Influence of Market and Non-Market Factors on the Growth of Electromobility in Metropolitan, Urban and Rural Regions in the Czech Republic

Abstract

This research aims to evaluate how socio-economic and environmental factors influence the development of electromobility. To this end, the research was applied to regions of the Czech Republic, divided into metropolitan, urban and rural types. Based on a panel multiple linear regression with fixed effects of regions and years, the influence of socio-economic and infrastructure variables, in particular average gross wages, the development of charging points by region type and other factors, is analysed. The results show that average wages are a consistently statistically significant factor in the growth of new electric vehicle registrations across the regions. In contrast, the current level of charging infrastructure has no direct effect; its influence is only felt after a delay. Interaction models further confirm that the influence of income is relevant in both urban and rural areas. The study provides new insights into the temporal dynamics of electromobility adoption and formulates recommendations for targeted regional transport policy with an emphasis on proactive infrastructure planning.

1. Introduction

The European Commission has created the European Green Deal (EGD), which is a new growth strategy for the EU. The EGD aims to transform the EU into a net-zero greenhouse gas emitter by 2050 [1]. The electrification of passenger transport is therefore one of the key objectives of the European Union’s plan to reduce greenhouse gas emissions and achieve carbon neutrality. On the other hand, the adoption of electric vehicles is influenced by various factors, including the cost of electric vehicles, the availability of charging infrastructure, government incentives and environmental impact. The prevailing view is that economic reasons are the most frequently considered factor when deciding between purchasing an electric vehicle or a combustion engine vehicle [2,3]. However, studies have shown [4,5,6] that the total cost of ownership of battery electric vehicles is lower than that of internal combustion engine vehicles in the long term. Nevertheless, this information is rarely considered decisive by the public when choosing an electric car. The preference of consumers for combustion engine vehicles is mainly attributed to insufficient familiarity with electric vehicles and their high purchase price [7,8,9].

The development of electromobility is closely linked to the broader sustainability agenda defined by the 2030 Agenda for Sustainable Development, particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). Electromobility represents a key component of the transition toward low-carbon and energy-efficient transport solutions, as highlighted by a range of studies focusing on the environmental and socio-technical dimensions of EV adoption [3,10]. At the same time, it forms an integral part of the Smart City concept, which emphasises intelligent mobility systems, digital solutions and data-driven approaches to urban governance [9]. In the Czech context, electromobility increasingly contributes to the implementation of national and European sustainable mobility objectives, while regional differences in settlement structure and socio-economic characteristics may influence the pace of this transition [11,12]. Incorporating a regional perspective therefore makes it possible to assess how individual parts of the Czech Republic respond to the challenges associated with the sustainable transformation of transport.

In addition to technological and socio-economic determinants, the strategic and institutional capacities of territories also influence their readiness for technological transformation. Smart governance, understood as the ability of local and regional administrations to coordinate resources, integrate digital tools and support data-driven decision-making, has been identified as a complementary driver of regional performance and innovation potential [13,14]. These governance-related factors are increasingly relevant in the context of electromobility, as the deployment of charging infrastructure, integration of renewable energy sources and development of intelligent mobility systems require both administrative preparedness and long-term strategic orientation.

In another study, Jaderná et al. [7], assessed consumer attitudes towards different types of electric vehicles, including fully electric vehicles, plug-in hybrid vehicles and mild hybrid vehicles, in comparison with traditional combustion engine vehicles. This research concluded that targeted marketing strategies are needed to highlight the economic benefits and practical advantages of electric vehicles. In [8], Formánek and Tahal extended the analysis to assess the influence of socio-demographic factors and how they determine consumer attitudes towards electric vehicles. Women and older individuals generally show a higher inclination towards environmentally responsible forms of transport. For example, women are more likely to consider hybrid vehicles a promising technology for the near future and perceive electric cars as a more environmentally friendly alternative. Similar to the above-mentioned research in the field of electromobility, a study of the Portuguese environment [9] showed that institutional and technological influences also play a role in the decisions and attitudes of individuals and institutions. Despite the growing use of digital tools, one-way communication focused on providing information prevails, and the use of interactive methods of communication and presentation of electric mobility remains limited.

Beyond the European context, recent research demonstrates that vehicle electrification has become a policy priority in many world regions and is driven by a combination of environmental, economic and technological considerations. Empirical studies from China, India, Thailand and Germany document how governments and markets use financial incentives, charging infrastructure and other measures to support the diffusion of electric vehicles alongside climate and air-quality objectives [10,15,16,17]. This global perspective highlights that EV adoption dynamics cannot be interpreted solely through an environmental policy lens, but also reflect broader economic, technological and institutional conditions that shape regional development trajectories.

At the same time, gradual improvement can be observed over time, suggesting that the pressure for openness and public engagement is long-term and cumulative in nature. These findings can be applied analogously to the adoption of new technologies in transport, where institutional and socio-demographic conditions significantly shape public opinion and attitudes towards the acceptance and use of electrified solutions.

The structure of the article is based on the presented topic, beginning with a review of the literature and other research. Particular emphasis is placed on studies published in the last two years, as this period has seen dynamic developments in attitudes towards electromobility. This is followed by a presentation of the statistical analysis methodology resulting from the research objectives and data used for the research. The discussion section then interprets the results achieved in the context of existing research and attempts to place them in a broader theoretical framework. The conclusion summarises the main findings and outlines their limitations and possible directions for further research.

2. Literature Review

The parameters influencing individual preferences for electric vehicles over traditional combustion engine vehicles typically include many diverse aspects, such as the cost of purchasing an electric vehicle, environmental impacts, or the health benefits of electric vehicles. Guo et al. [10] report that these factors have a positive influence on the willingness to accept electric vehicles, especially in congested urban areas. When examining the factors influencing consumer acceptance of electric vehicles, it was found that not only technical and economic aspects, but also psychological and social factors play a key role. According to Chawla et al. [15], the most important determinants include consumer brand loyalty, vehicle energy efficiency, and efficiency of charging infrastructure. Consumers who have had positive experiences with a particular brand are more willing to accept new technologies offered by that brand. The energy efficiency of a car is another critical factor. Higher energy efficiency means lower operating costs and longer range per charge, which are key considerations for consumers considering switching to EVs. The charging system and availability of charging infrastructure also play a significant role. An efficient and easily accessible charging network can significantly increase the attractiveness of EVs for consumers who are concerned about limited range and long charging times. Together, these factors form a complex picture that needs to be taken into account when designing strategies to promote the adoption of electric vehicles in the market [15].

2.1. Factors Considered When Deciding to Purchase an Electric Vehicle

Based on the theoretical research conducted, the table below summarises the key factors for choosing an electric vehicle. Several keywords (electric vehicle, factors, choice) were entered into the WoS (Web of Science) portal. From the original 1750 articles, 285 relevant sources were selected based on keywords in the abstracts. Further filtering was then carried out to remove duplicates and articles that were not related to the topic (or the time period 2014–2025) under investigation. After this selection, a total of 20 main professional articles were selected, the summaries of which are presented in Table 1.

