Regional Innovation Systems as a Remedy for Structurally Affected Regions—Empirical Evidence from the Czech Republic

Abstract

Structurally affected regions face a necessary economic transformation, for which the efficiency of public financial support is crucial. Regional innovation systems represent a modern approach to regional and urban development focusing on innovation and research activities. The aim of this study is to assess whether there is a difference in the effect of public R&D funding on the development of a region’s innovation environment between structurally affected and other regions. The beta convergence and the method of calculating the average efficiency of public funds were used to meet the objective of this research. The analysis was conducted on data from 2012 to 2022 in 14 regions of the Czech Republic. The results show that public support for R&D has a positive effect on development of the innovation environment in structurally affected regions in the Czech Republic and that there is a difference in the effect of this support regarding structurally affected regions versus developed regions. These affected regions on average respond better to public financial support for R&D than mature regions. In contrast, the efficiency of public financial support is lower in regions with a strongly developed innovation system.

1. Introduction

Innovation is a key driver of economic success in urban regions, especially in the context of globalization. While globalization facilitates benefits like the free movement of capital and information, it also intensifies competition in international markets, challenging regions to sustain their competitiveness and attract investment [1]. Therefore, it is crucial for regions to harness their unique qualities, characteristics, and opportunities to foster innovative activities that propel economic growth [2,3]. Structurally disadvantaged regions often have a manufacturing base that differs historically from that of more developed regions, which have long focused on high-tech and semi-high-tech industries. Consequently, public support for R&D typically favours urban centres with a history of advanced industrial characteristics. However, the necessity to transform structurally affected regions could represent a competitive advantage and an opportunity for innovation. This raises the question of whether more public R&D support should be directed towards these regions to capitalize on this potential.

There are several terms characterizing regions that are lagging behind in some respect, such as “peripheral”, “structurally disadvantaged”, or “structurally affected”. The main characteristic of “peripheral region” is its geographical distance from the developed centre. It is precisely this distance that causes their poorer economic development. In academic discourse, while the term “structurally disadvantaged” region usually represents one specific negative characteristic of a region (e.g., high unemployment, high crime, etc.), the term “structurally affected” region is understood to represent a much broader range of negative characteristics (such as economic, social and environmental) [4,5,6]. Therefore, for the purposes of our study, we understand the investigated regions as structurally affected.

The aim of this study is to assess whether there is a difference in the effect of public R&D funding on the development of a region’s innovation environment between structurally affected regions and other regions. To research this issue, we ask two research questions.

RQ1. 

Are structurally affected regions experiencing an increase or decrease in the effect of public R&D financial support?

Research question RQ1 is based on the assumption that regions facing challenges related to economic transition may have a greater need to innovate and adapt to new technologies and challenges [7]; therefore, there is room for new initiatives, and the effect of public support will be positive. Conversely, regions in which the innovation environment is already saturated (i.e., developed) may have a lower potential for increasing the efficiency of this public financial support, as the scope for new initiatives may already be limited. This variation in the characteristics of regions may lead to different trends in the efficiency of the financial support deployed. The discovery of a decline in the efficiency of this support in structurally affected regions may be a signal of inefficient or inappropriately targeted allocation of public funds to support R&D, and an impulse for regional policy to revise its strategies.

RQ2. 

Is there a difference in the effect of public R&D financial support on the development of the innovation environment between high-performing and peripheral regions?

Since the efficiency of public financial support is crucial in the process of transforming the economies of structurally affected regions [8], research question RQ2 seeks to answer the question of whether these regions have the same or different capacity to use this financial support compared to performing regions. This question is key to understanding the efficiency of public investment in supporting the development of an innovation environment as a way of transforming underdeveloped regions.

Evaluating the innovation environment of the region and the efficiency of R&D spending on urban development is necessary for several reasons. It allows policy makers to better identify weaknesses and strengths in regional innovation capacities [9], and design more effective development objectives and strategies based on an understanding of these factors. In addition, the results can be used to better and more efficiently allocate and spend public funds to support R&D and innovation initiatives in the regions.

