Innovation Efficiency Of China Research Articles

Research on the Technology Innovation Efficiency of China’s Listed New Energy Vehicle Enterprises

As a new industry, the healthy development of new energy vehicle industry is of great significance for effectively solving environmental problems and energy crises. The purpose of this study was to analyze the technological innovation process of China’s new energy vehicle enterprises and construct the evaluation index system of technology innovation efficiency of China’s new energy vehicle enterprises and SBM model-based network DEA model from the two aspects of technology research and development stage and achievement transformation stage. The overall technological innovation efficiency and staged efficiency of 16 listed new energy vehicle enterprises in China were measured and combined with the Malmquist Index Model. The technical change and efficiency change of 16 listed new energy vehicle enterprises in China, spanning from 2012 to 2018, were analyzed. The results show that the average overall efficiency of technological innovation of 16 new energy automobile companies is 0.5515, and the standard deviation is 0.2341. It reflects the low overall efficiency of technological innovation of Chinese new energy vehicle enterprises and the uneven development and the huge gap between different enterprises. From 2012 to 2018, the M index is less than 1, and the technical level change (TC) has a greater negative impact on the M index.

A research on the efficiency of regional green innovation in China—Based on DEA-SBM model and Malmquist index method

This paper selects panel data of 29 provinces in mainland China from 2010-2017 and combines a DEA-SBM model that takes into account non-desired outputs and the Malmquis index method to study the static and dynamic changes of regional green innovation efficiency. It is found that the overall level of green innovation efficiency in China is low, but the overall trend is on the rise and there is more room for improvement. The differences in green innovation efficiency values among the three regions of East, West and Central Asia are obvious, with the eastern region developing at a higher level than the central and western regions, and the gap is gradually narrowing. Technological progress is a key factor affecting total factor productivity.

University‐industry‐government triple helix relationship and regional innovation efficiency in China

AbstractBy utilizing panel data of provinces in China from 2012 to 2018, this paper measured innovation efficiency of each regional innovation system and the triple helix relationship of the university‐industry‐government system, then, empirically investigated the influence of the triple helix relationship on regional innovation efficiency. It has been found that: (1) the regional innovation efficiency in China increases slightly year by year, the regional differences are obvious, and university–industry bilateral cooperation is the tightest; (2) cooperation between universities and industries is most beneficial to improve regional innovation efficiency, cooperation between universities and governments significantly promotes scale efficiency in the long run, cooperation between industries and governments significantly promotes regional innovation comprehensive efficiency and pure technical efficiency, meanwhile inhibits scale efficiency, coordinated relation among universities, industries, and governments is beneficial to improve regional innovation comprehensive efficiency and scale efficiency. The research results provide useful theoretical support and policy enlightenment for improving regional innovation efficiency.

Green innovation efficiency: a threshold effect of research and development

Green innovation is an important topic of research worldwide currently due to the attention to climate change and environmental issues. To have an understanding of the green innovation evaluation, this study measured the green innovation efficiency by using a meta-frontier Malmquist–Luenberger productivity index. The study results indicated that the growth rate of green innovation efficiency found differs greatly based on the environmental issues. Taking the research capacity of research and development institutions as the threshold variable, a double threshold effect is found as an inverted N-shaped. The study explored that the educational level and maturity of the technology market have a significant positive correlation with regional green innovation efficiency. Unlike environmental regulation and degree of openness, an improvement in green innovation efficiency is found fully dependent on the technological progress and regional green innovation efficiency. This study will be useful for policymakers and researchers to enhance green innovation efficiency in China and the rest of the world with similar economic settings.

A Five-Stage DEA Model for Technological Innovation Efficiency of China’s Strategic Emerging Industries, Considering Environmental Factors and Statistical Errors

A Five-Stage DEA Model for Technological Innovation Efficiency of China’s Strategic Emerging Industries, Considering Environmental Factors and Statistical Errors

Research on Spatial Pattern and Spatial Effect of Marine Science and Technology Innovation Efficiency in China's Coastal Provinces

