Modelling the impacts of EU countries’ electric car deployment plans on atmospheric emissions and concentrations

Figure 1 shows a stylised overview of the modelling approach, highlighting the model soft-linkages. ‘Soft-coupling’ designates the exchange of a limited set of indicators between two models which are complementary in context or representation. Soft-coupling implies also that the two models may iterate, if necessary, to ensure that the feedback loop is closed. In contrast to this modelling approach, hard-coupling would entail model integration into a single, unified framework. As can be seen in Fig. 1, the first linkage is made from PRIMES-TREMOVE to DIONE and the second model coupling from DIONE to SHERPA. The set of indicators exchanged are total vehicle stock and car travel activity as well as air pollutant emissions, respectively. The modelling strategy of soft-linking is challenging but may be beneficial (see Krook-Riekkola et al. [38] for a discussion in the field of energy). As can be seen, the modelling exercise comprises three models (PRIMES-TREMOVE, DIONE and SHERPA) and a geographic information system software (QGIS).

The PRIMES-TREMOVE and the DIONE models are complementary models, which feature some common attributes. PRIMES-TREMOVE is an energy economic model for the transport sector and is part of the main PRIMES model, which simulates the overall energy system. The transport demand simulated in PRIMES-TREMOVE is based on the microeconomic theory and features a degree of engineering detail as regards the vehicle technologies. The model also handles, explicitly, policies related to the promotion of alternative fuels and low or zero emission vehicles (which is the scope of this paper). On the other hand, the DIONE model is a very detailed engineering model which calculates a larger (than PRIMES-TREMOVE) set of air pollutant emissions, based on technical specifications of vehicles’ powertrains and engine sizes. The two models are linked to develop the base scenario. However, only the DIONE model was used to quantify the NPF scenario, using as input data from the NPF assessment. On top of this, for the purposes of this specific paper, the aim was to scale down the pollutant emissions at a more refined geographical scale. Specifically, the SHERPA model needs the following air pollutant emissions as inputs: nitrogen oxides (NO_x), non-methane volatile organic compounds (NMVOC), ammonia (NH₃), particulate matter (PM_2.5), and sulphur oxides (SO_x). While PRIMES-TREMOVE output was available for NO_x, PM_2.5 and SO_x, the DIONE model additionally provided the NMVOC and NH₃ emissions, which were necessary for the analysis. Hence, the suite of models employed (i.e. PRIMES-TREMOVE, DIONE and SHERPA) ensures a consistent data transfer between the models.

The core model used in this study is the DIONE Fleet Impact Model (henceforth DIONE). Two streams of information feed this model: projections from PRIMES-TREMOVE and data from the NPF assessment (see Section 3). To accomplish the data exchange, an Excel template was created. As a result of processing this information in DIONE, two scenarios were constructed (see Section 4).

The modelling exercise can be summarised as follows: projections of car stock and its composition as well as travel demand are combined with consumption functions and fuel efficiency values to estimate energy consumption, in turn used together with emission factors to calculate emissions in each Member State until 2030. The change in emissions between scenarios is used to estimate the consequent change in urban background concentrations of air pollutants. In the next sections, all the models introduced in Fig. 1 will be briefly presented.

The PRIMES energy system model [4] simulates market equilibrium solutions for energy demand and supply in the EU. PRIMES has been developed and maintained by E3MLab/Institute of Communication and Computer Systems of the National Technical University of Athens. The sub-model of the PRIMES energy system model, which simulates in detail the transport sector and is relevant to this work, is called PRIMES-TREMOVE.

PRIMES-TREMOVE simulates the transport sector as a market equilibrium problem where demanders and suppliers, including self-supply behaviour for the users of private cars, are modelled as individual agents. The model projects the evolution of demand for passengers and freight transport by transport mode and transport mean. It is a dynamic system of multi-agent choices under several constraints, which are not necessarily binding simultaneously. It has a modular structure, featuring a module projecting demand for transport services for passenger and freight mobility and a supply module deriving ways of meeting the demand. The supply module projects the optimum technology and fuel mix to produce transport services which meet demand. It includes a vehicle stock sub-module which considers stock of transport means inherited from previous time periods and determines the necessary changes to meet demand. PRIMES-TREMOVE tracks vehicle vintages and formulates the dynamics of vehicle stock turnover by combining scrapping and new registrations.

Three private cars-related key variables/output from the PRIMES-TREMOVE model are employed in this exercise, namely: vehicle stock, transport activity and final energy demand. In PRIMES-TREMOVE, the stock of private cars is disaggregated by powertrain and size (small, medium, big). In total, nine powertrains are modelled including a distinction of hybrid and plug-in hybrid vehicle powertrains into gasoline and diesel powered [46]. The horizon of the model is 2050; for the purposes of the present paper, we focused on the 2030 horizon. PRIMES-TREMOVE model outputs are available on 5-year time steps.

