The Development of Electromobility in the European Union: Evidence from Poland and Cross-Country Comparisons

Abstract

The article presents the current situation with the implementation of electric cars in Poland against the background of pan-European trends and the current energy sector condition in Poland. The aim of the study was to show the ecological, economic, social, and technical difficulties and controversies related to the use of electric cars in Poland. The authors used statistical analysis and identified the main factors of electromobility development in Poland. Despite the declared electromobility in the Strategy of Responsible Development program, the degree of its implementation is far from the previously established goals. The article analyzes the feasibility of the above plan and indicates the factors inhibiting the development of electromobility in Poland. The strategy and decisions to further implement electric cars cannot be purely political. In justifying the need to promote the use of electric cars in Poland, one should take into account the specificity of the Polish economy, its energy system, the condition of its infrastructure, and real social needs. The authors formulated a hypothesis that the aforementioned program is based on simplistic assumptions and marginalizes or ignores significant barriers to the development of electricity-based transport in Poland. The successful implementation of that program is conditioned by comprehensive and costly activities aimed to transform the whole energy system and introduce an effective system of incentives for the buyers of electric cars.

1. Introduction

In February 2017, the government of Poland adopted the Strategy for Responsible Development [1]. One of its priorities is the development of electromobility in accordance with a government plan [2]. This program, referred to as the Electromobility Development Plan, was presented as early as in March 2017 [3]. It assumes, among others, the use of one million electric cars in Poland in 2025. The plan’s goals can be summarized in the following points:

• Creating conditions for the development of electromobility in Poland. The plan proposes steps that will develop the charging infrastructure. This will help reassure consumers that an electric vehicle is as functional as an internal combustion one.
• Development of the electric vehicle market. It is assumed that as a result of the implementation of the instruments proposed in the plan, at least 30% of the added value related to the production of electric vehicles registered in Poland in 2025 will be created in our country. An industrial analysis will be performed to determine the final value of this index.
• Stabilization of the power grid. The development of electromobility will adapt the network infrastructure to the changing needs of the economy. The appropriate level of investment should allow avoiding problems related to the need to connect to the network. Moreover, the stabilization of the investment environment for network operators will allow them to actively join the network of charging points for electric vehicles.

The authors of the plan identify the following benefits resulting from the implementation of new solutions [1,4]: adapting the Polish economy to European environmental regulations and improving air quality, increasing demand for electricity and the effectiveness of energy generation, increasing the country’s energy security, improving the country’s trade balance as well as improvements in scientific research, the car industry, and the market of additional services [1]. The development of electromobility is consistent with a vision of the EU’s modern, competitive, and climate neutral economy to be achieved by 2050 [5].

However, at the halfway point of implementing the plan of electromobility in Poland, progress made is far from satisfactory [6]. According to the estimates of the National Framework for developing infrastructure for alternative fuels, the number of electric cars in Poland in 2020should amount to 76.9 thousand (BEV and PHEV) [7]. The actual number of registered vehicles by the end of 2020 was 18,875 e-motorcars (10,041 BEVs and 8834 PHEVs) [8], with a high increase in new registrations recorded in 2020 (8637 electric cars registered in Poland at the end of December 2019 (5091 BEVs and 3546 PHEVs) [9]. The development of electromobility in Poland faced a number of barriers at the very beginning of implementing the plan. They include the lack of access to the proven technologies of production and effective resources as well as a relatively low level of general prosperity [10]. Special attention should be given to the following problems [11,12]:

• limited access to Polish coal;
• a high level of decapitalization and low efficiency of Polish power plants;
• low daily reserves at the time of high demand for charging services;
• inadequate number of charge stations for electric cars;
• lack of adaptation of household power connections to rapid chargers;
• uncertainty with regard to development trends in Polish energy policies;
• limitations in developing renewable energy sources in Poland;
• lack of production versions of Polish electric cars;
• inadequate incentives to purchase electric cars; and
• long-term return on investment in electric cars.

Unfortunately, no breakthrough changes have been recorded despite a period of four years of implementing the plan and a relatively good identification of barriers to electromobility [13].

This paper aims to determine whether the planned goals are achievable and to identify the factors that hinder electromobility development in Poland. The authors conject that the plan is based on overoptimistic assumptions and that it marginalizes, or even disregards, significant barriers to electromobility-based transport.

The successful implementation of the electromobility development plan is conditioned by comprehensive activities aimed to transform the energy system by developing a dispersed network of renewable energy sources, creating the charging infrastructure and introducing an effective system of incentives for the buyers of electric cars [14].

The structure of the paper comprises an assessment of Poland’s economic situation, particularly its energy industry, and the identification of infrastructural, technological, and demand-related barriers to electromobility development. The further parts of the work present a statistical analysis that allows for identifying the main drivers of electromobility development. The final part of the paper presents conclusions and recommendations for accelerating the electrification of road transport.

Poland’s experience in electromobility development and the results of research studies can be regarded as guidelines for countries representing a similar level of the economic and energy transformation [15].

2. Review of the Situation

The generation of electricity in Poland is based on hard coal and lignite. Although the role of solid fossil fuels has gradually decreased in favor of gas fuels and renewable energy sources (RES), hard coal still accounted for 76.8% (

Table 1. Electricity generation by energy carrier.