2. Literature Review

According to Asheim et al. [10], institutional theories of regional development represent the most dynamically developing approach to understanding the causes and possibilities of regional development; within these theories, so-called regional innovation systems are currently being applied and studied. A regional innovation system (RIS) is a collection of organisations, universities and research institutions, businesses and networks that together create an innovation environment, i.e., it consists of a knowledge and institutional infrastructure supporting innovation within the industrial structure of a region [11]. Regional innovation systems theory considers innovation as the result of the interaction between such a set of actors. Tödtling and Trippl [12] state that an innovation system in the regional context is understood as an infrastructure that enables the creation and sharing of knowledge, while the quality and added value of this structure then lead to the economic development of the region. According to Wang [13], successful regional innovation systems capable of disseminating knowledge and experience lead to job creation and new businesses with higher value added, and more skilled university graduates, fostering social interactions, networking and a more competitive environment.

Some authors emphasize the position of a city as a key organizing unit for innovative activity and consider urban regions as centres of the processes of innovation and entrepreneurship [14]. Geographers and regional scientists have found innovation to be highly concentrated within urban areas [15]. Nonetheless, as stated by Ellison and Glaeser [16], in the case of commercial innovation (i.e., products and services), the benefits of urban agglomeration decrease, and related activities can be spread out across regions. Yet the concentration of people and economic activities in urban environments has also given rise to a number of social and environmental problems [17]. In the light of these changes, sustainable economic growth and balanced development across a region come to the fore, primarily through the local dimension and the recognition of the concept of territorial cohesion [18,19]. Thus, urban areas can benefit from the increased innovation performance of surrounding regions, which is amplified through the development of innovation ecosystems.

The concept of regional innovation systems became particularly important in the context of regional development in peripheral and structurally affected regions facing a necessary economic transformation. According to Coenen et al. [20] or Chmelíková and Redlichová [21], old industrial regions are in declining sectors and technologies and, therefore, appropriate policy and financial support, strategic measures and activities capable of economically transforming these old industrial regions are needed. According to the authors, it is the existence of a quality innovation environment that is the way to the successful development of peripheral regions, which, in addition to restoring the economic maturity of the regions, also brings improvements in their competitiveness in the modern globalized environment at the national and supranational level.

Blažek and Uhlíř [22] argue that for a successful regional innovation environment, it is necessary to promote interaction between the individual members of innovation systems, which leads to a process of mutual learning and knowledge dissemination, while at the same time appropriate support is necessary to develop these RISs. The European Committee of the Regions through the Innovation Programme supports academics to carry out research that examines, for example, the achievement of the stated objectives of the regions’ innovation strategies and policies, the quality of regional RIS, the level of cooperation between their actors or the evaluation of the tools used to support these RISs. According to the European Committee of the Regions, these innovation systems are essential for ensuring competitiveness and addressing societal challenges such as adaptation to climate change, digital transformation, etc. [23].

A number of authors (e.g., Hajek et al. [24]; Asheim et al. [10]; Wanga [13]) have conducted research on innovation systems and argue that RIS evaluation does not have a generally valid and universal methodology. According to the authors, it is possible to assess, for example, the quality of RISs by monitoring their outputs (which can be, e.g., number of new patents, number of publications, number of university graduates) over a certain period and comparing the values with other regional systems. This, according to the authors, is indicative of the quality of the local innovation environment. Furthermore, it is possible to look for regional similarities of significantly successful innovation systems, as in the case of Silicon Valley and Route 128 (see Klímová [25]). According to Pokorný et al. [26], it is important for the region and regional policy to examine and evaluate the so-called RIS innovation environment and its quality, which makes it possible to identify trends in the development of regional innovation systems, to assess their quality and performance through appropriately chosen indicators (the exact list of which, according to the authors, is not generally valid, but includes, for example, the number of research institutions in the region, the level of investment in R&D, the size of the university-educated population, the number of patents, etc.) and to measure them regularly.

