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How to increase global resource productivity? 345 emissions and resource inputs for 50 countries, 2 regions, 41 product groups, 12 energy carriers and 9 resources. The regions are “OPEC” and “Rest of the World”. The explicitly modelled region “OPEC” and the 50 countries cover about 95% of world GDP and 95% of global CO2 emissions. The aggregated region “Rest of the World” is needed for the closure of the system. The model is documented in Meyer et al. (2007), Meyer and Lutz (2007) and Lutz et al. (2010). Current applications of the model can be found in Giljum et al. (2008a) and Lutz and Meyer (2009b). An update of the material models is provided in Lutz and Giljum (2009). The main difference to neoclassical CGE models is the representation of prices, which are determined due to the mark-up hypothesis by unit costs and not specified as long run competitive prices. But this does not mean that the model is demand side driven, as the use of input–output models might suggest. Even though demand determines production, all demand variables depend on relative prices that are given by unit costs of the firms using the mark-up hypothesis, which is typical for oligopolistic markets. CGE models assume polipolistic markets, where prices equal marginal costs, in contrast. The difference between CGE models and GINFORS can be found in the underlying market structure and not in the accentuation of either market side. Firms are setting the prices depending on their costs and on the prices of competing imports. Demand is reacting to price signals and thus determining production. Hence, the modeling of GINFORS includes both demand and supply elements. Allowance prices and carbon tax rates are endogenous to the model. To avoid long solving procedures, the prices are changed in an iterative process manually until the GHG reduction target is reached. Allowance prices increase the shadow prices of energy carriers and reduce energy demand according to the specific price elasticities. Different allocation methods therefore have no direct influence on energy demand and the emission levels in the model. Increasing profits of private companies in the case of grandfathering deliver other sector and macroeconomic impacts than government spending out of auctioning revenues, however. All parameters of the model are estimated econometrically, and different specifications of the functions are tested against each other, which gives the model an empirical validation. An additional confirmation of the model structure as a whole is given by the convergence property of the solution which has to be fulfilled year by year. The econometric estimations build on times series from OECD, IMF and IEA from 1980 to 2006. For a number of variables the data were only available for a shorter time period. The modelling philosophy of GINFORS is close to that of INFORUM type modelling (Almon 1991) and to that of the model E3ME from Cambridge Econometrics (Barker et al. 2007a). Common properties and minor differences between E3ME and GINFORS are discussed in Barker et al. (2007b). 3 Scenarios To investigate the impacts of an ETR for Europe six separate scenarios have been designed to understand a variety of tax reform options. Each scenario is identified by an acronym. The final letter indicates the baseline to which it is compared with L for low and H for high energy prices.
346 C. Lutz The scenario analysis allows for an understanding of different revenue recycling methods and various scales of ETR in order to meet different greenhouse gas emissions targets. All scenarios were examined in both E3ME and GINFORS (see Ekins and Speck 2010). The scenarios are: & BL: Baseline (low energy prices), & BH: Baseline sensitivity with high oil price (reference case), & Scenario S1L: ETR with revenue recycling designed to meet unilateral EU 2020 GHG target, & Scenario S1H: ETR with revenue recycling designed to meet unilateral EU 2020 GHG target (high oil price), & Scenario S2H: ETR with revenue recycling designed to meet unilateral EU 2020 GHG target (high oil price), 10% of revenues are spent on eco-innovation measures, & Scenario S3H: ETR with revenue recycling designed to meet cooperation EU 2020 GHG target (high oil price). The baseline with low energy prices BL has been calibrated to the 2007 PRIMES baseline to 2030, published by the European Commission (DG TREN 2008). For the high oil price baseline (reference case BH) the effect of a higher oil price, particularly over the period 2008–10 is assumed. In this scenario coal and gas prices develop in line with the increases to the oil price. In this scenario energy prices are close to the assumptions in the current IEA World Energy Outlook (2008). Different oil price assumptions are shown in Fig. 1. Each of the ETR scenarios has the same key taxation components: & a carbon tax rate is introduced to all non EU ETS sectors equal to the carbon price in the EU ETS that delivers an overall 20% reduction in greenhouse gas emissions by 2020, in the international cooperation scenario this is extended to 30%, & aviation is included in the EU ETS at the end of Phase 2, & power generation sector EU ETS permits are fully auctioned in Phase 3 of the EU ETS, 140 120 100 80 60 40 20 0 BL BH 1990 1995 2000 2005 2010 2015 2020 Fig. 1 International oil price in the low and high energy price scenarios in $2005/b
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154 P.J.J. Welfens et al. Keywords
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228 R. Bleischwitz economics and po
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244 R. Bleischwitz Ericsson M (2009
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246 R. Walz 1 Introduction Competen
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248 R. Walz Based on the pollution
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250 R. Walz procedures. However, ev
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252 R. Walz In addition to exports,
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