Specification	2011	2012	2013	2014	2015	2016	2017	2018	2019
Hard coal	87,326	80,528	81,568	76,162	77,693	79,400	79,022	81,257	76,538
Lignite	52,529	54,054	56,150	53,365	52,825	50,920	52,166	49,331	49,331
Gas fuels	5821	6259	5247	5329	6405	7831	10,141	12,709	15,131
Pumped storage	430	428	558	551	603	482	474	417	706
RES	13,137	16,879	17,066	19,841	22,679	22,808	24,050	2580	25,378
Biomass and biogas	7601	10,094	8622	9976	9932	7957	6416	6511	7602
water	2331	2037	2439	2182	1832	2139	2560	1970	1958
wind	3205	4747	6004	7676	10,858	12,588	14,909	12,799	15,107
photovoltaic	0	1	1	7	57	124	165	300	711
Other fuels	4305	3991	3968	3810	4739	5193	4613	4745	4597
Total	163,548	162,139	164,557	159,058	164,944	166,634	170,465	170,039	163,989

Source: author’s elaboration based on [17].

) of the total amount of energy generated in 2019. Considering that the remaining energy carriers are of secondary importance, it can be assumed that the role of hard coal in the Polish electro-energy industry is not likely to change considerably in the coming years. Hard coal remains a dominant fuel in electricity generation. Similar assumptions were adopted in Poland’s energy policy until 2040. The document contains the following statement: “The share of hard coal in the energy usage structure in 2030 will not exceed 56%”. Moreover, Poland has adopted the principle—which is justifiable from the perspective of energy security—that demand for electricity should be met on the basis of the country’s own resources, and that hard coal import–export relations should be supplementary in character [16].

Unfortunately, the consistent implementation of such energy policies is, in reality, a factor that hinders electromobility development. Apart from rich coal deposits (64.3 bn tons) and lignite deposits (23.3 bn tons), the market availability of those resources is continuously reduced [18]. Coal extraction has gradually decreased in recent years. In the 1970s and 1980s, coal extraction reached the level of approx. 190 m tons, and the export of coal in the late 1970s was as much as 40 m tons, with imports at a very low level [11]. The situation changed radically in the period of Poland’s economic and systemic transformation. Coal output and exports decreased sharply, while imports, particularly of scarce types of coal, gradually increased. Since 2008, Poland has been a net importer of hard coal, with its imports exceeding exports by a record 14.3 m tons in 2018 (

Table 2. Selected components of hard coal balances in Poland (millions of tons).

Description	2005	2010	2015	2018	2019	2005	2010
Extraction	97,904	76,728	72,686	63,857	62,080	97,904	76,728
Import	3372	13,603	8289	19,245	16,694	3372	13,603
Export	19,369	9965	9192	4908	4443	19,369	9965
Foreign trade balance	−15,997	3638	−903	14,337	12,251	−15,997	3638
Total usage	80,438	84,788	73,558	74,834	68,774	80,438	84,788

Source: authors’ research based on [19].

).

The current situation results from a number of factors: lower global coal prices, long-standing deficiencies of the Polish mining industry, and the general coal phase-out in favor of environmentally friendly renewable energy sources [20] as well as the EU climate policy, which leads to the increased prices of CO₂ emissions allowances (EUA), and the decreasing competitiveness of coal generated energy [21]. Setting increasingly ambitious climate goals in the European Union indicates that departure from coal as an energy carrier is decided. Moreover, market analyses indicate that the observed increased prices of CO₂ emission allowances represent a trend that is not likely to change in the coming years. Not so long ago, in 2013, EUA prices were below 5 EUR, representing historically minimal levels. However, as a result of the reform of the system and, particularly, reductions in the surplus of allowances, the costs of acquiring EUA allowances started to increase. In 2019, EUA prices averaged 24.90 EUR. In March 2020, as a result of the economic downturn in connection with the COVID-19 pandemic, the price of EUA decreased by 40%, reaching the level of 14.31 EUR. In December, it reached its record high of 33.44 EUR, resulting in the average price of EUA in 2020 at the level of 24.80 EUR [22]. Contract prices in July 2021 exceeded the level of 50 EUR for one ton of CO₂.

The problems of the Polish coal industry also result from inadequate investment outlays and the frequent cases of irrational deposit management, leading to the shrinking of coal operating resources (operational resources are deposits constituting part of balance sheet and off-balance sheet resources, which can be used when it is technically and economically viable and complies with laws including environmental regulations, less expected losses) [13]. These deposits were estimated in 2008 at 2090 m tons, which allowed for the uninterrupted mining of coal until 2035 [23]. Despite the threats identified by the Supreme Chamber of Audit, posed to the security of coal supplies from domestic deposits, adequate measures have not as yet been taken. At the end of 2016, coal operating deposits in coal mines for the period of granted licenses fell to 1560 m tons (including 1187 m tons at active and under construction levels) [24]. Therefore, in the absence of radical organizational changes and adequate investments in the coal mining industry, an increase in the volumes of electricity for electric cars is likely to deteriorate foreign coal trade balance or increase energy imports.

Another fact that causes concern is the condition of Polish coal power plants, which show a high degree of decapitalization. As long ago as in 2006, it was noted that aging power plants decreased Poland’s energy security. At that time, one third of power units in Poland were nearly 40 years old, and 500 MW units had been in operation for more than 25 years [25]. Despite a considerable lapse of time, the situation has not been much improved. In 2021, the average age of a power plant, measured from activating its first power unit, was 47 years. Power plants older than 50 years account for approx. 30% of installed power capacity [26]. Although new installations are being put into use (

Table 3. Chosen characteristics of Polish power plants.

Investor	Location	Fuel	Gross Capacity [MW]
ORLEN	Płock	Earth gas	596
ENEA	Kozienice	Hard coal	1075
TAURON	Jaworzno	Hard coal	910
PGE	Opole	Hard coal	1800
TAURON	Stalowa Wola	Earth gas	450
PGE	Turów	Lignite	496
PGNiG Termika	Żerań	Earth gas	499
Total	-	-	5826

Source: own research.

