Technological Innovation In China Research Articles

How does artificial intelligence development affect green technology innovation in China? Evidence from dynamic panel data analysis.

As the global climate problem becomes increasingly serious, the green technology innovation to achieve "carbon peak and carbon neutral" has gradually become the global consensus of major countries, and how the rapid development of artificial intelligence (AI) technology affects green technology innovation (GTI) has received a great deal of attention in the field of economics. Therefore, based on China's inter-provincial panel data from 2006 to 2019, the system GMM, dynamic panel threshold model, and quantile regression model were constructed to examine various influences of AI development on GTI under different environmental regulation intensity, research and development (R&D) investment, and institutional environmental threshold conditions. The findings presented that AI development significantly contributes to GTI and GTFP, with an impact coefficient of 0.0122 and 0.0084, and this influence is mainly reflected in the western region of China and is more obvious in the 2006-2012 period. AI development mainly enhances green technological efficiency, and it has dampening effects on green technological progress during the period 2013-2019. Additionally, there are non-linear threshold effects in the relationship between the level of AI development and GTI when environmental regulatory intensity, R&D investment, and institutional environment are in different level intervals. AI development will boost GTI only when the intensity of environmental regulation and institutional environment is above a certain threshold value. However, the AI development represented by industrial robot applications still has no obvious effect on GTI even when the R&D investment exceeds a certain threshold. Furthermore, the growth effect of AI development on GTI indicates a decreasing nonlinear pattern as the GTI's quantile rises under the condition that R&D investment and institutional environment intensity cross the threshold, while this growth effect increases gradually with the rise of GTI's quantile when the environmental regulation is above the threshold.

International Comparison of Scientific and Technological Innovation -Input and Output

Strengthening the country with science and technology is a new strategy for China's economic development. However, lack of core technology in many fields limits the development of China's innovation. This paper collects and analyzes the data on the input and output of scientific and technological innovation in China, the United States, Japan and other major countries, then puts forward suggestions on how to improve the structure of public financial expenditure on science and technology, how to obtain scientific and technological funds through multiple channels, and how to adjust the structure of personnel engaged in R&D activities.

Accelerating efficiency of green technology innovation on carbon mitigation under low-carbon regulation

According to the transaction cost theory for carbon mitigation, green technology innovation must achieve energy conservation and emission reduction through appropriate low-carbon regulation. As a consequence, this study compared the punishment effect of the carbon tax and the reward effect of carbon subsidy on the performance of green technology innovation in China’s provincial industries from 2006 to 2017. We also elucidated that the enterprises’ green technology innovation restricts and stimulates the role of low-carbon regulation on green efficiency in the case of the double threshold effect of carbon dioxide emission. Based on this framework, the integrated tax-subsidy policy for carbon mitigation has a significant differential effect on green efficiency, which changed from an inhibition effect in the high-carbon stage to a promotion effect in the low-carbon stage. And the validity of low-carbon regulations increases with the reduction degree of carbon dioxide emission. But it all depends on whether low-carbon regulations stimulate the enterprises’ willingness to green technology innovation, and whether innovations generate incentives that both increase carbon dioxide emissions and compensate for their innovation cost. Otherwise, enterprise performance should adopt follow strategy more than green technology innovation. Therefore, we further suggested that offsetting the costs of green technology innovation through carbon subsidies would improve the willingness of enterprises to make technological progress and increase the likelihood of carbon reductions which can help the peak in China’s carbon emissions.

Does fiscal expenditure promote green technological innovation in China? Evidence from Chinese cities

The fiscal behavior of the governments can affect the green economy. Using the panel data of 276 cities from 2004 to 2019, this study examines the role of fiscal R&D expenditure and education expenditure in the process of urban green technological innovation in China. It is found that fiscal R&D and education expenditure can significantly promote green technological innovation, but the impact of fiscal expenditure in different periods and regions presents heterogeneity. More importantly, the urban economic growth pressure and fiscal pressure may affect the effect of fiscal expenditure, leading to the non-linear impact of fiscal expenditure on green technological innovation. With the increase of economic growth pressure and fiscal pressure, the positive effect of fiscal R&D expenditure on green technological innovation becomes weaker, while the promotional effect of education expenditure on green technological innovation does not change significantly. In addition, the green technological innovation effect of fiscal R&D and education expenditure can also indirectly reduce pollution emissions. In conclusion, optimizing the structure of fiscal expenditure is not only conducive to green technological innovation, but also improves environmental quality.

