Carbon tariffs on Chinese exports: Emissions reduction, threat, or farce?

Energy Policy 50 (2012) 315–327

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Carbon tariffs on Chinese exports: Emissions reduction, threat, or farce?

Michael Hubler a,b,c,n

a Centre for European Economic Research, Germanyb Kiel Institute for the World Economy, Germanyc Potsdam Institute for Climate Impact Research, Germany

H I G H L I G H T S

c Carbon intensities and contents of trade by commodity and region using GTAP 7.c Net carbon exports: industrialized region �15%, developing region 12%, China 24%.c CGE analysis of carbon tariffs based on our carbon intensities.c The tariffs make China worse off than climate policy and are ambiguous for the developing region.c They have a small impact on reducing global emissions.

a r t i c l e i n f o

Article history:

Received 21 April 2011

Accepted 12 July 2012Available online 9 August 2012

Keywords:

Carbon content of trade

Carbon tariff

China

15/$ - see front matter & 2012 Elsevier Ltd. A

x.doi.org/10.1016/j.enpol.2012.07.025

espondence address: Centre for European Eco

annheim, Germany. Tel.: þ49 621 1235 340

ail addresses: [email protected], michael-hueb

e term ‘border tax adjustment’ (BTA) usuall

egion B with a lower carbon price to a region

ination with carbon tax rebates on exports fro

based import tariffs in our analysis, though.

rbon leakage abroad where no carbon price

policies in the ‘‘home country’’ can basically o

reduction of international fossil fuel price

ption abroad. (2) Relocation of production to

-riding of those who suffer from climate cha

t home.

a b s t r a c t

(1) We estimate CO2 implicitly exported via commodities relative to a region’s total emissions: We find

�15% for the industrialized, 12% for the developing region, and 24% for China. (2) We analyze a

Contraction and Convergence climate regime in a CGE model including international capital mobility

and technology diffusion: When China does not participate in the regime and instead a carbon tariff is

imposed on its exports, it will likely be worse off than when participating. This result does not hold for

the developing region in general. Meanwhile, the effect on emissions appears small.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

A drastic reduction in global CO2 emissions critically dependson the inclusion of the developing and emerging economies,especially of China. If China stays reluctant to join a bindingpost-Kyoto regime, China’s emissions can possibly be reduced byimposing a carbon tariff, or carbon-content-based border mea-sures,1 since the Chinese economy is carbon—as well as export-intensive. This idea aims at the reduction or avoidance of carbonleakage, this means an increase in emissions abroad due to anemissions reduction at home.2 Such a carbon tariff could also be

ll rights reserved.

nomic Research (ZEW), L7/1,

; fax: þ49 621 1235 226.

[email protected]

y refers to taxes on imports

A with a higher carbon price

m A to B. We will only apply

is implemented induced by

ccur through three channels:

s that stimulates fossil fuel

abroad (and exporting back).

nge abroad on the reduction

used as a threat in order to convince China to join a post-Kyotoclimate regime. Such policies have been controversially debatedby politicians and economists, especially in the USA (Krugman2009a,b; Friedman, 2009; Broder, 2009). However, Bhagwati andMavroidis (2007) for example (for other recent views see Sheldon,2011; Moore, 2011) question the economic, juristic and politicalfeasibility of carbon-content-based border tax adjustment (BTA).Herein, the accordance with the WTO legislation is a criticalaspect. And the economic effectiveness of such border measuresis unclear. Shedding light on the economic effectiveness requiresnumerical assessment beyond a purely theoretical understanding.

It is therefore the main research task of our paper to assesshow a carbon tariff on exports from China (and from otherdeveloping countries) would affect welfare, GDP, the energyproductivity, the terms of trade, the global carbon price andemissions or carbon leakage. Herein, we compare the impactson China with those on other developing countries. We apply anextended version of the computable general equilibrium (CGE)model DART.3 The model has a high sectoral resolution whichcaptures the different carbon intensities across sectors thatdetermine the rates of carbon-based tariffs. The model also

3 Dynamic Applied Regional Trade.

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dx.doi.org/10.1016/j.enpol.2012.07.025

dx.doi.org/10.1016/j.enpol.2012.07.025

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mailto:[email protected]

mailto:[email protected]

dx.doi.org/10.1016/j.enpol.2012.07.025

6 Following Armington (1969), most computable general equilibrium (CGE)

models distinguish imported varieties of a certain commodity (or sector) by

source country (as imperfect substitutes). This creates an import bundle which is

then combined with a variety of the same commodity produced in the domestic

region itself. A lower price of a variety stemming from one region will ceteris

M. Hubler / Energy Policy 50 (2012) 315–327316

captures productivity spillovers associated with internationaltrade and international investment beyond the pure trade effectsrepresented in usual CGE models. In this way, the model capturestrade externalities that can enhance the effectiveness of trade-related policy measures beyond direct trade effects. Therein,different futures are represented by a ‘‘green’’ scenario which isoptimistic regarding energy efficiency improvements in contrastto a ‘‘brown’’ scenario which is more pessimistic.

So far, several studies have estimated carbon emissions impli-citly embodied in traded commodities for different countries andspecifically for China.4 Shui and Harriss (2006) estimate that USCO2 emissions would be 3–6% higher if the goods imported fromChina were produced in the USA, and that 7–14% of China’s CO2

emissions can be attributed to exports to US consumers.Peters and Hertwich (2008) calculate carbon contents of trade

based on the GTAP 6 data set for 2001. They find net carbonimports for the Annex B region of 5.6% relative to total CO2

emissions produced in this region, and relative net carbon exportsof 8.1% for the non-Annex B region.5 In particular, according totheir calculations China’s net carbon exports amount to 17.8% ofits total produced emissions, US net carbon imports amount to7.3%, Japan’s to 15.3%, and Germany’s to 15.7%. Switzerland(122.9%) and Latvia (60.7%) are the most intensive net carbonimporters among Annex B countries, whereas Hong Kong(182.2%), the rest of the South African Customs Union (Lesotho,Namibia and Swaziland) (176.4%) and Mozambique (172.4%) arethe main net carbon importers among all countries. South Africa(38.2%) and the Russian Federation (21.6%) are the most intensivenet carbon exporters among all countries.

Pan et al. (2008) estimate China’s emissions in 2006 on aconsumption basis using data from Input–Output Tables of Chinain 2002 (NBS, 2006), the China Statistical Yearbook (variousyears), the International Energy Agency (IEA, 2006, 2007) andthe World Resources Institute (WRI). They apply the ‘‘Leontiefinverse’’ matrix and distinguish imported from domesticallyproduced intermediate goods, which is often neglected in theliterature. As a result, they find emissions amounting to 3.8 Gt ofCO2 rather than 5.5 Gt on the standard production basis. Thisimplies that China’s net carbon exports amount to 1.7 Gt in 2006.They conclude that ‘‘Moreover, in the current institutional con-text, production methodologies encourage leakage through tradethat may do more to displace than to reduce emissions. Bothequity and efficiency concern therefore suggest that emissionsembodied in trade should receive special attention in the dis-tribution of post-Kyoto abatement burdens.’’ Accordingly, ourCGE analysis captures supply and demand side carbon leakagebetween regions with and without climate policy.

In the CGE-model-based literature, Babiker and Rutherford(2005) and Bohringer et al. (2010) find that border tax adjustment(BTA) has a limited potential for reducing carbon leakage. More-over, possible competitiveness disadvantages for firms within theEuropean emissions trading scheme towards non-EU firms play acentral role. Alexeeva-Talebi et al. (2008a) compare border taxadjustment based on imported quantities multiplied by domesticcarbon intensity factors with an integrated emissions tradingscheme based on imported emissions actually created during theproduction of imported commodities. They conclude that bordertax adjustment protects domestic competitiveness more effec-tively, while an integrated emissions trading scheme achieves a

4 For a ‘‘review of input-output models for the assessment of environmental

impacts embodied in trade’’ see Wiedmann et al. (2007). For an overview of

quantitative analyses of CO2 embodiment in international trade see Liu and Wang

(2009).5 Herein, net carbon exports mean implicit CO2 exports via exports of

commodities minus implicit CO2 imports via imports of commodities.

greater reduction in emissions abroad. Alexeeva-Talebi et al.(2008b) conclude from their simulations of the European emissionstrading scheme that market-based policy measures such as theClean Development Mechanism, allowing for flexibility in thelocation of emissions savings, can be effective substitutes for bordertax adjustments in unilateral climate policy. Manders andVeenendaal (2008) find that border tax measures under theEuropean emissions trading scheme significantly reduce carbonleakage. Furthermore, border tax measures appear beneficial for theEU, while they may entail a welfare loss for the rest of the world.Herein, most of these studies use the GTAP 6 data for the base year2001, while our study uses the GTAP 7 data for 2004. Moreover, wefocus on China as a carbon as well as export-intensive economy, incomparison with the other developing countries.