Chen, Z. and Ma, L., 2020. Research on spatial pattern and spatial effect of marine science and technology innovation efficiency in china's coastal provinces. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 42-45. Coconut Creek (Florida), ISSN 0749-0208.The intensity of interaction between China's marine economy and marine science and technology is not symmetrical and there are regional differences. The development of marine economy promotes the progress of marine science and technology. Under the strategic background of “marine power”, marine economy plays an increasingly important role in the national economy. Ecological efficiency takes into account the ecological and economic benefits of economic activities. It requires that the intensity of environmental impact and resource utilization be adapted to the level of the earth's carrying capacity. It is a concentrated expression of the goal of sustainable development. The spatial spillover degree of energy ecological efficiency in a certain area is stronger than that of error impact in the adjacent area. There are positive spatial correlation and spatial agglomeration phenomena in marine economic efficiency of coastal areas. The spatial correlation patterns of different areas have no obvious difference. There is obvious spatial spillover effect under the influence of geographical distance. Therefore, improving the efficiency of marine science and technology innovation is of great significance for accelerating the construction of a powerful marine country and a powerful scientific and technological country.

Measuring Green Innovation Efficiency for China’s High-Tech Manufacturing Industry: A Network DEA Approach

Earlier studies on the innovation process in the high-tech manufacturing industry failed to take environmental pollution into account, making it difficult to estimate green innovation efficiency in the industry. From a perspective of innovation value chain, this paper decomposes green innovation process in the high-tech manufacturing industry into two stages: R&D stage and achievement transformation stage; a network DEA approach considering undesirable outputs is utilized to estimate the green innovation efficiency in China’s high-tech manufacturing industry. Compared with the method of conventional innovation efficiency without considering environmental pollution, the estimation method for green innovation efficiency can not only avoid bias of estimation results of provinces producing low pollution emissions like Inner Mongolia and Hainan but also reflect the volatility in efficiency of the high-tech manufacturing industry before and after the implementation of the environmental law.

Evaluation of Corporate Technology Innovation Efficiency: Evidence from Coastal Regions of China

Liu, Y.; Wang, W., and Duan, Y., 2020. Evaluation of corporate technology innovation efficiency: Evidence from coastal regions of China. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 186–190. Coconut Creek (Florida), ISSN 0749-0208.Coastal countries and regions worldwide tend to have better economic development and business performance because of their unique geographic advantages and favorable transportation conditions. Regarding corporate technology innovation efficiency in coastal regions, there is not sufficient in-depth research to offer theoretical and practical improvement measures. This study constructs an evaluation model of corporate technology innovation efficiency in coastal regions based on the DEA-Malmquist productivity index and take analyze China's coastal regions. This study shows a high level of corporate technology innovation efficiency in China's coastal regions, where enterprises tend to emphasize the balance between inputs and outputs to achieve economies of scale. The study suggests that enterprises should increase in-house research and development (R&D) and technology purchasing and asserts that an excessive amount of R&D cooperation will reduce corporate technology innovation efficiency.

Empirical Research on Innovation Efficiency in China Based on SFA Model

To study the specific situation of China’s innovation efficiency over the years, and explore the impact of state-owned enterprise-scale, tax incentives, regional export environment, R&D funds and R&D personnel on innovation activities, this paper selects the panel data of provinces and cities from 2009 to 2015, divides innovation activities into scientific research and development stage and value realization stage, and constructs SFA model for research. The results show that China’s innovation level is in the range of 0.5-0.8, and the overall development is unbalanced and uncoordinated. The government’s capital investment has an obvious promotion impact on the local innovation efficiency, and the effect of other factors varies from stage to stage. Finally, according to the research results, the existing problems are analysed, and substantive suggestions are put forward.

Do government subsidies promote efficiency in technological innovation of China’s photovoltaic enterprises?

Supported by preferential policies and government funding, the technological innovation of China’s photovoltaic industry has been improved greatly. As a capital-intensive practice, will innovation activities in China’s photovoltaic industry be affected by the continuous decline of government subsidies in recent years? Therefore, it is essential to study the relationship between government subsidies and innovation performance of Chinese photovoltaic industry. However, there is less empirical research on this topic. In this context, this study aims to supplement existing studies by proposing a micro-level perspective to measure the innovation performance based on the data of Chinese listed photovoltaic companies from 2012 to 2016. Moreover, we assess the effects of government subsidies and other influencing factors on innovation performance. The findings reveal that, firstly, the average innovation efficiency of Chinese listed photovoltaic companies is over 0.9, which is relatively high. Secondly, government subsidies make a positive influence on innovation performance. Thirdly, financial leverage and ownership concentration have significant positive impacts, while firm size has a significant negative effect on the innovation efficiency of China’s photovoltaic industry. The findings will serve as a reference for policy-making to promote further technological progress and sustainable development of China’s photovoltaic industry.

