Dynamic Impact of Technology and Finance on Green Technology Innovation Efficiency: Empirical Evidence from China's Provinces.

In the new stage of global economic development, we hope to achieve both economic development and environmental improvement through green technology innovation. How to effectively obtain the support of technology and finance to green technology innovation is an issue worth studying. This paper constructed an improved super-SBM-DEA efficiency measurement model and combined it with the window analysis method to measure the green technology innovation efficiency (GTIE) of Chinese provinces from 2006 to 2018. Then, based on the PVAR model, the impulse response function and Monte Carlo simulation were used to study the dynamic impact of various variables of technology and finance on GTIE. Finally, the variance decomposition was used to explore the contribution degree of each variable of technology and finance to improving GTIE. The results revealed the following: (1) the average value of China's provincial GTIE from 2006 to 2018 was 0.42, which is relatively low and shows a trend of volatility and rising. (2) From the impulse response results, it could be seen that various variables of technology and finance have always had a positive impact on GTIE. However, there are differences in the influence degree, shock effect, and dynamic transmission mechanism. (3) The results of the variance decomposition showed that government financial technology investment had the highest contribution to the improvement of GTIE, followed by bank technology credit, then by enterprise independent R&D investment, and finally venture capital. This paper offered a reference to developing countries with regard to improving their GTIE and studying the role of technology and finance.

Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China's Cities.

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China's GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city's GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China's GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Environmental regulation, environmental responsibility, and green technology innovation: Empirical research from China.

Innovation and green are the directions to promote the circular economy and environmental sustainability at the corporate level. This paper examines the impact of environmental regulation (pollution charge) on green technology innovation and the mediating role of corporate environmental responsibility. Our results indicate that: (1) Environmental regulations stimulate manufacturing enterprises' environmental responsibility and green technology innovation. It is worth noting that corporate environmental responsibility strengthens the relationship between environmental regulation and green technology innovation. (2) Further investigation reveals that R&D expenditure and environmental investment have greatly strengthened the positive effect of environmental regulation on green technology innovation. (3) With more detailed disclosure about enterprises' environment-related information, the more outstanding stimulation effects of environmental regulation. Discussions on the features of enterprise location have revealed that, if the goal of environmental protection is set too high or if the fiscal decentralization is too strong, implementation of environmental regulation would not achieve desirable results. Accordingly, we need to optimize the collection of environmental taxes, strengthen the enterprises' environmental responsibility, and increase investment in R&D and environment protection. Meanwhile, the execution of environmental regulation should also take into account the institutional environment and governance features of the enterprise locations.

Derecruitment volume assessment derived from pressure-impedance curves with electrical impedance tomography in experimental acute lung injury.

OBJECTIVE: To investigate the accuracy of derecruitment volume (VDER) assessed by pressure-impedance (P-I) curves derived from electrical impedance tomography (EIT). METHODS: Six pigs with acute lung injury received decremental positive end-expiratory pressure (PEEP) from 15 to 0 in steps of 5 cmH2O. At the end of each PEEP level, the pressure-volume (P-V) curves were plotted using the low constant flow method and release maneuvers to calculate the VDER between the PEEP of setting levels and 0 cmH2O (VDER-PV). The VDER derived from P-I curves that were recorded simultaneously using EIT was the difference in impedance at the same pressure multiplied by the ratio of tidal volume and corresponding tidal impedance (VDER-PI). The regional P-I curves obtained by EIT were used to estimate VDER in the dependent and nondependent lung. RESULTS: The global lung VDER-PV and VDER-PI showed close correlations (r = 0.948, P<0.001); the mean difference was 48 mL with limits of agreement of -133 to 229 mL. Lung derecruitment extended into the whole process of decremental PEEP levels but was unevenly distributed in different lung regions. CONCLUSIONS: P-I curves derived from EIT can assess VDER and provide a promising method to estimate regional lung derecruitment at the bedside.