Finally, Lessmann et al. (2009) examine a numerical, inter-temporal optimization framework with stable climate policycoalitions. They show that carbon-based import tariffs increasethe emissions target of the coalition in a welfare improving way ifthe tariff rate is small relative to the Armington elasticity ofimports.6

The first contribution of our paper is to calculate and illustrateimplicit carbon contents of commodities traded between (main-land) China, the industrialized countries and the developingcountries using the GTAP 7 data for 2004 and distinguishingintermediate inputs by source country (Section 3). The secondcontribution is to examine the welfare and emissions effects ofimposing a carbon tariff on exports from China and the otherdeveloping countries under a Contraction and Convergence cli-mate regime with emissions trading (Section 4). Herein, we applya computable general equilibrium (CGE) model that includesinternational capital mobility (foreign direct investment, FDI)and an innovative way of modeling international technologydiffusion via capital mobility and trade, distinguishing verticaland horizontal spillovers. This feature appears important whenanalyzing trade policy in combination with climate policy, becausetrade and FDI may create emissions reductions via technologyspillovers. Based on the results, the paper derives implications forpost-Kyoto policies (Section 5). The paper starts with an overviewof the underlying three-region model (Section 2).

2. The three-region model

The underlying DART7 model is a recursive dynamic multi-region, multi-sector CGE model of the world economy. The staticpart of the model is currently calibrated to the GTAP 7 database(Narayanan and Walmsley, 2008) that covers global productionand trade data (including taxes and subsidies, governments andhouseholds, consumption and savings) for 113 countries andregions, 57 sectors (commodities) and five production factors(skilled and ‘‘unskilled’’ labor, capital, land, natural resources) forthe benchmark year 2004.8 Carbon emissions are derived fromthe use of fossil fuels in production and for consumption in

paribus lead to a shift of demand towards this variety. The strength of this shift is

governed by the (Armington) elasticity of substitution between the imported

varieties as well as the elasticity of substitution between the import bundle and

the domestic variety.7 Dynamic Applied Regional Trade.8 The GTAP data are collected from various data sources and merged together

to a consistent data set. For detailed information see https://www.gtap.agecon.

purdue.edu/.

https://www.gtap.agecon.purdue.edu/


9 Full technological catching up would be far beyond the time horizon of our

analysis.10 For this section, we only need the GTAP 7 data set, not the CGE model itself.

The GTAP 7 data apparently incorporate some inconsistencies between inter-

mediate inputs in currency value terms and fossil fuel (emissions) inputs in

physical value terms and differences in accounting emissions. For details see

Peters and Hertwich (2008) and their supporting information. These inconsisten-

cies can be relevant when computing sector or product-specific results, but

probably not on the macro level. Nonetheless, the results should be treated with

some caution.11 We distinguish 30 sectors: agriculture and food (agr), textiles, apparel and

leather (tex), beverages and tobacco (bev), business services (bui), chemicals,

rubber and plastic (crp), culture and recreation (cus), coal (col), communication

(com), construction (con), crude oil (cru), electricity supply (egw), electrical

equipment (elm), ferrous metals (fem), financial intermediation (fin), gas (gas),

machinery (mac), metal products (met), minerals (min), non-ferrous metals (nfm),

non-metallic mineral products (nmm), other manufacturing (otm), paper products

and publishing (pap), petroleum and coal (oil), trade and wholesale (trd), public

services (pub), real estate (ree), transport machinery (trm), transportation (trn),

water supply (wat), wood (woo).12 The emissions intensity of gas in (mainland) China was obviously an

outlier. Therefore, we assume it is equal to the emissions intensity of gas in

developing countries. For further comments on accounting problems in the GTAP

data see Peters and Hertwich (2008) and their supporting information.

M. Hubler / Energy Policy 50 (2012) 315–327 317

combination with the associated carbon intensity factors of coal,gas and oil. The model runs under GAMS MPSGE. For a detaileddescription see Klepper and Springer (2000), Springer (2002) andKlepper et al. (2003) and Hubler (2011). A mathematical descrip-tion can be found in Appendix A.5.

The GTAP data can be aggregated according to modelers’needs. The version of the model scrutinized here distinguishesthree regions: (mainland) China (CHI), industrialized region (IND)and developing region (DEV). The industrialized region encom-passes the OECD countries plus Hong Kong, Macao, Taiwan,Singapore and South Korea, since they are important sources ofFDI into (mainland) China – and potential sources of technologytransfer into (mainland) China (compare Tseng and Zebregs,2002; Whalley and Xin, 2006). The model distinguishes theproduction factors labor, capital, land, and natural resources(fossil fuels). In order to analyze climate policies, CO2 emissionsare linked to the use of fossil fuels in production and consump-tion. The current sectoral aggregation covers 30 sectors in eachregion.

Each commodity market is perfectly competitive. Product andfactor prices are fully flexible. The model incorporates two typesof agents for each region: producers (one producer per productionsector and region) and consumers (one private and one publicconsumer per region). Producer behavior is derived from costminimization for a given output. Consumers receive all incomegenerated by providing primary factors to production processes.Consumers save a fixed share of income and invest it into capitalfor production in each period. Herein, investments are producedlike commodities by using production inputs. The disposableincome (net of savings and taxes) is then used for utility max-imization by purchasing and consuming commodities. The expen-diture function is modeled as a CES (constant elasticity ofsubstitution) composite, which combines an energy bundle witha non-energy bundle.

Factor markets are perfectly competitive with full employmentof all factors. Labor is a homogenous good, being mobile acrossindustries within regions, but being internationally immobile.While in the basic version of the DART model capital is alsointernationally immobile, in this version capital is internationallymobile between the industrialized region and China. The bench-mark values of foreign capital located in China are taken from theChina Statistical Yearbook (2006, 2007). All regions are linked bybilateral trade flows, and all commodities except the investmentgood are traded among regions. Domestic and foreign commod-ities imported from different regions are imperfect (Armington)substitutes.

The model is recursive-dynamic; it solves for a sequence ofstatic one-period equilibria for future time periods. The majorexogenous, regionally different driving factors of the modeldynamics are population growth, total factor productivity growth,human capital growth and investment in capital. The modelassumes constant, but regionally different growth rates of humancapital (educational attainment) taken from Hall and Jones(1999). Population growth rates and labor participation ratesare taken from the PHOENIX model (Hilderink, 2000). The result-ing GDP growth paths are in line with recent projections by OECD(2008).

Technological progress has an exogenous part in every region.It consists of improvements in total factor productivity and inenergy-specific technological progress. In the latter case, a givenoutput quantity can ceteris paribus be produced in a smallervolume of energy inputs. In (mainland) China, technologicalprogress in a certain sector additionally increases in the importintensity of the related product, with the foreign capital intensityin this sector (horizontal linkage) and with forward and backwardlinkages (vertical linkage) across sectors within the production

chain. Technological progress decreases the closer the Chinesetechnology level comes to the technology frontier given by theindustrialized region. This results in a process of technologicalconvergence.9 (For further details see Hubler, 2011) This novelway of modeling international technology diffusion via FDI andtrade appears important when analyzing trade policy in combina-tion with climate policy.

Like other CGE models, DART has a number of limitations thatwe need to consider when interpreting the model simulationresults. The likely impact of these limitations will be discussed inSection 4.

3. Carbon content of trade

We calculate the implicit carbon contents of traded commod-ities using the GTAP 7 data set for 2004 (Narayanan andWalmsley, 2008) in combination with emissions data computedfrom the GTAP 7 data set (Lee, 2008).10 Such implicit carboncontents capture all emissions that occur during the productionprocesses of commodities. Our calculation improves on Pan et al.(2008) by using the new GTAP 7 data and by distinguishingintermediate good inputs by country of origin (following Isard,1951; Ackerman et al., 2007). The latter aspect seems importantfor computing Chinese carbon contents of trade, since a substan-tial part of Chinese exports is produced by using importedintermediate goods (so that the value added is relatively low).

