Chapter 5. Assessment framework and results for the baseline scenario
This chapter introduces the assessment framework used in the analysis. It describes the simulation tool developed to evaluate the development of transport tax bases over time and to infer implications for tax revenues in the Slovenian road transport sector. The chapter highlights the model structure and scope and presents results for a baseline scenario. It also discusses the main technology scenario adopted for modelling the uptake of alternative fuel vehicles and provides sensitivity analysis to this key input parameter. Tax revenues from fuel excise levied on passenger cars may significantly decline under current policies when fuel-efficiency improves and the uptake of alternative fuel vehicles increases.
To evaluate the development of tax bases in the Slovenian road transport sector over time (energy use, vehicle stock and road use) and to evaluate the associated tax revenue implications, a simulation tool based on a vehicle stock model was developed. This chapter provides information on the model design and underlying assumptions, and presents results for a baseline scenario.
Section 5.1 provides an overview of the overall model and its scope. Section 5.2 introduces the model components, their input data and related assumptions in more detail. This section also discusses the main technology scenario adopted for modelling the uptake of alternative fuel vehicles. Section 5.3 provides information on how main model outputs, i.e., tax revenues, are derived for each tax base under current tax policies (baseline scenario). Section 5.4 presents results for the baseline scenario, against which tax counterfactuals are built in Chapter 6. Finally, Section 5.5 provides results of sensitivity analysis to some key input parameters.
5.1. Overview of the model and scope
5.1.1. Design requirements
The model captures the most important margins that are necessary to build relevant and robust scenarios to assess transport taxes in Slovenia and potential tax reform. It provides both appropriate detail and sufficient flexibility to answer strategic questions concerning transport tax policy in the presence of eroding fossil fuel tax bases. Several requirements need to be met that are summarised below and detailed further in the respective subsections:
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Tracing vehicles over their lifetime – this is to reflect that vehicle usage profiles and vehicle efficiency levels differ over the vehicles’ lifetime (e.g. older vehicles typically travel less and are less energy-efficient than newer vehicles);
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Reflecting the development of different and new vehicle technologies and their penetration into the vehicle sales market over time;
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Accounting for imported second-hand vehicles, which make up a relevant share of Slovenia’s vehicle sales market;
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Using data and forecasts available from national sources; where this is not possible, use recognised alternative sources.
5.1.2. Model structure
To calculate Slovenia’s vehicle stock for the time period 2017-2050 and derive the associated energy consumption, a vehicle stock model was built that allows retracing vehicles and their use over time. Figure 5.1 provides an overview of the functioning of the model.
In a nutshell, a vehicle stock model simulates the development of the vehicle fleet that is required to meet future vehicle activity (or transport demand). The evolution of vehicle activity over time is a main input to the model and captures how exogenous factors like GDP and population growth may affect travel demand. The model simulates how new or second-hand vehicles enter the existing vehicle stock, while older vehicles “die” (i.e. they are scrapped or exported) in line with a vehicle survival function that provides the probability for a vehicle of a certain age to survive one more year in the fleet. The vehicle activity, the derived vehicle stock and its structure (in terms of the vehicles’ age, technology and associated fuel-efficiency) determine the energy use of the vehicle fleet.
Tax revenues can then be derived from vehicle activity (in case of tolls), the vehicle stock (in case of vehicle registration taxes and annual circulation taxes) and energy use (in case of excise duty and carbon tax) – see Section 5.4. For tolls, excise duty and carbon tax, the vehicle activity of domestic and foreign vehicles on Slovenian roads is relevant. Registration taxes and annual circulation taxes for vehicles apply to domestic vehicles only.
Source: OECD/ITF representation.
5.1.3. Model scope
The scope of the model is defined by its geographic coverage, the time horizon it accounts for, and the types of vehicles it covers:
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The geographic coverage of the model is Slovenia. More specifically, the model considers vehicles registered in Slovenia and vehicles driven in Slovenia (whether foreign or domestic).
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The time horizon of the model is 2050 to allow for relevant future technology developments in the vehicle sales market. The base year is set to 2017. From 2020 onwards, model outputs are derived in 5-year steps.