The detailed documentation of the entire PRIMES model is available in E3MLab [9]. A detailed presentation and confirmation of the PRIMES model is provided in Capros et al. [4]. Most recent applications of the PRIMES and PRIMES-TREMOVE models available to the scientific community are presented in Capros et al. [3] and Siskos et al. [47].

DIONE is a tool that allows analysing scenarios of European road vehicle stock, activity, energy consumption and emissions (GHGs and air pollutants) up to 2050 [48]. It was developed for assessing transport and energy (policy) options, such as fleet emission targets, vehicle technology transitions, alternative fuels mixes, scrappage schemes, etc. It builds on a detailed representation of road vehicle types and powertrains, their activities and efficiencies. Seven categories of conventional passenger cars are available, classified by fuel type and engine size. Moreover, there are eight categories of electrified cars (hybrid electric vehicle (HEV), PHEV, range-extender electric vehicle (REEV), BEV and FCEV) as well as seven categories of flex-fuelled vehicles (gasoline-compressed natural gas (CNG), gasoline-liquefied petroleum gas and gasoline-ethanol 85).

DIONE contains a calibrated baseline, which is based on the country-specific stock and activity data collected in the EU research project TRACCS (see [54]). The original baseline was taken forward following the trends of the EU Reference Scenario 2012. Within the context of this study, the baseline was updated to EU Reference Scenario 2016 as described in Section 4. Four car sizes are included in DIONE: tiny (represented by an engine capacity of < 0.8 l), small (0.8–1.4 l), medium (1.4–2.0 l) and large (> 2.0 l). For internal combustion engine vehicles, estimation of energy consumption and emissions in DIONE is based on COPERT 4 [20]. For BEVs, the energy consumption factor was estimated from real drive data collected in the context of the Green eMotion project [30]. The DIONE fleet impact model can be employed to run scenarios on EU Member State level or aggregates thereof. The model is run for the period 2017–2030, with annual resolution.

DIONE is flexible enough to allow soft-linkages with other models (see Harrison et al. [32] for an example of DIONE being soft-linked with another model not covered in this paper). In this work, DIONE is soft-linked with PRIMES-TREMOVE and SHERPA. It also receives input data from the NPF assessment, i.e. future new vehicle fleet composition according to Member States’ electric car deployment plans, which is used as a basis for calculating emissions under the alternative scenario.

Further details on DIONE can be found in Krause et al. [37].

The Screening for High Emission Reduction Potential on Air (SHERPA) model [5, 43, 52] quantifies the relationships between emissions and urban background concentration levels, mimicking the behaviour of a complex full air quality model. The SHERPA model has been created both to support regional/local decision makers to design air quality plans, and to support the European Commission for Impact Assessment evaluations. It is distributed with default data covering Europe at ~ 7 × 7 km² spatial resolution, and allows decision makers to work on any European domain, without the need to perform complex scientific/technical tasks beforehand. The model is based on linearity assumptions, which have been tested and confirmed in previous works [51, 53]. Validation of the SHERPA model can be found in Pisoni et al. [43].

DIONE generates values for total NO_x, NMVOC, NH₃, PM and SO_x emissions for each Member State analysed, expressed in terms of percent reductions between scenarios. Once these results are introduced into SHERPA as inputs, the model delivers urban background concentrations of air pollutants, namely nitrogen dioxide (NO₂), PM_2.5 and PM₁₀. More specifically, for NO₂ modelling, SHERPA simulates at first the link between NO_x emissions and NO_x concentrations; and then converts NO_x concentrations to NO₂ concentrations using the relation as in Düring et al. [8]. For PM_2.5, precursor emission reductions (as primary PM and other precursor emissions) are directly linked to PM_2.5 concentrations. In the modelling of PM_2.5, both primary and secondary PM_2.5 components are taken into account.

In order to calculate atmospheric dispersion, the emissions are regionalized within the Member States as follows: initially, the emissions per Member State and sectors are derived from the GAINS model [35]. Then, sector-specific proxies are applied to compute the fraction of Member State level emissions to be associated to each cell (i.e. traffic emissions are distributed only on road-related grids, residential heating is linked to build-up, etc.). More details on the applied methodology can be found in Trombetti et al. [55].

These results on air pollutant concentrations can be further processed to generate maps. To do so, we used the open-source geographic information system software available from QGIS [44]. In this way, a geospatial representation of the results of the modelling exercise is given.

