The share of export to the US and EU denotes theshare of export to theUS and EU ofthe totalsectoral export.The energy intensity is defined as theratio of the sectoral total energy use in terms of coal equivalents to thesectoral value added. The

direct carbon emissions are sectoral carbon emissions from primary energy use, while the total carbon emissions are sectoral carbonemissions from both primary and second energy use. The latter includes the emissions from fossil-fuel use to generate electricity usedin this sector. As shown in Table 2, except for the electricity industry, whose carbon emissions stem largely from burning fossil fuels togenerate electricity for uses in other sectors, the total carbon emissionsin other sectors are higherthan their directcarbonemissions tovarying degrees. Carbon intensity is defined as the sectoral total carbon emissions per unit of output.

3.2. Model calibration

As commonly used in CGE analysis, a calibration procedure is adopted. The year 2007 is treated as the benchmark year. Scaleparameters and sharing parameters are calibrated based on the SAM of the year 2007. The elasticities of substitutions and theArmington elasticities are specified, as shown in Table 3, according to the related studies (Shi et al., 2010; Wu & Xuan, 2002) withsome modifications.2 The depreciation rate is set at 0.05.

Table 3

Substitution elasticities and Armington elasticities of commodities.

Commodities f e ke kel im ex

AGR 1.50 0.70 0.3 0.5 3 3.6

M_C 1.30 0.65 0.3 0.2 3 4

M_O 1.30 0.65 0.3 0.2 3 4

M_G 1.30 0.65 0.3 0.2 3 4

MFM 1.50 0.70 0.3 0.9 3 4

MNF 1.50 0.70 0.3 0.9 3 4

MIN 1.50 0.70 0.3 0.9 3 4

FOD 1.50 0.70 0.3 0.9 3 4

TOB 1.50 0.70 0.3 0.9 3 4.6

TEX 1.50 0.70 0.3 0.9 3 4.6

FUR 1.50 0.70 0.3 0.9 3 4.6

WOD 1.50 0.70 0.3 0.9 3 4.6

PAP 1.50 0.70 0.3 0.9 3 4.6

PRT 1.50 0.70 0.3 0.9 3 4.6

OIL 1.25 0.60 0.3 0.9 3 4.6

COK 1.25 0.60 0.3 0.9 3 4.6

RCM 1.50 0.70 0.3 0.9 3 4.6

MCM 1.50 0.70 0.3 0.9 3 4.6

CMF 1.50 0.70 0.3 0.9 3 4.6

RUB 1.50 0.70 0.3 0.9 3 4.6

CEM 1.50 0.70 0.3 0.9 3 4.6

GLS 1.50 0.70 0.3 0.9 3 4.6

NMM 1.50 0.70 0.3 0.9 3 4.6

STL 1.50 0.70 0.3 0.9 3 4.6

NFR 1.50 0.70 0.3 0.9 3 4.6

MET 1.50 0.70 0.3 0.9 3 4.6

EQP 1.50 0.70 0.3 0.9 3 4.6

TRM 1.50 0.70 0.3 0.9 3 4.6

EEQ 1.50 0.70 0.3 0.9 3 4.6

CUM 1.50 0.70 0.3 0.9 3 4.6

OTM 1.50 0.70 0.3 0.9 3 4.6

ELE 1.25 0.60 0.3 0.2 0.9 0.5

GAS 1.25 0.60 0.3 0.2 0.9 0.5

WTR 1.50 0.70 0.3 0.3 0.9 0.5

CNS 1.50 0.70 0.3 0.5 3 3.8

TRP 1.50 0.70 0.3 0.5 2 3

OSR 1.60 0.90 0.3 0.5 2 3

2 The elasticities will not change the results significantly. Robustness tests on these parameters were carried out and are available upon request.

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The model is recursively run up to the year 2030 in the method described in Section 2.5. The recursive, dynamic calibrationassumptions are shown in Table 4. The growth rate of the GDP, primary industry (including the sector of Agriculture [AGR]),tertiary industry (including sectors of Transportation, storage, post telecommunication and other information-transmissionservices [TRP] and other services [OSR]), total labor force and labor in primary industry are specified for calibration from the years2008 to 2030. In these assumptions, the actual economic data from the years 2008 to 2010 are used, while the dynamic path from2011 to 2030 is forecasted based on historical data and the related literature. Two stages are defined for China's economydevelopment from 2011 to 2030 according to Holz (2008): one is a relative faster-developing stage from 2011 to 2015, and theother is a more steadily growing stage from 2016 to 2030.