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The vehicle types that are covered are passenger cars and trucks with a maximum mass exceeding 3.5 tonnes. Note that 2- and 3-wheelers, light duty vehicles (i.e., vans), buses or special purpose vehicles are not considered in the analysis. Their respective fuel consumption and related tax revenues are therefore not included in the calculations.
5.2. Description of model components and related input assumptions
The following sections provide insight into the data inputs to, and functioning of, the three model components outlined in Figure 5.1 that allow the calculation of tax revenues from the three relevant tax bases: the “vehicle activity”, “vehicle stock” and “energy use” components. The main description of each component refers to the approach that was taken for passenger cars. Due to restricted data availability, a simplified approach was followed for trucks as outlined at the end of each subsection.
5.2.1. Vehicle activity component
The vehicle activity component provides total road transport demand forecasts in Slovenia in terms of vehicle-kilometres (vkm) to the year 2050. Transport demand from both foreign and domestic vehicles on Slovenia’s roads is considered.
The Statistical Office of the Republic of Slovenia (SURS) provided vehicle activity for the year 2016. For simplicity, vehicle activity in 2017 was assumed to be the same as in 2016. Up to the year 2035, vehicle activity forecasts stem directly from Slovenia’s national energy model. For the period from 2035 to 2050, vehicle activity is extrapolated by applying the growth rate from 2030 to 2035 to all following 5-year intervals.
Figure 5.2 provides an overview of the vehicle activity used in the analysis for the period from 2017 to 2050, for both passenger cars and trucks; values are indexed to the year 2017. The vehicle activity of trucks is projected to almost double by 2050. Passenger car activity increases by around 20% from 2030 to 2050.
Source: Slovenia national energy model (2017-2035) and extrapolation (2035-2050).
5.2.2. Vehicle stock component
The vehicle stock component defines the Slovenian vehicle stock over time including its age distribution and technology composition. It assesses the number of vehicles that are needed to satisfy the demand for road transport by domestic vehicles,1 which requires information on the composition of the vehicle stock in the base year, as well as assumptions concerning the lifetime and annual mileage of vehicles.
Information on the vehicle stock in the base year was obtained from Slovenia’s vehicle registration dataset (Ministry of Public Administration of Slovenia, 2017[1]). Table 5.1 shows the distribution of Slovenia’s passenger car fleet across different vehicle age groups and technologies in 2017. Vehicles were grouped into five age and five technology categories throughout the analysis. The latter distinguishes between petrol and diesel vehicles (including conventional hybrid vehicles), plug-in hybrid electric vehicles (PHEVs)2, battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs).
Concerning the annual mileage of a vehicle, the present analysis considers that, on average, older vehicles travel less than newer vehicles and vehicle usage profiles differ with vehicle technology given different running and upfront costs (e.g. diesel cars typically travel more than petrol cars). Table 5.2 shows the annual vehicle mileage assumptions by vehicle age category and vehicle technology for passenger cars as derived from Slovenian data.
The mileage values for diesel and petrol vehicles were derived from annual vehicle mileage data for Slovenia’s total vehicle fleet provided by SURS (in billion vkm in 2016). This data – disaggregated by vehicle technology and age group – was divided by the relevant vehicle stock in each group as reported in Slovenia’s vehicle registration dataset for 2017.
Data points for alternative vehicle technologies are rare, resulting from their currently very limited representation in Slovenia’s vehicle fleet. Consequently, it is not possible to derive a robust estimate for the average mileage for these vehicle technologies. It was therefore assumed that alternative-fuel vehicles follow the same pattern as diesel vehicles. This choice relates to the similarity of these vehicle technologies with respect to the distribution of costs; i.e., they are subject to high up-front but relatively lower running costs compared to petrol vehicles.
The model adjusts the vehicle stock over time in line with the assumptions on the vehicles’ lifetime and the penetration of new and imported cars. The future vehicle stock is matched with the vehicle activity forecast based on the mileage values outlined in Table 5.2, which are assumed to remain constant over the model horizon.
