Investigating the Impact of Innovation Policies and Innovation Environment on Regional Innovation Capacity in China

Abstract

Regional technological innovation serves as a key pillar for achieving high-level self-reliance and self-strengthening, a fundamental requirement for promoting balanced regional development, and a vital aspect of implementing the innovation-driven development strategy. We examine the impact of innovation policies on corporate innovation activities and the importance of the innovation environment in enhancing innovation capacity. By analyzing the implementation of innovation policies in three provinces in Central China from 2018 to 2022, this study introduces two indicators—policy intensity and policy volume—to assess the level of government policy support. The findings are as follows: First, local government innovation policies exert a significant positive impact on corporate innovation activities, with particularly strong effects observed in the implementation of R&D subsidies. Second, the implementation of regional innovation policies enhances regional innovation capacity by promoting increased R&D investment by firms. Third, the innovation environment moderates the influence of regional innovation policies on corporate R&D investment, amplifying the effect of R&D investment on regional innovation capacity. In summary, when formulating innovation policies, governments should consider the unique needs of different industries, regions, and enterprises, adopting flexible and diverse policy measures to optimize the innovation environment and accelerate the nation’s technological innovation development.

1. Introduction

Innovation is the primary driving force behind development, the cornerstone of high-quality growth, and a key mechanism for the transformation from extensive to intensive economic models [1]. Joseph Schumpeter [2] argued that innovation is the fundamental driving force of economic development, even to the extent that it embodies the very essence of progress itself. In recent years, an increasing number of economists have also recognized that it is innovation—rather than capital accumulation—that serves as the primary impetus for long-term national economic growth. The U.S. Department of Commerce estimates that, since World War II, 75% of America’s economic growth can be attributed to innovation. Further supporting this notion, research by Klenow and Clare [3] reveals that 90% of per capita income growth across 98 developed and developing countries is due to innovation. Consequently, on 30 December 2005, the State Council in China released the National Medium- and Long-Term Program for Science and Technology Development (2006–2020), setting forth the goal of enhancing indigenous innovation capabilities and building an innovation-driven nation. Enhancing the quality of regional innovation capacity requires closely aligning with local realities and comprehensively advancing innovation-driven development strategies to stimulate the intrinsic innovative vitality within regions and boost the momentum and potential for innovation-driven growth. Currently, while the eastern regions of China have a solid foundation in innovation and robust technological capabilities [4], challenges persist in improving innovation efficiency, enhancing independent innovation capacity, and optimizing industrial structures to further elevate regional innovation capabilities. Innovation is also the inexhaustible force for national development and a crucial factor in boosting productivity [5]. The ongoing wave of technological revolutions and industrial transformations is reshaping the global innovation landscape and reconstructing the global economic order [6].

In the study of regional innovation performance, most scholars focus on three key areas: measurement indicators, evaluation systems, and influencing factors. Regarding the measurement of innovation performance, scholars have gradually expanded from using singular absolute value indicators to incorporating relative value calculations [5]. In terms of the evaluation system for innovation performance, the scope of evaluation has progressively broadened, with some scholars integrating environmental regulation into their frameworks [7,8]. As for the factors influencing regional innovation performance, the majority of scholars approach the subject from two perspectives: innovation actors and the innovation environment. Research on regional innovation capacity is extensive, with studies predominantly focusing on four key areas as follows [9]: defining the concept of regional innovation capacity, identifying and categorizing its various influencing factors, assessing regional innovation capacity across different areas, and exploring strategies and pathways to enhance regional innovation capacity. Most studies have concentrated on the innovation output effects of government investment in science and technology, while overlooking the government’s contributions in areas such as scientific manpower, environmental development, and policy formulation to support technological innovation activities.

We aim to address the following two questions: By effectively responding to China’s major national strategic plans, how can a systematic blueprint for regional scientific and technological innovation be devised? Furthermore, as a critical strategic area in Central China, to what extent does the regional innovation environment influence the effectiveness of innovation efforts?

This study addresses these research gaps related to the neglect of regional innovation synergy and the role of policy in enhancing regional innovation capacity as follows: (1) By assigning weighted scores to regional innovation policy indicators, this research goes beyond the traditional analysis paradigm, which relies heavily on textual analysis, offering a more precise depiction of the impact of regional policies. (2) It adopts the perspective of the innovation ecosystem to explore the connections between regional innovation elements, identifying the key factors influencing regional innovation. (3) This study also considers the varying nature of enterprises within regions, particularly the distinction between state-owned and non-state-owned enterprises, which significantly affects their access to capital, resource endowments, management models, and institutional constraints.