), the capacity of new units merely compensates for shortages resulting from decommissioned units (contrary to previous announcements and the actual commencement of the investment project, the plan to launch a new coal unit at Ostrolęka Power Plant is not scheduled to be implemented) [18]. New high efficiency energy units (even more than 45%) have the capacity of approx. 6000 MWe and meet nearly a quarter of Poland’s peak demand for power (Table 3). According to forecasts, demand for electricity after the year 2030 may exceed 30 GW [22].

A short analysis of the condition of Polish power plants (Table 3) leads to the conclusion that without new large investments in hard coal energy or renewable energy sources, a considerable increase in demand for capacity and energy resulting from the planned development of electromobility would be even detrimental to the quality of electricity supplies. Warnings are already being issued for the years 2030–2035 with regard to the possible shortages of energy surplus not compensated for by system operators. The first major deficits are likely to occur as early as 2028 in connection with decommissioning subsequent energy units [27].

A similarly negative assessment concerns high voltage (HV) lines. As many as 80% of 220 kV transmission lines, 56% of 400 kV lines, and 34% of substations are more than 30 years old, and their further use requires major investment outlays. The condition of distribution networks is similar. Their average age is 30 years, and their depreciation rate amounts to 75% [28]. Electricity distribution difficulties are not a new issue [29]. In August 2015, the system recorded the cases of overheating and temporary limitations of electricity supplies in the periods of the highest temperatures (

Table 4. Selected energy generators in the National Energy System.

Year	2001	2002	2003	2004	2005	2006	2007	2008
Winter peak	22,868	23,207	23,288	23,108	23,477	24,640	24,611	25,120
Summer peak	16,436	16,95	17,233	18,324	19,004	19,593	20,276	20,430
Year	2009	2010	2011	2012	2013	2014	2015	2016
Winter peak	24,595	25,449	24,780	25,845	24,761	25,535	25,101	25,551
Summer peak	19,782	20,623	21,143	21,175	20,994	21,804	22,305	22,791
Year	2017	2018	2019	2020	2021	2022	2023	2024
Winter peak	26,231	26,440	26,150	27,088	27,491	27,751	28,040	28,401
Summer peak	23,221	23,680	24,350	24,507	25,083	25,410	25,762	26,180

Source: authors’ research based on [27].

). Restrictions resulting from the regulation of the Council of Ministers affected 1600 Polish enterprises [30].

The age of power plants and distribution lines is usually an indication of their efficiency and transmission losses. An obsolete energy system results in a low efficiency of energy generation, the risk of electricity outages as well as high CO₂ emissions per one unit of generated energy. Relatively new hard coal energy units in Poland emit approx. 800 kg CO₂/MWh (a lignite unit commissioned in 2011 at Bełchatów power plant with a rated power of 858 MW emits 995 kg CO₂/MWh). In the most modern hard coal units, for example, the one in Opole, CO₂ emissions amount to 691 kg per 1 MWh net of electricity, at Kozienice—700 kg CO₂/MWh, and at Jaworzno—750 kg CO₂/MWh [31]. The capacity and applied technologies place Polish hard coal power plants among the top CO₂ emitters. The inglorious ranking of 30 power plants includes as many as seven Polish entities (

Table 5. Top 30 emitters in 2019 (power plants) in the European Union.

Company					Installed Capacity 2019	Verified Emissions 2019		Electricity Generation 2019		Intensity	Emission 2019
EUTL ID	Company	Country Code	Power Plant	Fuel	MW	MICO	vs. 2018	TWh	vs. 2018	CO2, MWh	vs. 2018
PL1	PGE	PL	Belchatów	Lignite	5472	32.7	−15%	28.1	−15%	1.2	1%
DE 1606	RWE	DE	Neurath	Lignite	4210	22.6	−30%	21.1	−28%	1.1	−2%
DE 1649	RWE	DE	Niederaußem	Lignite	2795	18.4	−29%	16.2	−31%	1.1	3%
DE 1456	LEAG	DE	Jänschwalde	Lignite	2500	17.6	−23%	13.9	−20%	1.1	4%
DE 1607	RWE	DE	Weisweiler	Lignite	2362	13.3	−21%	10.7	−19%	1.2	−2%
DE 1454	LEAG	DE	Bosberg Werk IV	Lignite	1470	11.0	8%	10.5	7%	1.0	1%
DE 1450	LEAG	DE	Schwarze Pumpe	Lignite	1510	10.5	15%	9.3	−15%	1.1	0%
PL4	ENEA	PL	Kozienice	Hard Coal	2941	10.46	8%	11.4	8%	0.9	0%
DE 1460	LEAG	DE	Uppendorf	Lignite	1782	8.9	−24%	9.3	−23%	1.0	−1%
BG so	TPP	GB	Maritsa East 2	Lignite	1604	7.9	−18%	6.7	−21%	1.2	5%
DE 1453	LEAG	DE	Bosberg Werk III	Lignite	1000	7.6	−13%	6.4	−13%	1.2	0%
PL2	PGE	PL	Opole	Hard Coal	3339	7.0	−	10.0	25%	0.7	−25%
GR is	MH AE	GR	Demitrios	Lignite	1456	6.8	−26%	4.4	−28%	1.6	3%
PLS	Enea Elektrownia Potaniec	PL	Potaniec	Hard Coal	1882	6.8	19%	8.7	−15%	0.8	4%
8G9	Contour Global Maritsa East	GB	TEC Contour Global Mar	Lignite	908	5.7	17%	5.5	28%	1.0	−9%
PL3	PGE	PL	Turdw	Lignite	1488	5.5	20%	S.1	−22%	1.1	3%
NL 205957	RWE	NL	Eemshaven Centrale	Hard Coal	1580	5.4	−32%	7.2	−33%	0.8	1%
BE 750	Electrabel	BE	Electrabel Knippegros	Blast Furnace Gas	315	5.1	10%	2.3	10%	2.2	0%
DE 1380	Großkraftwerk Mannheim	DE	Mannheim	Hard Coal	1983	4.9	−27%	S.0	−30%	1.0	5%
ES 201	EDP	ES	Aboño	Hard Coal, Blast Furnace Gas	904	4.9	−31%	8.1	−39%	1.6	12%
DE 206180	Vattenfall	DE	Moorburg	Hard Coal	1600	4.7	−24%	5.8	−25%	0.8	1%
CZ 124	Elektrarna Pocerady a.s.	CZ	CEZ, a.s. Elektrama	Lignite	930	4.7	−14%	4.7	−13%	1.0	−2%
RO 87	CE Oltenia	RO	Rovinari	Lignite	1189	4.6	−11%	5.2	−7%	0.9	4%
FR 988	ENGIE THERMIQUE	FR	ETF centrale DK6	Natural Gas	796	4.6	2%	4.3	6%	1.1	−%
IT 521	ENEL	IT	Brindisi Sud	Hard Coal	2420	4.6	−17%	4.3	−22%	1.1	7%
	ENEL	IT	TorrevalDaliga Nord	Hard Coal	1845	4.5	−44%	4.6	−48%	1.0	8%
HU 142	RWE	HU	Métral Fromu ZRI.	Lignite, Natural Gas	950	4.5	14%	4.0	−14%	1.1	0%
PL 209933	ENEA	PL	Kozienice Block 11	Hard Coal	1075	4.4	2%	5.4	2%	0.8	0%
PL6	EDF	PL	Oddzial w Rybniku	Hard Coal	1797	4.4	−17%	4.6	−16%	1.0	−1%
IT S11	Taranto Energia	IT	Taranto	Blast Furnace Gas	N.A.	4.4	−7%	N.A.	N.A.	N.A.	N.A.
CZ 129	CEZ, a. s.	CZ	CEZ, Blektrama	Lignite	728	4.3	3%	4.7	1%	0.9	4%

