Perspectives on decarbonizing the transport sector in the EU-28

Energy Strategy Reviews 20 (2018) 124e132

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Energy Strategy Reviews journal homepage: www.ees.elsevier.com/esr

Perspectives on decarbonizing the transport sector in the EU-28
mez Vilchez b, Robert Kunze b, 1, Paul Deane c, Thomas Haasz a, Jonatan J. Go d David Fraboulet , Ulrich Fahl a, *, Eamonn Mulholland c a

Institute of Energy Economics and Rational Energy Use (IER), University of Stuttgart, Heßbrühlstraße 49a, 70565 Stuttgart, Germany Institute for Industrial Production (IIP), Chair of Energy Economics, Karlsruhe Institute of Technology, Hertzstr. 16, 76187 Karlsruhe, Germany c Environmental Research Institute, Energy Policy & Modelling Group, University College Cork, Lee Road, Sunday's Well, Cork, Ireland d  l'Energie Atomique, A, P120, 17 rue des Martyrs, 38054 Grenoble Cedex 9, France Cabinet du Haut Commissaire a b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 July 2017 Received in revised form 24 November 2017 Accepted 18 December 2017

The transport sector is of great importance at a global level in order to become a low-carbon economy by 2050. In the European Union the transport sector accounts for 20% of anthropogenic greenhouse gas emissions. Electric propulsion systems might be a feasible solution for greenhouse gas mitigation in the transport sector. Based on our cost assumptions, grid-connected electric vehicles play no major role in the analyzed scenarios until 2030 but reach high market shares (over 90%) under stringent greenhouse gas mitigation targets until 2050. Renewable electricity plays a crucial role in providing the additional power needed in the transport sector. Financial incentives seem to be effective in order to reach the cost optimal car mix in France and Germany. © 2017 Published by Elsevier Ltd.

Keywords: Transport Greenhouse gas mitigation Renewable energy Energy system optimization Policy analysis

1. Introduction In 2015, the transport sector played a major role in the EU in terms of energy use (33% of final EU-28 energy consumption) and emissions (20% of EU greenhouse gas (GHG) emissions, mostly CO2) [1,2]. The transport sector experienced a growth in its emissions from fuel combustion in contrast to other major energy sectors, e.g. industry, from 1990 to 2007 and a slow decrease until 2015 [2]. Moreover, traffic emissions are globally a major cause of local air pollution, most notably nitrogen oxides, carbon monoxide, and volatile organic compounds [3]. As in any area with intensive energy use, measures to achieve the EU climate and energy policy objectives must be pursued in the transport sector. Freight and passenger transport are crucial elements for the vitality of the economy. Within the transport sector, final energy demand is dominated by road vehicles. The technical potentials for

* Corresponding author. E-mail addresses: [email protected] (T. Haasz), jonathan.
mez Vilchez), [email protected] (R. Kunze), jp.deane@ [email protected] (J.J. Go ucc.ie (P. Deane), [email protected] (D. Fraboulet), [email protected] (U. Fahl), [email protected] (E. Mulholland). 1 Energy System Analysis Associates e ESA2 GmbH, Albert-Nestler-Str. 21, 76131 Karlsruhe, Germany. [email protected]. https://doi.org/10.1016/j.esr.2017.12.007 2211-467X/© 2017 Published by Elsevier Ltd.

increasing the efficiency of internal combustion engine technologies are limited. The main approach for resource conservation and emission reduction, while ensuring the to-date achieved high quality of mobility, is the substitution of conventional fossil fuels by renewable and lower-emission energy carriers [4,5]. In the 'Europe 20200 communication [6], the EU has set the overall objectives of 20% reduction of CO2 emissions, 20% of renewable energy and 20% improvement in energy efficiency by 2020 (reference year: 1990). The 2011 Transport White Paper [7] puts forward GHG emission reductions for the transport sector by 20% in 2030 (reference year: 2008) and 60% by 2050 (reference year: 1990). In its 2014 climate-energy package communication [8], the EC has put forward goals for 2030: 40% GHG reduction compared to 1990 and 27% share of renewables of EU energy consumption. Furthermore, the EC has recently proposed a 27% improvement in energy efficiency [9]. The EU's Renewable Energy Directive (RED) sets binding targets of 20% (16.7% in 2015 [10]) for gross final energy consumption and 10% (6.7% in 2015 [10]) for transport fuels to come from renewable energy sources by 2020. Liquid biofuels in road transport are expected to make the largest contribution to the target of 10% renewable energy share in the transport sector. However, progress on the targets has been challenging. Currently, almost 80% of biofuel is biodiesel used in road transport.

