Evaluation and Prediction of Regional Innovation Ecosystem from the Perspective of Ecological Niche: Nine Cities in Hubei Province, China as the Cases

Abstract

Based on niche theory, this paper constructs an evaluation index system for regional innovation ecosystem suitability from four aspects: innovation benefits, innovation technology, innovation culture, and innovation policy. The niche evaluation model is used to calculate and compare the suitability and evolutionary momentum of the innovation ecosystems in nine prefecture-level cities in Hubei Province from 2017 to 2022. Using the grey prediction model GM (1,1), the development of niche suitability for the nine prefecture-level cities in Hubei Province over the next five years is predicted. The results showed that, from the perspective of the niche, the overall suitability of the innovation niches in the nine prefecture-level cities of Hubei Province is relatively low, with higher evolutionary momentum, and the two exhibited a reverse development trend. The forecast results indicated that the suitability of the innovation niches in the nine prefecture-level cities of Hubei Province will follow different development trends in the next five years, with central cities showing higher development than non-central cities. Accordingly, this study provides a more scientific method for the evaluation, monitoring, forecasting, and improvement of regional innovation ecosystems at the city level. It supports policy formulation and strategic planning for the nine prefecture-level cities in Hubei Province and other regions from the aspects of innovation benefits, innovation technology, innovation culture, and innovation policy. At the same time, this study also provides new research pathways for the academic community, encouraging scholars to adopt multidisciplinary and multi-faceted approaches to explore the complexity and dynamics of regional innovation ecosystems in depth. It utilizes this information to optimize and adjust regional innovation policies to better adapt to the needs of global innovation competition.

1. Introduction

Innovation is increasingly becoming a core strategy for the development of countries worldwide. Under the current domestic and international circumstances, the construction of an innovation ecosystem has become a crucial part of national innovation-driven development, the national innovation system, and the construction of a strong science and technology country. The advancement of science and technology profoundly shapes the future and destiny of a nation and deeply influences the welfare of its people. In this process, the innovation ecosystem has emerged, integrating various innovation entities that can fully leverage their respective characteristics, collaborate in innovation, and create value. Through the evolution of symbiotic coexistence, this system has formed a network with ecosystem characteristics, which is highly compatible with China’s “innovation-driven” strategy proposed in the new stage of opening up to the outside world. However, despite the broad prospects of the innovation ecosystem concept, China faces many challenges in this area. Issues such as poor operational conditions and slow evolution still exist and need to be deeply explored and addressed. In-depth research on regional innovation ecosystems is of great practical significance for achieving the transformation of China from a major country in science and technology to a leading country in science and technology. Existing research must seriously review these issues and strive to promote the continuous improvement of China’s innovation system in the exploration of solutions, to meet domestic and international challenges and achieve significant progress in innovation-driven development. Under the premise of open innovation, the study of regional innovation ecosystems is closely related to the current global and Chinese innovation policies and economic development strategies. As the most populous country in the world with a large economic scale and a unique socialist market economic system, China’s experience in developing its innovation ecosystem is of reference significance for other developing countries. Moreover, China has a comprehensive statistical system and data resources, providing reliable data support for the study of regional innovation ecosystems. Hubei Province, as a major province within China, has differences in innovation policy orientation, economic development levels, and cultural backgrounds among its nine prefecture-level cities, which gives it good representation at the provincial and international city levels. Therefore, this paper, based on the ecological niche theory, constructs an evaluation index system and model for regional ecological niche suitability, and evaluates the regional ecological niche suitability of the nine prefecture-level cities in Hubei Province for a total of six years from 2017 to 2022. It also uses the GM (1,1) model to predict the regional ecological niche suitability for a total of five years from 2023 to 2027, thereby exploring the path towards higher quality development of China’s innovation ecosystem.

2. Literature Review

The concept of the regional innovation niche refers to the integrated characteristics of the natural and socio-economic environment within a specific geographical area that influence innovation activities. Previous research has predominantly focused on the components and influencing factors of regional innovation niches [1,2,3,4,5,6,7]. Smith et al. proposed mechanisms by which regional resource endowments, scientific and technological investments, talent agglomeration, and industrial clustering impact the innovation niche. Li et al. discovered that innovation policies, industrial upgrading, and scientific and technological innovation significantly influence the evolution and adjustment of regional innovation niches. Deng et al. demonstrated that the application of digital technology in innovation activities across different regions markedly affects the landscape of innovation niches. Xu et al. found that inter-regional cooperation and competition have a bidirectional impact on innovation niches. Hedong and Jie analyzed the impact mechanism of niche suitability on high-quality economic development, using innovation capability as an intermediary variable and constructing a threshold effect model. Li et al. measured and analyzed the impact of the innovation ecosystem’s niche suitability on the innovation efficiency of public R&D organizations. Huiyong et al. utilized the spatial Durbin model to study the spatial relationship between the suitability of high-tech industry innovation ecosystems and innovation performance.

Scholars have also begun to examine regional innovation ecosystems from the perspective of digitalization [8,9,10,11]. Zhihui and Jie, from an ecosystem perspective, analyzed the main components of regional innovation ecosystems and constructed an evaluation system for regional innovation ecosystems that considers the impact of digital development factors. Wei et al. empirically analyzed the impact mechanism of digital transformation on the suitability of regional innovation ecosystems based on panel data from 30 provinces in mainland China from 2008 to 2021.The aim of the research was to obtain a better understanding of the ecosystems that foster digital evolution at the regional scale. An empirical observation campaign in three European regions (Grand Est in France, Styria in Austria, and Värmland in Sweden) helped to provide a description of the nature of digitalization ecosystems, their practices, and internal collaborative dynamics. The results suggested that DIHs, despite their emerging and trial-and-error stage, were designed for promoting multi-actor collaborative platforms including non-local actors to stimulate the transition into Industry 4.0 by promoting place-based collaboration alliances that respond to local/regional contextual specificities and demands. These regional-based platforms facilitate public–private partnerships that co-design policy initiatives resulting from co-participation and the negotiation of spatially bounded oriented initiatives for digitizing.