A vehicles’ lifetime is defined exogenously by a vehicle survival curve (see Figure 5.3 for passenger cars). This curve provides the probability of a vehicle to stay in the vehicle fleet for one more year as a function of the vehicle’s age. The specific curve that is used in the present analysis was established for the European Commission (see Ricardo (2016[3])) and reflects the life of the EU’s average passenger car. According to the curve, a car that is 10 years old has a 97%-likelihood to stay in the fleet for at least one more year. This drops to an 84%-likelihood for a car that is 15 years old. Said differently, a new car has a likelihood of around 95% to stay in the fleet for 10 years or more. This likelihood drops to around 68% for 15 years or more, or 10% for 20 years or more.
Source: SULTAN Model (see Ricardo (2016[3]) for more information).
New vehicles enter the vehicle fleet every year, so that the overall travel demand is satisfied; they are either newly purchased or imported. In 2017, a majority of newly registered cars (first registrations) in Slovenia was running on fossil fuels: 53% on petrol and 45% on diesel (Ministry of Public Administration of Slovenia, 2017[1]). However, these shares are projected to change in the future when alternative fuel vehicles (BEVs, FCEVs and PHEVs) increasingly penetrate the fleet.
The future penetration of alternative fuel vehicles is modelled exogenously. Following a review of the current literature, which is discussed in Section 5.5, the main scenario is in line with the International Energy Agency’s (IEA) two-degree scenario (2DS) for Europe (IEA, 2017[4]). The policy-driven 2DS sets out a decarbonisation pathway in line with international policy goals. It depicts the vehicle technology shares that would be required in the future to meet a world where average global temperature increases are limited to 2°C at a 50% chance, at least.
Applying the IEA 2DS to the Slovenian case, Figure 5.4 shows the vehicle technology penetration in percent of new vehicle sales. By 2050, above 60% of newly purchased vehicles are either PHEVs, BEVs or FCEVs, compared to a 2% share in the base year. Because technology evolution remains largely uncertain, an alternative scenario that is more optimistic about the uptake of alternative fuel vehicles is discussed as further described in Section 5.5.
Note: PHEV – plug-in hybrid electric; BEV – battery electric vehicles; FCEV – fuel cell electric vehicles.
Source: Technology shares for 2017 stem from national vehicle sales data for Slovenia. Technology shares beyond 2017 are aligned with IEA 2DS (IEA, 2017[4]).
Around one third of passenger cars entering Slovenia’s vehicle fleet each year are car imports. As a result, it is also relevant to assess how these vehicles enter the fleet. Table 5.3 shows the age distribution of car imports, as identified in Slovenia’s vehicle registration dataset. In 2017, imported cars were relatively young (69% below 5 years). To model the vehicle turnover, it is assumed that both the total share of imports in 2017 (33%) and their age distribution remain constant over time.
The technology distribution of future car imports is of high uncertainty. Based on expert judgment from Slovenia, it is assumed that the current high share of diesel imports (90%) will diminish to the benefit of alternative fuel vehicles over time (see Figure 5.5). Such an evolution is in line with the 2DS and can be justified by the fact that vehicle stocks in car exporting countries adapt to the 2DS.
Note: PHEV – plug-in hybrid electric; BEV – battery electric vehicles; FCEV – fuel cell electric vehicles.
Source: Technology shares for 2017 stem from vehicle import data for Slovenia. Technology shares beyond 2017 are based on own calculation following IEA 2DS (IEA, 2017[4]).
Approach for trucks
Modelling the turnover of the vehicle stock for trucks follows a simplified approach compared to passenger cars. This is because of the relatively limited data availability for trucks. Notably, disaggregate data on the energy consumption of individual trucks in Slovenia is not available. As such, a detailed analysis of the age and technological distribution of trucks that would allow assessing the fleet’s energy consumption in more detail is not carried out.