Environmental regulation, R&D investment, and green technology innovation in China: Based on the PVAR model.

The unreasonable economic development model of human beings has caused the environmental pollution problem to become increasingly serious. In order to achieve a positive relationship and interaction between environmental regulation, research and development (R&D) investment, and green technology innovation, and effectively solve the “strange circle” problem between high-quality economic development and environmental pollution in China and even the world, this paper takes the panel data of industrial enterprises above designated size in Chinese mainland 31 provinces from 2009 to 2019 as a research sample. The comprehensive index of R&D investment and green technology innovation was established by the entropy method, and the panel vector autoregressive (PVAR) model was constructed from the dynamic endogenous perspective, and the dynamic interaction and regional heterogeneity between environmental regulation, R&D investment, and green technology innovation were empirically analyzed by using impulse response function and variance decomposition. We obtain the following findings: (1) Environmental regulation has a two-way interaction relationship with R&D investment and green technology innovation, and R&D investment has a promotion effect on the “green degree” of technological innovation, but its role is still weak and has lagging characteristics. (2) There is significant regional heterogeneity in the dynamic responses of the eastern, central and western parts of China. (3) In the long run, environmental regulation has a “negative crowding out effect” on R&D investment in the central region, and the phenomenon of “central collapse” still exists but will gradually weaken. Environmental regulation has a “positive innovation compensation effect” on green technology innovation. Green technology innovation and R&D investment have an obvious “Pareto improvement” effect on environmental regulation, especially in the eastern region. The conclusions of this study help to clarify the dynamic interaction between environmental regulation, R&D investment, and green technology innovation, further improve environmental regulatory policies and green technology innovation R&D decision-making, and provide an effective way to achieve green and sustainable development in China and other parts of the world.

The Impact of Factor Market Distortion on the Efficiency of Technological Innovation: A Spatial Analysis

The growth of scientific and technological innovation in China is facing a bottleneck under the influence of domestic and foreign environments. The economic internal circulation policy of China may explore new driving forces for innovation from the perspective of optimizing the efficiency of production factor allocation. This research applies the provincial data from 2001 to 2017 to empirically investigate the spatial effects of factor market distortions on the efficiency of technological innovation. The DEA (Data envelopment analysis) model with variable returns to scale is exploited to measure the efficiency of technological innovation. The production function approach can be harnessed to measure labor market distortions and capital market distortions. The spatial correlation test results and the spatial econometric results regressed with three spatial weight matrices draw the following conclusions: (1) No matter how the spatial connection is established, the efficiency of the scientific and technological innovation in China shows a strong positive spatial correlation. (2) Labor market distortion and capital market distortion lead to low factor allocation efficiency, which inhibits the improvement of scientific and technological innovation efficiency. (3) When considering inter-regional economic connections, the inhibitory effect of factor market distortions on the efficiency of technological innovation shows spillover effects on surrounding areas. (4) Human capital and advanced industrial structure are conducive to the improvement of scientific and technological innovation efficiency. Optimizing the efficiency of factor market allocation can become a significant path for China to release new room for improvement in scientific and technological innovation.

How Does the Digital Economy Contribute to Regional Green Development in China? Evidence-Based on the Intermediary Effect of Technological Innovation

This study aims to explore how the digital economy contributes to regional green development through the intermediary effect of technological innovation in China. Taking 30 provinces in China as the regional research objects, this study constructed a measurement index system for the development level of the digital economy and green development level and used the intermediary effect model and the threshold effect model for empirical testing. The empirical results show that: (1) the digital economy has a significant positive impact on the green development of China, and the digital economy can effectively promote green development at the provincial level; (2) technological innovation has a significant intermediary effect and a single threshold effect in the process of the green development of provinces driven by the digital economy; (3) the marginal effect of the digital economy on green development has an inflection point, from strong to weak. This study provides a reference for China to further plan the sustainable development strategy and provides experience for the sustainable development of other countries.