In recent years, some scholars have started to study regional innovation niches from an ecological standpoint. One paper, therefore, maps out a field of future research—the geography of sustainability transitions—that might be beneficially labored by both traditions. The paper introduces the core concepts, but also the limitations of the transitions literature [12]. The concept of ‘motors of creative destruction’ is introduced to expand innovation and technology policy debates to go beyond policy mixes consisting of technology push and demand pull instruments, and to consider a wider range of policy instruments combined in a suitable mix which may contribute to sustainability transitions [13]. Brown introduced niche theory into regional innovation research and proposed the concepts of “resource utilization mode” and “competition mechanism” to explore the differences in innovation resource utilization between regions and the impact of resource competition on the pattern of regional innovation niches [14]. Research has also focused on the sustainable development of regional innovation niches, with Wang et al. noting that the rational use of innovation resources, the protection of the ecological environment, and the promotion of coordinated socio-economic development are key factors in achieving the sustainable development of regional innovation niches [15]. Additionally, the influence of globalization on regional innovation niches has become an important research direction, as Miao et al. found that deep involvement in global value chains accelerates the reconstruction and transformation of regional innovation niches, positively impacting the enhancement of innovation capabilities in different regions [16]. Scholars have also turned their attention to the contribution of innovation niches to regional economic growth [17,18], with Gao highlighting that optimizing the structure of innovation niches, improving innovation efficiency, and strengthening innovation policy support are crucial pathways for fostering regional economic growth. Hua and Ming, in their review, identified key factors affecting regional innovation niches, which are closely related to resource allocation, policy environment, and industrial structure. Furthermore, Hong and Tao discussed the path of digital transformation of regional innovation niches, emphasizing the significant role of digital technology in optimizing the structure of regional innovation niches [19]. Recently, scholars have also focused on the sustainable development of regional innovation niches. The research by Liang and Ting showed that the sustainable development of regional innovation niches is essential in the context of a low-carbon economy. They stressed that protecting the ecological environment and promoting coordinated economic development are key pathways to achieving sustainable development of regional innovation niches [20]. Scholars have conducted research on the evaluation of regional innovation ecosystems from the perspective of strategic niche management [21,22,23]. Hongqi and Ping established an evaluation index system for the suitability of innovation niches in strategic emerging industries and used a comprehensive and phased suitability evaluation model to assess the innovation and development status of 30 regional strategic emerging industries from 2011 to 2020. Hongyan constructed innovation environment indicators based on the three stages of industrial value chain evolution and used the theoretical model of niche suitability to study the supply and demand gap of the regional innovation environment in 30 provinces and municipalities directly under the central government. Aishan et al. constructed a regional innovation ecosystem suitability evaluation system that includes both species and non-species dimensions, and evaluated the innovation ecosystems of 30 provinces in China from 2010 to 2019, and, based on the Fourier series modified-OGM model (FOGM), predicted the development status of China’s innovation ecosystem during the “14th Five-Year Plan” period. Jing and Ruyu found that there are two main differences in the evaluation index of niche suitability: the dynamic and static balance of innovation elements are different, and the measurement range is different; they conducted a root cause analysis from three aspects: different perspectives on understanding the connotation of the innovation ecosystem, different research content and objectives, and the difficulty of obtaining data [24]. Peili et al., based on the measurement of niche suitability and innovation value, constructed a panel data regression model and empirically tested data from 31 provinces, autonomous regions, and municipalities in mainland China from 2010 to 2020 [25].

In summary, research on regional innovation niches continues to expand its theoretical depth and breadth, with in-depth discussions from dimensions such as ecology, sustainable development, globalization, digital technology, and inter-regional cooperation. The focus has gradually shifted from discussions on static influencing factors to analyses of dynamic mechanisms, providing theoretical and practical guidance for enhancing regional innovation capabilities and sustainable regional economic development. Based on the aforementioned research, this paper studies regional innovation ecosystems from both micro and macro perspectives and, drawing on the niche model mentioned by scholars Yidan and Yimeng, innovatively constructs an index system from four basic dimensions and 20 basic variables to analyze and research the suitability and evolutionary momentum of regional innovation niches in nine prefecture-level cities in Hubei Province, China. This fills the research gap in niche studies between small-area cities under different indicator systems.

3. Research Theories and Methods

3.1. Research Ethics

All participants were involved in the processing and writing of this paper, with informed consent and on a voluntary basis, without any coercion or pressure. At the same time, the data used in this paper are open data, which do not involve the privacy and confidentiality of personal data, and can be accessed and collected from official Chinese databases (such as statistical yearbooks, various municipal and state statistical bureaus, etc.). In addition, this study does not involve any risks of harm, and has not caused physical, psychological, or social harm to the participants. In the part of the paper where the results of data processing are analyzed, the principles of objectivity, truthfulness, and fairness were followed to interpret and analyze the data results, conveying relevant information to the readers, and there was no academic misconduct or plagiarism.

3.2. Research Methods

3.2.1. Ecological Niche Model

In the natural science field, the best ecological niche of the research target could be applied in the social and economic field, but there was little promise to conduct a large number of experiments. Therefore, scholars resorted to the approach of experiments to come up with the best ecological niche. This is how the approach works: the maximum or minimum number in the evaluation index would be used to determine the best niche, which means the ecological niche would be 1 when the conditions of resources in the range fully meet the demand of development; the number is 0 when the demands cannot be met. This paper draws on the research of previous scholars Yang [26] and Song [27], using the ecological niche model. Suppose the amount of the innovative ecology system is m, then ${IF}_{ij}$(i = 1, 2, …, m; j = 1, 2, …, n) is the data value of the ith innovative ecology system on the ecological factor j. The steps for the construction of the model are as follows.

(1) The Normalization Processing of Data

Due to the difference in the index unit, normalization processing of the data is needed to remove the impact of dimension, hence the following formula:

$${IF}_{ij}^{\prime }=\frac{I{F}_{ij}-I{F}_{j\min }}{I{F}_{j\max }-I{F}_{j\min }}$$

(1)

That is, the difference between each variable and the minimum value of the variable sequence is divided by the difference between the maximum value and the minimum value of the variable sequence, and the maximum value of each variable is 1 and the minimum value is 0 after standardization. “${IF}_{jmin}$” in the formula denotes the minimum value of the ecological factor sequence of j in ${IF}_{ij}$(i = 1, 2, …, m; j = 1, 2, …, n).

(2) Optimal ecological niche for ecological factors ${IF}_{Oj}$.

Suppose ${IF}_{ij}^{\prime }$ represents the actual ecology of the ecological factor j of the ith innovative, ${IF}_{Oj}$(j = 1, 2, …, n) represents the best ecological niche of the jth ecological factor, namely,

$$I{F}_{Oj}=\max I{IF}_{ij}^{\prime }, (\mathrm{j}=1,2,\dots ,\mathrm{n})$$

(2)

(3) Innovation ecosystem unit i niche ${IN}_{Oi}$. The formula is as follows:

$$I{N}_{Oi}={\omega }_j{\displaystyle \sum _{j=1}^n\frac{\min \left\{\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right|\right\}+\epsilon \max \left\{\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right|\right\}}{\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right|+\epsilon \max \left\{\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right|\right\}}}$$

(3)

where ${IN}_{Oi}$ represents the ith innovation ecosystem suitability. The greater the value, the higher the regional innovation ecosystem suitability level, and the more active the innovation activities of the innovation ecosystem population in the region; ωj is the weight of the jth ecological factor, which reflects the degree of influence of this factor on the suitability of the innovation ecosystem; and ε (0 ≦ ε ≦ 1) is a model parameter whose value is generally calculated based on ${IN}_{Oi}$= 0.5.

(4) Calculation model parameter with the following formula:

$${\xi }_{ij}=\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right| i=1,2,\dots ,\mathrm{m};j=1,2,\dots ,\mathrm{n}$$

(4)

$$\begin{array}{l}{\xi }_{\max }=\max \left\{{\xi }_{ij}\right\};{\xi }_{\min }=\min \left\{{\xi }_{ij}\right\}\\ \overline{{\xi }_{ij}}=\frac{1}{mn}{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{\xi }_{ij}}}\\ \epsilon =\frac{\overline{{\xi }_{ij}}-2{\xi }_{\min }}{{\xi }_{\max }}\end{array}$$

(5)

(5) Calculate evolutionary momentum IM.