Economic considerations are a critical factor in influencing consumer preferences for electric vehicles. The high initial purchase price of electric vehicles is often cited as a significant barrier to adoption, despite their lower operating costs [3]. Financial incentives and government subsidies have the potential to mitigate this barrier by reducing the initial cost of electric vehicles [18]. A study in Turkey found that financial incentives significantly influenced consumers’ intentions to purchase electric vehicles, particularly among younger and higher-income groups [19]. The perceived value of electric vehicles, including their long-term cost savings and technological advances, may influence consumer preferences. Han & Sun [20] report that consumers’ willingness to pay for electric vehicle attributes varies significantly across regions and socioeconomic groups, highlighting the importance of targeted policies to promote electric vehicles.

Table 1. Summary of electromobility research based on key articles from Web of Science.

Factor	Context	Authors/Citations
Environmental concerns	Consumers who prioritise environmental sustainability are more likely to choose an electric vehicle over a combustion engine vehicle.	[21,22]
Economic factors	The high initial cost of electric vehicles is a barrier, but financial incentives (e.g., from the government) can help consumers decide to buy an electric vehicle.	[19,23,24]
Technological attributes	Vehicle range and charging time have a significant impact on the adoption of electric vehicles.	[3]
Infrastructure availability	Access to charging facilities increases the willingness to purchase electric vehicles.	[22]
Social influences	Peer pressure and social norms can help promote electromobility.	[25]
Psychological factors	Attitudes and beliefs about electric vehicles significantly influence intentions to purchase an electric vehicle.	[26,27]
Demographic factors	Younger consumers with higher incomes are more likely to purchase electric vehicles.	[19,20]

Source: Authors’ elaboration based on the reviewed literature.

Table 1. Summary of electromobility research based on key articles from Web of Science.

Factor	Context	Authors/Citations
Environmental concerns	Consumers who prioritise environmental sustainability are more likely to choose an electric vehicle over a combustion engine vehicle.	[21,22]
Economic factors	The high initial cost of electric vehicles is a barrier, but financial incentives (e.g., from the government) can help consumers decide to buy an electric vehicle.	[19,23,24]
Technological attributes	Vehicle range and charging time have a significant impact on the adoption of electric vehicles.	[3]
Infrastructure availability	Access to charging facilities increases the willingness to purchase electric vehicles.	[22]
Social influences	Peer pressure and social norms can help promote electromobility.	[25]
Psychological factors	Attitudes and beliefs about electric vehicles significantly influence intentions to purchase an electric vehicle.	[26,27]
Demographic factors	Younger consumers with higher incomes are more likely to purchase electric vehicles.	[19,20]

Source: Authors’ elaboration based on the reviewed literature.

Technological features such as vehicle range, charging time, and battery technology significantly influence consumer preferences for electric vehicles. Consumers who value technological progress are more likely to choose an electric vehicle [21]. Buhman’s study [27] in Spain found that consumer attitudes towards electric vehicles are influenced by their perceived usefulness and ease of use. However, concerns about limited range and charging time may deter potential buyers. A study in Thailand found that vehicle range and charger availability significantly influence consumers’ decisions to purchase electric vehicles [16].

The availability and spatial distribution of public charging stations is a key prerequisite for the widespread adoption of electric vehicles. Illmann and Kluge’s and Hafezi and Morimoto’s studies [17,28] confirm a long-term positive correlation between charging infrastructure density and the number of EV registrations, but the extent of this effect is modulated by technical parameters (e.g., Level 2 and Level 3 charging speeds) and the demographic and geographical characteristics of a region (urban versus rural areas). The example of the United Kingdom illustrates that an increase of one fast-charging point per 10km² correlates with an approximately 5% increase in annual electric vehicle registrations in urban areas. At the same time, a cycle [28] is evident, whereby higher electric vehicle market penetration motivates energy companies and private investors to further expand the network, creating positive feedback between demand and infrastructure development. The operational efficiency of a charging network also depends on its intelligent integration with the electricity grid and the deployment of Internet of Things (IoT) technologies. Dynamic load management, predictive maintenance and real-time monitoring of station availability significantly increase operational reliability and user comfort [29,30]. These technological innovations play a crucial role in eliminating range anxiety and thus support the mass adoption of EVs in light of growing demands for environmental sustainability and economic efficiency [30,31].

Social influences, such as peer pressure and social norms, also play a significant role according to some authors. Studies have found that social interactions and norms significantly influence consumer attitudes and the degree of acceptance of electric vehicles [32]. Similarly, Kant et al. [25] show that subjective and personal norms directly influence consumers’ intentions to adopt electric vehicles. The influence of social networks and community norms can either encourage or discourage the adoption of electric vehicles. Another study found that consumers influenced by social norms and with higher levels of environmental concern are more likely to accept electric vehicles [20]. Bozpolat [19] shows that younger consumers with higher incomes are more likely to accept electric vehicles due to their greater awareness of environmental issues and ability to cover higher initial costs. Younger consumers are also more receptive to new technologies, which increases the likelihood of electric vehicle adoption [25]. Individual preferences play a key role in shaping the choice between electric cars and cars with conventional combustion engines. Among other things, the brand of a vehicle is an interesting specific factor in individual preferences. The brand and country of manufacture of a vehicle influence the decision to purchase an electric car. Consumers often perceive brand quality as a key factor in their purchasing decisions, which is supported by their product knowledge and trust in a brand, even if their previous experience with said brand has only been with combustion engine vehicles [33]. Research shows that consumers tend to prefer brands that are known for their innovation and technological advancement, which is particularly important in the context of the development of electromobility. The country of manufacture may also play a role, as consumers may have greater confidence in vehicles manufactured in countries with a long tradition of automotive manufacturing and high quality standards [33]. Individual preferences play a key role in shaping the choice between electric vehicles and combustion engine vehicles. Environmental concerns, economic factors, technological attributes, social influences, infrastructure availability, psychological factors and demographic characteristics all contribute to this decision.

Demographic factors such as age, income and education are also closely linked to social influences. These significantly influence consumer preferences for electric vehicles. However, the influence of demographic factors in deciding on electric vehicles may vary between countries, regions and cultures. Consumers’ willingness to pay for electric vehicles varies significantly across regions and socio-economic groups, highlighting the need for and impact of targeted policies to promote electromobility as such [20].

Beyond individual behavioural determinants, institutional conditions and the organisational capacity of municipalities can also shape the adoption of new technologies. Recent research demonstrates that smaller municipalities, particularly in rural regions, often exhibit higher flexibility in decision-making and are more willing to adopt innovative solutions when these reduce operational costs or build on locally available energy resources [14]. Such institutional and organisational aspects complement the socio-psychological and demographic factors identified in previous studies on EV acceptance [20], and highlight the importance of governance capacity in supporting low-carbon mobility transitions.