3.3. Scenarios

In this paper, we focus on the BTA policies implemented by the US and EU that would be levied on the carbon content of their goodsimported from China. To discuss the impacts of the BTAs on China'ssectoral carbon emissions, three scenariosare developedunder whichcarbon tariffs will be implemented at different levels, i.e., US dollars 20, 50 and 80 per ton of carbon emissions (US$/tC), according torecent studies (Elliott et al., 2010; Peterson & Schleich, 2007). In each case, the BTAs will be imposed starting in the year 2020.

The BTAs have been designed separately by the US and EU with different destinations and different details (Asselt & Brewer,2010; Zhang, 2010c, 2012b). In the US, as specified in the American Clean Energy and Security Act (HR2998) (US House ofRepresentatives, 2009), the importers of primary emission-intensive products from countries that have not taken greenhousegas compliance obligation commensurate with those that would apply in the US have to surrender carbon-emission allowances.The eligible industrial sectors are qualified as sectors whose energy or greenhouse-gas intensity is above 5% and whose tradeintensity is at least 15% or sectors with an energy or greenhouse-gas intensity higher than 20%. In the EU, the coverage of targetedgoods includes energy-intensive primary goods and finished goods. For simplicity, in this paper, we assume that the BTAs will belevied on all of the products from China. This assumption of the simulation gives an upper bound on the impacts of the BTAs on

China. The large-country assumption is made, and carbon emissions are calculated based on both the primary and second energyuse for each sector in our model. These assumptions and specifications will be altered in Section 5 for sensitivity analyses.

4. Results and analysis

Based on our recursive dynamic CGE model for China, the impacts of the BTAs on China's sectoral carbon emissions aresimulated and extensively analyzed in this section. First, a general review of the overall effects of the BTAs on China's total carbonemissions and energy demands is provided. The impacts of the BTAs on sectoral carbon emissions and energy demands are thenanalyzed. Finally, an economic analysis is provided to better explain the different impacts on sector emissions.

4.1. A general review

The impact of the BTAs on China's total carbon emissions is shown in Fig. 2, which illustrates that the imposition of BTAs by the

US and EU on their imports from China will decrease the total carbon emissions in China. Moreover, the effects of BTAs will belarger with a higher tax rate. For example, with a border carbon tax level of US$ 20, 50 and 80 per tC, the total carbon emissionsduring production in China will be cut by approximately 0.020%, 0.048% and 0.075% in 2020 and 0.011%, 0.027% and 0.042% in2030, respectively. This result implies a positive effect played by the BTAs in mitigating China's total carbon emissions. However,the impacts are relatively small.

The impact of BTAs on China's total carbon emissions can be directly attributed to the decrease in China's energy use. Theimpacts of BTAs on China's energy output (X) and demand (Q) are shown in Table 5. The output and demand of each energysource will be decreased by BTAs, especially for COK; e.g., the output and demand of COK will be reduced by approximately0.118% and 0.104%, respectively, in 2020 due to the imposition of BTAs at a level of 50 US$/tC. The results in Table 5 alsodemonstrate that the output price (PX) and demand price (PQ) of all of the analyzed energy sources will be cut by BTAs. M_C andCOK are the two energy sources with the greatest price drops; e.g., in 2020, the output price of M_C and COK will be decreased byapproximately 0.171% and 0.141%, respectively, while the demand prices of M_C and COK will be cut by approximately 0.169%and 0.138%, respectively by BTAs levied at the level of 50 US$/tC. From the simulation results, we can easily find that the impacts

of BTAs on China's total carbon-emissions mitigation are very small. This pattern is attributed to the decreasing prices of primaryenergy, which will stimulate the use of fossil energy, especially M_C and COK.

Table 4

Recursive dynamic calibration assumptions (%).

