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Regional innovation capability plays a crucial role to the regional economy. In order to raise regional innovation capability, reinforce the enterprises’ innovation strength, and drive the fast economic development, the evaluation system needs to be established to analyze the factors influencing regional innovation capability. By studying the regio...
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... Regarding the evaluation index system construction, scholars generally base it on the constituent elements of regional synergy innovation capability, namely, innovative resource, innovative output, and innovative milieu [16][17][18][19]. They actively explore diversified research perspectives such as the university-industry perspective [20][21][22], input-output perspective [23], green low-carbon perspective [24], and technology-oriented perspective [25]. ...
... Internal expenditure of R&D funds (10 4 CNY) A11 [18,41] Full-time equivalent of R&D personnel (person-year) A12 [18,41] R&D personnel input (persons) A13 [18,24] Economic spillover GDP (10 9 CNY) A21 [41] New product sales revenue of industrial enterprises above designated size ( ...
... Internal expenditure of R&D funds (10 4 CNY) A11 [18,41] Full-time equivalent of R&D personnel (person-year) A12 [18,41] R&D personnel input (persons) A13 [18,24] Economic spillover GDP (10 9 CNY) A21 [41] New product sales revenue of industrial enterprises above designated size ( ...
Regional synergy innovation capability is an important driving force in promoting the sustainable and high-quality development of the regional economy. Taking the regional innovation development panel data of the Yangtze River Delta integration region from 2010 to 2019 as a sample, this study constructs an evaluation index system of regional synergy innovation capability, weights the index using the entropy weight method, and measures the capability of the Yangtze River Delta integration region (three provinces and one city) using the composite system synergy degree model. The empirical results show that the synergy of regional synergy innovation in the Yangtze River Delta integration has increased steadily, but there is still much room for improvement. Anhui has great potential for synergy innovation with Jiangsu, Zhejiang, and Shanghai. Therefore, this study proposes countermeasures and suggestions for the high-quality development of Anhui’s synergy innovation capability under the integration of the Yangtze River Delta. This study provides theoretical and methodological support for enhancing regional synergy innovation capability and provides decision support for the sustainable and high-quality development of the regional economy.
... Thus, this indicator is included in the innovation indicators system in this paper. In addition, to reduce the possible endogeneity in the estimations, the indicator representing the regional government expenditure on education is converted to the proportion of educational expenditure to fiscal expenditure, and the indicator representing the intensity of Internet and telecom infrastructure is calculated as the share of communication devices and Internet users in the population [69][70][71]. Besides the regional educational support and communication infrastructure, the intensity of services for local innovators is also of great importance in producing innovative outputs in a region. ...
As the continuous improvement of the quality of innovation becomes increasingly significant for balanced regional development in China, it is critical to provide insights into the sustainability of regional innovation in China from the viewpoint of value. This study estimates regional innovation values based on an improved regional innovation value model incorporating patent values and a regional innovation indicator system. Data for invention patents as well as regional innovation indicators in 282 cities from 1987 to 2019 in China are utilized for estimation. Based on the estimated parameters and Monte Carlo simulation, city-level innovation values are calculated as benchmarks, along with provincial and regional innovation values, to analyze the patterns of the spatial distribution and agglomeration of regional innovation value. The findings are as follows. (1) The regional innovation value model provides an effective way to measure regional innovation in terms of value. (2) The regional innovation values are unevenly distributed; cities with higher innovation values are clustered in Eastern China, while most other cities have much lower innovation values. (3) The innovation values in Eastern China are notably higher, and the differences in innovation values between Eastern China and other regions are large and show a trend of first widening and then narrowing during the sample period. (4) The sustainability of regional innovation is not widely achieved, since highly concentrated innovation value is found in only a few regions in the eastern coastal areas. These findings suggest that promoting China’s innovation capacity and the sustainable development of technological innovation requires continually implementing innovation-driven development strategies, cultivating high-value innovation, optimizing industrial transfer, improving the layout of the national research infrastructure, giving full play to spatial spillover effects, and promoting interregional innovation information exchange in order to achieve the balanced and sustainable development of regional innovation.
