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Service Contract in Support of the Impact Assessment of Various Policy Scenarios to Reduce CO2 Emissions from Passenger Cars

This report has been prepared by an external contractor and does not

necessarily represent the European Commission's view.

Contract: N° 070501/2004/392571/MAR/C1

Commissioned by: European Commission DG ENV

Status: Final Report

Centre for European Economic Research (ZEW) Mannheim

Germany

Mannheim, 16.10.2006

Authors: Dr. Sabine Jokisch Dr. Georg Bühler Centre for European Economic Research (ZEW)

Prof. Dr. Ferdinand Dudenhöffer Kai Pietron B&D Forecast

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Content

1 INTRODUCTION............................................................................................................................11

2 MODELS FOR IMPACT ANALYSIS...........................................................................................14

2.1 TREMOVE MODEL .................................................................................................................14 2.1.1 The transport demand module ............................................................................................15 2.1.2 The vehicle stock module ....................................................................................................16 2.1.3 The fuel consumption and emissions module......................................................................17 2.1.4 The welfare module ............................................................................................................17 2.1.5 Methodology for the Impact Assessment on transport sector .............................................18 2.1.6 Extension of the TREMOVE output to EU25......................................................................18

2.2 PACE-T...................................................................................................................................20 2.2.1 Model description ...............................................................................................................20 2.2.2 Methodology for the Impact Assessment on society level ...................................................24 2.2.3 Data input and calibration .................................................................................................25

2.3 B&D FORECAST MODEL...........................................................................................................27 2.3.1 Methodological overview ...................................................................................................27 2.3.2 Description of workflow of the B&D model / simulation....................................................29 2.3.3 Model description ...............................................................................................................30 2.3.4 Input Parameters of the B&D Forcar Simulation ..............................................................33

3 BASELINE .......................................................................................................................................37

3.1 TREMOVE..............................................................................................................................37 3.1.1 Exogeneous transport baseline...........................................................................................37 3.1.2 Vehicle Stock ......................................................................................................................41 3.1.3 Road transport emissions and fuel consumption ................................................................44

3.2 PACE-T...................................................................................................................................51 3.3 FORCAR BASELINE ................................................................................................................56

3.3.1 Initial position of Baseline-scenario...................................................................................57 3.3.2 Conclusions of FORCAR-Baseline-scenario ......................................................................57

4 SCENARIO BUILDING .................................................................................................................66

4.1 DESCRIPTION OF THE METHODOLOGY FOR POLICY SCENARIO BUILDING .................................66 4.1.1 Building of the cost curve for passenger cars (M1)............................................................66 4.1.2 Assessing the costs and reduction potential of other measures ..........................................67 4.1.3 Defining Policy Scenarios ..................................................................................................67

4.2 TECHNICAL OPTIONS TO REDUCE FUEL CONSUMPTION FROM PASSENGER CARS .......................68 4.2.1 TREMOVE modelling .........................................................................................................70 4.2.2 Sensitivity analysis with respect to autonomous weight increase.......................................71 4.2.3 Alternative cost curve methodologies .................................................................................72 4.2.4 Cost-effectiveness analysis of alternative assumptions ......................................................73

4.3 INTEGRATED APPROACH MEASURES IDENTIFIED BY TASK A ...................................................74 4.3.1 Technical options to reduce fuel consumption from light-commercial vehicles .................74 4.3.2 Application of fuel efficient mobile air conditioning systems .............................................75 4.3.3 Options to reduce vehicle and engine resistance factors....................................................75 4.3.4 Options to promote application of biofuels ........................................................................77

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4.3.5 Fuel efficient driving ..........................................................................................................77 4.3.6 Other measures...................................................................................................................78 4.3.7 TREMOVE Model runs and cost-effectiveness analysis .....................................................79

5 ANALYSIS OF THE SCENARIOS WITH TREMOVE .............................................................81

5.1 BUILDING THE POLICY OPTIONS ..............................................................................................81 5.2 ASSESSMENT OF THE SCENARIOS WITH TREMOVE ................................................................84

5.2.1 Transport demand...............................................................................................................84 5.2.2 Vehicle sales .......................................................................................................................84 5.2.3 Emissions............................................................................................................................85 5.2.4 Welfare ...............................................................................................................................86

5.3 ADDITIONAL TARGET BY 2015 .................................................................................................88

6 MACRO-ECONOMIC AND SECTORAL ANALYSIS ..............................................................89

6.1 DESCRIPTION AND ASSUMPTIONS OF THE SCENARIOS...............................................................89 6.1.1 Context................................................................................................................................89 6.1.2 General scenario setups .....................................................................................................90

6.2 MACRO-ECONOMIC ANALYSIS .................................................................................................91 6.2.1 Scenario: 120g-low costs....................................................................................................92 6.2.2 Scenario: 120g-high costs ..................................................................................................99 6.2.3 Scenario: 130g-high costs ................................................................................................106 6.2.4 Comparison of the scenario results ..................................................................................113

6.3 SECTORAL ANALYSIS.............................................................................................................116 6.3.1 Determinations on the three simulated scenarios.............................................................116 6.3.2 Scenario: 120g-IEEP(2004) / 120g-low-cost-scenario ....................................................117 6.3.3 Scenario: 120g-TNO (2006d) / 120g-high-cost-sceanrio.................................................127 6.3.4 Scenario: 130g-TNO (2006d) / 130g-high-cost-scenario.................................................137 6.3.5 Summary of the three scenarios 120g-IEEP, 120g-TNO, 130g TNO ...............................147 6.3.6 Differentiation volume-manufacturer vs. premium-manufacturer....................................151 6.3.7 Summary and perspectives ...............................................................................................161

7 CONCLUSIONS ............................................................................................................................165

8 REFERENCES...............................................................................................................................169

9 ANNEX 1 – TREMOVE RUNS USED FOR MACRO-ECONOMIC AND SECTORAL ANALYSIS .....................................................................................................................................171

10 ANNEX 2 – DETAILED PACE-T MODELLING RESULTS...................................................468

11 ANNEX 3 – DETAILED FORCAR MODELLING RESULTS ................................................482

12 ANNEX 4 – FINAL TREMOVE RUNS FOR COST-EFFECTIVENESS ANALYSIS AND SCENARIO BUILDING ...............................................................................................................498

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Figures FIGURE 2.1: TREMOVE MODULAR STRUCTURE.....................................................................................15 FIGURE 2.2: SCHEDULE OF B&D ANALYSIS ............................................................................................27 FIGURE 2.3: PLAYERS OF THE B&D ANALYSIS/SIMULATION...................................................................28 FIGURE 2.4: DESCRIPTION OF WORKFLOW ..............................................................................................29 FIGURE 2.5: BASIC PRINCIPLE – OVERVIEW.............................................................................................30 FIGURE 3.1: CRUDE OIL PRICES HYPOTHESIS...........................................................................................38 FIGURE 3.2: RELATIVE ANNUAL CAR MILEAGE AS A FUNCTION OF VEHICLE AGE (IN %) .........................42 FIGURE 3.3: MOVEMENT OF CAR-MANUFACTURER TO EU10 STATES......................................................58 FIGURE 3.4: COST PER EMPLOYEE AT CAR-MANUFACTURER EU25 VS. NON-EU ...................................61 FIGURE 3.5: COST PER EMPLOYEE AT SUPPLIER INDUSTRY EU25 VS. NON-EU......................................62 FIGURE 3.6: PRODUCTION OF CARS EU25 VS. NON-EU .........................................................................63 FIGURE 3.7: NUMBER OF EMPLOYEES AT CAR-MANUFACTURER (BASELINE-SCENARIO)

– EU25 VS. NON-EU ...........................................................................................................63 FIGURE 3.8: NUMBER OF EMPLOYEES AT SUPPLIER-INDUSTRY (BASELINE-SCENARIO) – EU25 VS. NON-

EU........................................................................................................................................65 FIGURE 4.1: PERCENTAGE VARIATION IN VEHICLE PURCHASE COST FOR REACHING ALTERNATIVE

TARGETS BY 2020 – TARGET-MEASURE COMBINATION: PERCENTAGE REDUCTION PER MANUFACTURER ...................................................................................................................69

FIGURE 4.2: ASSESSMENT OF THE IMPACT OF A DIFFERENT “SAFETY MARGIN” IN DRAWING THE COST CURVES ON THE COSTS OF CO2 REDUCTIONS REQUIRED TO MEET THE 2008/9 AND 2012 TARGETS (EXAMPLE FOR MEDIUM PETROL VEHICLE) ............................................................73

FIGURE 4.3: BUSINESS-AS-USUAL INTRODUCTION OF OPTIONS TO REDUCE VEHICLE AND ENGINE RESISTANCE FACTORS AS MARKET SHARE OF NEW CARS SOLD..............................................76

FIGURE 5.1: COST EFFECTIVENESS OF INTEGRATED APPROACH MEASURES .................................................82 FIGURE 5.2: MARGINAL COST ANALYSIS INTEGRATED APPROACH ...........................................................82 FIGURE 6.1: PROCEDURE OF FORCAR-SIMULATION OEM....................................................................118 FIGURE 6.2: DEVELOPMENT OF JOBS IN EU25 – 120G-IEEP-LOW-COST-SCENARIO /

