Innovation Efficiency Of China Research Articles

Does digital economy development promote innovation efficiency? A spatial econometric approach for Chinese regions

ABSTRACT In the context of the rapid development of the digital economy, it’s an important topic how to play the role of digital technology in improving innovation efficiency. Employing the spatial econometric model with province-level panel data during 2006–2018, the article explores the impact of the development of the digital economy on innovation efficiency in China. The analysis unveils three major findings. First, the innovation efficiency has significant positive spatial externalities and the digital economy has significantly positive direct effects and spatial spillover effects on innovation efficiency, but the above effects are heterogeneous for different regions and innovation subjects. Second, the impact of digital economy development on innovation efficiency has characteristics of a certain degree of lag effect and continuity. Third, the threshold effect analysis reveals the non-linear characteristic of the increasing marginal effect of the digital economy on innovation efficiency. Altogether, the development of the digital economy has become an important driving force for promoting China’s innovation efficiency. The findings of this paper provide empirical evidence for understanding the relationship between the digital economy development and innovation efficiency, giving significant implications for the innovative development of developing countries.

Influences of Environmental Regulations on Industrial Green Technology Innovation Efficiency in China

The Paris Agreement marks global response to climate change after 2020 and China has proposed the dual carbon goals, carbon peaking and carbon neutrality, in response. This paper analyses the contribution to dual carbon goals by analyzing the impact of environmental regulations (ERs) on green technology innovation (GTI) in China. First, considering variances in energy consumption structure across provinces and industries, industrial CO2 emission is calculated and set as an undesirable output of industrial GTI. Then, industrial green technology innovation efficiencies (GTIE) of 29 provinces in China between 2005–2017 are calculated using a non-oriented two-stage network SBM-DEA model assuming variable returns to scale. Last, dynamic evolution and regional differences of industrial GTIE during green technology R&D, green technology commercialization, and overall GTI stages are respectively observed, and the influences from different types of ERs, command-based (CER), market-based (MER), and voluntary (VER), on industrial GTIE are analyzed. We identify China is overall experiencing relatively low but gradually increasing industrial GTIE and Industrial GTIE present gradient changes across provinces with increasingly prominent regional difference. It is found that influences of types of ERs on industrial GTIE present dynamic effect, threshold effect, lag effect and regional differences.

The Green Innovative Power of Carbon Neutrality in China: A Perspective of Innovation Efficiency in China’s High-Tech Industry Based on Meta-Frontier DEA

The high-tech industry plays a crucial role in reducing carbon emission and achieving green economic development. This research uses Meta-Frontier data envelopment analysis to measure the innovation efficiency level of the high-tech industry in China’s provinces from 1999 to 2018, compares the difference in this industry’s innovation efficiency under the regional Frontier and common Frontier, and inspects the convergence condition of its innovation efficiency in the three major areas of eastern, central, and western China. The results show under the regional Frontier that the difference in innovation efficiency of the western region’s high-tech industry is the biggest, while the difference in the central region is the smallest, and under the national common Frontier the innovation efficiency level of the eastern region’s high-tech industry is the highest, while that of the western region is the lowest. The regional pattern of innovation efficiency in the high-tech industry is consistent with the development trend of the regional economy. Moreover, by using the ratio of the technology gap ratio, we find that the eastern region has the potential optimal technology in China, whereas the central and western regions have large room for improvement. Lastly, the stochastic convergence test shows that the innovation efficiency of the central region’s high-tech industry presents a convergence trend, but the same trend does not occur in the western and eastern regions as well as for the whole country.