The base scenario was constructed using the EU Reference Scenario 2016 [12] as a starting point and removing any incentives for alternative fuels at the Member State level. The base scenario includes the CO₂ emissions targets on car manufacturers for the period until 2021 but does not include the recent targets set on car manufacturers for 2025 and 2030. The fact that the same model (PRIMES-TREMOVE) was used to quantify both the EU Reference Scenario 2016 and the base scenario ensures consistency. PRIMES-TREMOVE follows a microeconomic approach where decision makers for the passenger transport sector seek to maximize their utility under budget and other constraints. The new vehicle choice determines the mix of new vehicle technologies and fuels in the new vehicle registrations for each consecutive time period. Alternative vehicle technologies and fuels compete based on cost and other considerations in a Weibull discrete choice model. Hence, if the costs of EVs decrease, the relative market share of gasoline and diesel cars decreases based on the relative ratios of their cost performance. In the presence of CO₂ targets for car manufacturers, shadow values apply in the cost of the various alternative options which do not comply with the target. The shadow values, which penalize vehicle options with high specific CO₂ emissions (in gCO₂/km) ensure that the CO₂ target is met. Low-emission vehicles such as EVs are favoured by the presence of CO₂ targets on car manufacturers. In the absence of incentives for alternative fuels at the Member State level, battery cost reductions still lead to EV market uptake. The initial projections of car travel activity in PRIMES-TREMOVE and DIONE differ. As can be seen in Fig. 2, the original DIONE values, based on the 2012 version of the EU Reference Scenario, were systematically higher. To update them to the Reference Scenario 2016 projections, a correction was introduced by adjusting the average base mileage, measured in km/car. The results shown in Figs. 2, 3, 4, 5 and 6 are for the EU28, but country-specific calculations were also made.

There also was a discrepancy between DIONE original and Reference Scenario 2016 stock, with DIONE car stock lower than in Reference Scenario 2016. Our aim was to mimic in DIONE the evolution of the total car stock projected by current PRIMES-TREMOVE outputs. For this reason, the default values in DIONE were updated to the projections within the Reference Scenario 2016. As a result, both models ended up with the same total car stock projections (Fig. 2).

Because of its implications for energy demand and emissions, of interest in this modelling exercise is the disaggregation of the total car stock by powertrain. In the base scenario, the EU Reference Scenario private car stock projections 2018–2030 for each country were used. To ensure that the level of compatibility between both models was good enough to proceed further with the scenario analysis, assumptions had to be adopted (see Table 3). For instance, whereas PRIMES-TREMOVE disaggregates BEVs and FCEVs into three sizes, DIONE only represents these powertrains as medium-sized cars. We thus had to assume that the medium-sized PHEV stock can be used as a proxy for the average of small, medium and large PHEV stocks in PRIMES-TREMOVE. When faced with discrepancies in the units of measurement, adjustments to ensure dimensional consistency were also made.

When it comes to tank-to-wheel (i.e. tailpipe) CO₂ emissions from cars, a gap between the PRIMES-TREMOVE and the DIONE output remains after the DIONE model update (Fig. 3). Although energy demand in DIONE was slightly lower than in PRIMES-TREMOVE (see Figure 11 in Appendix), the corresponding tailpipe CO₂ emissions are moderately higher. This gap can be explained not only by the model differences highlighted in Table 3 but also by the role biofuels play in the two models. Equation 1 is used to calculate CO₂ emissions from powertrain technologies which operate on blends of petroleum products and biofuels:

where EF = emission factor of the respective petroleum product, β = biofuel blend percentage (biofuel blending ratio), E = energy consumption of the overall blended petroleum product and biofuel.

This convention draws from the concept of “carbon neutrality”. Biofuels are considered as a carbon-neutral source of energy, stemming from the fact that biomass combustion releases the same amount of CO₂ as was captured by the plant during its growth. The modelling follows this convention, as is also stated in the Fuel Quality Directive (see Annex IV, section C.13 in EU [27]). In quantitative terms, the car CO₂ emissions mitigated through the use of biofuels in the EU28 are approximately 30 MtCO₂ in 2020, 28 MtCO₂ in 2025 and 27 MtCO₂ in 2030.

After the base scenario was simulated in DIONE, the NPF scenario was developed.

The difference between the base and the NPF scenario is in the projected EV stock. Figure 4 shows the simulated spread between both scenarios at the EU28 level. As can be seen, the divergence in EV stock between scenarios increases as the simulation time horizon extends. In 2030, EV stock in the NPF scenario reaches ca. 18.7 million cars, compared to 11.8 million under the base scenario.

Our overarching goal when constructing this NPF scenario was to use the information provided by the Member States in their NPFs. It needs to be noted that the scenarios that each Member State elaborated for its NPF typically considered the specificity of each Member State. The data summarised in Table 2 was utilised to build this scenario. Three groups of countries can be distinguished:

For the first group, the NPF values were incorporated in the modelling exercise. For these countries, EV market penetration is simulated to be higher in the NPF scenario than in the base scenario, with the exception of FI. The modelling in PRIMES-TREMOVE was influenced by the more ambitious EV stock estimate made earlier in the Impact Assessment, which was 35,000 EVs in use in 2020 [25]. However, the Finish government turned out to be more conservative (22,000 EVs in use in 2020) in its NPF.