... 2) Use equation (2) to calculate the degree matrix , that is, the sum of the elements in each row of , and * diagonal matrix composed of ; ...
... Shan constructed a system including 4 first class index and 24 second class index from the four aspects of innovation environment, innovation input, innovation output and management ability [2] . Li constructed a city innovation evaluation index system including 6 first class index and 23 second class index from three aspects: innovation environment, innovation input and innovation output [3] . ...
... There is no consensus in the academic circles on the concept of regional innovation capability. Shan (2017) defined the concept of regional innovation capability as "within a certain space, the innovation subject's comprehensive ability to allocate regional innovation output resources and strengthen regional economic strength," and divided the constituent elements of regional innovation capability into four aspects: input capability, innovation environment, management capability, and innovation output. Fu et al. (2020) pointed out that regional innovation capability refers to creating new technological ideas by effectively allocating innovation resources and transforming them into new products or services to achieve regional growth. ...
... Although these studies have different perspectives, there is still some consensus on some indicators. Innovation resources, innovation output, and innovation environment (Buesa et al., 2006;Zhao et al., 2015;Shan, 2017) is a common index when constructing the evaluation index system of regional innovation capability. Scholars have adopted patents (Quatraro,2009;Sleuwaegen & Boiardi, 2014;Xu & Zhai, 2020) successively as an important indicator of regional innovation output. ...
... Based on the measurement scale introduced by relevant scholars (Shan, 2017;Su et al., 2020;, the data collection indicators in this paper focus on the measurement of regional innovation capability to evaluate the input and output of regional innovation processes, mainly because these are relatively easy to quantify and measure (Asheim and Isaksen, 2002). Further, some databases such as the China Statistical Yearbook, Anhui statistical yearbook, and China Science and Technology statistical yearbook can retrieve the required data efficiently and at a low cost. ...
Regional innovation capability is considered an important driving force for the sustainable and high-quality development of regional economy. Therefore, data-driven evaluation method of regional innovation capability is proposed. First, the article collects regional innovation development statistics; second, min-max standardization method is used for dimensionless data processing, and anti-entropy method is used to calculate index weight; third, a composite system synergy degree model for objective and quantitative evaluation is built; finally, using Anhui Province as an example, the feasibility of the method was verified . The results indicate that the order and synergy degree of the innovation input and innovation output subsystems of Anhui Province have shown an upward trend from 2010 to 2019. However, innovation output has not been synchronized with innovation input. Therefore, suggestions for improving Anhui's regional innovation capability are proposed. This study provides theoretical and methodological support for evaluating and optimizing regional innovation capability.
... After the concept of RISs was put forward , a range of approaches have been used to analyze the RISs, including Data Envelopment Analysis (DEA) (Avilés-Sacoto et al., 2020), triple helix (Etzkowitz & Leydesdorff, 2000;Jiao et al., 2016;Lin et al., 2021;Han and Qin, 2022), quadruple helix, interview and so on. They have mainly studied the influencing factors (Li & Phillips, 2015;Kreiling et al., 2019), the internal structure (Zhao et al., 2015;Mattes et al., 2015;Kwon & Motohashi, 2017), internal efficiency (Dzemydaitė er al., 2016;Teng & Chen, 2019;Avilés-Sacoto et al., 2020), innovation capability (Park et al., 2021;Shan, 2017) and analyzing framework (Chen & Guan, 2011) of the RISs. ...