SMALL-CAR-SEGMENT......................................................................................................122 FIGURE 6.3: DEVELOPMENT OF JOBS IN EU25 – 120G-IEEP-LOW-COST-SCENARIO /

MEDIUM-CAR-SEGMENT...................................................................................................123 FIGURE 6.4: DEVELOPMENT OF JOBS IN EU25 – 120G-IEEP-LOW-COST-SCENARIO /

LARGE-SEGMENT..............................................................................................................124 FIGURE 6.5: PROCEDURE OF FORCAR-SIMULATION SUPPLIER-INDUSTRY.............................................125 FIGURE 6.6: DEVELOPMENT OF JOBS IN EU25 – 120G-IEEP-LOW-COST-SCENARIO /

SUPPLIER-INDUSTRY...........................................................................................................127 FIGURE 6.7: PROCEDURE OF FORCAR-SIMULATION OEM....................................................................128 FIGURE 6.8: DEVELOPMENT OF JOBS EU25 – SCENARIO 120G-TNO-HIGH-COST-SCENARIO /

SMALL-SEGMENT .............................................................................................................132 FIGURE 6.9: DEVELOPMENT OF JOBS EU25 – SCENARIO 120G-TNO-HIGH-COST-SCENARIO /

MEDIUM-SEGMENT ..........................................................................................................133 FIGURE 6.10: DEVELOPMENT OF JOBS EU25 – SCENARIO 120G-TNO-HIGH-COST-SCENARIO /

LARGE-SEGMENT..............................................................................................................134 FIGURE 6.11: PROCEDURE OF FORCAR-SIMULATION SUPPLIER-INDUSTRY.............................................135 FIGURE 6.12: DEVELOPMENT OF JOBS IN EU25 – 120G-TNO-HIGH-COST-SCENARIO /

SUPPLIER-INDUSTRY...........................................................................................................137

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FIGURE 6.13: PROCEDURE OF FORCAR-SIMULATION OEM....................................................................139 FIGURE 6.14: DEVELOPMENT OF JOBS EU25 – SCENARIO 130G-TNO-HIGH-COST-SCENARIO /

SMALL-SEGMENT .............................................................................................................142 FIGURE 6.15: DEVELOPMENT OF JOBS EU25 – SCENARIO 130G-TNO-HIGH-COST-SCENARIO /

MEDIUM-SEGMENT ..........................................................................................................143 FIGURE 6.16: DEVELOPMENT OF JOBS EU25 – SCENARIO 130G-TNO-HIGH-COST-SCENARIO /

LARGE-SEGMENT..............................................................................................................144 FIGURE 6.17: PROCEDURE OF FORCAR-SIMULATION SUPPLIER-INDUSTRY.............................................145 FIGURE 6.18: DEVELOPMENT OF JOBS IN EU25 – 130G-TNO-HIGH-COST-SCENARIO /

SUPPLIER-INDUSTRY...........................................................................................................147 FIGURE 6.19: DEVELOPMENT OF EMPLOYMENT AT CAR-MANUFACTURER IN EU25 – CO2

(SCENARIOS VS. BASE-LINE) ..............................................................................................148 FIGURE 6.20: DEVELOPMENT OF EMPLOYMENT AT SUPPLIER-INDUSTRY IN EU25 – CO2

SCENARIOS VS. BASE-LINE.................................................................................................150 FIGURE 6.21: IMPACT ON DEALERSHIP – 120G-IEEP CO2 SCENARIO .......................................................159 FIGURE 6.22: IMPACT ON DEALERSHIP – 120G-TNO CO2 SCENARIO........................................................160 FIGURE 6.23: IMPACT ON DEALERSHIP – 130G-TNO CO2 SCENARIO........................................................161

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Tables TABLE 2.1: VEHICLE STOCK IN THE EU10 COUNTRIES IN 2000 (IN 1.000 CARS) ..................................19 TABLE 2.2: REGIONS IN PACE-T .........................................................................................................21 TABLE 2.3: SECTORAL DISAGGREGATION IN PACE-T .........................................................................21 TABLE 2.4: SALES VOLUME SPLIT BY SEGMENT....................................................................................32 TABLE 2.5: SEGMENT SPLIT DIVIDED BY FUEL-TYPE.............................................................................32 TABLE 2.6: RETAIL PRICE INCREASE BY SEGMENT................................................................................32 TABLE 2.7: TRANSPORTATION BY SEGMENT (IN KM)............................................................................32 TABLE 2.8: TURNOVER SUPPLIER INDUSTRY........................................................................................33 TABLE 2.9: SALES VOLUME DEALERSHIP.............................................................................................33 TABLE 2.10: ALLOCATION TO SEGMENTS ..............................................................................................34 TABLE 3.1: AVERAGE ANNUAL GROWTH RATES OF GDP AND POPULATION: EU25 .............................37 TABLE 3.2: BASELINE PASSENGER TRAVEL DEMAND (IN BILL. PKM PER YEAR) ....................................39 TABLE 3.3: BASELINE TRANSPORT DEMAND (ANNUAL GROWTH RATE)................................................40 TABLE 3.4: BASELINE PASSENGER TRAVEL DEMAND (IN BILL. VKM PER YEAR)....................................41 TABLE 3.5: NUMBER OF VEHICLES IN THE EU25 ..................................................................................42 TABLE 3.6: NUMBER AND SHARE OF THE CAR TYPE..............................................................................43 TABLE 3.7: AVERAGE MILEAGE (VKM/PASSENGER CAR) OVER THE FORECAST PERIOD.........................44 TABLE 3.8: NEW DIESEL CARS TEST CYCLE FUEL EFFICIENCY ..............................................................45 TABLE 3.9: CAR MAKERS AGREEMENTS IMPLEMENTATION ASSUMPTIONS (EU15 AVERAGE)...............45 TABLE 3.10: FUEL EFFICIENCY IMPROVEMENTS FOR NON CAR ROAD VEHICLES .....................................46 TABLE 3.11: EXTRA FUEL CONSUMPTION FOR CARS WITH AIR CONDITIONING (LITRE PER 100 VKM),

TREMOVE BASELINE.......................................................................................................47 TABLE 3.12: EMISSIONS OF CNG CARS COMPARED TO EMISSIONS OF GASOLINE EURO 4 CARS..............48 TABLE 3.13: WELL-TO-TANK EMISSION FACTOR AND FUEL PATHWAY BY 2012 IN TREMOVE BASELINE

..........................................................................................................................................49 TABLE 3.14: OVERVIEW OF VEHICLE STOCK, DEMAND AND CO2 EMISSIONS IN TREMOVE BASELINE.50 TABLE 3.15 AUTOMOBILE INPUT DATA IN 2000 ....................................................................................51 TABLE 3.16 ENERGY IMPORT PRICES (IN 2000$/BOE)............................................................................52 TABLE 3.17 BASELINE CAR PURCHASE PRICES (IN €).............................................................................52 TABLE 3.18 BASELINE AVERAGE ANNUAL FUEL COSTS AT CONSTANT PRICES (IN €) .............................53 TABLE 3.19 BASELINE AVERAGE ANNUAL OTHER COSTS (IN €).............................................................53 TABLE 3.20 BASELINE MACROECONOMIC DEVELOPMENT RELATIVE TO 2000 (INDEX=1,0000 IN 2000)55 TABLE 3.21: CAR-PRODUCTION IN EU25- AND NON-EU-STATES .........................................................59 TABLE 3.22: PRODUCTION OF CARS EU25 – SPLIT EU15 VS. EU10........................................................59 TABLE 3.23: ASSUMED SHARES OF CAR PRODUCTION VALUES TO SUPPLIER AND CARMAKER ................59 TABLE 3.24: COST-STRUCTURE OF AUTOMOTIVE SUPPLIER....................................................................60 TABLE 3.25: COMPARISON OF LABOUR COSTS PER YEAR / CAR-MANUFACTURER INDUSTRY ..................60 TABLE 3.26: COMPARISON OF LABOUR COSTS PER YEAR / SUPPLIER INDUSTRY .....................................61 TABLE 3.27: DEVELOPMENT OF PRODUCTION AND EMPLOYEES AT CAR-MANUFACTURER – EU25 VS.