We compute the carbon intensities that contain the emissionsover all intermediate production stages that occur when produ-cing one unit of each commodity or sector (i) in each region (r) –including intermediate good imports from abroad. (For details seeAppendix A.1.) Fig. 1 shows the results for the benchmark year2004.11 The figure illustrates that products from (mainland) China(CHI) have the highest carbon (CO2) intensities (except transpor-tation trn), on average about 3.1 kg/US$. Especially, the Chinesecarbon content of electricity generation (egw) is extremely highdue to the importance of inefficient coal power in (mainland)China.12 As expected, commodities produced in the developingcountries (DEV) have the second highest carbon contents, onaverage about 1.6 kg/US$, and commodities produced in theindustrialized countries (IND) have the lowest carbon intensities,on average about 0.7 kg/US$.

In the following step, total carbon contents of traded commod-ities per year can easily be computed by multiplying the carbon

Fig. 1. Regional carbon intensity factors of commodities.

Fig. 2. Total implicit carbon contents of exported commodities.

Fig. 3. Interregional carbon flows via commodity trade.

14 Compared with Peters and Hertwich (2008) who calculate the carbon

contents of trade based on GTAP 6 for the year 2001, implicit carbon exports of


intensity factors shown in Fig. 1 by the related volumes ofcommodity trade. (Note that implicit carbon trade within regionsis not included.) Fig. 2 shows the results for exports of eachregion. As expected, the ranking of implicit Chinese carbon exportvolumes is similar to the ranking of commodity export volumes.The three highest and almost equal carbon volumes are embodiedin exports of textiles, apparel and leather (tex); electrical equip-ment (elm) and machinery (mac). Chemicals, rubber and plastic(crp) contribute the fourth highest carbon export volume which isclearly lower than the three highest volumes. All other productscontribute lower carbon export volumes. The other developingcountries obviously export substantial carbon volumes via trans-portation services (trn);13 non-ferrous (nfm) and ferrous (fem)metals; via agricultural and food products (agr); via crude oil(cru); and via petroleum and coal products (oil).

Fig. 3 illustrates the result of summing up over carboncontents of traded commodities per region for the benchmarkyear 2004. The triangle in Fig. 3 visualizes the total quantities ofCO2 in Gt (Giga tons) that are implicitly traded between regions.While about 1.6 Gt flow from the developing countries to theindustrialized countries, (mainland) China alone exports about1.1 Gt to the industrialized countries. CO2 exports from theindustrialized region to the developing region and China, as wellas CO2 flows from the developing region to (mainland) China andvice versa, are relatively low. Fig. 3 does not show implicit carbontrade within regions. The implicit carbon trade within the

13 The high volume of carbon exports via transportation services stems from

the high export volume of transportation services given by the GTAP 7 data.

industrialized region (between industrialized countries) is sub-stantial; it amounts to 2.7 Gt. The implicit carbon trade within thedeveloping region amounts to 0.7 Gt.

The percentage numbers show net CO2 exports (implicit CO2

exports minus imports) relative to total emissions that areactually generated in each region. As expected, China is a majornet carbon exporter (24% of total Chinese emissions), while theindustrialized region is a net carbon importer (15% of totalemissions). The developing region is a net carbon exporter aswell (12% total emissions).14

China have risen from 0.8 Gt (24.4% of total Chinese emissions) in 2001 to 1.4 Gt

(31.3%) in 2004. Relative carbon imports of China have risen from 0.2 Gt (6.6%) to

0.3 Gt (7.7%). Thus, net carbon exports of China have risen from 0.6 (17.8%) to

1.1 Gt ð23:6%� 24%Þ. According to Pan et al. (2008), China’s net CO2 exports

amount to 1.7 Gt in the year 2006.


In order to get a better understanding of the robustness of thedata that we use, we can compare the CO2 emissions of our GTAP-7-based computation to the emissions gained from other sources.According to the focus of our analysis, let us look at total CO2

emissions produced in China. Our computation yields Chineseemissions of 4.9 Gt for the base year 2004. Klepper and Springer(2000) for example use GTAP 3 with the base year 1992. They findemissions of about 3.7 Gt in China plus Hong Kong as a projectionfor 2003. Peters and Hertwich (2008) use GTAP 6 with the baseyear 2001. They find Chinese emissions of about 3.3 Gt in 2001.IEA (2011, pp. 47/49) reports Chinese emissions of 5.1 Gt or 5.2 Gtrespectively in 2005. EIA (2011) reports Chinese emissions of5.5 Gt in 2005. Hence, our computation is in line with other data.

Differences in the computation results on the macro andsectoral level can occur because of different calculation methodsand the distinction or not-distinction between intermediateinputs by source country, different sectoral resolutions, thetreatment of fossil fuels as a feedstock (e.g. for plastic products),the treatment of fossil fuel refinement regarding emissions, andthe distinction between fuels by source country or by sub-types offossil fuels (e.g. different types of coal an oil products). And ingeneral, large-scale data sets covering numerous countries andsectors are due to measurement and reporting errors and datainconsistencies have been adjusted.

Moreover, we compute the carbon intensity of Chinese exportstaking general equilibrium (CGE) effects into account in order tocheck the robustness of our findings. (For further details seeAppendix A.4 and Section 4.) This computation shows that theCGE-based carbon intensity declines from about 1.5 kg/US$ in2012 to about 1.0 kg/US$ in 2025. The analysis carried in thissection so far and in most other studies that compute carboncontents and intensities of trade do not take these generalequilibrium effects into account. Indeed, in this section we foundan average carbon intensity of commodities from China of about3.1 kg/US$ and of commodities from other developing countriesof about 1.6 kg/US$. The reason why our new general equilibriummeasure yields lower values is presumably the replacement ofpart of the Chinese exports by Chinese supply to the local marketand by domestic supply to the local markets in the industrializedand developing region. As a consequence, emissions decline to asmaller extent. However, such an indirect, more theory-orientedmeasure appears less appropriate for the analysis of carbon-basedborder measures because these are referring to the actual, directcarbon intensities of products. The general equilibrium effectswill be a result of the general equilibrium analysis rather than apresumption with practical policy applicability.

Herein, the sectoral aggregation of the model with sectors suchas ‘metal products’ or ‘agriculture and food’ is relatively crude. Inreality one would like to look at more specific products like ‘steelscrews’ or ’wheat’ making the calculation of carbon intensitieseven more complicated.

Against the backdrop of the carbon contents of trade that wehave computed, one could expect that climate policy in theindustrialized region alone might cause carbon leakage: Produc-tion might increase in developing countries such as China as aresponse, and trade of carbon-intensive commodities to theindustrialized region might increase. Therefore, it seems worth-while to examine the effectiveness of policies that might lowerimplicit carbon trade, in particular carbon-based tariffs.

15 Herein, we neglect the following aspect: Part of intermediate goods used in

Chinese production are imported and thus have been subject to a carbon tax in the

region of origin. Under a carbon tariff, Chinese exports are taxed again based on

their full carbon content. Deducting these carbon taxes already paid would reduce

the effect of the border tax adjustment under scrutiny. In this respect, a carbon

tariff would have a stronger effect if Chinese emissions were higher.

4. Carbon tariffs

This section uses the carbon intensities of commodities com-puted in the previous section to set carbon-content-based tariffson imports from a region without a carbon price, in particular

China, to a region with a carbon price under a Contraction andConvergence policy (C&C, introduced by GCI 1990, for moredetails see Appendix A2 and A3).15 The tariff rates are set in sucha way that exporters from China or the developing region have topay a carbon tax bill as if a carbon price (restricted to exports)were in place in China or the developing region. Herein, thecarbon price is given by the carbon price in the industrializedregion that has implemented a climate policy scheme. The carbonprice rises over time because the emissions targets becometighter. The tariff rates also increase in the carbon intensities ofexported commodities. The required carbon intensity factors havebeen calculated in Section 3.

Since the future technological development and thus theresulting CO2 emissions are uncertain, we make an upper-lower-bound or pessimistic-optimistic scenario comparison. The opti-mistic scenario is called green; the pessimistic scenario is calledbrown. Therein, the scenario design follows IEA (2007). (For a moredetailed description see Hubler, 2011.) In scenario green, weassume exogenous energy efficiency gains of 1% per year inall regions. We assume total factor productivity gains of 0.5% peryear in China (CHI), 1.3% in the developing region (DEV) and0.86% in the industrialized region (IND). In CHI, we additionallyassume energy efficiency and total factor productivity gainsvia international technology diffusion associated with importsand FDI inflows. The resulting time paths of CO2 emissions comeclose to the Alternative Policy Scenario by IEA (2007) and areoptimistic regarding CHI’s future business as usual (BAU) emis-sions intensity.

