Is a Carbon-Neutral Pathway in Road Transport Possible? A Case Study from Slovakia

Abstract

Transformation of European transport belongs among the key challenges to achieve a reduction of 55% by 2030 and climate neutrality by 2050. This study focuses on GHG emissions in road transport in Slovakia, as it currently accounts for 19% of total GHG emissions (road transport emissions account for 99% of transport emissions). The main driver for this study was the preparation of Slovakia’s Climate Act and investigation of where are the limits of greenhouse gas emission reduction by 2050. With the aim of achieving maximum reduction in emissions by 2050 compared to 2005 levels, various scenarios were developed using the COPERT model to explore emission reduction strategies. The scenarios considered different subsectors of road transport, including passenger cars, light-commercial vehicles, heavy-duty vehicles (buses and trucks), and L-category vehicles and examined encompassed reduction of transport demand, improving energy efficiency, and utilizing advanced technologies with alternative fuels (hybrids, PHEV, CNG, LNG or LPG). However, the economic aspects of specific mitigation options were not considered in this analysis. The results show that there is a possibility of 77% GHG emission reduction by 2050 in comparison with the 2005 level. This reduction is accompanied by a shift in vehicle technologies to alternative fuels like electricity, hydrogen, and to a smaller extent biofuels and biomethane. This study shows that it will be possible to achieve 86.7% zero-emission cars and an additional 12.9% low emission and alternative fueled cars by 2050. By identifying and assessing these scenarios, policymakers and stakeholders can gain insights into the possibilities, challenges, and potential solutions for meeting the climate targets set by the European Union’s Fit for 55 climate package.

1. Introduction

Transportation, as a means of moving people and goods, has been associated with various challenges and issues throughout history. Even in ancient Roman cities, the mass transport of goods brought about its own set of constraints. The introduction of vehicles with combustion engines in the late 1800s, for instance, resulted in car accidents and raised concerns about air pollution. Steam engines, while considered promising, presented their own significant challenges, and were eventually limited to railways.

During the early days of road transport, electric vehicles briefly dominated, particularly in the United States. However, with the advent of mass production, vehicles powered by gasoline and diesel combustion engines became the prevailing choice over an extended period. Each of these transformative shifts in transportation initially faced considerable resistance from society.

Over time, the range of issues escalated from local to regional and to global scales. The great manure crisis of the late 1800s raised concerns about air quality in urban environments, while the widespread adoption of combustion engines led to air quality challenges in various regions worldwide. The subsequent rapid expansion of transportation further exacerbated environmental problems.

In the present day, the primary focus of discourse centers on the adverse effects of internal combustion engines on noise pollution on the local scale, air quality on the regional scale, and climate change on the global scale. While air quality concerns tend to have a predominantly regional character with varying levels of impact across different world regions, climate change is a global issue intricately linked to the transport sector. It is worth noting that the effects of climate change also vary in intensity across the globe and on a global scale, GHG emissions from transportation play a pivotal role in climate change, affecting the global climate system. The Intergovernmental Panel on Climate Change (IPCC) has repeatedly highlighted the need for substantial emission reductions from all sectors, including transportation, to limit global warming and its associated impacts.

In the context of the European Union (EU), transportation is a significant contributor to greenhouse gas (GHG) emissions, making it a crucial area of concern for climate action. The EU has been actively working to reduce its overall GHG emissions, aiming to achieve its climate targets as outlined in the Paris Agreement. The transportation sector, including road, aviation, and maritime transport, accounts for a substantial portion of the EU’s total GHG emissions. Addressing the challenges posed by transportation emissions is essential not only for individual regions and countries but also for mitigating the impact of climate change on a global level. Concerted efforts to transition to low-carbon and sustainable transportation systems are crucial for a more sustainable and climate-resilient future.

Climate change could have a large impact on economy, natural and managed ecosystems, and human health and mortality [1,2]. As a result, the Paris Agreement (PA) [3] was signed in 2016 and a large part of the world committed itself to mitigate or halt global warming. After the announcement of the US withdrawal from the PA, the European Union (EU) became a leader in the fight against climate change. In this direction, the European Union has committed itself to carbon neutrality by 2050 (except for Poland). In 2019 the European Union updated and improved its framework document on energy policies as a package under the name “Clean energy for all Europeans” [4]. As a result of calling for more aggressive greenhouse gas (GHG) reduction targets, the EU introduced the “Fit for 55” package (also called “FF55”) in summer 2021 that calls for reducing emissions to 55% below 2005 levels by 2030 and carbon neutrality by 2050 [5]. This brought the Slovak government to introduce the first proposal of a national climate act. The near-term (2030) goals for effort sharing regulation (ESR) sectors were formulated based upon estimates of national policies (i.e., national programs, action plans) and technology options to reduce national emissions. The long-term (2050) goal (carbon neutrality) was based on a prolongation of policies and technology options, with a more aggressive approach to reduce GHG emission as much as possible in the ESR sectors and to find a solution to go carbon neutral in 2050.