NON-EU ...........................................................................................................................64 TABLE 3.28: EMPLOYEES AT CAR-MANUFACTURER BY SEGMENT AND REGION......................................64 TABLE 4.1: PERCENTAGE VARIATION IN VEHICLE PURCHASE COST FOR REACHING AVERAGE 120G/KM

BY 2012, DEPENDING OF TARGET-MEASURE COMBINATION ...............................................68 TABLE 4.2: WELFARE IMPACT OF ALTERNATIVE 2012 SCENARIOS AT M1 VEHICLE LEVEL – TASK A

CORE HYPOTHESIS..............................................................................................................70

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TABLE 4.3: GHG EMISSIONS (2010-2020) AND COST-EFFECTIVENESS OF ALTERNATIVE 2012 SCENARIOS AT M1 VEHICLE LEVEL – TASK A CORE HYPOTHESIS.......................................71

TABLE 4.4: SOCIETAL COSTS, CO2 SAVINGS AND COST EFFECTIVENESS OF FOUR DIFFERENT REDUCTION SCENARIOS FOR PASSENGER CARS (CUMULATED OVER 2010-2020) UNDER 3 COST HYPOTHESIS .......................................................................................................................74

TABLE 4.5: COST AND ABATEMENT HYPOTHESIS FOR N1 VEHICLES SCENARIOS ..................................75 TABLE 4.6: COST AND ABATEMENT HYPOTHESIS FOR MAC SCENARIOS ..............................................75 TABLE 4.7: GHG EMISSIONS (2010-2020) AND COST-EFFECTIVENESS OF SELECTED INTEGRATED

APPROACH INDIVIDUAL MEASURES ANALYSED WITH TREMOVE .....................................80 TABLE 5.1: IMPACT ON TRANSPORT DEMAND.......................................................................................84 TABLE 5.2: IMPACT OF THE POLICY SCENARIOS ON VEHICLE SALES.....................................................85 TABLE 5.3: IMPACT OF THE POLICY SCENARIOS ON GREENHOUSE GASES EMISSIONS ...........................86 TABLE 5.4: IMPACT OF THE POLICY SCENARIOS ON POLLUTANT EXHAUST EMISSIONS.........................86 TABLE 5.5: WELFARE ANALYSIS OF THE POLICY SCENARIOS................................................................87 TABLE 5.6: COST EFFECTIVENESS OF GHG ABATEMENT 2010-2020 ...................................................87 TABLE 5.7: SOCIETAL COSTS, CO2 SAVINGS AND COST EFFECTIVENESS OF TWO ADDITIONAL

SCENARIOS INCLUDING ADDITIONAL 120G TARGET BY 2015..............................................88 TABLE 6.1: ASSUMPTIONS ON FUEL CONSUMPTION AND CAR PURCHASE COSTS IN THE SCENARIOS .....90 TABLE 6.2 AVERAGE CAR PURCHASE COSTS IN SCENARIO 120G-LOW COSTS (IN €)...........................92 TABLE 6.3 AVERAGE ANNUAL FUEL COSTS IN SCENARIO 120G-LOW COSTS (IN €) ............................93 TABLE 6.4 MACROECONOMIC SIMULATION RESULTS OF SCENARIO 120G-LOW COSTS RELATIVE TO

BASELINE (IN %) ................................................................................................................95 TABLE 6.5 SECTORAL EFFECTS OF SCENARIO 120G-LOW COSTS RELATIVE TO BASELINE (IN %) .........98 TABLE 6.6 AVERAGE CAR PURCHASE COSTS IN SCENARIO 120G-HIGH COSTS (IN €) ........................100 TABLE 6.7 AVERAGE ANNUAL FUEL COSTS IN SCENARIO 120G-HIGH COSTS (IN €)..........................101 TABLE 6.8 MACROECONOMIC SIMULATION RESULTS OF SCENARIO 120G-HIGH COSTS RELATIVE TO

BASELINE (IN %) ..............................................................................................................102 TABLE 6.9 SECTORAL EFFECTS OF SCENARIO 120G-HIGH COSTS RELATIVE TO BASELINE (IN %).......105 TABLE 6.10 AVERAGE CAR PURCHASE COSTS IN SCENARIO 130G-HIGH COSTS (IN €) ........................107 TABLE 6.11 AVERAGE ANNUAL FUEL COSTS IN SCENARIO 130G-HIGH COSTS (IN €)..........................107 TABLE 6.12 MACROECONOMIC SIMULATION RESULTS OF SCENARIO 130G-HIGH COSTS RELATIVE TO

BASELINE (IN %) ..............................................................................................................109 TABLE 6.13 SECTORAL EFFECTS OF SCENARIO 130G-HIGH COSTS RELATIVE TO BASELINE (IN %).......112 TABLE 6.14 MACROECONOMIC EFFECTS IN 2020 COMPARED RELATIVE TO BASELINE (IN %) .............113 TABLE 6.15: BASIC SPLIT OF SEGMENT BY SIZE AND FUEL-TYPE (IN MILL. CARS).................................117 TABLE 6.16: RETAIL PRICES BY SEGMENT IN THE 120G-IEEP/ LOW-COST- SCENARIO (IN €) ................119 TABLE 6.17: LABOUR COST ADVANTAGES IN THE 120G-IEEP/ LOW-COST-SCENARIO NON-EU VS. EU 25

PER CAR (IN €)..................................................................................................................121 TABLE 6.18: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER SMALL-CAR-SEGMENT BASELINE-

VS. 120G LOW-COST-SCENARIO........................................................................................122 TABLE 6.19: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER MEDIUM-CAR-SEGMENT BASELINE-

VS. 120G LOW-COST-SCENARIO........................................................................................123 TABLE 6.20: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER LARGE-CAR-SEGMENT BASELINE-

VS. 120G LOW-COST-SCENARIO........................................................................................124 TABLE 6.21: LABOUR COST ADVANTAGES IN THE 120G-IEEP/ LOW-COST-SCENARIO NON-EU VS. EU 25

PER CAR (IN EURO)/ SUPPLIER-INDUSTRY ........................................................................126

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TABLE 6.22: NUMBER OF EMPLOYEES IN THE SUPPLIER-INDUSTRY (BASELINE- VS. 120G LOW-COST-SCENARIO) .......................................................................................................................127

TABLE 6.23: RETAIL PRICES BY SEGMENT IN THE 120G-TNO SCENARIO (IN €) ....................................129 TABLE 6.24: LABOUR COST ADVANTAGES IN THE 120G-TNO / HIGH-COST-SCENARIO NON-EU VS. EU

25 PER CAR (IN €) .............................................................................................................131 TABLE 6.25: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER SMALL-CAR-SEGMENT (BASELINE-

VS. 120G HIGH-COST-SCENARIO)......................................................................................132 TABLE 6.26: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER MEDIUM-CAR-SEGMENT BASELINE-

VS. 120G HIGH-COST-SCENARIO .......................................................................................133 TABLE 6.27: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER LARGE-CAR-SEGMENT (BASELINE-

VS. 120G HIGH-COST-SCENARIO)......................................................................................134 TABLE 6.28: LABOUR COST ADVANTAGES IN THE 120G-TNO / HIGH-COST-SCENARIO NON-EU VS. EU

25 PER CAR (IN €) / SUPPLIER-INDUSTRY..........................................................................136 TABLE 6.29: NUMBER OF EMPLOYEES IN THE SUPPLIER-INDUSTRY (BASELINE- VS. 120G LOW-COST-

SCENARIO) .......................................................................................................................137 TABLE 6.30: RETAIL PRICES BY SEGMENT IN THE 130G-TNO SCENARIO..............................................140 TABLE 6.31: LABOUR COST ADVANTAGES IN THE 130G-TNO / HIGH-COST-SCENARIO NON-EU VS. EU

25 PER CAR (IN €) .............................................................................................................141 TABLE 6.32: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER SMALL-CAR-SEGMENT (BASELINE-

VS. 130G HIGH-COST-SCENARIO)......................................................................................142 TABLE 6.33: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER MEDIUM-CAR-SEGMENT BASELINE-

VS. 130G HIGH-COST-SCENARIO .......................................................................................143 TABLE 6.34: NUMBER OF EMPLOYEES IN AT CAR-MANUFACTURER MEDIUM-CAR-SEGMENT (BASELINE-

VS. 130G HIGH-COST-SCENARIO)......................................................................................145 TABLE 6.35: LABOUR COST ADVANTAGES IN THE 130G-TNO / HIGH-COST-SCENARIO NON-EU VS. EU

25 PER CAR (IN €) / SUPPLIER-INDUSTRY..........................................................................146 TABLE 6.36: NUMBER OF EMPLOYEES IN THE SUPPLIER-INDUSTRY (BASELINE- VS. 130G HIGH-COST-

SCENARIO) .......................................................................................................................147 TABLE 6.37: NUMBER OF EMPLOYEES AT CAR-MANUFACTURER IN EU25 (BASELINE- VS. CO2-

SCENARIOS) .....................................................................................................................149 TABLE 6.38: NUMBER OF EMPLOYEES AT SUPPLIER-INDUSTRY IN EU25 (BASELINE- VS. CO2-

