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Climate Change Economics, Vol. 10, No. 1 (2019) 1950001 (27 pages) World Scientific Publishing CompanyDOI: 10.1142/S2010007819500015SECTORAL TARGETS TO ADDRESSCOMPETITIVENESS — A CGE ANALYSIS WITHFOCUS ON THE GLOBAL STEEL SECTORVICKI DUSCHA*, EVERETT B. PETERSON†, JOACHIM SCHLEICH*,†,‡,¶and KATJA SCHUMACHER§*FraunhoferInstitute for Systems and Innovation ResearchBreslauer Straße, 76139 Karlsruhe, Germany†Department of Agricultural and Applied EconomicsVirginia Polytechnic Institute and State University202A Hutcheson Hall, Blacksburg, VA 24061, USA‡Grenoble Ecole de Management Univ Grenoble Alpes ComUE 12, rue Pierre Semard,38000 Grenoble, France§Öko Institut, Schicklerstrasse 5-7, 10179 Berlin, Germany¶[email protected]ceived 21 September 2018Accepted 31 October 2018Published 11 December 2018In the wake of the Paris Climate Agreement, countries may employ sectoral approaches. Theseallow for efficiency gains while at the same time addressing the concerns of competitiveness andcarbon leakage. Applying a multi-country, multi-sector dynamic CGE model, this paper exploresthe role of sector emission targets for the steel sector in an international agreement, their interaction with emissions trading systems, and to which extent sector targets may address competitiveness concerns. To better reflect technological realities, the steel sector is disaggregated intoits two main industries: primary fossil fuel-based steel production (BOF) and secondary scraprecycling steel production (EAF). The policy simulations suggest that sectoral targets may effectively counter the (negative) output and competitiveness effects of differences in the stringencyof climate policy across countries. BOF steel contributes significantly more to emission reductions than EAF steel. Moreover, the output effects of BOF and EAF are of opposite signs.Keywords: Sectoral targets; steel sector; competitiveness; carbon leakage; climate policy.1. IntroductionThe Paris Agreement (UNFCCC, 2015) allows countries to nationally determine thetype of commitment they would undertake. Countries’ nationally determined contributions (NDCs) may, for example, involve economy-wide absolute targets, intensitytargets, or sectoral targets. Although the Paris Agreement does not directly mention¶ Correspondingauthor.1950001-1

V. Duscha et al.carbon markets, it includes provisions that may advance international carbon marketsin a post-Kyoto climate regime. In particular, Article 6 of the Paris Agreementrecognizes that countries can employ “cooperative approaches” to implement theirNDCs, thereby using “internationally transferred mitigation outcomes” (ITMOs).More than half the countries plan using international market mechanisms to meet theirNDCs (http://cait.wri.org/indc/). Conceivably, these mechanisms also refer to sectorfocussed approaches such as sectoral targets, i.e., the joint binding agreements betweensectors and governments of countries (e.g., Baron et al., 2009; Sawa, 2010). Somecountries may employ such sectoral targets as a first step toward more ambitionseconomy-wide targets in the future (see e.g., Den Elzen et al., 2008; Barrett, 2010;Böhringer et al., 2014). Sectoral approaches may also emerge from industry initiatives,including ICAO and IMO. A first example could be the so-called Global MarketBased Measure, a carbon offsetting and reduction scheme for international aviationagreed upon by the ICAO in 2016.Sectoral targets may lead to ITMOs further on called “emission certificates” beingtraded across countries, thus allowing for efficiency gains while at the same timeaddressing the concerns of competitiveness and carbon leakage when the stringency ofclimate policy differs across countries. Even before the emergence of the Paris Agreement, sectoral targets have been one proposition for future agreements on climate change,having primarily been proposed for energy-intensive sectors, such as the cement, steel, orelectricity sectors, but their role may have been strengthened by the new agreement.Quantitative analyses of sectoral targets for individual sectors are mainly based onpartial equilibrium models (e.g., Mathiesen and Maestad, 2004; Meunier and Ponssard,2012; Karali et al., 2014) but also on computable general equilibrium (CGE) analysisto capture economic effects on trade and production (e.g., Voigt et al., 2011; HamdiCherif et al., 2011; Gavard et al., 2011; Mu et al., 2017).In this paper, we explore the impacts of emission targets in the steel sector in ageneral equilibrium framework, their interaction with an emissions trading system(ETS), and to which extent sector targets may address competitiveness concerns. Thesteel sector seems particularly suited for a sectoral targets approach for two reasons.First, steel production is relatively CO2-intensive, accounting for about 3–5% of theglobal CO2 emissions. Secondly, the steel industry is trade-intensive, with more than aquarter of finished steel products being exported (World Steel Association, 2014).Steel is mainly produced via two technological routes: a basic oxygen furnace(BOF), which produces primary steel from virgin raw materials, or an electric arcfurnace (EAF), which produces secondary steel from recycled metal products. Thus,BOF production is mainly associated with direct CO2 emissions, whereas EAF causesprimarily indirect CO2 emissions via electricity use. The shares of BOF and EAF differconsiderably across countries (see Table A.1).While some engineering-economic bottom-up models distinguish betweendifferent steel production technologies, this is typically not the case for econometrically estimated (macro) economic models or for CGE models. Exceptions include1950001-2

