The impact of technology transfer on the green innovation efficiency of Chinese high-tech industry

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.

1. Introduction

High-tech industry (HTI) refers to technology-intensive industries with high research and development (R&D) intensity and high product added value. It is characterized by innovation and environmental friendliness. It plays an important role in enhancing the competitiveness of the manufacturing industry and promoting economic structural optimization, making it a crucial area in international competition. Technology transfer is an important pathway to promote green development in industries (Fernandes et al., 2021; Zheng et al., 2022). China has actively pursued practical exploration of technology transfer. In September 2017, the State Council of China issued the National Technology Transfer System Construction Plan, with a view toward using technology transfer to provide support for improving the capability of green innovation. According to data released by the National Bureau of Statistics of China, the domestic technology acquisition (DTA) expenditure of Chinese HTI increased from 2.0241 billion yuan in 2011 to 25.1917 billion yuan in 2020. Foreign technology introduction (FTI) funds decreased, however, from 6.9650 billion yuan in 2011 to 18.0730 billion yuan in 2020 (as shown in Figure 1). Facing the dual constraints of limited innovation resources and deteriorating ecological environment (Peng et al., 2022), it is essential to examine the relationship between technology transfer and green innovation capability from the perspective of process to promote the sustainable development of Chinese HTI. Relevant empirical studies, however, remain lacking.

FIGURE 1

Figure 1. Expenditure of two types of technology transfer from 2011 to 2020.

Some studies have evaluated the green innovation efficiency (GIE) of Chinese HTI. Research to date can be divided into two categories: (1) the “black-box” perspective, which regards the green innovation of HTI as a “black-box” and evaluates the input-output conversion efficiency of this “black-box” (Li L. et al., 2018; Luo et al., 2019); and (2) the process perspective, which regards the green innovation of HTI as a multistage continuous process and evaluates the input-output efficiency of each stage (Deng et al., 2020). Compared with the “black box” perspective, the process perspective can further our understanding of industrial GIE and its components.

Some research has analyzed the impact of technology transfer on GIE in HTI from the “black box” perspective. Liu et al. (2020) found that in the areas with strong competitiveness of HTI, DTA was significantly positively related to the GIE of HTI, whereas the role of FTI was just the opposite. However, few studies have examined the differences between the two ways of technology transfer in improving the efficiency of green innovation from a process perspective (Liu et al., 2020). In addition, when measuring GIE in HTI, existing studies often choose sulfur dioxide (SO2) emissions as a single undesirable output indicator, which may lead to measurement bias of the results of GIE (Yang et al., 2020).

There are two main contributions in this paper. First, from the perspective of process, we decomposed the green innovation process of HTI into three stages: R&D, commercialization, and diffusion. On this basis, a three-stage index system of green innovation in HTI was constructed, and the network data envelopment analysis (NDEA) model considering environmental pollution was used to measure the GIE of inter-provincial HTI in China. Second, after measuring the GIE, a series of regression models are constructed to examine the differences between the two ways of technology transfer, DTA and FTI, in promoting the GIE of HTI.

In Section 2, we review the theoretical literature on the impact of technology transfer on GIE in HTI. In Section 3, we introduce our research methods, including the three-stage NDEA model and the regression model. In Section 4, we provide estimated results of the impact of technology transfer on industrial GIE. In Section 5, we render conclusions and limitations.

2. Theoretical background

The use of technology transfer can help enterprises overcome internal constraints that affect their green development, such as lack of capacity or input (Ghisetti et al., 2015). Technology transfer plays a crucial role in facilitating green innovation (Leiponen and Helfat, 2010; Hu et al., 2017). DTA and FTI are the two main types of technology transfer (Li et al., 2020; Qian et al., 2022).

The use of technology transfer can help enterprises overcome internal constraints that affect their green development, such as the lack of capacity or input (Ghisetti et al., 2015). Technology transfer plays a crucial role in facilitating green innovation (Leiponen and Helfat, 2010; Hu et al., 2017). DTA and FTI are the two main types of technology transfer (Li et al., 2020; Qian et al., 2022).