The actual ecological niche of the innovation ecosystem is set as ${IF}_{ij}^{\prime }$ = (${IF}_{i1}^{\prime },{IF}_{i2}^{\prime }$, …, ${IF}_{in}^{\prime }$), then the optimal ecological niche is ${IF}_{Oj}({IF}_{O1},{IF}_{O2}$, …, ${IF}_{On}$). We can determine the expression of evolutionary momentum, which reflects the evolutionary space of the innovation niche of the evaluation object.

$$I{M}_i=\sqrt{\frac{{\displaystyle \sum _{j=1}^n\left|{IF}_{ij}^{\prime }-I{F}_{Oj}\right|}}{n}} i=1,2,\dots ,m;j=1,2,\dots ,n$$

(6)

3.2.2. GM (1,1) Model

On the premise of fully understanding the grey theory, the grey prediction model learned was used to analyze the problems involved in this paper. The following is an explanation and description of the model. The basic steps of the GM (1,1) model are as follows:

Input: Set X⁽⁰⁾ as GM (1,1) modeling sequence, X⁽⁰⁾ = (x⁽⁰⁾(1), x⁽⁰⁾(2), …, x⁽⁰⁾(n)).

Step 1: Generate X⁽¹⁾; X⁽¹⁾ is the sequence of 1-AGO, X⁽¹⁾ = (x⁽⁰⁾(1), x⁽⁰⁾(2), …, x⁽⁰⁾(n)), where x⁽¹⁾(k) = ${\sum }_{i=1}^k{x}^{\left(0\right)}\left(i\right)$, k = 1, 2, …, n.

Step 2: Generate Z⁽¹⁾; Z⁽¹⁾ is generation sequence of the neighboring average value of X⁽¹⁾, Z⁽¹⁾ = (z⁽¹⁾(1), z⁽¹⁾(2), …, z⁽¹⁾(n)), Z^(k) = 0.5x⁽¹⁾(k) + 0.5x⁽¹⁾(k − 1); k = 2, 3, …, n;

Step 3: Define the basic formula of GM (1,1) model: x⁽⁰⁾(k)+ αz⁽¹⁾(k) = b. Set $\widehat{a}={(a,b)}^T$, grey differential equation of the least squares estimated parameters listed to the maximum $\widehat{a}={\left(B^{T}B\right)}^{-1}{B}^T{Y}_n$, with $Y_n={[x^{\left(0\right)}\left(2\right),x^{\left(0\right)}\left(3\right)\dots x^{\left(0\right)}\left(n\right)]}^T$. B is shown in Formula (6):

$$B=\left[\begin{array}{ccc}-z^{(1)}& (2)& 1\\ \begin{array}{c}-z^{(1)}\\ \dots \end{array}& (3)& \begin{array}{c}1\\ \dots \end{array}\\ -z^{(1)}& (n)& 1\end{array}\right]$$

(7)

Step 4: Generate $x_p^{(1)}(k+1),x_p^{(1)}(k+1)=\left[x^{(0)}(1)-\frac{b}{a}\right]e^{-ak}+\frac{b}{a}$.

Step 5: Generate $x_p^{(1)}(k+1)$, the inaccessible original data are in the prediction value of (t + 1), substitute $x^{(0)}(k+1)=x^{(1)}(k+1)-x^{(1)}(k)$ into $x_p^{(1)}(k+1)$, hence Formula (8):

$$x_p^{(0)}(k+1)=\left[x^{(0)}(1)-\frac{b}{a}\right]e^{-ak}(1-e^a)$$

(8)

Output: the predicted data sequence is $x_p^{(0)}$.

4. Research Design

4.1. Source of Data

Based on the diversity in innovation policy orientation, economic development levels, and cultural backgrounds among the nine prefecture-level cities in Hubei Province, as well as their good representativeness within the province, and also considering the authenticity and availability of data, this paper evaluated the suitability of the regional innovation ecosystem in nine prefecture-level cities in Hubei Province (Wuhan, Yichang, Xiangyang, Suizhou, Xianning, Jingmen, Jingzhou, Shiyan, and Enshi) by selecting various indicator data from 2017 to 2022. The data sources for this paper included “Hubei Statistical Yearbook”, the statistical yearbooks of various cities and prefectures, “the China Statistical Yearbook”, “China Statistical Yearbook on Science and Technology”, as well as the statistical bureaus of various cities and prefectures, and CEI database [28,29,30,31]. Some of the data were obtained through interpolation method or linear fitting calculations based on the above data sources.

4.2. Index Selection

The evaluation of regional innovation capacity needs to be carried out based on a scientific, reasonable, and objective index system. Different indicators may present different results, that is, the selection of indicators will affect the final evaluation results. The index system is the primary condition for objective evaluation results, and comprehensive screening should be carried out from multiple levels, multiple angles, and various aspects to ensure that the final evaluation results can objectively and reasonably reflect the current situation and have real reference value. Based on the analysis of factors in the evaluation of innovation and development, this paper comprehensively considered the limitations of each indicator, that is, whether the region and time are fully covered, and determined 20 indicators according to the principles of scientificity, systematicness, operability, effectiveness, and comparability (see

Table 1. Evaluation index for regional innovation development.

Number	Index	Number	Index
1	Per capita gross domestic product	11	Industrial enterprises above designated size: Number of enterprises with R&D activities
2	R&D expenditure intensity	12	Number of high-tech enterprises
3	Proportion of financial expenditure on science and technology	13	Business environment index
4	Tertiary industry GDP share	14	Import and export volume as a share of GDP
5	Proportion of invention patents among patent applications authorized	15	The number of high-tech enterprises increased
6	Disposable income of residents (yuan)	16	Number of national-level technology business incubators
7	Proportion of fiscal education expenditure (%)	17	Proportion of fiscal and social security employment expenditures
8	Added value in high-tech industries	18	Proportion of fiscal transportation expenditure
9	R&D personnel (annual per capita)	19	Proportion of fiscal health and medical expenditures
10	Technology market turnover(million)	20	Public library collections per capita (volume)

for details).

4.3. Establishment of Index System

Starting from the niche theory, this paper focused on constructing the suitability evaluation system of a regional innovation ecosystem, which included innovation benefit, innovation technology, innovation culture, and innovation policy. Therefore, to construct a complete, scientific, and systematic evaluation index system of regional innovation capacity, more accurate analysis of each index was needed. Principal component analysis (PCA) is an important and recognized multivariate statistical method. By analyzing the cross-sectional data of each city and prefecture, the results (KMO = 0.777, p value = 0.000) showed that principal component analysis could be carried out. When four main factors were extracted, the cumulative contribution rate of variance reached 84.32%, as shown in

Table 2. Total variance of interpretation.