Endogenous growth theories emphasize the internal factors driving economic growth, particularly technological progress generated within the economy. These theories highlight that R&D expenditures are pivotal for enhancing the innovation environment in an urban region [27], thereby increasing economic efficiency. Todling and Triple’s study found that financial support for regional innovation systems (RIS) is crucial for their efficiency and positive impact on economic development. They argue that this support must encompass a wide range of aspects beyond technological innovation, including research activities and the promotion of human and social capital. Gutiérrez et al. [28] assert that public spending on R&D invariably fosters economic growth and development. While Shao and Wang [29] conclude that government subsidies have a significant stimulating impact on enterprise technological innovation activities, Dvouletý et al. [30] emphasize heterogeneity in the effects of investment grants (subsidies). However, the efficiency of this spending in developing the innovation environment varies by region. Several authors (e.g., [31,32,33,34]) have noted that previous studies on RIS have predominantly focused on developed regions, leaving a gap in understanding how this approach can benefit peripheral and structurally affected regions. In these disadvantaged areas, R&D support is crucial for fostering regional innovation systems that drive urban development and economic transformation [31,33].

3. Materials and Methods

3.1. Research Design

In our study, the main and independent variable was considered to be the total direct public R&D support, including all the funds provided by public budgets to support R&D. The effect of this support was assessed on eight selected indicators that characterize the maturity of the innovation environment and ecosystem in the region. The indicators were chosen based on the recommendations of the strategy of the Ministry of Industry and Trade of the Czech Republic [35], which uses these indicators to assess the maturity of the innovation environment in the regions. At the same time, the indicators listed below are also regularly monitored by the Innovation Centre of the South Moravian Region [36] for annual evaluation of the development of the innovation ecosystem in the region. The choice of indicators is also consistent with previous published studies, such as, e.g., the study by Uzlov and Li-chun [37], which used these indicators to design a methodology for evaluating the innovation ecosystem and innovation performance of the region. In our study, we evaluate the impact of public financial support on science and research through eight indicators of the regional innovation environment, specifically (1) number of research and development institutions in the region, (2) number of registered patents, (3) number of people working in science and research, (4) number of publications in the top 10% of the most cited scientific outputs worldwide, (5) employment in high-tech industries, (6) gross domestic product at current prices, (7) R&D expenditure financed from corporate sources, and (8) average gross monthly wage.

All data for each indicator were obtained from the publicly accessible database of the Czech Statistical Office. The justification of the indicators and their connection with the innovation performance of the region are as follows. The number of scientific research institutes (Number of research and development institutions in the region) is indicative of the density of the innovation ecosystem in the region. A higher number of these workplaces indicates a higher potential for the emergence of new ideas, innovations and technologies, which positively contributes to the competitiveness of the region [38]. Since many studies on innovation performance consider patents as an R&D output (e.g., [39,40]) and as a source for innovation measurement [41], the indicator Number of registered patents is evaluated in our study as well. According to Jang et al. [42], a higher number of registered patents indicates that new technological solutions and innovative products are being discovered in a given innovation ecosystem.

As human capital is also an important factor in innovation performance and development, we included the Number of people working in science and research indicator in our evaluation, which reflects the level of human capital in a region or ecosystem. It was monitored as the number of full-time workers in science and research [43], i.e., in employment, service or membership relationships, which does not include, e.g., persons on maternity leave, apprentices or persons working on the basis of agreements on work performed outside the employment relationship. Higher numbers of people working in science and research can lead to more scientific research activities [44].

The Number of publications in the top 10% of the most cited scientific outputs worldwide attests the quality of scientific research in a given location [35]. A higher number of these publications indicates that scientific research in the region is important and valued internationally. Given the fact that high-tech industries have the ability to generate higher value added per employee and thus contribute to the economic growth of the region [45], the Employment in high-tech industries indicator was used. This indicator refers to the number of employees in the region in industries such as those involved in the manufacture of basic pharmaceutical products and pharmaceutical preparations, manufacture of computers, electronic and optical instruments and equipment, telecommunication activities, information technology activities, and information activities.

Gross domestic product at current prices is included in our study as an indicator of the economy’s performance [46]. A higher value of this indicator indicates a higher level of economic activity and prosperity, which can substantially boost investment (by regions or firms) in research and development and thus increase the innovative productivity of the region [47]. Given the fact that public R&D subsidies may stimulate private R&D investment (e.g., [48,49,50]) the indicator of R&D expenditure financed from corporate sources is considered to be an indicator of private sector involvement in innovation activities. The level of this corporate R&D expenditure can support the innovation environment of a region and its economic growth [35]. The last indicator, i.e., Average gross monthly wage, is evaluated since higher average wages in the region can attract new and skilled workers who can potentially find employment in higher value-added industries that contribute to economic growth [35].