DTA is an important channel to obtain external technology. Enterprises can obtain the technical knowledge needed for their product or process innovation from local universities and research institutions to supplement or replace expensive R&D activities (Caloghirou et al., 2004). Because the technology gap of these domestic enterprises is relatively small, recipients can better digest, and absorb domestic technology (Deng and Lu, 2021). Furthermore, the same knowledge background of domestic enterprises can reduce transaction costs and information asymmetry (Li, 2011). Some high-tech enterprises improve the efficiency of innovation through industry-university-research cooperation (Chen et al., 2016). It is often difficult for developing countries to acquire specialized, diversified, and advanced technical knowledge when acquiring domestic technologies, however, and it may be difficult to help them accelerate their innovation process (Elia et al., 2020).

FTI is another important channel of technology transfer. Latecomer countries can carry out technological innovation based on introduced technology and catch up technologically in a short time (Awate et al., 2015; Yu et al., 2019). The introduction of high technology will enable developing countries to obtain technology spillover effects (Belitz and Molders, 2016). Nevertheless, technology introduction may cause developing countries to fall into the “technology dependence trap,” which can lead to the inhibition of their independent innovation capacity (Laursen and Salter, 2006; Choi, 2017). In addition, the introduced technology may contain highly polluting mechanical equipment, which may cause damage to the environment (Peters and Hertwich, 2006; Tukker et al., 2013). Therefore, the influence of FTI on GIE is uncertain.

Green innovation requires enterprises to deal with complex technological and economic problems, therefore, requires knowledge input from different technological sources (Cainelli et al., 2015; Ketata et al., 2015). The effectiveness of green innovation is influenced by the source of technical knowledge, but most importantly, by the combination of technical knowledge in the green innovation process (Ben Arfi et al., 2018). Therefore, the process perspective will provide a new understanding of the differences in the role of different technology transfer modes in the improvement of GIE in HTI.

3. Methodology

3.1. Model

To analyze the influence of technology transfer on the GIE of Chinese HTI, we set the following model:

$\begin{array}{ll}{GIE}_{it}={\alpha }_0+{\alpha }_{1}D{TA}_{it}+{\alpha }_2{FTI}_{it}+{\gamma }_k{\varnothing }_{it}+{\epsilon }_{it}& (1)\end{array}$

where the subscripts i and t represent the province and year, respectively; GIE represents the GIE of HTEs; DTA represents the domestic technology acquisition; FTI represents the foreign technology introduction; ∅ is the control variable vector; and ε represents the random error.

3.2. Dependent variable

From the perspective of process, the green innovation process of HTI can be divided into three stages: R&D, commercialization, and diffusion (Lin et al., 2023). The input-output variables of these three stages are shown in Table 1.

TABLE 1

Table 1. Input-output variables in the process of three-stage green innovation.

The input in the R&D stage is R&D full-time equivalent and R&D expenditure (Wang et al., 2016; Du et al., 2019). Its output is patent applications and patents in force (Zhang et al., 2019). These outputs are also inputs in the commercialization phase. Supplementary input in the commercialization stage is the expenditure for new product development (Du et al., 2019). The output is the sales income of new products. This output is also the input of the diffusion phase. The supplementary input in the diffusion stage is the number of new product development projects (Chen et al., 2021b). The desirable output of the diffusion stage is the main business income (Lin et al., 2023), and its undesirable output is environmental pollution emissions. Due to the availability of data, SO2 emissions and wastewater emissions were selected as undesirable outputs in this paper (Yang et al., 2020; Chen et al., 2021a).

Stochastic frontier analysis (SFA) and DEA are common methods used to measure GIE. The SFA method offers advantages in dealing with measurement error and statistical interference (Zhu et al., 2021), but it is difficult to use when dealing with the input-output efficiency evaluation of multiple stages and multiple outputs (Li T. et al., 2018). The DEA method is often used to measure the relative efficiency of the same kind of decision-making units (DMUs) with multiple inputs and outputs (Tang and Qin, 2021), and it can provide improvement basis for increasing desirable outputs and reducing undesirable outputs of non-effective DMUs (Liu et al., 2020).

From the process perspective, the process of green innovation in HTI includes multiple stages and involves a variety of inputs and outputs. Therefore, the network DEA model is needed to measure GIE (Cook et al., 2010; Du et al., 2019).