SCENARIOS) .....................................................................................................................150 TABLE 6.39: EXPORT RATE (OUTSIDE THE EU) OF A PREMIUM-MANUFACTURER .................................151 TABLE 6.40: ABSOLUTE RETAIL PRICES EXCL. TAX (IN €)– ALL THREE SCENARIOS ..............................152 TABLE 6.41: PETROL- / DIESEL-SHARE BY SEGMENTS ..........................................................................152 TABLE 6.42: EFFECTS ON DEALERSHIP – BEST CASE SCENARIO ............................................................153 TABLE 6.43: 120G-IEEP: EFFECTS ON SALES MARGIN AND VOLUME – BEST CASE ...............................154 TABLE 6.44: 120G-TNO: EFFECTS ON SALES MARGIN AND VOLUME – BEST CASE ...............................154 TABLE 6.45: 130G-TNO: EFFECTS ON SALES MARGIN AND VOLUME – BEST CASE ...............................155 TABLE 6.46: EFFECTS ON DEALERSHIP – MEDIUM CASE SCENARIO.......................................................155 TABLE 6.47: 120G IEEP: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – MEDIUM CASE ....156 TABLE 6.48: 120G TNO: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – MEDIUM CASE ....156 TABLE 6.49: 130G TNO: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – MEDIUM CASE ....156 TABLE 6.50: EFFECTS ON DEALERSHIP – WORST CASE SCENARIO.........................................................157 TABLE 6.51: 120G IEEP: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – WORST CASE ......157 TABLE 6.52: 120G TNO: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – WORST CASE.......158 TABLE 6.53: 130G TNO: EFFECTS ON SALES MARGIN AND VOLUME BY SCENARIO – WORST CASE.......158

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1 Introduction

Within the Kyoto Protocol, the European Union has committed itself to a substantial reduction of 8% in overall CO2 emissions. Due to the expected tremendous increase of transport services – especially in road transport – by 50% in the period between 1990 and 2010 and due to the fact that 28% of the overall CO2 emissions in the EU is caused by the transport sector today, it will be difficult for the European Union to fulfil its commitment to reduce CO2 emissions without a considerable contribution of the transport sector.

In the “Community strategy to reduce CO2 emissions from passenger cars and to improve fuel economy” (COM(95) 689 final), the Community chooses the per-kilometre emissions of new passenger cars (120 g CO2/km by 2012) as one main strategic variable in reducing CO2 emission from passenger cars. Three "pillars" are defined that might contribute in various combinations to achieving this goal:

• Voluntary agreements with the automobile industry on fuel economy improvements. • Fuel-economy labelling of cars and • Fiscal measures.

With regard to the first pillar, the European, Japanese and Korean Automobile Manufacturers Associations (ACEA, JAMA and KAMA, respectively) entered into commitments to achieve new passenger car fleet average CO2 emissions of 140 g CO2/km by 2008/2009 (measured according to Directive 93/110/EC). The European Parliament and Council Decision of 22 June 2000 establishing a scheme to monitor the average specific emissions of CO2 from new passenger cars (Decision 1753/2000) supplemented this step. It should be recalled that the targets of the Commitments must mainly be achieved by technological developments affecting different car characteristics and market changes linked to these developments.

In addition to the three pillars of the strategy, several other options are feasible in support of them that could contribute to achieving CO2 emission reductions beyond the period of the current Commitments by ACEA, JAMA and KAMA. Overall, the aim of the Commission is to achieve the Community goal of 120 g CO2/km in a “sustainable way”, taking into account the three pillars of the Gothenburg Council (environmental, social, economic aspects).

The Commission has decided that a joint evaluation shall be carried out, involving the Commission, the stakeholders, selected national experts and consultants in order to address the two key issues:

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A The costs and reduction potential of technologies and other measures to reduce CO2 emissions

B An extended impact assessment of policy scenarios to reduce CO2 emissions from passenger cars in the EU

In Task A (Service Contract undertaken by TNO, LAT and IEEP) a number of technical and non-technical measures is reviewed in terms of their potential contribution to CO2 reduction in passenger cars and their costs. These measures have been identified by the European Commission and can be regarded as complementary options in the context of a so-called Integrated Approach, and include the following technical and non-technical measures:

Technical measures

• technical options to reduce fuel consumption at the vehicle level

• application of fuel efficient air conditioning systems

• options to reduce vehicle and engine resistance factors

• options for application of alternative fuels based on fossil energy

• increased application of biofuels

• possibilities to include N1 vehicles into the Commitments

Non-technical measures

• fuel efficient driving

• CO2 based taxation schemes for passenger cars

• options for improved energy or CO2 labelling

• public procurement proposals

This report presents the findings of the Task B project “Service contract in support of the extended impact assessment of various policy scenarios to reduce to reduce CO2 emissions from passenger cars”.

The objective of this service contract is to assess the economic, environmental and social aspects of different policy scenarios to reduce CO2 emissions from passenger cars - scenarios based on the measures analysed by Task A - covering all the sectors of the economy and the external trade dimension. The assessment covers the transport sector and in particular the automotive sector in the EU. The work support an Impact Assessment undertaken under the procedures of the Commission.

The effects on the transport sector are quantified with the transport emission model TREMOVE, and the model runs have been carried out by the consultant in charge of the

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development of the TREMOVE model (K.U. Leuven / Transport and Mobility Leuven), and by the services of the Commission (DG Environment, Unit Energy and Environment). On the macroeconomic level, we use the dynamic general equilibrium model PACE-T to simulate the impacts of the relevant regulation measures on the macro-economy as well as the individual sectors and trade flows of the European countries. On the level of the individual automobile markets and sub-markets, B&D Forecast uses its market analysis and forecasting tool FORCAR to break down the results for the automobile market as a whole to the impacts on specific segments of the market, as well as on specific car producers and different groups of suppliers.

The assessment of the several scenarios is done in comparison with a baseline scenario (TREMOVE 2.4) which has been defined by the European Commission and implemented into the models. Results are going to be presented for the EU25. A national investigation analysis is done in this report, when there are remarkable differences within the EU countries implemented in TREMOVE. To capture the differences between the EU15 and the EU10, which obviously are expected to exist, the results of the models are reported for both regions, too. The period to be analysed is 2000 to 2020 although the reduction target should be met in 2012. The annex of this report contains the time series data for each indicator and each country.

This draft final report is structured as follows. In the second section of the report, the three models TREMOVE, PACE-T and FORECAST are explained. The third section presents the Baseline for the analysis. The forth chapter describes the way policy scenarios to be modelled with TREMOVE are build on the individual measures analysed by Task A. The fifth chapter describes the result of the TREMOVE runs. The sixth chapter describes the results of the analysis of the macro-economic and Sectoral impact of a simplified sub-set of three scenarios which are all build on technical measures to reduce CO2 emission from passenger cars. Finally the seventh chapter summarizes the impacts of the scenarios and recalls the caveats of the modelling undertaken, in order to provide insights to the Impact Assessment report.

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2 Models for impact analysis

2.1 TREMOVE model

TREMOVE is a policy assessment model to study the effects of different transport and environmental policies on the emissions of the transport sector. The model estimates transport demand, modal shifts, vehicle stock renewal, emissions of air pollutants and welfare level. TREMOVE models both passenger and freight transport in 21 countries including the EU15 plus Switzerland, Norway, Czech Republic, Hungary, Poland and Slovenia. The four new Member States are selected on the basis of data availability. TREMOVE covers the period 1995-2020.

TREMOVE has a modular structure and consists of three inter-linked ‘core’ modules. The first module is the transport demand module which describes transport flows and the users’ decision making process when it comes to making their modal choice. The key assumption here is that the transport users will select the volume of transport and their preferred mode, period, region etc. based on the generalized price for each mode. The second module is the vehicle stock turnover module which describes how changes in demand for transport or changes in vehicle price structure influence the share of age and type of vehicles in the stock. The third core module is named fuel consumption and emission module. This module is used to calculate fuel consumption and emissions, based on the structure of the vehicle stock, the number of kilometres driven by each vehicle type and the driving conditions.

Additional to the core modules TREMOVE is extended with a lifecycle emission module and a welfare cost module. The lifecycle emissions module enables to calculate emissions during production of fuels and electricity. The welfare cost module has been developed to compute the cost to society associated with emission reduction scenarios in European urban and non-urban areas. The welfare effect of a policy change is calculated as the discounted sum of changes in utility of households, production costs, external costs of congestion and pollution and benefits of tax recycling.

The five above mentioned modules make use of specific country data which means that there are 21 separate models with a similar modular structure that work independent from each other. Additionally, TREMOVE includes one maritime transport demand model for all 21 countries in aggregate. Because of the focus of the project, this model is not relevant for the analysis of this project and is therefore not described in the following sections.

Figure 2.1 shows the inter-linkages of the TREMOVE model. As indicated, outputs from the vehicle stock as well as fuel consumptions and emission modules are fed back

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into the demand module. As fuel consumption, stock structure and usage influence usage costs, they are important determinants of transport demand and modal split.

Figure 2.1: TREMOVE modular structure

Source: TREMOVE model description, version (2.41).

TREMOVE measures differences between the base case and a simulation. The separate values of welfare for simulation cannot be interpreted on their own. Only the difference between both values makes sense. Therefore, a TREMOVE model run always consists of two parts: a baseline run and a simulation run. The base case run uses an input database to contract a coherent reference case for transport demand, vehicle stocks and emissions. This reference case (or base case) has been developed for all countries and model regions considered.