We consider public financial support for science and research as an independent variable that influences the level of the innovation environment in the region, i.e., it affects the eight indicators defined above. The quality of this environment and ecosystem then represents an effective way of regional development, especially in the case of structurally affected regions [20]. This concept is illustrated in Figure 1.

3.2. Data

The difference in the effect of direct public support for science and research on the development of the innovation environment was examined among NUTS 3 regions within the Czech Republic. There are 14 NUTS 3 regions in the Czech Republic, three of which are recognized as peripheral. The definition of peripheral (structurally affected) regions is given by Act No. 248/2000 Coll. Ref. [51] on support for regional development and reflects the socio-economic analysis of the conditions of the territory. According to this law, these regions are faced with the decline in industrial orientation and the resulting poor situation in the labour market (imbalance between supply and demand on the market, growing unemployment), a significant increase in the risk of poverty of the population, the ageing of the region (outflow of young people and low birth rate), and environmental pollution (caused by past intensive economic activity). The peripheral regions in the Czech Republic include Moravian-Silesian region, Ústecký region, and Karlovy Vary region. The Moravian-Silesian and Ústecký regions have already established an innovation centre, which deals with support, strategies and development of the local innovation environment [52]. The second group of regions consists of the other eleven regions of the Czech Republic without structural disabilities.

The analysis was performed on data from 2012 to 2022 obtained from the Czech Statistical Office. Descriptive statistics of the data are presented in

Table 1. Descriptive Statistic.

Variable	Unit	N	Mean	StDev	Min	Max
Total public support for R&D.	Millions of CZK	154	1077	5069.9	4.3	24,021.2
Number of research and development institutions in the region.	Number	154	145	172.2	20	759
Number of registered patents.	Number	154	24	39.7	1	209
Number of science and research workers (full-time).	Number	154	1337	4054.6	67	19,591
Number of publications in the top 10% of the world’s most cited scientific outputs.	Number	154	88	320.2	0	1467
Employment in high-tech industries.	Number	154	10,900	17,265.5	970	92,900
Gross domestic product of the region at current prices.	Millions of CZK	154	249,170	330,185.1	81,072	1,926,323
R&D expenditures financed from corporate sources.	Millions of CZK	154	2183	4751	105	27,717
Average gross monthly wage.	CZK	154	30,192	6585.9	21,663	54,015

.

3.3. Methods

The β-convergence method and the method of calculating the average efficiency of public funds were used to meet the objectives of this study.

In order to assess the development of the efficiency of public support in terms of selected indicators across regions and to answer the research question RQ1, the concept of beta-convergence based on the neoclassical theory of economic growth [53] was applied, as previously used by, e.g., Hong et al. [54], or Desli and Gkoulgkoutsika [55]. The beta-convergence method allows one to assess whether poorer regions are catching up with richer regions through the growth coefficient. Real convergence can also be seen as a structural convergence of the technologies used, in our case the calculated efficiency. In order to present beta-convergence, a two-dimensional graph is used, in which the X-axis is plotted with the level of efficiency of public funds at the beginning of the study period (i.e., in 2012), and the Y-axis with the average growth coefficient of the indicator under study over the study period (i.e., 2012–2022). The data for each of the eight indicators were first recalculated in relation to public funding for science and research in the regions. This adjusted data on particular indicators tell us about their “efficiency” and thus how much CZK 1 million invested in science and research generates in patents, high-tech employees, publications, etc. Based on the economic theory [56], the efficiency of public expenditure on R&D for each indicator was calculated as follows:

$$E_{ijk}={\displaystyle \frac{I_{ijk}}{P{E}_{jk}}}$$

(1)

where E_ijk is the efficiency of public expenditure on R&D for indicator “i” in period “j” and region “k”, I_ijk is the value of indicator “i” (see Figure 1) in the period “j” in the region “k”, and PE_jk is the public expenditure for R&D in period “j” and region “k”.

Subsequently, the average growth coefficient was calculated from the adjusted data. The formula is as follows:

$$k=\sqrt[n-1]{{\displaystyle \frac{E_n}{E_1}}}$$

(2)

where k is the growth coefficient, n − 1 is the number of values (in region “k”, for indicator “i”) minus 1, E_n is the efficiency in the last monitored year for indicator “i” and region “k”, and E₁ is the efficiency in the first monitored year for indicator “i” and region “k”.