T. Haasz et al. / Energy Strategy Reviews 20 (2018) 124e132

The objective of this paper is to outline the status quo of renewable energy in the transport sector regarding the EU goals and to analyse pathways to reach the targets. In particular, a modelbased analysis is conducted to show possible least cost evolutions in the transport sector to achieve the EU goals with a focus on emission reductions in French and German road transport. We focus on France and Germany since the transport sector in these two countries had the highest GHG emissions in the EU-28 [2]. Three scenarios (Continuation of Current Policy, Low Commitment (LC) of Member States (MS) and High Commitment (HC) of MS) for the years 2020, 2030 and up to 2050 are analyzed and their effects on the energy system and GHG emissions are quantified. The modelling exercise is grounded on an optimization model (TIMES PanEU) and a simulation model (TE3). TIMES PanEU minimizes the objective function representing the total discounted system costs over the time horizon from 2010 to 2050. In addition to the optimization model, the system dynamics method is applied to perform simulations and policy analysis using TE3. In this dynamic model, four policy instruments have been examined to approximately arrive at the car mix obtained in TIMES PanEU. The instruments represented in TE3 are emission standards for new conventional cars, taxing conventional fuels, subsidising electric cars and investing in public refuelling/recharging infrastructure. In the remainder of the paper we first introduce measures to reduce GHG emissions in the transport sector (section 2). Afterwards, the two models used to conduct the analysis are introduced together with the general scenario assumptions (section 3). Section 4 is split into the model results for the entire energy system of the EU-28 as well as the model results for the simulations on the car stock in Germany and in France. The last section (section 5) summarizes the results and points to future research needs. 2. Measures to decarbonize the transport sector Conventional combustion engines are by far the predominant propulsion system in Europe's transport sector. Since the fuels are mostly based on fossil energy carriers they face important drawbacks such as high import dependency, or CO2 and NOx emissions. In order to minimize the associated negative impact of fossil fuel use, we distinguish between three measures for decarbonizing the transport sector. Firstly, improving fuel efficiency can be a shortterm measure to reduce GHG emissions from fossil fuel usage. Secondly, nearly carbon neutral fuels (well-to-wheel) (i.e. biofuels) that emit as much CO2 as was captured while the feedstock grew. In combination with greater fuel economy this can have additional benefits in terms of resource consumption. A drawback of biodiesel usage is generally higher NOx emissions, which are due to effects of the fuel on factors such as injection timing and ignition delay [11]. Thirdly, energy carriers that do not result in GHG emissions (tankto-wheel) while being utilized in a vehicle, such as electricity, are considered [12]. Passenger cars have experienced an improvement in fuel economy over the course of the last decade, largely following the adoption of the EU CO2 performance standards for new passenger cars [13] limiting new car emissions to 95 gCO2/km by 2021. However, there is evidence that cars have higher emissions in reality than shown in standardized tests [14]. An alternative to the technical approach for increasing fuel efficiency are higher occupancy rates which would reduce emissions per person kilometer if vehicle kilometers are reduced [15]. Biofuels face a variety of challenges. The resource potential for Bioenergy in 2020 in the EU-28 is limited to 267 Mtoe according to the Reference Bioenergy Scenario by JRC [16]. To put this into perspective, final energy consumption in the transport sector in the EU-28 was 359 Mtoe in 2015 [1]. In addition, there are limitations