Components	Initial Eigenvalue			Extract Sum of Squares Loading			Rotated Sum of Squares Loading
	Sum	% of Variance	Accumulation%	Sum	% of Variance	Accumulation%	Sum	% of Variance	Accumulation%
1	8.532	53.088	53.088	8.532	53.088	53.088	5.020	31.374	31.374
2	2.319	14.429	67.517	2.349	14.681	69.537	4.415	27.596	58.970
3	1.378	8.574	76.091	1.494	9.337	78.875	2.292	14.327	73.297
4	1.325	8.229	84.32	0.875	5.467	84.32	1.767	11.044	84.32
5	0.713	4.436	88.756
6	0.583	3.627	92.383
7	0.335	2.084	94.467
8	0.253	1.574	96.041
9	0.188	1.170	97.211
10	0.125	0.779	97.99
11	0.085	0.528	98.518
12	0.056	0.349	98.867
13	0.041	0.256	99.123
14	0.034	0.212	99.335
15	0.025	0.156	99.491
16	0.023	0.143	99.634
17	0.021	0.131	99.767
18	0.018	0.112	99.879
19	0.015	0.091	99.970
20	0.005	0.030	100.000

below.

Specifically, the first principal component had a relatively larger load in per capita GDP, added value of high-tech industry, technology market turnover, disposable income of residents, and financial expenditure. These primary variables included the main indicators of innovation efficiency. The second principal component had a relatively large load in the intensity of R&D expenditure, R&D personnel, the proportion of financial science and technology expenditure, the proportion of invention patents in the number of patent applications authorized, the proportion of tertiary industry GDP, and the number of high-tech enterprises, indicating that the second principal component at a certain level represented the index of innovation technology. The third principal component had a relatively larger load on the proportion of enterprises with R&D activities, the annual increase in high-tech enterprises, the business environment index, and the proportion of import and export GDP, which indicated that the third principal component reflected the elements of innovation culture. The fourth principal component had a relatively larger load in the proportion of financial science and technology expenditure, the proportion of tertiary industry GDP, the annual increase of high-tech enterprises, the business environment index, and the number of per capita possessions of public libraries. Therefore, the fourth principal component reflected innovation policies to a certain extent. At the same time, each index contained a certain amount of information in different principal components, such as the added value of the high-tech industry and technology market turnover. The increase in the added value and the turnover would also promote the increase in technology investment, so it had a relatively larger load in the innovation resources of the second principal component. A higher proportion of financial science and technology expenditure, higher business environment index, and higher proportion of import and export GDP could also reflect the analytical effect of regional innovation policy. Therefore, it had a greater load on the fourth principal component of innovation resources, showing the intersection of various indicators in different innovation systems. For specific indicator systems, check the chart of evaluation index system of niche suitability of regional innovation systems in Figure 1.

4.4. Determine the Weight of the Evaluation Index System

The number of weighting methods for evaluation indicators abound, which can be divided into two categories in general. One is the objective weighting method, which determines the weight of indicators according to the relationships in the data or the differences therein, which are not subject to subjective factors, but the calculation process is fairly complicated. The second is the subjective weighting method, which determines the weight according to the subjective judgment of the experts on the importance of each indicator. Whether the weight is determined on a scientific and reasonable basis depends on the knowledge scope and experience of the relevant experts, which is easily affected by subjective factors, but with convenient operation. In fact, there are certain advantages and inevitable limitations in any method to measure regional innovation capacity. Based on the above analysis, a weighting method that was both subjective and objective was constructed based on the analytic hierarchy process and entropy method, and a regional innovation capacity combination evaluation model was also constructed, as shown in Figure 2 below.

Combined with the subjective and objective weighting method, the weights of each evaluation system and its indicators are shown in

Table 3. Evaluation index system and index weight of niche suitability of regional innovation system.

Evaluation System Number	Name of Evaluation System	Combination Weight of Evaluation System	Name of Evaluation Index	Combination Weight of Index
1	Innovation Benefits	0.285	Per capita GDP	0.154
			Added value in high-tech industries	0.143
			Technology market turnover	0.142
			Disposable income of residents	0.124
			Proportion of fiscal education expenditure	0.113
			Proportion of fiscal education expenditure	0.085
			Proportion of fiscal health and medical expenditures	0.072
			Proportion of fiscal transportation. expenditures	0.093
			Proportion of fiscal and social security employment expenditures	0.074
2	Innovation Technology	0.312	R&D personnel	0.126
			R&D expenditure input intensity	0.153
			Proportion of fiscal science and technology expenditures	0.104
			Proportion of invention patents among patent applications authorized	0.113
			Added value in high-tech industries	0.081
			Technology market turnover	0.074
			Proportion of tertiary industry	0.111
			Number of high-tech enterprises	0.078
			Number of national-level technology business incubators	0.066
			Industrial enterprises above designated size—proportion of enterprises with R&D activities	0.094
3	Innovation Culture	0.186	The number of high-tech enterprises increased	0.178
			Number of national-level technology business incubators	0.087
			Business environment index	0.275
			Import and export volume as a share of GDP	0.194
			Public library collections per capita	0.094
			Industrial enterprises above designated size—proportion of enterprises with R&D activities	0.172
4	Innovation Policy	0.217	Proportion of fiscal science and technology expenditures	0.143
			Proportion of fiscal education expenditure	0.103
			Number of high-tech enterprises	0.123
			Business environment index	0.132
			Number of national-level technology business incubators	0.114
			Industrial enterprises above designated size—proportion of enterprises with R&D activities	0.132
			Import and export volume as a share of GDP	0.097
			Tertiary industry GDP share	0.092
			per capita ownership of public library collections	0.064

below.

5. Empirical Analysis

5.1. Measure of Niche Suitability and Evolutionary Momentum

The innovation ecosystem niche suitability evaluation index system contained 20 empirical measured indicators. Since the original data of each indicator had different dimensional units, it was impossible to directly calculate and compare them quantitatively. Therefore, dimensionless processing was carried out on the collected original data, transforming the original absolute value of each indicator into a relative value, thus eliminating the dimensional influence. To retain the evaluation result in the interval range of [0, 1], this paper adopted the commonly used maximum and minimum normalization methods to standardize the original data.

After data normalization, calculating the best ecological niche of the ecological factor was much needed. Since the standardized value was between the interval [0, 1], thus, $I{F}_{Oj}=\max {IF}_{ij}^{\prime }=1$. To calculate the suitability of innovation ecosystem according to Formula (3), the model parameter ε should be considered first. ε can be calculated through the following steps:

$$\begin{array}{l}{\xi }_{\max }=\max \left\{{\xi }_{ij}\right\}=1; {\xi }_{\min }=\min \left\{{\xi }_{ij}\right\}=0\\ \overline{{\xi }_{ij}}=\frac{1}{mn}{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{\xi }_{ij}}}=\frac{1}{180}{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{\xi }_{ij}}} i=1, 2, \dots ,m;j=1, 2, \dots ,n\\ \epsilon =\frac{\overline{{\xi }_{ij}}-2{\xi }_{\min }}{{\xi }_{\max }}=\overline{{\xi }_{ij}}=\frac{1}{180}{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{\xi }_{ij}}}\end{array}$$

Based on the above steps, the model parameter ε value can be calculated. The results can be seen in

Table 4. Values of the parameter model ε of nine prefecture-level cities in Hubei Province during 2017–2022.

Year	2017	2018	2019	2020	2021	2022
ε	0.6552	0.6566	0.6620	0.6525	0.6370	0.6339

.