The model assumes an average vehicle lifetime of 11 years for all trucks (European Commission, 2014[5]). After each 5-year interval that is covered in the model, the relevant amount of trucks is replaced with new vehicles. A distinction between new and imported vehicles is not made for this vehicle type. As is the case for passenger cars, the technology penetration of new trucks follows IEA’s 2DS – see Figure 5.6, which assumes a constant high share of trucks running on diesel. BEVs start phasing in from 2025; however, at a slow rate, attaining a share of 6% by 2050.
Note: CNG – Compressed Natural Gas; LPG – Liquefied petroleum gas; PHEV – plug-in hybrid electric; BEV – battery electric vehicles; FCEV – fuel cell electric vehicles.
1. The data for these series are close to zero and, therefore, not visible in the figure.
Source: Technology shares for 2017 stem from vehicle registration dataset (Ministry of Public Administration of Slovenia, 2017[1]). Technology shares beyond 2017 are based on own calculation following IEA 2DS (IEA, 2017[4]).
Output of the vehicle stock component
The output of the vehicle stock component is the number of vehicles by technology and vehicle age that make up Slovenia’s total vehicle fleet in a given year over the model horizon (2017-2050) – information that feeds into the energy use component of the model. Figure 5.7 shows the development of the size of the vehicle fleet (for passenger cars and trucks) over time. It closely follows the trend of the vehicle activity forecasts.
Source: OECD/ITF calculation.
5.2.3. Energy use component
The energy use component of the model defines the amount of energy (i.e., fuel and electricity) that is necessary to cover vehicle activity in Slovenia – whether carried out by domestic or foreign vehicles – in a given year. The energy consumption of domestic vehicles can be directly derived from the vehicle stock component by multiplying the energy consumption of a vehicle (in litre fuel/km or kwh/km) with the annual domestic mileage of that vehicle (in km), accounting for the vehicle’s technology and age, and summing over the whole vehicle stock. Vehicle-kilometres by foreign vehicles are assumed to follow the energy use pattern of domestic vehicles.
Energy consumption of petrol and diesel cars can be derived from vehicles’ CO2 emission values, which directly relate to fuel use. Data on emission values per vehicle are readily available in Slovenia’s 2017 vehicle registration dataset. Conversion factors allow deriving a vehicle’s fuel use from its specific CO2 emission values (see Table 5.4).
However, the CO2 emission values in the vehicle registration data represent test values, which do not necessarily reflect real-world emissions (and hence energy consumption). Test values are obtained when testing vehicles on pre-defined test-cycles and under specific conditions. It has been shown that test values regularly deviate from real-world emissions. As a result, the present analysis applies correction factors to the CO2 emission values reported in the vehicle registration dataset. ICCT (2015[6]) has established such values and shown that these correction factors have increased in recent years and are projected to increase further over time if test procedures do not change in the future.
ICCT (2015[6]) distinguishes correction factors for petrol, diesel and plug-in hybrid vehicles and different road types (urban and interurban). For the purposes of this study, an average correction factor by vehicle technology was derived – see Table 5.5. These correction factors adjust the CO2 emission values that are recorded in Slovenia’s vehicle registration dataset for petrol, diesel and PHEV vehicles. The adjustment accounts also for the age of the vehicles and the finding that the deviation between test values and real-world emissions has increased over time.
Defining the fuel consumption via CO2 emission values is convenient for developing projections for vehicles’ fuel consumption. This is because European Commission targets with respect to the energy efficiency of vehicles are set in terms of vehicles’ CO2 emissions (not their fuel consumption). More specifically, the average CO2 emission value for the total new passenger car fleet in 2021 is to meet 95gCO2/km (see Regulation No 333/2014 of the European Parliament and of the Council), as tested on the New European Driving Cycle (NEDC). Further targets will be set as relative reduction targets in comparison to the 2021 target to avoid irregularities with the phase-in of new test procedures. It is assumed that an intermediate 15% reduction target for 2025 and a 35% reduction target for 2030 will be agreed.3
The model assumes that new vehicle sales in Slovenia meet, on average, the European Commission targets, which yields the average CO2 emission values (in NEDC terms) by vehicle technology as shown in Table 5.6. Note that these values depend on the assumed technology penetration in new vehicles sales: the more zero-emission vehicles (BEVs, FCEVs) are sold in the future, the higher the average emission values of other vehicle technologies may be, while still meeting the overall vehicle emission target. Also, average emission values across the whole fleet depend on the assumed technology penetration across in new sales. The development of passenger car CO2 emission values after 2030 is assumed to be in line with IEA’s projections for Europe (IEA, 2017[4]). These are very moderate improvements of around 1% for each 5-year interval.