Assume that $x_{ij}^l$ and $y_{rj}^l$ represent the ith input and the rth output of the jth DMU at the lth node (phase), respectively; $z_{f_{(l,l^{{}^{\prime }})j}}^{(l,l^{{}^{\prime }})}$ represents the intermediate output of the jth DMU between the lth node (phase) and the l′th node (phase); and the subscript $f_{(l,l^{{}^{\prime }})}$ indicates the number of intermediate outputs between the lth node (phase) and the l′th node (phase), $f_{(l,l^{{}^{\prime }})}=1,\cdots ,F_{(l,l^{{}^{\prime }})}$. The NDEA model can be expressed as follows (Tavana et al., 2013):

$\begin{array}{ll}{\gamma }^{\ast }=\min {\sum }_{\begin{array}{c}l=1\\ {\theta }_k,\lambda ,S^{-}\end{array}}^k{W}_l\left({\theta }_l-{\epsilon }_x^l{\displaystyle {\sum }_{i=1}^{m_l}\frac{w_i^{l-}{S}_i^{l-}}{x_{i_0}^l}}\right)& (2)\end{array}$

$\begin{array}{l}s.t.\\ {\sum }_{j=1}^n{x}_{ij}^l{\lambda }_j^l+S_i^{l-}={\theta }_h{x}_{i0}^l,\\ {\sum }_{j=1}^n{y}_{rj}^l{\lambda }_j^l\ge y_{r0}^l,\\ {\sum }_{j=1}^n{z}_{f_{(l,l^{{}^{\prime }})j}}^{(l,l^{{}^{\prime }})}{\lambda }_j^l={\sum }_{j=1}^n{z}_{f_{(l,l^{{}^{\prime }})j}}^{(l,l^{{}^{\prime }})}{\lambda }_j^{l^{{}^{\prime }}},\\ i=1,\cdots ,m_l,l=1,\cdots ,k,\\ r=1,\cdots ,s_l,l=1,\cdots ,k,\\ f_{(l,l^{{}^{\prime }})}=1,\cdots ,F_{(l,l^{{}^{\prime }})},\forall (l,l^{{}^{\prime }}),\\ {\theta }_l\le 1,l=1,\cdots ,k,\\ {\lambda }_j^l\ge 0,j=1,\cdots ,n,l=1,\cdots ,k,\\ s_i^{l-}\ge 0,i=1,\cdots ,m_l,l=1,\cdots ,k,\end{array}$

where $w_i^{l-}$ represents the weight of the ith input of the lth node (phase), which satisfies $\sum _{i=1}^{m_l}{w}_i^{l-}=1$; ${\epsilon }_x^l$ is used to measure the dispersion of various inputs of the lth node (phase); ${\epsilon }_x^l$ represents the relaxation of the ith input of the lth node (phase); and W_l represents the weight of the lth node (phase).

3.3. Explanatory variables and control variables

3.3.1. Explanatory variables

Explanatory variables include DTA and FTI. DTA is measured by the ratio of the expenditure on the purchase of domestic technology to the main business income of HTI. FTI is measured by the ratio of expenditure on the introduction of foreign technology to the main business income of HTI.

3.3.2. Control variables

In addition to these two types of technology transfer, the existing literature also has identified other influencing factors of GIE in HTI, including enterprise scale (ES), marketization level (ML), foreign direct investment (FDI), human capital (HC) level, and environmental regulation (ER) (Li L. et al., 2018; Li T. et al., 2018; Peng et al., 2018; Zhang et al., 2022).

(1) ES

Green innovation is costly and risky. Large-scale enterprises have more abundant resources for green innovation, and thus, they are more able to bear the costs and risks of green innovation (Li T. et al., 2018). As the enterprise grows in scale, however, its innovation management efficiency also may decrease (Zhu et al., 2021).

(2) ML

The cooperation between technology suppliers and consumers has an important impact on improving the utilization rate of technology (Li L. et al., 2018). The market is a platform for technology transfer and diffusion. A mature market can enhance the cooperation between technology suppliers and demanders, thus promoting the transfer and diffusion of technology more effectively (Li T. et al., 2018).

(3) FDI

FDI from developed countries usually has technology spillover effect on enterprises in developing countries (Sari et al., 2016; Vujanovic et al., 2022). This provides a technological basis for enterprises in developing countries to achieve green innovation (Feng et al., 2018; Luo et al., 2021).

(4) HC

The HC level affects firms' ability to absorb external technologies (Kneller and Stevens, 2006; Huang et al., 2019). Firms with higher HC levels are better able to adopt external technologies than others (Blalock and Gertler, 2009; Guo et al., 2022).

