- Original Paper
- Open Access
Industry strategies for the promotion of E-mobility under alternative policy and economic scenarios
© The Author(s). 2018
- Received: 2 October 2017
- Accepted: 2 May 2018
- Published: 22 May 2018
In this study, we consider the European electro-mobility market from an industrial perspective, and focus on effects of market conditions and manufacturer strategies, with the objective to gain insight on what could inhibit the successful market penetration of electric powertrain vehicles.
We use the EC-JRC Powertrain Technology Transition Market Agent Model (PTTMAM), a system dynamics model based around the interactions of conceptual market agent groups in the EU. We assess strategies employed by automobile manufacturers towards the development and market penetration of electric vehicles.
Impacts on electric powertrain sales shares (up to 2050) related to industrial strategies, represented by learning effect, marketing effort and R&D funding, are presented under different scenarios related to policy, regulation and market conditions.
It is concluded from the results presented here that competition between electrical powertrain options may be more inhibitive than competition against conventional counterparts, with both monetary and non-monetary industry support for immature powertrains key to their long-term success when supportive policies are designed to be technology neutral.
- Automobile industry
- System dynamics modelling
The 2016 European Strategy for Low Emission Mobility  supports the transition towards low and zero emission vehicles, building on the aim of the 2011 Transport White Paper  to reduce road transport emissions and the use of conventional vehicles in cities. It is widely accepted that electric vehicles (EVs) will form a major part of achieving the targets set in these documents. Although EVs are not completely without environmental impact, due to upstream greenhouse gas (GHG) emissions and manufacturing processes, studies have suggested that EVs can have substantially lower overall GHG emissions than conventionally fuelled Internal Combustion Engine Vehicles (ICEVs) [3, 4]. On that basis, this paper explores the potential of e-mobility in the European Union (EU).
Key acronyms for powertrains used in this paper
Any vehicle powered by an electric powertrain.
Plug-in Electric Vehicle
A vehicle with an electric powertrain which requires (at least in part) charging from an external source.
Battery Electric Vehicle
A vehicle with an electric powertrain fully powered by an internal battery charged from an external source.
Plug-in Hybrid Electric Vehicle
A vehicle with an electric powertrain which can be powered by either an internal battery charged from an external source or from an internal conventionally fuelled combustion engine.
Fuel Cell Electric Vehicle
A vehicle with an electric powertrain powered by a hydrogen fuel cell.
There is already a significant body of work studying the various policies related to EVs – recent examples include [9–12]. However, further support to the transition may be outside the control of authorities, such as market conditions and the strategies adopted by the industry.
Modelling the transition of automobile technologies has long been a subject of research, from studies of car ownership in the early twentieth century [13, 14] through the increased interest in vehicle choice from the late 1970s [15–17] and more recently the impact of zero tailpipe emission vehicles as part of emission reduction strategies [18, 19]. However, the number of studies considering the industrial perspective is currently low in the English language (see [20–22]). The German automotive industry has been examined using system dynamics in various theses published in German [23–25], as has the Japanese market .
Complex real-world systems can be studied with system dynamics (SD) modelling, which is based on theory of nonlinear dynamics and feedback control . Accumulations (stocks) and movement (rates) within a system of interrelating variables are represented by simultaneous equations that are repeated over a period of time, allowing evolution of the outputs. Developed by Jay W. Forrester in the 1950s and applied extensively in business management, it is being increasingly applied to other disciplines. System dynamics modelling has long been applied to transport  and in particular the uptake of alternative fuel vehicles . Most previous models have focused on specific technologies, market agents or regions, whereas the Powertrain Technology Transition Market Agent Model (PTTMAM) is one of the most comprehensive models of its type.
Variables tested by thematic area
Policy and Regulation
• Emission regulations
• Purchase subsidies
• Infrastructure subsidies
• GDP ratio
• Oil price
• Learning rate
• Marketing effort
• R&D share
It was of interest in this study to look at both push and pull effects on EV uptake, coming from policy and regulation, market conditions and manufacturer strategies. In this section we will describe the inputs and scenarios we have applied. To create scenarios that represent the influence of policy, manufacturer and economic market conditions on EV uptake, we have chosen to focus on a number of variables within the PTTMAM. It is assumed that EV uptake will be sensitive to all of these variables, and that increasing them will be beneficial for uptake, whereas decreasing them would inhibit uptake, relative to a baseline scenario. In the results section we explore the impacts of the scenarios on sales market shares of BEV, PHEV and FCV.
Two levels of fleet emission regulatory targets and four levels of purchase subsidies were considered, and each of the economic conditions and manufacturer strategy variables were tested at a Base value and two extremes. Whereas the minimum represents a halving of the baseline values, the maximum entails doubling. As previous studies identified strong interactions between powertrains, certain tests were carried out with FCV excluded to explore the effect of if it never actually enters the market at larger scale, though it is noted that there are already some variants on sale in Europe (e.g. Toyota Mirai and Hyundai ix35). A full list of scenarios is available in the Appendix, and the scenario inputs are described in more detail in the next section.
