Industrial Technological Innovation Research Articles

Influence Mechanism of Industrial Agglomeration and Technological Innovation on Land Granting on Green Total Factor Productivity

The influence of land granting on green total factor productivity (GTFP) and its mechanisms remain under-explored. Based on dynamic spatial panel data of 30 provincial administrative regions in China from 2007 to 2017, this study attempts to establish the framework of a transmission mechanism among land granting, manufacturing agglomeration and technological innovation, and green total factor productivity, and measures their interactions by the mixed directional distance function and Global Malmquist–Luenberger index model containing undesirable output. Then, this paper empirically estimates the impacts of land granting, industrial agglomeration and technological innovation on green total factor productivity in China with a dynamic spatial mediating effect model. The results show that the development paradigm of China’s industrial green economy is characterized by path dependence, and industrial GTFP has a significant spatial effect. Local governments rely on low-price competition to obtain cost advantages and facilitate the agglomeration of local manufacturing industries, and promote the impact of negotiation on industrial GTFP through the mediating effect of manufacturing agglomeration. There exists a non-linear relationship between manufacturing agglomeration and industrial GTFP. The land acquired through negotiation has a promoting effect on green technology innovation, and will foster industrial GTFP through the intermediary effect of technological innovation. No obvious non-linear relationship is observed between technological innovation and industrial GTFP. The grant of “Tender, Auction and Listing” has no significant influence on the industrial GTFP, and the mediating conduction effect on the GTFP of industry is not established. Industrial structure, government management and infrastructure will significantly promote the improvement of industrial GTFP, while the level of transportation will inhibit the improvement of industrial GTFP. Through administrative intervention in low-production capacity departments, local governments can integrate regional resource endowments, give play to the comparative advantages of industries, and achieve industrial structure upgrades and core competitiveness, which will be conducive to the improvement of industrial GTFP. High-level transportation conditions increase energy consumption and greenhouse gas emissions in transportation operation, which makes no contribution to the enhancement of industrial GTFP.

Research on carbon productivity and its spatial convergence of steel industry in China.

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency

Improving technological innovation efficiency and eco-efficiency is the key to achieving a high-quality, sustainable mining industry in China. In order to explore the internal process of technological innovation to promote economic and environmental benefits and to clarify the optimization path to improve the overall efficiency of the mining industry. A two-stage DEA model is used to construct indicators to measure Chinese 30 provinces mining technological innovation efficiency (MTIE), mining eco-efficiency (MEE), and mining comprehensive efficiency (MCE). Because of the lag in innovation, the study period was divided into nine periods from 2008 to 2018 into three-year time slices, and the dynamic evolution of time and space was analyzed. It was found that: (i) MCE and MTIE maintained the same changing trends, with the MCE being generally higher in areas with higher levels of mining economic development; (ii) the MTIE and MEE in the western area were higher than in the other regions, of which Qinghai was the only province that had efficiencies of more than 1 in both stages; (iii) most provinces belonged to type II regions with high MEE-low MTIE or type III regions with low MEE-low MTIE. The scientific achievements output in the first stage was generally insufficient, and the inputs and outputs in the second stage were redundant. There are three paths to optimize efficiency: unilateral optimization, gradual optimization, and jumping optimization. Finally, based on the above findings, policy recommendations are given from three aspects; reducing excess capacity, increasing investment, and building a platform, all of which can provide optimized plans and references for realizing the high-quality development of the Chinese mining industry.

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.

The Geographical Pattern and Diversity of Strategic Industry Technological Innovation in the Guangdong-Hong Kong-Macao Greater Bay Area

The Geographical Pattern and Diversity of Strategic Industry Technological Innovation in the Guangdong-Hong Kong-Macao Greater Bay Area

The Impact of the Internet on Industrial Green Productivity: Evidence from China

The expansion of the Internet has played a considerable role in improving industrial green development. This paper both analyzes how the development of the Internet affects Industrial Green Total Factor Productivity (IGTFP) and its mechanism of action, and, using the Dynamic Spatial Durbin Model (DSDM) and Chinese panel data, empirically investigates the impact and spatial spillover effect of the development of the Internet on IGTFP. The results show not only that the development of the Internet has a significant promoting effect on improvements to IGTFP in this region and the surrounding areas, but also that the long-term effect is greater than the short-term effect. The Mechanism test shows that Internet development can promote IGTFP through both the upgrading of industrial structure and technological innovation, with the intermediary effect of technological innovation obviously greater than that of industrial structure upgrading. The above research conclusions are still valid after performing a series of robustness tests, and provide empirical evidence for the implementation of the “Internet+” action plan and the popularization and application of the industrial Internet in China.