Regional innovation systems (RISs) characterized periodical and is suitable for analyzing the Chinese economy which has entered the new normal. However, few scholars analyze and identify the stage of RISs from the perspective of life cycle. This paper divides the life cycle of RISs into four stages: initial stage, growth stage, maturity stage, and decline stage. Based on the analysis of the characteristics in every stage of the life cycle in the RISs, this paper constructs entropy weight disturbing attribute model to identify the stage of 31 provincial innovation systems in China. The research results show that all the RISs in maturity stage belong to the eastern developed regions, and innovation achievement capacity is significant. The RISs are widely distributed which are in the growth stage. The innovation efficiency capacity, innovation growth capacity and innovation network capacity of RISs which are in growth stage are in a high level. The provinces with the innovation system in the initial stage are mainly in the backward areas of the west, and the innovation growth capacity is better than other innovation capacities. Mastering the evolutionary stage of the RISs is helpful to promote the implementation of innovation-driven development strategies.
... Chen et al. measured and ranked the urban innovation capacity of Liaoning Province from three dimensions of innovation input, innovation output, and innovation environment [26]. According to the theory of regional innovation, Shan used the analytic hierarchy process (AHP) to construct the evaluation system [27]. Pei et al. proposed a model to evaluate urban innovation capability using machine learning [28]. ...
The digital economy has aroused widespread concern. This paper studies the impact of the digital economy on innovation using a panel threshold model. Taking 30 provinces, municipalities, and autonomous regions in China as the research object, the time span is from 2013 to 2019. The data are from the National Bureau of Statistics of China (NBSC), China National Intellectual Property Administration (CAIPA), the China Stock Market and Accounting Research (CSMAR), and the Ministry of Industry and Information Technology (MIIT)of China. Data analysis is performed with ArcGIS 10.2 and STATA 16 software. The influence mechanism of digital economy on innovation is innovatively analyzed from the aspects of innovation elements, innovation tools, innovation subjects, and innovation environment. A digital economy development level index system is constructed using the entropy method, and the development level of China’s digital economy in time and space is analyzed. On this basis, the nonlinear impact of digital economy on innovation, i.e., the threshold effect, is innovatively studied using the panel threshold model. It is found that China’s digital economy develops rapidly, but there is a serious spatial imbalance, and there are great differences in the different dimensions of the digital economy. At the same time, the impact of digital economy on innovation has a double threshold effect with industrial structure as the threshold variable and a single threshold effect with urbanization level as the threshold variable. Specifically, the promoting effect of digital economy on innovation increases with the optimization of industrial structure or the improvement of urbanization level. This study enriches the theoretical research on the impact of digital economy on innovation, and it has important support and reference value for China’s development of digital economy and improvement of innovation capacity.
... The innovation environment refers to various software and hardware environments that affect the innovation of the innovation subject in the innovation process, such as a good policy environment, social environment, and cultural environment [91]. Compared with the existing research [92,93], this study increased the number of legal entities in cultural and related industries above the designated size (A11) [94] and the number of public library institutions (A12) [95] in the innovation environment criteria layer. Innovation input refers to the resources consumed for scientific research and technological innovation, mainly embodied in human and capital input [96]. ...
Coordinating regional innovation–economy–ecology (IEE) systems is an important prerequisite for overall continuous regional development. To fully understand the coordination relationship among the three, this study builds a data-driven multimodel decision approach to calculate, assess, diagnose, and improve the regional IEE system. First, the assessment indicator system of the regional IEE system is established. Secondly, the range method, entropy weight method, and weighted summation method are employed to calculate the synthetic developmental level. Thirdly, a multimodel decision approach including the coupling degree model, the coordination degree model, and the obstacle degree model is constructed to assess the spatiotemporal evolution characteristics of the regional IEE system coupling coordination and diagnose the main obstacles hindering its development. Finally, the approach is tested using Anhui Province as a case study. The results show that the coupling coordination degree of the Anhui IEE system presents a stable growth trend, but the coupling degree is always higher than the coordination degree. The main obstacle affecting its development has changed from the original innovation subsystem to the current ecology subsystem. Based on this, some countermeasures are put forward. This study, therefore, offers decision support methods to aid in evaluating and improving the regional IEE system.