For the graphical representation, the indicator R2 is referred to as the coefficient of determination, which indicates the strength of the observed tendency. On the basis of this graphical representation—the correlation diagram—it is possible to trace the behaviour of the individual regions over the period, specifically their relative development from the initial year to the last year of observation.

The second research question used the method of calculating the average efficiency of public funds in line with the study by Khan and Murova [57]. From the adjusted data for particular indicators, the average efficiency of public financial support for science and research for particular indicators, regions and the time series 2012–2022 is as follows:

$$A{E}_k={\displaystyle \frac{{\displaystyle \sum }{E}_{ik}}{N_k}}$$

(3)

where AE_k is the average efficiency of public expenditure on R&D, ΣE_ik is a sum of efficiency of indicator “i” and region “k”, and N_k is a number of values in region “k”. In other words, it is the calculation of the average efficiency, where N_k is the number of values, i.e., the number of elements (or data points) in the data set. These results tell us about the average efficiency of public financial support in the region in terms of the given indicator.

4. Results

The results identifying the growth or decline in the efficiency of the public funds to support R&D in particular regions of the Czech Republic are shown in Figure 2 (indicators 1 to 4) and Figure 3 (indicators 5 to 8).

The highest increase in the efficiency of public funds within the indicator (1), Number of science and research institutions in the region in the analyzed period 2012–2022, was achieved in the Liberec and Olomouc regions. Two of the three structurally affected regions (Moravian-Silesian and Ústecký) had a growth coefficient value higher than 1, i.e., the efficiency of public financial support in terms of the number of R&D institutions in the given period also increased. On the contrary, e.g., in the case of the capital city of Prague, the efficiency of public financial support in terms of the number of R&D institutions was observed to be decreasing in this period compared to other regions. In the peripheral Karlovy Vary region, the efficiency of this public support in the initial phase (in 2012) was the highest (i.e., CZK 1 million generated one R&D institution); however, the decreasing efficiency of this support was observed during the period under study. Nonetheless, despite this decrease, the public support efficiency was still the highest at the end of the period.

As for the indicators (2) Number of registered patents, (3) Number of employees in science and research, and (4) Number of publications in the top 10% of the most cited scientific outputs in the world, public financial R&D support was most beneficial in the Liberec and Plzeňský regions according to the highest growth rates in the observed period. Within the group of peripheral (structurally affected) regions, in the Moravian-Silesian Region, an increasing efficiency of public subsidies for R&D regarding these indicators was confirmed, while in the Ústecký Region, the efficiency was increasing from the point of view of indicators (2) and (4), whereas from the point of view of indicator (3), the effect of public financial support on the number of R&D institutions was decreasing over the period. The effect of this support was decreasing most significantly in the Karlovy Vary Region compared to other regions.

Figure 3 shows the beta-convergence of indicators (5) to (8). Employment in high-tech sectors, i.e., indicator (5), was the only indicator in which all three peripheral (structurally affected) regions had the growth rate higher than 1 over the observed period, and thus, there was an increase in the efficiency of public financial support in relation to this indicator (in the Moravian-Silesian region, this growth value was the third highest). High-tech industries are endowed with the ability to create high added value per employee, which contributes to the economic growth of the region, as well as the ability to innovate and create new technologies and products that can improve the competitiveness of the region [45]. This factor is a key element for structurally affected regions in the process of their economic transformation, leading to the successful development of the territory. On the contrary, a decrease in efficiency growth was observed for the capital city of Prague and the Central Bohemian Region.

Regarding the indicators (6) Gross domestic product at current prices, (7) R&D expenditure financed from corporate sources, and (8) Average gross monthly wage, public financial support was most effective in the Liberec and Olomouc regions, while an increase in efficiency was also observed in the structurally affected regions, namely in the Moravian-Silesian and Ústecký regions. On the other hand, in the Karlovy Vary Region, there was a decrease in the efficiency of the public funds spent on R&D support for these indicators. Throughout the whole period, public R&D expenditures had an increasing effect on corporate expenditures for science and research, i.e., indicator (7), as well as on average gross monthly wages, i.e., indicator (8), in all regions except the peripheral (structurally affected) Karlovy Vary Region.