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.

Analysis of the Coupling Coordination and Spatiotemporal Evolution of High‐Tech Industrial Technological Innovation and Regional Economic Development

Improving the coordination between technological innovation in high‐tech industries and regional economic development is an important measure for all provinces (cities) to implement the innovation‐driven development strategy. Based on the analysis of the mechanism of high‐tech industrial technological innovation and regional economic development, this paper constructs the measurement index system of high‐tech industrial technological innovation and regional economic development, and the chain network DEA model, entropy weight method, coupling coordination model, and exploratory spatial data analysis technology are comprehensively used to empirically analyze the technological innovation efficiency of high‐tech industries, regional economic development level, and the coupling coordination relationship between them and the spatiotemporal evolution trend in 24 provinces (cities) of China from 2009 to 2018. The results show that (1) there are significant differences in the average level of technological innovation efficiency of high‐tech industry in each stage, which is the most prominent in the technology research and development stage, followed by the industrialization stage, and finally in the technology transformation stage. (2) The degree of coupling and coordination between high‐tech industrial technological innovation and regional economic development is increasing year by year, which is in the adaptation stage of high‐tech industrial technological innovation and regional economic development. (3) From the perspective of spatial pattern, there is a positive spatial autocorrelation between the coupling coordination degree of high‐tech industrial technological innovation and regional economic development. The number of provinces (cities) with low coupling coordination degree is much larger than that with high coupling coordination degree, showing a serious imbalance of regional heterogeneity and forming two spatial aggregation areas of “high‐high” diffusion effect and “low‐low” low‐speed growth.

Research on the Influence of the Coupling Coordination of the Logistics Industry and Manufacturing Industry on the Upgrading of the Manufacturing Industry

One of the objectives of coupling coordination between the logistics industry and manufacturing industry (hereinafter referred to as “two industries coordination”) is to improve the competitiveness of the manufacturing industry. Using the slacks-based method (SBM) and coupling coordination degree model, the coupling coordination scheduling between the logistics industry and manufacturing industry is measured, and the coupling coordination measurement system of the logistics industry and manufacturing industry under low-carbon constraints is constructed. The DEA Malmquist model is used to measure the level of manufacturing upgrading, and a threshold regression model is used to study the mechanism of the two industries’ coordination on manufacturing upgrading. The empirical study found that during 2009-2019, the average coordination of the two industries in the Yangtze River Delta was 0.64, which was basically in the stage of good coordination, and the coupling level showed a gradual upward trend. From 2009 to 2019, the average total factor productivity index of the manufacturing industry was 1.23, showing an upward trend. The coordination of the two industries can promote the upgrading of the manufacturing industry, and there is an obvious inverted U-shaped relationship. There is a double threshold for industrial scale and technological innovation. When the scale of the manufacturing industry is between 79.1 billion yuan and 97.4 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater. When the R&D expenditure is less than 9.89 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater. The study found that the larger the industrial scale of the manufacturing industry is, the more conducive it is to the coordination of the two industries, and the coupling coordination does not increase with the increase in R&D investment. The manufacturing industry is mainly light industry, so the proportion of optimal assets is relatively low under low-carbon constraints. The upper limit of optimal science and technology expenditure shows that there is the problem of excessive investment of optimal science and technology funds under low-carbon constraints.

Industry Development Tendency and Innovation Strategy Preference of Five Typical Industries under the Background of Low‐Carbon Sustainable Development in China

This paper tries to investigate the future development tendency of five typical industries in China and find out whether there exists a different innovation strategy preference between Chinese firms of low‐ and high‐knowledge density industry in the background of low‐carbon sustainable development. First, this paper finds that the innovation driven‐based trend of industrial development is further accelerated in China. Firms in industries with high knowledge and technology density, such as specialized‐supplier, scale‐intensive, and science‐based industries, are more likely to choose exploratory innovation strategy than exploitative innovation strategy. Firms in industries with low knowledge and technology density like service‐ and supplier‐dominated industries are more likely to choose the exploitative innovation strategy. Second, results indicate that exploratory innovation can bring higher technological innovation performance than exploitative innovation. And the effect of the ambidextrous strategy is better than the single exploitative strategy in industries with high knowledge and technology density like specialized‐supplier, scale‐intensive, and science‐based industries. Third, this paper suggests that innovation strategy preference plays a mediating role between industry type and technology innovation and a moderating effect of knowledge management capacity between innovation strategy and firms’ technology innovation.