In Slovakia, the transport sector is one of the largest contributors of GHG emissions, making up to 19% of national total emissions in 2019 [6], and it is the only sector with a long-term rising trend from 1990. As a result of the rising trend of emissions, the transportation sector has to play the main role if significant emission reductions need to be achieved. In this study, the focus is on road transport. Road transport is responsible for 99% transport sector GHG emissions, excluding the EU ETS pipeline subsector (according to IPCC nomenclature). Breakdown of road transportation energy use and tank to wheel emissions by each road transport subsector in 1990 and the reference year (2019) are summarized in

Table 1. 1990 and 2019 Slovakia road transport energy use and GHG emissions by subsector (according to Slovakia NIR 2023 [6]).

Subsector	Vehicle Types	1990		2019		1990		2019
		Energy Use		Energy Use		GHG Emissions		GHG Emissions
		PJ	%	PJ	%	kt	%	kt	%
1.A.3.bi	Passenger cars	19.43	31.8	66.6	61.0	1453.9	31.7	4617.8	60.5
1.A.3.bii	Light-commercial vehicles	4.29	7.0	12.5	11.4	318.56	7.0	876.75	11.5
1.A.3.biii	Heavy-duty vehicles Buses	36.70	60.1	29.85	27.3	2766.1	60.32	2115.4	27.7
1.A.3.biv	Motorcycles Microcars Mopeds ATVs	0.61	1.0	0.27	0.3	47.40	1.03	18.35	0.2
1.A.3.b	All subsectors	61.03		109.20		4585.9		7628.3

. In the last 30 years, the major share of energy use (in petajoules, PJ) and GHG emissions shifted from heavy-duty vehicles to passenger cars. Whereas in 1990 approximately two-thirds of the energy use and emissions were accounted for by freight transport, in 2019 approximately three-quarters of energy use and emissions were accounted for by passenger transportation using cars.

This study focuses on finding the best path of how the road transport sector can assist in meeting or exceeding the targets introduced in the Slovak Climate Act proposal and whether there is a possibility to completely decarbonize the road transport at latest by 2050. This study explores options for reducing emissions in the transport sector in the year 2050 compared to 2005. To do this, a scenario approach was used. These scenarios look across all transportation subsectors (cars, light-commercial vehicles, heavy-duty vehicles—buses and trucks) and the L-category (motorcycles, mopeds, microcars and ATVs—all terrain vehicles), and includes strategies for reducing or changing travel and transport demand, improving engine efficiency, and using advanced technologies with alternative fuels using all available national action plans and strategies connected to the transport sector. A limitation of this study is that in the scenarios the issues of economic and dynamic specific mitigation options are not considered in order to keep the analysis simple and evaluable. As this study centers on the Climate Act and the strategies to attain its objectives, it considers solely the national level, overlooking regional variations. This approach is justified due to the concentration of transport intensity in economically advanced regions, and the model operates using national averages. Scenarios are presented with explicit assumptions on emissions to introduce potential future situations to the policymakers and stakeholders. In addition, the discussions about the possibilities to reach the targets and implement technologies and challenges represented in the European Union’s Fit for 55 climate package can begin.

2. Materials and Methods

2.1. COPERT Model

This study uses the COPERT model [7] to calculate the amount of GHG emissions per vehicle category for a year. The version of the COPERT model used in this study is V5.5.1. The COPERT model is a macroscale emissions model funded by the European Environmental Agency and developed by EMISIA, S.A., EMISIA S.A. is the spin-off of the Laboratory of Applied Thermodynamics of the Aristotle University: (https://www.emisia.com/, accessed on 1 July 2023), and is widely used in Europe. The model is used mostly for research activities as a standardized comparison model, but it does have wider usage. O’Driscoll et al. used the COPERT model to assess speed dependent emissions factors [8]. The study [8] compared measurements between PEMS and COPERT speed dependent emission factors. The study [8] showed that difference between measurements and modelling emissions is declining with newer technologies. Brady and O’Mahony [9] evaluated the Irish national plan for 10% of the national road fleet to be powered by electricity by 2020. The results showed that each of the EV scenarios examined would bring reduction in CO₂ emissions. Quaassdorff et al. [10] used the COPERT model to validate Madrid’s emission predictions. The study [10] focused on traffic simulation to obtain data on vehicle activity. It was found, however, that the data obtained by the engine data logger are more appropriate and more consistent with real working conditions, which is beneficial for further improving the accuracy of the model. The data of the COPERT model mainly come from the bench test data accumulated by EU countries and the JRC (JRC Energy, Mobility and Climate: https://op.europa.eu/en/web/who-is-who/organization/-/organization/JRC/COM_CRF_3415 Joint Research Centre, accessed on 1 July 2023). The JRC is the European Commission’s in-house science and knowledge service and scientific advisor. The COPERT model always reflects and incorporates the latest developments and scientific knowledge into emission calculations, which are incorporated annually in a major update. The emission calculation methodology is described in the EMEP/EEA Atmospheric Emissions Inventory Guidebook [11] on exhaust emissions from road transport. The emission calculation includes all GHG gases (CO₂, CH₄, and N₂O), as well as all relevant air pollutants (NO_X, SO_X, NMVOC, PAH, CO, PM₁₀, PM_2.5, and NH₃). The CO₂ emissions are modelled based on the carbon content of each fuel provided by the users and basic emission factors deriving the CO emissions.