Scientific culture is an important social foundation for shaping the internal drivers of scientific and technological innovation in China

Scientific culture is an important social foundation for shaping the internal drivers of scientific and technological innovation in China

Does green finance promote enterprises’ green technology innovation in China?

In the carbon neutrality strategy, understanding the effects of green finance on green technology innovation is conductive to promoting the green transformation of the economy. Based on the micro-level and provincial panel data of Shanghai and Shenzhen A-share listed companies from 2012 to 2019, this study explored the impact of green financial development on the enterprises’ green technology innovation. Both mediating effect and moderating effect models were employed to determine the impact of green finance on green technological innovation. It was found that green finance significantly improved the enterprises’ green technology innovation, despite sufficient incentives for “quantity” and relatively insufficient motivation for “quality”. The mechanistic tests demonstrated that the green finance could encourage enterprises to improve green technology innovation by alleviating corporate financing constraints. The green innovation effect of green finance was gradually increased when the regional intellectual property protection was improved. The heterogeneity test indicated that the incentive effect of green financial development on green technology innovation was more evident in state-owned enterprises, enterprises with good internal control quality, and enterprises in the growth period. If only enterprises in the recession stage received green financial support, a “green innovation bubble” might occur. The research conclusions enrich the theories on the driving factors of enterprise green innovation and provide empirical evidence for enhancing the competitiveness of enterprise green innovation and achieving carbon neutrality.

Dynamic Effects and Regional Differences of Industrialization and Urbanization on China’s Energy Intensity under the Background of “Dual Carbon”

Based on China’s provincial panel data during 2012–2019, this paper performs an empirical analysis of the dynamic effect and regional difference of industrialization and urbanization on the energy intensity in China by separating the energy intensity into three levels including low, middle and high and using the dynamic panel data with system GMM estimation. The results show that the energy intensity will increase by 0.4298% for every 1% increase in the industrialization level on the premise of keeping other variables unchanged. For every 1% increase in the urbanization level, the energy intensity will increase by 0.5674% on average. For every 1% increase in energy intensity in the previous period, the energy intensity in that year will increase by 0.7968% on average. Moreover, there are regional differences in the effects of industrialization and urbanization on the energy intensity in areas with different energy intensities. In addition, all of the factors including the development level of the regional economy, energy price, and technological innovation have different effects on the energy intensity in China. Meanwhile, there exist the rebound effects of the technological innovation in China, and the energy price has an induced effect on the technological innovation. Undoubtedly, industrialization and urbanization jointly promote the increase in energy intensity. At the same time, the level of economic development, energy prices and technological innovation are also reasons for the differences in the energy intensity among regions. Therefore, in order to effectively reduce energy intensity while carrying out technological innovation, promoting high-quality development and increasing income, it is necessary to improve the internal quality of industrialization and urbanization, and to promote new resource-saving and environmentally friendly methods of industrialization and urbanization.

How Does Internet Development Affect Green Technology Innovation in China?

With the advent of the information age, the Internet is a new tool for “mass entrepreneurship and innovation initiative” and contributes to the emergence of new products, new technologies and new business formats. However, the question of how the Internet development affects green technological innovation has not been well answered. This article measures the comprehensive Internet development level of 264 prefecture-level cities from 2003 to 2018 in China, and then conducts empirical research. The results show that Internet development can increase high-quality and low-quality green technological innovation. This conclusion remains robust after endogeneity testing. Secondly, Internet development can not only directly promote green technology innovation, but also indirectly play a positive role through financial development and industrial structure upgrading. Thirdly, the positive effect of Internet development on green technology innovations presents significant nonlinear characteristics of increasing marginal effect. It indicates that Metcalfe's law is valid in this paper.