2.1.1 The transport demand module

The strength of the transport demand module is that it is an integrated simulation model which differentiates between private and business transport. Business transport is furthermore subdivided into passenger and freight transport. Within these model components, the following choices are going to be analysed and quantified on basis of generalised prices:

• Choices in the labour and leisure/consumption markets • Geographic choices: region and trip distance • Choice in period: peak or off-peak • Choice of modes and road types – urban • Choice of modes and road types – non-urban

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TREMOVE models the transport activity in a country without explicit origin-destination disaggregation and simulates in a coherent way the changes in volume of passenger and freight transport, modal and vehicle choice relative to a transport and emissions baseline. Thereby, the model relies on a certain assumption of how people and firms behave “on average”. For non-work and commuting passenger trips, transport demand and modal choice is determined by generalized prices and observed consumer preferences. In case of freight transport and business trips, the desired production quantities and substitution possibilities between transport modes and with other production factors are taken into account. In TREMOVE, the choice of transport type is modelled with a nested utility function. The represented discrete choice process has 10 levels. The complete “private” decision tree is disaggregated into 136, the complete “business” decision tree in 140 different types of transport possibilities. In the business tree 68 choices are related to passenger trips and 72 to freight transport.

2.1.2 The vehicle stock module

The vehicle stock module is directly linked to the transport demand module and depends on the vehicle kilometres that are quantified within the demand module. As explained in section 2.1.1 the transport demand module delivers the figures separate for each vehicle category on each road type and according to the time of the day (peak/off-peak). In the vehicle stock module, for each vehicle category, the evolution of the detailed vehicle fleet and the related number of vehicle-km is estimated, based on three major elements:

• the historical vehicle fleet • the growth of the transport volumes • the characteristics of the available vehicle types and technologies on the market The analysis of the number of vehicles in each vehicle type and category can be roughly described by three calculation steps. In the first step, the surviving stock of the vehicles has to be quantified. In the second step, the new sales of vehicles (be aggregate category) have to be assessed by adding the necessary number of vehicles to the surviving stock in order to meet the overall number of driven km in the respective years. In the third step, the overall number of vehicle sales by category has to be broken down into the different detailed vehicle types that are included in TREMOVE. For cars, this is done by using a sales logit model in which market shares are determined by the parameters lifetime driving price, acceleration, engine size and fuel type.

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2.1.3 The fuel consumption and emissions module

Based upon the detailed vehicle stock characteristics and the number of vehicle-km, TREMOVE calculates the emissions per region, period and network type. TREMOVE covers several pollutants, e.g. CO2, NOx and SO2. A variety of methods is used to calculate the emissions included in TREMOVE. The methods depend on the pollutant, the transport mode and the vehicle type. For detailed information see the description of TREMOVE on www.tremove.org.

In case of road transport emissions and fuel consumption, TREMOVE is based upon the Copert III emission calculation.1 This method is modified in TREMOVE for the calculation of road transport emissions e.g. TREMOVE differentiates the fuel consumption for diesel passenger cars according to car size and takes into account:

• the effect of airco on fuel consumption, • biofuels and CNG • adaptations to COPERT fuel consumption factors based on the CO2 monitoring dB • … Because the “pre-processor” emissions will become more and more substantial in the next years, a lifecycle emission analysis is incorporated in the model. The lifecycle emission module calculates the “pre-processor” emissions for all relevant fuels that are and will be used in the following years in the road transport sector and additionally electricity, kerosene, train diesel and gasoil for the other transport sectors.

2.1.4 The welfare module

The difference in social welfare resulting from a policy analysis is calculated as the sum of four components that have to be expressed in monetary terms in order to be able to compute an overall impact on social welfare. The first two components are known as the producers’ and consumers’ surplus and are described as changes in utility of households and as changes in production costs. TREMOVE calculates these differences for the simulation run versus the baseline run. The functional form is a nested CES function for the quantification of the effect on consumers and producers. The third component is the change in government revenues. Taxes and VAT are calculated at the lowest nodes in the CES tree in the demand module. A value for base case and simulation case is for each year transferred to the welfare module. This value is adapted

1 Copert = COmputer Program to calculate Emissions from Road Traffic, a computer program

developed in the framework of European Environment Agency’s CORINAIR project. Ntziachristos L., Samaras Z. (2000) Copert III computer programme to calculate emission factors from road transport. Modelling emission factors (version 2.1). Report to the European Environment Agency.

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for the value of marginal cost of public funds. The value of marginal cost of public funds expresses that different forms of taxes have different efficiencies. In TREMOVE we assume that the transport taxes replace labour or general taxes, or in other words, if no transport taxes were raised, labour or general taxes should be higher. The fourth component is the change in external effects. The external effects that are covered by the model are congestion costs and several environmental costs relying on the emission model. Positive external effects are not included in the model.

2.1.5 Methodology for the Impact Assessment on transport sector

The assessment at the transport sector level is achieved by providing a standard output from the TREMOVE runs. This standard output includes

• absolute and relative changes in fuel consumption per fuel type relative to the base case

• absolute and relative changes in emission reductions relative to the base case • absolute and relative changes in vehicle sales and stocks relative to the base case • changes in fuel mix and size structure • volume and percent changes in traffic demand relative to the base case by mode and

vehicle type • welfare impact and decomposition of cost to society (including impact on public

budget) • changes in generalised prices for transport (urban/rural), changes in average speed

(road category) In this interim report, the assessment on the transport sector level reports relative changes to the base case for transport demand, fuel consumption and the stock of passenger cars. For the final report this analysis is extended for absolute changes as well as for the remaining indicators listed above.

2.1.6 Extension of the TREMOVE output to EU25

The impact assessment of this project focuses on the EU25 member states. At the moment, the output of the TREMOVE model covers all EU15 members and 4 (CZ, HU, PL, SK) of the EU10 countries. For the impact assessment at the EU25 level it is therefore necessary to make some assumption in order to translate the TREMOVE output into an EU25 picture to assess the impact on the transport and society level.

Of course, we are aware of the fact that a further extension of the EU member states by Bulgaria and Romania is expected in 2007. The extension of the TREMOVE output to EU27 could be reached in a similar way as for the EU25 countries (see the explanation below). However, Bulgaria and Romania are relatively small countries with respect to

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their GDP compared to the EU25 as a whole.2 Consequently, in a macroeconomic model like PACE-T the extension by these two countries will have a fairly small additional impact on the simulation results for the EU as a whole. Therefore, this analysis abstracts from the extension to EU27 and only focuses on the EU25 region.

Since the TREMOVE output covers only 19 of the EU25 countries, it is necessary to extent these figures for the six remaining countries CY, EE, LT, LV, MT, and SK. Assuming that the EU10 countries reveal very similar structures with respect to transportation demand, we map these six countries to the four EU10 members covered in TREMOVE

In a first step, the number of vehicles in the stock is quantified between the years 2000 and 2020. For the countries not covered in TREMOVE, the year-2000 stocks of passenger cars are adopted from UN (2001) and IRF (2002). Table 2.15 reports the figures on car stocks in 2000 for all EU10 countries.

Table 2.1: Vehicle Stock in the EU10 countries in 2000 (in 1.000 cars)

CY CZ EE HU LT LV MT PL SI SK

267,6 3484,5 463,9 2629,3 1172,2 556,3 189,1 11040,0 849,6 1274,2

Given the stocks of passenger cars in 2000, we calculate the ratio α of vehicles in all EU10 countries to the number of vehicles in CZ, HU, PL, and SK as follows:

2000; 10

2000; , , ,

EU

CZ HU PL SK

vehiclesvehicles

α = .

This ratio is used to update the number of passenger cars between 2001 and 2020 for the whole EU10 region by taking into account the development of car stocks in the four regions covered in the TREMOVE output, i.e.

; 10 ; , , ,t EU t CZ HU PL SKvehicles vehiclesα= ⋅ , where 2001,..., 2020t = .

With respect to the assessment on society level, the input data for passenger cars in the PACE-T model (i.e. purchase prices, fuel prices, fuel consumption, other costs) are yearly averages for the representative car in each country/region, as explained in section 0. Due to the lack of data, we again have to make a simple assumption for those EU10 countries which are not covered by TREMOVE. As an approximation for the whole EU10 region, we simply take the weighted averages of the input data for CZ, HU, PL,

2 In 2000 GDP in Bulgaria amounted to 13,7 bill. € and in Romania to 40,3 bill. € while GDP in the

EU25 region was 9057,5 bill.. € (see: http://europa.eu.int/comm/economy_finance/ indicators/annual_macro_economic_database/ameco_applet.htm). Taken GDP in Bulgaria and Romania together, it is only 0,6 percent of GDP in the EU25.

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and SK reached from the TREMOVE output. Take for example average purchase prices in the EU10, these are reached as

; ;

; 10;

t i t ii

t EUt i

i

avpprice vehiclesavpprice

vehicles

⋅=∑

∑

where 2000,..., 2020t = and , , ,i CZ HU PL SK= .

With respect to the assessment at the transport sector, further assumptions are necessary when translating the TREMOVE output into an EU25 picture. As far as relative changes are considered (with respect to 2000 or with respect to the baseline), changes for the whole EU10 region (or for the single countries) are simply reflected by the changes in the four Eastern European countries given in the TREMOVE output.