The COPERT model is a widely accepted model across the EU, particularly in small countries with limited resources for the development of national transport models or using robust economic models. The COPERT model was selected as the most comprehensive emission calculation model and as a seamless extension of the historical emission trend calculations. Slovakia employs the COPERT model for emission estimation, making it a logical continuation of the calculation process, ensuring the consistency of data and computations for a longer period.

There are also alternative models available for similar study, but these models are based more on economics and Slovakia lacks data to prepare enough comprehensive scenarios. The PRIMES model used for Slovakia’s Low Carbon Strategy [12] is too robust a model to evaluate only road transport and the comprehensiveness of the vehicle fleet is too low to apprehend the penetration of smaller groups of technologies (alternative fuels). The model TR³E used by Rabiega et al. [13] is an economic model and this study excluded the effect of economy such as carbon pricing, fuel, and vehicles prices as well as other market variables. These are excluded from our model and the only economic limitation in the study is the total annual registration of vehicles. The registration includes newly purchased vehicles and individually imported vehicles. This limitation was set so as not to overshoot the vehicle fleet and purchasing capacity of Slovakia.

2.2. Base Year and Activity Data

The base year for calculation was 2019 and the reference year 2005. The year 2019 was designated as the base year because the following years 2020 and 2021 were affected by the COVID-19 pandemic and the emissions from road transport drastically decreased due to the curfew. The environmental data (monthly minimal and maximal air temperatures and average monthly humidity) are from the Slovak Hydrometeorological Institute (www.shmu.sk). Fuel specification data and lubricant specification data were measured by The Research Institute of Petroleum and Petroleum Gas (VURUP) using the standardized method ASTM D 5443. Statistical fuel consumption was obtained from the Fuel Quality Report under Article 7a. Stock configuration data was sourced from the Vehicle Evidence Database (IS EVO) of the Police of the Slovak Republic, as presented in

Table 2. Stock and aggregated activity data for base year (2019) (according to Slovakia NIR 2023 [6]).

Category of Road Vehicle	Activity Data		Category of Road Vehicle	Activity Data
	Number of Vehicles	Average Mileage (km/veh)		Number of Vehicles	Average Mileage (km/veh)
Passenger Cars	2,287,768	9782.7	Heavy Duty Trucks	70,571	17,625.9
Petrol Mini	9458	5268.0	Petrol > 3.5 t	129	4995.5
Petrol Small	816,290	4254.1	Rigid ≤ 7.5 t	21,259	27,366.4
Petrol Medium	357,939	4551.7	Rigid 7.5–12 t	12,807	32316.7
Petrol Large	48,230	4866.2	Rigid 12–14 t	3247	30,240.9
2-Stroke	156	1548.8	Rigid 14–20 t	4085	21,343.5
Hybrid Mini	65	4909.2	Rigid 20–26 t	1041	12,396.2
Hybrid Small	379	11,669.2	Rigid 26–28 t	34	10,585.2
Hybrid Medium	6257	16,039.0	Rigid 28–32 t	217	15,139.0
Hybrid Large-SUV-Executive	2608	11,547.8	Rigid > 32 t	158	7175.0
Diesel Mini	364	2394.7	Articulated 14–20 t	27,573	56,764.5
Diesel Small	25,600	10,093.2	Articulated 20–28 t	20	20,432.6
Diesel Medium	819,620	17,472.8	Articulated 50–60 t	1	14,806.7
Diesel Large-SUV-Executive	150,793	12,955.1	Buses	7650	31,864.3
LPG Bifuel Mini	28	8037.2	Urban Buses Midi ≤ 15 t	707	38,296.9
LPG Bifuel Small	23,043	18,878.9	Urban Buses Standard 15–18 t	277	34,843.4
LPG Bifuel Medium	20,611	19,843.9	Urban Buses Articulated > 18 t	45	24,047.9
LPG Large-SUV-Executive	4324	17,812.9	Coaches Standard ≤ 18 t	6321	43,368.7
CNG Bifuel Small	1249	13,162.4	Coaches Articulated > 18 t	57	57,001.4
CNG Bifuel Medium	697	11,961.7	CNG Buses	243	20,712.5
CNG Large-SUV-Executive	57	8088.9	L-Category	133,751	909.1
Light Commercial Vehicles	246,175	10,722.1	Mopeds 2-stroke < 50 cm3	560	190.4
Petrol N1- I	25,074	6191.1	Mopeds 4-stroke < 50 cm3	27,850	312.3
Petrol N1-II	9367	7192.0	Motorcycles 2-stroke > 50 cm3	1507	984.8
Petrol N1-III	2511	8178.4	Motorcycles 4-stroke < 250 cm3	45,144	762.0
Diesel N1- I	16,451	12,497.2	Motorcycles 4-stroke 250–750 cm3	27,426	1227.1
Diesel N1-II	70,231	12,890.6	Motorcycles 4-stroke > 750 cm3	31,264	1978.0
Diesel N1-III	122,541	17,383.5