Psychological factors such as attitudes, beliefs and perceived control significantly influence consumer preferences for electric vehicles. A study by Zhao et al. [26] in China found that consumers’ motivations, attitudes, and internal activities have a significant influence on their purchase intentions in the context of electric vehicles. The perceived usefulness and ease of use of electric vehicles also play a key role in shaping consumer attitudes and intentions. A study in the United States found that these factors significantly influence consumer attitudes, highlighting the need to address range anxiety and charging concerns [3].

It can therefore also be expected that the decision to purchase an electric vehicle will be influenced by the place of residence of potential customers in a particular type of region. Metropolitan and urban regions show a higher rate of electric vehicle adoption compared to rural areas, mainly due to the concentration of infrastructure investment and higher income levels, which facilitate the development of charging stations and accelerate the adoption of new technologies [34,35]. Rural regions offer potential for renewable electricity generation, the effective use of which through community charging stations can reduce energy costs and increase the resilience of the distribution network [34]. Despite lower population densities, rural areas exhibit similar patterns of vehicle use to urban agglomerations, indicating that with adequate infrastructure and targeted policy support, they can achieve comparable levels of EV adoption [35]. Connecting urban and rural networks for renewable energy supply would further optimise resource use and increase regional self-sufficiency, while carefully balancing the benefits for rural communities with the demand of large cities [36]. The adoption of electric vehicles in rural areas therefore not only depends on the availability of infrastructure, but also on the integration of renewable energy sources and targeted incentives that can offset regional disparities and contribute significantly to sustainable development [37,38]. This research builds on the findings of studies [11,12] that have shown that regional context can influence the approach of households and local governments towards electromobility.

To provide an international benchmark for the contextual information discussed above,

Table 2. Structural indicators relevant to EV adoption.

Country	EV Sales 2024 (Units)	Population (Million)	EV per 100,000 Inhabitants
Czech Republic	16,500	10.9	1514
Turkey	105,000	86.3	1216
United Kingdom	540,000	67.6	7983
United States	1,700,000	333	5106

Source: Authors’ elaboration based on publicly available datasets: IEA Global EV Data Explorer (2024); World Bank Population Database (2024).

summarises key structural indicators related to the adoption of electromobility for the Czech Republic, Turkey, the United Kingdom and the United States. As these countries were referenced in the literature review, the comparison allows the position of the Czech Republic to be interpreted in relation to markets with different socio-economic structures and levels of EV uptake [39,40]. The table reports population size, population density, GDP per capita and the number of new EV registrations per 100,000 inhabitants using the latest available data (2023–2024).

The results illustrate substantial international differences in the relative intensity of EV adoption. While the United Kingdom and the United States show high ratios of new EV registrations per capita, the Czech Republic and Turkey remain at significantly lower levels despite recent year-on-year growth. For broader global context, China—the world’s largest EV market—registered approximately 10 million new EVs in 2024, which corresponds to around 710 EVs per 100,000 inhabitants [41]. Although China dominates in absolute volumes, the relative penetration rate is considerably lower than in the United Kingdom or the United States.

2.2. Regional Context of Electromobility Development in the Czech Republic

Previous research and strategic documents point out that the extent of electromobility adoption may be influenced by regional specifics such as economic performance, population density, infrastructure availability and level of urbanisation [11,12]. In the Czech Republic, these factors are reflected in significant differences between regions, both in terms of socio-economic structure and spatial layout. For example, Prague and the South Moravian region are characterised by higher incomes, a higher concentration of human capital and more developed infrastructure, while regions such as Karlovy Vary, Ústí nad Labem and Vysočina are among the economically weaker areas with lower innovation capacity [12].

For the purposes of this study, the regions of the Czech Republic were classified into metropolitan, urban and rural regions. This classification is not based on a single official typology, but was created based on the prevailing spatial structure of settlements, population density and the presence of significant urban centres in individual regions. The criterion for inclusion in a given category was the dominant character of the municipalities forming the regional unit. Prague was designated as the only metropolitan region, as it is the only area with a clearly metropolitan structure. Urban regions are regions with a significant proportion of medium-sized and larger towns, or those directly within the hinterland of the regional capital city (South Moravian, Central Bohemian, Plzeň, Liberec, Pardubice, Ústí nad Labem and Hradec Králové regions). Rural regions include areas with a predominance of smaller settlements and lower population density, without strongly urbanised centres (Karlovy Vary region, Vysočina region, Olomouc region, Zlín region, Moravian-Silesian region, South Bohemian region). This typology was used as an interpretative framework for evaluating spatial patterns in the adoption of electromobility and also served as the basis for constructing categorical and interaction variables in regression models.

Alternative European classification systems, including the DEGURBA methodology and typologies employed by Eurostat and ESPON, were also considered. However, these frameworks are not analytically suitable for the Czech NUTS-3 level, as they classify most regions into the same category and therefore fail to capture the functional spatial differences documented in national statistics and previous research [11,12]. For this reason, a territorially grounded typology based on Czech Statistical Office indicators was applied, enabling the differentiation of metropolitan, urban and rural regions required for the econometric and cluster-based analyses.

In developing the regional typology, we relied on territorial characteristics published by the Czech Statistical Office, which distinguish regions according to population density, the share of inhabitants living in urban settlements, the presence of regional centres and functional commuting patterns. Urban regions are characterised by a substantial concentration of medium-sized towns and strong service and employment functions, whereas rural regions exhibit low population density, a predominance of small municipalities and a fragmented settlement structure without a dominant core. These criteria reflect long-term socio-spatial structures in the Czech Republic [12] and provide an analytically appropriate basis for the spatial analysis of electromobility adoption.

2.3. Research Objectives

Although the topic of electromobility is widely represented in European professional literature, most studies focus on the national level or on developed markets with a high share of EVs. However, there is a lack of systematic quantitative analysis that would examine regional differences in the adoption of electric vehicles in the Czech Republic in detail, particularly in terms of socio-economic and infrastructural determinants. This study therefore responds to this hitherto under-researched area and seeks to contribute to a deeper understanding of spatial inequalities in the adoption of low-emission technologies.

The study uses panel data for the period 2018–2024 and sets the following research objectives:

• To assess the impact of average gross wages on the rate of electric vehicle registration in individual regions.
• To assess the role of public charging infrastructure, including the time lag between its construction and its impact on the number of electric vehicle registrations.
• Compare the levels of electromobility adoption between metropolitan, urban and rural regions.
• Identify regions that deviate from expected trajectories and group them according to similarity of development through a cluster analysis.

3. Methodology

Quantitative methods were chosen to match the nature of the panel in the data and the spatial structure of the research. The combination of a fixed-effects panel regression, interaction models and a supplementary cluster analysis makes it not only possible to test the relationships between socio-economic factors and the number of electric vehicle registrations, but also to take into account the time lag of certain influences and identify regions with similar development trajectories.