The results for the average efficiency of public support for R&D, calculated for each region separately, are shown in Figure 4 and Figure 5. As seen, with the exception of indicator (4), the Karlovy Vary Region achieved the highest average efficiency for public spending on R&D in the analysed period 2012–2022. While the relative growth of this efficiency based on the beta-convergence analysis was rather decreasing, and the Karlovy Vary Region lagged behind other regions, in absolute terms (using the average efficiency), this support was the most efficient. This may be due to the fact that the Karlovy Vary Region has a very poor innovation environment, so the effect of increasing R&D funding may be higher on average than in other regions. However, given the lagging level of the innovation environment, the growth rate of this efficiency may tend to lag behind that of other regions.

As for indicator (1), Number of science and research institutions in the region, structurally affected regions belong to the group of regions with higher average efficiency of public R&D spending. For example, in the Karlovy Vary Region, on average, the amount of public R&D support of CZK 1 million contributed to the creation of over 1.4 science and research institutions. The lowest average efficiency for public support was confirmed in the capital city of Prague. This result was similar for indicators (2), Number of registered patents, and (3), Number of employees in science and research. In the Karlovy Vary Region, CZK 1 million in public R&D support brought more than six new employees in the analysed period. Regarding indicator (4), Number of publications in the top 10% of the most cited scientific outputs in the world, public financial support was most effective in the Vysočina Region and the Královehradecký Region, and least effective in the Plzeňský Region. Overall, structurally disadvantaged regions are among the regions with higher average efficiency of public financial support for R&D.

Figure 5 shows the efficiency of public support for R&D in terms of indicators (5), (6), (7), and (8). Similarly, as in Figure 4, the highest average efficiency was found for the Karlovy Vary Region, while the lowest values were confirmed for the Prague Capital City and the South Moravian Region. In developed regions with a relatively high-quality and developed innovation environment (e.g., Prague, South Moravian Region, Central Bohemia Region), public financial support for R&D is, on average, less effective than in regions with a significantly underdeveloped innovation environment (peripheral Karlovy Vary Region).

5. Discussion

As demonstrated by previous research, e.g., [31,32,33,34], actual studies on RIS have predominantly been focused on developed regions, leaving a gap in understanding how this approach can be beneficial in the process of urban development in regions with structural disadvantages. Therefore, our study was focused on the concept of RIS in this type of region.

Public financial support for science and research is crucial for the success of regional innovation ecosystems and thus helps the process of urban development [58,59]. This support contributes to the stimulation of innovation activities, the creation of new patents and technologies, and the competitiveness and attractiveness of innovation ecosystems and regions themselves [60,61]. As stated by Lyu et al. [62], these ecosystems are crucial in the process of urban development, as they enable modern and appropriate possibilities that include not only technological progress, but also economic growth and improvements in the quality of life of the population.

Regarding public spending on R&D in poor regions, it can only be effective if there is a functioning and, to some extent, developed innovation ecosystem in those locations, as stated by Celli et al. [63]. Overall, the findings of this study based on beta-convergence analysis confirm that the growth in public support efficiency was higher in those structurally affected regions where the innovation environment is more advanced, such as in the Moravian-Silesian and Ústecký regions. In contrast, the growth rate in the Karlovy Vary Region was significantly lower, given the backwardness of the innovation environment there.

The high-tech sectors are key to a region’s competitiveness [64], and a representation of high-tech sectors is often considered to be a characteristic feature of the advanced economy of a certain territorial unit, i.e., region [45]. The results in our study confirmed that public financial support for R&D had increasing efficiency in terms of employment in the high-tech sectors in all three structurally affected regions. On average, this public support was most effective in the region with the most backward innovation ecosystem. Conversely, regions classified as mature locations with a developed innovation ecosystem experienced a decreasing effect of this support on employment in the high-tech sectors over time, and the efficiency of financial support was lower on average as well. Juchniewicz [65] argues that the lack of high-tech sectors and employees significantly reduces the competitiveness of a region and its opportunity for successful development; therefore, our findings may be a positive signal in the process of transformation of structurally affected regions in the Czech Republic.