2. The Literature Review and Research Hypothesis

Existing research on regional innovation performance primarily focuses on the macro level, examining factors such as policy, environment, industrial agglomeration, and culture. At the micro level, studies tend to explore topics such as intellectual property, innovation elements, and regional infrastructure. On the macro level, some studies analyze regional innovation performance from a policy perspective. For instance, Wang Xinliang and colleagues suggest that the implementation of talent settlement policies can significantly enhance regional innovation performance by promoting talent agglomeration [10]. Similarly, Zhang Xichun and others found that innovation policies can markedly improve scientific output within regions [11]. Song Hua et al. argue that innovation investment has a significant positive effect on regional innovation performance, and the implementation of regional policies positively moderates the relationship between regional innovation investment and innovation performance [12].

On the other hand, some research considers regional innovation performance in the context of economic globalization. Wang Xinliang and colleagues assert that the development of big data technology optimizes the business environment, reduces transaction costs, and enhances regional innovation performance [13]. Deng Jianjun et al. highlight how local governments leverage foreign investment to acquire advanced international technologies, significantly boosting regional innovation performance through the technology spillover effect [14].

Scholars have also examined the influence of local culture on regional innovation. Some findings suggest that cultural values and embedded cultural meanings can negatively moderate the process of regional innovation driven by investment, while the prevalence of egalitarianism within a region positively moderates this relationship [15]. Furthermore, performance orientation and gender egalitarianism are found to have a significant positive effect on regional innovation performance, while institutional collectivism exerts a significant negative impact [10].

In Central China’s three provinces, enterprises face constraints due to economic limitations, resulting in low success rates and high costs for innovation activities. The development trajectory of new technologies may be difficult. In such circumstances, enterprises often aim to maximize returns in the medium to long term [16]. However, innovation efforts may not yield significant short-term returns and could even result in losses, leading enterprises to reduce R&D investments, particularly in long-term projects.

To address these challenges, governments have introduced a series of innovation policies to incentivize enterprises to actively pursue technological innovation and increase R&D expenditures [17]. The most significant province in Huazhong District, Wuhan, launched new policies to support seed industry development in Donghu High-Tech Zone and Hannan District as part of efforts to build the Wuhan National Modern Agricultural Industry Science and Technology Innovation Center. These policies, such as the “Implementation Rules of Several Policies for the Development of Wuhan National Modern Agricultural Industry Science and Technology Innovation Center” and the “Special Support Policy for the Development of Hannan Modern Seed Industry Town (2023–2025)”, outline that from 2022 to 2025, both the municipal and district levels will allocate special funds annually for three consecutive years to support the R&D, application, and promotion of new agricultural varieties, technologies, models, and equipment by relevant industries under the “One Core, Two Wings” initiative. Leading and specialized enterprises with original achievements, both domestic and international, will receive the highest relocation rewards and financial support [15,16,17].

Current policy research focuses on innovation funding, tax relief, and financial aid, aiming to demonstrate that these policies can enhance corporate innovation efficiency [18]. Well-designed policy support can significantly strengthen companies’ technological innovation capabilities and promote scientific advancement.

However, the effectiveness of policy support varies depending on the growth stage of the enterprise, region, and industry. In the short term, policy support significantly promotes technological innovation output, while in the long term, government subsidies have a more pronounced effect on realizing innovation value [19]. For enterprises at different development stages, the effectiveness of policy support varies. Local industrial policies primarily promote innovation output for mature enterprises, while the impact on growing enterprises is less evident [20]. Additionally, the degree of government intervention in the market varies by region. In areas where industrial policies have a significant influence, government intervention is relatively limited, leaving the implementation of policies more dependent on market forces. This suggests that policy formulation must consider regional conditions to achieve better outcomes. For instance, in the rapidly growing digital economy, government subsidies and industry entry restrictions have had a positive effect, while tax incentives have had a smaller impact [21]. This may be due to the unique characteristics and development trends of the digital economy, which require more targeted policy support. In other industries, a combination of credit, tax relief, R&D expense deductions, and government subsidies can effectively encourage private enterprises to pursue technological innovation [22,23]. Yu Liping [24] utilized interprovincial panel data from 31 provinces in mainland China from 2011 to 2019, employing panel data models and panel threshold models to analyze the impact of government innovation policies on employment. The study revealed that the strength of these policies significantly influences both employment levels and the innovation capabilities of enterprises. This highlights the importance of tailoring policy design to the specific characteristics and development needs of each industry to achieve optimal results [23,24,25]. One study focusing on Jiangsu’s manufacturing sector explored the impact of central and local government R&D subsidies on the innovation activities of manufacturing enterprises in China [26]. The findings show that, overall, R&D subsidies effectively promote exploratory innovation in enterprises, with local government subsidies proving even more significant. Therefore, the government must adopt diverse and flexible policy measures to promote scientific and technological innovation, addressing the unique needs of different enterprises, regions, and industries [27]. Reasonable policy support from the government can unleash the innovation potential of enterprises, rapidly advancing scientific and technological innovation and providing the necessary momentum for national economic and social development [28,29]. Studies have also shown that innovation policies can stimulate patent output; however, the quality of these patents tends to decline, which hinders regional innovation capacity [30].