Analysis on Spatial Correlation Network of Green Innovation Efficiency of China’s High-Tech Industry

Analysis on Spatial Correlation Network of Green Innovation Efficiency of China’s High-Tech Industry

Impact of Green Finance and Environmental Regulations on the Green Innovation Efficiency in China

Innovation is the first driving force for development, and green innovation efficiency (GIE) plays a very important role in regional sustainable development. Data from 31 provinces and cities in China from 2011 to 2020 were used to select the proportion of energy saving and environmental protection costs in GDP as the green financial value, and the proportion of industrial pollution control input in GDP as the environmental regulation index. Green innovation efficiency is measured from two aspects of input and output by DEA method, and carried out for 31 provinces and cities in three regions. Using the DEA-Malmquist index to measure regional green innovation efficiency, the results show that the green innovation efficiency in three regions basically presents an upward trend, but the upward trend of green innovation efficiency is different between the three regions. A Tobit regression model is constructed to explore the impact of green finance and environmental regulations on the green innovation efficiency in these three regions. Research indicates that environmental regulations, the proportion of output value of tertiary industry in GDP, industrial structure, and foreign direct investment have significant impacts on the green innovation efficiency in all regions. Green finance, industrial structure, and power consumption have a significant impact on the green innovation efficiency in eastern China. Industrial structure has a significant impact on green innovation efficiency in central China, while power consumption and industrial structure have a significant impact on green innovation efficiency in western China. Therefore, each region needs to improve the standard of environmental regulation innovation, and introduce and use foreign investment in a scientific and reasonable way so as to promote the improvement of industrial infrastructure.

Analysis on the innovation efficiency of China's electronic and communication equipment industry

The development of electronic and communication equipment industry is the driving force of economic growth, and innovation is the basic motivation for the development of electronic and communication equipment industry. The evaluation of innovation efficiency of electronic and communication equipment industry (IEEC) is conducive to economic growth and better development of electronic and communication equipment manufacturing industry. The existing researches mainly focus on the efficiency evaluation of high-tech industry, and there is few study on the IEEC. To fill the gap, based on DDF model and Global-Malmquist index, this paper estimates the IEEC in 20 provinces of China from 2009 to 2018. The results show that: (1) From the perspective of time dimension, China's IEEC shows an overall growth trend in the past decade from 2009 to 2018. (2) From the regional perspective, IEEC exists differences among regions. Under the meta-frontier and group frontier, the value of IEEC in the central region is higher than the value of IEEC in the eastern region and western region. (3) From the decomposition index, the growth of IEEC depends more on technological progress, rather than efficiency improvement. Combined with the above conclusions, some policy implications are proposed.

Factors influencing technological innovation efficiency in the Chinese video game industry: Applying the meta-frontier approach

Exploring technological innovation efficiency in China's video game industry allows us to reconceptualize the industry's state of development, identify barriers to technological innovation, and seek solutions. This study analyzed this industry's technological innovation efficiency across different regions in China using the meta-frontier approach and empirically tested the effects of human capital, financial capital, region, and policy factors using an econometric model. There were several main findings: (1) Beijing, Jiangsu, and Guangdong had higher meta-frontier technology ratios and more technological innovation efficiency, whereas Jilin and Hubei had lower ratios. Innovation efficiency in the latter regions showed more room for improvement, given the current technology level and factor input conditions. (2) Regarding group-frontier and meta-frontier technology efficiency, all regions (except Beijing) had room for improvement in the entire industry and individual companies. (3) Technological innovation efficiency fluctuated slightly across regions and over time. Beijing, Jiangsu, and Guangdong maintained high technological innovation efficiency, whereas Liaoning, Jilin, and Fujian did not. (4) A region's video game industry tended to have higher technological innovation efficiency if the industry had innovative video game practitioners who were good learners, more research and development investment, greater economic development, and better infrastructure support, among other factors.