125

with respect to the use of biofuels in conventional internal combustion engines (ICE) at present. Both EN 590 and TS 15940 diesel state an upper limit of 7% of fatty acid methyl ester (FAME) content, more commonly referred to as biodiesel. Hydrotreated Vegetable Oils (HVO) are a more recent development and can be used in EN 590 diesel fuel without any limit [17]. Hydrogen electrolysis account for an efficiency of approximately 60% and electric fuel cell efficiency is typically slightly above 50%. The overall conversion is thus ranging from 30% to 45%. This seems modest in comparison to battery system efficiency (above 80%), but there may be applications where the overall conversion efficiency might be outweighed by other benefits. Firstly, hydrogen allows for long-term energy storage and may be produced by excess intermitted renewable electricity in future that would otherwise be curtailed. Secondly, hydrogen fuel cells can be seen as a good compromise between fuel range and global efficiency which is at least on par with conventional combustion engines while maintaining the advantages of pure electric propulsion such as being noiseless and emission free. However, fuel cell electric vehicles might be more expensive in comparison to hybrid fuel cell electric vehicles or battery electric vehicles [18]. The conversion from electrical to mechanical energy and viceversa is highly efficient. Introducing the electric energy vector at least somewhere along the on-board propulsion system allows alleviating various drawbacks of conventional combustion engines. For example, mechanical energy recovery becomes possible in the deceleration phase. Furthermore, electric vehicles might enable vehicle to grid storage and thereby supporting large share of intermitted renewable electricity generation in the European electricity system if the findings of [19] for Germany can be generalized for the EU-28. In Addition to GHG mitigation, electric mobility might become cost-competitive due to fast decreasing battery prices by 2030 for various vehicle categories and users with high annual mileages. Given strong cost reductions for Li-ion batteries in the long-run, full electric cars could become cost-optimal for every type of user and car [20]. 3. Methodology and scenario framework For the analysis of both, the energy system and the transport sector in the EU-28, the two model applications the Pan-European TIMES model (TIMES PanEU) and the TE3 (Transport, Energy, Economics, Environment) model are used, which are briefly introduced below. A more detailed presentation of the two models can be found in Kunze et al. [21]. Firstly, TIMES PanEU which was developed in a collaborative effort within the EU-funded NEEDS project [22], is used to determine cost optimal development pathways for the three analyzed scenarios within this study. TIMES PanEU is a multi-regional model containing all countries of the EU-28 plus Switzerland and Norway. The model minimizes the objective function representing the total discounted system costs over the time horizon from 2010 to 2050. Perfect competition among different technologies and pathways of energy conversion as well as perfect foresight are assumed in the model. TIMES PanEU covers on a country level all socio-economic sectors connected by energy supply and demand, namely the supply of resources, the public and industrial generation of electricity and heat, as well as the end use sectors industry, commerce, agriculture, households and transport (for a detailed description of the sectors and data please see Refs. [23e25]). For all relevant technologies and energy carriers, GHG emissions (CO2, CH4, N2O) are modelled in TIMES PanEU. Secondly, the cost optimal GHG mitigation pathways from the TIMES PanEU model are used as framework conditions for policy analysis. For this purpose, the TE3 model is used. TE3 is a multi-

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country computer simulation model capable of generating scenarios as well as of facilitating policy analysis, particularly in the context of electric car market uptake [26]. Core to the TE3 model, which is grounded on system dynamics, is the explicit consideration of feedback loops. TE3 is structured in nine interlinked modules: population-GDP, car stock, technology choice, travel demand by car, infrastructure, production costs, energy, emissions, and policy. Nine car technologies and seven types of fuels are represented in TE3. The model is solved in Vensim® using Euler numerical integration with a one-year time step. In this work, the TE3 model focuses only on the passenger car market in France and Germany between 2010 and 2030, since these two countries had the highest GHG emissions in the transport sector in 2015 [2]. In this regard, TE3 complements, to some extent, the analysis produced by the TIMES PanEU model. In taking TIMES Pan EU as a reference, the purpose of TE3 is not to match the results in an accurate manner but to approximate the main patterns, by incorporating policy variables in the modelling exercise. Table 1 shows the main scenario assumptions which are consistent throughout the three analyzed scenarios. The analysis of renewable energies in the transport sector of the EU-28 as a whole and for selected member countries is based on different levels of greenhouse gas mitigation targets as well as sectors covered by these targets. While the Current Policy scenario aims at a reduction of GHG emissions in the European Emission Trading Scheme (ETS) of 75% by 2050 it does not include an economy wide GHG emission reduction target. In contrast, in the Low Commitment (LC) and the High Commitment (HC) scenarios, the ETS is replaced by an economy wide GHG emission reduction target after 2030. In 2050 a GHG reduction of 80% and 90% is to be achieved in the Low Commitment and the High Commitment scenario, respectively. In addition, the Current Policy scenario does not feature any policy measures in the transport sector, whereas national targets for e-mobility and biofuel utilization are included in the Low Commitment and the High Commitment scenario. In order to achieve comparable results by the soft-linked models, key assumptions like the specific investment cost development of batteries for passenger cars were harmonized.