Substitute the obtained value into Formula (3), and simplify the calculation formula of innovation niche value:

$$I{N}_{Oi}={\omega }_j{\displaystyle \sum _{j=1}^n\frac{\epsilon }{\left|{IF}_{ij}^{\prime }-1\right|+\epsilon } }i=1, 2, \dots , \mathrm{m};j=1, 2, \dots ,n$$

Considering the index system, index weights of niche suitability evaluation of regional innovation system in Table 3, and each classification index were interactive, and the weights of 20 indicators calculated were obtained after weight processing, as shown in

Table 5. Weight of regional innovation system niche suitability evaluation index.

Index	Weight
Per capita GDP	0.043890
Added value in high-tech industries	0.066027
Technology market turnover	0.063558
Disposable income of residents	0.035340
R&D personnel	0.039312
R&D expenditure input intensity	0.047736
Proportion of invention patents among patent applications authorized	0.035256
Proportion of tertiary industry	0.054596
Number of high-tech enterprises	0.051027
Number of national-level technology business incubators	0.061512
Business environment index	0.079794
Import and export volume as a share of GDP	0.057133
Public library collections per capita	0.031372
The number of high-tech enterprises increased	0.033108
Proportion of fiscal science and technology expenditures	0.095684
Proportion of fiscal education expenditure	0.046576
Proportion of fiscal health and medical expenditures	0.020520
Proportion of fiscal transportation expenditure	0.026505
Proportion of fiscal and social security employment expenditures	0.021090
Industrial enterprises above designated size—proportion of enterprises with R&D activities	0.089964
Sum	1.000000

.

The innovation niche was calculated from the standardized values of all cities  in each (known) year. The formula for calculating evolutionary momentum could be simplified as follows:

$$I{M}_i=\sqrt{\frac{{\displaystyle \sum _{j=1}^n\left|{IF}_{ij}^{\prime }-1\right|}}{20}} i=1, 2, \dots , \mathrm{m};j=1, 2, \dots ,n$$

Through the above steps, the suitability and evolutionary momentum of the innovation ecosystem in nine prefecture-level cities in Hubei Province from 2017 to 2022 were calculated (

Table 6. Comparison of suitability and evolutionary momentum of innovation ecosystem in nine prefecture-level cities of Hubei Province.

Year		Wuhan	Xianning	Jingzhou	Xiangyang	Yichang	Jingzhou	Shiyan	Suizhou	Enshi
2017	Suitability	0.9608	0.4746	0.4617	0.5360	0.5702	0.4820	0.4746	0.4692	0.4597
	Suitability ranking	1	6	8	3	2	4	5	7	9
	Evolutionary Momentum	0.3296	0.8578	0.9022	0.7786	0.7858	0.8425	0.8662	0.8772	0.8863
	Momentum Ranking	9	5	1	8	7	6	4	3	2
2018	Suitability	0.9258	0.4912	0.4626	0.5286	0.5783	0.4854	0.5037	0.4619	0.4601
	Suitability ranking	1	5	7	3	2	6	4	8	9
	Evolutionary momentum	0.3868	0.8421	0.8977	0.8080	0.7651	0.8396	0.8498	0.8778	0.8993
	Momentum ranking	9	5	2	7	8	6	4	3	1
2019	Suitability	0.9208	0.4895	0.4690	0.5234	0.5836	0.4804	0.4826	0.4575	0.4813
	Suitability ranking	1	4	8	3	2	7	5	9	6
	Evolutionary momentum	0.3851	0.8527	0.9029	0.8161	0.7583	0.8424	0.8651	0.8865	0.8847
	momentum ranking	9	5	1	7	8	6	4	2	3
2020	suitability	0.9554	0.4940	0.4711	0.5305	0.5901	0.4753	0.4832	0.4540	0.4743
	Suitability ranking	1	4	8	3	2	7	5	9	6
	Evolutionary momentum	0.3009	0.8482	0.9002	0.7947	0.7456	0.8456	0.8810	0.8859	0.8882
	Momentum ranking	9	5	1	7	8	6	4	3	2
2021	suitability	0.9442	0.4701	0.4708	0.5298	0.5930	0.5037	0.4991	0.4661	0.4834
	Suitability ranking	1	8	7	3	2	4	5	9	6
	Evolutionary momentum	0.3466	0.8811	0.9058	0.7805	0.7245	0.8192	0.8442	0.8675	0.8634
	Momentum ranking	9	2	1	7	8	6	5	3	4
2022	Suitability	0.9456	0.4842	0.5100	0.5355	0.5843	0.4914	0.4838	0.4521	0.4759
	Suitability ranking	1	6	4	3	2	5	7	9	8
	Evolutionary momentum	0.3517	0.8500	0.8441	0.7891	0.7436	0.8282	0.8742	0.8764	0.8676
	Momentum ranking	9	4	5	7	8	6	2	1	3

).

5.2. Analysis on the Suitability of Innovative Ecosystem in Nine Prefecture-Level Cities in Hubei Province

Taking nine prefecture-level cities in Hubei Province as independent measurement units, because the horizontal comparison of different regions at the same time could not effectively reflect the regional level differences and development characteristics, and in order to bring each measurement unit in Hubei Province into the horizontal and vertical comparison with regional and time axes at the same time, it was necessary to evaluate and analyze the innovation niche suitability of the nine prefecture-level cities in Hubei Province from the point of view of development characteristics.

The annual average value was the annual arithmetic average of the suitability of each measurement unit during the study, which could integrate the dual factors of time and numerical changes and transform the time-series values that changed with time into the mean values that could be compared intuitively.

The results of the suitability measurement showed that there was no intuitive change tendency of innovation niche suitability data of the nine prefecture-level cities in Hubei Province from 2017 to 2022 (Figure 3). To grasp and intuitively compare the suitability of each measurement unit during the study, the starting point for analysis was the annual average value and annual average growth rate of innovation niche suitability of each measurement unit in Hubei Province. (The numbers following the city names are the ecological suitability rankings.)

Since the regional innovation ecosystem had the characteristics of self-organization and dissipation, and was always in the dynamic evolution process, the innovation niche occupied by the innovation unit was also dynamically changing. Therefore, for a region, it was normal for its innovation niche suitability to fluctuate slightly around a certain value, but for the regional set, namely the prefecture-level city level, the overall development status and trend could be reflected through individual state feedback.

During the study, Wuhan and Yichang maintained a relatively leading suitability level, and their annual suitability ranked as the top two cities in the city-state region, but Wuhan and Yichang’s suitability declined all year round, showing a relatively obvious negative growth trend. The growth rate ranked last among cities and states; on the contrary, the suitability level of Jingmen City was relatively low over the years, with the annual average suitability ranking seventh in the city-state region, but the annual growth rate of its suitability ranked first with an overall growing trend.