Convergence analysis of regional sustainable innovation efficiency in China

Convergence analysis of regional sustainable innovation efficiency in China

Measurement of Innovation Efficiency in China’s Electronics and Communication Equipment Manufacturing Industry-Based on Dynamic Network SBM Model

In recent years, China’s electronics and communication equipment manufacturing (ECEM) industry has overgrown, and the government should assess the innovation performance of the industry for its sustainable development. However, most previous studies on the innovation efficiency of the ECEM industry have ignored the link and carry-over variables. This paper uses the number of patent applications as a link variable to consider the stage of innovation activities. It divides the innovation activities of the electronics industry into two stages: technology development and results in transformation. To consider the dynamics of innovation activities, this paper uses capital stock as a period carry-over variable and evaluates the change of innovation efficiency over time. In this paper, the DNSBM model is used to measure the innovation efficiency of the ECEM industry in 26 Chinese provinces from 2013–2019. This model includes both stage link variables and period carry-over variables, thus allowing for overall efficiency and stage efficiency and period efficiency. The results show that the overall innovation efficiency values in the Chinese ECEM industry are low, there are considerable differences between the two-stage efficiency values in the east, central and western regions, and the overall efficiency values show a slow upward trend.

Research on Regional Differences and Influencing Factors of Chinese Industrial Green Technology Innovation Efficiency Based on Dagum Gini Coefficient Decomposition

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects.

Spatial-Temporal Evolution and Influencing Factors of Urban Green Innovation Efficiency in China.

Promoting green urban development has become a common consensus to address environmental pollution and ecological damage, but we know little about the measurement and drivers of urban green innovation efficiency (GIE). In this article, firstly, we established a framework for assessing urban green innovation efficiency through multidimensional data, then used the spatial econometric model to reveal the spatiotemporal evolutionary characteristics of urban GIE, and, finally, analyzed the influencing factors and spatial spillover effects of urban GIE. The results show the following: (1) The overall urban GIE in China was low and had significant spatial agglomeration, mainly concentrated in the Yangtze River Delta and Pearl River Delta regions with spatial locking characteristics, while the GIE of cities in undeveloped regions does not change much. (3) There was much room for improvement in the input-output system of green innovation, considering that the sources of inefficiency in most cities were insufficient investment in scientific and technological innovation personnel and innovation environment, excessive environmental pollution, and limited technological output. (4) Foreign direct investment, financial development, and manufacturing industry agglomeration had positive effects on urban GIE. These research findings and policy implications are of certain reference value for other emerging developing countries to implement urban governance and green development.

Spatial spillovers and threshold effects of internet development and entrepreneurship on green innovation efficiency in China

Green innovation is a critical component of high-quality development that is particularly integrated with the Chinese 14th five-year plan. At present, internet development and entrepreneurship are considered to be the important driving forces to improve green innovation efficiency adhered by sustainable development goals. Thus, based on the panel of 30 provincial administrative regions in China from 2006 to 2018 and 196 cities from 2012 to 2018, the spatial spillover, threshold effects, causality of Internet development on green innovation efficiency (GIE) are comprehensively investigated. The results reveal that internet development and entrepreneurship have a positive effect on the GIE. This conclusion is still valid after a series of tests of robustness including iv estimation. There are also significant spatial spillover effects among internet development, entrepreneurship, and GIE. For instance, the local internet development and entrepreneurship can significantly strengthen the green innovation efficiency of neighboring regions in China. The threshold model reveals that advancement in regional entrepreneurship lifts the positive impact of internet development on GIE. Manifestly, the development of Internet and entrepreneurship have a marginal increasing non-linear impact on the GIE, that significantly varied across different thresholds of both variables. Taking broadband China as a quasi-natural experiment, this paper tests the causality between the development of Internet and the efficiency of green innovation. The results show that the broadband China policy has significantly promoted the efficiency of green innovation. These research findings offer valuable policy recommendations for government and stakeholders.