(5) ER

ER increases the expenditure of controlling environmental pollution for enterprises and squeezes out the funds for technological innovation (Zhang et al., 2021). ER, however, also can encourage enterprises to carry out technological innovation, which may introduce more benefits (Li and Zeng, 2020).

These factors are widely used in the empirical study of GIE in Chinese HTI. Zhang et al. (2022) confirmed that ES is significantly positively correlated with industrial GIE. Li T. et al. (2018) confirmed that ML has a positive impact on GIE. Peng et al. (2018) found a significant positive correlation between FDI and GIE. Yang et al. (2022) found that the HC level has a significant positive impact on GIE. Li L. et al. (2018) found that ER has a significant negative impact on GIE. Therefore, we chose FDI, HC level, ML, ER, and ES as control variables.

FDI is expressed as a ratio of the number of foreign-funded enterprises in HTI (Xu et al., 2020). ML is expressed by the ratio of non-state-owned enterprises in the main business income of this (Wang et al., 2021). HC level is expressed by the proportion of employees in the local population (Wang and Zhao, 2021). Per capita GDP is used as the proxy variable for ER (Antweiler et al., 2001). ES is expressed by the average value of the main business income of enterprises (Li T. et al., 2018).

The data used to calculate SO2 emissions and solid waste emissions come from the China Environmental Statistics Yearbook 2012–2021. Data used to calculate environmental regulation come from the China Statistical Yearbook 2012–2021. The data used to calculate other variables come from the Statistical Yearbook of China's High-tech Industry 2012–2021.

The descriptive statistical results of the variables are shown in Table 2. Considering the integrity of the data, we selected the panel data of 30 provinces in China from 2011 to 2020 to examine the impact of technology transfer on the GIE of China's HTI.

TABLE 2

Table 2. Descriptive statistical results.

4. Results and discussion

4.1. Measurement of GIE

Based on the panel data of 30 provinces in China from 2011 to 2020, we use equation (2) to calculate the GIE of China's HTI (see Table 3).

TABLE 3

Table 3. GIE in Chinese HTI.

As shown in Table 3, Jiangsu, Guangdong, Qinghai, and Xinjiang are the provinces with high GIE in China's HTI, and the green innovations of these four provinces are all effective. Except for these four provinces, green innovation in HTI in other provinces is ineffective. Among them, Heilongjiang has the lowest green innovation efficiency, with an efficiency value of only 0.5053. The average GIE of China's HTI is 0.7727.

4.2. Regression results

The value of GIE calculated by the NDEA model is between 0 and 1. For restricted dependent variables, the use of OLS regression can lead to inconsistent estimates. Tobit regression is a common method to analyse this type of sample data (Chen, 2014). Therefore, we use the Tobit model to analyse the impact of technology transfer on the GIE of China's HTI.

Equation (1) is used to analyse the influence of technology transfer on GIE in HTI. Model 1, Model 2, and Model 3, respectively, introduced a series of control variables by stepwise regression method. These control variables include ES, ML, FDI, HC, and ER. The final estimated results are shown in Table 4. The results of the likelihood ratio test (LR test) confirm that the Tobit regression method of random effects should be used for all three models.

TABLE 4

Table 4. Influence of technology transfer on GIE in Chinese THI.

Models 1, 2, and 3 show that DTA has a significant positive impact on GIE. The coefficients are 0.0231, 0.0209, and 0.0210 respectively. Models 1, 2 and 3 also show that there is a positive correlation between FTI and GIE, with coefficients of 0.0191, 0.0486, and 0.0692 respectively, but it is not statistically significant. The coefficients of ES and ML are significantly positive, indicating that both ES and ML can promote GIE. After considering ER, the impact of FDI and HC on the GIE is no longer significant. The relationship between ER and GIE forms an inverted U. This relationship shows that moderate ER is conducive to green innovation, but that strict ER may be harmful to GIE in HTI.

For comparison, Table 5 shows the estimated results of the fixed-effects model (using Cluster-Robust Standard Errors). It can be found that whether using Tobit random effect model or fixed effect model, the results show that DTA has a significant positive impact on GIE. The FTI is positively related to GIE, but it is not statistically significant.

TABLE 5

Table 5. Estimated results of fixed effect model.