Source: authors’ research based on [27].

).

However, it turns out that the modernization of hard coal power plants will not suffice to meet the EU’s CO₂ emission requirements. Even the newest projects may not be competitive in the EU energy generation market. The problem lies in the participation of generating units in the power market. According to the regulation of the European Parliament and of the Council, new power plants emitting more than 550 kg CO₂/MWh of electricity, which start energy generation after the regulation becomes effective, cannot participate in capacity mechanisms, and, consequently, benefit from state aid. Simultaneously, previously installed units emitting more than 550 kg CO₂/MWh, and more than the annual average of 350 kg CO₂ per each installed kW can participate in the European capacity market until 1 July 2025 [32].

The costs of energy generation in hard coal and lignite units are strongly correlated with the changes in CO₂ emissions allowance and fuel prices. Moreover, a low efficiency of hard coal-based electricity generation is accompanied by relatively high emissions per one unit of generated energy. If we assume that the hard coal heating value is 21.24 MJ/kg, the lignite heating value is 9.47 MJ/kg (

Table 6. Average heating values (WO) and CO₂ emission ratios (WE) of coal used by power plants and heat and power generating plants in 2018.

Type of Fuel	WO [MJ/kg]	WE [kg/GJ]
Hard coal	21.24	93.54
Lignite	9.47	105.95

Source: authors’ research based on [33].

), the net efficiency of electricity generation ηn = 40%, and electricity transmission and distribution losses are at the level of ΔE% = 6%, it can be easily calculated that the end user receives 2.22 kWh of electricity from 1 kg of coal used by a power plant, and 0.99 kWh of electricity generated by lignite.

If we assume that a compact electric car uses 14 kWh of energy to cover the distance of 100 km (including city cars using 12–15 kWh and middle-class cars using approximately 20 kWh per 100 km), a power plant uses 6.4 kg of hard coal for this purpose (12.6 kg of CO₂ emissions) or 14.1 kg of lignite (14.3 kg of CO₂ emissions). A comparable petrol engine car uses an average of 4.5–5 L of fuel per 100 km (city consumption), emitting approx. 9 kg of CO₂. Therefore, the environmental benefits of electromobility development based on hard coal generated energy, measured by CO₂ emissions, are doubtful.

E-vehicles are characterized by slightly lower power costs. The average price of energy offered by public electric chargers amounts to 1.5 PLN/1 kWh, so the cost of covering the distance of 100 km is approx. 21 PLN, while the cost of the traditional fuel is approx. 25 PLN. However, the development of electromobility is likely to increase the costs incurred by the owners of e-vehicles for maintaining road infrastructure. The price of energy used to charge a vehicle will include a road tax, as is the case of traditional fuels. An additional tax will reduce the benefits of using e-vehicles. The costs of maintaining e-vehicles are considerably lower when their owners decide to use home chargers. The choice of a popular G11 tariff (depending on energy suppliers) reduces costs by more than half, and night tariff G12 reduces charging costs by as much as 70% compared with traditional fuels [34]. The greatest benefits of the use of e-vehicles result from photovoltaic systems. In favorable weather conditions, charging a vehicle is practically free. However, the question arises as to how long it is possible.