Variables 2008 2009 2010 20112015 20162030

Growth rate of real gross domestic product 9.600 9.200 10.400 7.500 6.000

Growth rate of primary industry 5.400 4.200 4.300 3.500 3.000

Growth rate of tertiary industry 10.400 9.600 9.600 9.000 8.000

Growth rate of total labor 0.323 0.349 0.365 0.500 0.400

Growth rate of labor in primary industry 2.628 3.452 3.323 2.500 2.000



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4.2. Sectoral carbon emissions

The impacts of BTAs on both the sectoral direct carbon emissions (CO2) and total emissions (TOTCO2) will differ amongsectors, as shown in Fig. 3. For some of the sectors, BTAs will reduce the sectoral carbon emissions. For example, for the industrialsectors of the manufacture of nonmetallic mineral products (NMM), manufacture of glass (GLS) and smelting and pressing offerrous metals (STL), the total carbon emissions stemming from the consumption of both primary and second energies will bereduced by approximately 0.186%, 0.174% and 0.166%, respectively, due to BTAs with a level of 50 US$/tC in 2020. In contrast, theBTAs will increase carbon emissions from the industrial sectors of the manufacture of measuring instruments and machinery forcultural activity and office work (CUM), manufacture of electrical and electronic equipment (EEQ), mining and processing ofnonferrous metal ores (MNF) and manufacture of textiles (TEX). Our results show that the carbon emissions from these sectorswill be increased by approximately 0.226%, 0.172%, 0.106% and 0.097%, respectively, by BTAs levied at a level of 50 US$/tC in 2020.

To find out why carbon emissions will decrease in some industrial sectors but increase in others with the levy of BTAs, sectoralenergy demands are calculated. Fig. 4 represents the changes of sectoral total energy demands as well as demands for M_C and

OIL due to BTAs. M_C and OIL are chosen as the representative energy types whose output prices are reduced the most and leastby BTAs, and their results are shown in Table 5. From Table 5, we can see that BTAs also affect the sectoral total energy demand intwo directions. In some of the sectors, the total energy demand will be reduced. For example, in the industrial sectors of themanufacture of nonmetallic mineral products (NMM), manufacture of glass (GLS) and smelting and pressing of nonferrous metals(STL), the total energy demand will decrease by approximately 0.198%, 0.185% and 0.170%, respectively, due to BTAs at a level of50 US$/tC in 2020. In contrast, the total energy demand will be increased in the industries of the manufacture of measuringinstruments and machinery for cultural activity and office work (CUM) and the manufacture of electrical and electronicequipment (EEQ). The total energy demand in these sectors will be increased by approximately 0.199% and 0.142% by the BTAslevied at 50 US$/tC in 2020, respectively.

Another interesting result, shown in Fig. 4, is that the energy-substitution effect differs among the energy types. Taking M_Cand OIL for example, it can be seen that the demands for M_C in most of the studied sectors will increase as a result of the BTAs.What's more, the increasing rate of M_C use is obviously higher than the sectoral total energy demand, while the decreasing rateof M_C use is lower than the sectoral total energy demand. For example, for the sectors CNS, EQP and MIN, the total energy

demand will be decreased by BTAs; however, the use of M_C will be increased. The sectoral demand of OIL shows a patternopposite to that of M_C. The main reason for this result can be found in Table 5: the prices of M_C will show the greatest decreasedue to BTAs. Because M_C will emit more carbon than OIL, the energy-substitution effects triggered by the impacts of BTAs onenergy prices will offset the effects of BTAs on carbon-emission mitigation.

-0.080

-0.070

-0.060

-0.050

-0.040

-0.030

-0.020

-0.010

0.000

20 50 80

changeoftotalcarbon

emissions(%)

carbon tax level(US$/tC)

2020

2025

2030

Fig. 2. Impacts of the studied BTAs on China's total carbon emissions (%).

Table 5

Impacts of the studied BTAs on the energy sources in 2020 at a tax level of US$ 50 per tC (%).

X PX Q PQ

COK 0.118 0.141 0.104 0.138

ELE 0.021 0.129 0.021 0.129

GAS 0.016 0.119 0.016 0.119

OIL 0.058 0.078 0,064 0.075

M_C 0.007 0.171 0.013 0.169

M_G 0.004 0.096 0.066 0.075

M_O 0.004 0.095 0.067 0.074



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4.3. Economic analysis of the results

Why will the BTA policies imposed by the US and EU have different impacts on China's sectoral emissions and energy demand?What are the main factors that drive these results? In this subsection, the transmission mechanism of BTAs is further discussedfrom both the supply and demand perspectives. The BTAs will first take effect in the international trade module, where the export

prices in different sectors faced by Chinese enterprisers will be decreased by BTAs even under the large-country assumption forChina. As shown in Table 6, all of the export prices (PE) will be cut by BTAs. With BTAs levied at a level of 50 US$/tC in 2020, thedecrease of PE varies from 0.417% (sector GLS) to 0.038% (sector CUM).