4.3. Robustness test

We used three methods to test the robustness of the estimates. First, we introduced more control variables into the regression model. Considering the influence of location factors, MID is used to represent the dummy variable of the central region, and WEST is used to represent the dummy variable of the western region. Model 7 shows that although location factors have a significant impact on GIE, the estimation results of independent variables do not change with the addition of more control variables. Second, this paper uses short panel data (N = 30, T = 10). Due to the small-time dimension T, it is difficult to test the hypothesis of autocorrelation and heteroscedasticity. In this case, we use the panel corrected standard error (PCSE) method to give a consistent estimate. Model 8 shows that the estimates are still robust. Finally, the regression model used to discuss the impact of technology transfer on GIE may have endogenous problems (Zhou et al., 2020). Instrumental variable (IV) method is a common method to deal with the endogeneity of panel data (Lu et al., 2018). We use lag variables as a tool to deal with endogeneity problems. In Model 9, the Wald test shows that the original hypothesis of exogenous is accepted. At the same time, the results of the independent variables are also robust (see Table 6).

TABLE 6

Table 6. Results of robustness tests.

5. Discussion

Some studies use the NDEA model to measure the innovation efficiency of China's HTI (Chen et al., 2018; Wang et al., 2020), but these studies do not consider environmental pollution in the innovation process of HTI. Differently from previous research, this paper uses the three-stage network DEA model to measure the GIE of China's HTI, because this deepens our understanding of the GIE in China's HTI.

DTA has significantly promoted GIE in Chinese HTI. This result is in accord with the findings of Liu et al. (2020), even though the two studies apply different measurement methods for dependent variables and independent variables (Liu et al., 2020). Under the development concept of green innovation, China actively supports enterprises to form strategic alliances with universities and research institutes for collaborative technological research. This enhances the green innovation capability of Chinese HTI; thus, DTA has a significant positive effect on the GIE of HTI.

The impact of FTI on the GIE of China's HTI is not significant. This result is different from the discovery made by Liu et al. (2020), who found that FTI hinders GIE in areas where HTI is highly developed. Although FTI will produce a technology spillover effect to some extent (Belitz and Molders, 2016). However, due to the lack of the technology absorptive capacity of most enterprises, it is difficult for China's HTI to obtain the corresponding economic and environmental benefits, so its impact on the GIE of HTI is not significant.

Existing research on the GIE of China's HTI generally regards green innovation as a “black box” (Li L. et al., 2018; Luo et al., 2019). According to the process perspective, green innovation in HTI is a three-stage system including R&D, commercialization, and diffusion. Therefore, we establish a three-stage NDEA model considering environmental pollution to measure GIE. This method provides an improved method for measuring the GIE of China's HTI.

The effectiveness of green innovation is affected by the source of technological knowledge, but most importantly by the combination of technological knowledge in the process of green innovation (Ben Arfi et al., 2018). However, there is little literature on the impact of various ways of technology transfer on GIE in China's HTI from a process perspective (Liu et al., 2020). We bring the two main ways of technology transfer, DTA and FTI, into a unified framework and discuss the impact of technology transfer on GIE from a process perspective. This research has deepened our understanding of the role of technology transfer in improving GIE in HTI.

6. Conclusion

Based on panel data from 30 provinces in China from 2011 to 2020, we used the three-stage NDEA model to evaluate the GIE of China's HTI and used the Tobit model to analyze the impact of DTA and FTI on the GIE of HTI. The results show that the average GIE of China's HTI is 0.7727. Except for four provinces, green innovation in most provinces is ineffective. DTA significantly promotes the improvement of GIE of China's HTI, while the impact of FTI on the GIE of HTI is not significant.

6.1. Implications for practice and policy

At present, the competition in high-tech field is increasingly fierce, and DTA has become an important approach to elevate GIE in Chinese HTI. In the process of actively promoting the construction of the national technology transfer system, China should pay more attention to improving its national technology trading network platform to provide information resources for high-tech enterprises to obtain appropriate domestic technologies. Moreover, it should actively support high-tech enterprises to build strategic alliances supporting industrial green innovation with universities and research institutions and should proceed green R&D and commercialization activities in line with market demand.

Although FTI has no significant positive impact on the GIE of HTI, we should not give up opening and technological cooperation. While actively introducing foreign advanced technology, it is necessary to enhance the absorptive capacity to realize the integration and utilization of foreign technological resources. In addition, an international technology transfer platform must be established to provide information services for the introduction of foreign technology. This can not only reduce the opportunity cost of introducing technology to high-tech enterprises, but also improve the applicability of imported technology to these enterprises.

