Innovation Efficiency Of China Research Articles

The Impact of Global Value Chain Position on Emerging Industrial Cluster Innovation Efficiency in China

The Impact of Global Value Chain Position on Emerging Industrial Cluster Innovation Efficiency in China

The impact of digital industrialization and industrial digitalization on regional green innovation efficiency in China -From the perspective of the innovation value chain

To systematically detect the influence of digital economy on regional green innovation, this study regards regional green innovation as a two-stage sequential process, and constructs a network SBM model to evaluate the overall and stage green innovation efficiency of 30 provinces in China from 2011 to 2020. Digital economy development is decomposed into digital industrialization and industrial digitalization, and an empirical study based on fixed effects regression model of panel data is conducted to determine their influence on the overall and stage green innovation efficiency. The results suggest the following: (1) The overall efficiency in China remained at a low level, with the efficiency of research and development being slightly higher than that of achievement transformation. (2) From the national perspective, there is no discernible effect of digital industrialization on overall efficiency, but there is a negative impact on research and development and a significant positive impact on achievement transformation. Industrial digitalization significantly improves achievement transformation and overall efficiency but has no discernible effect on the research and development stage. (3) In the eastern region, industrial digitalization promotes the improvement of both stages and overall efficiency, while digital industrialization does not have a significant impact. In the central region, digital industrialization and industrial digitalization have significant positive impacts on both overall efficiency and the efficiency of achievement transformation but have no impact on research and development. In the western region, digital industrialization inhibits the overall and achievement transformation efficiency, and industrial digitalization does not have significant impact on the overall efficiency or the efficiency of each stage. Finally, some relevant policy recommendations are proposed.

New measurement and spatio-temporal heterogeneity of regional green innovation efficiency in China

New measurement and spatio-temporal heterogeneity of regional green innovation efficiency in China

Evaluation and convergence analysis of science and technology innovation efficiency in China’s national central cities based on the super-efficiency SBM-ML model

ABSTRACT Scientific and Technological Innovation (STI) is an important driving force for economic development and social progress. National central cities are the main connection points of China’s economic activity network and play a leading role in the national economic development of the country. In order to explore the efficiency of STI and the convergence characteristics of STI in China’s national central cities, this paper selects S&T innovation input-output indicators and constructs a system of indicators for high-quality economic development. It measures the STI innovation efficiency of China’s national central cities by combining with the super-efficiency SBM-ML model and then constructs a model to analyze its convergence situation. The results of the study show that: from 2011 to 2021, the STI efficiency of China’s national central cities has reached an effective level, and the overall trend has increased; the total factor productivity of STI in China’s national central cities has obvious differences, with Beijing having the highest total factor productivity, while Shanghai has the lowest; the STI innovation efficiency of China’s national central cities exhibits both absolute β-convergence and conditional β-convergence, but there is no significant α convergence, and regional differences are obvious.

How producer services agglomeration affects urban green innovation efficiency in China: a spatial correlation and nonlinear perspective

How producer services agglomeration affects urban green innovation efficiency in China: a spatial correlation and nonlinear perspective

Dynamic evolution of green innovation efficiency in China: spatial heterogeneity and convergence analysis

ABSTRACT Green innovation plays a pivotal role in fostering sustainable and robust economic growth. Hence, it is of great practical significance to accurately assess green innovation efficiency and analyze its evolving trends and discrepancies from a spatiotemporal dynamic perspective. This study analyzes the green innovation efficiency across 30 provinces in China from 2001 to 2019 using the Super-SBM model, Dagum Gini coefficient, Kernel density estimation, and convergence models. The results reveal a consistent enhancement in China's green innovation efficiency, yet substantial room for further improvement persists, standing at 0.679 in 2019. Both China as a whole and its four major regions exhibit a polarisation trend in green innovation efficiency, characterised by notable internal disparities, albeit displaying a declining pattern. Interregional divergence is the primary source of the overall disparity, followed by intraregional disparities and the intensity of transvariation. China's green innovation efficiency demonstrates sigma and beta convergence trends. The study also highlights how uneven regional development and resource allocation disparities lead to an imbalanced convergence of regional green innovation efficiency.