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

0.250

AGR

CEM

CMF

CNS

COK*

CUMEEQ

ELE*

EQP

FODFUR

GAS*

GLS

M_

C*

M_

G*

M_

O*

MCM

MET

MFMMIN

MNF

NFR

NMM

OIL*

OSR

OTMPAP

PRT

RCMRUBSTL

TEX

TOB

TRMTRP

WOD

WTR

changeofsectoralcarbonem

issions(%)

TOTCO2

CO2

Fig. 3. Impacts of the BTAs on China's sectoral carbon emissions in 2020 at a tax level of US$ 50 per tC (%).

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400

AGR

CEM

CMF

CNS

COK*

CUMEEQ

ELE*

EQP

FOD

FUR

GAS*

GLS

M_

C*

M_

G*

M_

O*

MCM

MET

MFMMIN

MNF

NFR

NMM

OIL*

OSR

OTMPAP

PRT

RCMRUBSTL

TEX

TOB

TRMTRP

WOD

WTR

changeofsectoralenergyusing(%)

TOTAL

M_C

OIL

Fig. 4. Impacts of the BTAs on China's sectoral energy demands in 2020 at a tax level of US$ 50 per tC (%).



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The changes of the stylized facts of 37 sectors as listed in Table 2 due to the applied BTAs are shown in Figs. 5 to 7 with sectoralexport-price changes for comparison. The sectors with the greatest decreases in export prices can be characterized asexport-oriented, energy-intensive and carbon-intensive. For these sectors, the sectoral carbon intensity plays a dominant role,

Table 6

Impacts of the BTAs on the sectoral supply in 2020 at a tax level of US$ 50 per tC (%).* Energy sectors.

X D E PX PD PE

AGR 0.001 0.001 0.113 0.101 0.101 0.069

CEM 0.052 0.020 1.100 0.097 0.090 0.326

CMF 0.011 0.008 0.071 0.074 0.075 0.062

CNS 0.009 0.009 0.136 0.090 0.090 0.052

COK 0.118 0.103 0.492 0.141 0.138 0.223

CUM 0.086 0.054 0.116 0.044 0.052 0.038

EEQ 0.063 0.044 0.104 0.052 0.056 0.043

ELE 0.021 0.021 0.021 0.129 0.129 0.128

EQP 0.027 0.018 0.096 0.070 0.068 0.085

FOD 0.011 0.013 0.075 0.088 0.088 0.067

FUR 0.005 0.019 0.061 0.075 0.078 0.060

GAS 0.016 0.016 0.119 0.119

GLS 0.157 0.000 1.519 0.119 0.085 0.417

M_C 0.007 0.008 0.026 0.171 0.171 0.163

M_G 0.004 0.004 0.052 0.096 0.096 0.082

M_O 0.004 0.004 0.049 0.095 0.095 0.082

MCM 0.015 0.015 0.017 0.079 0.079 0.080

MET 0.019 0.008 0.067 0.076 0.074 0.087

MFM 0.107 0.107 0.248 0.105 0.105 0.140

MIN 0.071 0.061 0.375 0.096 0.094 0.172

MNF 0.021 0.027 0.362 0.065 0.064 0.161

NFR 0.009 0.022 0.203 0.068 0.065 0.114

NMM 0.157 0.042 1.144 0.125 0.100 0.341

OIL 0.058 0.057 0.173 0.078 0.077 0.103

OSR 0.016 0.020 0.099 0.082 0.083 0.044

OTM 0.016 0.031 0.050 0.062 0.065 0.048

PAP 0.007 0.003 0.157 0.088 0.087 0.121

PRT 0.001 0.013 0.043 0.070 0.073 0.061

RCM 0.060 0.011 0.511 0.087 0.076 0.185

RUB 0.002 0.004 0.009 0.071 0.071 0.074

STL 0.151 0.055 1.205 0.097 0.077 0.328

TEX 0.014 0.006 0.043 0.081 0.083 0.075

TOB 0.029 0.029 0.081 0.075 0.075 0.051

TRM 0.017 0.020 0.020 0.067 0.067 0.059

TRP 0.024 0.023 0.030 0.073 0.073 0.076

WOD 0.020 0.016 0.046 0.082 0.081 0.088

WTR

0.008

0.008

0.383

0.383

-0.450

-0.400

-0.350

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0 10 20 30 40 50 60 70 80

changeofsectoralPE(%

)

ratio of export (%)

CUMEEQ

TEX

FURPRT

GLS

NMM

STL

CEM

COK*

RCM

WODMET

Fig. 5. Impacts of the BTAs on the sectoral export prices in 2020 with a tax level of US$ 50 per tC against a sectoral ratio of export to output.