Sectoral Targets to Address CompetitivenessLutz et al. (2005) for macroeconometric models and Schumacher and Sands (2007) forCGE models. Since the regional scope of these models is limited to one country(Germany), they cannot adequately capture competitiveness and leakage effects.To explore the implications of sectoral targets for the steel industry, and as the mainmethodological contribution of the paper, we first modify an existing dynamic CGEmodel to more adequately reflect steel production technologies. The model is thenapplied to investigate two policy scenarios which differ by the number of sectorswithin and across countries facing emission targets and to which extent trading ofemission certificates is allowed between the target sector (steel) and sectors subject toemissions trading.The remainder of the paper is organized as follows. Section 2 presents the mainfeatures of the model and includes the disaggregation of the steel sector into BOF andEAF steel. Section 3 describes the specific emission targets in each scenario. Section 4presents the model results. The final section discusses the main findings andconcludes.2. Modeling2.1. Empirical modelThe analyses rely on a multi-country, multi-sector, recursive dynamic CGE modelwhich is based on the GDyn (Ianchovichina and McDougall, 2001) and GTAP-Emodels (Burniaux and Truong, 2002; Nijkamp et al., 2005), utilizes the GTAP 7database, and includes domestic trade and transport margins (Peterson and Lee, 2009).Accordingly, households and firms are assumed to act perfectly rational but myopic.That is, they maximize utility or profits given the information available in a particularperiod.1 As is typical for CGE models (e.g., Dellink et al., 2004; Babiker and Eckaus,2007; Guivarch et al., 2011; Capros et al., 2013; Nordhaus and Sztorc, 2013), relativefactor prices drive companies’ input portfolio and output prices drive demand andsupply. Factor prices and output prices adjust instantaneously so that all markets clearin all time periods.2The base year of the model is 2012. The underlying GTAP database, whose baseyear is 2004, is updated to 2012 using observed changes in GDP, CO2 emissions, andpopulation in each region in the model for the 2004–2012 period.3 In the update1Forexample, Babiker and Eckaus (2007), Guivarch et al. (2011), or Capros et al. (2013) also employ such recursivedynamic models, whereas Dellink et al. (2004) assume perfect foresight. In this case, household’s and firms’ decisionsalso take into account utility and profits of all future periods. For further discussion of this assumption in the climatepolicy context, please see Babiker et al. (2009).2In contrast, Babiker and Eckaus (2007) or Guivarch et al. (2011) allow for labor market rigidities.3Employing the more recent GTAP v8 database, which uses 2007 as the base year, would also involve updating to a2012 reference year that we used in the analysis. This would require using the same observed changes in GDP, CO2emissions, etc. from 2007 to 2012 that were used when we updated the v7 database. Potential changes in intermediateinput use and consumption shares for a given region between 2004 and 2007 should be small, given short three-yeardifference between the reference years.1950001-3