Based on this, the following hypothesis is proposed:

H1. 

The implementation of regional innovation policies can enhance regional innovation capacity.

The process of implementing regional innovation policies can direct innovation resources towards regional innovation hubs. As a result, governments frequently adopt measures such as R&D subsidies and tax incentives to enhance the innovation capacity of cities. In order to secure increased innovation subsidies from the government, innovative firms respond to national innovation policies by increasing their R&D investments, thereby improving their innovation capabilities and concentrating in innovation hub cities. This concentration of innovative entities in hub cities further amplifies government support for innovation. Firms, as the primary drivers of innovation, benefit from innovation policy support, which essentially facilitates constructive management of the differences in market perceptions between firms and governments—both key actors in market activities. This process aims to define common problems and formulate joint solutions based on provisional agreements that might coexist with disagreements and dissent [17,29]. Policy support typically comes with subsidies, industry protection, and increased revenues playing a facilitative role. The government can directly provide financial subsidies to companies to boost their R&D expenditures, while also sending signals to the market through official documents, encouraging firms to increase their R&D investments [18,30]. Through an empirical analysis of Shanghai’s innovation policies, Li Zhan et al. found that regional innovation policies encourage firms to increase their R&D investments [31]. Other studies have pointed out that the implementation of government innovation policies not only boosts corporate R&D investments but also alleviates the financial constraints on R&D [32]. Moreover, talent policies introduced by the government have promoted corporate innovation, leading to an increase in invention patents [21,33].

Therefore, this study proposes the following research hypothesis:

H2. 

The implementation of regional innovation policies enhances regional innovation capacity by promoting increased R&D investment by firms.

The regional innovation environment is a complex system, and different environments lead to varying policy outcomes. Overall, the optimization of the regional innovation environment encourages numerous firms to accelerate their technological R&D, solidify their technological foundations, and bridge the gap between technology and the market [34]. The innovation environment encompasses not only healthy competition among firms within the system but also the promotion of policies and an innovation culture [35]. When regional firms operate in an environment with abundant innovation resources and sound institutional frameworks, their innovation vitality is stronger, and they are more likely to receive policy support. Scholars highlighted a significant positive correlation between the innovation environment and innovation performance [36]. Aghion et al., in an empirical study of 692 German manufacturing firms, found a positive correlation between the level of intellectual property protection in a region and firms’ R&D investments. They further emphasized that optimizing the regional innovation environment is key to enhancing R&D efficiency and strengthening innovation capabilities [37]. In summary, a favorable regional innovation environment positively influences firms’ R&D investments and the effectiveness of policy measures.

Therefore, the following research hypothesis is proposed:

H3. 

The regional innovation environment positively moderates the impact of regional innovation policy implementation on regional innovation capacity.

The three hypothesized relationships are illustrated in the following Figure 1:

3. Research Methods

Description of Variables

Based on the approach of Peng Jisheng [38], the explanatory variable in this study is regional innovation policy. To ensure the accuracy and effectiveness of policy intensity assessments, the weight distribution in

Table 1. Policy weights.

Weight	Issuing Authority
5	Joint issuance by the Municipal Party Committee, Municipal People’s Congress, and Municipal Government
4	Issuance solely by the Municipal People’s Congress, Municipal Party Committee, or Municipal Government
3	Joint issuance by other municipal departments (excluding the Municipal Government, Party Committee, and People’s Congress)
2	Issuance solely by other municipal departments
1	Others

was formulated based on consultations with experts, including officials specializing in relevant research from the Jiangsu Provincial Department of Science and Technology, as referenced in Peng’s study. Following a thorough analysis of the “Explanation on Drafting Procedures of the Jiangsu Provincial Department of Science and Technology Normative Documents”, we established a standard for assigning values to the strength of technological innovation policies. Additionally, after systematically reviewing all technological innovation policies, we meticulously defined each policy’s distinct benchmarks across the dimensions of policy measures and objectives, thereby preliminarily establishing a standard for assigning values to the objectives and measures of each technological innovation policy.

First, each policy is assigned a weight based on the government department that formulated and issued the policy, thereby quantifying the policy intensity. Additionally, the number of innovation policies issued annually by various departments of municipal governments is tallied to form a policy quantity variable. These two variables are used to assess the innovation policy intensity of municipal governments.