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while the exportratio only plays a minor one. Forexample,for sectors GLS, NMM, STLand CEM, whose exportpriceswill be reduced bymore than 0.3% in 2020 due to BTAs with a tax level of 50 US$/tC, the ratios of sectoral export are all below 20%, as shown in Fig. 5,which is much lower than those for sectors CUM, EEQ, TEX, FUR, PRT, etc. However, as shown in Fig. 6, the energy-intensity of sectorsGLS, NMM, STL and CEM are among the highest. Moreover, the carbon-intensity of these sectors shown in Fig. 7 are also among thehighest. For sectors that are export-oriented but not energy- and carbon-intensive, e.g., CUM, EEQ, TEX, FUR and PRT, the decreases ofexport prices will be extremely small. Meanwhile, for sectors that are energy- and carbon-intensive but not export-intensive, e.g., TRP,M_G, M_O and OIL, the reduction of export prices will be modest.

The reductions in export prices will then affect the whole economy from both the demand and supply sides. On the supply side, asexport prices decrease differently in different sectors, the sectoral export profits will be reduced, and producers will accordinglysubstitute away from these goods towards domestic products, which can be termed the substitution effect. This effect will somewhatstimulate domestic supply. On the demand side, as prices decrease due to the BTAs, the domestic income will be reduced, and thedemand will be decreased, which can be termed the income effect. This effect will somewhat reduce domestic demand. The final

effects on sectoral output and demand, i.e., whether positive or negative, will depend on the relative strength of these two effects.Table 6 reflects the impacts of the BTAs on the sectors from the supply side. It can be seen that output prices (PX) and domestic

prices (PD) for all of the sectors will decrease following the decrease of export prices. However, the impacts of the BTAs onindustrial outputs (X), exports (E) and domestic products (D) will vary across sectors. The sectors whose outputs suffer thelargest decreases from the BTAs are those whose export prices are reduced the most. For example, for sectors NMM, STL and GLS,the sectoral total output will be reduced by 0.157%, 0.157% and 0.151%, respectively, due to BTAs at a level of 50 US$/tC. The

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-0.050

0.000

0 0.2 0.4 0.6 0.8 1 1.2

changeofsectoral

PE(%)

energy intensity

(tons of coal equavalent per ten thousand RMB yuan)

ELE*

STL

COK*

GLS

CEMNMM

OIL*

TRP

M_C*

RCM

M_G*

MINMNF

Fig. 6. Impacts of the BTAs on the sectoral export prices in 2020 with a tax level of US$ 50 per tC against the sectoral energy intensity.

-0.450

-0.400

-0.350

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-0.050

0.000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

changesofsectoralPE(%)

sectoral carbon intensity(tons of carbon emissions per ten thousand RMB yuan)

COK*

STL

GLS

CEMNMM

ELE*

M_C*

RCM

MFM

OIL*

TRP

M_G*

MIN

MNF

Fig. 7. Impacts of the BTAs on the sectoral export prices in 2020 with a tax level of US$ 50 per tC against the sectoral carbon intensity.



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sectors whose outputs will be increased are those whose export prices are reduced the least. For example, for sectors CUM andEEQ, the sectoral total output will be increased by 0.086% and 0.063%, respectively, due to BTAs at a level of 50 US$/tC.

Table 7 reflects the impacts of the BTAs on the sectors from the demand side. It is worth noticing that sectoral import prices(PM) will be affected by the BTAs under the large-country assumption of China. The impacts of the BTAs on sectoral import pricesare all negative but are smaller than those of export prices. Therefore, sectoral demand prices (PQ) will be negatively affected byBTAs. It can also be seen that imports (M) in all of the sectors will be decreased due to the income effect, while the total demands(Q) will be affected differently across sectors. The sectors whose demands are reduced most are the sectors MFM and COK, withthe reduction of 0.139% and 0.104%, respectively, in 2020 at a BTA level of 50 US$/tC.