Evaluation Research of Green Innovation Efficiency in China’s Heavy Polluting Industries

Recently, green innovation efficiency, which considers innovation and environmental factors, is gradually becoming important for the sustainable development of Chinese heavy polluting industries because of the increasing strictness in China’s environmental regulations. Previous studies ignore the impact of external environmental factors on the efficiency of green industry innovation and fail to explain the complex relationship between environmental and technical efficiency fully. Therefore, a non-radial directional distance function-data envelopment analysis (DDF-DEA) three-stage green innovation efficiency evaluation model was constructed to measure the green innovation efficiency of China’s heavy polluting industries objectively and explore the impact mechanism of external factors. Then, the aforementioned model was used to conduct an empirical test on China’s heavy polluting industries. Results indicate that the green innovation efficiency of heavy polluting industries is generally low in China, and the entire industry is in the transitional stage of “effective innovation but not green.” The uncertainty of the effect of the environmental regulation policy, the over-reliance on external technologies, and the scale diseconomies of industries, which are the key factors in improving the green innovation efficiency of China’s heavy polluting industries, have a significant negative impact on green innovation efficiency. The conclusions of this study can provide a useful reference for China and other emerging markets to formulate reasonable environmental regulations and green transition of heavy polluting industries.

Innovation Efficiency of China’s High-End Manufacturing Industry: Evidence from Super-SBM Model and Malmquist Index

Technological innovation is an important engine to support the development of China from a big manufacturing country to a powerful manufacturing country. With the support of government policies, China’s high-end manufacturing industry urgently needs to solve the problems of insufficient technological innovation and weak core competitiveness. This paper uses the super-SBM model to measure the impact of incentive policies related to high-end manufacturing industry on innovation efficiency of China’s high-end manufacturing industry in 2012–2017. The DEA Malmquist model is used to analyse the dynamic change trend of efficiency. The results show that the innovation efficiency of China’s high-end manufacturing industry shows a trend of gradual optimization, stepping into the middle and high-end levels, but there are large differences in innovation efficiency among industries, and the optimization process is not stable; at the same time, China’s high-end industry is not stable. Manufacturing industry needs to improve its pure technical efficiency as soon as possible and solve the problems of low efficiency of system and management level. Accordingly, this paper puts forward a series of policy suggestions, such as increasing financial and talent support, encouraging technological innovation, and promoting industrial synergy.

Research on Evaluation of Technology Innovation Efficiency in China’s Pollution-Intensive Industries

Based on 20 pollution-intensive industries’ data in China during 2007-2016, this paper employs two-stage SBM model to evaluate the technology innovation efficiency of Chinese pollution-intensive industries. The results show that: the industry with the efficiency value between 0.6 and 1 in the R&D stage and the commercialization stage only accounts for 30% and 50%. Also, the R&D ability limits the improvement of technology innovation efficiency in pollution-intensive industries.

Does market competition promote innovation efficiency in China’s high-tech industries?

ABSTRACTDespite extensive discussion about the relationship between market competition and innovation performance, the impact of market competition on the innovation efficiency of high-technology industries in a transitional economy is still unclear. The article is based on panel data of 17 subsectors of China’s high-tech industry spanning the 2001–2016 period. Using stochastic the frontier analysis model, we empirically test the impact of market competition on two-stage innovation efficiency from the perspective of the industry. The results indicate that market competition and firm scale have positive and significant effects on the efficiency of two-stage innovation, the effects of industry export intensity and government intervention on R&D efficiency are negative, but the effects are different for the efficiency of the commercialisation of technology. Based on the results of the empirical analysis, this paper compares the trends and the differences between R&D efficiency and commercialisation efficiency, and then uses cluster analysis to reclassify China’s high-tech industries into three categories, thus revealing the disconnect and imbalance within China’s high-tech industries. At the end of the article, we present some possible policy recommendations.