As far as absolute changes in the transport sector are analysed, we calculate for each indicator (vkm, pkm…..) the weighted average over the four EU10 countries covered in TREMOVE multiply the respective values of the four EU10 countries Czech Republic, Hungary, Poland and Slovenia with a multiplying factor to get the EU10 values. This multiplying factor corresponds to the ratio from the four EU10 countries at the EU10 and is based on the 2000 figures (European Commission 2005). Of course, this assumption is very ad-hoc. However, first it is almost impossible to find predicted data on all indicators from one consistent source. Second, given that this multiplying factor is almost constant with respect to all indicators in the EU10 of 2000 and given further uncertainties about future developments that can never be covered by any model, we think that this ad-hoc assumption is nevertheless justified.

2.2 PACE-T

The policy simulations in this study are carried out within the dynamic computable general equilibrium (CGE) model PACE-T. General equilibrium models are widely used to quantify the economic impacts of various policy interventions (e.g. international trade, tax policies, climate policy in general). In the following the general features of the model are firstly outlined. Afterwards the specific modelling of the transport sector is discussed.

2.2.1 Model description

PACE-T is a multi-regional, intertemporal general equilibrium model of the world economy which is based on the fundamental GTAPinGAMS structure (Rutherford 1998). The concrete analysis of different regulation measures to reduce CO2 emissions from passenger cars mainly focuses on the European area. Therefore, the model exhibits a relatively large disaggregation of the European countries (eight regions) while the

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remaining world is highly aggregated (three regions). An overview of all regions in PACE-T is given in Table 2.2.

Table 2.2: Regions in PACE-T

DE Germany REU15 Rest of EU15

ES Spain EU10 New EU member states

FR France USA USA

IT Italy JAP Japan

NL Netherlands ROW Rest of the world

UK United Kingdom

The sectoral disaggregation in PACE-T is comparable to other models with the focus on energy policy analysis. The full input-output decomposition of national accounts has been re-aggregated into 12 sectors (see Table 2.3; this is similar to Rutherford and Paltsev 2000). This sectoral disaggregation reflects the special relevance of energy resources and energy intensive economic sectors for climate protection.

Table 2.3: Sectoral Disaggregation in PACE-T

AGR Agrar OIL Oil

SER Services COA Coal

MAN Manufacturing GAS Gas

EIS Energy intensive production PC Refined oil products

TRN Transport services and trade ELE Electricity

MVH Motor vehicles and parts and transport equipment CGD Aggregate savings good

The model is set up as a full dynamic model with optimal savings and investment decisions. Period length is chosen to be four years with the model horizon extending from 2006 to 2030. The implementation of 4-year periods allows an accurate assignment of the Kyoto CO2 targets in 2008 and 2012.

International trade is modelled in the Armington fashion, i.e. goods produced in different countries are treated as imperfect substitutes and their import shares depend on their relative prices (Armington 1969). The only exception is the international crude oil market, which has been homogenised so that every country either imports or exports crude oil.

The model is formulated as a system of nonlinear inequalities using GAMS/MPSGE (Rutherford 1999) and solved using PATH (Dirkse and Ferris 1995). The inequalities correspond to the three classes of conditions associated with a general equilibrium: (i) exhaustion of product (zero-profit) conditions for constant returns to scale producers, (ii) market clearance for all goods and factors, and (iii) income balance for the representative consumers in each region. The fundamental unknowns of the system are three vectors: activity levels (production indices), non-negative prices, and consumer

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incomes. In equilibrium, each of these variables is linked to one inequality condition: an activity level to an exhaustion of product constraint, a commodity price to a market clearance condition, and a consumer income variable to an income definition equation. An equilibrium allocation determines production, prices and incomes.

For each region the model considers one representative household who maximises the present value of his lifetime income given the following constraints: (i) an intertemporal budget restriction, (ii) exhaustion of production in each period by consumption (domestic or export) and investment, and (iii) the equation of motion for the capital stock, i.e. capital stocks evolve through depreciation and new investment. The solution to the optimisation problem of the household yields an optimal path for consumption over time.

All essential functions in the model are of the nested constant elasticity of substitution (CES) type with extreme cases Cobb-Douglas (CD) and Leontief (L). The derivation of cost and demand functions is very technical. We therefore use in the following the simplified notation Output = CES(Input).

Production of commodities in each region is captured by aggregate production functions which characterise technology through substitution possibilities between various inputs. Nested CES cost functions with three levels are employed to specify the substitution possibilities in domestic production between capital, labour, energy and non-energy, intermediate inputs. The production function has the following structure:

Y = L(intermediate inputs, crude oil, CES(energy, CES(primary inputs))),

where

energy = CES(electricity, CES(coal, CES(gas, refined oil))).

At the top level, non-energy inputs are employed in fixed proportions with crude oil and an aggregate of energy, capital, and labour. At the second level, a CES function describes the substitution possibilities between the energy aggregate and the aggregate primary inputs. Finally, at the third level, the elasticity of substitution between capital and labour is constant. As to the formation of the energy aggregate, we allow sufficient levels of nesting to permit substitution between primary energy types, as well as substitution between a primary energy composite and secondary energy, i.e. electricity.

On the output side, total domestic production splits up into domestic use and exports. With respect to domestic demand, the model assumes a hypothetical Armington commodity A which incorporates domestic and foreign goods, i.e.

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A = CES(domestic production, CES(foreign production from other countries)).

Domestic demand is composed of final demand of private and public consumers and intermediate demand from other sectors. All demand fractions are of the same structure as the Armington commodity.

Final demand C of the representative agent is given as a CES composite of transport services (see explanations below) and an aggregate of other consumption commodities:

C = CES(passenger transport services, other consumption commodities aggregate).

The consumption commodities aggregate CC combines consumption of an energy aggregate E with a non-energy consumption bundle:

CC = CES(CES(non energy commodities), E).

The energy aggregate in final demand consists of the various energy goods trading off at a constant elasticity of substitution, i.e.

E = CES(refined oil products, coal, gas).

With respect to the intertemporal structure of the model, we adopt the common assumptions on the accumulation of productive capital: Capital in each period is composed of the capital from the previous period (less depreciation) plus new investment. Depreciation and capital maturation rates have been adjusted to match the requirements of the 4-year periods in the model. The capital stock produces proportional capital services each year that enter the production functions. The proportionality factor has been calibrated according to the baseline interest rate. Investment into conventional productive capital is a Leontief-composite of output of all productive sectors with the share parameters determined through baseline investment.

An essential feature of the transport sector in PACE-T is the modelling of passenger cars as durable consumption goods. The representative household does not consume cars as such but transport services TRN. These services are produced with various inputs which are mainly composed of capital services of the automobile stock in the respective economy, fuel and other expenditures (for operation and maintenance). The production function for transport services has the following shape:

TRN = CES(operation and maintenance, CES(capital services, fuel)).

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The automobile capital stock is treated similar to the stock of productive capital, i.e. the stock of automobile capital is composed of the stock in the previous period less depreciation plus new investment. This captures the fact that adjustment of the average automobile characteristics is not instantaneous. Cars from earlier vintages contribute to a certain extent to the production of current transport services. The durability of cars is assumed to be three periods, i.e. 12 years. Technology coefficients are time varying depending on the development of fuel and other costs. Car exports are assumed to be transferred to a world market which is the pool for all imports of automobiles.

Besides the application of the sectoral, macroeconomic database GTAP5 (GTAP 2002), the calculation of the value of the automobile stock and the investment in new cars is calculated from TREMOVE data (see below). In order to preserve macroeconomic consistency, the consumption of passenger car transportation services has been subtracted from the aggregate consumption value of the respective national accounts.

2.2.2 Methodology for the Impact Assessment on society level

The impact assessment of the considered scenarios on society level is carried out by applying the PACE-T model. Changes in the considered scenarios are reported with respect to the base case. Referring to the Technical Annex, the impact assessment at society level is reached as follows:

• Changes in GDP, productivity, the disposable income of the representative household, capital and investment are a standard output in PACE-T. Additionally, we report changes in real consumption since this is an important driver of GDP development and overall welfare. Since there is no possibility to quantify aggregated effects on unemployment with PACE-T, changes in employment are reported as employment shifts between the different production sectors.

• International trade patterns are reflected in exports and imports which also serve as an indicator for the competitiveness of the considered countries. We therefore report changes in trade patterns (over all sectors) for the considered scenario. Additionally, since this study focuses on passenger cars, we also report the changes in car exports and imports.

• Impact on public finances are reported as far as they refer to changes in taxation at the transport level and, depending on the scenario, possible financing tools for subsidies. With respect to distributional effects associated with the considered scenarios, we follow the neoclassical theory of the functional distribution of income.3 This is reflected in the development of wage rates of workers and the interest rate for capital owners.

3 See Mankiw, G. (2003), p.46ff.

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• For an overall evaluation of the considered scenarios we report social welfare which is measured as an equivalent income variation.

2.2.3 Data input and calibration

Data on international trade: GTAP

Baseline data determine parameters of the functional forms from a given set of baseline quantities, prices, and elasticities4. The main data source for the calibration of national and international commodity flows is the GTAP5 database (GTAP 2002) which provides a consistent representation of energy markets in physical units as well as detailed accounts of regional production and consumption as well as bilateral trade flow. GTAP5 data are based on the year 1997 and are disaggregated into 65 regions and 58 production sectors. This disaggregation is partially reversed in PACE-T to generate the 11 regions and 12 sectors as described in the previous section.