Through the descriptive analysis of the suitability evaluation results of the above nine prefecture-level cities, there was a large gap in the suitability level of innovation niche among the nine prefecture-level cities in Hubei Province throughout the study, and the trend of polarization is obvious (Figure 4). Among them, Yichang City and Jingzhou City maintained obvious leading advantages in innovation suitability level and growth rate, indicating that their innovation resources stock and innovation activity demand had a high degree of coupling, and the regional innovation ecosystem had good innovation vitality and innovation sustainability. Wuhan City, Xiangyang City, Shiyan City, and Xianning City belonged to the type featuring a relatively high suitability level with a dearth of growth traction, indicating that the innovation resources were rich, but the importance of resource input was still undervalued, and that the input power and integration ability of innovation resources were also out of line with the resource advantages, which was not enough to support the innovation resources to meet the needs of innovation activities continuously. The level of innovation niche suitability in Enshi Prefecture and Jingmen City was overall lagging behind, but their growth or flat trend was obvious, with Jingmen City in particular. This indicated that although these cities had relatively deficient innovation resources, their internal innovation input structure was constantly optimized and resource integration efficiency was relatively high. Therefore, the degree to which innovation resources meet the demand for innovation activities could maintain a relatively stable level. The level and growth of innovation niche suitability in Suizhou were not optimistic, indicating that innovation resources in Suizhou were weak in supporting innovation activities. (The numbers following the city names represent the annual average growth rate ranking of ecological suitability.)

5.3. Evolutionary Momentum Analysis of Innovation Ecosystem Suitability in Nine Prefecture-Level Cities in Hubei Province

The evolutionary momentum measured the improvement space of innovation niche suitability in each region after reasonable measures were taken to improve the existing innovation environment and improve the innovation ability of innovation subjects, which was a measure of the future development potential of innovation niche suitability. The annual average of the evolutionary momentum of innovation niche suitability in the nine prefecture-level cities and states in Hubei Province is shown in Figure 5. The results showed that during the study, the evolutionary momentum of the relatively backward regions led by Jingmen City and Enshi Prefecture was generally higher, while the evolutionary momentum of the regions with strong innovation strength, such as Wuhan City and Xiangyang City, was generally lower, which took on a generally reverse trend compared to the ecological niche of regional innovation. (The numbers following the city names are the rankings for the evolutionary momentum.)

During the study, the average annual growth rate of the innovation niche suitability in nine prefecture-level cities in Hubei Province fluctuated greatly, and only Wuhan, Xiangyang, and Shiyan showed an overall trend of improvement in innovation niche suitability (Figure 6). Wuhan ranked first with an average annual growth rate of 2.34%, followed by Xiangyang and Shiyan. Xianning City, Jingzhou City, Suizhou City, and Enshi Prefecture had a slightly negative annual growth rate of niche suitability evolutionary momentum and maintained a basically flat trend; Jingmen City and Yichang City maintained an overall weakening trend with relatively obvious negative growth rates (−1.28% and −1.08%). It is worth mentioning that the order of strength between the annual mean value and the annual growth rate of innovation niche evolutionary momentum of prefecture-level cities changed significantly: During the study, Jingmen and Enshi maintained a relatively leading level of evolutionary momentum of fitness, and their annual evolutionary momentum of suitability ranked among the top two cities and states, but the evolutionary momentum of suitability of Jingmen and Enshi declined all year round. There was a relatively obvious negative growth trend, and the growth rate ranked last among cities and prefectures. On the contrary, Wuhan had a relatively low level of evolutionary momentum of suitability over the years. The annual average evolutionary momentum of suitability ranked ninth in the city-prefecture region, but the annual growth rate of evolutionary momentum of suitability ranked first, with an overall growing trend. (The numbers following the names of the cities are the rankings for the annual average growth rate of evolutionary momentum.)

As shown in

Table 7. Comparison of innovation niche suitability and annual mean evolutionary momentum ranking in nine prefecture-level cities in Hubei Province.

City	Niche Suitability Ranking	Evolutionary Momentum Ranking
Wuhan	1	9
Xianning	6	5
Jingmen	7	1
Xiangyang	3	7
Yichang	2	8
Jingzhou	5	6
Shiyan	4	4
Suizhou	9	3
Enshi	8	2

, the distribution of evolutionary momentum was basically THE opposite to that of niche suitability in both economically developed and underdeveloped regions, that is, the prefecture-level cities with higher ecological niche suitability had lower evolutionary momentum (such as Wuhan, Yichang, and Xiangyang). Provinces with a lower niche suitability had higher evolutionary momentum (such as Suizhou, Enshi, and Jingmen). Compared with regions with relatively higher innovation suitability levels, regions with lower niche suitability levels at the present stage, that is, regions with insufficient innovation resources to meet the requirements of innovation activities, would have more room for improvement of innovation suitability in the future. Increasing the improvement of innovation impact factors in these regions would significantly improve the innovation niche suitability level of this region. The law of the change in niche suitability and evolutionary momentum value was in the opposite direction. Suizhou was a special case. At present, both the innovation niche suitability and evolutionary momentum of Suizhou were at a low level, that is, the evolutionary momentum of Suizhou was low and has yet to reach the expected level of evolutionary momentum, indicating that the innovation resources of Suizhou not only had a low degree of satisfaction for innovation activities at the present stage, but also had relatively limited room for improvement in the future. To some extent, it reflected that the innovation development of Suizhou lagged behind, and the innovation environment was in urgent need of improvement. At the same time, it is worth mentioning that Jingmen City, with the highest evolutionary momentum value (0.8922), was nearly 2.6 times that of Wuhan City with the lowest evolutionary momentum value (0.3501), which once again verified the imbalance and difference in the development of regional innovation suitability in Hubei Province.

5.4. Single Index Analysis of Innovation Ecosystem Suitability in Nine Prefecture-Level Cities in Hubei Province

Based on four indexes of “innovation efficiency, innovation technology, innovation culture, and innovation policy”, this paper analyzed the advantages and disadvantages of innovation resources in the innovation ecosystem of nine prefecture-level cities and prefectures in Hubei Province. In doing so, certain goals were expected to be accomplished: going deep into the system and exploring the relevant factors that directly affect the suitability level of innovation niche in each region to provide suggestions and references for integrating regional innovation resources, improve the quality of interaction between innovation subjects and the environment, and optimize various innovation functions of the system, thus improving the suitability level of the regional innovation niche.

5.4.1. Innovation Benefits

Innovation benefits measured the benefits that innovation entities in each region obtain from innovation in excess of normal benefits and were the profits made from the subject elements and service element resources that participated in innovation activities. The innovation benefit niche suitability values and rankings of the nine prefecture-level cities in Hubei Province are shown in

Table 8. Innovation benefit niche suitability of nine prefecture-level cities in Hubei Province in 2021.

	Ecological Niche Suitability (Combination)	Ranking	Ecological Niche Suitability of Innovation Benefits	Ranking
Wuhan	0.9442	1	0.8432	1
Xianning	0.4701	8	0.4489	7
Jingmen	0.4708	7	0.4208	9
Xiangyang	0.5298	3	0.5138	6
Yichang	0.5930	2	0.5620	2
Jingzhou	0.5037	4	0.5345	5
Shiyan	0.4991	5	0.4482	8
Suizhou	0.4661	9	0.5395	4
Enshi	0.4834	6	0.5619	3

and Figure 7. Wuhan City, Xiangyang City, and Enshi Prefecture ranked in the top three and were far ahead of the other prefecture-level cities. There was a big gap, with Shiyan and Jingmen being the lowest. At the same time, through a comparison with the comprehensive niche suitability ranking, it was found that the niche suitability ranking of prefecture-level cities in terms of innovation benefits had changed significantly.

