In recent years, China’s high-tech industry has made remarkable technological progress, but it has also brought serious environmental pollution, which has aroused great concern about its environmental efficiency. Although foreign technology transfer is considered as important ways for technological progress of the high-tech industry, the existing research on what role foreign technology transfer plays in improving the environmental efficiency of the high-tech industry is still lacking. Based on China’s interprovincial panel data from 2008 to 2017, we evaluated the environmental efficiency of the high-tech industry using the super-efficiency slacks-based measure (SBM) model with undesirable outputs. We then used the Tobit model to analyze the impact of technology introduction (TI) and foreign direct investment (FDI)—two major types of foreign technology transfer—on the environmental efficiency of the high-tech industry. The results of the super-efficiency SBM model show that the average environmental efficiency of China’s high-tech industry is only 0.4375. Except for Guangdong, Shanghai, and Beijing, most of the provinces in China have low environmental efficiency. The provinces with high environmental efficiency are in the eastern region, whereas the provinces with low environmental efficiency are concentrated in the central and western regions. Tobit regression results confirm the difference in the role of technology import and foreign direct investment in the improvement of environmental efficiency in China’s high-tech industry. Technology introduction has a significant positive impact on environmental efficiency. FDI also promotes environmental efficiency, but it is not statistically significant. These findings were confirmed by a series of robust tests. This study not only deepens our understanding of the environmental efficiency of China’s high-tech industry but also expands the theoretical research on the relationship between technology transfer and environmental efficiency.

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.

In this paper, we assess, in the framework of Global Navigation Satellite System (GNSS) meteorology, the accuracy of GNSS propagation delays corresponding to the Saastamoinen zenith hydrostatic delay (ZHD) model and the Vienna Mapping function VMF1/VMF3 (hydrostatic and wet), with reference to radiosonde ray-tracing delays over a three-year period on 28 globally distributed sites. The results show that the Saastamoinen ZHD estimates have a mean root mean square (RMS) error of 1.7 mm with respect to the radiosonde. We also detected some seasonal signatures in these Saastamoinen ZHD estimates. This indicates that the Saastamoinen model, based on the hydrostatic assumption and the ground pressure, is insufficient to capture the full variability of the ZHD estimates over time with the accuracy needed for GNSS meteorology. Furthermore, we found that VMF3 slant hydrostatic delay (SHD) estimates outperform the corresponding VMF1 SHD estimates (equivalent SHD RMS error of 4.8 mm for VMF3 versus 7.1 mm for VMF1 at 5° elevation angle), with respect to the radiosonde SHD estimates. Unexpectedly, the situation is opposite for the VMF3 slant wet delay (SWD) estimates compared to VMF1 SWD estimates (equivalent SWD RMS error of 11.4 mm for VMF3 versus 7.0 mm for VMF1 at 5° elevation angle). Our general conclusion is that the joint approach using ZHD models and mapping functions must be revisited, at least in the framework of GNSS meteorology.