. Originally obtained individual data on vehicles were aggregated for the needs of the COPERT model and thereafter to ensure privacy protection and GDPR. Stock and activity data, including the number of vehicles and average annual mileage, were extracted from the project ‘Improving the allocation of road transport emissions in AEA module and coherence between AEA and PEFA modules’ [14]. The number of vehicles and average annual mileage play a crucial role in estimating emissions. The number of vehicles was directly obtained from the IS EVO, which serves as the vehicle registration information system responsible for collecting, recording, and storing information about registered vehicles in the Slovak Republic. It captures data related to car owners, car registration numbers, and other relevant details. The IS EVO is operated by the Ministry of Interior of the Slovak Republic through the Central Body of State Administration. The annual average mileage is calculated using software developed during the project [14], utilizing data from the Periodical Technical Inspection (PTI) database operated by the Ministry of Transport of the Slovak Republic, which was made available for the project’s purposes [14]. The model [7] estimates the carbon, hydrogen, and other heavy metals based on the type and amount of fuel used. These values directly affect the calculation results of emissions.

Circulation data were collected using the National Traffic Census 2015 [15] and Google api based on GPS position. The census takes place every five years and its first step is a statistical survey in Slovakia, followed by modelling of the traffic based on the survey and its switching to map layers. The speed data was taken as the default values of the COPERT model [7].

2.3. Calibration and Prediction of Vehicle Fleet

Recently, in the COPERT model the CLI (command line interface) module was introduced, which allows new technologies that are not directly defined by the model to be brought into the modelling. This includes emission-intensive technologies such as LNG, flexi-fuel, e-fuel, or hydrogen engines. The CLI module allows the calculation of emission projections using the COPERT model. To support the CLI module, EMISIA prepares and periodically updates the Sybil baseline [16]. The Sybil baseline is a database of vehicle fleets, activity data, and emission factors for technologies not included in the basic COPERT model, made for the 27 EU countries. The Sybil baseline is prepared based on data from the following:

• National statistical data reported to EUROSTAT [17]
• Results from projects (FLEETS [18], TRAACS [19], NMP [20])
• EC Statistical Pocketbook [21]
• ACEA (The European Automobile Manufacturers’ Association) [22]
• ACEM (The Motorcycle Industry in Europe) [23]
• Monitoring of CO₂ emissions from passenger cars under Regulation (EU) 2019/631 [24]
• EAFO (European Alternative Fuels Observatory) [25]
• NGVA EUROPE/NGV GLOBAL (The Natural and Bio-Gas Vehicle Association) [26,27]
• UNFCCC National inventory reports [28]
• Vehicle age distribution based on the Weibull distribution.

The Weibull distribution is a continuous probability distribution. It models a broad range of random variables, largely in the nature of time to failure or time between events. Zachariadis et al. [29] introduced the Weibull distribution to dynamic modelling of vehicle populations and internal technological turnovers before 2010. This approach is still widely used to predict vehicle fleet turnover and lifespan of vehicles in Europe [30]. The Sybil baseline data had to be adjusted and calibrated to real national circumstances maintaining the base trend. The base trend was adjusted as follows:

x_n/x_(n−1) = a

(1)

In the equation, x_n is the number of vehicles in a predefined category, x_n−1 is the number of vehicles in a predefined category in the year prior, and a is the development coefficient. The calibration is then made with the following equation:

a × y_n−1 = V

(2)

where y_n−1 is the number of vehicles in a predefined category from the IS EVO year prior to the calculated year and the same year as x_n−1.

The actual and predicted trends of vehicles based on emission production in individual scenarios are shown in Figure 1. In this study conventional vehicles are counted as diesel oil and petrol fueled, ZEV vehicles are all types of vehicles not producing GHG emissions (electric and hydrogen), and alternative vehicles are all other technologies which still produce a reduced amount of GHG emissions.

2.4. Scenarios

For this study a Reference scenario or Business as Usual (BAU) scenario, a set of Silver Bullet (SB) scenarios and a Low Carbon (LC) scenario were prepared. Each individual SB scenario represents one of the possible measure in the LC scenario. The Final LC scenario is the sum of all SB scenarios (measures) and their synergic effect. Each of these scenarios assumes different fleet turnover and development strategies, but all of them contain the same technological base with different strength and speed of development. Road transport will be affected by policy measures mainly from three areas: energy, transport, and environment. Energy policy measures are mainly focused on energy efficiency and renewable fuels (biofuels) in transport. Transport policy measures, on the other hand, focus on transport infrastructure and intensity, and environmental policy measures focus directly on reducing emissions of greenhouse gases. In addition, the policy measures can be divided into four categories:

• Measures affecting transport intensity at the national level (e.g., modal shift, taxes)
• Measures that affect emission factors (e.g., EU Directives)
• “Soft” measures that cannot be quantified
• Measures that can be quantified but not at national level (e.g., non-motorized transport).