The assumed average electricity consumption of PHEVs, BEVs and FCEVs in Slovenia is provided in Table 5.7 and stem from advertised values for best-selling respective vehicle models in Europe in 2017, corrected by 50% to account for a divergence to real-life driving based on expert judgment. For PHEVs and BEVs it is assumed that values will very moderately improve over time, i.e. less than 1% in each 5-year interval. This is in line with IEA’s assumption for BEVs (IEA, 2017[4]). FCEVs are assumed to improve from 0.37 kWh/km in 2017 to 0.20 kWh/km in 2050 in line with IEA (2017[4]) for Europe.
Output of the energy use component (passenger cars)
The output of the energy use component is the energy use by energy type (petrol, diesel, electricity) that meets the demand for transport activity on Slovenian roads in a given year. Figure 5.8 and Figure 5.9 show how petrol, diesel and respectively electricity consumption of passenger cars develop over time (2017-2050) according to the model. Results are based on the input assumptions described above and assume an increase in vehicle activity to 2050 given in Figure 5.2. The skyrocketing electricity consumption is only shown for completeness. It can be explained by the very low starting value in 2017 and reflects the uptake of electric vehicles presented in Figure 5.4.
Source: OECD/ITF calculations.
Source: OECD/ITF calculations.
Approach for trucks
The approach for trucks is in principle the same as for passenger cars. However, CO2 emission values are not available as testing and reporting procedures similar to passenger cars have not been put in place. Based on expert judgment, it was assumed that the average real-life fuel consumption for diesel trucks was 40 litres/100 km in 2017 and that this drops with the renewal of the fleet and the phase-in of fuel-efficiency standards for trucks similar to those of passenger cars to close to 20 litres/km by 2050.
Figure 5.10 shows the resulting modelled reduction in diesel consumption to 2050. The increase in vehicle activity is balanced by increased fuel-efficiency over time. Given the assumed low penetration of electric trucks, the consumption of other energy sources remains negligible.
Source: OECD/ITF calculations
5.3. Deriving model outputs for a baseline scenario
Final model outputs – being toll and tax revenues from energy use, vehicles and road use in Slovenia over the model horizon (2017-2050) – are derived by combining the intermediate outputs of the different model components (tax bases) with the current tax structure in Slovenia.
The baseline scenario assumes that the toll and tax system in 2017 as described in Chapter 3 remains in place. The following sections treat fuel and carbon taxes, vehicle taxes and tolls in turn, and provide most relevant information as to how revenues were calculated and calibrated (where possible). Actual quantified outputs for a baseline scenario are provided in Section 5.4.
5.3.1. Fuel and carbon taxes
Excise duties (including additional taxes for strategic stockpile, energy efficiency, renewables and cogeneration) and carbon taxes apply to all petrol and diesel fuel sales as explained in Chapter 3. For electricity, only the output tax is included in the analysis, while any other tax or input taxes on fuels that are used in electricity production are disregarded for simplicity. Respective revenues are obtained by multiplying tax rates (per unit of energy) with the petrol, diesel and electricity purchases that are calculated in the model’s energy use component.
It is assumed that all energy purchases resulting from vehicle activity on domestic roads result in tax revenue for Slovenia. In other words, it is assumed that the fuel and electricity for all kilometres driven on Slovenian roads are purchased in Slovenia. This approach has been chosen to disregard potential strategic behaviour linked to fuel tourism into (or out of) Slovenia in the baseline scenario. Section 6.1.1 discusses fuel tourism in more detail.