Data on passenger cars: TREMOVE

Input data on passenger cars in PACE-T are harmonized with the data provided in the TREMOVE database v2.42. In order to calculate international car stocks and the average properties of cars, we selected all relevant data from the demand, emissions and stock tables of the TREMOVE database.

Since the focus of this project is on the reduction of CO2 emissions of passenger cars, we have taken into account only the following vehicle types5:

• PCGS small gasoline cars, • PCGHS small hybrid gasoline cars, • PCDS small diesel cars, • PCGS_CNG small natural gas cars, • PCGM medium gasoline cars, • PCGHM medium hybrid gasoline cars, • PCDM medium diesel cars, • PCDHM medium hybrid diesel cars, • PCGM_CNG medium natural gas cars, • PCGB big gasoline cars, • PCGHB big hybrid gasoline cars, • PCDB big diesel cars, • PCDHB big hybrid diesel cars, 4 Table 8.3 which gives an overview of the elasticities in PACE-T is provided in the Annex. 5 Small diesel hybrids are not introduced in the market in the current baseline and are therefore not

considered.

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• PCGB_CNG big natural gas cars, and • PCL medium and big LPG cars. All data selected from the TREMOVE database is aggregated in all essential dimensions in order to yield data in each country, for each period and vehicle type. Since PACE-T only incorporates one single car type (“all”), this data on the different vehicle types is further condensed to the single car category representing a typical car of the respective period and region.

The implementation of passenger cars into PACE-T requires in a first step the extraction of vehicle kilometres and the number of vehicles from the demand and stock tables of the TREMOVE database.

In order to calculate the value of the automobile capital stock, it is further necessary to compute average purchase prices. These were calculated in accordance to the TREMOVE model by applying 1995 road vehicle purchase resource costs and the price indices for road vehicle cost evolution. For unconventional cars (small diesel, hybrids, CNG, LPG) an increment is added to the purchase cost of their conventional equivalent. Given these purchase costs for all vehicle types, average car purchase costs are calculated as weighted averages over all car types. The value of the automobile capital stock is then simply the average car costs multiplied with the overall number of passenger cars (sum over all vehicles of the different car types).

For the calculation of fuel costs, fuel consumption as given in the TREMOVE emissions table was recalculated using the densities6 of the different fuel types in order to reach fuel consumption in litres for gas, diesel, CNG and LPG cars. Fuel costs are then calculated from road fuel resource costs per litre. The calculation takes into account the assumption that all petrol and diesel road vehicles use blended fuels, i.e. the share of biofuel in both petrol and diesel blends increases to 5,75% by 2010 (with a linear interpolation between 2005 and 2010). The share of biofuel remains at 5,75% beyond 2020.

All other costs are calculated on an annual basis from fix resource costs, variable resource costs, fix tax, variable tax, VAT on fix resource costs, and VAT on variable resource costs as selected from the TREMOVE demand table taking into account the computed purchase and fuel costs.

Finally, PACE-T incorporates taxes in accordance to the TREMOVE model. The calculation takes into account road fuel taxes, road fuel VAT for the different fuel types as well as VAT on car purchase and registration taxes. 6 The respective densities are assumed as 0.75 kg/l for gasoline, 0.85 kg/l for diesel, 0.8 kg/m3 for

CNG and 0.52 kg/l for LPG.

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The TREMOVE database does not cover all regions considered in PACE-T. In order to calculate the number of vehicles and all automobile costs, we had to map some countries to regions covered by TREMOVE. This mapping refers to the USA and Japan which are mapped with the EU15 countries and to ACE and ROW which are mapped with the new EU members (CZ, HU, PL, SI). Car stocks in 2000 in the USA, Japan, ACE and ROW are calculated from data provided in UN (2001) and IRF (International Road Federation, 2002). The growth rate for the car stock is assumed to be the same as for the countries with which these regions are mapped. All cost data is calculated as weighted averages from the countries with which the four PACE-T regions are mapped.

2.3 B&D Forecast model

2.3.1 Methodological overview

B&D studies the economic and business impacts on the car manufacturer (OEM), the supplier industry and the dealership in the EU-25 member states caused by the target of 140g CO2/km CO2 emission in 2008 and 120g CO2/km CO2 emission in 2012 and 130g CO2/km respectively.

At this step B&D analyses the impacts of the different measures on the automotive sector in the EU. The following chart illustrates the proposed procedure.

Figure 2.2: Schedule of B&D analysis

Source: B&D Forecast

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Step 1:

The above chart shows that in step 1, the EU car-market is split into sub-segments. Sub-segments are illustrated by squares in the chart. After the segments are defined, we study the impacts of the defined measures on the different car segments. Decisive variables at this step are new car registrations, which signalizes the car demand. To get a clear-cut picture of the measures on the different carmakers we have to define accurate car-market-segments. The following segmentation approach is used:

• EU25-total market • Major markets: Germany, UK, France, Italy, Spain (Total Market Analysis) • Product-price-segmentation for German Car Market.

Price groups according TREMOVE segmentation, i.e. small gasoline car (-1,4l), medium gasoline car (1,4 - 2l), big gasoline car (+2l), small diesel car (-1,4l), medium diesel car (1,4 - 2l), big diesel car (+2l).

New car registrations for a longer time series are available at B&D-Forecast. Using these registration time series data we estimate and simulate the impacts on the measures on the different car segments. For estimation and simulation we use experience of similar effects in the past, e.g. the reaction of car segments to the introduction of catalytic converters in the early 90ties. A comparison of competitive conditions between EU-car-market and US-car-market further allows evaluating impacts of regulations on EU-car-markets.

Step 2:

After the study of the regulatory impacts on the different market segments, next step of analysis is to deduct the implications of car demand on the different players. At this step we focus on different carmaker groups, supplier and car dealer/garages.

Again squares in Figure 2.2 marked by CD 1 (car dealer 1) to Sup n (supplier n), are used to illustrate the approach. Thereby, we cluster the player according to the following chart.

Figure 2.3: Players of the B&D analysis/simulation

Source: B&D Forecast

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Clustering in that manner allows deriving the impacts of the measures on the different players. At the level of the carmaker the following differentiation seems to be worthwhile.

• European Premium (Audi, BMW, Porsche, Mercedes, Landrover, Jaguar, Volvo, Saab)

• European Volume Manufacturer (Alfa-Fiat-Lancia, Opel, Ford, Renault, PSA-Peugeot-Citroen, VW-Skoda-Seat)

• Non-European with European Production Facilities (Toyota, Honda, Hyundai) • Non-European without European Production Facilities (Daewoo/Chevrolet,

Chrysler, GM, Ford-US, Mazda, Subaru, Suzuki) For suppliers and car dealers/garages we deduct generally the impacts on the different groups of enterprises (company-size).

2.3.2 Description of workflow of the B&D model / simulation

B&D studies the impacts of the different measures on the automotive sector in the EU. The findings of the simulations of TREMOVE and IEEP are starting point of B&D analysis. Therefore B&D all data will be integrated in the B&D forecast-tool FORCAR.

The results of the FORCAR simulations will be matched with the output of the TREMOVE and IEEP simulations and the Baseline scenario.

Figure 2.4: Description of workflow

Source: B&D Forecast

On the basis of that balance B&D considered the impacts on the different players (OEM, supplier and car-dealer) in the automotive sector. Which effects will arise on the specific business by increasing costs of CO2 emissions reduction? What changes in business are caused by increasing technology costs?

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Figure 2.5: Basic principle – overview

Source: B&D Forecast

2.3.3 Model description

B&D-Model starts with the different parameters given by IEEP, TNO or TREMOVE for the car market. Major parameters are time series of registrations according to the listing below.

Sub-Model OEM Effects

The objective is to transform these registrations in production volumes. Our model thus transforms registrations to production volumes for different manufacturer and vehicle segments/classes. The model further differentiates between EU15, EU 10 and rest of world production of carmaker. For that reason, we study the price-cost-relations given by TREMOVE, TNO assumption. Thus, if the margins are constant, i.e. if retail-price and induced cost will rise by the same percentage, the model assumes that allocation of production at OEM-level will show the same share. If OEM-margins decrease, the model assumes a decreasing share of OEM-production in EU15. Thus production is shifted to EU10.

Our base model thus transfers registrations into production volumes in EU10, EU15. Having done this, we have explained the effect of 120g CO2 legislation on allocation of car production.

Sub-Model Supplier Effects

Allocation of car production is essential to determine allocation of supplier production. The model transfers therefore production volumes of OEM to supplier production at the different levels of supplier (tier1, tier 2, ,…) Essential for the allocation of the supplier production are again price-cost-margin effects. So given, the TNO, TREMOVE results

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show a constant development of OEM-Margin (technology cost is 100% included in retail price) no additional competition and price-pressure on OEM side is given compared to the situation without decrease of CO2 emissions. Our model thus is based on production elasticities.