In scenario brown, we do not assume any energy-specificefficiency gains in any region. We do not assume energy efficiencyor total factor productivity gains associated with imports or FDIinflows, either. We assume total factor productivity gains of 2%per year in China (CHI), and 1.3% in the developing region (DEV)and 0.86% in the industrialized region (IND) as before. Impor-tantly, China’s short-run growth rates are lower than in the green

scenario, but its long-run growths rates are higher. This happensbecause of the convergence behavior with declining growth ratesover time in scenario green. The resulting time paths of CO2

emissions come close to the High Growth Scenario by IEA (2007).They are more pessimistic regarding future emissions intensities,especially of CHI. In this scenario, the Chinese economy alsostrongly grows towards the end of the time horizon in 2030 andthus produces high emissions.

The BAU emissions paths for scenarios green and brown andthe emissions paths resulting from the policy experiments,explained in the following, are illustrated in Fig. 4. Obviously,DEV grows strongly and overtakes IND at the end of the timehorizon with respect to emissions. While CHI’s emissions growmoderately in scenario green, they come close to those of IND andDEV in scenario brown.

We then analyze several policy scenarios for each technologyscenario. Herein, a ‘‘þ ’’ before a region’s name indicates that thisregion is included in the climate policy regime, a ‘‘� ’’ indicatesthat it is not. þbta indicates that border tax adjustment isimposed on imports from regions outside the climate policyregime to regions within the climate policy regime.

Fig. 4 illustrates the emissions paths under the differenttechnology and policy scenarios. (Scenario þchi-devþ ind is notshown.) In BAU, there is no climate policy in any region, while in

2004 2010 2020 20300

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2 in

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Scenario brown

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INDbauCHI−chi+dev+indDEV−chi+dev+indIND−chi+dev+indCHI−chi+dev+bau+btaDEV−chi+dev+ind+btaIND−chi+dev+ind+btaCHIpolDEVpolINDpol

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Fig. 4. Time paths of regional CO2 emissions under scenarios green and brown.


‘‘pol’’, climate policy covers all regions. Obviously, the inclusion ofregions to the Contraction and Convergence (C&C) regime reducesemissions substantially. Border tax adjustments on imports(þbta), on the contrary, show small impacts on CHI’s emissions,while there is no impact on DEV’s and IND’s emissions when theiremissions are capped with or without BTA.

Nevertheless, BTA tax rates reach substantial values. In sce-nario �chiþdevþ indþbta green, the carbon-based tariff ratetBTAð2013,CHI,IND,iÞ varies between 0.07% for real estate; 0.2%for communication, public services and others; and almost 6% forgas and electricity. According to this scenario, the CO2 price willrise up to 65 US$ per ton of CO2 in 2030. As a consequence, thecarbon-based tariff rate will vary between 2% for real estate;around 30% for paper, oil and metal products; more than 40% forchemicals and water; around 50% for coal; more than 60% forferrous and non-ferrous metals; almost 80% for non-metallicmineral products; and 265% for electricity, given that China’senergy supply will still strongly rely on coal.16

Tables 1 and 2 describe the technology and policy scenarios indetail. Several aspects are important for the interpretation of theresults:

(1) In China, as well as in any other country, markets are farfrom perfect with respect to perfect competition, market clear-ance, balanced budgets, immediate adjustment without adjust-ment costs, and so forth. Herein, non-clearing markets could lead

16 Again, the Chinese gas sector appears as an outlier; the related border tax

rate would be about 365%.

to unemployment and over- or undercapacities when workers arenot able to move immediately to another production sector,tangible capital cannot be transferred to another sector immedi-ately, and consumers are not willing or able to change theiraccustomed consumption patterns immediately. Public and pri-vate debts and surpluses could be exacerbated. As a consequence,reactions to shocks such as new policies occur too fast and do notincorporate all relevant associated costs in the model comparedto reality.

(2) Moreover, the model is bound to the benchmark situationin certain respects, for example consumer preferences and carbonemissions factors. As a consequence, the farther the time horizonof the policy analysis, the less likely it becomes that the worldeconomy will be as given by the benchmark data. This means,uncertainty increases in the time horizon. Speculating aboutbehavioral and structural changes, one would expect that theadjustment and substitution possibilities will improve over time.As a result, policy-driven adjustments would be eased and the riseof policy costs would be dampened over time when taking thisaspect into account—which is not done in our model nor in mostCGE models.

(3) Our C&C scenario reduces per capita emissions from their2012 levels to equal regional levels of about 2 t of CO2 in 2050.This policy favors high-population countries like China. We do notconsider a scenario, where global emissions must not exceed acertain level in order to meet a certain CO2 concentration or atemperature target, such as the 2 degree target. The latter wouldrequire that regions within the regime cut emissions to a largerextent the more countries are outside the regime that do not

Table 1Economic indicators of scenario green.

Region CHI DEV IND World CHI DEV IND World

Policy bau¼�chi�dev� ind

GDP 2030 (bill. 2004-US$) 6594 15,069 49,327 70,991

CO2 emissions 2030 (Gt) 8.54 17.82 17.13 43.49

Energy productivity 2030 wrt. 2004 1.79 1.38 1.31 1.37

Terms of trade 2030 wrt. 2004 0.86 1.11 1.00 –

Policy �chi�devþ ind �chi�devþ indþbta

Welfare effect 2004–2030 wrt. bau (%) 0.14 �0.29 �0.19 – �1.38 �0.95 0.04 –

CO2 emissions 2004–2030 wrt. bau (%) 1.88 8.96 �17.96 �11.24 1.11 7.79 �17.96 �12.92GDP 2030 (bill. 2004-US$) 6552 14,713 49,088 70,354 5366 12,764 49,045 67,176

CO2 emissions 2030 (Gt) 8.75 20.93 8.92 38.60 8.58 20.38 8.92 37.87

CO2 price 2030 (2004-US$ per ton) – – – 79.12 – – – 85.50

Energy productivity 2030 wrt. 2004 1.73 1.23 1.74 1.63 1.72 1.25 1.72 1.63

Terms of trade 2030 wrt. 2004 0.87 1.04 1.02 – 0.78 1.00 1.01 –

Policy �chiþdevþ ind �chiþdevþ indþbta


CO2 emissions 2004–2030 wrt. bau (%) 5.09 �18.97 �13.07 �28.53 3.52 �19.02 �13.03 �29.46GDP 2030 (bill. 2004-US$) 6600 14,621 49,143 70,365 5492 14,579 49,128 69,199

CO2 emissions 2030 (Gt) 9.69 10.22 11.17 31.08 9.28 10.19 11.20 30.68

CO2 sales 2030 (Gt) 2.26 �2.26 0 2.29 �2.29 0




Policy þchi�devþ ind þchi�devþ indþbta

Welfare effect 2004–2030 wrt. bau (%) 0.00 �0.25 �0.13 – 0.22 �0.83 �0.04 –

CO2 emissions 2004–2030 wrt. bau (%) �30.67 8.60 �13.18 �21.08 �30.95 7.45 �13.05 �22.36GDP 2030 (bill. 2004-US$) 6805 14,804 49,177 70,786 6828 13,532 49,170 69,529

CO2 emissions 2030 (Gt) 2.72 20.86 10.75 34.32 2.67 20.30 10.80 33.76

CO2 sales 2030 (Gt) 1.83 �1.83 0 1.88 �1.88 0




Policy pol¼þchiþdevþ ind

Welfare effect 2004–2030 wrt. bau (%) 0.22 �1.09 0.00 –

CO2 emissions 2004–2030 wrt. bau (%) �30.74 �16.12 �10.58 �40.34GDP 2030 (bill. 2004-US$) 6843 14,523 49,180 70,545

CO2 emissions 2030 (Gt) 2.79 11.08 12.07 25.94

CO2 sales 2030 (Gt) 1.75 1.40 �3.15 0

CO2 price 2030 (2004-US$ per ton) – – – 56.03




reduce emissions.17 As a consequence of our scenario assumption,global emissions cuts will be insufficient to reach an ambitioustemperature target, if not all regions are ‘‘on board’’.