The Impact of Economic Environmental Regulations on Industrial Firms' Technological Innovation - An Empirical Study Based on the Eastern Region

While enterprises continue to develop, they have caused certain damage to the ecological environment, and studies have shown that technological innovation is beneficial to the protection of the ecological environment. Can environmental regulations promote technological innovation? And does heterogeneity exist among different environmental regulations on technological innovation? These two questions are of great academic importance for guiding enterprises to achieve their technological innovation goals and promoting the government to formulate a unified and effective environmental regulation strategy. In this paper, based on the panel data of 13 provinces and cities in the eastern region of China from 2012-2021, regression analysis is conducted using STATA software to study the impact of economic-type environmental regulations on industrial enterprises' conducting technological innovation. The results show that: cost-based environmental regulations have a relatively obvious hindering effect on industrial enterprises to carry out technological innovation, and cannot effectively promote enterprises to sustain product and technological innovation within the system; investment-based environmental regulations have long-term benefits on industrial enterprises to carry out technological innovation, and can enhance the motivation of enterprises to carry out technological innovation.

The Effect of Internal and External Sources on Product Innovation: Colombian Context

Product innovation have been recognized as an important factor to firms' survival. To develop product innovations, companies require different knowledge sources. This research aims to identify the effects of both internal and external sources on product innovation. Using logistic regression, we test the proposed model using data from the Colombian Manufacturing Industry Technological Development and Innovation Survey (TDIS). The results suggest that internal, market and free access sources have a significant positive impact on the probability of companies developing product innovation.

Spatial-Temporal Evolution Analysis of Industrial Green Technological Innovation Efficiency in the Yangtze River Economic Belt

Spatial-Temporal Evolution Analysis of Industrial Green Technological Innovation Efficiency in the Yangtze River Economic Belt

Effects of environmental regulation on CO2 emissions: An empirical analysis of 282 cities in China

This paper takes 282 cities in China from 2003 to 2016 as research samples, uses the system-generalised method of moments (GMM) and dynamic panel model to analyse the direct and indirect effects of environmental regulation on CO 2 emissions, and subdivides the research sample to analyse the impact of urban heterogeneity on the emission-reduction effect of environmental regulation. The results show that the direct impact of environmental regulation on CO 2 emissions presents an inverted U-shaped relationship. At present, environmental regulations effectively restrain CO 2 emissions in Chinese cities. Overall, environmental regulation can effectively restrain CO 2 emissions by forcing industrial structure upgrades and technological innovation. However, under the constraints of environmental regulation, foreign direct investment (FDI) shows a pollution–haven effect. From the subsample perspective, the impact of urban heterogeneity on the emission reduction effect of environmental regulation is quite different. This study helps policy-makers better understand the impact of environmental regulation on CO 2 emissions and formulate effective policies to achieve emission reductions.

A Public Health-Oriented New Energy Industry Competitiveness Evaluation.

This paper develops a regional new energy industry (NEI) competitiveness evaluation index system based on the theoretical framework of the “diamond model” and the unique characteristics of the new energy industry and made an empirical analysis of Jilin province with analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The analysis results show that the new energy industry in Jilin Province has a good foundation, and the government's role, talent support, technological innovation, and market factors are acceptable. However, due to location factors and resource endowments, regional GDP and per capita consumption expenditure are low. The lack of intrinsic motivation of industrial competitive advantage has resulted in the poor overall competitiveness of the new energy industry in Jilin Province. Then, it is advised to take targeted measures to work closely with national NEI development strategy, optimize the industrial technology innovation ability and the talent support, actively guide social capital flows and new energy consumption, optimize regional energy consumption structure and industrial structure by creating a complete NEI chain, and revitalize the whole economy.

Evaluation of biomedical industry technological innovation ability based on a grey panel clustering model.

