External shocks, tacit knowledge and innovation: a natural experiment in China during the COVID-19 pandemic

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.

This study investigates the present situation of and changing trend in the innovation efficiency of health industry enterprises in China. Based on panel data for 192 listed health companies in China from 2015 to 2020, we analyse innovation efficiency using the DEA–Malmquist index and test convergence using σ-convergence and β-convergence models. From 2016 to 2019, comprehensive average innovation efficiency increased from 0.6207 to 0.7220 and average innovation efficiency decreased significantly in 2020. The average Malmquist index was 1.072. Innovation efficiency in China as a whole, North China, South China, and Northwest China showed σ-convergence. Except for the Northwest region, absolute β-convergence was evident, and in China as a whole, North China, Northeast China, East China, and South China, conditional β-convergence was evident. Overall innovation efficiency of these companies has increased annually but needs further improvement, and the COVID-19 pandemic has had a great negative impact on it. Innovation efficiency and trends in it vary across regions. Furthermore, we should pay attention to the impacts of innovation infrastructure and government scientific and technological support on innovation efficiency.

In this study, we bridge an important gap in the literature by comparing the extent to which external technology spillovers, as indicated by foreign direct investment (FDI), and internal technology spillovers, as indicated by university-institute-industry cooperation (UIC), influence innovation efficiency in China. We divide the innovation process into two sequential stages, namely the knowledge creation and technology commercialization stages, and employ a network data envelopment analysis approach to measure innovation efficiency at each stage. The spatial analysis of the distribution of knowledge creation efficiency and technology commercialization efficiency reveals the heterogeneity of innovation efficiency at the provincial level. Then, a panel data regression is used to analyze the effect of FDI and UIC on innovation efficiency at each stage, using data from 2009 to 2015 for 30 provinces in China. By comparing FDI with UIC, we find that FDI has a higher coefficient and stronger significance level at the knowledge creation stage, while only industry-institute linkages exhibit a stronger association with innovation efficiency at the technology commercialization stage.

Based on China's provincial panel data from 2011 to 2021, this study examines the relationship between Internet development and regional innovation efficiency. The results show that Internet development can effectively improve innovation efficiency in China, and the promoting effect in the central region & western region is obviously stronger than that in the eastern region. Internet development can accelerate the convergence of the nationwide innovation efficiency gap in China, but it will hinder the convergence of innovation efficiency in the central region & western region and expand the innovation efficiency gap among its provinces. The coupling coordination degree between Internet development and innovation efficiency in China shows a steady upward trend, but there are obvious regional differences. During this period, the average coupling level in the eastern region has reached 7, which is in the primary coordination stage, while that in the central region & western region is only 5, which is on the verge of imbalance.