The Evaluation, Dynamic Evolutionary Characteristics and Influencing Factors of Green Innovation Efficiency in China

The Evaluation, Dynamic Evolutionary Characteristics and Influencing Factors of Green Innovation Efficiency in China

Examining the impact of urban management on regional innovation efficiency in China’s manufacturing industry with a focus on modern sustainable energy: utilizing a non-radial distance function method

This study examines the impact of urban management on the efficiency of regional innovation in China’s manufacturing industry, with a specific focus on modern sustainable energy. The research takes into consideration the regional diversity in innovation development across China’s three major regions and integrates common Frontier theory with the MSBM model. By employing a non-radial distance function approach, this study develops the MMSBMP model, incorporating various improved methods proposed by researchers. The Luenberger index methodology is utilized to assess the innovation efficiency of the national manufacturing industry from 2017 to 2021, enabling the identification of efficiency losses. The findings highlight significant disparities and opportunities for enhancing innovation efficiency across the three major regions, both at the common Frontier and the group Frontier. However, caution is advised due to the potential overestimation of regional Frontier efficiency values resulting from variations in technical reference datasets. Analysis of the Total Growth Rate (TGR) values reveals distinct development characteristics among the regions, with the eastern region exhibiting smaller extremes and the central and western regions displaying larger extremes. This comprehensive examination of China’s manufacturing industry emphasizes the influence of urban management. By investigating the consequences of urban management practices, this research provides insights into the relationship between urban development strategies and the performance, efficiency, and sustainable growth of the manufacturing industry. The study highlights the significance of urban management in shaping regional innovation efficiency and emphasizes the continual improvement of the industry’s innovation performance in the context of modern sustainable energy.

The effect of producer service agglomeration externalities on urban green innovation efficiency in China

For China's urban industrial layout and green innovation, determining which types of producer service agglomeration externalities (PSAE) are most conducive to the rise of green innovation efficiency (GIE) has theoretical and practical relevance. Based on the panel data of 274 prefecture-level cities in China from 2006 to 2019, this paper adopted the super-SBM model with unexpected output to evaluate the urban GIE. It used the Moran I index to judge the spatial auto-correlation of GIE, followed by the spatial Durbin model (SDM) to examine the spatial spillover effect of PSAE on the urban GIE. Then we used a comprehensive threshold effects model to investigate the non-linear relationship. The results demonstrate that Marshall-Arrow-Romer externality (MARE) and Jacobs externality (JACE) are curbing the urban GIE. Porter externality (PORTERE) inhibits the region and promotes the surrounding area. Furthermore, these effects also depend on the stage of economic development.

Impact of Heterogeneous Environmental Regulations on Green Innovation Efficiency in China’s Industry

Innovation is the primary driving force for development, and green innovation efficiency (GIE) plays a key role in regional sustainable development. Moreover, environmental regulations (ERs) are also crucial for innovation and green transformation. Considering the heterogeneity of ERs, we assess the dynamic GIE in the industrial sectors of China. We detect their spatial clustering characteristics, and distinguish the impacts of ERs. Results suggest that there exist significant differences in GIE. Provinces such as Hainan, Guangdong and Zhejiang are ranked high, while Gansu, Inner Mongolia and Ningxia are ranked at the bottom, which shows some spatial dependence. The relationship between the administrative regulation and GIE demonstrates a U-shape, and has not reached a critical point, whereas the relationship between the market-based regulation and GIE possesses an inverted U-shape, which is highly significant. Furthermore, a positive linear relationship exists between the lagged public participation regulation and GIE. This paper also proposes that the economic development level and industrial structure are vital factors in accelerating industrial GIE. These conclusions provide scientific support for formulating regional transformation strategies.

Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Green innovation is an important concept of high-quality development to achieve resource conservation and ecological protection. To explore whether there is an imbalance in the development of green innovation in China and find the reasons for this phenomenon, it is of vital importance to investigate the regional differences of green innovation efficiency (GIE) and its influencing factors. Many scholars have studied the performance of green innovation from the efficiency perspective but concentrated on provincial-level analysis and lacked heterogeneity analysis of the influencing factors. To fill this gap, this study explores the regional differences of GIE of 285 prefecture-level and above cities in China during the period 2003–2019, and then employs the spatial error model (SEM) to explore the heterogeneity of influencing factors. The results show that: (1) The GIE in China and its three regions underwent an overall increasing process, revealing regional heterogeneity, with most efficient cities agglomerated in the Eastern region. (2) The spatial difference of GIE in China was narrowing, and the within-region Gini coefficient in the three regions presented a similar trend. Between-region difference contributed the largest to the regional differences, especially between the Central and Western regions. The kernel density estimation results showed that GIE presents significant spatial characteristic of polarization. (3) The SEM model analysis indicated that economic development, government motivation, industrial structure, financial support, and population scale affected GIE profoundly in China, and there was significant spatial heterogeneity in the impact of each influencing factor. Western cities were mainly driven by governmental support in green innovation, while Eastern and Central cities were driven by economic development and improved industrial structure.

The contribution of enterprises, universities, and research institutes to regional innovation efficiency in China

ABSTRACT This study estimated the contribution of three innovation entities – enterprises, universities, and research institutes – to regional innovation performance and investigated the impact of several exogenous environmental variables on regional innovation efficiency. Stochastic frontier models were employed, and the sample consisted of panel data from 30 provinces and municipalities in China between 2010 and 2021. The results demonstrated that the three innovation entities contributed to regional innovation performance; however, the elastic coefficients of universities and enterprises were higher than those of research institutes, suggesting that China did not shift from a research institute- to enterprise-centered innovation system. The impact of several exogenous environmental variables, such as regional openness, government support, and information infrastructure, on innovation performance varied across regions.

The impact of green finance and local regulations on industrial green innovation efficiency in China.

When the incentive mechanism of green finance fails to fully promote green technology innovation in industrial enterprises, local government environmental regulations become an important tool in correcting this market failure. However, due to the "follow the cost" hypothesis, the moderating effect of the local government environmental regulation is heterogeneous. In order to explore the impact mechanism of green finance development on the efficiency of green technology innovation in industrial enterprises, spatial effects as well as the heterogeneous moderating effect of local government environmental regulation, this paper systematically evaluates the development level of green finance in 30 provinces in China from 2009 to 2019. It estimates the efficiency of green technology innovation in industrial enterprises using the super-efficiency SBM model, and empirically analyzes the impact mechanism and moderating effect using the spatial Durbin model. The results show that: (1) green finance not only positively impacts the efficiency improvement of green technology innovation in industrial enterprises but also has significant spatial spillover effects; (2) local government environmental regulation has a nonlinear "inverted U-shaped" moderating effect between the green finance development and the efficiency of green technology innovation in industrial enterprises. Based on the research conclusions, this paper proposes policy recommendations from the perspectives of deepening the regional connectivity of green finance and promoting joint regulation by local governments.

How do R&D capital market distortions affect innovation efficiency in China? Some evidence about spatial interaction and spillover effects

Improving innovation efficiency (IE) and refining capital market distortions are important considerations for promoting national innovation strategies. This study uses the dynamic spatial Durbin model and mechanism tests to explore the direct impacts, spatial effects, and influencing mechanisms of R&D capital market distortions on IE based on Chinese provincial panel data from 2009 to 2019. The main results can be summarized as follows. First, China’s IE is characterized by temporal inertia and spatial agglomeration. In both the spatial and temporal dimensions, IE in neighboring areas in the previous period can inhibit local IE in the current period. Second, R&D capital market distortions have a weakening effect on IE in the short- and long-term. Third, R&D capital market distortions have significant spatial interaction and persistent positive spatial spillover effects on IE. Finally, R&D capital mismatch and technology lock-in effects are essential mechanisms related to R&D capital market distortions that can inhibit IE. Based on these results, policy recommendations have been proposed to improve IE.