(4) With respect to climate policy analysis, standard CGEmodels represent the reduction in the use of fossil fuels in formof a substitution of fossil fuel inputs by other inputs in theproduction structure. They do not represent investments in low-carbon technologies over time like Integrated Assessment modelswith a fully-fledged energy generation module (as for exampleREMIND, Leimbach et al., 2010). As a result, in standard CGEmodels emissions reductions are mainly achieved by reducing theenergy intensity of production. In Integrated Assessment modelson the contrary, the reduction of the emissions intensity of energygeneration plays a major role. This additional decarbonizationoption can substantially reduce mitigation costs, especially whenthe energy module includes advanced or breakthrough technol-ogies like carbon capture and storage (CCS).

(5) Technological progress in production sectors plays ingeneral a major role for mitigation costs. General (total factorproductivity) or labor-specific technological progress raises out-put and thus emissions and thus the emissions reduction gap

17 This can result in more or less infeasible situations, if too few countries are

in the regime, so that the burden for the countries within the regime becomes too

heavy.

towards the emissions target. Energy-specific technological pro-gress reduces the emissions stemming from a given outputvolume. As a result, mitigation costs rise in general or labor-specific and decrease in energy-specific technological progress.(More precisely, mitigation costs increase in labor-specific rela-tive to energy-specific technological progress, compare Hubleret al., 2011.) Therefore, we contrast two technology scenarios,wherein general progress dominates in ‘‘brown’’ and energy-specific progress dominates in ‘‘green’’. Furthermore, endogenous

technological progress in the energy efficiency of productionopens another channel for emissions reductions. However,the option for energy efficiency improvements is likely usedin the business as usual scenario already. As a consequence,mitigation might only to a minor extent decrease due to endo-genous technological progress compared to the new business asusual (BAU) benchmark (compare Hubler et al., 2011).

(6) The inclusion of a region into the C&C regime implies thatfirms in this region have an incentive to use less fossil fuel inputsfor production in order to reduce their emissions intensity ofproduction and hence their carbon bills. Border measure policies,on the contrary, do not create such an incentive, since the actualinputs of firms cannot be measured. They simply imply a punish-ment for exporting emissions-intensive products based on bench-mark data. As a consequence, imports will shift from high-carbonto low-carbon products, and imports will decline in total as well.The difference is that the former policy is an input tax at a fixed

Table 2Economic indicators of scenario brown.

Region CHI DEV IND World CHI DEV IND World

Policy bau¼�chi�dev� ind

GDP 2030 (bill. 2004-US$) 8726 15,265 49,197 73,188

CO2 emissions 2030 (Gt) 14.07 16.84 16.49 47.40



Policy �chi�devþ ind �chi�devþ indþbta


CO2 emissions 2004–2030 wrt. bau (%) 5.82 10.16 �16.83 �5.49 3.97 8.85 �16.83 �8.69GDP 2030 (bill. 2004-US$) 8693 14,856 48,911 72,460 6806 12,492 48,856 68,155

CO2 emissions 2030 (Gt) 15.65 20.23 8.91 44.79 14.87 19.50 8.91 43.28




Policy �chiþdevþ ind �chiþdevþ indþbta


CO2 emissions 2004–2030 wrt. bau (%) 11.41 �17.54 �11.61 �17.99 7.89 �17.65 �11.53 �20.99GDP 2030 (bill. 2004-US$) 8800 14,810 48,986 72,596 7085 14,655 48,969 70,708

CO2 emissions 2030 (Gt) 17.54 10.16 11.17 38.87 16.11 10.10 11.23 37.45

CO2 sales 2030 (Gt) 2.27 �2.27 0 2.33 �2.33 0




Policy þchi�devþ ind þchi�devþ indþbta

Welfare effect 2004–2030 wrt. bau (%) �1.27 �0.53 �0.16 – �0.91 �1.49 �0.02 –

CO2 emissions 2004–2030 wrt. bau (%) �33.96 14.83 �16.01 �24.00 �34.32 12.85 �15.79 �26.01GDP 2030 (bill. 2004-US$) 8620 14,672 48,901 72,193 8660 12,706 48,886 70,253

CO2 emissions 2030 (Gt) 4.55 22.07 9.40 36.02 4.47 21.11 9.49 35.07

CO2 sales 2030 (Gt) 0.50 -0.50 0 0.59 �0.59 0




Policy pol¼þchiþdevþ ind

Welfare effect 2004–2030 wrt. bau (%) �0.95 �1.68 0.04 –

CO2 emissions 2004–2030 wrt. bau (%) �31.59 �18.48 �11.47 �44.33GDP 2030 (bill. 2004-US$) 8636 14,395 48,941 71,972

CO2 emissions 2030 (Gt) 5.14 9.82 11.43 26.39

CO2 sales 2030 (Gt) �0.09 2.61 �2.52 0

CO2 price 2030 (2004-US$ per ton) – – – 88.71



18 For a more detailed critical discussion of limitations of CGE models in a

trade- and development-related context see Ackerman and Gallagher (2008).


rate for each fossil fuel input (coal, gas and oil), while the latter isan output tax at a fixed rate for each commodity (metal products,chemical products and so forth).

(7) The regional aggregation is large except China, which is thefocus of our analysis. Therefore, we cannot disentangle howcertain countries with different characteristics are affected; forexample least developed countries such as Ethiopia, fast-growinghigh-population economies such as India, or oil exporters such asSaudi Arabia—which are all merged in region DEV. As a conse-quence, high-population countries within region DEV benefitfrom the per capita-based C&C policy, while oil exporters sufferfrom climate policy. The overall effect of a C&C policy for regionDEV is therefore ambiguous. The aim of this aggregation is,however, to contrast the effects for China with those for OECDcountries and those for the rest of the world without tumblingover numerous country-specific effects. Furthermore, an impactof a certain size in absolute terms, for example a tax revenuethrough border tax adjustment, has a higher relative impact onCHI than on IND and DEV due to CHI’s smaller economic size.

(8) Long-term policy analysis is sensitive to the time horizonunder scrutiny. Since the regions CHI and DEV are supposed toachieve sustained long-run growth, while emissions targets willbecome more stringent in the long-run, climate policy will hit theseregion harder the longer the time horizon. Moreover, a numericalmodel as explained in Section 2 is determined by the benchmark yeardata, for example with respect to energy and emissions intensities of

production. Therefore, uncertainty rises, the longer the time horizon.Taking this into account, we have chosen 2030 as a medium-termcompromise, and we contrast two technology scnarios.

(9) Finally, there is uncertainty in the choice of elasticities ofsubstitution in nearly all CGE and Integrated Assessment models.They can significantly influence the results. For example, the elasticityof substitution between fossil and non-fossil inputs in production andenergy generation can influence mitigation costs. The direction andmagnitude of these effects are not clear-cut, though: On the one hand,a better possibility to substitute inputs enhances the possibility toreplace fossil inputs in case of climate policy. On the other hand, itraises output in the business as usual (BAU) scenario and thusenlarges the mitigation effort. Elasticities of substitution may alsochange over time, becoming higher in the long-run than in the short-run, which is not taken into account in this study. This effect couldmitigate the rise in mitigation costs over time. Moreover, higherArmington elasticities (between exports and imports and importsfrom different regions for each good) will presumably result instronger trade and leakage effects of climate policy and might evenchange the sign of certain sectoral effects (compare Saito, 2004).

Keeping in mind the assumptions and caveats listed above,18 wecan now examine the simulation results reported in Tables 1 and 2.


Welfare and terms of trade. We measure welfare effects as theHicks-equivalent Variation between a policy scenario and BAU(relative to households’ expenditures in BAU), cumulated from2004 until 2030 and discounted at a rate of 2% per year. As a result,in technology scenario green, under all policies without BTA, CHI isat least equally off as in BAU. The exact reason is difficult to separatein a complex CGE. Nevertheless, a look at the terms of trade gives ahint: The terms of trade (in 2030 relative to the 2004 value set to1) are in all policy scenarios without BTA higher than in BAU.Accordingly, fossil resource prices and prices of goods that CHI usesas intermediate inputs fall relative to the prices of commodities thatCHI exports. Moreover, in scenario green that is optimistic withrespect to CHI’s emissions intensity, CHI becomes a permit seller andbenefits from the related revenues. This happens because the C&Cpolicy favors high-population countries like China. It is even betterfor CHI, when more countries join the C&C regime that buy CHI’spermits. CHI’s permit sales are also beneficial for the otherregions—but only to a very small extent. A possible reason is thatCHI’s exports become more expensive, as indicated by the terms oftrade. This terms of trade effect is disadvantageous for the otherregions that import CHI’s products, and counteracts the efficiencygains with respect to emissions permits trade.