Bai J. On regional innovation efficiency: evidence from panel data of China's different provinces, Regional Studies. The main goals of this paper were to estimate the regional innovation efficiency in China and to investigate major factors affecting efficiency scores. Stochastic frontier methods with a translog production function were applied. The samples were composed of the panel data of China's thirty provinces for the period between 1998 and 2007. The empirical results show that innovation efficiency in China remained at a lower level and had much room for improvement. The impact of major factors and their interplay with innovation efficiency was negative. This indicated that internal construction of the regional innovation system was far from perfect. The innovation efficiency in the eastern regions was higher than that in the central and western regions. Bai J. 区域创新效率：来自中国不同省份的经验证据，区域研究。本文研究的目的主要是为了评估中国区域创新的效率，并分析影响效率的主要因素。本文采用超越对数形式的随机前沿模型，并以1998–2007 年中国大陆30个省区的面板数据为考察样本。研究表明，中国的区域创新效率处于一个较低的水平，仍有较大的提升空间；区域创新主体及其之间的联结关系对创新效率有显著的负面影响；中国东部的创新效率高于中西部地区。 区域创新系统 区域创新效率 随机前沿模型 Bai J. A propos de l'efficacité de l'innovation régionale: des données provenant d'une enquête à échantillon constant auprès de diverses provinces chinoises, Regional Studies. Cet article cherche principalement à estimer l'efficacité de l'innovation régionale en Chine et à examiner les facteurs qui influent sur le taux d'efficacité. On a appliqué des modèles frontières aléatoires à fonction de production translog. Les échantillons étaient composés de données provenant d'une enquête à échantillon constant auprès de trente provinces sur la période de 1998 à 2007. Les résultats empiriques montrent que l'efficacité de l'innovation en Chine est restée à un niveau inférieur et qu'elle aurait pu être nettement mieux. L'impact des facteurs importants et de leur interaction avec l'efficacité de l'innovation s'est avéré négatif. Cela a indiqué que l'établissement interne du système d'innovation régional laissait à désirer. L'efficacité de l'innovation dans les régions de l'est était supérieure à ce qu'elle ne l'était dans les régions du centre et de l'ouest. Système d'innovation régional Efficacité d'innovation régionale Modèle frontière aléatoire Bai J. Regionale Innovationseffizienz: Belege aus den Paneldaten der verschiedenen Provinzen Chinas, Regional Studies. Die wichtigsten Ziele dieses Beitrags sind eine Schätzung der regionalen Innovationseffizienz in China und eine Untersuchung der wichtigsten Faktoren, die sich auf die Effizienzwerte auswirken. Zum Einsatz kamen stochastische Grenzmethoden mit einer Translog-Produktionsfunktion. Die Stichproben stammten aus den Paneldaten der 30 chinesischen Provinzen im Zeitraum von 1998 bis 2007. Aus den empirischen Ergebnissen geht hervor, dass die Innovationseffizienz in China weiterhin auf einem niedrigen Niveau angesiedelt ist und erheblich verbesserungsfähig ist. Die Auswirkung von wichtigen Faktoren und ihre Wechselwirkungen mit der Innovationseffizienz fielen negativ aus. Dies lässt darauf schließen, dass der interne Aufbau des regionalen Innovationssystems bei weitem nicht perfekt ausfällt. Die Innovationseffizienz lag in den östlichen Regionen höher als in den zentralen und westlichen Regionen. Regionales Innovationssystem Regionale Innovationseffizienz Stochastisches Grenzmodell Bai J. Eficiencia de la innovación regional: ejemplos de los datos del panel de las diferentes provincias de China, Regional Studies. El principal objetivo de este artículo es calcular la eficiencia de la innovación regional en China e investigar los principales factores que afectan a las puntuaciones de eficiencia. Para ello aplicamos los métodos estocásticos fronterizos con una función de producción translog. Las muestras fueron obtenidas a partir de los datos de panel de las treinta provincias de China para el periodo entre 1998 y 2007. Los resultados empíricos indican que la eficiencia de la innovación en China sigue en un nivel más bajo y que queda mucho margen de mejora. El impacto de los principales factores y su interacción con la eficiencia de la innovación fue negativo. Esto indica que la construcción interna del sistema de innovación regional dista mucho de ser perfecta. La eficiencia de la innovación en las regiones orientales era superior que en las regiones del centro y oeste del país. Sistema de innovación regional Eficiencia de la innovación regional Modelo estocástico fronterizo

PurposeFinancial misappropriation is a significant challenge to China’s innovation-driven growth model. This paper investigates the impact of regional-level financial misappropriation on innovation efficiency across 30 provinces and administrative municipalities in China.Design/methodology/approachThe paper uses the Data Envelopment Analysis method to estimate the innovation efficiency at regional level, then, employs panel Tobit and indirect-transmission-channel models to analyze the direct and indirect impact of financial misappropriation on regional innovation efficiency in China.FindingsThe findings of the paper suggest that financial misappropriation significantly reduces regional innovation efficiency in China both directly and indirectly. Financial misappropriation hinders the transformation of scientific and technological achievements and, at the same time, it retards high-tech industrial development.Research limitations/implicationsThe research adopted the non-parametric approach over the parametric approach due to limitations of data availability. Both approaches have their own criticisms. However, the focus in this generates the efficiency scores that could be used for the analysis principal question of this research.Practical implicationsThe results show if the innovation efficiency issues are not addressed at regional levels the national efficiency objecting may achieve suboptimal results.Social implicationsThe benefits of innovation may not flow on to regional economies creating social disparity.Originality/valueThis paper is the first of its nature empirically testing the direct and indirect effects of financial misappropriation on regional innovation efficiency in China by using regional financial corruption data of 30 Chinese provinces and administrative cities.