Technological innovation in the biomedical industry is the basis for improving the core competitiveness of the biomedical industry and promoting the high-quality development of the industry. However, the technological innovation capacity of China's biomedical industry is not balanced, and there are great differences between regions. Therefore, accurately grasping the status quo of technological innovation in China's biomedical industry and assessing regional differences are of great significance and effect for the country to formulate targeted policies and systems. In view of this, this paper designs a two-stage biomedical industry technological innovation capability evaluation index system from the perspective of the innovation value chain. According to the panel data of China's biomedical industry from 2012 to 2018, a grey relational clustering model based on panel data is constructed and used to evaluate the technological innovation capability of China's biomedicine industry from two dimensions: the level of technological research and development and the ability to transform technological achievements.

Research on the Function of Government Fund Support in Pharmaceutical Industry Technology Innovation

Pharmaceutical technology innovation process needs a lot of human, material and financial support, so the government must give full play to its financial support function. Specifically, the government should improve the fund investment mechanism of the pharmaceutical industry, improve the financial investment management system of pharmaceutical technology innovation, strengthen the function and mechanism of the capital market, improve the risk investment system, focus on cultivating a good credit environment, and increase the support of national policy-based financial institutions. Through direct investment, financial subsidies, tax incentives and low-interest financing and other means, vigorously promote the improvement of pharmaceutical innovation capacity.

EDUCATIONAL ROBOTICS AS A MEANS OF DEVELOPING PUPILS’ AND STUDENTS’ COMPUTATIONAL THINKING

The accelerating development of technology over the past decades has brought many radical changes in all aspects of life and has unquestionably affected the functioning of our society. The expansion of the digital space and the technological innovation in industry, commerce and household have given rise to a large number of new concepts relating to digital and information technology and their use. In the context of supporting these activities and needs, the FEP document has been updated for the educational area Information and Communication Technology in elementary schools but also in general and technical secondary schools. This innovation is built primarily on the development of computational thinking, which is based on two pillars—algorithmization/programming and educational robotics. The present paper uses educational research to analyse the area of educational robotics as one of the important tools for promoting the concept of developing computational thinking including a description of the specific tools for the implementation of this type of teaching. The aim of the research study was to analyse the current level of pupils’ awareness of and practical experience with educational robotics, both in school and in extracurricular activities. The research tool for data collection was an online questionnaire designed by the authors.

Market co-creating and shaping through investments in innovation: a comparative analysis of two public funding programmes in Brazil

ABSTRACT This paper documents the incipient emergence of an active role of the state in Brazil that has been oriented towards creating and shaping markets through investments in innovation. It draws on a comparative analysis of two case studies of funding programmes: the BNDES–FINEP Joint Programme for Supporting Industrial Technological Innovation in the Sugar-based Economy and Sugar-Chemicals Sectors (PAISS), and the Ministry of Health’s Production Development Partnerships Programme (PDPP). In the study, evidence was found within both programmes of five dimensions of effective public–private partnerships, these being that public agencies (i) seized mapped opportunities; (ii) took the lead; (iii) engaged in risk-sharing and institutional building; (iv) pursued risk diversification and competition; and (v) sought an equitable sharing of rewards. After a discussion of the main policy lessons, the paper concludes with a reflection on the specific challenges for building equitable public–private partnerships in the light of the increasingly competitive and global scope of the economy.

Research on the Effects of Environmental Regulations on Industrial-Technological Innovation Based on Pressure Transmission

This paper investigates the transmission of pressure between the public, relevant government departments, and industrial firms through the use of formal environmental regulations. The data include formal environmental regulations issued from 2005 to 2019 in 179 cities in 27 provinces in China. The intermediary effect model and the threshold effect model are used to carry out research studies on the relationships between public-participated environmental regulations, formal environmental regulations, and industrial-technological innovations. Results indicate that: (1) Pressure is transmitted between the public, and relevant government sectors and industries. For instance, public-participated environmental regulations pressure relevant government departments to apply strong formal environmental regulations on industrial sectors. (2) Labor and capital have a positive moderating effect on the effect of formal environmental regulations on industrial-technological innovations. (3) Both public-participated and formal environmental regulations promote industrial-technological innovations. (4) There is a threshold effect in formal environmental regulations. For instance, when the intensity of public-participated environmental regulations is higher than 93, the role of formal environmental regulations in promoting industrial-technological innovation can be completely maximized.