5. Robustness analyses and extensions

5.1. Altering the key assumptions and specifications

In our model proposed above, there are the two key assumptions and specifications that are likely to affect the simulationresults. One is the large-country assumption, and the other is the method used to calculate sectoral carbon emissions. These twoassumptions and specifications are altered in this subsection to determine whether our results will still hold. To that end, fourscenarios are designed, as shown in Table 8.

Table 7

Impacts of the BTAs on the sectoral demands in 2020 at a tax level of US$ 50 per tC (%). * Energy sectors.

Q D M PQ PD PM

AGR 0.010 0.001 0.152 0.098 0.101 0.051

CEM 0.020 0.020 0.145 0.090 0.090 0.048

CMF 0.004 0.008 0.108 0.074 0.075 0.036

CNS 0.010 0.009 0.140 0.090 0.090 0.047

COK 0.104 0.103 0.259 0.138 0.138 0.086

CUM 0.016 0.054 0.050 0.028 0.052 0.017

EEQ 0.019 0.044 0.062 0.047 0.056 0.021

ELE 0.021 0.021 0.068 0.129 0.129 0.076

EQP 0.028 0.018 0.111 0.064 0.068 0.037

FOD 0.018 0.013 0.139 0.087 0.088 0.046

FUR 0.023 0.019 0.126 0.076 0.078 0.042

GAS 0.016 0.016 0.119 0.119

GLS 0.008 0.000 0.127 0.082 0.085 0.042

M_C 0.013 0.008 0.260 0.169 0.171 0.087

M_G 0.066 0.004 0.146 0.075 0.096 0.049

M_O 0.067 0.004 0.145 0.074 0.095 0.048

MCM 0.019 0.015 0.127 0.078 0.079 0.042

MET 0.010 0.008 0.115 0.073 0.074 0.038

MFM 0.139 0.107 0.210 0.094 0.105 0.070

MIN 0.066 0.061 0.171 0.092 0.094 0.057

MNF 0.001 0.027 0.083 0.055 0.064 0.028

NFR 0.014 0.022 0.087 0.063 0.065 0.029

NMM 0.044 0.042 0.171 0.099 0.100 0.057

OIL 0.064 0.057 0.145 0.075 0.077 0.048

OSR 0.022 0.020 0.093 0.082 0.083 0.047

OTM 0.038 0.031 0.113 0.063 0.065 0.038

PAP 0.009 0.003 0.132 0.085 0.087 0.044

PRT 0.016 0.013 0.116 0.072 0.073 0.039

RCM 0.024 0.011 0.120 0.072 0.076 0.040

RUB 0.000 0.004 0.104 0.070 0.071 0.035

STL 0.057 0.055 0.142 0.076 0.077 0.047

TEX 0.002 0.006 0.121 0.081 0.083 0.040

TOB 0.029 0.029 0.127 0.075 0.075 0.042

TRM 0.026 0.020 0.111 0.065 0.067 0.037

TRP 0.025 0.023 0.085 0.073 0.073 0.042

WOD 0.018 0.016 0.129 0.080 0.081 0.043

WTR

0.008

0.008

0.383

0.383

Table 8

Four scenarios for the alternative assumptions.

Large country Small country

Total carbon emissions BAU Scenario A1

Carbon emissions based on primary energy Scenario A2 Scenario A3



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The impacts of BTAs on the total carbon emissions under four scenarios are shown in Fig. 8. One important finding is that theassumptions of the modeled unit being a large country or small country lead to different results. Under the small-countryassumption, because China's exports have no ability to affect the world export prices, all of the prices decreased by BTAs will bereflected in the export prices faced by China's export enterprises. However, under the large-country assumption, because thechanges of China's export will affect the export prices, the loss caused by BTAs will be greatly relieved. This buffering can also bereflected in changes in the average export prices, as shown in Fig. 9. In the real world, because some sectors have a greater abilityto affect the export prices, while others have less, the true impacts of BTAs on China will lie in between the results estimatedunder these two assumptions.