An Analysis on the Influence of R&D Fiscal and Tax Subsidies on Regional Innovation Efficiency: Empirical Evidence from China

To promote the National Mid-andLong-Term Scientific and Technical Development Program, utilizing the technical innovation data from 30 provinces of China from2002–2016, this paper evaluates the inter-provincial differences of China’s regional innovation efficiency from four aspects of technical efficiency, efficiency index change, returns to scale, and projection analysis by using the DEA-Malmquist index method and constructs of the DEA-Tobit random response model to explore the impact of government funding on regional innovation efficiency. The research results show that: (1) The local development of regional innovation efficiency in China is unbalanced, and the level of pure technical efficiency restricts the improvement of innovation efficiency. (2) In the prophase of the scientific and technical development plan, technological progress has led to the growth of total factor productivity, resulting in the formation of scale effect; in the later stage, the scale return shows an overall increasing trend, and the continuous expansion of technological scale and opportunities has improved the regional innovation efficiency. (3) The R&D fiscal and tax subsidies have policy sustainability, and the direct government funding can significantly improve innovation efficiency, while the enterprises investment is opposite, and the pretax additional deduction has a negative but not significant impact. The government should give priority to direct subsidy and supplemented by tax preference, making reasonable policy allocations to expand the policy effect.

Research on the spatial association network structure for innovation efficiency of China's new energy vehicle industry and its influencing factors.

It is of great significance to study the spatial network of the new energy vehicle (NEV) industry innovation efficiency and its factors to promote the rational allocation of innovative resources and the coordinated development of Chinese NEV industry. First, the Super Efficiency Data Envelope Analysis model is used to measure innovation efficiency in the NEV industry in Chinese provinces, and based on the results, the improved gravity model is applied to construct a spatial correlation network. Then, by applying social network analysis (SNA) to study NEV industry development node spatial correlations, we conclude that there is no overall hierarchical structure. The SNA are applied to examine spatial correlations with respect to NEV industry innovation efficiency in each province, and to analyze the role and position of each province in the spatial correlation network. Finally, the influencing factors of spatial correlation of the innovation efficiency of China’s NEV industry has been discussed. The result shows that the difference in spatial distance and R&D investment has a significant impact on the spatial correlation of the NEV industry.

Impact of intellectual property protection on the innovation efficiency in China’s hi-tech industry

ABSTRACT A plethora of publications have discussed the correlation between intellectual property protection (IPR) and innovation capability; however, regarding the hi-tech industry in transition economies, the relationship remains unclear. Based on the data from 30 hi-tech industries in Mainland China during 2004–2016, the study used the SAR Tobit model to analyse the direct, mediating and moderating effects of IPR on China’s hi-tech industry’s innovation efficiency. The findings illustrated that both R&D efficiency and business transformation efficiency of China’s hi-tech industry have significant positive spatial correlation and spatial spillover effect. In addition, IPR significantly promoted R&D efficiency and business transformation efficiency. Market segmentation exerted a significant negative moderating effect between IPR and R&D efficiency, and a significant positive moderating effect between IPR and business transformation efficiency. The technology market development has an intermediary effect between IPR and two-phase innovation efficiency. In particular, the non-linear influence of IPR on business transformation efficiency is the hinge on the level of market segmentation. Ultimately, several possible policy suggestions are proposed.

Impact of sci-tech finance on the innovation efficiency of China's marine industry

To realize the high-quality development of China's marine economy, it is important to vigorously promote blue finance and increase the support of science and technology finance for the innovation development of the marine industry. Based on the existing literature research, this paper uses two-step empirical evidence to investigate the role of science and technology finance on the innovation efficiency of the marine industry by combining the development status of science and technology innovation, science and technology finance in the marine industry. The results of the study show that government financial investment in science and technology finance has a significant contribution to the innovation efficiency of the marine industry; The innovation efficiency of marine industry is positively but not significantly affected by the investment of enterprises' funds in science and technology, and the innovation efficiency is inhibited by the investment in entrepreneurial risk management. In addition, there are still large differences in the efficiency of innovation in the marine industry among different regions. Moreover, we propose the government support the marine industry science and technology innovation fully, optimize risk investment and improve the investment return guarantee mechanism, and unblocked the indirect financing channels of the financial market for the sea-related science and technology innovation enterprises, and give policy recommendations to develop the marine industry science and technology innovation activities.