Specifically, the methodology involves referencing the approach of Fan Xia et al. [39] by extracting innovation policies from the Beida Fabao database for the cities in the three provinces of Central China from 2018 to 2022. The reason why we focus on this period is that, as early as 2016, China’s National Development and Reform Commission issued the “13th Five-Year Plan for Promoting the Rise of the Central Region”, which emphasized supporting Wuhan and Zhengzhou in becoming national central cities in Central China, strengthening the status of provincial capitals such as Changsha, and enhancing their capacity for resource aggregation, technological innovation, and service functions. The plan also aimed to elevate their levels of modernization and internationalization. Additionally, it sought to further develop Luoyang, Yichang, Yueyang, and other regional center cities within Central China, accelerating industrial transformation and upgrading, extending industry and service chains, and creating growth nodes to stimulate regional development. After filtering the policies relevant to innovation research, departmental approval documents and policies with weak relevance are excluded. A policy weighting table (in Table 1) is then created following Peng Jisheng’s standards for policy intensity, assigning weights from 1 to 5 to each policy. Subsequently, the policies of a given city for the year are multiplied by their respective weights, and the policy intensity value is calculated by summing the results.

The regional innovation environment, as referenced in the “China Regional Innovation Capacity Report 2023”, is defined by five aspects: infrastructure environment, market environment, labor quality, entrepreneurship level, and sustainable development capacity. Relevant indicators are selected, and the factor analysis method is applied to obtain a composite score index for the regional innovation environment in each city. The specific indicators used are detailed in

Table 2. Innovation environment indicator system.

	Variable	Measure
Regional Innovation Environment Indicator System	Innovation Infrastructure	Number of mobile phone users (per 10,000 households)
		Number of R&D institutions affiliated with municipal government departments
	Market Environment	Ratio of total imports and exports to GDP (gross domestic product) (%)
		Per capita consumption expenditure of urban residents (in CNY)
		Per capita total investment in fixed assets (in CNY)
	Labor Quality	Ratio of education expenditure to GDP (gross domestic product) (%)
		Percentage of students enrolled in secondary and tertiary education (%)
	Entrepreneurship Level	Urban registered unemployment rate (%)
	Financial Environment	Amount of venture capital received by tech incubators in the current year (CNY 10,000)
		Total amount of incubation funds in tech incubators (CNY 10,000)

. Additionally, government subsidies are empirically tested using data on the total subsidies received by listed companies in a given year.

Regional innovation capacity is defined by the number of patents granted to firms. Referring to the “China Regional Innovation Capacity Report 2023”, this research focuses on substantial innovation output by firms as a measure of innovation capacity. The number of patents granted is considered more effective in evaluating innovation output compared with the number of patent applications. Therefore, the number of patents granted to firms within a region is used to assess regional innovation capacity.

The mediating variable in this study is corporate R&D investment, measured by the R&D expenditures of listed companies within the region. Control variables include firm size, firm ownership, return on assets (ROA), and debt-to-asset ratio.

Firm size: often correlates with resources available for innovation, as larger firms typically have more capital and capacity for R&D activities, which can influence both innovation output and operational efficiency.

Firm ownership: ownership type (e.g., state-owned, private, or foreign) can affect managerial incentives, risk tolerance, and access to capital, each of which impacts a firm’s innovation capabilities and strategic orientation.

Firm age: the age of a firm often affects its innovation behavior, as older firms may have more established routines and resources, while younger firms might display greater agility and willingness to adopt novel approaches.

Return on assets (ROA): measures a firm’s profitability and efficiency in using its assets, serving as an indicator of financial health that may influence a firm’s ability to invest in innovation.

Debt-to-asset ratio: Indicates a firm’s financial leverage and risk level, which can impact its flexibility and willingness to engage in innovation, as higher debt levels may lead to more conservative risk-averse decision making.

The specific meanings and measurement methods for each variable are detailed in

Table 3. Variable definitions.

Variable Type	Measure	Variable Definition	Symbol
Dependent Variable	Regional Innovation Capacity	Number of patents granted to listed companies within the region	TP
Independent Variables	Policy Quantity	Total number of policies issued by various departments in the region annually	PN
	Policy Intensity	Weighted value of policies issued by various departments within the region	PD
Mediating Variable	Corporate R&D Investment	R&D expenditures as a percentage of corporate operating revenue	RD
Moderating Variable	Regional Innovation Environment	Composite score index of the regional innovation environment	IE
Control Variable	Firm Size	Logarithm of total assets at the end of the year	SIZE
	Firm Ownership	1 for state-owned enterprises, 0 for private enterprises	STATE
	Return on Assets	Logarithm of net profit/total assets	ROA
	Debt-to-Asset Ratio	Total liabilities/total assets	DLR
	Firm Age	Age of the firm	AGE

.