The BAU scenario assumes that there will be no external influence; that means no new policy measures implemented after the end of 2019. It describes future emissions in the road transport sector based only on the predicted basic vehicle trend (Figure 1). The transport intensity and demand (passenger and freight) on the road will increase according to the Low Carbon Development Strategy of the Slovak Republic [12]. This transport intensity was calculated by the CPS+ macroeconomic model (Compressed Primes Model) [12]. There are only the following 5 known measures:

• Directive (EU) 2018/2001 of the European Parliament and of the Council (RED II) on the promotion of the use of energy from renewable sources [31]
• Sale of low-emission vehicles (electric hybrids or plug-in hybrids) or directly zero-emission vehicles (electric cars) [32]
• Energy efficiency [33]
• Regulation (EU) 2019/631 of the European Parliament and of the Council of 17 April 2019 setting CO₂ emission performance standards for new passenger cars and light-commercial vehicles [34]
• Regulation (EU) 2019/1242 of the European Parliament and of the Council of 20 June 2019 setting CO₂ emission performance standards for new heavy-duty vehicles [35].

The revised Renewable Energy Directive [31] sets new targets for the blending of renewable fuels (biofuels) into fossil fuels as is shown in Figure 2.

The estimated percentage of electric vehicles (EVs) in the passenger car category is shown in Figure 3. This is a more conservative estimate of the number of EVs in 2030 than in the EU Reference scenario [36]. According to the EU Reference scenario, the share of EVs in the EU as a whole is expected to reach 25% in 2030, but due to low infrastructure in Slovakia this is not achievable.

In our study, energy efficiency, which represents improvements in efficiency, is incorporated into the model similar to the real options. The potential for enhancing combustion and engine efficiency to achieve “ultra-efficiency” was estimated at 15% for passenger cars equipped with spark-ignition engines, as reported in the ERTRAC Report [37]. For passenger cars with compression-ignition engines, the estimated improvement by 2050 was 12%. However, for light- and heavy-duty vehicles, the potential improvement by optimizing engine efficiency was found to be 10%. Additionally, energy efficiency directly interacts with and is influenced by Regulation (EU) 2019/631 [34] and Regulation (EU) 2019/1242 [35].

The SB scenarios (measures) and the LC scenario are built on policies, strategies, and action plans that were not put into force before 2020. A brief description of each SB scenario is shown in

Table 3. Brief description of SB scenarios (measures).

Policy	Scenario Summary
High decarbonisation of passenger car scenario (EVPC)	Intense ZEV penetration to the fleet, 65% of PC is electric, 20% hydrogen, 10% PHEV until 2050, support according to Action plan for the Development of Electric Vehicles in the Slovak Republic [32] and European Hydrogen Strategy [38]
High technical inspection scenario (STK_TAX)	Strict periodic technical inspection should result in the capture and removal of the oldest and non-compliant vehicles from transport, phaseout 0.01–0.05% vehicles older than 15 years, gradually diminishing effect, based on National Air Pollution Control Program [39] and Act on Vehicle registration fee based on gCO2/km emissions (in force from 1 July 2023) [40]
PT modal shift scenario (PC_modal)	Increase in PC occupancy by 50% compared to 2020 by 2050, decrease PC mileage by 33% and increase bus mileage by 10%, based on Strategic Transport Development Plan 2030 [41]
Goods transport modal shift scenario (HDV_modal)	Decrease of goods transported by HDV by 50% by 2050 based on Strategic Transport Development Plan 2030 and Low Carbon Development Strategy of the Slovak Republic [12]
City cycling scenario (Cyclo)	Short distance travelling in cities by bicycles will rise by 6% according to National Strategy for the Development of Cycling Transport and Cycling Tourism in the Slovak Republic [42]
2035 Passenger car fossil fuel phaseout scenario (2035_PC)	Application of EU proposed directive on reducing GHG emission to 0% from new passenger cars from 2035
Last mile decarbonisation scenario (EVLCV)	Higher penetration of ZEV to the LCV fleet, 50% of LCV is electric and 3.1% hydrogen until 2050
HDV fleet turnover scenario (HDV_phaseout)	Promotion and higher penetration of low GHG emission HDV to the vehicle fleet, 10% of HDV is LNG fuelled, 1.7% is hybrid, 4% electric, and 8% hydrogen until 2050 based on National Air Pollution Control Program [39], Integrated National Energy and Climate Plan for 2021 to 2030 for Slovakia [43] and European Hydrogen Strategy [38]

. Each scenario represents a single mitigation option possible to use to reduce GHG emissions. The LC scenario is prepared afterwards as a combination of all measures from each SB scenario.