6.2. Limitations and future research

According to the process perspective, we analyzed the influence of DTA and FTI on GIE in HTI under a unified framework and conducted an empirical test on the effect of these two types of technology transfer on efficiency improvements at each stage of green innovation. This study had two shortcomings, though. First, we introduced environmental pollution into the GIE analysis framework to explore the influence of technology transfer on GIE in HTI. However, because of the availability of data, we did not consider other undesirable output factors, such as wastewater and carbon dioxide emissions, when measuring the GIE. Second, Chinese HTI include pharmaceutical manufacturing, aviation equipment manufacturing, communication equipment manufacturing, and other sub-industries. Differences in the technological characteristics of these various sub-industries will affect the decision making about technology transfer. When we studied the relationship between technology transfer and GIE in HTI, we did not consider industry heterogeneity. These areas will be the focus of our next study.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

Methodology, software, validation, formal analysis, data curation, and writing—original draft preparation: SZ. Conceptualization, writing—review and editing, supervision, and project administration: FP. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Project of Humanities and Social Sciences of Ministry of Education of China, Grant No. 18YJC630131.

Acknowledgments

The authors are grateful to the reviewers and the editor for their constructive comments and suggestions for this paper.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Antweiler, W., Copeland, B. R., and Taylor, M. S. (2001). Is free trade good for the environment? Am. Econ. Rev. 91, 877–908. doi: 10.1257/aer.91.4.877

CrossRef Full Text | Google Scholar

Awate, S., Larsen, M. M., and Mudambi, R. (2015). Accessing vs sourcing knowledge: a comparative study of R&D internationalization between emerging and advanced economy firms. J. Int. Bus. Stud. 46, 63–86. doi: 10.1057/jibs.2014.46

PubMed Abstract | CrossRef Full Text | Google Scholar

Belitz, H., and Molders, F. (2016). International knowledge spillovers through high-tech imports and R&D of foreign-owned firms. J. Int. Trade Econ. Dev. 25, 590–613. doi: 10.1080/09638199.2015.1106575

CrossRef Full Text | Google Scholar

Ben Arfi, W., Hikkerova, L., and Sahut, J. M. (2018). External knowledge sources, green innovation and performance. Technol. Forecast. Soc. 129, 210–220. doi: 10.1016/j.techfore.2017.09.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Blalock, G., and Gertler, P. J. (2009). How firm capabilities affect who benefits from foreign technology. J. Dev. Econ. 90, 192–199. doi: 10.1016/j.jdeveco.2008.11.011

CrossRef Full Text | Google Scholar

Cainelli, G., De Marchi, V., and Grandinetti, R. (2015). Does the development of environmental innovation require different resources? Evidence from Spanish manufacturing firms. J. Clean. Prod. 94, 211–220. doi: 10.1016/j.jclepro.2015.02.008

CrossRef Full Text | Google Scholar

Caloghirou, Y., Kastelli, I., and Tsakanikas, A. (2004). Internal capabilities and external knowledge sources: complements or substitutes for innovative performance? Technovation 24, 29–39. doi: 10.1016/S0166-4972(02)00051-2

CrossRef Full Text | Google Scholar

Chen, F. Y., Wu, C., and Yang, W. N. (2016). A new approach for the cooperation between academia and industry: an empirical analysis of the triple helix in east China. Sci. Technol. Soc. 21, 181–204. doi: 10.1177/0971721816640617

CrossRef Full Text | Google Scholar

Chen, Q. (2014). Advanced Econometrics and Stata Application. Beijing: Higher Education Press.

Google Scholar

Chen, X., Liu, X., Gong, Z., and Xie, J. (2021a). Three-stage Super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the chinese high-tech industry. Comput. Ind. Eng. 156. doi: 10.1016/j.cie.2021.107234

CrossRef Full Text | Google Scholar

Chen, X., Liu, X., Zhu, Q., and Jiang, J. (2021b). R&D innovation efficiency of Chinese high-tech industries: Three-stage network approach with fairness consideration. J. Oper. Res. Soc. 73, 1562–1577. doi: 10.1080/01605682.2021.1920346