V. Duscha et al.simulation, we also assume that all Annex I countries meet their national targets underthe Kyoto Protocol, with the exception of the United States. We also do not allow for“hot air” for Russia or the Ukraine, so no national targets are imposed for these regionsin the update simulation.The model consists of 32 country/regions and has 18 industries/sectors4. The EU 27has been aggregated into five countries (France, Germany, Italy, Spain, UK) and fourregions according to their main steel-production routes (BOF versus other processes)and their economic development (EU15 versus EU12): BOF 15, BOF 12, REU 15, andREU 12.5 For the remainder of this paper, we specify two groups of sectors. The ETSsector includes electricity; refined petroleum and coal; chemicals, rubber, and plasticproducts; other mineral products (cement); paper products; nonferrous metals as wellas the BOF and EAF steel industries. Note that these industries/sectors are part of theEuropean Union ETS. All remaining industries/sectors belong to the group of the nonETS sector. The sub-group ETS S includes all ETS sectors except for BOF and EAFsteel.2.2. Disaggregation of the steel sector2.2.1. Production technologies and market sharesPrimary steel is produced via sintering plants (ore concentration)/coking plants, blastfurnaces (iron making), and converters (steel production). Secondary steel relies onsmelted down scrap and is mostly produced via arc furnace process and to a lesserextent in induction furnaces. The main energy inputs are electricity in the EAF processand coke in the BOF process with EAF steel requiring less than half the primaryenergy use of BOF steel. Hence, CO2 emissions are much lower for EAF steel than forBOF steel (ca. 0.4 t versus 1.7–1.8 t of direct CO2 emissions per ton of crude steel,IEA, 2012). The indirect CO2 emissions for steel production depend primarily on thecarbon intensity of the power mix. In 2013, BOF steel accounted for 71.2% of globalcrude steel production and EAF steel for 28.2% (World Steel Association, 2014), butshares vary significantly across regions (Table 1). For major steel-producing countries,the share of EAF is over 60% in the USA, Mexico, India, Italy, and Spain, but lessthan 30% in China, Russia, the Ukraine, Japan, and Australia (World Steel Association, 2014).2.2.2. Splitting EAF and BOF steel in GTAPTo allow for a more realistic modeling of steel production, the GTAP sector ferrousmetals (i s) is disaggregated into BOF steel and EAF steel industries. When splittingthe sector using quantity shares of BOF and EAF, we preserved the value of total steel4AppendixA.1 provides an overview of the production structure together with overview tables of the regions andsectors employed in the model. Appendix A.3 offers sensitivity analyses of the results to the elasticity of substitutionbetween energy and capital, which is a key parameter in CGE-based simulations (Antimiani et al., 2015).5See Table A.2 for the composition of these regions.1950001-4

Sectoral Targets to Address CompetitivenessTable 1. Overview of global crude steel production in 2013.RegionChinaEU 27EU 15EU 12JapanUSAIndiaRussiaRest AsiaSouth AmericaCIS excl. RussiaOther EuropeNorth America excl. USMiddle EastAfricaOceaniaGlobalTotal production of crudesteel (in 1,000 t)Share of global crude steelproductionShare ofEAF ource: World Steel Association (2014).production since the GTAP database uses values (not quantities). Thus, prices andquantities are not separately identified. This sector disaggregation mainly affects threeparts of the model: inputs used in steel production, export sales, and domestic sales forintermediate use. To disaggregate input use by ferrous metals in the GTAP databaseinto inputs used by BOF and EAF steel producers, we employ the followingprocedure6: First, total input costs are allocated to BOF and EAF steel based on theproduction share of BOF and EAF steel in the Steel Statistical Yearbook for the year2004 (the base year in the GTAP data). Individual inputs are then allocated to the twoprocesses as follows: coal, other minerals, which include metal ores, refined petroleumand coal products, which include coke, used by the ferrous metals sector are majorinputs for the BOF production route and are therefore allocated to the BOF steelindustry. Electricity, gas, labor, and capital are major inputs for both production routes.Therefore, we split those production factors between BOF and EAF steel based onestimated cost of each input for BOF and EAF processes in 2011 and the estimatedtotal input cost for BOF and EAF steel production.7 All remaining intermediate inputsare allocated on a proportional basis to ensure that the estimated total cost for eachproduction process is met.6See Appendix A.3 for an exemplary calculation of the disaggregation process.7The estimates for the cost shares of BOF and EAF are taken from estimates by Metalsat www.steelonthenet.com.1950001-5Consulting International (MCI)