In addition, all SB scenarios include also the RED directive in biomethane blending to methane-based fossil fuels (CNG and LNG), which are considered alternative fuels. This measure is not considered in the BAU scenario as it is still not in force even if it is part of a measure from this scenario.

To evaluate the impact of climate change and possible rise of average ambient temperature to GHG emission production by road transport, the default average temperatures in the model were substituted by data from the KNMI-RACMO22E climate model obtained from the COPERNICUS Climate Data Store [44]. The optimistic RCP 2.6 scenario was used as the study worked with the ambition to fulfil the Paris Agreement.

3. Results and Discussion

Scenarios in this paper are used to estimate the GHG emission reduction potential and assess the possible impact associated with the implementation of the proposed policy measures. The presented scenarios should not be taken as an exact prediction of the future, but rather as plausible assumptions about the utilization of specific mitigation pathway strategies.

3.1. Reference Scenario (BAU)

The Reference scenario aligns with the historical trend of GHG emissions, exhibiting primarily rapid growth due to significant past consumption of petrol and predominantly diesel oil, as depicted in Figure 4. It is expected that the tipping point for GHG emissions will likely occur around the year 2030, resulting from the failure to achieve the necessary reductions mandated by the Paris Agreement and other commitments made by Slovakia. By 2030, GHG emissions are anticipated to be approximately 66% higher than the reference year, with a subsequent decline to reference year levels following the tipping point, as illustrated in

Table 4. Overall trend of GHG emission projections under BAU and LC scenarios as comparison in the transport sector.

Year	BAU Scenario	LCS Scenario
	Gg CO2 Equivalents
1990	4585.89	4585.89
1995	4112.70	4112.70
2000	4142.50	4142.50
2005	6240.52	6240.52
2010	6499.42	6499.42
2015	7005.14	7005.14
2019	7628.26	7628.26
2020	6806.59	6806.59
2025	9637.17	8122.74
2030	10,339.81	7497.21
Comparison with 1990	125.47%	63.48%
Comparison with 2005	65.69%	20.14%
2035	9777.33	5911.91
2040	8952.41	4058.95
2045	7565.19	2492.65
2050	6410.66	1450.00
Comparison with 1990	39.79%	−68.38%
Comparison with 2005	2.73%	−76.76%

.

The emissions in this scenario stay considerably higher compared to the first recorded/calculated year (1990). This can be interpreted as a result of the slow development of road transport in previous years and a small portion of LCV vehicles, which only gained importance later while today this category is playing an important role in the decarbonization of last mile logistics as it is assumed that this vehicle segment will be still growing in the future.

There is also a great gap between the EU Reference scenario 2020 for Slovakia and the reference scenario prepared for this study. This gap is a result of different input data sources. The EU Reference scenario uses only internationally available statistical databases while not fully accepting national circumstances, whereas the BAU scenario of this paper arose from national data and individual datasets provided only for the purpose of this study and needed to be aggregated. There are also differences between national and international statistical data, which were explored as statistical deviation in fuel reporting in the transport sector [45]. The analysis in this study showed, that data from the Statistical Office of the SR and reports under the Article 8 of the EU Fuel Quality Directive of the Ministry of the Environment of the SR, proved to be the least consistent data having the highest rate of variability. The highest consistency and transparency in data was shown the FQD art.7a report. The consequence, of such significant deviations may subsequently be the incorrect estimates of emissions and the deviation of Slovakia from the trajectory leading to carbon neutrality.

3.2. Silver Bullet Scenarios (SB)

Silver Bullet scenarios describe possible future GHG emissions in which only one mitigation option is deployed and implemented. The emissions are calculated to gain an understanding of the total GHG reduction potential of a particular mitigation option. The SB scenarios each modify only one element of the BAU scenario, such as passenger transport modal shift (transport demand) or high efficiency ZEV penetration.

Figure 5 shows the reduction potential in GHG emissions for each SB scenario and BAU scenario. The most promising scenarios to reduce emissions are the mitigation option to ban sales of fossil fuel cars and the high penetration of zero emission technologies to the vehicle fleet. In 2050 these two mitigation options can achieve 22% (1500 kt CO₂) reduction compared to the reference year, but the ban mitigation option has a slightly higher reduction rate between 2030 and 2050. This is due to the fact that this mitigation option is in 2030 not in force.

On the other hand, the weakest mitigation options are the introduction of strict periodic technical controls with an environmental registration tax for fossil fuel vehicles and the promotion of commuting by bicycle. The bicycle city commuting reduction contributes only 1.7% (110 kt CO₂) to the BAU scenario in 2050. This is much less than anticipated by Bucher et al. [46] in their study, where the possible reduction rate is more than 10%.

3.3. Low Carbon Scenario (LC)

None of the individual mitigation options processed through the Silver Bullet scenarios can achieve the goal that Slovakia has committed itself to by signing the Paris Agreement and to the goals set by the EU FitFor55. However, many of the examined policy measures are complementary and can be combined in a single scenario. This scenario should be sufficiently ambitious and realistic to drive the emissions to the point where they can be balanced enough by the LULUCF sinks (Figure 6).