4.1. The contribution of the transport sector to greenhouse gas mitigation efforts Fig. 1 shows the final energy consumption of the transport sector across all modes in the EU-28 from 2010 to 2050. Due to missing GHG reduction targets for the transport sector in the Current Policy scenario, only minor reductions in final energy consumption take place (9% in 2050 compared to 2010). It should be noted, that only a few energy saving measures are modelled for aviation and navigation (e.g. winglets, weight savings). In contrast, stronger final energy consumption reductions take place in the Low Commitment (LC) and the High Commitment (HC) scenario in 2050 if the transport sector is subject to an economy wide GHG reduction target. Final energy consumption is 29% lower in the Low Commitment (LC) scenario and 32% lower in the High Commitment (HC) scenario in 2050 relative to the 2010 figures. In both scenarios in which the transport sector is subject to GHG mitigation targets (i.e. Low Commitment (LC) and High Commitment (HC)) only minor reductions in final energy consumption take place until 2030 in comparison to the Current Policy scenario. This highlights, that from this perspective the marginal abatement costs in the transport sector are higher than in other sectors such as conversion and supply. Fig. 2 reveals the development of the car stock from 2010 to 2050 by energy carrier used. It can be seen that the car stock

4. Results analysis In the two subsequent sections, the results of the three scenarios are described focusing on the transport sector but also highlighting structural changes in the power sector due to electrification of the transport sector.

Fig. 1. Final energy consumption of the transport sector in the EU-28 by transport mode from 2010 to 2050.

Table 1 Main scenario assumptions for the scenario analysis (based on [27e35] and own calculations).

Development of the population and gross domestic (GDP) product in the EU-28 population million GDP 1012 V2010 Households and residential buildings number of apartments million number of residential buildings million living space million m2 Assumption on renewable energy maximum renewable electricity generation TWh renewable energy in gross electricity consumption % renewable energy in gross final energy consumption % Energy carrier prices oil V2010/GJ natural gas V2010/GJ hard coal V2010/GJ lignite V2010/GJ

2010

2020

2030

2040

2050

504 12.3

517 14.2

525 16.7

528 19.2

526 21.9

197.9 114.9 15,856

260.2 147.2 20,502

273.6 154.4 22,041

267.9 156.1 22,619

259.8 152.0 22,320

955

1800 30 20

2750

3900

5090 60 54

10.65 5.73 2.91 1.25

10.34 5.11 2.70 1.25

14.27 7.35 2.89 1.25

16.05 8.15 3.04 1.25

17.99 9.00 3.18 1.25

T. Haasz et al. / Energy Strategy Reviews 20 (2018) 124e132 Hydrogen FC hybrid Hydrogen FC

Number of vehicles [1000 veh]

250000

Hydrogen IC Electricity Methanol FC

200000

Methanol IC Combined Combus on

150000

DME LPG Biodiesel

100000

Ethanol PHEV Ethanol hybrid

50000

Ethanol Natural Gas PHEV Natural Gas hybrid

0

Natural Gas

Gasoline PHEV Gasoline hybrid Gasoline Diesel PHEV Diesel hybrid Diesel

Fig. 2. Car stock by energy carrier in the EU-28 from 2010 to 2050.