3. The Availability of the Charging Infrastructure for E-Vehicles

The authors of the electromobility plan see benefits in optimizing KSE operations (national electricity system), assuming that users will charge vehicles benefiting from tariff comparison rates. This assumption seems to be a bit too optimistic. We suppose that one cannot expect drivers to adapt their daily schedules to recommended night charging hours. Simultaneously, from 12 p.m. to 5 p.m., and from 9 p.m. to 12 p.m., when demand for fast charging is the highest, KSE’s demand is nearly equal to electricity generation in domestic power stations [35]. Increasing demand for electricity and fast charging may have a positive impact on the charging infrastructure, but electricity prices in such distribution networks are likely to be changeable. The development of electromobility not coordinated with the possibility of charging electric vehicles at specific hours may even lead to the deterioration of the situation of the domestic producers and distributors of electricity as well as an increase in imported energy [36]. It should be noted that Poland has been a net importer of electricity since 2016 (Figure 1).

At the end of 2020, Poland had 1364 publicly available charging stations for e-vehicles, of which only 33% accounted for DC high voltage stations, while 67% represented AC low voltage stations [8] in which charging takes several hours. Member states are obligated to ensure the availability of charging stations at least in large agglomerations and suburban areas in 2020. The number of stations recommended in the directive should correspond to at least one station per 10 vehicles [37]. Therefore, the use of one million e-vehicles will require 100 thousand publicly available chargers. If we estimate the cost of building a low-capacity station at 16–70 thousand PLN, and in the case of capacity exceeding 22 kW, even 200 thousand PLN, the development of the required infrastructure can amount to as much as 10 bn PLN [38]. Additional outlays will certainly affect charging costs, and the commissioning of subsequent stations is also determined by the availability of funds as well as the capacity of nearby energy networks. In extreme cases, to close gaps in the charging infrastructure, it will be necessary to use diesel chargers. Such solutions, regarded as temporary, are applied in Australia [36,39].

Another issue is adapting individual electricity connections and installations to the needs of those who intend to charge e-vehicles independently. Legal and technical problems occur in multi-family houses. Legal regulations concerning the management of such property are ambiguous with regard to the individual charging infrastructure. Additionally, there are doubts as to the financial settlement of the use of energy for charging private vehicles. Moreover, co-tenants frequently express concerns about the safety of chargers. Difficulties in installing the charging infrastructure constitute a major barrier to electromobility development because, as the results of the surveys indicate, as many as 96.7% of respondents prefer using their individual home chargers. However, only 43% of them declared such a possibility [40].

Technical barriers also concern single-family houses. According to typical contracts for electricity distribution, delivered power does not exceed 6 kW. Moreover, some recipients have access only to single-phase connections. The technical parameters of such installations allow for the use of one charger (up to 3.7 kW), and the charging of batteries takes as long as 8 h, with a limited reserve of power for other household needs. The use of fast chargers (of at least 7.5 kW) will require contractual changes or new connections, and, obviously, will be conditioned by the distributor’s ability to meet power supply requirements. The effects of the above restrictions could be mitigated if electromobility development was accompanied by an increasing number of RES installations. Possible energy surpluses could be used for charging e-vehicles, mitigating impacts from energy transmission problems. Unfortunately, the share of RES in energy generation in Poland is still low [41].

4. Technical and Other Demand-Related Barriers

Political ambitious plans are not coupled with the effects of construction work, which cannot be attributed to their insufficient knowledge but the availability of technology and the shortage of funds for further development research studies. On 28 July 2020, Electro Mobility Poland S.A., responsible for developing the concept of e-vehicles, presented two prototypes of a Polish e-vehicle called Izera. It should be noted that a competition for the design of a Polish e-vehicle was already resolved in September 2017. Unfortunately, despite declarations concerning encouraging domestic designers to present their suggestions as well as positive post-competition opinions, four winning designs were never implemented. Ultimately, cooperation started with the Italian company Torino Design, which developed the concept of Izera. The representatives of Electro Mobility Poland S.A. claim that vehicles will be available on the market in 2024. The cost of constructing a plant is estimated at 4–5 bn PLN, and the company is to be taken over by the state treasury. Izera prototypes are similar to solutions already available on the markets. However, information about the final versions of the Polish e-vehicle and its price has not been disclosed yet. Similarly, it is hard to predict global trends in electromobility in three years’ time when the Polish plant starts producing its vehicles [42,43].

The history of the Polish e-vehicle is longer than that of the Electromobility Company. A prototype of e-vehicle ELV001 was presented as early as June 2011. The vehicle built in Mielec is still in the design phase. The Electric Car Factory, on the basis of the Fiat 500 from Tychy, intends to produce the electric vehicle FSE01. This vehicle is to be assembled at the Bielsko-Biała plant, and the target output is up to one thousand vehicles annually. Finally, at FSO in Warsaw, AK Motors is planning to produce a two-seat e-vehicle called Syrena nixi. According to the designers, its range will be up to 150 km, and fast charging for only 15 min. Syrena will be a micro-car with the curb weight of less than 400 kg and a 20-horsepower engine, so it could be driven by B1 driver license holders. However, it is hard to predict the future of the above-described designs.

Market analyses indicate that the operations of large automotive concerns are more effective than those carried out by new entities relying on public aid programs. The markets offer a wide range of e-vehicles, and their recognizability, also in Poland, is increasing. A total of 24% of survey respondents were able to name as many as five models of electric cars (Figure 2).