The second implication relates to the method of calculating carbon emissions. By calculating total carbon emissions ratherthan just calculating emissions from direct fossil-fuel use, as shown in Table 2, most of the sectors will emit more carbon and thuswill be more affected by BTAs. Table 9 reports the three sectors whose carbon emissions will be either decreased or increased themost under the four scenarios. While there are marked differences in their emissions based on different emissioncalculation

methods, the assumptions of a large or small country have much greater effects on their emission levels, either direct or total,than the method of emission calculation. For most of the sectors, the effects of BTAs on carbon emissions at the sectoral level arelimited (Fig. 2). At the aggregated level, such sectoral effects are cumulative. By comparing the results of Scenario A2 and BAU orScenario A3 and Scenario A1 as shown in Figs. 8 and 9, respectively, we can see that the effects of BTAs on total carbon emissions aremarkedly larger based on the method of total carbon-emission calculation than based on the method of direct carbon-emissioncalculation.

-0.100

-0.090

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-0.030

-0.020

-0.010

0.000

BAU Scenario A1 Scenario A2 Scenario A3

changeoftotalcarbo

nemissions(%)

2020

2025

2030

Fig. 8. Impacts of the BTAs on the total carbon emissions under the alternative assumptions in 2020 at a tax level of US$ 50 per tC (%).

-0.180

-0.160

-0.140

-0.120

-0.100

-0.080

-0.060

-0.040

-0.020

0.000


changeofaverageexportprice

s(%)

2020

2025

2030

Fig. 9. Impacts of the BTAs on the average export prices under the alternative assumptions at a tax level of US$ 50 per tC (%).



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5.2. Factoring in technological progress

An interesting issue is that the results will change when technological progress is factored into the model. An efficiencyparameter is set in the production function, as shown in Eq. (12).

KEi;t acap;iCAPi;tke aenergy;i energy;i;tENERGYi;t

ke

1=ke: 12

As shown in Eq. (12), KEi,t denotes the composite of capital (CAP) and energy composite (ENERGY), and energy,i,t denotes the

technological change in the production process, which will minimize the required energy input. To analyze the technologicalchange in energy-saving technology, it is assumed that the annual increasing rate of energy,i,t is captured by an autonomousenergy efficiency-improvement (AEEI) parameter. In the proposed model above, the AEEI parameter is set as zero. To examine theeffects of technical progress, we simulated three scenarios of different AEEI values, as shown in Table 10.

As we can see in Fig. 10, the impact of technological progress in China's production depends on the timing and the technicalprogress rates. The marked impacts of BTAs on total emissions occur in 2020 for a technological progress rate of 2% per year.However, for a technological progress rate of 0.5% per year, marked effects will not occur until 2025.

One interesting question still remains regarding whether and how technological progress itself works to mitigate carbonemissions. From Fig. 11, we can find an obvious indication that technological change will greatly help reduce the carbon emissionsin China. Even under the scenario with the lowest rate of technical progress, where AEEI is set at 0.005, that is, 0.5% per year, thereduction of carbon emissions will be greater than the impacts of BTAs.

6. Conclusion and policy suggestions

The proposed border carbon adjustments target major emerging economies, such as China and India. In this paper, weanalyzed the sectoral carbon-emission impacts of the BTAs implemented by the US and EU on China. We built a recursive dynamicmulti-sector CGE model for the Chinese economy up to the year 2030 and simulated three scenarios with tax levels of US$ 20, 50and 80 per ton of carbon, respectively.

Our simulation results show that BTAs will decrease China's total carbon emissions and total energy consumption. A higher taxlevel relates to a more significant impact. The main reason lies in the decrease of export prices and its further impacts on thewhole economy. However, the impacts of BTAs on sectoral carbon emissions and energy demands will vary across sectors. Sectorsthat are export-oriented and energy- and carbon-intensive are likely to be affected more by BTAs, and these sectoral carbonemissions will be reduced most. However, in other sectors, carbon emissions are likely to be increased by BTAs. The main reasonlies in the outputdemand effect, which will cut down sectoral output and demand and further decrease sectoral energy demandand carbon emissions. Moreover, the substitution effect among different energy sources will also reduce the effects ofcarbon-emission mitigation. That is, as prices of M_C and COK with relatively high emission intensity will be decreased the most

by BTAs, the demands for M_C and COK will increase, which will stimulate sectoral carbon emissions.An interesting finding derived from the simulation results is that BTAs will affect China's economy in a different way comparedwith carbon-tax policies. The BTAs will in general decrease overall prices in China, while carbon-tax policies will increase theprices by adding carbon costs. Furthermore, BTAs may even stimulate some sectoral carbon emissions and energy demands as aresult of the overall decrease of energy prices. In contrast, carbon-tax policies will increase the domestic prices, especially for thecarbon-intensive sectors, thereby decreasing sectoral fossil-energy demands as well as carbon emissions. In conclusion, the price

Table 9

Impacts of the studied BTAs under the alternative assumptions on the sectoral carbon emissions (%).