The basic analytical framework of this study is a fixed-effects panel regression (Model 1), which allows us to model the relationships between explanatory variables and the number of newly registered electric vehicles. This approach eliminates the influence of time-invariant characteristics of regions (e.g., geographical, historical, or institutional factors) and shared macro trends that manifest themselves across years. It thus allows for a more robust identification of the net effect of variables such as wage levels or the number of charging stations. Model 1 is used to address research objective 1 (the impact of wages) and research objective 2 (the impact of infrastructure). In addition to socio-economic factors, the analysis also took into account the territorial classification of regions, which distinguishes between metropolitan, urban and rural regions. This classification makes it possible to test whether differences in the adoption of electromobility are related to the level of urbanisation of a given territory (research objective 3: the impact of regional typology). This research objective is addressed using a cross-sectional model (Model 2), which analyses differences between individual types of regions using categorical variables, and also using an interaction model, which tests the varying strength of the influence of wages by type of territory without the influence of the metropolitan region.

For research objective 4 (the influence of region typology), the initial framework is the calculation of residuals (Model 1), on which the analysis of regional deviations is based. This focuses on detecting regions where electromobility development systematically deviates from model expectations, while also searching for groups of regions with similar profiles using a cluster analysis (Model 3 and Model 4). An overview of all model specifications used, their methodological approaches, input variables and analytical purpose is provided in

Table 3. Summary table of model specifications.

Model	Name	Dependent Variable	Independent Variables	Method	Specificity/Purpose
Model 1	Panel regression (FE)	EVs per 100,000 inhabitants	Wage, Charging	Fixed effects (FE + lag)	Main model—income, infrastructure incl. lag (research objectives
Model 2	Regression by region type	EVs per 100,000 inhabitants	Metro, Rural (dummy)	OLS	Excluding Prague, test of the influence of region typology (research objective 3)
Model 3	Residual analysis	Residuals from Model 1	Not applicable (residual-based analysis)	Averaging	Detection of regions with positive/negative deviation from model expectations (objective 4)
Model 4	K-means cluster analysis	EVs over time (panel)	EVs over time by region	K-means clustering	Group typology of regions according to registration trends (Objective 4)

Source: Authors’ elaboration.

.

Panel regression (Model 1)—research objectives 1 (impact of wages) and 2 (impact of infrastructure): Panel regression with fixed effects for regions and time periods was chosen to estimate the relationship between socio-economic factors and the rate of registration of new electric vehicles. This model eliminates the influence of time-invariant regional characteristics (e.g., geographical disposition, permanent economic structure) and at the same time checks for common macroeconomic influences across years (e.g., general trends in electromobility, national policy, inflation).

The suitability of the chosen fixed effects approach was verified using a model comparison. The results showed significant differences in the coefficients between the fixed and random models, particularly for the wage and charging variables. The fixed effects model achieved higher explanatory power (R² = 0.682 vs. 0.598) and consistent estimates. Based on these differences, the fixed effects method was preferred, which is consistent with the implication of Hausman’s test of the inconsistency of random effects in the presence of correlation with error.

The basic form of the model is defined as follows:

$$E{V}_{it}={\alpha }_i+{\lambda }_t+{\beta }_1·{\mathrm{W}\mathrm{a}\mathrm{g}\mathrm{e}}_{it}+{\beta }_2·{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}}_{it}+{\epsilon }_{it}$$

(1)

where:

EV_it is the number of newly registered electric vehicles per 100,000 inhabitants in the region and year;

α_i represents the fixed effects of the region;

λ_t are the fixed effects of the year;

Wage_it is the average gross wage in euros;

Charging_it is the number of newly established charging points;

ε_it is a random component.

To select the appropriate panel approach, a Hausman test was performed, which showed systematic differences between FE and RE estimates (p < 0.01). Based on this, the fixed effects method was chosen as consistent and preferred. Multicollinearity between predictors was tested using the variance inflation factor (VIF). Both explanatory variables (Average Wage (EUR), number of charging points) showed VIF values of ≈2.34. This is well below the critical threshold of 5, confirming that the model does not suffer from serious multicollinearity. The high VIF value for the constant (≈84.9) is normal and does not indicate any interdependence between the predictors.

The models were also estimated using robust Driscoll–Kraay standard errors, which ensure consistency of estimates even in the presence of heteroscedasticity, spatial correlation, and autocorrelated residuals over time. The calculations were performed in The calculations were performed in R (version 4.3.2) using the plm package (version 2.6-3); robust Driscoll–Kraay standard errors were computed via the vcovSCC estimator.

As an extension of the main model, an analysis was performed to take into account the possibility of a delayed effect of infrastructure development on consumers’ decisions to purchase electric vehicles. Economic and behavioural research shows that infrastructure can influence behaviour with a certain time lag. For this purpose, a lagged variable, chargingi, t-1, was introduced into the model equation, defined as the number of newly established public charging points in the previous year. The variable was created as a one-year shift within each region, using a group-wise shift (group by (region) → shift). Due to the construction of the variable, 14 observations from 2018 were excluded from the analysis. The model equation was as follows:

$$E{V}_{it}={\alpha }_i+{\lambda }_t+{\beta }_1·{\mathrm{W}\mathrm{a}\mathrm{g}\mathrm{e}}_{it}+{\beta }_2·{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g}}_{i,t-1}+{\epsilon }_{it}$$

(2)

A potential methodological concern in studies of regional EV adoption is the risk of endogeneity in key explanatory variables, particularly charging infrastructure and average wages. First, public charging infrastructure may react to rising EV uptake rather than precede it, creating a possibility of reverse causality. Network operators, municipalities and private investors often expand charging capacity in response to demand pressure, which implies that contemporaneous measures of infrastructure may incorporate behavioural feedback rather than represent an exogenous driver of adoption. In order to partially mitigate this issue, the analysis includes a one-year lag of newly installed charging points, which separates the temporal sequence of infrastructure investment and subsequent EV registrations. The lagged term is statistically significant, whereas the contemporaneous term is not, which is consistent with behavioural adjustment over time. While this does not fully eliminate endogeneity, it reduces the likelihood that the estimated effect reflects mere co-movement rather than a directional relationship.

Given the potential risk of endogeneity, a set of robustness checks was conducted. First, we re-estimated the model excluding Prague, whose extreme socio-economic profile may distort the coefficients for other regions; the results remained stable and statistically significant. Second, alternative specifications including additional socio-economic indicators (e.g., population density, education) were tested, but these introduced multicollinearity without improving explanatory power, which is consistent with previous empirical findings on regional EV adoption [17,28,33]. The introduction of a one-year lag for charging infrastructure confirmed the expected behavioural adjustment over time. While these procedures cannot substitute for a full causal identification strategy, they ensure that the substantive conclusions are not artefacts of a particular model specification and remain robust to reasonable analytical variations.

Second, average wages may capture broader regional characteristics beyond household purchasing power, such as socio-cultural tendencies, political priorities, technological orientation of the regional economy, or environmental attitudes. These factors could influence EV adoption pathways independently of income. To evaluate whether the wage effect is sensitive to model specification, we estimated auxiliary regressions excluding Prague—the only metropolitan region with extreme values—and tested alternative specifications including additional socio-economic controls. Across all variants, the wage coefficient remained positive and statistically significant, suggesting that the effect is robust to reasonable changes in model design.