Sub-Model Dealer Effects

Performance of dealerships is due to two variables: new car sales and maintenance services. Registration forecast of TNO directly provide sales figures for new cars for the different brands and dealerships of the brands. Development of retail prices is essential to the dealer margins for new cars. Thus we again use the above mentioned retail-technology cost results of TNO. If margins decrease the model assumes a concentration process in dealer net. Again the model is driven by an elasticity which defines the conversion rate of margins into concentration.

The second important variable to define dealer profit is maintenance services. For our model the car transport demand in kilometres thus defines the demand for maintenance. Car transport demand is given by TREMOVE baseline scenario. In addition the model integrates the demand for more expensive lubricants and tires. Again the model takes reacts on margins for that product, i.e. if retail price of lubricants will increase by the same percentage as cost, no margin-effect will occur. Thus the model calculates the profit-loss effects of the 120g CO2-policy on dealer service income

Taking the two effects – new car profits per dealership and maintenance profit per dealership – in account, the model results in total dealer profit situation derived from the started policy measures.

Our conversion rate of total dealership margins thus explains the concentration in the car dealership due to the policy measures.

The development of car sales in the EU25, the development of the retail price structure as well as the development of the manufacturers’ costs structure (production- , retail and sales-costs) as basis.

The changing price structure in the EU-market will lead to changes in the behaviour of the consumers and in consequence to a new split of sales volumes between different segments.

The main variables defined within the model are illustrated by the following descriptions

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Table 2.4: Sales volume split by segment

2000 2005 2008 2012 2020

Large Cars x1 x2 x3 x4 x5

Medium Cars y1 y2 y3 y4 y5

Small Cars z1 z2 z3 z4 z5

Table 2.5: Segment split divided by fuel-type

2000 2005 2008 2012 2020

Large Cars x1 x2 x3 x4 x5

Gasoline x11 x21 x31 x41 x51

Diesel x12 x22 x32 x42 x52

CNG x13 x23 x33 x43 x53

Hybrid x44 x24 x34 x44 x54

Medium Cars y1 y2 y3 y4 y5

Gasoline y11 y21 y31 y41 y51

Diesel y12 y22 y32 y42 y52

CNG y13 y23 y33 y43 y53

Hybrid y14 y24 y34 y44 y54

Small Cars z1 z2 z3 z4 z5

Gasoline z11 z21 z31 z41 z51

Diesel z12 z22 z32 z42 z52

CNG z13 z23 z33 z43 z53

Hybrid z14 z24 z34 z44 z54

All sales volume data in the simulation are on a yearly base 2000 to 2020 and provided by country or summed by EU 15 / EU 10 split or EU-total market.

Table 2.6: Retail price increase by segment

2000 2005 2008 2012 2020

Large Cars ∆p1L ∆p2L ∆p3L ∆p4L ∆p5L

Medium Cars ∆p1M ∆p2M ∆p3M ∆p4M ∆p5M

Small Cars ∆p1S ∆p2S ∆p3S ∆p4S ∆p5S

Table 2.7: Transportation by segment (in km)

2000 2005 2008 2012 2020

Large Cars d1L d2L d3L d4L d5L

Medium Cars d1M d2M d3M d4M d5M

Small Cars d1S d2S d3S d4S d5S

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Table 2.8: Turnover Supplier industry

2000 2005 2008 2012 2020

Supplier Total u1 u2 u3 u4 u5

TIER 1 u11 u21 u31 u41 u51

TIER 2 u12 u22 u32 u42 u52

…

TIER 5 u15 u25 u35 u45 u55

Note: u = ulargecars + umediumcars + usmallcars

Table 2.9: Sales Volume Dealership

2000 2005 2008 2012 2020

Dealership Total w1 w2 w3 w4 w5

Large Dealer / Dealer-Groups w11 w21 w31 w41 w51

Medium Dealer w12 w22 w32 w42 w52

Small Dealer w13 w23 w33 w43 w53

Note: w = wlargecars + wmediumcars + wsmallcars

2.3.4 Input Parameters of the B&D Forcar Simulation

B&D uses a number of different input parameter/date for their simulations. Some of the data required for the simulation are provided by project-partners (TNO/Task A, TML or ZEW other data are provided by the B&D Forecast data pool and the B&D simulation tools like FORCAR.

The FORCAR Market Analyst Tool allows the explicative forecast of different markets. All factors which have an important impact on the car market development are taken into consideration. The FORCAR Market Analyst calculates the impact of each explicative factor for the market or a segment of the market.

For the simulation of the “Impact assessment for the automotive sector in the EU” the following input factors are necessary. Basic output data split of brands and segments:

• Sales volumes per manufacturer per segment • Retail price increase and net costs to the consumer per manufacturer per segment for

reaching 140 g CO2/km in 2008 • Retail price increase and net costs to the consumer per manufacturer per segment for

reaching 120 g CO2/km in 2012 • Total technology costs to manufacturers (excl. taxes & margins, excl. fuel savings)

manufacturer (2008 to reach 140g CO2/km) • Total technology costs to manufacturers (excl. taxes & margins, excl. fuel savings)

per manufacturer (2012 to reach 120g CO2/km)

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Summary by segment: The basic output data split of brands and segments lead to a summary by segment. The B&D standard segmentation and is normally divided by body and size is summarized to small, medium and large cars.

Table 2.10: Allocation to Segments

Small Medium Large

Mini x

Small x

Lower Medium x

Medium x

Upper Medium x

Luxury x

Sport x

MPV x

Vans x

Off-Road / SUV x

Pick-Up x

The integration of the fuel-type / powertrain delivers the following market segmentation for the simulation

Small gasoline

Medium gasoline

Big gasoline

Small diesel

Medium diesel

Big diesel

Small CNG

Medium CNG

Big CNG

Small gas Hybrid

Medium gas Hybrid

Big gas Hybrid

Medium diesel Hybrid

Big diesel Hybrid

BMW DaimlerChrysler Fiat Ford GM Porsche PSA Renault Volkswagen ACEA Total Toyota Nissan Mitsubishi Honda Mazda Suzuki Subaru JAMA Total Hyundai Chevrolet / Daewoo KAMA Total Small Eur. Manuf. Other imports

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Development of sales / retail costs and margin

Higher technology costs lead to costs pressure. So the development of sales and retail costs as well as the margin is another important factor.

Development of production and site factors

An increasing cost pressure means it is important for car manufacturer to cut down production costs. Therefore the question of the cheapest production site is fundamental. Only a “healthy cost structure” will guarantee a strong market position in the future. So the decision where to build the cars of the future is influenced by the following costs factors:

• labour costs • logistic costs • costs of capital • environmental requirements and costs • aid payments and regional promotion These listed factors are followed by the decision of equipment investments of car manufacturer and supplier.

Relationship car manufacturer and supplier

Car manufacturers and suppliers have a strong relationship. Over the years supplier more and more played the role of a technology partner on the one hand and a parts, component or module supplier on the other hand. But the supplier industry is strongly connected to the OEM on the cost side as well. Higher cost pressure to the manufacturer means higher cost pressure for the supplier, too. Consequently, lower sales volumes of the OEM leads to lower trading volume of supplier industry. Thus, we analyse the trade volume and the market structure of the supplier industry. Furthermore the reallocation added value split OEM and supplier (in %) is an important factor in the simulation.

Exchange rate €/$

For the European car manufacturers – especially the premium brands – the export markets play an important role to cover decreases of inner European sales by exports. Therefore the development of the exchange rate between the Euro and the dollar is another factor in the simulation.

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That factor leads to an analysis of the volume car export of European manufacturer (OEM ACEA) and the volume car import of European manufacturer (OEM ACEA) from their plants outside EU e.g. see BMWs production site in Spartenburg, USA or Mercedes-Benz site in Tuscalosa, USA.

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3 Baseline

3.1 TREMOVE

3.1.1 Exogeneous transport baseline

The TREMOVE baseline is based on output of the European transport model SCENES, combined with specific data from some Member States, and consistent in the basic macro-economic and demographic hypothesis with DG TREN Energy Outlook 2030. The overall population growth rates for EU25 are also provided by DG TREN (2005) and Eurostat (for FR and IT). The key assumptions are summarised in the table below:

Table 3.1: Average annual growth rates of GDP and Population: EU25 GDP Population 2000-2010 2010-2020 2000-2020 2000-2010 2010-2020 2000-2020

AT 1,93% 1,95% 1,94% 0,30% 0,22% 0,26% BE 2,02% 2,03% 2,02% 0,30% 0,22% 0,26% DE 1,24% 1,69% 1,46% 0,08% -0,02% 0,03% DK 1,66% 1,52% 1,59% 0,24% 0,11% 0,17% ES 2,76% 2,64% 2,70% 1,11% 0,21% 0,66% FI 2,34% 1,88% 2,11% 0,23% 0,21% 0,22% FR 1,95% 2,06% 2,01% 0,46% 0,33% 0,4% IE 5,04% 3,54% 4,29% 1,3% 0,96% 1,13% IT 1,19% 2,02% 1,61% 0,29% -0,06% 0,12% LU 4,05% 4,83% 4,44% 0,84% 0,88% 0,86% NL 1,42% 1,83% 1,63% 0,46% 0,32% 0,