5.3.2. Vehicle taxes
Two types of vehicle taxes exist in Slovenia, a one-time motor vehicle registration tax and an annual motor vehicle tax. Revenues from vehicle taxes are calculated by multiplying tax rates with the respective new vehicle stock (registration tax) or the total vehicle stock (annual tax) in a year.
The registration tax is paid on newly registered vehicles in Slovenia (whether new or imported). It applies to passenger cars, but not to trucks. For passenger cars, the tax rate depends on a vehicle’s i) type of fuel (distinguishing electricity, diesel and other), ii) CO2 emission value (in g/km), iii) EURO standard, iv) engine capacity (in cm3). The tax rate applies ad valorem as a percentage of the vehicle’s sales price (exclusive of VAT). Electric vehicles with zero tailpipe emissions pay the lowest rate (0.5%).
Revenues from the registration tax in the base year are calculated by multiplying tax rates with sales prices. Detailed data on revenues from the registration tax in 2017, provided by the Slovenian Ministry of Finance, allows the distributions of vehicles across all parameters that define a vehicle’s registration tax for the base year to be determined. To calculate future revenues, the relevant distributions are shifted for future years to reflect the uptake of new vehicle technologies (in line with the shares provided in Figure 5.4 and Figure 5.5), and of increasingly fuel-efficient conventional vehicles (diesel and petrol). As this study does not aim to assess the future development of EURO standards or engine capacity for petrol or diesel vehicles, respective distributions are assumed constant. This translates into the assumption that potential future changes in such distributions would result in revisions to the registration tax system.
Registration tax calculations are calibrated against the actual tax revenue in 2017. Future changes to tax revenue are the result of changes in the size and composition of the vehicle fleet, vehicle technology shares and fuel-efficiency values (i.e. CO2 emission values) as defined in the vehicle stock and energy use component respectively.
The annual motor vehicle tax applies to registered passenger cars and trucks. In the case of passenger cars, the tax depends on the vehicle’s engine capacity. Vehicles with zero tailpipe emissions (BEVs and FCEVs) are exempt from the tax. For trucks, the tax depends on the vehicle weight (or engine power in some instances) and EURO class.
Similar to the method applied for calculating the motor vehicle tax revenue, the dataset on vehicle registrations in 2017 is used to define distributions across all relevant vehicle parameters for the base year. These distributions are then adjusted over time to account for the uptake of alternative fuel vehicles. In the model, future changes to the motor vehicle tax are the result of changes in the size of the vehicle fleet and its composition in terms of new vehicle technologies, as defined in the vehicle stock component.
5.3.3. Distance-based charging
In Slovenia, passenger cars using the motorway are subject to purchasing a vignette, while trucks pay tolls levied on a per-kilometre basis.
Calculating the revenue from vignettes requires assumptions concerning the number of domestic and foreign vehicles that acquire a vignette. Respective assumptions were implemented in the model and calibrated against the total 2017 vignette revenue obtained from ASECAP (2018[7]).
Vignettes are currently not dependent on the energy efficiency of vehicles. In the model, future changes in vignette revenue therefore only stem from changes in Slovenia’s vehicle stock (as calculated in the vehicle stock component) and the estimated number of foreign vehicles using Slovenia’s motorways. The latter is assumed to develop in line with the vehicle activity of these foreign vehicles on domestic motorways, as defined in the vehicle activity component. This translates into the assumption that the annual mileage per foreign vehicle on Slovenia’s roads remains constant. The share of vehicle activity on motorways was obtained from SURS for the base year and is assumed to remain constant over time.
The revenue from tolls for trucks is estimated in the model by multiplying the domestic vehicle activity (from the vehicle activity component) with toll rates. In 2017, toll rates varied with the EURO class and the number of axles of a truck. Slovenia’s vehicle registration dataset provides information on these two parameters for all trucks registered in Slovenia in the base year. This allows establishing a distribution of trucks along the relevant parameters and applying the relevant toll rate. Foreign vehicles driving on Slovenia’s roads are assumed to follow the same distribution as national vehicles along these parameters.
Toll revenues are calibrated against actual revenue observed in 2017 that was obtained from ASECAP (2018[7]). Future changes in toll revenue are the result of changes in total vehicle activity of domestic and foreign trucks on Slovenia’s motorways.