Polish e-vehicle FSE 01, presented to the general public, uses lithium-ion 14 kWh batteries, which allow for covering the distance of 102 km. The cost of covering the distance of 100 km can range from 6 PLN to 7 PLN (in the case of G12 night tariff), and up to as much as 21 PLN (in the case of public chargers). Charging based on single phase chargers will take approx. 6 h, and half of that time for three phase chargers. The expected price of the car is 100 thousand PLN. Both technical parameters (range) and the price indicate that it will not be popular among buyers. Its combustion engine prototype, the Fiat 500, in its basis version costs 50 thousand PLN, its average fuel consumption is 3.8 liters of petrol, and its range is 850 km (the cost of covering 100 km amounts to approx. 19 PLN). Considering exclusively purchase and energy prices, the economic benefits of an e-vehicle will be visible after covering the distance of at least 400 thousand km. An average vehicle in Poland covers the distance of 8500 km annually [35], so the benefits will be seen after as many as 47 years. These benefits will never occur in the case of the frequent use of public chargers. After all, it is the price, according to potential users, that is a decisive buying factor (95% of responses), while environmental effects are indicated by merely 2.7% of respondents [40]. Therefore, it can be concluded that electromobility development, instead of directly engaging public funds in the construction of e-vehicles, should be stimulated by an attractive system of e-vehicle purchase incentives, allowing for improving return on investment [45].

Unfortunately, Poland belongs to a group of countries in which the system of purchase incentives is not satisfactory. The National Fund for Environmental Protection and Water Management is implementing a program “A green vehicle—co-financing of the purchase of electric motorcars”, but it is not very effective. A subsidy for a vehicle accounts for 15% of eligible costs, but not more than 18,750 PLN. It is conditioned by a number of factors, for example, the coverage of 10,000 km annually and a maximum price not exceeding 125 thousand PLN. The available budget for this purpose is also limited, amounting to 37.5 m PLN, thus allowing for co-financing the purchase of 2000 vehicles. Setting a maximum level of prices of eligible vehicles considerably reduces the choice of available products. Moreover, due to low subsidies under the co-financing program, the prices of electric vehicles are still as much as almost twice as high as those of similar combustion cars (for example, Nissan Leaf—from 27,000 EUR vs. Nissan Micra—from 15,000 EUR or Dacia Sandero—from 9300 EUR in the entry level segment; Skoda Eniaq—from 40,000 EUR vs. Skoda Karoq—from 2100 EUR in the medium segment). Not surprisingly, buyers have shown little interest in the program [46].

Most of the EU countries offer higher subsidies for the purchase of e-vehicles (

Table 7. A system of subsidies for the purchase of e-vehicles in various countries of the EU.

Country	Type of Incentive for Buyers of Low-Emission Vehicles	Share of Low-Emission Vehicles in Total Sales
Austria	Subsidy for buyers: 3000 EUR for BEV and FCEV, and 1250 EUR for PHEV and EREV.	3.5%
Croatia	Subsidy for buyers: 9200 EUR for BEV and 4600 EUR for PHEV.	No data
Estonia	Bonus of 5000 EUR for buyers of BEV priced below 50,000 EUR.	0.3%
Finland	Subsidy of 2000 EUR for buying or leasing BEV priced below 50,000 EUR.	6.9%
France	Bonus (max. of 7000 EUR) for vehicles emitting up to 20 g CO2/km. Bonus up to 5000 EUR for owners of old cars on purchase of new or second-hand vehicle emitting up to 50 g CO2/km (priced up to 60,000 EUR).	2.8%
Greece	Refund of 15% of BEV net price (up to 5500 EUR), and, additionally, 1000 EUR on scrapping old vehicle.	0.4%
Spain	Subsidy for BEV (max. of 5000 EUR) and PHEV (up to 2600 EUR).	1.4%
Netherlands	Subsidy program (SEPP) for buyers of BEV (new—4000 EUR or second-hand—2000 EUR).	15.0%
Ireland	Subsidy up to 5000 EUR for BEV and PHEV emitting up to 50 g CO2/km.	4.1%
Luxembourg	Tax refund of up to 5000 EUR for BEV or FCEV. Possible tax refund up to 2500 EUR for PHEV emitting up to 50 g CO2/km.	No data
Germany	Bonus for the purchase of BEV and FCEV: 9000 EUR (priced up to 40,000 EUR) or 7500 EUR (in the case of higher prices). Bonus for PHEV: 6750 EUR (priced up to 40, 000 EUR) or 5625 EUR (higher prices).	3.0%
Poland	Subsidy of 15% (max. 18,750 PLN/4067 EUR/) for BEV priced up to 125,000 PLN.	0.5%
Portugal	Subsidy of 3000 EUR for BEV.	5.7%
Romania	Subsidy for BEV (10,000 EUR) and PHEV emitting up to 50 g CO2/km (4250 EUR). Additional bonus of 1250 EUR for scrapping old car.	0.9%
Slovakia	Subsidy for BEV (8000 EUR) and PHEV (5000 EUR).	0.4%
Slovenia	Subsidy for BEV (7500 EUR) and PHEV/EREV (4500 EUR).	0.9%
Hungary	Subsidy for BEV amounting to 7350 EUR (priced up to 32,000 EUR) and 1500 EUR (priced 32,000 EUR—44,000 EUR).	1.9%
Italy	Bonus of up to 6000 EUR for vehicles emitting up to 70 g CO2/km (priced up to 50,000 EUR).	0.9%

Source: authors’ research based on [21,33].

). Only six countries do not offer direct incentives (Belgium, Bulgaria, Cyprus, Denmark, Latvia, and Malta) but only tax incentives or exemptions. Simultaneously, Lithuania is the only member state that does not offer any support for the buyers of electric vehicles.

5. Technical and Demand-Related Barriers

The research was conducted among 28 European Union countries and was based on the secondary data obtained from the Eurostat in 2019. The aim of the research was to examine the determinants of the electromobility development in the EU. Based on statistical analysis of the variables, the procedure of the linear regression and agglomeration analysis for all EU countries were calculated in the Statistica^® program (TIBCO Team, Statsoft, Cracow, Poland). There were seven variables used in this research, as indicated in

Table 8. Electromobility variables in the EU in 2019.