Based on the analysis of the National Innovation Strategy of the Czech Republic [35], it is possible to say that the benefits of public financial support for science and research and development of regional innovation systems, which were identified in this study, are currently following the same trend as in the period 2012–2022. These regional innovation systems have been strengthened through increased public financial support and targeted strategic actions for innovation and research. Improvements were particularly evident in structurally affected regions, where public support contributed significantly to the growth of the innovation environment and regional competitiveness.

As demonstrated by previous research [66,67,68], research centres create positive regional impacts and may be perceived as being associated with cultivating a regional advantage through bringing technology, finance and skills into the region. The results of our study showed that, from the point of view of the creation of R&D institutions, i.e., research centres, public support in the Czech Republic led to the greatest effects precisely in underdeveloped (structurally affected) regions. Moreover, these effects may also generate additional positive regional impacts, such as on the labour market through attracting highly qualified labour [66], and potentially also on the wage levels for research workers [69], which subsequently translate into growth in average wages within the region in general.

Although public support for R&D is generally justified and accepted through the argument that markets fail to provide sufficient incentives for private firms to achieve socially optimal levels of private R&D funding [70,71], the debate on the issue of additionality or substitutability between public R&D support and private R&D efforts is still ongoing, and opinions remains inconclusive [72]. As the results of our study showed, in the case of regions with a worse innovation environment, i.e., structurally affected regions, public R&D support has a positive effect on R&D expenditures financed from private sources, which is also in line with previous findings by Lee [73], who found out that public R&D expenditures complement the innovation activities of firms with lower technological capabilities, whereas it replaces R&D activities in firms with expertise. These findings are confirmed also by other studies (e.g., [74,75]) confirming the greatest benefit from the availability of public R&D resources for regions with low innovation capacity.

6. Conclusions

The aim of this study was to examine the difference in the effect of public funding for R&D on the development of the innovation environment between structurally disadvantaged and performing regions in the Czech Republic in the period 2012–2022. Using the method of beta-convergence and calculating the average efficiency of public funds, our results show that public support for R&D has a positive effect on development of the innovation environment in structurally affected regions in the Czech Republic (RQ1), and that there is a difference in the effect of this support regarding structurally effected regions versus developed regions (RQ2).

An increase in the effect of public financial support on the development of the innovation environment in the region was observed in all structurally affected regions regarding the most analyzed indicator. These regions also, on average, responded better to public financial support for R&D than developed regions. In contrast, the efficiency of public financial support was, on average, lower in regions with a strongly developed innovation system (e.g., Prague, South Moravian Region). Moreover, the results show that the innovation environment in a region that is classified as a structurally affected territory and is significantly underdeveloped (in our case, it was the Karlovy Vary Region), is, on average, the most affected by public financial support for R&D in the given territory. In such a territory, public support was the most effective in terms of most analyzed indicators (seven out of eight indicators). However, if we compare this efficiency of public funding for R&D with other regions, it turned out that the growth of efficiency of public funding for R&D in this territory is slowing down or is not fast enough.

This study enriches the literature on regional development and innovation by demonstrating that public policy for R&D helps to improve the innovation environment, especially in structurally affected regions, while the efficiency of this support is higher than in the case of such public support directed to developed regions. The findings can help regional policy makers to better design and target strategies for urban development and cultivation of an innovation environment in their regions or help to allocate public funds to efficiently bridge regional gaps. We recommend that regional innovation policymakers in these regions prioritize the allocation of public financial resources to address the unique needs of structurally affected regions and thus maximize their impact on innovation activities. Additionally, we suggest fostering collaborative networks among research institutions, universities and businesses to facilitate knowledge and resource sharing, which is crucial in the process of regional and urban development. It is also important to invest in education and training programs to build a skilled workforce capable of supporting innovation and create incentives for private sector investment in R&D to complement public funding. Based on the results of our study, more developed regions with already saturated innovation ecosystems respond less, on average, to public financial support for R&D development. Therefore, we recommend shifting focus from direct financial support towards fostering a more diverse and interconnected innovation ecosystem, encouraging cross-sector collaboration and partnerships that can drive innovation.

To gain a more comprehensive understanding of how the concept of the regional innovation ecosystem works in peripheral regions, future research should be focused on the optimal allocation of public funds for R&D support in regions of this type. This could be a valuable area of research, as it may help regional policy makers to design more effective strategies in the process of urban development for strong innovation environments with a competitive advantage.