There has been only a limited number of comprehensive studies focusing on emissions reduction in road transport across European countries. A study conducted by Rabiega et al. [13] explores decarbonization possibilities in road transport in Poland. Despite economic limitations, the study estimates a similar proportion of electric vehicles (EVs) will achieve 65% by 2050. This suggests that the electrification of the passenger car fleet may be driven more by social and behavioral changes in society rather than solely by economic variables. However, differences can be observed in the case of internal combustion engine (ICE) vehicles, where Rabiega et al. [13] estimate a 26% share of ICE vehicles in 2050, the scenarios for Slovakia indicate a potential reduction of ICE vehicles to approximately 4.5% in 2050, representing the maximum achievable level in current conditions.

Similar comprehensive studies exist for other regions, although they may have slightly different economic and socio-economic variables, making direct comparisons with European studies more challenging. For example, Chang and Chung [47] examined potential policies to reduce greenhouse gas emissions by 2050 in Taiwan, starting from 2005. They found that curbing the growth of ICE vehicles, promoting the use of plug-in hybrid electric vehicles (PHEVs), and introducing carbon allowances would be necessary for Taiwan. However, our results from the SB scenarios demonstrate that simply halting the growth of vehicles and supporting PHEVs would not be sufficient, and additional measures need to be explored and evaluated.

In contrast, Davis et al. [48] conducted a study on GHG emissions from road transport in Canada, focusing solely on a BAU scenario and identifying potential areas for future policy option. They estimated a 9% increase in GHG emissions by 2050 compared to 2014, with consistent emission growth. This finding contrasts with our results, where even in the BAU scenario, GHG emissions are expected to decrease after 2030, with emissions in 2050 only 2.7% higher than in 2005 (Table 4).

In addition to the result of the SB scenarios, it is evident that the electricity and hydrogen-driven mitigation option exhibit a high level of synergetic effect when combined with the ban of fossil fuel passenger cars from 2035. The ultimate goal of both options is to significantly increase the adoption of zero-emission vehicle (ZEV) passenger cars by 2050. This alignment in objectives allows for a seamless integration of efforts, leading to more effective and efficient results. As a consequence of this synergy, there is a potential to gradually reduce or phase out support for the procurement of ZEVs after 2035. This is because both mitigation options are projected to be driven by market forces at that point, making additional support unnecessary. It is important to highlight that achieving this harmonized approach can result in a substantial reduction in GHG emissions and contribute significantly to the long-term goal of decarbonizing the transportation sector.

With such a complementary strategy in place, the transition to a low-carbon and sustainable transport system becomes more feasible, paving the way for a cleaner and greener future. The difference in strength of these two mitigation options will be 23 kilotons of GHG emissions. ZEV vehicles should accommodate about 85% of all passenger vehicles in 2050 with additional 14% of low-carbon intensity vehicles (hybrids and plug-in hybrids) until 2050. Fuel-cell electric vehicles represent 20% of ZEV vehicles according to the Hydrogen Council in 2050 [49]. The Hydrogen Council defines itself as “a global CEO-led initiative of leading companies with a united vision and long-term ambition: for hydrogen to foster the clean energy transition for a better, more resilient future”. Samsun et al. [50] support this as the level of 20% is very feasible level for hydrogen powered passenger vehicles, but also urge a more ambitious ramp-up in the coming years to achieve the set target. Furthermore, the penetration of ZEV results in a shift in the distribution of fuel consumption, becoming more diversified with a greater emphasis on electricity consumption (Figure 7).

The implementation of a modal shift in road transport, both for passenger and freight transportation, has the potential to significantly reduce GHG emissions by approximately 1 519 kilotons. This shift is necessary to alleviate the pressure on the road transport infrastructure and overall transportation intensity.

In the case of passenger transport, the modal shift involves encouraging passengers to shift from using personal cars to using public transportation. This change is expected to lead to a 50% increase in the occupancy rate of passenger vehicles compared to the 2019 rate of 1.13. As a result, the total distance traveled by passenger vehicles is projected to decrease by up to 33% by 2050. The modal shift for passengers will involve a dual approach, with a portion of travelers transitioning to public road transport with others shifting to rail transport. Furthermore, Wang et al. [51] examined the feasibility and conditions for the successful functioning and implementation of Demand-Responsive Transport (DRT) systems and identified the factors that enable their full potential utilization.

Regarding freight transport, which is currently heavily reliant on road transportation, the freight modal shift policy aims to reduce the volume of goods transported by trucks by 50% by 2050 [12,41]. This shift involves transferring some of the goods to railways, leading to a decrease in annual vehicle mileage and thus contributing to the reduction of GHG emissions.