Electromobility Variable Symbol	Characteristics [Measure]
E1	New registered electric cars [per100k inhabitants]
E2	Renewable energy sources share in electricity production [%]
E3	Gross domestic product per capita
E4	Electricity price [kWh/euro with taxes]
E5	Number of fast chargers [No./1000 km square]
E6	Number of normal chargers [No./1000 km square]
E7	Battery electric vehicle benefits [euro]

Source: Authors own elaboration based on Eurostat [44].

. Variable E1 was a dependent variable, when the others were independent. The E1 variable is a direct measure of the electromobility.

The correlation between independent variables was calculated to check the interdependencies between variables and to discover the shape of these relations. The correlation examination between variables has sense only if there is a cause-and-effect relationship, which can be logically explained [47]. All obtained values were statistically significant in all calculation results in this paper. Variables E1, E5, and E6 were calculated on the basis of the Eurostat data or extracted from this database by the authors [48]. Comparability of data was therefore achieved and reproducibility of the adopted linear regression method. The detailed method explanation in this study is further accompanied by the presentation of the obtained calculations and results. The secondary data for variables used in calculations are presented in

Table A1. Raw data used in the research for 28 European Union countries in 2019.

Country	Variables
	E1	E2	E3	E4	E5	E6	E7
Austria	101.97657	75.14	38,170	0.2074	8.3508053	44.577337	3000
Belgium	74.897331	20.828	35,950	0.286	15.684101	197.92618	0
Bulgaria	4.2149286	23.509	6840	0.0958	0.585612	0.630659	0
Croatia	3.8194603	49.783	12,450	0.1324	2.653036	8.4720282	9200
Cyprus	0.9008958	9.756	24,530	0.2236	0	4.1085523	0
Czechia	6.1717203	14.047	18,330	0.177	4.9319167	5.1981642	9800
Denmark	94.079048	65.351	49,720	0.2924	13.275566	51.990177	0
Estonia	5.7939346	21.998	15,760	0.1411	4.1678612	4.4545394	5000
Finland	34.152041	38.069	37,230	0.1783	1.0635556	5.291115	2000
France	64.880956	22.384	33,270	0.1913	4.9284231	50.378829	7000
Germany	72.375112	40.816	35,840	0.2878	17.620777	95.45192	9000
Greece	1.7539661	31.295	17,750	0.1551	0.159197	0.3032325	5500
Hungary	16.612884	9.992	13,270	0.1097	1.5374195	6.3647017	7350
Ireland	69.514386	36.492	60,170	0.2546	3.6543392	11.586238	5000
Italy	17.844961	34.969	26,910	0.2341	3.5115553	27.587947	6000
Latvia	4.5605252	53.423	12,510	0.164	3.4035929	1.2668081	0
Lithuania	5.6547928	18.79	14,010	0.1254	1.8803889	1.2077295	0
Luxembourg	153.64761	10.857	83,640	0.1799	5.0096339	346.82081	5000
Malta	39.839553	8.039	21,960	0.1304	0	323.80952	0
Netherlands	337.59422	18.219	41,870	0.2055	34.475465	1309.2217	4000
Poland	3.8015563	14.355	13,020	0.1376	1.1948345	1.6217887	4400
Portugal	66.822657	53.774	18,630	0.2181	3.6016973	16.556552	3000
Romania	7.775946	41.709	9110	0.1421	0.5563271	0.8825941	10,000
Slovakia	3.1147665	21.945	15,860	0.1585	6.0988047	7.1390691	8000
Slovenia	29.963819	32.633	20,700	0.1666	8.679785	22.291266	7500
Spain	21.568638	36.932	25,200	0.2394	2.5456165	9.0270088	4000
Sweden	150.76123	71.187	43,920	0.2076	10.571332	8.9709622	6000
United Kingdom	56.283021	34.769	32,910	0.221	24.939156	90.243499	3530

Note: Variables’ symbol meanings explained in Table 8. Source: [55].

in Appendix A.

6. Results

This paper researched a set of Eurostat data for 28 EU countries in 2019. The adopted methods were linear regression and agglomeration analysis [49]. First, the linear regression results are presented and verified. Before all analyses were applied, we examined the correlation to check the interdependencies between variables. We claim that the correlation examination between independent variables has sense. In this paper, we proved by calculations in the Statistica@ software (version 13.3) by StatSoft Poland that independent variables were not correlated to each other. The dependent variable was strongly correlated [50,51].

In the linear regression analysis, the described, dependent variable was E1—the number of new registered electric cars [100 thousand inhabitants], and the rest of the variables presented in Table 8, were independent variables (not correlated to each other). The result of the linear regression procedure is presented in

Table 9. Dependent variable E1 linear regression summary.

N = 28	b*	Std. Err. b*	b	Std. Err. b	t(21)	p
Intercept			−0.074500	0.031116	−2.39426	0.026065
E2	0.272886	0.062206	0.206350	0.047039	4.38680	0.000258
E3	0.362848	0.070781	0.341736	0.066663	5.12632	0.000045
E6	0.708108	0.093426	0.769197	0.101486	7.57935	0.000000

Note: Model characteristics: R = 0.96972873; R² = 0.94037380; Corr. R² = 0.92333775; F(6, 21) = 55,199; p < 0.0000001 Error std. estimation: 0.058558158; symbols meaning: b*—standardized coefficients, b—directional coefficients of equation; Source: authors’ calculations.

.

Based on the calculation, we identified that all cases were important for building a linear model in the regression procedure for EU member states N = 28. We decided to calculate a model to describe dependences between variables. In the results of the calculations, we discovered that only three variables were statistically significant determinants of the electromobility in the European Union. These are E2, E3, and E6 with growing relevance in ascending order. The detailed results of the backward step regression based on all cases N = 28 is presented in

Table 10. Summary of the linear regression of dependent variable E1.