Given the scope of this study and data availability, more advanced causal identification strategies (e.g., instrumental variables, quasi-experimental designs or policy discontinuities) are beyond the present framework. We explicitly acknowledge these limitations and consider them a promising direction for future research. Nevertheless, the robustness checks performed suggest that the main findings remain substantively consistent and are not driven by model artefacts or reverse causality alone.

Cross-sectional regression by region type, including interaction without metropolitan areas (model 2)—research objective 3 (influence of region typology):

Within the analytical framework of this study, the individual regions of the Czech Republic were classified as metropolitan, urban and rural regions, as this classification made it possible to monitor whether the level and dynamics of electric vehicle adoption are related to the urban structure of a region. The type of region is therefore used as an independent variable in cross-sectional and interaction regression models (see Model 2 and the follow-up analysis without the metropolitan variable) and also serves as an interpretative key in several graphs in the results section, where the distinction between regional typologies is visually highlighted.

To quantify the influence of region type, cross-sectional models were estimated using the OLS method. In the first phase, Model 2 was estimated based on the entire sample of regions, including the capital city of Prague. Dummy variables for metropolitan and rural regions were included in the equation, with urban areas as the reference category. The model equation was as follows:

$$E{V}_{it}={\delta }_0+{\delta }_1·{\mathrm{M}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{o}}_i+{\delta }_2·{\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_i+u_{it}$$

(3)

where Metro_i and Rural_i are categorical (dummy) variables for metropolitan and rural areas, with urban regions as the reference category. Model 2 showed that metropolitan regions—represented exclusively by Prague—have a significantly higher rate of electric vehicle registrations (coefficient ≈ 83; p < 0.001).

This approach made it possible to test whether there are systematic differences in the level of electric vehicle registrations between the three types of areas. In the second step, Prague was excluded from the sample, as it was the only metropolitan region to show widely different values. As a result, the metropolitan dummy variable was no longer included, and the comparison was limited to rural and urban regions only.

$$E{V}_{it}={\delta }_0+{\delta }_1·{\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}_i+u_{it}$$

(4)

Based on the assumption that wage levels may have a different effect on electromobility depending on the type of region, an interaction equation was tested:

$$E{V}_{it}={\gamma }_0+{\gamma }_1·\left({\mathrm{W}\mathrm{a}\mathrm{g}\mathrm{e}}_{it}·D_{\mathrm{U}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}}\right)+{\gamma }_2·\left({\mathrm{W}\mathrm{a}\mathrm{g}\mathrm{e}}_{it}·D_{\mathrm{R}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{l}}\right)+{\alpha }_i+{\lambda }_t+{\epsilon }_{it}t\}$$

(5)

This equation allows us to distinguish between the effect of wages on electric vehicle registrations in rural regions compared to urban regions, which serve as a reference category. The results confirmed that the impact of income on electromobility is evident in both groups, but stronger in urban regions. This interaction approach allowed for a more refined analysis of the spatial differentiation of the impact of income and thus a deeper understanding of regional patterns of electromobility adoption in the Czech Republic.

The final model specification deliberately focuses on average wages and charging infrastructure as the two core explanatory variables. Given the small number of NUTS 3 regions in the Czech panel, adding multiple socio-economic indicators substantially reduces degrees of freedom and leads to unstable or collinear estimates. Several alternative variables, such as GDP per capita, population density and education indicators, were explored in preliminary testing, but their inclusion did not increase explanatory power and frequently introduced multicollinearity. For this reason, the final specification prioritises parsimony, model stability and theoretical relevance.

It is also important to note that many determinants frequently discussed in the EV adoption literature—such as vehicle brand preferences, driving range, charging time, psychological attributes or individual environmental attitudes—cannot be incorporated in the regional models because no consistent indicators for these factors exist at the NUTS 3 level. These determinants operate primarily at the household or individual level and are therefore conceptually misaligned with the aggregated regional dataset used in this study. The modelling framework therefore focuses exclusively on structural, regionally measurable variables.

Although the behavioural and psychological dimensions of consumer decision-making are widely documented in the electromobility literature (e.g., refs. [15,25,27,32,34]), these determinants cannot be directly incorporated into a region-level analysis. The reason is that the available data take the form of aggregated indicators at the NUTS 3 level, whereas psychological, social or motivational factors are inherently individual-level variables, typically measured through survey instruments administered to households or individuals.

Including such individual-level determinants in an econometric model based on regional aggregates would constitute an ecological fallacy, i.e., a situation in which characteristics of a population are incorrectly interpreted as characteristics of the individuals within that population. This methodological problem is well known in socio-economic research and represents a fundamental limitation when attempting to combine micro- and macro-level determinants within a single quantitative model. Since no regional-level indicators of psychological factors exist for the Czech Republic, and no valid aggregation or transformation of individual-level findings is possible, these determinants must be addressed exclusively in the theoretical part of the study rather than in the econometric modelling.

For this reason, psychological, social and behavioural factors are reflected in the literature review through empirical studies (e.g., refs. [15,25,27,34]), whereas the empirical model is intentionally restricted to structural variables that can be validly measured at the regional level (income, infrastructure). This approach is consistent with methodological standards of regional analysis and ensures that the model does not violate the principle of maintaining the correct level of data aggregation.

The exclusion of Prague from the interaction models is motivated by its extreme socio-economic position and its status as the only metropolitan region in the dataset. Including Prague in interaction terms produced disproportionate leverage effects and masked meaningful contrasts between urban and rural regions. Removing it allows the interaction models to capture differences between these two region types more accurately.

Residual analysis (Model 3)—research objective 4 (influence of regional typology):

Furthermore, an analysis of residuals and calculation of their average values by region was performed to reveal systematic deviations from the model predictor. This residual deviation served as input for identifying regions that are significantly above or below the predicted development level.

$$\overline{{\epsilon }_i}={\displaystyle \frac{1}{T}}\sum _{t=1}^T{\epsilon }_{it}$$

K-means cluster analysis (Model 4)—research objective 4:

The cluster analysis was included as a complementary step to the econometric models in order to capture the temporal trajectories of EV adoption across regions. While the regression models and the residual analysis identify the main determinants and systematic deviations from predicted values, the clustering groups regions according to the similarity of their development dynamics over time. This provides an additional perspective on regional heterogeneity and visually reinforces patterns already indicated by the regression framework, particularly the distinct position of metropolitan Prague, the intermediate trajectory of strong urban regions, and the slow-growth profile of the remaining areas.

To identify patterns in the dynamics of electromobility, a K-means cluster analysis was performed on the development of values over time. The input was a normalised matrix:

$$\left|\begin{array}{ccc}{\mathit{EV}}_{1,2018}& \cdots & {\mathit{EV}}_{1,2024}\\ ⋮& \ddots & ⋮\\ {\mathit{EV}}_{14,2018}& \cdots & {\mathit{EV}}_{14,2024}\end{array}\right|$$

where each row corresponded to one region and each column to the value for a given year. The aim was to divide the regions into homogeneous groups with similar levels of development. The results were visualised using the time trajectories of individual clusters.

