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.

The sustainable development of China’s high-tech manufacturing (HTM) sector is restricted by dependence on technology introduction and foreign direct investment (FDI), low input-output efficiency, and environmental pollution. This study aimed to examine the roles of technology introduction and FDI in improving the technical efficiency of Chinese HTM from an environmental perspective. By integrating stochastic frontier analysis (SFA) and projection pursuit (PP) based on the real-coded accelerated genetic algorithm (RAGA), this study constructed a RAGA-PP-SFA model that considers undesirable outputs. This model includes various outputs, including environmental pollution, in the production function to improve estimation accuracy. Moreover, to verify the robustness of the estimation results, the results were provided when environmental pollutants were taken as input factors. The results showed that technology introduction could significantly promote HTM’s technical efficiency, while FDI had no significant positive effect. By comparing the estimated results with those that did not consider environmental pollution, this study not only reveals different roles of technology introduction and FDI in improving HTM’s technical efficiency but also confirms that ignoring environmental pollution will overestimate their roles (especially the role of FDI) in such improvement.

Promoting technology transfer is an important strategic measure for China to promote industrial innovation. However, there is little research exploring the influence of technology transfer on the green innovation efficiency (GIE) of China's high-tech industry (HTI). From the perspective of process, green innovation in HTI is a continuous three-stage system including research and development (R&D), commercialization, and diffusion. Therefore, we measure the GIE of China's HTI by using a three-stage network data envelopment analysis (NDEA) model considering environmental pollution and establish a series of regression models to investigate the role of the two main ways of technology transfer, domestic technology acquisition (DTA) and foreign technology introduction (FTI), in improving the GIE of HTI. The results show that the average GIE of China's HTI is 0.7727 from 2011 to 2020. Except for Jiangsu, Guangdong, Qinghai, and Xinjiang, green innovation in HTI in other provinces in China is inefficient. DTA has significantly promoted GIE in HTI. FTI has a positive impact on the GIE of HTI but is not statistically significant. The robustness test confirmed these results. This study is helpful to understand the differences between the effects of DTA and FTI on the GIE of China's HTI, to provide a basis for adjusting technology transfer policies.