Statistics	Value
R multiple	0.969728726
Multiple R2	0.940373803
Corrected R2	0.923337746
F(6, 21)	55.1990309
p	0.00000000000891364673
Stand. Estim. Err.	0.058558158

Source: authors’ calculations.

. The regression procedure aims to indicate the primary model of the phenomena based on the reduced number of dependent and independent variables.

The regression procedure aims to indicate the primary model of the phenomena based on the reduced number of dependent and independent variables. As a result, we obtained the simplified equation, representing the relations between P34, P30, and P33 variables. The linear model formula (linear polynomial) is presented as Equation (1).

E1 = E6 × 0.769197 + E3 × 0.341736 + E2 × 0.206350 − 0.074500 ± 0.058558158

(1)

In this equation, the variables’ meaning is the same as in Table 9.

We verified the model, which consists of checking the model assumptions [52]:

• The significance of linear regression;
• The importance of partial regression coefficients;
• No collinearity (redundancy) between independent variables;
• Homoscedasticity assumption, which means that the variance of the random component (εi) is the same for all observations;
• No residual autocorrelation;
• Normal residual distribution; and
• The random term ε_i has the expected value equal to 0.

We verified the obtained model, and we proved that [50]:

• The linear regression’s significance is represented by the values F(6, 21) = 55,199, p < 0.000001, which proves that the obtained equation is significant. Additionally, multiple correlation coefficient R = 0.94, which supports linear dependencies between chosen variables to the model (Equation (1));
• Calculated p < 0.000001 value proves the significance of partial regression coefficients;
• There is no collinearity between independent variables. The tolerance for both variables is high, and its value is close to the 1;
• The homoscedasticity assumption is fulfilled and supports the linearity of the model;
• There is no residual autocorrelation;
• There is also normal residual distribution as illustrated in Figure 3; and
• The random term ε_i has the expected value equal to 0 because the average value of the Cook’s distance equals 0.

The obtained linear model describes the E1 variable using three independent variables: E6, E3, and E2. The regression results, which reduced the number of variables used in the final model, were used to describe statistically significant relations between E1, E6, E3, and E2 variables. Therefore, we proved the interchangeable character of these electromobility related variables. In the discussion of the model, we can explain that E1 (New registered electric cars [per100k inhabitants]) can be influenced by the availability of the chargers E6 (Number of normal chargers [No./100 km square]). Additionally significant for the model was variable E3 (Gross Domestic Product per capita) and E2 (Renewable energy sources share in electricity production [%]), which illustrates the wealth of the society and readiness to cover expenses of the electromobility transformation.

Then, an agglomerative hierarchical clustering procedure [52] was calculated (Figure 4), where the criterion for choosing the pair of clusters to merge at each step is based on the optimal value of an objective function.

There are two main groups of EU countries when the electromobility factors have been analyzed (as presented in Table 8). There is a visible group of involved countries, and countries that struggle with different barriers. Poland is located in this second group, which proves what was discussed in point 4 with regard to technical and other demand-related problems.

7. Discussion

The authors hypothesized that the electromobility program of implementing electromobility in Poland is based on overly simplified assumptions. This program marginalizes or ignores significant barriers to the development of electricity-based transport in Poland.

A significant limitation is the large share of energy from non-renewable sources in the electricity balance. The predominance of fossil energy sources and the implementation of an increasingly ambitious climate policy has resulted in a strong increase in electricity prices. For this reason, the competitiveness of environmentally friendly transport is weakening [53].

The successful implementation of this program is conditioned by comprehensive and costly steps aimed at transforming the whole energy system and introducing an effective system of incentives for the buyers of electric cars.

The results of the performed statistical analyses prove that development of the infrastructure is crucial for electromobility. The proposed Equation (1) is a result of the linear regression. Equation (1) illustrates the causality between the described variable E1 (New registered electric cars [per100k inhabitants]) and the availability of the chargers E6 (Number of normal chargers [No./100 km square]). Additionally significant for the model are variables E3 (Gross Domestic Product per capita) and E2 (Renewable energy sources share in electricity production [%]).

Such independent variables influencing the electromobility in Poland prove the similarity of the problems in this part of the European Union. Additionally, there are studies that have proven the identified pattern of better and worse performers of electromobility among European countries [14,54]. There is dependency between each country’s development as represented by the GDP per capita and investments in renewable energy sources. Such a pattern is visible in Figure 4.

8. Conclusions

Poland’s experience in electromobility development and the results of the research studies can be regarded as guidelines for countries representing a similar level of the economic and energy transformation.

According to the authors, attention should be paid primarily to the development of the charging infrastructure and increasing the share of renewable sources in the energy balance. Solar energy is the cheapest source of energy and in the near future, it may turn out to be the main determinant of the development of electromobility the more the technology of energy storage gradually develops. There are also known solutions in which e-vehicles connected to the charging infrastructure constitute an energy reservoir, mitigating the daily fluctuations in the availability of energy produced in PV installations.

In countries with average per capita income including Poland, attention should be paid to the system of subsidizing the purchase of an electric vehicle. As shown by the research, the issue of profitability plays a key role in deciding to buy an electric car.

An exciting direction of some further research is comparing the approach to electromobility in the Visegrad Group countries, since the agglomerative analysis gathered them in the linkage distance lower than 1. Comparison in local clustered groups can bring new insights into determinants of electromobility.

This research contributes to the strategic management literature and entails some practical managerial implications for policymakers. The model outlined in this paper is significant because it offers an opportunity to fill a void in understanding how governments might successfully develop their electromobility programs, avoiding Polish problems. Creation of positive conditions for the automotive industry and developing the required infrastructure are the most important factors in the progress of electromobility.