continuously increases over time in line with the assumption on the development of the population in the EU-28 (see Table 1). Regarding the powertrains in use, the scenario analysis shows that conventional combustion engines dominate the car stock until 2030 and in the Current Policy scenario this trend even continues until 2050. In 2030 ethanol supports the greenhouse gas mitigation efforts in the transport sector. However, future ethanol usage depends on the mitigation target and at 90% GHG reduction in 2050 compared to 1990 (HC scenario) biomass is strongly gasified to produce hydrogen for the transport sector limiting the production and usage of ethanol. To reduce GHG emissions, the market share of cars with diesel engines decline steadily and in the long-term it is likely that they will only be used as range extenders. Similar findings apply to gasoline engines, however, they might have a market share of over 50% in the Low Commitment (LC) scenario. Under the very stringent mitigation target of the High Commitment (HC) scenario electricity plays a crucial role in the car fleet as slightly over 90% are either fully electric or plug-in-hybrid electric vehicles (PHEV) in 2050. Large shares of full electric vehicles can be found in Austria, Belgium, Germany, UK and the Netherlands as well as in Italy. On the one hand, these cars could be the second car of a household focusing on daily commutes. For example, Bubeck et al. [19] demonstrated that electric powertrains can be a costcompetitive alternative for frequent travelers in Germany. On the other hand, they could be used for long-range and cross-country travels if a sufficient fast charging infrastructure is in place. In addition, hydrogen is used to further reduce GHG emissions from road transportation. These vehicles are especially used in Germany, but also in Austria and France. Since hydrogen is going to be used for buses and trucks in many countries in the EU-28, it would be possible to refuel in all countries but likely in more remote areas such as industrial or commercial areas. Due to the capacity expansion of renewable energy (especially photovoltaics, wind onshore and wind offshore) and their lower capacity credit, the installed capacity in the EU-28 increases by approximately 200 GW from 2010 to 2020. An additional 150 GWe170 GW are built in the following 10 years until 2030 depending on the scenario. There are only minor differences between the three scenarios until 2030 which can be explained largely by the residual capacities that are still in operation. In addition, the decline of nuclear capacities until 2030 is compensated by renewable energy and supplemented through natural gas capacities. In 2040 the total installed capacity is only between 60 GW and 125 GW higher compared to 2010. The higher installed capacities in the Low Commitment (LC) and the High Commitment (HC) scenario can be attributed to coal and lignite fueled power

plants with carbon capture and storage (CCS) capability. With more stringent GHG mitigation targets in 2050 capacities in carbon neutral electricity generation capacities increase strongly. In comparison to the Current Policy scenario an additional 60 GW of nuclear capacities are installed in the Low Commitment (LC) as well as in the High Commitment (HC) scenario. Furthermore, in the High Commitment (HC) scenario wind and solar capacities play a crucial role to generate carbon free electricity. The installed wind capacities reach almost 470 GW and solar capacities over 320 GW in 2050 in the High Commitment (HC) scenario. Electricity generation in the EU-28 develops in line with the installed capacities (see Fig. 3). Triggered by the EU Emission Trading Scheme (ETS), coal and lignite fired power plants almost completely fade out of the system in the Current Policy scenario until 2050. Conversely, wind power, solar energy and biomass increase. Especially, offshore wind, which is only of minor importance today show significant growth rates and thus provides for nearly 300 TWh in the year 2050. Additionally, further power is assumed to be provided via electricity imports but at a small magnitude. In 2050 the Low Commitment (LC) scenario already illustrates a higher electricity generation by about 700 TWh in comparison to the Current Policy scenario. The increase largely stems from coal and gas fired power plants with carbon capture and storage (CCS), nuclear energy as well as electricity imports from North Africa thereby substituting some electricity generation from biomass (125 TWh). In the High Commitment (HC) scenario the total electricity generation from fossil fuels is approximately 140 TWh in 2050 in the EU-28. However, total electricity generation is nearly 1230 TWh higher in the High Commitment (HC) scenario compared to the Current Policy scenario. The strong growth of generated electricity can be attributed mainly to nuclear energy (470 TWh), wind onshore and offshore (480 TWh) and photovoltaics (270 TWh). Thus, the increased electricity generation follows the trend of electrification within the demand sectors which was already illustrated for road transportation. The largest contribution to the reduction of CO2 emissions is related to the conversion sector, especially to the public electricity and heat production. This result is on the one hand given by the emission limits of the ETS and on the other hand, by the finding that CO2 emissions can be reduced most cost-effective in the transformation sector. This is achieved by an increased use of renewable energy and the use of CCS with very low specific CO2 emissions according to the assumed capture rates. Up to the year 2030 the transport sector shows the smallest contribution to GHG reduction of all sectors compared to 2010. The small contribution of the

net electricity generation [TWh]

300000

127

5000

Storage

4500

Net imports

4000

Others / Waste non-ren.

3500 3000

Other Renewables Biomass / Waste ren.

2500 Solar

2000 Wind offshore

1500 1000 500 0

Wind onshore Hydro incl. pump storage Nuclear Natural gas Oil Lignite Coal

Fig. 3. Net electricity generation in the EU-28 by energy carrier in the analyzed scenarios from 2010 to 2050.