5.4.2. Innovative Technology

Innovative technology mainly measures the innovation activities in the innovation area that are based on scientific and technological knowledge and the resources created, with the purpose of creating new technological niches. As shown in

Table 9. Suitability of innovative technology niches in nine prefecture-level cities in Hubei Province in 2021.

	Ecological Niche Suitability (Combination)	Ranking	Ecological Niche Suitability of Innovation Policy	Ranking
Wuhan	0.9442	1	0.9987	1
Xianning	0.4701	8	0.4681	8
Jingmen	0.4708	7	0.4952	6
Xiangyang	0.5298	3	0.5178	4
Yichang	0.5930	2	0.5661	2
Jingzhou	0.5037	4	0.5046	5
Shiyan	0.4991	5	0.5327	3
Suizhou	0.4661	9	0.4432	9
Enshi	0.4834	6	0.4689	7

and Figure 8, Wuhan, Yichang, and Shiyan cities in Hubei Province were ranked in the top three for the suitability of the innovative technology ecological niche, while Suizhou city had the lowest ranking.

5.4.3. Innovation Culture

Innovation culture measured the combination of distinctive innovative intellectual wealth and innovative material forms created and formed by regional innovation entities in innovation and innovation management activities. As shown in

Table 10. Suitability of innovation cultural niche in nine prefecture-level cities in Hubei Province in 2021.

	Ecological Niche Suitability (Combination)	Ranking	Ecological Niche Suitability of Innovation Policy	Ranking
Wuhan	0.9442	1	0.3515	1
Xianning	0.4701	8	0.1858	5
Jingmen	0.4708	7	0.1944	4
Xiangyang	0.5298	3	0.2098	3
Yichang	0.5930	2	0.2498	2
Jingzhou	0.5037	4	0.1821	6
Shiyan	0.4991	5	0.1757	7
Suizhou	0.4661	9	0.1499	8
Enshi	0.4834	6	0.1392	9

. and Figure 9, Wuhan City, Xiangyang City, and Yichang City ranked in the top three in terms of innovation cultural niche suitability, while Suizhou City and Enshi Prefecture ranked last again.

5.4.4. Innovation Policy

Innovation policy mainly measures the sum of a series of direct or indirect public measures and policies formulated and applied by a national and regional government with the goal of promoting the generation, utilization, and diffusion of innovative activities and regulating the behavior of innovative subjects. As shown in

Table 11. Suitability of innovation policy niche in nine prefecture-level cities in Hubei. Province in 2021.

	Ecological Niche Suitability (Comprehensive)	Ranking	Ecological Niche Suitability of Innovation Policy	Ranking
Wuhan	0.9442	1	0.5442	1
Xianning	0.4701	8	0.2787	7
Jingmen	0.4708	7	0.2824	6
Xiangyang	0.5298	3	0.3037	3
Yichang	0.5930	2	0.3513	2
Jingzhou	0.5037	4	0.2854	5
Shiyan	0.4991	5	0.3034	4
Suizhou	0.4661	9	0.2553	9
Enshi	0.4834	6	0.2776	8

and Figure 10, Wuhan City, Xiangyang City, and Yichang City ranked in the top three in terms of innovation policy niche suitability, while Suizhou City and Enshi Prefecture ranked last again.

The changing trend of the regional innovation niche suitability of the four individual indicators of “innovation benefits, innovative technology, innovation culture, and innovation policy” is as follows (Figure 11). The niche suitability of the innovation ecosystem and each innovation driving factor shared the same trend. The niche suitability for the innovation-driven elements of innovation culture and innovation policy was lower than that of other driving elements. The niche suitability of innovation benefits and innovative technology coincided with the suitability curve of the innovation ecosystem.

5.5. Prediction of Ecological Niche Suitability of Nine Prefecture-Level Cities in Hubei Province

To the intuitive reflection of the niche suitability trends of nine prefecture-level cities in Hubei Province in the next five years, the gray prediction model was used to predict the ecological niche suitability trends of nine prefecture-level cities in Hubei Province in the next five years (2023–2027) based on original data (2017–2022). The ecological niche suitability of the city was predicted. The specific results are shown in Figure 12.

As shown in the figure above, the niche suitability of Wuhan City was still higher than that of other prefecture-level cities, and the niche suitability of other prefecture-level cities remained stable in the range [0.4, 0.6]. From the perspective of the growth value, the niche suitability of Wuhan, Jingmen, Jingzhou, and Enshi will show slow growth in the next five years; the niche suitability of Xianning and Shiyan will show a downward trend; and the niche suitability of Xiangyang, Yichang, and Suizhou will be relatively stable in the next five years. Judging from specific numerical values, the ecological niche suitability of such central cities as Wuhan, Yichang, and Xiangyang was higher than that of its other non-central peers, which reflected the regional dualization effect to a certain extent.

6. Discussion

This study, building upon and absorbing the ecological niche theory, adopted an innovative methodological approach known as the subjective–objective weighting method for the selection and weighting of evaluation indicators. This approach not only enhances the scientific and rational selection of indicators but also strengthens the accuracy and reliability of the evaluation results. This research focused on nine prefecture-level cities in Hubei Province, using two sets of models to conduct an in-depth analysis of the ecological niche suitability and evolutionary momentum of these cities’ regional innovation ecosystems. The application of these dual models provides new perspectives and analytical tools for the study of regional innovation ecosystems. The findings of this research are consistent with the existing literature and theories, demonstrating continuity with previous studies, while also showcasing uniqueness in terms of the specific research subjects, theories, methods, and the significance and value of the research. Especially in the integration of environmental theory with regional innovation niches, this study emphasizes the impact of regional environmental factors on innovation activities. Environmental theory, which focuses on how environmental factors shape the behavioral patterns of organizations and individuals, aligns well with the concept of regional innovation niches. The regional innovation niche not only includes the participants in innovation activities but also encompasses the social, economic, and cultural contexts in which these activities occur. By comparing the innovation niches and momentum of different countries and regions, this study revealed the positioning and characteristics of Hubei Province within the global innovation ecosystem. This comparative research method not only provides an empirical basis for regional innovation policies in Hubei Province and China as a whole but also offers valuable references for international readers. Foreign readers can use this study to understand global innovation trends, identify potential issues, and recognize areas for improvement within their own regional innovation ecosystems. This is of great significance for promoting experience exchange and learning from best practices among different regions. Furthermore, the findings and recommendations of this study hold guiding significance for policymakers, who can use this information to optimize and adjust regional innovation policies to better adapt to the needs of global innovation competition.