Using all A-share listed companies registered in the Central China region (Hubei, Hunan, and Henan provinces) from 2018 to 2022 as the research sample, data on patent grants and financial figures were sourced from the CSMAR (China Stock Market & Accounting Research) database. Central China, located in the heart of China with its advantageous geographic position, serves as a crucial hub connecting the eastern coastal region with the central and western areas of the country. Studying enterprises in Central China offers valuable insights into how the central region acts as a bridge in both domestic and international markets within the context of regional economic integration. Furthermore, the three provinces in Central China exhibit a diverse industrial structure. Wuhan is known for its concentration of high-tech industries, while Changsha and Zhengzhou are central to traditional manufacturing and agricultural economies. Researching innovation and development within these industries can reveal how different sectors are transforming and upgrading under national innovation-driven policies. Central China is a key area for the national “Rise of Central China” strategy. In recent years, the three provinces have received increased government support in areas such as innovation policies, infrastructure development, and industrial upgrading. Selecting enterprises from this region as research subjects helps analyze the impact of government policies on regional economic development.

The relevant innovation policy variables were derived from the Beida Fabao database, while other variables were collected from the statistical yearbooks of the respective cities. During the data analysis, pre-processing steps were carried out, including the removal of financial sector companies, exclusion of all ST stocks, and winsorization of the variables at the 1% and 99% levels. Ultimately, a sample of 1381 companies was obtained.

To investigate the direct effect of regional innovation policies on the enhancement of regional innovation capacity, the following baseline regression model is constructed based on the aforementioned research hypotheses:

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}P{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(1)

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}P{N}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(2)

where α₀ represents the estimated parameters for the independent and control variables; TP_it denotes the innovation performance of firm i in year t; PD_it refers to the policy intensity in the location of firm i in year t; PN_it stands for the policy quantity in the location of firm i in year t; Control_it represents the control variables; δ_t accounts for year fixed effects; ε_it is the random error term.

To test Hypothesis 2, firm R&D investment is introduced to examine the mediating effect between regional innovation policy and regional innovation capacity. Specifically, the model for the R&D investment level of firm i in year t is as follows:

$$R{D}_{it}={\alpha }_0+{\alpha }_{1}P{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(3)

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}R{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(4)

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}P{D}_{it}+{\alpha }_{2}R{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(5)

$$R{D}_{it}={\alpha }_0+{\alpha }_{1}P{N}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(6)

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}R{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(7)

To test Hypothesis 3, the regional innovation environment is introduced to examine the moderating effect between regional innovation policy and regional innovation capacity. Specifically, the model incorporates the R&D investment level of firm i in year t as follows:

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}P{N}_{it}+{\alpha }_{2}I{E}_{it}\times R{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(8)

$$T{P}_{it}={\alpha }_0+{\alpha }_{1}P{D}_{it}+{\alpha }_{2}I{E}_{it}\times R{D}_{it}+{\displaystyle \sum Contro{l}_{it}}+{\delta }_t+{\epsilon }_{it}$$

(9)

4. Results and Discussion

4.1. Descriptive Statistic

Table 4. Descriptive statistics.

Variable	Mean	Median	Sd	Min	Max
TP	12.68	1	76.52	0	1628
PN	55.35	55	22.21	27	106
PD	131.3	133	48.27	55	229
RD	5.400	4.040	9.844	0	307.7
IE	28.67	28.51	2.122	24.91	32.83
SIZE	22.23	22.14	1.429	17.65	29.30
STATE	0.346	0	0.476	0	1
ROA	4.250	3.607	15.80	−356.5	100
DLR	29.35	44.31	23.11	2.398	379.02

presents the descriptive statistics for each variable. The standard deviations indicate significant differences in regional innovation capacity and policy intensity across different regions. The variance in educational attainment levels across regions is also notable, reflecting substantial disparities. However, the regional innovation environments are generally maintained at a high level across most areas.

These findings suggest that while some regions benefit from stronger innovation policies and higher educational resources, the overall innovation environment remains robust, contributing positively to regional innovation performance.

Overall, these variables reveal the extent of variation in company and production data across different samples, with TP and RD exhibiting the greatest fluctuations, while IE and SIZE remain relatively stable. The extreme values in ROA and DLR may also affect the overall trend analysis.

4.2. Benchmark Regression Analysis

Table 5. Benchmark regression results and the mediation effect results.