Exploring the Effect of Digital Economy on PM2.5 Pollution: The Role of Technological Innovation in China

PM2.5 emission causes serious harm to health and hinders the sustainable development of economy and society. Among all the factors affecting PM2.5 pollution, the role of new economic forms and information technology innovation is lacking. This study aims to explore the impact of digital economy on PM2.5 pollution and its influencing mechanism using data from 281 prefecture-level cities from 2011 to 2016. The empirical results demonstrate that digital economy is conducive to reducing PM2.5 pollution. In other words, the digital economy is conducive to alleviating PM2.5 pollution. Further analysis shows that the digital economy promotes technological innovation, which is an important mediating mechanism affecting PM2.5 pollution. Additionally, the inhibitory effect of digital economy on PM2.5 pollution is more significant in the eastern and central regions. Unfortunately, the negative impact of digital economy on PM2.5 pollution is not significant in the western region. The conclusions provide a new strategy for reducing pollution emissions and improving environmental quality and technological innovation.

How does digital finance influence green technology innovation in China? Evidence from the financing constraints perspective

Green innovation activities of enterprises often fall into the dilemma of financing constraints. Hence, it is urgent that the financial system develops and becomes innovative, especially for a developing country like China. Existing literature mainly explores the influencing factors of green innovation from the environmental regulation, economic growth and government subsidies perspective. However, few papers critically study how digital finance influences green innovations. To explore the internal influencing mechanism of digital finance on the quantity and quality of green technology innovation, panel data of 271 prefecture-level cities from 2011 to 2019 in China is used. Based on theoretical analysis and empirical research, we get the following conclusions. First, digital finance can improve the quantity and quality of green technological innovation, which has become a significant driving force for green innovations in China. The results are still robust after the endogeneity tests. Second, the results of heterogeneous analysis indicate that the different dimensions of digital finance can improve green innovations. They include breadth of coverage, depth of use, and degree of digitization. The positive effect of digital finance is most substantial in eastern cities, followed by central cities, and weakest in western cities. Third, digital finance indirectly improves green innovation mainly by alleviating financing constraints. Our empirical results provide policy enlightenment for realizing the coordination of China's urban digital transformation and green economic development.

The Effect of Tax-Collection Mechanism and Management on Enterprise Technological Innovation: Evidence from China

Tax collection and management is an important cornerstone to safeguard a country’s financial strength, and it is also a key task to realize the modernization of a country’s tax governance. This study examines the effect of tax-collection mechanism and management on technological innovation in China from 2007 to 2020. By using a fixed effect model, the findings indicate that a higher intensity of regional compulsory tax collection and management rises the level of technological innovation of enterprises. Strengthening tax collection and management is more conducive to raising the level of technological innovation of midwestern enterprises, non-high-tech enterprises and enterprises in a poor market environment. The concrete impact mechanism shows that tax collection and management improve the level of enterprises and technology innovation by using a tax credit-rating system and enhancing enterprises’ information transparency; the application of the Golden Three system has been more conducive to strengthening tax collection and management to enhance the level of enterprises’ technological innovation. The findings of this study have policy implications for China regarding rising innovation through tax collection

Digital finance and innovation inequality: evidence from green technological innovation in China.

Green technological innovation is an overarching approach to tackling environmental pollution, and mitigating the inequality in green technological innovation between regions can contribute to environmental equity. This study measures the inequality in green technological innovation via an index and examines the influence of digital finance on that inequality using data from 285 cities in China from 2011 to 2018. The results are as follows: (1) Digital financial development alleviates the inequality in green technological innovation, and the greater the inequality is, the stronger the effect of digital finance. The development of digital finance can help regions lagging behind in terms of green technology to catch up. (2) When the financial supervision and environmental restrictions within a region are strict, the development of digital finance is more effective at reducing the inequality in green technological innovation; a higher capacity for governance in the local government and a smaller digital divide strengthen the positive effects of digital finance. (3) The development of digital finance alleviates interregional inequality in green credit, reduces market segmentation, and increases spending on environmental protection, thus narrowing the gap in the capacity for green innovation between regions. These findings not only enrich the literature on digital finance and green technological innovation but also serve as a reference for governmental departments as they optimize their strategy for developing digital finance and coordinating green development across regions.

Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.

Can Digital Transformation Promote Green Technology Innovation?