Additionally, the last mile logistics in freight transport represents another area for potential GHG emission reduction. However, this study primarily focuses on the conventional approach to decarbonization, which involves the electrification of vehicles using electricity and hydrogen as primary sources. Figliozzi [52] also evaluated new possible advanced technologies as sidewalk autonomous delivery robots (SADRs), drones, or road autonomous delivery robots (RADRs). RADRs are more efficient than electric vans but are limited by delivery to only a low number of costumers. Awwad et al. [53] also summarized some of the solutions such as a statistical method to allocate vehicles to delivery locations, use of e-vehicles, airborne fulfilment centers, reducing the number of failed deliveries, proper fleet planning, carbon emission trading, use of alternative fuels, while the use of urban consolidation centers have the potential to reduce CO₂ emissions.

The combination of these measures forms a critical part of the efforts to achieve significant emission reductions in the road transport sector. By promoting a modal shift and adopting alternative modes of transportation, it will be possible to pave the way for a more sustainable and environmentally friendly transport system.

The implementation of additional mitigation options, such as phasing out diesel oil-powered heavy-duty vehicles and replacing them with alternative fueled powertrains like diesel hybrids, LNG, electricity, or hydrogen-powered engines, may individually have a relatively small impact. However, combining these measures can play a crucial role in GHG emission reduction.

Delgado et al. [54] explored various technological possibilities to reduce GHG emissions in road freight transport, including improvements in aerodynamics, tires, and hybrid start/stop systems; all of which would be beneficial in reducing emissions.

It is important to note that phasing out diesel oil powertrains may initially lead to a temporary increase in emissions. However, this could be attributed to the COPERT model’s tendency to assign more annual mileage to newer technologies.

Initiatives promoting cycling have a more localized impact on air quality rather than a national or global impact on GHG emissions. On the other hand, the Periodic Technical Inspections (PTI) in 2050 are estimated to have an impact of only about 1 kiloton of GHG emissions. However, the true potential of PTI lies in its support of the electricity and hydrogen mitigation options.

4. Conclusions

Most studies typically adopt an approach based on economic feasibility. However, our study deviates from this convention by excluding most of the economic variables. The innovation of our study lies in its comprehensive examination of emissions at the national level, a perspective rarely explored by previous research. Only a limited number of studies have operated at this level, with most focusing on specific aspects, such as freight transport, public transport, and various types of promotions for zero-emission vehicles (ZEVs). The added value of our study lies in addressing this literature gap for smaller countries with limited access to data.

Regarding emissions, the BAU scenario results in a 66% increase in greenhouse gas emissions by 2030, equivalent to a rise of 4000 Gg of CO₂ equivalents compared to 2005. This increase is predominantly driven by the passenger car and light commercial vehicle segments, with heavy commercial vehicles (trucks over 3.5 tons) contributing only about 5% to the overall increase.

Analyzing each Silver Bullet scenario, we observed that no single policy or measure can fully achieve the goals set in the Fit for 55 goals. The scenario with the lowest outcome for the 2030 goal is the phaseout of heavy-duty vehicles, as it leads to even higher emissions compared to the BAU scenario. On the other hand, the scenario with the strongest impact, focused on the electrification of individual car transportation, appears to be a promising approach for achieving the Fit for 55 goal. Additionally, phasing out passenger cars altogether would contribute to the overall goals set by the Paris Agreement.

In our Low Carbon scenario, we aimed to achieve the highest possible reduction in emissions in Slovakia by 2050, targeting a 78% share of zero-carbon vehicles. Vehicles using alternative fuels, such as PHEV, hybrids, biofuel, biomethane, LNG, CNG, and LPG, are projected to comprise an 11% share, while vehicles with internal combustion engines are expected to decline to as low as 11% by 2050, representing approximately 340 thousand vehicles. The majority of internal combustion engine vehicles will belong to the L-category (50%) and light-commercial vehicle category (28%).

According to the BAU scenario, the increasing trend in vehicle numbers will stabilize after 2035. In contrast, the more optimistic LC scenario will experience stabilization after 2030 and a subsequent decrease in vehicle numbers after 2040. The passenger car intensity and transport demand, as per the BAU scenario, could potentially reach 51.48 billion pkm in 2040, a 68% increase compared to 2019. However, in the LC scenario, this volume will decrease by 50%, along with a significant reduction in overall kilometers traveled by passenger cars, thereby contributing significantly to emissions reduction.

The LC scenario, currently regarded as highly ambitious, yet realistic, incorporates mitigation options that can significantly reduce GHG emissions. By 2030, emissions would increase by only 20% compared to 2005 levels, and by 2050, the measures introduced in this scenario could result in a 76% reduction in emissions compared to 2005. The most impactful measure in this scenario would be the mandatory reduction of CO₂ emissions from new vehicles by 100% after 2035 (banning fossil fuel passenger cars). Any changes to this target date would have a significant impact on future mitigation, and there is a risk that Slovakia or other similar countries may struggle to fulfill their commitment to carbon neutrality due to road transport.

In summary, while individual mitigation options may seem relatively modest in their impact, combining various measures and strategies can significantly contribute to achieving carbon neutrality. This highlights the importance of adopting a comprehensive and integrated approach to decarbonization in the road transport sector.