	(1)	(2)	(3)	(4)	(5)
PN	0.003 ***				0.016 ***
	(0.001)				(0.005)
PD		0.002 ***		0.005 **
		(0.001)		(0.002)
RD			3.142 ***
			(0.708)
SIZE	0.043	0.055	9.127 ***	−0.417 ***	−0.427 ***
	(0.055)	(0.056)	(2.152)	(0.084)	(0.084)
STATE	5.604 ***	5.685 ***	13.770 ***	−0.304	−0.283
	(0.529)	(0.544)	(4.744)	(0.191)	(0.190)
AGE	0.062 ***	0.063 ***	−11.976	−1.077 **	−1.104 **
	(0.006)	(0.006)	(10.810)	(0.434)	(0.433)
ROA	−0.002	−0.002	1.129 **	−0.089 ***	−0.089 ***
	(0.001)	(0.001)	(0.505)	(0.020)	(0.020)
DLR	−0.004 ***	−0.004 ***	0.285 *	−0.054 ***	−0.055 ***
	(0.001)	(0.001)	(0.152)	(0.006)	(0.006)
N	1373	1372	1380	1380	1381

Robust statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

demonstrates the effects of regional innovation policy intensity and policy quantity on regional innovation capacity. The results in columns (1) and (2) indicate that both policy intensity and policy quantity have a significant positive effect on innovation capacity across the three provinces of Central China. This suggests that the number of policies has a more pronounced impact on innovation capacity, and both factors are significant, confirming the validity of Hypothesis 1.

Additionally, the regression results reveal if a company is state-owned and if its size significantly influences its innovation capacity, underscoring the critical role of ownership structure and firm size in innovation activities.

Through stepwise regression analysis to examine the mediating effect, the results in column (3) of Table 5 reveal that even after accounting for firms’ R&D investment, the R&D efforts of enterprises in Central China continue to have a significant positive impact on regional innovation capacity. This suggests that R&D investment by local firms contributes to enhancing regional innovation capabilities. After controlling for the mediating variable, firm R&D investment, as shown in columns (4) and (5) of Table 5, the impact of both policy intensity and the number of innovation policies on regional innovation capacity slightly declines but remains significant. Simultaneously, the effect of R&D investment as a mediating variable on regional innovation capacity is also significant, indicating that R&D investment plays a mediating role between regional innovation policies and regional innovation capacity.

Simultaneously, in the context of the current wave of regional competition, the competition within high-tech industries is increasingly becoming a central focus. In the process of advancing technological innovation and industrial development, local governments are actively vying to attract and concentrate high-tech resources and high-caliber talent within these industries. Referring to the “High-Tech Industry (Manufacturing) Classification (2017)” [40] issued by the National Bureau of Statistics,

Table 6. Results of the heterogeneity test.

	(1)	(2)	(3)	(4)
PN		0.004 **		0.002 **
		(0.002)		(0.002)
PD	0.002 ***		0.001 **
	(0.001)		(0.001)
SIZE	−0.032	−0.039	0.071 **	0.071 **
	(0.063)	(0.059)	(0.030)	(0.030)
STATE	−0.217 ***	−0.176 ***	−0.275 ***	−0.276 ***
	(0.065)	(0.059)	(0.087)	(0.087)
ROA	−0.003	−0.003	0.003	0.003
	(0.002)	(0.002)	(0.003)	(0.003)
DLR	−0.004	−0.004	−0.006 ***	−0.006 ***
	(0.003)	(0.003)	(0.002)	(0.002)
N	870	870	503	503

Robust statistics in parentheses, *** p < 0.01, ** p < 0.05.

presents the results of the heterogeneity analysis on whether a firm belongs to the high-tech sector. High-tech enterprises are selected because they are typically at the forefront of innovation and technology adoption, making them ideal for studying how advanced management practices and innovative approaches impact organizational performance and competitive advantage. Their dynamic nature and rapid adaptation to new technologies provide valuable insights into the effectiveness of strategic management practices in driving sustainable growth and industry leadership. The results indicate, as shown in columns (1) and (2) of Table 6, that firms categorized as high-tech demonstrate a stronger positive relationship between policy intensity and policy quantity on regional innovation capability compared with non-high-tech firms in columns (3) and (4). This finding suggests that the technological level of firms can further enhance their ability to benefit from policy incentives.

4.3. Moderating Effect Analysis

The moderating role of the regional innovation environment.

Table 7. Results of the moderating effect of the regional innovation environment.