... Compared with extant literature [41,42,66], this research has some advantages. First, a four-dimensional evaluation system of coupling coordination development of regional innovation EROB composite systems was constructed. ...
To promote coupling coordination development for regional innovation environment-resource-output-benefit (EROB) composite systems, we propose a data-driven integrated model method for measurement, evaluation, and identification. First, we construct an evaluation indicator system of coupling coordination development of regional innovation EROB composite systems. Second, we apply the entropy method to measure indicator weights and comprehensive development indices of regional innovation composite systems. The coupling coordination degree model is used to calculate and evaluate four subsystems’ coupling coordination development levels. The obstacle degree model is used to identify the main obstacle factors affecting coupling coordination development. Finally, using panel data of the Yangtze River Delta region (three provinces and one city) between 2014–2019 as a case study, we test the integrated model method. The results show that the comprehensive development level of the regional innovation EROB composite system in the Yangtze River Delta region maintained a stable growth trend; the coupling coordination development level among four subsystems continuously improved, with the main obstacle being the innovation resource subsystem. Accordingly, targeted policy suggestions are put forward. This study not only provides theoretical and methodological support for evaluating and optimizing regional innovation composite systems but also provides decision-making support for sustainable and high-quality development of regional economies.
... This improved gravity model is then Total profits of industrial enterprises above the designated size 0.042 Which are the economic benefits of innovation [47,48]. Per capita regional GDP 0.038 Which shows a city's economic growth and indirectly indicates the potential for improving innovation capability [42,49]. ...
Understanding the evolutionary characteristics of innovation network structure can improve urban innovation and regional construction. Urban innovative development is affected by various factors, which can be analyzed via models of innovation networks. We establish a multi-criteria evaluation system of innovation capability and use an improved gravity model to construct an innovation network for 2015–2018, employing social network methods to analyze structural characteristics and spatial patterns. Results show that: (1) The innovation of cities in the urban agglomeration in the middle reaches of the Yangtze River has gradually increased, with an accompanying increase in the complexity of innovation networks. The cities of Wuhan, Changsha, and Nanchang are located at the absolute core of this network, which exhibits a Matthew effect, and has a triangle integration mode of growth. (2) The attraction of innovative resources and the promotion of individual innovation are increasing every year within the cities. The aggregation pattern of innovation shows a multi-core state in the urban agglomeration in the middle reaches of the Yangtze River, but the innovation radiation pattern has changed from a single center to a double center. (3) Multiple spatial innovation axes are seen in the network, with a location and direction consistent with the urban agglomeration’s development axis in the Yangtze River’s middle reaches and a triangle integration growth mode. Policy implications are proposed for regional innovation and development, and our results can provide future policy guidance and direction for governmental entities and other stakeholders.
... At present, the comparison of innovation capability between different regions through an index framework involves more indexes and more scientific methods. For example, Shan (2017) compares the innovation capability of Hangzhou and Ningbo in terms of input, output, and environment of innovation through an analytic hierarchy process. However, the innovation system framework is considered neither a theoretical guide nor a rational choice based on empirical reasons, but merely a response to the need to provide rapid measurements of very complex phenomena (Cirillo et al., 2019). ...
The city is both a carrier and a subject of innovation. Based on the triple helix theory of industry-university research and the theory of spatial correlation, this study constructs a collaborative innovation framework both within the cities and between cities, and uses a network data envelopment analysis (DEA) model and spatial econometric model to measure and analyze the collaborative innovation efficiency in 75 innovative cities in China. The results show that col-laborative innovation efficiency within cities is on the rise, and the efficiency of "research to production" is significantly higher than that of "learning to research." Industrial structure and foreign factors have inhibited the efficiency improvements, and infrastructure and living standards have different promoting effects on different stages of efficiency. Between cities, capital flows have obvious spillover effects, which promote the efficiency of innovation networks, while the long-term characteristics of institutional learning have a near-term negative impact.