In both technology scenarios, green and brown, CHI achieves thehighest welfare improvement if it stays outside the regime, whilethe rest of the world is inside. This implies that terms of tradeimprovements and leakage effects are more beneficial for CHI thangains from participating in the international permit market. Theinclusion of DEV into the regime causes a welfare loss of around 1%compared with BAU for DEV, while the inclusion of IND creates onlya small welfare loss for IND. Apparently, the disadvantages ofclimate policy for fast growing economies and for oil exporterswithin DEV dominate. The introduction of BTA, however, causes asubstantial drop in the terms of trade and in welfare of the targetedregion, in particular a welfare loss of 1–2% for CHI.

However, the picture looks partly different in technology sce-nario brown. On the one hand, BTA has similar effects as before. Onthe other hand, the inclusion of CHI into the regime causes a welfareloss of around 1% compared with BAU for CHI, similar to the welfareloss for DEV, which becomes stronger in brown than in green.Different to scenario green, DEV does not benefit from the inclusionof CHI, possibly because CHI does now buy permits from the marketinstead of selling permits. In summary, whether CHI loses or gainsfrom joining the regime compared with BAU depends on thetechnology scenario. But in both scenarios, CHI loses when joiningthe regime compared with the situation, where CHI is excluded,while all other regions are included (�chiþdevþ ind), since CHI canstrongly benefit from leakage effects in the latter case.

In summary, we find the following important result regardingwelfare effects that holds in both technology scenarios: WhenChina does not participate in the regime and instead a carbontariff is imposed on its exports, it will likely be worse off thanwhen participating. This result does not necessarily hold for thedeveloping region in general.

Emissions and energy intensity. Leaving CHI outside the policyregime, while the rest of the world is included (�chiþdevþ ind)creates an increase in CHI’s emissions of about 5% in green andabout 11% in brown relative to the BAU scenario. This translatesinto leakage rates19 of about 9% in green and 29% in brown in2030. The leakage rate for the brown scenario is in line withBabiker and Rutherford (2005), for example, who find leakagerates in a range of 15–30%. Thus, the leakage rate for the green

19 The increase in carbon emissions in the area without a carbon price divided

by the reduction in carbon emissions in the region with a carbon price (in

percent).

scenario appears relatively low. Similarly, leaving DEV outside theregime while the rest of the world is included (þchi�devþ ind)creates an increase in DEV’s emissions of about 8% in green andabout 15% in brown relative to BAU. Furthermore, CHI’s and DEV’senergy intensities strongly improve compared with BAU whenthey are ‘‘on board’’, while they worsen compared with BAU whenthey are not. However, the border measures with fixed tax rateson produced commodities, achieve only small improvements inenergy intensity and emissions reductions. Emissions reductionsvia BTA are around 1–2% in each region, CHI and DEV, and arethus smaller than the leakage effects that we observed.

Interestingly, the implicit inter-regional CO2 flows computedin the previous section are almost as high as the explicit CO2 flowsthrough permit trading in 2030 that we find here. In both cases,IND imports around 2–3 Gt of CO2 a year from CHI and IND.Herein, CHI’s supply or demand of permits strongly depends onCHI’s technological progress, in our case expressed by the twotechnology scenarios, as discussed above.

5. Conclusion

In the first step, we calculate implicit carbon flows throughcommodity trade between an industrialized region, a developingregion, and mainland China, based on the GTAP 7 data for 2004.We find substantial implicit carbon flows from China and thedeveloping region to the industrialized region, in accordance withthe literature. Most of China’s implicit carbon exports occurthrough textiles, apparel and leather; electrical equipment; andmachinery. Our findings indicate the possibility of significantcarbon leakage effects, which is confirmed by our computablegeneral equilibrium analysis. Moreover, we compute carbonintensity factors of all commodities. While we are able tocompute carbon intensity factors accurately for the relativelyaggregated categories of commodities used in the GTAP data,actual policy applications will require much more disaggregatedcalculation of carbon contents of specific products by country oforigin. Data availability may limit accuracy in such real-worldcontexts.

In the second step, we analyze carbon tariffs by applying thecarbon intensity factors of commodities that we computed in thefirst step to our computable general equilibrium (CGE) model. Theresulting carbon-content-based tariffs are imposed on importsfrom a region that is not part of a global post-Kyoto Contractionand Convergence regime (GCI, 1990), in particular China or theregion of the other developing countries, to a region that is part ofthe regime. Throughout the model analysis, we distinguish a‘‘green’’ scenario that is optimistic with respect to (China’s)energy productivity and includes international technology spil-lovers, and a ‘‘brown’’ scenario that is optimistic with respect toChina’s overall growth but pessimistic with respect to China’senergy productivity.

We find as a main result: When China does not participate inthe regime and instead a carbon tariff is imposed on its exports, itwill likely be worse off than when participating. This result doesnot necessarily hold for the developing region in general. There-fore, carbon-based tariffs might encourage China to join a Con-traction and Convergence policy regime, but they might notnecessarily encourage developing countries in general. The reasonis probably that the Chinese economy is carbon-intensive as wellas export-intensive. In this case, a carbon tariff might be athreat—in case of other developing countries it might not be athreat. Moreover, we find relatively small carbon emissionsreductions stemming from the introduction of carbon tariffs. Thisresult casts doubt on the effectiveness of carbon tariffs as a directinstrument for reducing leakage and emissions in general.


The full inclusion of China into a global post-Kyoto regime ismore effective with respect to emissions reductions than theintroduction of carbon tariffs. Herein, China significantly loses inthe ‘‘brown’’ (high growth, low energy productivity) scenariocompared to business as usual. It can be equally off or slightlygain in the ‘‘green’’ (lower growth, high energy productivity)scenario. As a result, there is a stronger incentive for China to joina climate policy regime with international emissions trading inthe ‘‘green’’ scenario because China can benefit more from sellingemissions permits. On the contrary, there is a stronger incentivefor China not to join the regime in the ‘‘brown’’ scenario becauseChina can then emit more and benefit more from leakage effects.As a consequence, the effectiveness of carbon-based bordermeasures for encouraging China to join a climate regime is higherin a ‘‘green’’ world.

Independent of the assumptions on technical progress, Chinaachieves the highest welfare gain when it is excluded fromclimate policy while all other countries are included, since itcan then benefit from leakage effects. The industrialized anddeveloping region seem not to benefit much from China’s inclu-sion into this policy regime, or even slightly lose in case of thedeveloping countries. A probable reason is that the introductionof a carbon price in China raises the price of Chinese commoditieswhich the other countries, especially the industrialized countries,import at a large scale.

Herein it is important to note that in our policy scenario allregions follow the same emissions targets, no matter China joinsthe regime or not. Therefore, global emissions will be drasticallyhigher, if China does not join the regime. This implies that climatestress and damages will be higher in most regions. This is nottaken into account in our model as in most other numericalmodels and left for future research. In this sense, while reducedclimate damages do not show up in our analysis, it is still crucialthat China will be ‘‘on board’’ in a global climate coalition. Butpossibly the full inclusion of developing countries such as Chinain a climate coalition is not yet politically feasible.

In reality, there is unlikely to be an equalized global carbonprice in combination with a per-capita-based allocation of carbonpermits in the near future. If China does not have the possibility ofparticipating in such a carbon market, the attractiveness ofjoining the climate regime will be reduced. Hence, the incentivenot to join will increase. Thus, the potential of border measures asa threat can be better exploited when the alternative of joiningthe regime offers an efficient global carbon pricing scheme withbenefits for developing and emerging economies. In the case ofdifferent carbon prices across countries within a kind of globalcarbon pricing scheme, there would however still be an economicreason to employ border measures in order to adjust for thesecarbon price differences.

One reason for the small emissions reductions due to carbontariffs found in the analysis can be that such carbon tariffs areimposed on commodities based on base year carbon contents thatare fixed over time, and not directly on fossil fuel inputs. Thiscancels firms’ incentive to reduce fossil fuel inputs. Anotherreason is probably that China’s exports are responsible for aboutone quarter of total Chinese emissions so that three quarters ofemissions are unaffected. In order to address the first reason,carbon intensity factors would need to be measured and adjustedsimultaneously in order to create a better incentive for emissionsreduction via tariffs. But this would require Chinese firms toprovide exact information on their energy inputs (or emissionsoutputs) regularly to European, US and other policy makers inreality. This requirement does not appear as a feasible option.