Basic diagnostic tests were performed to the extent necessary to verify the suitability of the chosen model framework. Their results did not give rise to any reason to revise the specification used.

3.1. Data Set

For the purposes of the analysis, a panel data set covering 14 regions of the Czech Republic in the period 2018–2024 was compiled. The input data comes from publicly available databases from the Czech Statistical Office (ČSÚ), the Ministry of Transport and the Clean Transport platform. The file contains both socio-economic indicators and specific indicators of electromobility and infrastructure.

An overview of all variables used in the models is provided in

Table 4. Overview of variables used in regression and classification analysis.

Indicator	Designation	Unit	Period	Data Source
Number of new electric vehicle registrations	ev_registr	number/100,000 inhabitants	2018–2024	Clean Transport
Average gross monthly wage	wage	EUR	2018–2024	Czech Statistical Office
Number of public charging stations	charging	number	2018–2024	Clean Transport
Delayed infrastructure (t − 1)	charging_t-1	number	2018–2024	Own calculation
Gross domestic product per capita	gdp	EUR	2018–2024	Czech Statistical Office
Dummy variable: urban region	urban_dummy	0/1	static	Own calculation
Dummy variable: rural region	rural_dummy	0/1	static	Own calculation
Interaction wage × urban	wage*urban	—	2018–2024	Own calculation
Interaction wage × rural	wage*rural	—	2018–2024	Own calculation
Region type (category: metropolitan/urban/rural)	region_type	category	static	Own calculation

Note: A detailed table of normalised variables used in the empirical analysis is provided in Appendix A. Source: Authors’ elaboration based on data from the Czech Statistical Office and Clean Transport database.

, including their names, units, time coverage and data sources. To provide a concise overview of the dataset used in the empirical models,

Table 5. Summary statistics of the variables used in the analysis (2018–2024).

Variable	Mean	Std. Dev.	Min	Max	Unit
Average wage	~1470	~250	1100	2300	EUR
EV registrations per 100,000 inhabitants	~38	~45	0	338	cars/100 k
Charging points	~55	~65	0	278	units
Charging points (t − 1)	~45	~55	0	255	units
Observations	98	–	–	–	–

Source: Authors’ elaboration based on the dataset provided in Appendix A.

reports the summary statistics of the main variables. These statistics reflect the overall structure and variability of the regional panel data used in Models 1–4. Detailed information on the dataset and the normalised variables used in the empirical analysis is provided in Appendix A.

3.2. Analytical Workflow (Flow Chart)

To enhance the clarity of the methodological design, this subsection provides a structured overview of the analytical workflow applied in this study. A structured overview of the analytical workflow applied in this study is presented in Scheme 1. The workflow summarises the sequence of data preparation steps, econometric modelling, complementary analyses and interpretation procedures.

4. Results

This chapter presents the results of a quantitative analysis of the development of electromobility in individual regions of the Czech Republic in the period 2018–2024. First, an introductory descriptive overview of the development of the main indicators—the number of registered electric vehicles and public charging stations—is provided. This is followed by analytical outputs broken down according to the research objectives defined in Section 3 and the methodological approaches outlined in Section 4.

4.1. Descriptive Overview of the Development of Electromobility

Between 2018 and 2024, there was a gradual but noticeable increase in the number of registered electric vehicles in the Czech Republic. As shown in Figure 1, the rate of growth varied considerably between different types of regions. The metropolitan region (Prague) shows a significantly above-average trajectory, and since 2021 electromobility has been developing very dynamically. In contrast, rural regions recorded only a slight increase without any significant jumps throughout the period under review, indicating persistent barriers to the adoption of electromobility in these areas. Urban regions are located between these extremes—some (South Moravian region and Central Bohemian region) are approaching the metropolitan trajectory, while others remain closer to the rural profile.

Although developments in recent years indicate an accelerating trend, it is important to note that the overall share of newly registered electric cars in the Czech Republic remains low.

Household purchasing power is considered one of the key determinants of electric vehicle adoption. Data (2018–2024) shows that the metropolitan region (Prague) achieves significantly higher values, both in absolute terms and in terms of growth dynamics. Some urban regions (e.g., South Moravia, Central Bohemia) also perform above average, while rural areas have long-term lower wage levels. This income gap can significantly affect the ability of households to invest in new technologies, including electric vehicles, and thus forms one of the essential foundations for explaining regional differences in the development of the EV market. In Figure 2, this determinant is represented by the average wage in individual regions in 2024, including a typology of individual regions.

Another key determinant of the development of electromobility is the availability of public charging infrastructure, which is shown in Figure 3. Here, too, there are noticeable differences in development depending on the type of region. The metropolitan region (Prague) has long had the highest density of charging stations and also shows the fastest growth of charging infrastructure, especially after 2021. Some urban regions, such as South Moravia and Central Bohemia, also show steady infrastructure development, which corresponds to their higher economic performance and higher demand. In contrast, rural regions are growing more slowly and remain at significantly lower levels.

4.2. Research Objective 1: Impact of Wage Levels

Model 1—Panel regression with fixed effects (FE): Model 1 confirms that the average wage in the regions has a statistically significant and positive impact on the number of newly registered electric vehicles (p < 0.01). The estimated coefficient for the wage variable is approximately 0.19, indicating that an increase of EUR 100 in the average regional wage is associated with about 19 additional EV registrations per 100,000 inhabitants across the full panel. This result is consistent with the hypothesis of research objective 1 and suggests that income level is a key socio-economic factor influencing households’ willingness to invest in electromobility.

Figure 4 visualises the positive linear relationship between average monthly wages and the number of registered electric vehicles per 100,000 inhabitants, with the relationship shown separately for metropolitan, urban and rural regions. In all types of regions, a positive correlation between income level and the rate of electric vehicle adoption is confirmed, which is consistent with the results of regression models. The steeper slope of the regression line in metropolitan areas suggests that the relationship may be more pronounced in higher income brackets.

4.3. Research Objective 2: Impact of Charging Infrastructure

Model 1—Extension with a lagged infrastructure variable: While the effect of charging infrastructure in the same year was not statistically significant (p = 0.84), its lagged value Charging_{t-1} shows a statistically significant positive relationship with electric vehicle registrations (coefficient = 0.20; p = 0.009). This shows that household decisions are influenced more by infrastructure that was already available in the previous period. The result corresponds to the hypothesis of research objective 2, which suggests that the impact of infrastructure will be delayed.

Figure 5 shows the relationship between the number of public charging stations registered in the previous year and the number of electric vehicle registrations per 100,000 inhabitants in the following year, with individual points colour-coded according to region type. Despite the higher dispersion of values, a positive trend is evident in all categories, which corresponds to the results of the panel regression model with its lagged variable. Figure 5 also shows that metropolitan and urban regions generally have higher values for both variables, while in rural areas the distribution of values is flatter and more dispersed.