	(1)	(2)	(3)	(4)	(5)
PN	−0.419			0.163
	(1.524)			(0.175)
PD		−0.270			0.063
		(0.343)			(0.043)
RD			6.992 **
			(3.252)
IE	2.044	0.867	3.760 ***	0.577	0.622 **
	(3.143)	(2.294)	(1.390)	(0.366)	(0.281)
PN × IE	0.016			−0.005
	(0.055)			(0.006)
PD × IE		0.007			−0.002
		(0.011)			(0.001)
RD × IE			−0.209 **
			(0.102)
SIZE	7.433 ***	12.975 ***	8.318 ***	−1.102 ***	−1.152 ***
	(2.174)	(4.963)	(2.241)	(0.268)	(0.271)
STATE	12.984 ***	−18.016	13.758 ***	−0.798	−0.759
	(4.961)	(11.225)	(5.035)	(0.601)	(0.602)
AGE	−18.016	0.945 *	−17.403	−1.038	−1.019
	(11.230)	(0.510)	(11.397)	(1.367)	(1.367)
ROA	0.956 *	0.099	1.136 **	−0.269 ***	−0.260 ***
	(0.510)	(0.151)	(0.529)	(0.063)	(0.064)
DLR	0.109	0.099	0.176	−0.049 ***	−0.048 **
	(0.150)	(0.151)	(0.156)	(0.019)	(0.019)
N	1373	1372	1380	1380	1381

Robust statistics in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of the moderating effect tests of the regional innovation environment. The results in columns (1) and (2) show that the regression coefficients of policy intensity (PD), policy quantity (PN), and the interaction terms PD × IE and PN × IE are all insignificant, indicating that the regional innovation environment does not moderate the effect of regional innovation policies on firms’ R&D investment. Similarly, the results in columns (4) and (5) reveal that the regional innovation environment does not moderate the impact of regional innovation policies on firms’ R&D investment. However, as shown in column (3), the regression coefficients of firms’ R&D investment (RD) and the interaction term RD × IE are both significant. This suggests that the regional innovation environment moderates the effect of firms’ R&D investment on regional innovation capacity in the context of regional innovation policies.

4.4. Robustness Test

To ensure the robustness of the conclusions, this study replaces the explained variable by using the number of patent applications as a proxy for regional innovation capacity and re-runs the regression analysis. Due to space limitations, only the baseline regression results are presented, as shown in

Table 8. Results of the robustness test.

	(1)	(2)
PN	−0.171 *
	(0.093)
PD		−0.079 *
		(0.047)
SIZE	8.598 ***	8.556 ***
	(2.026)	(2.031)
STATE	13.770 ***	12.914 ***
	(4.744)	(4.702)
AGE	−11.399	−11.047
	(10.602)	(10.603)
	(1)	(2)
ROA	0.777	0.789
	(0.487)	(0.489)
DLR	0.171	0.151
	(0.143)	(0.144)
N	1372	1371

Robust statistics in parentheses, *** p < 0.01, * p < 0.1.

. The regression results in columns (1) and (2) of Table 8 indicate that, even after replacing the variable measuring regional innovation, the results remain significantly positive, confirming the robustness of the findings.

5. Discussions and Implications

Based on innovation policies issued by the governments of three central provinces in China from 2018 to 2022, combined with patent applications and the financial data of A-share listed companies, this study investigates the impact and mechanisms of regional innovation policies on corporate innovation performance. This study particularly examines the moderating roles of regional innovation environments and government subsidies, providing empirical evidence for evaluating the effectiveness of regional innovation policies. The results indicate that both the intensity and quantity of regional innovation policies significantly promote firms’ R&D investment and enhance regional innovation capabilities. However, the impact of regional innovation policies on firms’ R&D investment is relatively small, suggesting that the improvement of regional innovation capabilities depends more on internal drivers within firms, while the government’s primary role lies in regional economic development and improving the institutional environment. Firms’ R&D investment plays a significant mediating role in the relationship between regional innovation policies and regional innovation capabilities, demonstrating that such policies can increase R&D investment, thereby enhancing innovation performance. While the regional innovation environment does not significantly moderate the effect of regional innovation policies on innovation capabilities, it does influence the relationship between policies and firms’ R&D investment, reinforcing the impact of R&D on regional innovation capabilities.

Based on these findings, several policy implications can be drawn:

• Targeted innovation policy design: Local governments should tailor innovation policies to the specific industries and operational scopes of firms. Different industries face unique technological challenges and market demands; therefore, governments should differentiate between high-tech industries, traditional manufacturing, and services when formulating support measures. For example, high-tech enterprises can receive more R&D funding and tax incentives, while traditional manufacturing may benefit from technological transformation and industrial upgrades. Additionally, policy formulation should consider the region’s economic development level and innovation base. Developed regions may focus on supporting advanced innovation and industrial upgrading, while less developed areas should prioritize infrastructure development and talent cultivation. Such precise policy adjustments can better meet the needs of firms and promote balanced regional economic development, enhancing overall innovation capacity.
• Optimizing the innovation environment: Local governments should continue to improve the innovation environment by enhancing related reward systems and subsidy policies to increase the efficiency of innovation resource utilization and reduce obstacles faced by firms during innovation processes. First, establishing robust innovation reward mechanisms that recognize and incentivize firms excelling in technological development and product innovation can encourage broader participation in innovation activities. Second, providing multiple forms of innovation subsidies, such as R&D expense subsidies and innovation project funding, can help firms reduce innovation costs. Third, simplifying the subsidy application and review process can enhance efficiency, ensuring timely disbursement of funds. Lastly, stronger supervision of subsidy usage ensures that funds are allocated effectively. These measures can alleviate financial pressures on firms during innovation, encouraging greater R&D investment and improving innovation output efficiency. Additionally, fostering a transparent and accountable innovation ecosystem will help build trust between the government and businesses, promoting stronger collaboration between public institutions and private enterprises, which is crucial for sustaining long-term innovation-driven growth and ensuring regional competitiveness in the global economy.
• Promoting industry–academia collaboration: Local governments should actively promote collaborations between industry, academia, and research institutions, establishing effective cooperation mechanisms to enhance firms’ independent innovation capabilities and improve the responsiveness to regional innovation policies. First, governments can facilitate platforms for such collaborations, enabling resource sharing across sectors. By setting up innovation incubators and technology transfer centers, firms, universities, and research institutes can better align technological development and project partnerships. Second, encouraging universities and research institutes to increase technical support for firms through joint R&D can bridge the gap between theoretical research and practical application. Moreover, governments can establish incentive mechanisms that reward universities and research institutes contributing to industrial advancement, fostering deeper collaboration. By aligning academic research objectives with industrial needs, governments can nurture a more dynamic and productive innovation ecosystem, which will not only enhance the innovation capacity of individual firms but also improve the region’s overall competitiveness.
• Multifaceted approach addressing policy objectives: To continuously refine the design of our policy framework and enhance the impact of innovation policies on corporate R&D, a multifaceted approach addressing policy objectives, implementation methods, and evaluation mechanisms is essential [41,42,43,44]. First, in terms of policy orientation, it is advisable to place greater emphasis on the quality of innovation rather than mere quantity. For example, reducing or eliminating direct subsidies based on the number of patent applications and approvals could help curb “non-market-motivated patents” and the focus on patent volume over substance, thereby guiding firms to continuously enhance the quality of their innovations. Secondly, for demand-side policies such as government procurement, the economic regulatory function of government procurement in promoting technological innovation should be strengthened by refining product catalogs and establishing relevant standards and procedures. Finally, it is crucial to improve coordination among policy objectives, formation processes, policy structures, and stakeholder interests. The introduction of third-party evaluation agencies and the establishment of a comprehensive system for assessing and dynamically adjusting innovation policy effectiveness would further ensure policy responsiveness and efficacy.

6. Limitations

First, it is essential to deepen our understanding of the complexity in the co-evolution of innovation and policy. In response to the challenges posed by global technological competition and innovation issues, the concept of innovation policy has continuously expanded and generalized throughout the intricate evolutionary process. The policy system has become somewhat all-encompassing, a “bit of everything” [45]. Against the backdrop of deepening globalization, the layering of governance failures has further underscored the inherent complexity of technological innovation [46]. Compared with other public policies, innovation policy transformation needs to pay closer attention to the evolution of innovation models, particularly those centered around innovation ecosystems. While this study is quantitative, it remains theory-driven and lacks more micro-level explanations, particularly concerning the dynamics of innovation and the logic of policy change. Therefore, policy explanations in the field of technological innovation should place greater emphasis on innovation actors and innovation activities [43]. More extensive use of case studies is needed to gain deeper insights into the evolution of new concepts and paradigms, such as transformative innovation policy. Additionally, it is crucial to address the unintended consequences of technological innovation on areas like energy, the environment, urban development, and education, highlighting the need for a stronger social dimension in policy analysis.

Secondly, this study examines the relationship between local government debt and regional innovation efficiency from a linear perspective. Existing research predominantly assumes a linear correlation between local government debt and innovation, yet this may not accurately reflect the dual effects of local government debt on regional innovation observed in practice [47]. Given the complex nature of public finance and innovation, adopting a nonlinear hypothesis allows for a more sophisticated interpretation of the dual effects. Research on nonlinear relationships suggests that local government debt may have a U-shaped or inverted U-shaped effect on innovation efficiency. For instance, scholars utilized a panel threshold model to show that local government debt has a positive impact on innovation up to a certain threshold, after which further debt accumulation becomes detrimental. Therefore, adopting a nonlinear hypothesis may offer a more nuanced understanding of the connection between these two factors.

Thirdly, the model captures only within-individual variation, overlooking differences between individuals, which may render it insufficient for studies involving inter-individual relationships. Additionally, the interpretive scope of the model’s results is limited to individuals within the sample, making it challenging to generalize findings to the broader population or other individuals outside the sample.