The alternative assumption under discussion is that carbonintensity factors of imported products from different countriesare homogenous and equal to the carbon intensity factor of the

corresponding domestically produced products in the importingcountry. This policy, however, discriminates against exporterswith low carbon intensities and benefits exporters with highcarbon intensities. And due to on average lower emissionsintensities of importing high-income countries compared withlow-income exporting countries, the effects of border tax adjust-ment would be lower than in our analysis based on exporters’carbon intensities.

Hence, the following carbon tariff policy could be a feasiblecompromise with respect to Chinese exports: Carbon intensityfactors are estimated (on a rough sectoral base) and updated aftera certain period of time (for instance after five years) whenchanges in the emissions intensity can be measured and proven.This will take emissions intensity improvements of exportingeconomies like China on a macro level into account, without largebureaucratic efforts to measure and verify carbon emissions ofsingle firms continuously. It would create an incentive forgovernments to foster energy and emissions saving policies suchas in the Chinese Five Years Plan.

Future research may explicitly model endogenous technologi-cal progress instead of exogenous technological progress incombination with endogenous technology spillovers as in thecurrent analysis. Moreover, it appears useful to model renewableenergies and possibly CCS (carbon capture and storage) since thedeployment of new technologies strongly affects future emissionspaths and since coal power plays a major role in China and couldbe combined with CCS technologies. Alternatively, the distinctionbetween lower short-run elasticities and higher long-run elasti-cities of substitution between fossil and non-fossil inputs inenergy generation and production could be a less demandingsolution within a common CGE framework. This could mimic theincreasing availability of technological options over time. Themechanism of modeling international productivity spilloverscould be extended to modeling international transfers of advancedenergy generation technologies in a richer energy-oriented modeltoo. It might turn out that international transfer of low carbonenergy technologies is a more promising option than imposingtrade barriers for successfully dealing with climate change.

Acknowledgment

I thank Daiju Narita, Johannes Brocker, Sonja Peterson, TillRequate and Matthias Weitzel for their great help and thereviewers for their very thoughtful and helpful comments.

Appendix A

A.1. Carbon intensities

This section explains the calculation of the carbon intensitiesof traded commodities from the GTAP 7 data. In the first step, wederive an input-output table, in other words a 90�90 Leontieftechnology matrix L. In each column, it describes the productionof a commodity i (in a sector i) in region r. The first columnscontain all commodities i produced in the first region, thefollowing columns contain all commodities produced in thesecond region and so on. Within each column, commodities i

are listed in the same order representing the intermediate goodinputs that are necessary to produce one output unit of commod-ity i in region r. At this point, the GTAP 7 data set does not providebilateral trade flows of intermediate goods. It does, however,provide bilateral data on total trade flows m (for intermediateinput use plus consumption) and it does provide bisectoral

data on total imported intermediate inputs i of firms (without

21 Pauwelyn (2007), for example, notes: ‘‘Such competitiveness provisions

would essentially aim at leveling the playing field by imposing the same or similar

costs on imports, as US federal climate policy imposes on domestic US production.

To level the playing field on world markets, US exports could also be exempted

from domestic climate restrictions.’’ For further details see Houser et al. (2008).

Moreover, Meade (1974) and Grossman (1980) show under which conditions an

equal border tax on all imports and a corresponding subsidy on all exports lead to

a readjustment of the exchange rate without real economic effects. In our current

analysis with import tariffs at different rates but no export subsidies, these criteria

are not fulfilled.


distinguishing inputs by source country). Therefore, we use thefollowing weighting algorithm to compute bilateral intermediategood flows ib from sector ii in region rr to sector i in region r:

ibðrr,r,ii,iÞ ¼ iðr,ii,iÞmðrr,r,iÞPrrmðrr,r,iÞ

ð1Þ

The underlying assumption is that the distribution of sourcecountries of imports is the same for intermediate goods importsas for total imports.

In the second step, we compute the Leontief inverse w contain-ing the volumes of all commodities that are necessary to satisfythe demand for one unit of each commodity, and additionally tosatisfy the need for intermediate inputs throughout all productionstages. Herein, X is a 90�90 identity matrix.

w¼L� wþX 3 w¼ ½X�L��1 ð2Þ

In the third step, we derive the direct emissions per unit of outputi, denoted by the 1�90 vector e. These direct emissions occur ineach production stage via direct inputs of fossil fuels (coal, gasand oil).20 For this purpose, we use the data on direct emissionsthat Lee (2008) computes from the GTAP 7 data. She takes intoaccount that, depending on the region, a certain share of oil andgas goes into plastic products within the chemical sector. She alsotakes into account that the oil sector encompasses processeswhere oil inputs are not burned, but refined in order to gainimproved oil products. In this case, she assumes that the resultingemissions are zero.

In the fourth step, we multiply e with w. As a result, we obtainthe 1�90 carbon intensity vector z that contains the emissionsover all intermediate production stages that occur when produ-cing one unit of each commodity i in each region r.

z¼ e� w ð3Þ

A.2. Climate policy

This section explains how the Contraction and Convergence(C&C, GCI 1990) climate policy is implemented in the model. C&Chas been frequently suggested in the scientific and politicaldebate. It treats all people in all countries equally with respectto emissions and it favors high-population countries and poorcountries hat have today much lower per capita emissions thanthe industrialized countries. Therefore, it has a realistic chance ofbeing accepted by the developing countries and of becomingimplemented on a global scale. It appears interesting in particularwith respect to China to analyze such a C&C policy. In mathema-tical terms, the endowment with emissions permits for region r attime or year t follows the rule (Peterson and Klepper, 2007):

yCO2 ðt,rÞ ¼ yCO2 ð2012,rÞ2050�t

37þyCO2 ð2050Þ

t�2013

378tZ2013

ð4Þ

Regional emissions in 2012, denoted by yCO2 ð2012,rÞ, are derivedfrom the solution of the CGE for 2012. The global emissions levelin 2050 is set exogenously to about 18.3 Gt CO2 which corre-sponds roughly to a 450 ppm CO2 intensity target (compare IPCC,2001). As a result, per capita emissions converge step by step fromtheir regionally different levels in 2012 to an equalized level of 2 tper capita in 2050. Given these regional permit endowments,permits can be traded across regions and across all sectors,yielding efficient emissions reductions in the participating regions.

20 Assume that steal production uses electricity and burns oil when running

machines. Then, only these direct emissions from burning oil are included at this

stage of the calculation.

A.3. Carbon tariffs

This subsection introduces carbon-based tariffs on imports from aregion without a carbon price to a region with a carbon price. Thepolitical debate also includes carbon tax rebates on exports from aregion with a carbon price to a region without a carbon price. Thelatter is especially discussed with respect to competitiveness andfairness aspects concerning exporters that face a carbon price, whiletheir foreign rivals do not.21 (But not implemented in our model.) Inour border tax adjustment (BTA) experiments, the carbon-based advalorem tariff rate tBTAðt,rr,r,iÞ is endogenously adjusted, where rr

denotes exporting regions (for example CHI) outside the regime, and r

denotes importing regions within the regime (for example IND), and i

denotes sectors or commodities. In the absence of the first bestsolution, a carbon price in all sectors in all regions, we aim at a secondbest solution by pricing imports as if they had been produceddomestically. The tax rate depends on the carbon intensities ofcommodities in the exporting region, denoted by zðrr,iÞ. This impliesthat policy makers exactly know the real implicit carbon contents ofthe imported products, at least in the benchmark year. Furthermore,the tariff rate depends on the current carbon price pCO2 ðtÞ and on thecurrent (Armington composite) import price of the commoditypMðt,r,iÞ. The border tax adjustment is then given by the followingconstraint:22

tBTAðt,rr,r,iÞ ¼pCO2 ðtÞ

pMðt,r,iÞzðrr,iÞ ð5Þ

Thus, imports of commodities are due to the same carbon tax as thecorresponding domestically produced commodities. As a result,across sectors, the carbon-based tax rate is mainly determined bythe carbon intensity. Over time, it basically follows the developmentof the carbon price.