Do the coupling effects of environmental regulation and R&D subsidies work in the development of green innovation? Empirical evidence from China

Global environmental problems will require the development of new technologies. How will green technology-related public policy affect this process? By exploiting an economic assessing model of green technical innovation efficiency, this paper examines the impact of two different environmental policies (environmental regulation and government subsidy) and their interaction on green technical innovation. The analysis was conducted by collecting innovation-related data on 30 provinces in China over the period 2006–2016. It was found that there are dramatic regional differences in green innovation efficiency in China. The average efficiency in the eastern region is about 0.8, followed by the western region, the central region and the north-eastern region, with 0.4, 0.2 and 0.2, respectively. During the sample period, there was a statistically significant effect of the coupling of the two policy instruments on the innovative efficiency of green technology of China, which is − 0.01. Regulatory policies have a remarkable incentive influence on the performance of green innovation in the central region (0.5) but a restraining effect on the eastern region (− 0.9). R&D subsidies play an essential role in inducing green technology only in the central region (0.2). The paper concludes that both regulative policy and governmental subsidies have failed to achieve the goal of promoting green innovation development in China. These two figures show the average regional efficiency of the two types of innovation with or without environment-related factors using innovation-related data on a panel of 30 provinces in China over the period 2006–2016.

Effects of Environmental Regulations on Technological Innovation Efficiency in China’s Industrial Enterprises: A Spatial Analysis

China’s traditional industrial model is characterized by high energy consumption and high pollution, which results in many environmental problems that cannot be ignored. To achieve sustainable development, the Chinese government has proposed five development concepts of “innovation, coordination, green, openness, and sharing”. This initiative highlights the urgency of China’s efforts to strengthen environmental regulation. Based on the panel data of industrial enterprises in China from 2006 to 2015, this study not only investigates the spatial features of technological innovation efficiency, but also examines the relationship between technology innovation efficiency and environmental regulations from a spatial perspective. The results indicate that first, China’s provincial-level technological innovation efficiencies are uneven in space. Second, voluntary regulation positively affects the technological innovation efficiency of industrial enterprises at the provincial level, while mandatory regulation has no significant impact. Third, there is a spatial spillover effect in voluntary regulation at the provincial level. One highlight implication is that the government should promulgate environmental regulations based on each province’s technological innovation potential, due to the spatial differences in technological innovation activities.

Analysis of the scientific and technological innovation efficiency and regional differences of the land–sea coordination in China's coastal areas

The overall strategy for land-sea coordination is important for scientific and technological innovation efficiency in China's coastal areas. In this paper, the country's mixed land and sea regions were selected for study, including two land and ocean subsystems with input and output index systems. Based on the variation coefficient method, the global Malmquist–Luenberger (GML) index was used to calculate the scientific and technological development efficiency of coordinated plans for China's coastal land and sea areas from 2006 to 2015 and to analyze the regional differences among these plans. The conclusions are as follows. First, the total factor productivity growth rate of science and technology innovation efficiency in China's coastal areas is primarily larger than 1 and at the frontier of efficiency. The efficiency of mixed areas is higher than that of separated land and sea areas, and the differences in regional technological innovation efficiencies has gradually narrowed. The change in technical efficiency is consistent with changes in the GML index, and the financial crisis of 2008–2009 had a significant positive impact on pure technical efficiency. Second, the coordinated plans for land and sea areas are realized and spatial differences in scientific and technological innovation efficiency in China's coastal areas are classified according to scientific and technological innovation in China's coastal areas. Hebei and Liaoning are classified according to the land efficiency type; Jiangsu and Zhejiang are classified as the marine efficiency-driven type; and the Shandong, Tianjin, Shanghai, and Pan-Pearl River Delta regions are classified as the efficiency of mixed land and sea areas type. Drivers of regional differences mainly include economic development levels, differences in industry structure, the personnel structure of scientific and technological systems, and the state of production and the influence of the government.

Environmental Regulation, Two-Way Foreign Direct Investment, and Green Innovation Efficiency in China's Manufacturing Industry.