V. Duscha et al.The export sales of ferrous metal products in the GTAP database are allocated toBOF and EAF steel products using COMTRADE export data. We identified a list offour-digit HS codes that are either primarily associated with BOF steel or EAF steelproducts.8 Then, the level of ferrous metal product exports in the GTAP database isdisaggregated into BOF and EAF steel product exports based on the observed share ofBOF steel exports between a given country bilateral pair in the COMTRADE data.9 Asa validity check, we compared the outcome with trade data provided by World SteelAssociation (2014), thereby assuming that flat products are mainly made from BOFsteel, whereas long products are mainly made from EAF steel. The trade figures forBOF and EAF based on the World Steel Association (2014) data are quite similar tothose resulting from our approach.The domestic sales of ferrous metal products are disaggregated into sales of BOFand EAF steel products as follows. First, sales of ferrous metal products to the privateand government households are allocated to BOF and EAF steel products based on theproduction share of BOF and EAF steel in each region. The sales of ferrous metalproducts for domestic intermediate use are allocated to BOF and EAF steel products ona proportional basis to ensure that the estimated total sales/cost for each process isobtained. The factor of proportionality is determined by the total sales for each processless the value of exports, sales to private and government households, and own-useintermediate input use.We also assume that BOF and EAF steel are not substitutable. This approachreflects the (yet) rather limited substitutability of BOF and EAF steel. In practice, BOFsteel is typically used for flat products, e.g., in the automobile industry. In contrast,EAF steel is typically used for long products, e.g., for the construction industry.Because of technological progress, the quality of EAF steel is expected to continue toimprove, thus increasing substitutability of EAF and BOF steel. Mathiesen andMaestad (2004), for example, employ an elasticity substitution of 0.5. Given thegenerally small changes in BOF and EAF steel in our analyses, the difference betweenusing an elasticity of substitution of zero or 0.5 is rather negligible. The assumption ofzero substitution between BOF and EAF is, however, in contrast to other macroeconomic analyses, for example, Lutz et al. (2005) and Schumacher and Sands (2007)which distinguish different steel production technologies in their macroeconomicmodels but assume crude steel from different production routes to be homogeneousproducts.8HS codes 2618, 2619, 7201, 7202, 7203, 7205, 7212, 7217, 7219, 7220, 7223, 7225, 7226, and 7229 are associatedwith BOF steel exports, whereas HS codes 7204, 7213, 7214, 7215, 7216, 7218, 7221, 7222, 7224, 7227, 7228, and7301-7307 are associated with EAF steel exports.9Where differences existed between the COMTRADE data for BOF and EAF steel products and the production datafrom the Steel Statistical Yearbook, the trade shares of BOF and EAF steel were set equal to the production shares ofBOF and EAF steel for that region. If the COMTRADE data reported zero trade in steel products between a givenbilateral country pair but the GTAP data reported a positive value, exports were allocated using the average export shareof BOF steel across all bilateral trade pairs.1950001-6

Sectoral Targets to Address Competitiveness3. ScenariosOur analysis focusses exemplarily on the year 2020. The focus of the paper is,however, on understanding the mechanisms driving the results. We define a basicforecast scenario and two policy scenarios. In the forecast scenario, the growth ratesin country/region GDP, population, and CO2 emissions are based on the currentpolicies scenario as defined in the World Energy Outlook 2010 (IEA, 2010). Inparticular, no additional climate policies are implemented in the forecast scenario.World population reaches 7.6 billion in 2020, GDP growth evolves at an average rateof 4% between 2010 and 2020, and CO2 emissions increase by 16% to 35.2 Gt CO2between 2012 and 2020.3.1. Description of policy scenariosWe implement two policy scenarios, which differ by the countries facing emissiontargets and by the countries and sectors allowed to trade certificates (Table 2). In thebase scenario, all countries face two emission reduction targets for the period2012–2020: one for all ETS sectors (i.e., also including steel) and one for non-ETSsectors. There are two four-year time periods in the model: 2013 through 2016 and2017 through 2020. In the base scenario, trading of certificates is only allowed for theETS sectors within the EU. This scenario serves as a reference to the policy scenariowith sectoral targets.In the sectoral target scenario, the ETS emission target is further disaggregated intotwo targets: one for the steel sector and one for the ETS S sectors. Hence, each countryfaces three targets (steel, ETS S , non-ETS sectors). In this scenario, trading of certificates is allowed between steel industries across all regions. In addition, because thesteel sector is part of the EU ETS, trading of certificates is allowed between steel andthe other ETS S sectors within the EU 27.3.2. Emission targetsThe level of ambition of country targets differs across countries to reflect the principle of “common but differentiated responsibility.” For Annex I countries, we assume that national CO2 emissions in 2020 will be 30% below 1990 levels. This levelis consistent with the reduction range for Annex I countries emphasized by the IPCCfor meeting the 2 C target and with suggestions by the European Commission(IPCC, 2007; European Commission, 2009). According to Den Elzen et al. (2008),non-Annex I countries must reduce their emissions by 15–30% below baseline in2020 so that the 2 C target may be met. We therefore set the emission targets for allnon-Annex I countries to 15% below forecast levels in 2020. Hence, the stringencyof climate policy differs across regions in all scenarios. Following the Effort SharingDecision of the EU (European Commission, 2009), we assume that the ETS sectorsaccount for 60% of the required national emission reductions between 2005 and 2020in all countries.1950001-7