4.4. Research Objective 3: The Influence of Regional Typology

Model 2—Cross-sectional regression by region type: Regression by region type and interaction effects shows that metropolitan regions (represented by Prague) have significantly higher levels of electric vehicle registrations than urban and rural regions. The difference between metropolitan and rural areas is statistically significant (p < 0.05), while the difference between metropolitan and urban areas is less pronounced but also negative. The output contributes to the fulfilment of research objective 3, which focuses on spatial differences in the development of electromobility.

To better illustrate these model-based differences between region types, Figure 6 presents the observed levels of electric vehicle registrations in metropolitan, urban and rural areas.

This model was followed by an extension in which the interaction between income and region type was tested. The model with interactions (without the metropolitan variable) showed statistically significant positive relationships in both groups: urban (coefficient = 0.21; p = 0.0076) and rural (coefficient = 0.17; p = 0.0363). This shows that higher income is a relevant factor across all types of regions. The metropolitan region—Prague—was excluded from this additional analysis because its extreme income values and rate of electric vehicle registrations significantly exceed those of other regions and strongly influence the model results.

Figure 7 shows the linear dependence between wage levels and the number of new electric vehicle registrations by region type. Although a positive trend is evident in both territorial groups, the relationship is more pronounced in urban regions, which is confirmed by the results of the interaction and regression. This result can be interpreted as a greater sensitivity of urban residents to income factors when deciding to purchase an electric vehicle.

A closer inspection of the lower-income segment in Figure 7, however, reveals that some rural regions display slightly higher EV-per-capita values than selected urban regions. This deviation should not be interpreted as stronger household willingness to adopt EVs in rural areas. Rather, it likely reflects the fact that the absolute number of EV registrations in these regions is still small, so even minor fluctuations can produce noticeable differences in per-capita indicators. In addition, the available dataset does not distinguish between registrations by households, firms or public institutions, whose relative shares may differ across regions, and average wages do not capture intra-regional income variability. Importantly, this deviation appears only at the lowest income levels. As wages increase, the relationship becomes fully consistent with the regression results: higher incomes are systematically associated with higher EV adoption, and urban and metropolitan regions clearly outperform rural ones.

4.5. Research Objective 4: Regional Deviations from Predicted Development

Model 3—Residual analysis based on Model 1: This shows deviations between actual and predicted values (residuals) from the main panel regression model (Model 1), according to region typology. The aim is to determine whether the model systematically underestimates or overestimates the development of electromobility in certain types of regions, which is graphically illustrated in Figure 8. Residuals to the left of zero (orange area) represent positive deviations, i.e., situations where the model predicted a lower number of electric cars than were actually registered. Residuals to the right of zero (blue area) correspond to negative values, which means that the model overestimated actual development. The results show that the Prague metropolitan region has a significantly positive residual, indicating that the model underestimated the actual development of electromobility in the capital. Electromobility is growing faster here than would correspond to the economic and infrastructure parameters used in the model. Conversely, several rural regions, particularly the Karlovy Vary and Vysočina regions, show significantly negative residuals, which means that Model 1 predicted higher development of electromobility than actually occurred. The situation is more volatile in urban regions. For example, the Central Bohemian region shows a rather positive residual (the model underestimated growth), while the Pardubice region and the Liberec region are slightly below the prediction. This suggests that even within a single typological group, there is some internal variability.

Overall, however, the figure confirms expectations: metropolitan and selected urban regions tend to grow faster than the model predicts, while most rural regions lag behind the predictions. This confirms that the type of region plays a role in the predictive accuracy of the model and is an important factor in interpreting the results.

Cluster analysis (Model 4): To identify different dynamic patterns in the adoption of electromobility, a K-means cluster analysis was applied to panel data on the number of newly registered electric vehicles in the regions of the Czech Republic in 2018–2024. The aim was to group regions into relatively homogeneous clusters based on the similarity of their development trajectories over time. The number of clusters was set at k = 3 for the sake of analytical clarity and the meaningful interpretation of development patterns in the regions. The resulting cluster structure and regional development trajectories are illustrated in Figure 9.

The results can be interpreted as follows:

• Cluster 0 (dynamically growing) only includes the capital city of Prague, which is characterised by significantly accelerating growth in electric vehicle registrations, especially after 2021. Prague dominates both in absolute terms and in terms of development dynamics, forming a separate cluster representing the fastest phase of electromobility adoption in the Czech Republic.
• Cluster 1 (moderately growing) consists of the Central Bohemian and South Moravian regions, i.e., urban regions with a significantly above-average but less steep growth trajectory than in the case of metropolitan Prague. Both regions show continuous development of electromobility and can be described as secondary drivers of the transition to alternative fuels.
• Cluster 2 (slow growth) includes all rural regions and some urban regions (e.g., South Bohemia, Hradec Králové, Zlín, Pardubice). These regions show low absolute values and only a slight year-on-year increase in registrations. Although conditions are improving (infrastructure, awareness), the adoption of electromobility remains limited.

The regression results provide a clear interpretative framework for understanding the cluster trajectories. Prague forms a separate, fast-growing cluster, consistent with its strongly positive residuals and the high income sensitivity identified in Model 1. The South Moravian and Central Bohemian regions fall into an intermediate cluster, reflecting their above-average socio-economic performance and the stronger income responsiveness observed in the interaction model. Regions in the slow-growth cluster exhibit lower income levels and weaker infrastructural dynamics, which aligns with the regression-predicted limitations and their negative residuals. The cluster analysis therefore reinforces, rather than supplements, the spatial heterogeneity captured by the econometric models.

The results confirm a strong correlation between the typology of regions and the trajectory of electromobility development. All rural regions were classified in the slowest cluster, while Prague forms a separate group with the highest dynamics. Urban regions were divided between the slower and moderately dynamic clusters, confirming their internal heterogeneity.

These findings show that the typology of a region is an important predictor of the development of electromobility, but it is not the only one. Individual factors of regions, such as economic structure, infrastructure distribution, or population density apparently play a significant role. When creating policies to support electromobility, it is therefore necessary to not only take into account regional typology, but also the local specifics and potential of the given territory.

5. Discussion

This study analysed the factors influencing regional differences in the development of electromobility in the Czech Republic in the period 2018–2024. The results show that the average wage, type of territory and delayed effect of infrastructure play a key role. These findings are consistent with the conclusions of foreign studies [11,12], which also confirm the influence of economic conditions and infrastructure on EV adoption over time. Across models, it has been shown that higher wages statistically significantly increase the number of EV registrations. Model 1 indicates that a EUR 100 increase in average wages is associated with an average increase of approximately 19 EV registrations per 100,000 inhabitants. This relationship is also confirmed in the interaction model, where the wage effect is significant in both urban and rural areas. Income is therefore a key determinant across territory types. While the direct impact of charging infrastructure in the same year was not confirmed, its delayed effect (charging_t−1) showed a significant positive relationship. This result may indicate a reactive approach to infrastructure development—i.e., that infrastructure development follows increased demand rather than preceding it. This has implications for the timing of public support and the evaluation of its effectiveness. The residual analysis of Model 1 identified regions with significant deviations.