Using the index of the degree of digital transformation of enterprises constructed based on text analysis, and combining the data of Shanghai and Shenzhen A-share listed companies from 2007 to 2020, a panel data model was established to empirically study the impact of digital transformation on green technology innovation and the mechanism of action and to further analyze the impact of heterogeneity. The results show that digital transformation can significantly promote green technology innovation, and its internal mechanism is that digital transformation can improve the level of green technology innovation by alleviating financing constraints and attracting government subsidies. Compared with nonstate-owned enterprises and small and medium-sized enterprises, digital transformation plays a more significant role in promoting green technology innovation in state-owned enterprises and large-scale enterprises. Therefore, the government should regulate the market order and formulate reasonable financial policies to provide policy and financial support for enterprises to carry out digital transformation, mobilize the willingness of enterprises to carry out green technology innovation and improve the level of green technology innovation in China.

Spatial–Temporal Evolution and Sustainable Type Division of Fishery Science and Technology Innovation Efficiency in China

Science and technology innovation is an important driving force to promote the development of fishery industry, and is very important to improve the quality of fishery development. In this study, the Super-SBM model was used to evaluate the fishery science and technology innovation efficiency of 30 provinces and cities in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2011 to 2020. Combined with the kernel density estimation, the spatial and temporal differentiation characteristics were analyzed. Then, from the two dimensions of investment scale and innovation efficiency, the sustainable development types of fishery science and technology innovation were classified. The results show the following: (1) From the perspective of efficiency change, the overall efficiency of fishery science and technology innovation in China increased first and then decreased during 2011–2020, but the overall efficiency level was low, and the efficiency difference between regions gradually widened, and the eastern coastal regions became the development core of fishery science and technology innovation. (2) From the perspective of spatial differentiation characteristics, there was a large gap between the coastal and inland areas in China. The high-efficiency areas were mainly concentrated in the coastal provinces and cities, such as Guangdong, Jiangsu, Shandong, Shanghai and Tianjin, showing a decreasing trend from east to west. (3) From the perspective of investment scale and innovation efficiency, the study regions can be divided into four types: leading area, breakthrough area, catch-up area and backward area. This paper mainly calculates the efficiency of fishery science and technology innovation in various regions, and divides the type areas of fishery science and technology innovation and development. According to the advantages and problems of different types of areas, different development strategies and correction measures are proposed, which can effectively improve the efficiency of resource utilization, avoid resource waste and realize the sustainable development of fishery.

The dynamic association between public environmental demands, government environmental governance, and green technology innovation in China: evidence from panel VAR model

The dynamic association between public environmental demands, government environmental governance, and green technology innovation in China: evidence from panel VAR model

The Impact of Coordinated Development of Ecological Environment and Technological Innovation on Green Economy: Evidence from China.

Promoting the coordinated development of ecological environment and technological innovation is significant to the development of a green economy. In this study, we construct an index system of ecological environment, technological innovation, and green economy based on the panel data of 30 provinces and cities in China from 2005 to 2016, using the entropy weight method, the coupling coordination model, and the panel vector autoregressive model (PVAR) to calculate the comprehensive development levels of ecological environment, technological innovation, and green economy and the coordination degree between ecological environment and technological innovation, and then further explore the impact of the coordinated development level of ecological environment and technological innovation on the development of a green economy. The research results include: First, from 2005 to 2016, the comprehensive development levels of ecological environment, technological innovation, and green economy in China’s 30 provinces and cities achieved different degrees of improvement as a whole. Among them, the comprehensive development level of green economy was the highest, followed by the development level of technological innovation, and the comprehensive development level of ecological environment was the lowest. Second, from 2005 to 2016, the coordination degree between ecological environment and technological innovation in China’s provinces and cities increased year by year, but on the whole, the coordination degree between ecological environment and technological innovation in various regions was in a state of imbalance. Third, there was a long-term equilibrium relationship among the coordinated development levels of ecological environment, technological innovation, and green economy. Fourth, through pulse analysis and Monte Carlo simulation, we found that the coordinated development level of ecological environment and technological innovation had a lagging positive impact on green economy. Finally, we provide a summary of the results of this study.