V. Duscha et al.Table 2. Policy scenario definitions.ScenarioBase scenarioCountry groupTargetsETS (incl. steel)Non-ETSXXXXXXEUOther Annex INon-Annex ISectoral targetsscenarioEUOther Annex INon-Annex ITradingSteelETS SNon-ETSXXXXXXXXXAllowed within EU ETSNo tradingNo tradingAllowed for steel sector acrossregions as well as within EUETS (steel þ ETS S ); notrading for non-steel sectorsoutside of EU ETSFurther, we set a sectoral target that requires the steel sector to reduce directemissions by 10% below forecast in 2020 in all countries.10, 11 This target is in linewith meeting the Best Available Technology for BOF and EAF steel (EuropeanCommission, 2012). In the scenarios involving sectoral steel targets, the ETS S sectors’ reduction target is set such that the aggregate target of ETS S and the steel sectorcorresponds with the ETS sectors’ target in the base scenario. Table 3 displays thenational emission targets for the policy scenarios together with the emissions in theforecast scenario. The targets are in line with model scenarios in the IPCC AR4 reportlimiting CO2-equivalent concentrations to low level of about 450 ppm CO2-eq (likelyto limit global warming to 2 C above pre-industrial levels, IPCC, 2014a, b).12 Thesetargets do not account for emission changes from land use, land-use change andforestry (LULUCF), or from deforestation and degradation (REDD).All emission reduction targets are applied equally across all time periods in themodel, i.e., half the required reduction must be met in period 2013–2016 and the otherhalf in 2017–2020. To meet national, non-ETS, or nonsteel targets, countries employ adomestic price on CO2 emissions. Trading of credits from CDM or JI projects is notallowed in any scenario.10Since the majority of the direct CO emissions in the steel sector stems from BOF steel production, the majority of2the emission reductions will be achieved by reducing per-unit use of fossil fuel and/or by lowering output.11The 10% reduction below forecast in 2020 in the steel sector is in the same order of magnitude as the reductionsrealized in the steel sector in the base scenario.12The IPCC AR5 scenarios are based on model inter-comparison projects and individual model exercises leading up toat least 2050. Our 2020 targets are well within their range. Sectoral targets were not explicitly modeled in the exercisesincluded in the IPCC report. The model inter-comparison exercise by the Energy Modelling Forum (EMF 27), forexample, included different technology scenarios (for industrial technologies only CCS was explicitly included) andgrouped countries/regions to allow for emission trading among some (groups of) countries.1950001-8

Sectoral Targets to Address Competitiveness4. Results4.1. CO2 certificate prices in the policy scenariosFor policy scenarios involving several targets and markets, countries may face morethan one certificate price. Table 4 shows the CO2 certificate prices for the steel sectorand the ETS/ETS S sectors in the different policy scenarios for major steel-producingcountries and regions in 2020.13In the base scenario, countries face a country-specific uniform certificate price forthe steel sector and for ETS S .14 The differences in prices between countries reflectdifferences in the marginal abatement costs, reflecting that the levels of ambition of theemission targets differ significantly. They are most lenient for China and India andmost ambitious for Japan and the USA.In the sectoral targets scenario, each country also faces a price for certificates inthe steel market, which is identical across all regions because these certificates can betraded globally. Certificate prices for the steel sector are lower than those in the basescenario in all countries but China and India. Certificate prices in the ETS S sectorare slightly lower than those in the base scenario in all countries, but they aresignificantly above the certificate prices for the steel sector in all countries but Chinaand India.Table 3. CO2 emission targets by region.Base year (2012)Forecast (2020)Policy scenarios (2020)MtMtCompared to forecastChinaJapanIndiaUSABrazilEU 15403,857 15% 34% 15% 38% 15% 19321,02935,222 15% 31% 15% 21%Source: POLES Forecast.13Further results, which are not reported here to save space, are available from the authors upon request.14The CO certificate price in the EU 27 in the base scenario is substantially higher than the price currently observedin2the EU ETS due to several reasons: The 2020 target implemented is substantially higher than the 2020 GHG reductionstargeted at under the EU 20-20-20 policy package. In addition, our scenarios do not allow for using credits from offsetprojects (e.g. CDM) to reduce mitigation costs. The relatively high CO2 certificate prices in the base scenario in manyOECD countries imply that competitiveness and leakage effects are more pronounced than might be expected underknown current policy scenarios.1950001-9