5.4. Results for the baseline scenario
Figure 5.11 shows the overall model outputs, i.e., the revenue for the different tax bases considered in the analysis (energy use, vehicle stock and road use), for passenger cars and trucks for the baseline scenario (i.e. under current policies) for the IEA 2DS. The following main observations can be made:
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For passenger cars, the dominant share of tax revenue in 2017 comes from excise duty (65%), followed by revenue from vignettes (18%) in the base year. Other tax revenues sum to around 17%. By 2050, the share of excise duty revenues declines significantly to less than 50% of revenues in the base year.
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In the case of trucks, main revenues are collected from tolls (49%) and excise duty (43%) in the base year. Other tax revenues sum to less than 7%. By 2050, the share of toll revenues increases to more than 60%.
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Overall revenues from passenger cars decrease by around 44% in the period from 2017 to 2050. This is mainly due to the erosion of the fuel tax base and resulting decreases in excise duty and carbon tax revenues (-56%). The drop in revenues from the excise duty is driven by the fuel-efficiency improvements and uptake of alternative fuel vehicles according to EU emission standards and the IEA 2DS. In addition, revenues will decline as a result of a reduction in vehicle taxes, arising from a growing share of electric vehicles in the fleet, which are currently exempt or subject to reduced rates. These losses are partly offset by a slight increase in vignette sales, thanks to an increasing size of the total vehicle fleet.
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Overall, revenues from trucks increase by around 47%. This is mainly due to an increase in vehicle activity resulting in an increase in toll revenues. Revenues from excise duty remain relatively stable over the years as the effect of increased activity is here balanced by the assumed fuel-efficiency gains of diesel trucks. Also, other tax items remain relatively stable given the assumed continued dominance of diesel trucks to 2050.
Source: OECD/ITF calculations.
5.5. Alternative fuel technology scenario
The evolution of tax bases and revenues depends on assumptions regarding the uptake of new vehicle technologies that, at present, remain largely uncertain. The model derives baseline results following IEA’s 2°C scenario for Europe (IEA 2DS) from 2017 (IEA, 2017[4]). Outputs are based on scenario analysis and should not be interpreted as predictions of tax revenue or bases (see Box 5.1).
Analysing the future technology take-up in the vehicle fleet and potential impacts on tax revenue requires careful consideration of the uncertainty related to future technological developments. The present report builds on scenario analysis of technological developments and their relationship with tax bases and revenues, but does not aim to make predictions about the future.
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Predictions attempt a statement on the most likely outcome or estimated development of future, uncertain events.
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Scenarios, on the other hand, describe potential and plausible outlines of future events, based on a set of assumptions regarding key relationships, without an attempt to evaluate the likelihood of these events.
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Scenario analysis can be seen as a what-if analysis that is useful to inform decision-makers about potential future opportunities and challenges in the presence of uncertainty that may otherwise be neglected. Results from scenario analysis have no predictive intent, but show potential future developments conditional upon the assumptions taken.
The main scenario adopted in the present analysis applies the penetration of alternative fuel technologies as described in the International Energy Agency’s 2°C Scenario for Europe (IEA 2DS) to the Slovenian case. “The 2DS describes an energy system consistent with an emissions trajectory that recent climate science research indicates would give an 80% chance of limiting average global temperature increase to 2°C” (IEA, 2018[8]).
The IEA 2DS was chosen based on a review of the literature that estimates the uptake of alternative fuel vehicles measured as a percentage of new car sales. Table 5.8 contrasts the results from 2017 studies of comparable markets. Both the Bloomberg New Energy Finance and Slovenia’s own national energy model use values for the uptake of electric vehicles in new passenger car sales go well beyond the IEA 2DS.