The K-means cluster analysis further divided the regions into three groups according to the trajectory of electromobility development. In this context, the reference to “existing policies” relates to the financial and regulatory instruments supporting EV adoption in the Czech Republic in recent years, such as national purchase subsidy schemes, tax exemptions for electric vehicles and selected municipal measures. The results of this study indicate that these instruments have not produced uniform outcomes across regions. Several regions consistently fall below the model-predicted adoption trajectories, suggesting that factors not captured by the structural variables in our dataset—such as micro-level consumer preferences, perceived usability of EVs or differences in institutional uptake—may also play a role. Another possible explanation is that the currently available financial incentives may not be sufficient to offset the relatively high purchase price of EVs in lower-income regions. As the analysis is based exclusively on region-level panel data, however, it is not possible to determine the relative importance of these mechanisms. Further research combining regional indicators with micro-level behavioural or economic data would be required to identify the underlying drivers in more detail.

The analysis also confirmed the importance of spatial context. These cluster-based trajectories are closely aligned with the patterns identified in the regression and residual analyses. Prague forms a distinct and accelerating trajectory that corresponds to its systematically positive residuals and adoption levels exceeding what can be explained by income and infrastructure alone. The South Moravian and Central Bohemian regions follow a medium-growth path consistent with the stronger wage sensitivity observed in the interaction model. The remaining regions cluster into a slow-growth group, reflecting the limited adoption predicted by the regression model in areas with lower income levels and weaker infrastructural dynamics. In this way, the cluster analysis does not introduce an alternative interpretation, but rather visualises and reinforces the spatial differentiation captured by the econometric framework.

Given the strong role of income and the persistent differences between metropolitan, urban and rural regions identified in the regression, residual and cluster analyses, some degree of territorial differentiation in support instruments could be considered. For regions that consistently lag behind the model-predicted trajectories, targeted incentives conditioned on longer-term vehicle ownership or operational sustainability could reduce the risk of market distortions and short-term resale effects. Such measures could potentially help address structural barriers in the least economically performing regions, while ensuring that support remains effective and does not generate unintended redistribution across territories. However, the present study cannot evaluate the effectiveness of specific policy designs, as this would require micro-level behavioural and economic data. Any adjustment of support schemes would therefore need to be grounded in further evidence.

The classification of regions according to typology (metropolitan, urban, rural) and their inclusion in clusters suggests that the degree of urbanisation is related to the pace of electromobility adoption. Most urban and metropolitan regions are located in clusters with higher dynamics, while rural regions more often fall into the lagging group. However, the influence of urban centres within regions is also an important finding. For example, the Moravian-Silesian region, although structurally weak, shows above-average residuals, probably due to its metropolitan area. In contrast, the South Moravian region with Brno achieves both high growth dynamics and results in line with predictions. This confirms that, in addition to the type of region as a whole, its internal urbanisation structure is also key. It can therefore be concluded that regional differences in electromobility cannot be explained solely by socio-economic variables, but also by urbanisation and spatial factors. Policy recommendations should therefore not only reflect the type of region, but also its internal structure, including the presence of urban centres and spatial development trajectories

Institutional capacities may further contribute to deviations between predicted and observed trajectories. As shown by Hlaváček [14], smaller municipalities—particularly in rural settings—tend to demonstrate greater decision-making flexibility and may adopt innovative solutions more readily when supported by local energy resources or cost-saving opportunities. In the context of electromobility, such institutional adaptability may include the integration of local photovoltaic systems, community energy initiatives or tailored municipal support mechanisms. These institutional characteristics can partially explain why some rural regions deviate from model-predicted adoption levels despite structural constraints.

In conclusion, the results can be placed in the context of previous findings. The study confirms that economic factors are among the strongest determinants of EV adoption [11,42], with the effect of income across territory types being reflected in the data and more strongly in metropolitan and urban regions [20,25]. The study also shows the time-lagged effect of infrastructure development, in line with the literature emphasising the importance of charging availability and the link between demand and the network of charging stations [17,28]. The study complements existing knowledge in two ways. It provides the first panel quantification at a regional level in the Czech Republic, taking into account fixed effects and infrastructure delays, thereby expanding on studies that have been predominantly national or metropolitan in focus [42] and it explicitly tests the role of regional typology (metropolitan/urban/rural) and the interaction of income with the type of territory, thereby expanding behavioural and socio-psychological approaches [18,19,21] with a spatial dimension. A residual and cluster analysis also adds value by identifying deviating regions and latent development trajectories—information that conventional regression models do not usually provide. The interpretation simultaneously links institutional and size effects known from municipal research [9] with the adoption of new technologies in transport and shows that regional administration and capacities can accelerate or slow down the adoption of electric vehicles.

6. Conclusions

The aim of this study was to quantify the impact of selected socio-economic and infrastructure factors on the regional adoption of electromobility in the Czech Republic in 2018–2024. Based on a fixed-effects panel regression, it was shown that average wages are a consistent and statistically significant predictor of growth in the number of new electric vehicle registrations. The significance of this factor persisted across different model specifications (including interaction models), confirming the hypothesis that the availability of capital at a household level is a key prerequisite for the adoption of new technologies in personal transport. Furthermore, it was found that the impact of charging infrastructure is delayed—only the lagged effect (charging_t−1) was statistically significant. This result supports the hypothesis that infrastructure construction often follows an increase in demand, which can lead to reactive planning. The recommendations therefore point towards a proactive infrastructure policy that would anticipate the development of electromobility rather than merely reacting to it retrospectively.

Significant differences between regions were further confirmed according to the typology of regions. The interaction model showed that the influence of income is positive in both urban and rural regions and is not just stronger in metropolitan areas. Residual analysis identified regions where electromobility development deviates significantly from model predictions. This suggests the existence of other influences (e.g., cultural or institutional) that were not captured in the models. A K-means cluster analysis then added the perspective of dynamics over time and showed that regions can not only be segmented according to initial conditions, but also according to growth trajectories. In some cases, it was found that the presence of a strong urban centre can increase performance even in otherwise structurally weaker regions. At the same time, linking different types of regions can lead to synergistic effects, where urban investment and rural renewable energy potential can contribute to the further expansion of electromobility and strengthen the energy self-sufficiency of regions.

However, the conclusions of the research should be interpreted within certain limits. The research was conducted in a single country and on a sample of fourteen regions, with the representation of metropolitan, urban and rural regions not being entirely balanced. The 2018–2024 time span captures a dynamic phase in the market and for public policies, so some short-term fluctuations such as COVID-19, inflation and changes in subsidies may have partially influenced the results. We used aggregated regional data, which cannot fully capture the differences within regions or all the relevant factors influencing the purchasing preferences of the population.

Follow-up research could be extended to more EU countries (NUTS2/NUTS3) or extend the time span of evaluated data, or distinguish between BEVs and PHEVs and the share of EVs among new vehicle registrations. It would also be valuable to supplement the data with information on total ownership costs (including vehicle and energy prices), fast charging availability, the share of company registrations, or the possibility of home and work charging.