Global value chain embeddedness and innovation efficiency in China

ABSTRACT Combined with the global value chain (GVC) and the innovation value chain, this study analyses whether and how global value chain participation (GVCPA) and global value chain position (GVCPO) affect innovation efficiency (IE) to explain the difference of IE in China’s provinces from 2005 to 2016. It uses Stochastic Frontier Analysis to examine the influencing factors of IE. Results show that both GVCPA and GVCPO are important factors influencing IE, with GVCPA promoting IE and GVCPO inhibiting IE. Moreover, human capital (HC) positively affects the relationship between GVCPO and commercialisation efficiency as well as the relationship between GVCPA and R&D efficiency. The findings suggest that a region should not only encourage industrial enterprises to be embedded in the GVC but also develop education and improve the quality of HC.

Local government competition and regional innovation efficiency: From the perspective of China-style fiscal federalism

Abstract Through data envelope analysis, this article finds that the innovation efficiency of China’s three regions is shrinking. From the perspective of Chinese fiscal federalism, we try to study the influence of two forms of local government competition (vertical competition and horizontal competition) on regional innovation efficiency. We find that both fiscal decentralization and competition among local governments effectively promote regional innovation efficiency, while there is no effective interaction mechanism between them. The attitude of local governments toward scientific and technological (S&T) activities also directly affects the efficiency of regional innovation. From the top of policy design, in order to establish a national innovation system and improve innovation efficiency, appropriate fiscal autonomy should be granted to local governments. Suggestions include that local governments should strengthen S&T activities and cooperate with each other, which is an important S&T policy guidance for central government in the future.

Regional difference and convergence analysis of marine science and technology innovation efficiency in China

Based on panel data of 11 coastal provinces and cities in China during 2006–2015, stochastic frontier analysis(SFA) is used to calculate the efficiency of marine science and technology innovation in this study. Then, the regional differences and influencing factors of this efficiency are analyzed, and finally, a panel unit root test method is used to conduct a stochastic convergence analysis of the efficiency of marine science and technology innovation. According to the results, the overall marine science and technology innovation efficiency shows an upward trend during the statistical period and substantially differs among the 11 coastal provinces and cities. Among them, the 10-year average efficiency of marine science and technology innovation in Shandong province is the highest. In addition, there is a significant negative relation between the degree of government support and efficiency of marine science and technology innovation. The scale of marine scientific research institutions, structure of scientific research personnel, development degree of marine economy, and structure of marine industry have significant positive impacts on the marine scientific and technology innovation efficiency. In summary, there is no stochastic convergence among the Bohai Rim, Yangtze River Delta, and Pan-Pearl River Delta regions. Expanding the scale of marine scientific research institutions and promoting the development of senior personnel in the marine industry, development of marine secondary industries, and integration of marine science and technology innovation with economic development are expected to play positive roles in improving the marine science and technology innovation efficiency.

Investigating the Spatial Heterogeneity and Correlation Network of Green Innovation Efficiency in China

With environmental problems becoming increasingly serious worldwide, scholars’ research views on innovation have begun to pay more attention to the technological value from an ecological perspective, instead of simply analyzing the importance of technological innovation from the perspective of economic value. Currently, improving green innovation efficiency (GIE) has been considered as a critical path to realizing economic transformation and green development. Based on the global Super-Epsilon-based measure (EBM) model, Moran index, vector autoregression (VAR) model, and block model, this study investigated the temporal and spatial characteristics of GIE in 30 provinces in China from 2009 to 2017, and analyzed the spatial heterogeneity and spatial correlation network characteristics. The results showed that in spatial terms, China’s GIE presented an extremely unbalanced development model. In provinces with a higher GIE, there was an overall improvement of GIE, but there was a lower impact in provinces with a lower GIE. The efficiency of China’s green innovation could be divided into four blocks. The first block was the main overflow, the second block was the broker, the third block was the bilateral spillover, and the fourth block was the net benefit. The four blocks had their own functions, and a very significant correlation was observed among them.