Results for the IEA 2DS, especially with regards to passenger cars, can be interpreted as a lower bound for the penetration of electric vehicles when compared to other studies from 2017. This is also the case when comparing the IEA 2DS with more recent scenarios by the IEA. For example, IEA’s “EV30@30” scenario reflects climate ambition in line with a “Beyond 2DS” world (limiting temperature rise to 1.75 degrees, instead of 2.0 degrees) and results in a combined share of PHEV, BEV and FCEV of around 96% in new vehicle sales by 2050 (IEA, 2018[10]; IEA, 2018[11]).
Given the uncertainty around technological developments, an alternative scenario is presented, which accounts for a more ambitious outlook on the uptake of electric vehicles in the passenger car fleet (Figure 5.12). Under this alternative scenario, electric vehicles make up 70% of newly purchased cars in 2030, and more than 90% by 2040. Figure 5.13 provides the tax revenues that result from such a penetration pattern as derived by the model under current policies. In this scenario, tax revenues would fall by another 25% compared to the baseline scenario, to around 30% of 2017 levels.
Note: PHEV – plug-in hybrid electric; BEV – battery electric vehicles; FCEV – fuel cell electric vehicles; conventional hybrid vehicles are included in petrol or diesel vehicles, depending on the fuel they use.
Source: OECD/ITF calculations.
Source: OECD/ITF calculations.
References
[7] ASECAP (2018), General National Report: 2017 Motorway development in Slovenia, European Association of Operators of Toll Road Infrastructures, http://www.asecap.com/member-s-national-reports.html?download=311:slovenia.
[9] Bloomberg New Energy Finance (2017), Electric Vehicle Outlook 2017.
[5] European Commission (2014), Questions and Answers on the Commission strategy for reducing Heavy-Duty Vehicles’ (HDVs) fuel consumption and CO2 emissions, European Commission Memo, http://europa.eu/rapid/press-release_MEMO-14-366_en.htm.
[6] ICCT (2015), Quantifying the impact of real-world driving on total CO2 emissions from UK cars and vans, https://www.theccc.org.uk/wp-content/uploads/2015/09/Impact-of-real-world-driving-emissions-for-UK-cars-and-vans.pdf.
[11] IEA (2018), Correspondance with ITF, including provision of data on “EV30@30” for 2030-2050.
[10] IEA (2018), Global EV Outlook 2018: Towards cross-modal electrification, IEA, Paris, https://dx.doi.org/10.1787/9789264302365-en.
[8] IEA (2018), Glossary of definitions used by the International Energy Agency, https://www.iea.org/about/glossary/ (accessed on 31 January 2018).
[4] IEA (2017), Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations, IEA, Paris, https://doi.org/10.1787/energy_tech-2017-en.
[1] Ministry of Public Administration of Slovenia (2017), Vehicle registration records 2017 (database), https://podatki.gov.si/dataset/evidenca-registriranih-vozil-presek-stanja.
[3] Ricardo (2016), Consideration of the impacts of Light-Duty Vehicles scrappage schemes; Final report for the European Commission, DG Climate Action, https://ec.europa.eu/clima/sites/clima/files/transport/vehicles/docs/ldv_scrappage_schemes_en.pdf.
[2] Statistical Office of Slovenia (2018), Vehicle-kilometres Slovenia 2015-2016 – provisional data, https://www.stat.si/StatWeb/nk/File/NewsAttachment/2481.
Notes
← 1. Vehicle activity data from SURS allow distinguishing vehicle activity between domestic and foreign vehicles on Slovenian roads. For defining the domestic vehicle stock, only vehicle activity of domestic vehicles is relevant. The share of vehicle activity of domestic vehicles in total vehicle activity is assumed constant to 2050.
← 2. Throughout the analysis it is assumed that PHEVs drive on petrol besides electricity.
← 3. The European Commission, in November 2017, proposed new reduction targets of 15% (2025) and 30% (2030) for average CO2 emissions from new passenger cars (COM(2017)676). In October 2018 the European Parliament voted for amendments that would set more ambitious targets at 20% (2025) and 40% (2030) respectively, while the Council position keeps the 2025 target at 15% as in the original proposal and would set a 35% reduction target for 2030. (During the writing of this report, a provisional agreement was reached setting a reduction target of 15% by 2025 and of 37.5% by 2030.)