The influence of the bank–firm relationship on enterprises’ technological innovation efficiency: Evidence from China

Technological innovation efficiency is an important factor in achieving high-quality and sustainable economic development. Using bank–firm and technological innovation efficiency data from 2008 firms from 2011 to 2020, this paper reveals the mechanisms in and heterogeneity of the impact of the bank–firm relationship on technological innovation efficiency in China. The results indicate the following. (1) The technological innovation efficiency of enterprises in China has shown an upward trend, but remains below the optimal level. In addition, the level of technological innovation efficiency of state-owned enterprises is higher than that of non-state-owned enterprises. (2) The bank–firm relationship significantly improves technological innovation efficiency, and a series of endogeneity and robustness tests show that the results are robust. (3) The bank–firm relationship promotes technological innovation efficiency through the channels of financing constraints and information asymmetry. (4) A heterogeneity analysis shows that the positive role of the bank–firm relationship on technological innovation efficiency is more noticeable in state-owned firms, developed financial regions, and non high-tech industries. Finally, valuable policy advice is proposed based on the empirical results.

Research and evaluation of spatiotemporal dynamic of network green innovation efficiency in China—based on meta-Frontier theory

Green innovation has emerged as a crucial driver for advancing green and high-quality development. Exploring the evolutionary patterns of green innovation efficiency is crucial for achieving the “dual carbon” goals and realizing the benefits of both economic growth and environmental sustainability under the framework of new development concepts. This study employs the network SBM-DEA model under meta-Frontier and group-Frontier. Additionally, it considers the GML index and Moran’s I to conduct a comprehensive analysis of the evolving efficiency of green innovation in Chinese provinces from 2008 to 2020, then uses the Tobit regression model to verify the influencing indicators for green innovation efficiency. The examination covers various aspects, including the stage of green innovation, the diversity of technology accumulation, the comparability of pre- and post-development, the spillover effects in geographical space, and the diversity of influencing factors. The research findings indicate the following: 1) The group division exhibit a high level of geographical correlation, and the efficiency of green innovation in the two-stage and network displays heterogeneity across distinct frontiers. The efficiency loss in the Green Achievement Transformation stage is bigger than that in the Green Technology Research and Development stage. 2) There is an overall increase in green innovation efficiency of each type during most years, and the spatial correlation and stability of the two-stage and network green innovation efficiency have improved year by year. Provinces with higher Green Innovation Environment Composite Index have the highest concentration of “high-high” efficiency agglomeration. 3) Environmental regulation intensity, factor endowment, property rights structure, foreign direct investment and energy consumption have varying degrees of constraints on green innovation, and the regional economic development level can significantly improve the efficiency of various green innovations. Finally, this paper provides some suggestions, including stimulating innovation vitality, formulating differentiated policies, strengthening regional innovation collaboration, and mobilizing resources from various stakeholders. These recommendations aim to provide guidance and reference for promoting green innovation and achieving sustainable development in different regions.

What limits the innovation efficiency in China's ICT industry? A two-stage dynamic network slacks-based measure approach

ABSTRACT Based on the innovation value chain theory, the innovation process of the information and communication technology (ICT) firms is not only decomposed into R&D and commercialisation activities, but also its dynamic characteristics should be included in the evaluation system. To seek the underlying reasons for the inefficiency, a two-stage dynamic network DEA model within the SBM structure has been applied to assess the innovation efficiency along with the R&D capability and commercialisation performance. The results show that the innovation efficiency of the Chinese ICT industry shows a continuous decline due to the constraints of firms’ R&D capabilities. Similarly, the heterogeneity exists within the ICT industry; The four sub-industries has its own strengths, but they also exhibit high market efficiency and low R&D efficiency. Private companies show a higher level of R&D and commercialisation than SOEs in both stages.