5.1 Policy and regulation
Two levels of EU fleet emission regulations were applied. Base represents the current fleet emission regulation target in place , i.e. only a 95gCO2/km target from 2021. A second Mid scenario has tighter targets based on : 85gCO2/km in 2025, 75gCO2/km in 2030 and 25gCO2/km in 2050). Within the PTTMAM, manufacturers are represented as one conglomerate market agent. The specific emission target is calculated as per the regulations, based on average vehicle mass and the target as given. Average emissions for new vehicles of each powertrain and size in each MS are calculated endogenously. This is based on the annual powertrain sales shares of new vehicles, calibrated emission inputs at the start of the time frame, and the improvement over time is related to the degree of manufacturer investment in R&D of components relevant to the emissions efficiency (termed ‘environmental attribute’ in the PTTMAM). Thus, excess emissions are determined from the difference between this and the specific target emissions, and manufacturers are penalised according to the regulations (which is up to €95 per gCO2/km).
Purchase Subsidy (Cost differential from ICEV)
Infrastructure Subsidy (100%)
Not available (N/A)
5.2 Economic conditions
Gross domestic product (GDP) and oil price were chosen as indicators of crucial economic conditions. Both GDP and oil price influence total sales turnover, therefore these variables are beyond the direct control of the industry but could severely impact technology transitions.
5.3 Manufacturer strategies
To represent manufacturer strategies, three further variables were chosen, which could reasonably be argued are within the sphere of influence of the manufacturer.
Effect of learning on cost and cumulative manufacturer spend of individual components under base scenario
Learning Effect on Cost
Cumulative R&D spend to 2050 (€B)
Electric Drive System
Hydrogen Storage Tank
Fuel Cell System
Marketing effort represents the strength of promotion of a powertrain and feeds into awareness of the user. It directly influences the environmental importance a user gives a powertrain in their purchase decision, and through marketing effect, total social exposure and ultimately the willingness to consider a powertrain in their decision set. It is translated into marketing effect using a base response (i.e. the growth in awareness resulting from the base marketing effort) of 25% a year. The base marketing effort for each country and powertrain is determined in a base level of forecast sales share of 0.35 that triggers marketing and sensitivity to changes of 1.2. It is then adjusted according to the degree of extra effort required to encourage the adoption of lower-emission powertrains in order to avoid potential emission penalties, as well as in proportion to any subsidies that are available. Thus the manufacturer is assumed to reduce marketing of this powertrain.
The R&D share of the funds is the proportion of revenue made available for developing immature powertrain components. An assumption is made in the model that 5.6% of revenue funds R&D and 75% of this is allocated to the improvement of these components. The total investment by 2050 is shown in Table 4, though this is in relation to a capped amount of estimated spend to full maturity. The total amount of R&D funds are shared between powertrains according to their relative future profits, a measure of the current powertrain maturity and adjusted to reduce potential future emissions and avoid regulatory penalties. The component share of the total powertrain R&D spend is determined as a function of the remaining potential improvement in the component, the contribution of the component to powertrain attributes and the importance of the attributes to the user.
6.1 Impacts on EV sales shares
6.1.1 BEV sales share
In the period 2030–2050, greater differences due to fleet emission targets are more visible than in the initial 2015–2030 period. Perhaps most strikingly, the scenario with no targets and all variables at maximum have an increased market share of BEV compared to the same scenario with targets in place. This remains the most successful scenario (in terms of BEV sales share). This finding alone would suggest that when conditions are favourable towards BEV, fleet emission targets are unimportant. The same observation holds (to a lesser extent) for the marketing and GDP variables. Other anomalies witnessed are that reduced R&D and Learning effect are more beneficial for BEV than the maximised counterparts or baseline scenarios for the period from 2030. This is also true to a much more marginal extent from the about 2045 for marketing. These three manufacturer strategies, when maximised, are therefore more supportive of FCV than BEV. Under Mid Targets, there is a rapid sales growth around 2030, which increases even more after 2045. By 2050, the sales are at around the same amount as the baseline scenario. This again coincides with the introduction of FCV, suggesting that BEV benefits under conditions or strategies that are otherwise unfavourable towards e-mobility as they affect FCV more.
6.1.2 PHEV sales share
Up to 2050, there are greater differences between Base and Mid Targets for PHEV than for BEV, suggesting PHEV success is more sensitive to targets being in place than BEV. However, leading from the observations up to 2030 and similar to BEV, the All Minimised scenario continues to lead to a high sales rate and by 2050 is as successful as the All Max scenario. Thus, conditions and strategies that are unfavourable towards e-mobility push sales towards PHEV.