This paper aimed to delve into the regional innovation niche from four dimensions: innovation efficiency, innovation technology, innovation culture, and innovation policy. This was, to some extent, an extension and development of the research on regional innovation niches by scholars such as Smith and Li [1,2]. These scholars have emphasized the significant role of scientific and technological talent, resource endowment, and innovation policy in the formation of the ecological niche. However, there are some differences in the quantitative assessment of regional innovation niches by scholars like Jin and Zeng. They have respectively studied the impact of the suitability of the ecological niche on public research and development institutions [6], and explored the relationship between the suitability of high-tech industrial innovation ecosystems and innovation performance using the spatial Durbin model [7]. Yu, on the other hand, constructed a panel data regression model based on the measurement of ecological niche suitability and innovation value [25]. In contrast, this paper employed a method that combined the entropy weight method with the ecological niche model to conduct a comprehensive study of the regional innovation niche system, demonstrating the integration and application of different research methods to some extent. Moreover, with the advent of the digital age, some scholars have begun to examine regional innovation ecosystems from the perspective of digitalization. For instance, Li and Liu constructed an evaluation system for regional innovation ecosystems that considers the impact of digital development factors [8], while He and Dong conducted empirical analysis on the impact mechanism of digital transformation on the suitability of regional innovation ecosystems [9]. Drawing on these research findings, this paper started from the perspective of innovation technology and constructed a series of sub-indicators to study the regional innovation ecosystem of nine prefecture-level cities in Hubei Province. On the issue of sustainable development, this paper not only provided an in-depth analysis of the regional innovation niche of the nine prefecture-level cities in Hubei Province but also predicted the development status for the next five years using the grey prediction model. It is worth noting that unlike the improved grey model based on the Fourier series used by scholars such as Ye [23], there is still a need for further integration and improvement of the modeling methods in this paper. Therefore, future research needs to innovate and optimize the existing models to enhance the accuracy and reliability of predictions. This will not only provide a more scientific basis for the formulation of regional innovation policies but will also help to promote the sustainable development of the regional economy.

At the same time, this study also provides new research pathways for the academic community, encouraging scholars to adopt multidisciplinary and multi-faceted approaches to explore the complexity and dynamics of regional innovation ecosystems in depth. Future research can build on this foundation to further explore the applicability and limitations of the ecological niche theory in different cultural, economic, and social contexts, and how interdisciplinary integration can enhance the depth and breadth of research on regional innovation ecosystems.

Considering the relatively narrow research scope of this article, which was limited to nine prefecture-level cities within Hubei Province, there is a certain limitation in the scope of the research, and there was a lack of significant integration between the research methods and models, with the ecological niche model and the grey prediction model not being closely linked together, and instead merely calculating and analyzing the specific data involved in both. At the same time, this study was based on a small sample of data and did not employ an improved grey prediction model based on the Fourier series, which lacks persuasiveness in terms of predictive accuracy. Therefore, there is still room for improvement in this study. Future research should expand the scope to increase its representativeness and extensiveness, improve and integrate the models, and enhance the accuracy and precision of data processing.

7. Conclusions and Recommendations

7.1. Conclusions

This paper offered a detailed illustration of the calculation process of regional innovation niche suitability and evolutionary momentum and adopted the niche suitability model to analyze the regional innovation niche suitability and evolutionary momentum of nine prefecture-level cities in Hubei Province from 2017 to 2022. Through longitudinal analysis in the time dimension and four indicators of “innovation benefits, innovation technology, innovation culture, and innovation policy”, the comparison analysis was conducted on the regional innovation niche suitability and comprehensive niche suitability of nine prefecture-level cities in Hubei Province. The following conclusions could be reached.

(1) Taking the nine prefectural-level cities in Hubei Province as an independent measurement unit, it was learned that the regional innovation ecological niche suitability and the evolutionary momentum of the nine prefectural-level cities in Hubei Province showed an inverse change law, and the prefectural-level cities with high ecological niche suitability had a lower evolutionary momentum, indicating that there was a larger space for progress; the prefectural-level cities with a low ecological niche had a higher evolutionary momentum, indicating that they are actively engaged in innovation activities, but their basic innovation resources and innovation capabilities are insufficient. A comparison between different cities led us to some discoveries: between central cities and non-central ones, there is heterogeneity between niche suitability and evolutionary momentum due to a difference between their regional innovation capacity and the duality of innovation at a certain level.

(2) In order to further illustrate the differences in the suitability of the regional innovation ecological location of the nine prefectural-level cities in Hubei Province, through an in-depth comparison of the four dimensions of “innovation efficiency, innovation technology, innovation culture, and innovation policy”, the overall suitability of central cities and non-central cities still showed a large difference in suitability, regardless of the indicator dimension. No matter under which index dimension we looked, the suitability ranking of central cities was higher than that of non-central cities, which also echoed the overall conclusion that central cities and non-central cities have different innovation capacities, and the dichotomy of regional innovation was still obvious.

(3) This study delineated the positioning and characteristics of Hubei Province within the global innovation ecosystem, providing an empirical basis for the formulation of regional innovation policies in Hubei Province and China as a whole. It also offers significant reference value to the international academic community and policymakers. The research findings facilitate the understanding of global innovation trends, the identification of potential issues, and insights into areas for improvement within regional innovation systems, thereby promoting the exchange of experiences and learning from best practices, which is of notable importance for regional collaboration and knowledge sharing. Concurrently, the study has also opened new avenues for academic exploration, encouraging scholars to employ interdisciplinary and multidimensional approaches to thoroughly analyze the complexity and dynamics of regional innovation ecosystems, contributing a deeper understanding to academic research and policy formulation.

7.2. Recommendations

To this end, the study proposed recommendations from four aspects regarding the suitability and evolutionary momentum of regional innovation ecological niches in nine prefectural-level cities in Hubei Province:

Firstly, technology-based enterprises should strive to enhance the level of innovation efficiency. For governments in central cities, it is recommended to strengthen industrial policies and innovation incentives to attract more high-tech and innovative enterprises to relocate to central cities, increase the concentration of innovative industries, and thereby improve innovation benefits. For non-central cities, it is advised that governments increase support for local enterprises, establish industrial alliances and innovation alliances, promote local industrial upgrading and technological innovation, enhance their competitiveness in the field of innovative industries, and improve innovation benefits.

Second, enterprises and research institutions should strive to enhance the level of innovation technology. For governments in central cities, it is recommended to increase investment in scientific research, facilitate cooperation between research institutions and businesses, encourage the exchange and sharing of technology, and improve the independent research and development capabilities of innovative technologies. For governments in non-central cities, it is advised to strengthen the cultivation and introduction of technical talent, establish platforms for technological innovation, and encourage enterprises to increase their investment in scientific and technological research and development, thereby raising the level of local innovative technology.

Third, enterprises, governments, and research institutions should strive to enhance the atmosphere of innovation culture. For central cities, it is recommended that governments and businesses strengthen the promotion and education of innovation culture, encourage enterprises and individuals to be bold in trying and innovating, and create an innovative atmosphere. For governments and businesses in non-central cities, it is suggested to carry out training and exchange activities related to innovation culture, reinforce the construction of an innovation culture, and foster a societal understanding and attention to innovation.

Fourth, regional governments should focus on optimizing the innovation policy system. For governments in central cities, it is recommended to further refine the innovation policy system, streamline the approval process, lower the barriers to innovation, and attract more outstanding talents and projects. For governments in non-central cities, it is advised to develop targeted regional innovation policies, encourage cooperation between local governments and businesses, provide tax and financial support, and foster the development of the innovative economy.