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.

Based on the panel data of China’s 30 regions during 1998–2008, this study conducted an empirical analysis on the regional innovation efficiency of China, and mainly analyzed the influences of local government on regional innovation efficiency. Our results show that regional innovation efficiency in China is low. The local government R&D funding has a significantly negative impact on regional innovation efficiency. This indicates that the role of local government has not been effectively carried out in China’s regional innovation system. Besides local government, other factors, such as universities, research institutes, financial institutions in regional innovation system, also have significant negative impact on innovation efficiency. This indicates that internet construction of the regional innovation system is far from perfect. Improvement of the regional innovation environment and construction of a system network bear significant importance in improving innovation efficiency.

Association between Covid-19 and Pulmonary Embolism (AC-19-PE study)

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.

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.

Does haze pollution inhibit innovation efficiency in China? The mediating role of human capital and the moderating role of government attention

IntroductionAnalyzing the dynamic evolution and convergence of innovation efficiency in China’s new energy enterprises is critical for optimizing energy structures and guiding high-quality development under dual-carbon goals. This study examines spatiotemporal patterns and drivers from 2015–2023.MethodsInnovation efficiency was measured via SFA model, differentiating R&D and transformation phases. σ- and β-convergence tracked disparities and catch-up dynamics. A threshold regression model identified nonlinear macroeconomic impacts on convergence, using 2,182 firm-year observations across 30 provinces.Results(1) The innovation efficiency of China’s new energy enterprises is relatively low, with significant spatial and temporal differences but a consistent upward trend. Specifically, the innovation efficiency of China’s new energy enterprises ranges from 0.55 to 0.71 in the R&D phase and from 0.13 to 0.51 in the transformation phase. (2) 1/3 of the new energy enterprises show a high R&D-high transformation mode, while another 1/3 operate under a low R&D-low transformation mode. The σ-convergence of innovation efficiency across provinces is not evident, except for the R&D phase of enterprises in the eastern and western regions, where substantial β-convergence is observed. (3) The threshold model suggests that urbanization construction and economic development play a crucial role in influencing the convergence of innovation efficiency among China’s new energy enterprises.DiscussionPersistent R&D-transformation gaps necessitate region-specific policies. Western China should enhance technology absorption, while central/eastern regions require optimized innovation ecosystems.

By 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.

Environmental problems not only relate to residents' happiness but also challenge the innovation development of industries. This study first measures the innovation efficiency of China's high-tech industries using the super-efficiency data envelopment analysis model and portrays its spatial characteristics through the Moran's I index and the local indicators of spatial association map. Second, we use the entropy weight method to construct the local living environment index from both natural and social environments. Finally, we utilize spatial econometric models to analyze the impact of local living environment on high-tech industries' innovation efficiency. The results reveal that, first, the spatial variation of innovation efficiency in China's high-tech industries is significant, with efficiency being higher in the east than the Midwest, and higher in the south than the north. Second, innovation efficiency has a positive adjacent and geographical spatial autocorrelation, and low-low agglomeration and low-high agglomeration dominate the types of spatial correlation. Finally, the contribution of the local living environment to the innovation efficiency of high-tech industries is positive and significant. This contribution has an obvious spatial spillover effect and regional heterogeneity. This study can help regional governments to improve local living environments and promote industrial innovation and development.