This paper adopts 2009 to 2015 panel data from 27 manufacturing industries in China. A Super-SBM model is used to measure the green innovation efficiency (GIE) of China’s manufacturing industry. A panel data model is then built to systematically examine the impact of environmental regulation (ER) and two-way foreign direct investment (FDI) on the GIE of China’s manufacturing industry under a unified analysis framework. The results are as follows: (1) the overall level of the green innovation efficiency in China’s manufacturing is low, and there is still great potential for improvement. Considering industry heterogeneity, the green innovation efficiency of patent-intensive manufacturing is significantly higher than that of non-patent-intensive manufacturing; (2) in terms of the whole manufacturing industry, ER and the interaction between ER and outward foreign direct investment (OFDI) have significantly negative effects on GIE, OFDI has significantly positive effects on GIE. (3) when considering industry heterogeneity, for patent-intensive manufacturing, ER and the interaction between ER and inward foreign direct investment (IFDI) have significantly negative effects on GIE, while IFDI has significantly positive effect on GIE. For non-patent-intensive manufacturing, ER and the interaction between ER and OFDI have significantly negative effects on GIE, while IFDI and the interaction between ER and IFDI have significantly positive effects on GIE.

Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis

Considering the interrelationships between periods and the influences of non-operational factors, a new framework based generalized three-stage DEA model, grey relational analysis theory and disparity disassembly model is proposed in this paper. Then, we measure the technical efficiency, scale efficiency, and pure technical efficiency of innovation in China's semiconductor industry between 2009 and 2014. In addition, we conducted projection analysis of the inputs to innovation and the disparities analysis in innovation efficiency across the industrial chain and within each segment. The results of our analyses reveal four key findings. The overall innovation efficiency of China's semiconductor industry is increasing; however, each segment of the industrial chain had different trends and different levels of innovation efficiency. All segments show a consistently upward trend except for package testing, which dipped in 2012 due to the time lag between the inputs and outputs associated with major technological advancements. The most efficient innovation is occurring in design and package testing, followed by manufacturing, materials, and equipment, in that order. Low levels of innovation efficiency were found to be the most significant factor restricting further improvement in the design, manufacturing, and equipment segments of the industrial chain. But the opposite is true for the package testing segment, where pure technical efficiency is the main factor. A range of redundancies in input were found across the industrial chain, mostly in manufacturing and equipment. These two segments are capital-intensive and characterized by a high level of technical complexity coupled with a long research cycle. The disparities in innovation efficiency in and between the segments decreased over the period. However, interestingly, the main disparities were found among the enterprises within each segment, which we attribute to the Chinese government's concerted efforts to support particular companies. Package testing and manufacturing had the highest levels of disparity due to relatively high agglomeration of these two segments. The materials segment had the lowest disparity, with equipment and design falling somewhere in-between.

Research on the Efficiency of Green Technology Innovation in China’s Provincial High-End Manufacturing Industry Based on the RAGA-PP-SFA Model

This study offers a RAGA-PP-SFA model to measure green technology’s innovation efficiency in the high-end manufacturing industry. The study’s aim is to solve the shortcomings of traditional SFA methods that are unable to improve multi-output efficiency. The RAGA-PP-SFA model presented here is based on the multi-emission and multi-output characteristics of high-end manufacturing innovation activities. Using panel data from 2010 to 2015 on China's high-end manufacturing industry and considering factors such as environmental regulation, government subsidy, and market maturity, this paper empirically examines and compares the efficiency of green technology innovation versus traditional technology innovation, as well as regional heterogeneity in China's high-end manufacturing industry. The study ultimately found a low level of green technology innovation efficiency in China’s high-end manufacturing industry. However, an overall rising trend shows that the green development of China's high-end manufacturing industry has achieved remarkable results. Green technology innovation efficiency in high-end manufacturing industries across various regions was generally lower than the efficiency of traditional technology innovation. Both types of efficiency showed a pattern of “high in the east and low in the middle and in the west”. High-high efficiency is primarily found in the east, whereas the west is characterized by low-low efficiency. There are significant differences between regions, pointing to an equal rate of development. Government subsidies and enterprise scale had a significant negative impact on green technology innovation efficiency in regional high-end manufacturing industries, while market maturity and industrial agglomeration had a significant positive impact. Based on the study’s findings, environmental regulation and openness to the outside world play insignificant roles in green technology innovation efficiency.