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sales Germany and France

New Car Stock

+ + sales rate

B + simulated aggregate sales

+ aggregate new car stock

Older Car Stock

ageing + -

scrappage rate + B +

AVERAGE AGEING TIME +

+ - market share car stock by tech simulated aggregate + total car stock



INITIAL OLDER CAR STOCK

INITIAL NEW CAR STOCK

total car stock by tech

+

+ aggregate older car stock

simulated aggregate scrappage

+

Fig. 4. Excerpt of the TE3 Car Stock module. Source: Own work using [36].

transport sector to reduce emissions in the period up to 2030 is due to the fact that potentials for GHG avoidance are sufficiently available in other sectors of the energy system at lower cost than the avoidance costs of alternative fuels and engines in the transport sector. From 2040 onwards, however, the reduction of transport CO2 emissions increases significantly. In result, the transport sector is of great importance for the EU-28 in order to achieve GHG emission reductions of 80% or 90% in 2050 in comparison to 1990. In comparison to the Current Policy scenario, the total discounted system costs increase by almost 249 billion V2010 in the Low Commitment (LC) scenario. The fulfilment of the more ambitious GHG mitigation target of the High Commitment (HC) scenario is approximately six times more expensive (1435 billion V2010) than the GHG mitigation target of the Low Commitment (LC) scenario. 4.2. Policy measures for sub sector personal road transportation in France and Germany In this section, the results of the modelling exercise for France and Germany until 2030 related to policy measures using TE3 are presented i.e. the more detailed focus of the TE3 model is used to analyse possible combinations of policy instruments which achieve the modelling results from the TIMES model (cf. section 4.1). TE3 uses a stock-and-flow structure that de-couples inflows (sales/ registrations rate) and outflows (scrappage/discarded rates) from the state variables (car stocks), as shown in Fig. 4. Two minor balancing (B) or negative feedback loops are represented in the figure. The state variables are disaggregated by type of car powertrain technology (see Equation (1) where dt denotes delta time with a one-year time step).

Stockcountry ðtÞ ¼ technology

Zt h i salescountry ðtÞ  scrappagecountry ðtÞ dt tech tech t0

þ Stockcountry ðt0 Þ tech (1) The output of each model for France and Germany under the Current Policy scenario between 2010 and 2030 is illustrated in Fig. 4.2 In the upper section, the results of the TIMES PanEU model are shown. As can be seen, gasoline hybrid cars increase their share at the expense of gasoline and diesel cars in both markets. In

2

The results shown for PanEU are based on an interpolation between years.

Germany, liquefied petroleum gas (LPG) and natural gas slowly penetrate the market. In the Current Policy scenario, the rest of car technologies have very low market uptake in both countries until 2030. The lower section of Fig. 5 depicts the evolution of the car mix in France and Germany under the Current Policy scenario using the TE3 model. The most striking difference between the output of each model, in terms of alternative technology uptake, is that a higher number of gasoline PHEVs is simulated in TE3 in both car markets under this scenario. From a consumer perspective, this powertrain becomes attractive earlier in the simulation model than in the optimization model. Driving range is modelled in TE3 as a car attribute influencing choice, with the assumption that consumers show no ‘range anxiety’ towards PHEVs. This helps explain the relatively high attractiveness of this powertrain. The following key differences, based on the results of the optimization model, between the Current Policy and Low Commitment (LC) scenarios can be highlighted: (i) compared with the Current Policy scenario, there is an increasing number of gasoline PHEVs and cars powered by ethanol in the Low Commitment (LC) scenario; (ii) fuel cell cars do not successfully penetrate the market given the considered time horizon; (iii) the share on battery electric vehicle (BEV) is still surprisingly low in all scenarios, mainly because of the conservative assumption concerning the electric vehicle (EV) battery price evolution. Other market take-up scenarios found in the literature, as reviewed by Ref. [37], are significantly more optimistic on this share and current numbers might be interpreted as a more promising development [38] than assumed in this work. In the remainder of the section, the alternative scenario Low Commitment (LC) is explored using the TE3 model.3 The comparison of the results of both models for the High Commitment (HC) scenario is beyond the scope of this paper but can be found in Ref. [21]. How might the optimal development pathway illustrated by the TIMES PanEU results for the alternative scenarios be simulated? To answer this question, a set of policy variables are applied to drive the dynamic behaviour of the analyzed system. This part of the modelling exercise is performed using policy inputs incorporated in the TE3 model. These inputs represent the following policy instruments and measures: emission standards for new conventional cars, taxing conventional (i.e. gasoline and diesel) fuels, subsidising electric cars and investing in public refuelling/ recharging infrastructure. In view of the differences in results from

3 In previous work [18], a second alternativescenario (HC) and an additional policy measure (promotion of higher average caroccupancy rates) were also examined.