Top three decreased sectors NMM 0.186 0.358 0.169 0.325

GLS 0.174 0.335 0.163 0.315

STL 0.166 0.329 0.153 0.307

Top three increased sectors CUM 0.226 0.445 0.182 0.363

EEQ 0.172 0.346 0.151 0.309

MNF 0.106 0.197 0.110 0.201

Table 10

Four scenarios of technological progress.

AEEI value

BAU 0.000

Scenario C1 0.005

Scenario C2 0.010

Scenario C3 0.020



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mechanisms that lead to different effects of BTAs and carbon tax policies play a significant role and should be given sufficientattention in policy decisions regarding carbon-emission abatement.

Moreover, the impacts of BTAs are relatively small in China and are insufficient for achieving the main aim of BTAs, i.e., avoidingcarbon leakage. As indicated by our results, the overall carbon emissions in China will only be modestly decreased. However,cooperative agreements, such as technology sharing as well as energy-saving and next-generation low-carbon technologies, will bemore productive for protecting the global environment, as discussed by Weber and Peters (2009) and Bassi and Yudken (2011).Higher relative prices of different energy types will lead to decreases in coal and oil consumption and cut down aggregate energyintensity.

There remain several limitations of this research, and further progress could be made in several aspects. First, this studyconcentrates on the impacts of the BTAs on China's sectoral carbon emissions. However, it will be still interesting and important tostudy the responding policies by China's government, e.g., carbon tax or clean energy development strategies, and the combinedeffects of these policies. Secondly, this study accounts for a single-country general equilibrium model for China only, but it would bemore desirable to provide a multi-country and multi-sector model to study the global impacts of BTAs on carbon emissions. Thetechnological developments and China's commitment to reduce carbon intensity should also be considered in a fine-scale analysis.

Acknowledgments

This study was supported by the National Natural Science Foundation of China under grant no. 71203214 and no. 91224004

and the Chinese Academy of Sciences. An earlier version of the study was presented at the 19th Annual European Association ofEnvironmental and Resource Economists Conference, Prague, the Czech Republic, June 2730, 2012. The study has benefited from

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Fig. 10. Impacts of the BTAs on the total carbon emissions under alternative technology scenarios in 2020 at a tax level of US$ 50 per tC (%).

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Fig. 11. Differences of baseline carbon emissions under alternative technology scenarios from the BAU scenario (%).



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helpful comments from two anonymous referees and constructive discussions with Chen Xikang. That said, the views expressedhere are those of the authors. The authors bear sole responsibility for any errors and omissions that may remain.

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ZhongXiang Zhang, PhD in economics, is a distinguished professor at Fudan University and the Chinese Academy of Sciences, China and an adjunct senior fellowat EastWest Center, USA. He is co-editor of both Environmental Economics and Policy Studies and International Journal of Ecological Economics & Statistics, and isserving on the editorial boards of other nine international journals. He authors over 180 journal articles, book chapters and other publications, and authors/edits20 books and special issues of international journals ( Energy Economics, Energy Policy, International Economics and Economic Policy, International Environmental

Agreements, Journal of Policy Modeling, and Mineral Economics). He is among the most cited authors by the IPCC Climate Change 2001 and 2007, and by Trade andClimate Change: WTO-UNEP Report. He is among Social Science Research Network Top 100 Economics Authors, and among IDEAS/RePEc list of both the leadingenergy economists and the leading environmental economists in the world. Based on the number of journal pages weighted by the number of authors, he isamong the Top 1000 Economists in the worldwide ranking.

Qin Bao, PhD, is an assistant professor at Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Ling Tang is an associate professor at School of Economics and Management, Beijing University of Chemical Technology, Beijing, China.

Shouyang Wangis a chair professor and vice president of Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. He is theauthor or co-author of 25 books and over 200 papers in leading journals on decision analysis, financial management and economic analysis. He is a fellow at TheAcademy of Sciences for the Developing World, and a receipt of the Fudan Prize of Outstanding Contributions to Management.


Evaluación del Impacto de una BTA en la Economía China

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