A.4. CGE-based carbon intensities

This section examines how the carbon tariff affects emissionsand exports in relation to each other. This leads to carbonintensity factors comparable to those in our previous analysis.However, the carbon intensities computed within our CGE frame-work include all global general equilibrium effects. In particular,we compute the relative change in world wide emissions inscenario �chiþdevþ indþbta relative to scenario �chiþdevþind for each year. This relative change rises from �0.02% in 2012to �0.72% in 2025. Additionally, we compute the relative changein Chinese exports between the same scenarios. This relativechange rises from �0.25% in 2012 to �8.64% in 2025. We thenderive the following impact measure:

oðtÞ ¼ EM�chiþdevþ indþbtaðt,WorldÞ�EM�chiþdevþ indðt,WorldÞ

X�chiþdevþ indþbtaðt,CHIÞ�X�chiþdevþ indðt,CHIÞð6Þ

This impact measure describes the change in global emissionsEMðt,WorldÞ relative to the change in Chinese exports Xðt,CHIÞ due

22 Re-arranging the equation and multiplying by the volume of imports

Mðt,r,iÞ yields: Mðt,r,iÞ � pMðt,r,iÞ � tBTAðt,rr,r,iÞ ¼Mðt,r,iÞ � zðrr,iÞ � pCO2 ðtÞ: Now, the

left hand side is the total tax to be paid for importing commodity i into region r,

given the ad valorem tax rate tBTAðt,rr,r,iÞ. The right hand side computes the

carbon content of commodity i and prices it at the current carbon price.

Table 3Functions, sets and parameters.

Symbol Explanation

f ð:Þ General function

cesð:Þ ½cetð:Þ� Constant elasticity of substitution [transformation] function

cdð:Þ Cobb–Douglas function

ltf ð:Þ Leontief function

t Time, year [2004; 2025] (climate policy starts in 2012)

r½rr� Region {IND, DEV, CHI}

i½ii� Sector, commodity (30 sectors, see footnote in Section 3)

e Fossil fuels {col, gas, oil} (subset of i)

r Time discount rate (0.02 per year)

dðrÞ Capital depreciation rate

sðrÞ Saving rate

lðt,rÞ Population growth rate plus rate of educational improvement

WAðrÞ Rate of exogenous general technological progress

WEðrÞ Rate of exogenous energy biased technological progress

tð:Þð:Þ Tax rate

Uðrr,r,iÞ Transportation costs (of transporting from rr to r)

Table 4Variables.

Symbol Explanation

W(r) Cumulated, discounted welfare effect

Uðt,rÞ Utility of the representative consumer

Pð:Þ Expenditure

Cðt,rÞ Consumption (private and public)

Dðt,r,iÞ Production for domestic use

Xðt,r,iÞ½Xðt,rr,r,iÞ� Exports [bilateral trade from rr to r]

Mðt,r,iÞ Imports

Kðt,rÞ½ ~K ðt,r,iÞ� Capital endowment [production input]

Fðt,CHIÞ½Fðt,CHI,iÞ� Endowment of CHI with capital from IND [production

input]

Lðt,rÞ½Lðt,r,iÞ� Labor endowment [production input]

Bðt,rÞ½Bðt,r,iÞ� Land and natural resources endowment [production

input]

EMðt,r,eÞ½EMðt,r,e,iÞ� CO2 Emissions permits endowment [production input]

Nðt,r,iÞ½Nðt,r,ii,iÞ� Intermediate good input [flow from ii to i]

Rð:Þ Total tax revenue

pð:Þ Price

Aðt,r,iÞ Total factor productivity

Eðt,r,iÞ Energy input

FIðt,iÞForeign capital intensity in China

F

K

� �

MIðt,iÞImport intensity in China

M

DþX

� �

NIðt,ii,iÞ Intermediate good flow intensity in China (from ii to i)

N

DþX

� �

VIðt,iÞ Vertical linkage intensity in China

with respect to upstream u and downstream d sectors

½P

ua iFIðt,r,uÞNIðt,r,u,iÞþP

da iFIðt,r,dÞNIðt,r,i,dÞ�

YLðt,r,iÞLabor productivity

DþX

L

� �

YEðt,r,iÞEnergy productivity

DþX

E

� �

TAðt,iÞ Rate of endog. general tech. progress in China

TEðt,iÞ Rate of endog. energy biased tech. progress in China


to the introduction of the border tax adjustment policy for eachperiod of time t in the CGE. This analysis shows that oðtÞ declinesfrom about 1.5 kg/US$ in 2012 to about 1.0 kg/US$ in 2025.

A.5. CGE model

This section contains key equations of the CGE model. Forfurther details and data sources see Klepper and Springer (2000),Springer (2002), Klepper et al. (2003) and Hubler (2011).Tables 3 and 4 explain the meaning of the parameters andvariables.23 The equations are written in quantities here, whileall prices are endogenous (The GAMS MPSGE model is solved inprice form though.)

Cumulated, discounted welfare effects excluding climatechange damage are derived from the relative Hicks-equivalentvariation of policy scenario 1 compared with reference scenario 0:

WðrÞ ¼

P2025t ¼ 2004fP½p

Cð2004,rÞ,U1ðt,rÞ��P½pCð2004,rÞ,U0

ðt,rÞ�gð1�rÞðt�2004Þ

P2025t ¼ 2004 P½pCð2004,rÞ,U0

ðt,rÞ�ð1�rÞðt�2004Þ

ð7Þ

Households equate expenditure to income:

pCC ¼ pK KþpLLþpBBþpCO2 EMþRð:Þ 8ðt,rÞ ð8Þ

Capital accumulation with a constant depreciation rate andsaving rate:

Kðtþ1,rÞ ¼ ½1�dðrÞ�Kðt,rÞþsðrÞYðt,rÞ ð9Þ

Splitting capital supply in IND into domestic use and FDI to CHI:

Kðt,INDÞ ¼ cet½ ~K ðt,INDÞ,Fðt,CHIÞ� ð10Þ

Exogenous labor augmentation (via population growth and edu-cational improvements):

Lðtþ1,rÞ ¼ ½1þlðt,rÞ�Lðt,rÞ ð11Þ

Basic production structure (producers minimize costs takinginput and output taxes tð:Þ into account):

cetðD,XÞ ¼ ltf/N,cesfB,cd½ ~K ,L,E�gS 8ðt,r,iÞ,rAfIND,DEVg ð12Þ

Basic production structure in China (producers minimize coststaking input and output taxes tð:Þ into account):

cetðD,XÞ ¼ ltf/N,cesfB,cd½cdð ~K ,FÞ,L,E�gS 8ðt,CHI,iÞ ð13Þ

Imported and domestically bought commodities form a consumption

23 The 30 production sectors are listed in footnote 11 in section 3.

bundle:

Cðt,rÞ ¼ ces½Dðt,r,iÞ,Mðt,r,iÞ� ð14Þ

Linking CO2 emissions to fossil fuels (col, gas, oil) in an energybundle:

E¼ cdfcru,egw,ltf ½EMðeÞ,e�g 8ðt,rÞ ð15Þ

Armington aggregation of imports from different regions (whereexport subsidies, and carbon- and non-carbon-based importtariffs tð:Þ are imposed on traded commodities):

Mðt,r,iÞ ¼ cesfltf ½Xðt,rr,r,iÞ,Uðrr,r,iÞ�g ð16Þ

Exogenous total factor productivity improvement:

Aðtþ1,r,iÞ ¼ ½1þWAðrÞ�Aðt,r,iÞ 8rAfIND,DEVg ð17Þ

Exogenous and endogenous total factor productivity improve-ment in China:

Aðtþ1,CHI,iÞ ¼ ½1þWAðrÞþTA

ðt,iÞ�Aðt,CHI,iÞ ð18Þ

Exogenous energy efficiency improvement:

Eðtþ1,r,iÞ ¼ ½1�WEðrÞ�Eðt,r,iÞ 8rAfIND,DEVg ð19Þ

Exogenous and endogenous energy efficiency improvement inChina:

Eðtþ1,CHI,iÞ ¼ ½1�WEðCHIÞ�TE

ðt,iÞ�Eðt,CHI,iÞ ð20Þ


Herein, the strength of total factor productivity improvements inChina increases in the intensities of foreign capital, of verticallinkages within the production chain, of imports, and in thedistance to the technology frontier:

TAðt,iÞ ¼ f ½FIðt,iÞ,VIðt,iÞ,MIðt,iÞ�½YLðt,IND,iÞ�YLðt,CHI,iÞ� ð21Þ

The strength of energy efficiency improvements increases in thesame factors:

TEðt,iÞ ¼ f ½FIðt,iÞ,VIðt,iÞ,MIðt,iÞ�½YEðt,IND,iÞ�YEðt,CHI,iÞ� ð22Þ

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Carbon tariffs on Chinese exports: Emissions reduction, threat, or farce?