T. Haasz et al. / Energy Strategy Reviews 20 (2018) 124e132

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Fig. 5. Car stock by technology in France and Germany under the Current Policy scenario (2010e2030) using TIMES PanEU (top) and TE3 (bottom).

TIMES PanEU between the Current Policy and the Low Commitment (LC) scenario, it follows that policies that aim at increasing the market attractiveness of gasoline PHEVs and cars powered by ethanol4 are favoured in this part of the modelling exercise. When EVs are part of the picture, more stringent emission standards for conventional cars have two main opposing effects: (i) it gives car manufacturers the incentive to develop and offer zeroemission vehicles; (ii) it may improve the efficiency of new conventional cars, thereby reducing operating costs and making EVs harder to compete with. As a result of modelling, it is assumed that the average emission standards for new cars set by the EU target apply, but there are limits to the improvement of gasoline and diesel cars. The effectiveness of taxes in incentivising consumer behaviour has been stressed by, for instance, [39]. Energy taxes levied on conventional fuel represent a large portion of the final gasoline and diesel price paid by the consumer, which affects the operating cost and, in turn, the total cost of ownership (TCO) calculation. At the current stage, subsidies for EVs are needed in order to -vis conventional cars, for proincrease its attractiveness, vis-a spective car purchasers. However, our analysis indicates that, even if relatively high subsidies are offered, as is the case in Germany at the moment, the overall market attractiveness of BEVs may still not exceed that of conventional cars. In the model, PHEVs are subsidised between 2016 and 2018. The simulated subsidy amounts to 4000 EUR per gasoline PHEV in both markets. However, this subsidy scheme does not reflect the actual situation in Germany, for example, where PHEVs receive a subsidy of 3000 EUR and only BEVs receive a subsidy of 4000 EUR [40]. It is assumed that providing a temporary subsidy also increases the popularity of this technology. As a result, the attractiveness of this technology

4

In the TE3 model, this type of car technology is termed flexible-fuel (FF).

increases significantly at the expense of conventional and hybrid electric vehicle (HEV) technologies. The availability of electric vehicle supply equipment (EVSE) in the public space and the influence on recharging times are important attributes shaping the attractiveness of EVs and, consequently, driving the choice of technology by the market. Public EVSE can be categorised as ‘slow’ or ‘fast’, which affect recharging times differently. In the context of our simulation, the deployment of EVSE is necessary for rapid penetration of PHEVs. Furthermore, investment in refuelling infrastructure for alternative fuels, particularly for ethanol, needs to be considered. In TE3, the type of biofuel considered is E85. The assumed cost of ethanol infrastructure deployment is 18,000 EUR (for comparability, the estimated value for a new E85 dispenser in the US is $23,000 [41]). Fig. 6 illustrates the assumed development of this alternative fuel infrastructure for France and Germany. In 2016, a significant increase in

Fig. 6. Simulating deployment of E85 refuelling infrastructure (2010e2030). Own simulated values and historical data from Refs. [42,43].

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E85 availability is simulated for Germany after a period of stagnation. The reason why more E85 refuelling stations are simulated for Germany than for France is because technology choice is influenced by the availability of alternative fuel infrastructure relative to conventional fuel infrastructure. The number of stations selling gasoline is larger in Germany than in France throughout the simulation period. The impacts of these last two policy inputs lead to the simulation output under the LC scenario outlined at the bottom of Fig. 7. The effect of simulating a purchase subsidy for PHEVs is visible. For comparison, the results of TIMES PanEU for this scenario are included at the top of the figure. As can be seen, the simulation model underestimates, particularly in Germany, the number of HEVs and PHEVs by 2030, compared to the output of TIMES PanEU. Nevertheless, the selected policy measures seem to be well suited to reach a cost efficient pathway for both France and Germany.