6.1.3 FCV sales share
Looking at Base conditions between 2030 and 2050, there is what could be considered as a failure to penetrate the market under all scenarios. Indeed from 2030 there is no growth in FCV sales share under any scenario without Mid Targets. Thus we can deduce that, in the confines of this model and our scenarios, manufacturer strategies will not support FCV without long-term, ambitious emission fleet targets in place. There is a very different story regarding mid-term targets however. The baseline scenario has a rather rapid sales growth between 2025 and 2045, which increases further from 2045 when new targets are visible to manufacturer. Marketing and R&D funding exhibit similar and expected patterns of market penetration though marketing was most influential by the end of the time period. Learning rates scenarios display similar market uptake to the baseline scenario but in the opposite way as expected (lower learning increases share and higher learning decreases share). The 2030 trend continues because this variable supports the earlier maturing PiEV over the lagging FCV technology. Indeed, lower learning rates have led to one of the most successful FCV sales shares by 2050. Furthermore, lower GDP and Oil price begin to produce greater FCV success around this time. The slowing in sales is due to the fact that from 2035 the current targets are being met, and no new tighter targets are visible to the manufacturer – therefore there is no additional incentivisation for FCV development or marketing that exists under the other variables. These findings suggest that manufacturer strategies are important for FCV success. This supposition is further supported by the large bandwidths between Max and Min scenarios for the manufacturer strategy variables. When all variables are set to their minimum values FCV experiences moderate sales growth from about 2035, leading to a 2050 share of around 10% of the market. When all variables are set to maximum values however, the market stagnates around 2035 and there is little sales growth until 2045 when there is a push in FCV by the manufacturer in anticipation of the 2050 target. Therefore, even with targets in place, certain market conditions and manufacturing strategies are needed to support FCV development.
6.2 Testing for the absence of FCVs
6.3 Testing for subsidy regimes
Before concluding on our results, which have been extensively discussed in the previous section, it is worth emphasising the limitations of this research. There have been restrictions to what we could consider and implement due to the structure of the model employed. As such, our recommendations are a base for further study rather than a list of definitive actions. The main limitation of this study is the representation of the manufacturer agent as a conglomerate without competing manufacturers. This prevents us from analysing lock-in effects once a certain manufacturer has decided to invest heavily in a given technology. Thies et al.  considered this for two manufacturers. Further work could revolve around explicitly modelling competition among more than two manufacturers. Secondly, we have not considered in detail the wider impact on air pollution and GHG emissions that the introduction of new electric powertrain technologies may have should they become widespread. This may dependen on many factors, mainly upstream emissions, resource sources, power grid implications and the opportunity for energy storage from batteries. Nor have we explicitly considered any other externalities of sustainability concerns of EVs (e.g. resource depletion, transport poverties, social exclusion). A further restriction is the conservative approach to user choice modelling that is currently employed in the PTTMAM. We recognise the sophistication of the automobile market and purchase preferences and this has already been identified as an area of ongoing improvement and will be addressed in future work. Similarly, our model pre-dates the changing attitude towards diesel fuel due to its high NOx emissions and observed related accelerated shift towards EVs. Such changing preferences will be incorporated into future model developments. Finally, we appreciate that with such a long-term time horizon to 2050, that there exists many other uncertainties both in the political-economic future of the EU and within the structure of the transport system, through the introduction of new technologies (e.g. autonomous, smart and connected vehicles) and a possible move towards a sharing economy and mobility-as-a-service (MAAS). However, all research has its boundaries and these limitations do not invalidate our findings, merely contextualise them.
Under maximised manufacturer strategy variables and the most favourable economic market condition scenarios, FCV benefits the most of the of the powertrains considered;
Manufacturer strategies are more crucial for FCV than for PiEV success;
Strong marketing when both BEV and FCV are introduced is important for the success of both powertrains;
A lower learning rate benefits BEV, as high learning rates benefit the competing FCV.
FCVs may not experience market success in the absence of fleet emission regulations or if PiEVs gain early success (for which economic conditions have a notable influence);
Differing subsidy regimes, for both purchase and infrastructure, resulted in marginal change in overall EV share, but impact individual EV shares;
PHEV is the least affected powertrain by either of the offered subsidies.
Manufacturers must pay close attention to GDP and oil price conditions despite any of the three strategies examined;
Greater policy focus is required on the market interaction between PiEVs and FCVs rather than only the competition between EVs and conventional vehicles;
Fleet emission targets are less important when GDP and oil price conditions are otherwise favourable towards e-mobility;
Long-term purchase subsidies are important when economic conditions and manufacturer strategies are unfavourable towards e-mobility;
If the policy goal is to promote FCVs, subsidising the deployment of hydrogen infrastructure has a greater leverage than purchase subsidies.
In conclusion, although technology-neutral policies may be desirable as they do not allow the “cherry-picking” of available technologies, they may inadvertently lead to the suppression of the less mature technologies. Strong marketing is highly influential on uptake at the initial introduction of a new technology, and as this initial uptake determines later success it should be carefully planned for each technology. Manufacturer commitments to new technologies, both monetary and non-monetary, have different influences on the development and maturity of powertrains and so merit further investigation, alongside adoption of future focused strategies that may capitalise on changing attitudes towards mobility, technology and environmental impacts.
GH developed and analysed the scenarios in conjunction with CT. JGV provided additional comments and insights on both literature review and discussions. All authors read and approved the manuscript.
The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission. Otherwise, the authors declare that they have no competing interests.
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