V. Duscha et al.Table 4. CO2 certificate prices ( /t CO2)for steel and ETS/ETS S sectors in thepolicy scenarios in 2020.Base scenarioETSChinaJapanIndiaUSABrazilEU 27RussiaSectoral targetsSteelETS S15101661112857844111150101244981384.2. Effects of implementing a price for CO2 emissions for the steel sectorsImplementing emission targets involves direct and indirect certificate price effects onthe production costs for BOF and EAF steel industries. The direct certificate priceeffect is an increase in input costs for all fossil fuel inputs. The indirect effect is anincrease in the price of other intermediate inputs used by the steel sector that are fossilfuel-intensive and are also subject to a certificate price (e.g., electricity or coke). Thisindirect certificate price effect can be particularly large for EAF steel production incountries in which the power generation relies strongly on fossil fuels.Several other factors also affect the production of steel: (i) the “own-use effect,”(ii) trade effect, (iii) changes in domestic demand, and (iv) a “general equilibriumeffect.” In the underlying GTAP data, steel is an important input into steel production,accounting for approximately 40% of the cost of all intermediate inputs across allregions. This likely is a reflection of the importance of “unfinished” steel in morerefined steel products. Thus, an increase (decrease) in steel prices from higher (lower)fuel input prices is further magnified due to the “own-use” effect.15Because steel is traded intensively, differences in the direct and indirect certificateprice effects across countries may affect trade patterns. An extreme example is EAFsteel production in Brazil and China. In Brazil, the CO2 intensity of electricity is closeto zero due to the large use of hydropower. Thus, the indirect certificate price effect forBrazilian EAF steel producers is very small. In contrast, given the large use of coalfired power plants in China, the indirect certificate price effect is much larger. Ingeneral, countries in which steel production is less CO2-intensive (directly and indirectly) enjoy a comparative advantage compared with countries with a high CO2that in the GTAP database scrap, which is part of the “recycling” sector (ISIC two-digit number 37), is includedwith ISIC sector 36 in the GTAP sector manufacturing, n.e.c. In our aggregation, recycling is included in “othermanufacturing” (oman).15Note1950001-10

Sectoral Targets to Address Competitivenessintensity in steel production. At the same time, countries with a lower certificate pricefor the power sector enjoy a comparative advantage in EAF steel production comparedwith countries with higher certificate prices.Although trade in steel products is important, the majority of steel production isused domestically as intermediate inputs in the manufacturing and service sectors. Forexample, in our model, approximately one-half of BOF and EAF steel production isused domestically as an intermediate input in other manufacturing. As othermanufacturing and services are part of the non-ETS sector and face emission targets ineach region, differences in the certificate prices between the non-ETS and ETS sectorswithin a region and differences in the certificate prices for the non-ETS sector betweenregions will affect the competitiveness of other manufacturing sectors in each region.Since no substitution is allowed between nonenergy intermediate inputs in the model, areduction in other manufacturing production will reduce the demand for BOF and EAFsteel products.Finally, the “general equilibrium effects” capture changes in supply and demand inresponse to price changes. For example, higher certificate prices result in a decrease inthe total demand for carbon-intensive fossil fuels. As a result, prices for fossil fuels candecrease, offsetting part of the price increase due to the higher certificate prices. Thiseffect is also referred to as “fossil fuel channel of carbon leakage” as a reduction inenergy prices in response to a climate policy might stimulate renewed demand and thuslead to an increase in emissions (e.g., Babiker, 2005). Finally, the implementation ofclimate change policies will affect the overall demand for capital and labor in eachregion’s economy, thus altering the costs for these factors not only for the steel sectorsbut for all other sectors as well.16These effects have different orders of magnitude across countries and counterbalance or amplify each o

SECTORAL TARGETS TO ADDRESS COMPETITIVENESS — A CGE ANALYSIS WITH FOCUS ON THE GLOBAL STEEL SECTOR VICKI DUSCHA *, EVERETT B. PETERSON †, JOACHIM SCHLEICH ,‡ ¶ and KATJA SCHUMACHER§ *Fraunhofer Institute for Systems and Innovation Research Breslauer Straße, 76139 Karlsruhe, Germany †Department of Agricultural and Applied Economics Virginia Polytechnic Institute and State University