5. Conclusions In 2015, the transport sector was the largest consumer of final energy (33%) followed by the residential (25%) and industrial sector (25%) in the European Union (EU-28) [1]. With respect to GHG emissions, the transport sector is among the largest (20%) emitters of CO2 equivalents in the EU-28 s to the energy and conversion sector (28%) in 2015 [2]. Therefore, the transport sector is of great importance if the target of becoming a low-carbon economy by 2050 is to be reached by the EU. The goal of the European Commission is to reduce GHG emissions in the transport sector by 54%e 67% in 2050 compared with 1990 [44]. The envisaged measures include greater fuel efficiency, less carbon intensive energy carriers and better utilization of transport networks [44]. To assess the contribution of the transport sector to mitigate GHG emissions in the EU-28 three scenarios are compared. In the first scenario (Current Policy), the transport sector is not subject to

any GHG mitigation targets and thus final energy consumption only decreases by 9% in comparison to 2010. Consequently, the transport sector will be the largest emitter of GHG emissions in 2050 while significant reduction is achieved in the energy and conversion sector which is subject to the ETS. In the second and third scenario, an economy wide GHG mitigation target is in place from 2020 onwards that replaces the ETS after 2030 entirely. The GHG mitigation targets in 2050 are 80% (Low Commitment) and 90% (High Commitment) compared to 1990, respectively. By introducing an economy wide GHG mitigation target final energy consumption in the transport sector decrease by 21% in the Low Commitment and 24% in the High Commitment scenario. The majority of this reduction takes place in individual motorized transport. Much of the reduction in final energy consumption is due to the high penetration rate of grid-connected electric vehicles (PHEV and BEV) in the Low Commitment and the High Commitment scenario. Further measures to reduce GHG emissions in the transport sector in the long-run include a high share of hydrogen fuel cell busses and heavy duty trucks as well as grid-connected light-duty trucks. Through these measures, GHG emissions from the transport sector excluding aviation are approximately 130 Mt CO2 and 60 Mt CO2 in the Low Commitment and High Commitment scenario in 2050, respectively. To conclude, the transport sector is of great importance for the EU-28 in order to achieve GHG emission reductions of 80% or 90% in 2050 in comparison to 1990. However, these savings come at significantly higher costs of 249 billion V2010 in the Low Commitment and 1435 billion V2010 in the High Commitment scenario in comparison to the Current Policy scenario. Furthermore, the results indicate that based on the development of battery prices in the scenarios, the marginal abatement costs (or higher willingness to pay for comfortable, individual, and highly-motorized mobility) in the transport sector are higher in comparison to other sectors (e.g. energy industries and conversion).

Fig. 7. Car stock by technology in France and Germany under the LC scenario (2010e2030) using TIMES PanEU (top) and TE3 (bottom).

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In order to study how the cost optimal technology pathways can be supported by policy measures, the optimization model TIMES PanEU is soft-linked to the simulation model TE3. The scenario results are closely reproduced by simulations with TE3 for Germany and France as case studies. The policy measures in question include regulatory, economic and suasive instruments. Introducing tighter emission standards for combustion engines shows the trade-off between lower carbon-intensity and reduced competitiveness of electric vehicles. If higher shares of electric vehicles are to be achieved, it seems reasonable to tax conventional cars while subsidising electric vehicles. However, subsidies are not a sufficient condition for a fast growth in market share. Since these findings apply to France and Germany, it would be interesting to analyse if they can be generalized and applied to other case studies. Therefore, the question remains, how subsidies can be offered for a prolonged period. The practicable implementation of information campaigns that lead to desirable higher occupancy rates proves difficult [45]. Some progress may be made through car sharing concepts, but further research is needed. From an economic perspective, the comparably low mitigation efforts in the transport sector compared to other sectors seem not to be efficient for the whole economy, if stringent GHG mitigation targets are to be achieved. Therefore, an accelerated electrification of road transport would not only decrease the pressure to mitigate in other sectors but also decrease current oil dependency of the EU, lower local emissions and increase the energy efficiency of the transportation sector. Analyzing these effects is of high value for all affected sectors. Funding This work was funded by the European Commission under the 7th Framework Program (Grant Agreement number m612743) for Research and Technological Development. Conflicts of interest None. Acknowledgements The authors would like to thank Patrick Jochem (KIT) for his contribution to this paper and Ramachandran Kannan of the Paul Scherrer Institute for his review of the research. Furthermore, the
my De
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