Because of the missing observations, building the NPF scenario for the countries listed in the second and third groups required an additional assumption. The countries in the second group reported EV stock estimates until at least the year 2020 in their NPFs. On the other hand, the time horizon chosen for the modelling exercise extended until 2030. Several approaches to deal with this issue were identified at an early stage of the research design: (i) use average values of the first group, (ii) take into account socio-demographic characteristics, (iii) assume constant values of the EV stock beyond the last year for which an NPF value is available (i.e. 2020 or 2025, depending on the country); or (iv) adopt the values of the base scenario beyond the last available year. The first two were not tested in this study on the following grounds: (i) the average car stock of the first group is more than one million lower than the average EU28 car stock, (ii) the NPF values differ, often substantially, from the estimates calculated in EU [25], which had taken into account socio-demographic and economic aspects. The last two were tested (see Appendix). As expected, the third approach resulted in large divergences by 2030, at least for some countries. For this reason, the fourth approach was preferred and reported in this paper.

For the third group, given the absence of NPF data, the values of the base scenario were used. This implies that, for these six countries, the results of the base and NPF scenarios are equal (to some extent, the same occurs for the countries of the second group in either 2025 or 2030).

As indicated in Section 2.1, the quantification of the NPF scenario was done in DIONE. We assumed that each future EV stock estimate identified in the NPF assessment replaced a medium-sized conventional (gasoline or diesel) car. As a general rule, we assumed that two EVs replaced one gasoline and one diesel car. There were exceptions to this rule whenever we found a situation that would have led to a future negative stock of medium-sized conventional cars. This occurred with AT and IE. In the Austrian case, each EV was assumed to replace 0.7 diesel and 0.3 gasoline cars to avoid a negative stock of medium-sized gasoline cars. In the case of IE, each EV was assumed to replace 0.4 diesel and 0.6 gasoline cars to avoid a negative stock of medium-sized diesel cars.

Only FR and UK communicated in their NPFs a clear split between BEV and PHEV stock estimates. In the absence of this information, we assumed that EVs referred to BEVs. As a result, the base and NPF scenarios do not only differ in the total number of electric cars projected, but also on the relative shares of BEVs, PHEVs and FCEVs (see Fig. 5). The modelling decision not to assign a proportion to PHEVs and FCEVs requires justification. In the case of PHEVs, for the prospective car purchaser the main advantages of PHEVs over BEVs are driving range and refuelling/recharging time. Data from EAFO [10] suggests that the number of PHEV models available in the EU in 2017 was slightly higher than for BEV. For the German market at least, we found mixed evidence as to whether the purchase price of a PHEV is higher than that of its BEV counterpart: whereas the PHEV version of the Volkswagen Golf was found to be more expensive than its BEV counterpart [56], the Hyundai IONIQ PHEV was found to be cheaper than the BEV version [34]. However, we identified five reasons why these two advantages, both linked to the battery, might disappear in the future: (i) battery improvements (see Nitta et al. [40]) that lead to greater energy density and electric range favour BEVs; (ii) we expect the trend of battery cost reductions (see Nykvist and Nilsson [42]), which is more beneficial to BEVs, to continue in the next years; (iii) the deployment of ultra-fast (i.e. up to 350 kW) recharging stations (see e.g. Ionity [36]) has the potential to lower average recharging time; (iv) the impact the recent introduction of the worldwide harmonized light vehicles test procedure has had on PHEV sales and supply plans (see e.g. [1]); and (v) public awareness that PHEVs are low-emission cars while BEVs are zero tailpipe emission cars is likely to increase over time. Today, the leading EV markets are China and Norway, when measured respectively in total stock and sales market share. On the former BEVs accounted for 77% of the EV stock in 2017, on the latter for 64% [29]. With regards to FCEVs, the number of model offerings is at present very low, and restricted to the segment of large vehicles (see data from EAFO [10]). We assumed that this powertrain remains an unattractive option until 2030 due to uncertainty about (i) the cost evolution of key FCEV components (for the fuel cell system, see e.g. DOE [7]), and (ii) the required level of investment to guarantee an adequate network of hydrogen refuelling stations (for a cost estimate per station, see e.g. e-Mobil BW [21]). In recent research, BEVs are shown to clearly dominate over PHEVs and FCEVs in the future EU car market under various scenarios (see Figure 49 in EC [11]).

This and other aforementioned modelling assumptions, adopted in the light of missing or insufficient information available in several NPFs, are summarised in Table 4.