Evaluation of Green Innovation Efficiency in Chinese Provincial Regions under High-Quality Development and Its Influencing Factors: An Empirical Study Based on Hybrid Data Envelopment Analysis and Multilevel Mixed-Effects Tobit Models

In the context of China’s high-quality economic development, in-depth research on green innovation efficiency and its influencing factors is crucial for promoting economic transformation and energy conservation. This study employs the Hybrid Data Envelopment Analysis (DEA) method to measure the green innovation efficiency of 30 provinces in China from 2013 to 2019. Subsequently, based on the Multilevel Mixed-Effects (MME) Tobit model and a spatial econometric model, the study investigates the factors influencing green innovation efficiency under the backdrop of high-quality development, and conducts various robustness tests from different perspectives. The results indicate the following: Firstly, the overall level of green innovation efficiency in China is relatively low, but it shows a steady growth trend, with significant differences in green innovation efficiency among provinces in different stages of high-quality development. Secondly, the level of digital economic development, optimization of industrial structure, scale of knowledge dissemination, and degree of openness to the outside world have significant positive effects on green innovation efficiency. On the other hand, the scale of technological innovation, degree of environmental regulation, and guarantee of green innovation have significant negative effects, and the low quality of technological innovation hinders the improvement of green innovation efficiency. Thirdly, the new factors emerging under the backdrop of high-quality development exhibit certain spillover effects on green innovation efficiency. The green innovation efficiency of a province may be influenced by relevant factors in neighboring provinces. This provides new insights for provinces to enhance their green innovation efficiency. The contribution of this study lies in the incorporation of newly emerged factors in the context of high-quality development into the evaluation framework of green innovation efficiency. It accurately measures the green innovation efficiency of each province in China and, based on the analysis of influencing factors, provides novel insights for enhancing green innovation efficiency across provinces.

Supply chain finance and innovation efficiency: An empirical analysis based on manufacturing SMEs.

This paper firstly demonstrates the positive and negative effects of supply chain finance on the innovation efficiency of China's small and medium-sized enterprises (SMEs) in the manufacturing industry from the theoretical point of view. Based on the data of 267 manufacturing companies in China Growth Enterprise Market from 2015 to 2019, the DEA-SBM method was used to measure the comprehensive innovation efficiency of different companies, and it was further decomposed into technological innovation efficiency and organizational innovation efficiency. Afterwards, it conducts an empirical analysis through the double fixed effect model, and explores the difference in the impact of supply chain finance on innovation efficiency in enterprises with different industries and different property rights. The results show that supply chain financial services have a strong positive impact on the comprehensive innovation efficiency, technological innovation efficiency and organizational innovation efficiency of manufacturing SMEs. Further, supply chain finance has the most significant improvement on the technological innovation efficiency of the sample of private traditional enterprises, but it has a significant inhibitory effect on the organizational innovation efficiency of the sample of state-owned high-tech enterprises. Therefore, this paper suggests that the development of supply chain financial services should increase support for traditional manufacturing industries; appropriately tilt resources to private enterprises; improve relevant supply chain financial laws and regulations, establish and improve corresponding institutional arrangements, and encourage state-owned enterprises to participate in market competition.

Does the Digital Economy Improve the Innovation Efficiency of the Manufacturing Industry? Evidence in Provincial Data from China

This paper selects panel data on 30 Chinese provinces from 2011 to 2020 and uses an econometric approach to empirically examine the impact of development in the digital economy on the innovation efficiency of China’s manufacturing industry. The results of this study show that during the period under investigation, the innovation efficiency of China’s manufacturing industry is in a stage of continuous improvement, and the development in the digital economy significantly promotes the level of manufacturing innovation efficiency, becoming an important driving force for the improvement in manufacturing innovation efficiency in the digital era. Moreover, the digital economy not only directly promotes manufacturing innovation efficiency but also has an indirect impact on such efficiency with industrial structure upgrading and technology spillover. With upgrading industrial structure and technology spillover, the digital economy has an enhanced effect on the innovation efficiency of the manufacturing industry. Using an analysis with the spatial Durbin model, it is found that the digital economy has a positive impact on the innovation efficiency of the manufacturing industry with spatial spillover characteristics.