CrossRef Full Text | Google Scholar

Chen, X. F., Liu, Z. Y., and Zhu, Q. Y. (2018). Performance evaluation of China's high-tech innovation process: analysis based on the innovation value chain. Technovation 74, 42–53. doi: 10.1016/j.technovation.2018.02.009

CrossRef Full Text | Google Scholar

Choi, H. (2017). The social construction of imported technologies reflections on the social history of technology in modern Korea. Technol. Cult. 58, 905–920. doi: 10.1353/tech.2017.0108

PubMed Abstract | CrossRef Full Text | Google Scholar

Cook, W. D., Liang, L., and Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega-Int. J. Manage. S. 38, 423–430. doi: 10.1016/j.omega.2009.12.001

CrossRef Full Text | Google Scholar

Deng, P., and Lu, H. (2021). Transnational knowledge transfer or indigenous knowledge transfer: which channel has more benefits for China's high-tech enterprises? Eur. J. Innov. Manag. 25, 433–453. doi: 10.1108/EJIM-07-2020-0269

CrossRef Full Text | Google Scholar

Deng, Q., Zhou, S., and Peng, F. (2020). Measuring green innovation efficiency for China's high-tech manufacturing industry: a network DEA Approach. Math. Probl. Eng. 2020, 8902416. doi: 10.1155/2020/8902416

CrossRef Full Text | Google Scholar

Du, J. L., Liu, Y., and Diao, W. X. (2019). Assessing regional differences in green innovation efficiency of industrial enterprises in China. Int. J. Environ. Res. Public Health 16, 940. doi: 10.3390/ijerph16060940

PubMed Abstract | CrossRef Full Text | Google Scholar

Elia, S., Munjal, S., and Scalera, V. G. (2020). Sourcing technological knowledge through foreign inward licensing to boost the performance of indian firms: the contingent effects of internal R&D and business group affiliation. Manag. Int. Rev. 60, 695–721. doi: 10.1007/s11575-020-00419-6

CrossRef Full Text | Google Scholar

Feng, Z., Zeng, B., and Ming, Q. (2018). Environmental regulation, two-way foreign direct investment, and green innovation efficiency in china's manufacturing industry. Int. J. Environ. Res. Public Health 15, 2292. doi: 10.3390/ijerph15102292

PubMed Abstract | CrossRef Full Text | Google Scholar

Fernandes, C. I., Veiga, P. M., Ferreira, J. J. M., and Hughes, M. (2021). Green growth versus economic growth: do sustainable technology transfer and innovations lead to an imperfect choice? Bus. Strat. Env. 30, 2021–2037. doi: 10.1002/bse.2730

CrossRef Full Text | Google Scholar

Ghisetti, C., Marzucchi, A., and Montresor, S. (2015). The open eco-innovation mode. An Empirical Investigation of Eleven European Countries. Res. Policy 44, 1080–1093. doi: 10.1016/j.respol.2014.12.001

CrossRef Full Text | Google Scholar

Guo, R., Ning, L. T., and Chen, K. H. (2022). How do human capital and R&D structure facilitate FDI knowledge spillovers to local firm innovation? A panel threshold approach. J. Technol. Transfer 47, 1921–1947. doi: 10.1007/s10961-021-09885-y

CrossRef Full Text | Google Scholar

Hu, D., Wang, Y., and Li, Y. (2017). How does open innovation modify the relationship between environmental regulations and productivity? Bus. Strateg. Environ. 26, 1132–1143. doi: 10.1002/bse.1974

CrossRef Full Text | Google Scholar

Huang, J., Cai, X., Huang, S., Tian, S., and Lei, H. (2019). Technological factors and total factor productivity in china: evidence based on a panel threshold model. China Econ. Rev. 54, 271–285. doi: 10.1016/j.chieco.2018.12.001

CrossRef Full Text | Google Scholar

Ketata, I., Sofka, W., and Grimpe, C. (2015). The role of internal capabilities and firms' environment for sustainable innovation: evidence for germany. R&D Manage. 45, 60–75. doi: 10.1111/radm.12052

CrossRef Full Text | Google Scholar

Kneller, R., and Stevens, P. A. (2006). Frontier technology and absorptive capacity: Evidence from OECD manufacturing industries. Oxford B. Econ. Stat. 68, 1–21. doi: 10.1111/j.1468-0084.2006.00150.x

CrossRef Full Text | Google Scholar

Laursen, K., and Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among UK manufacturing firms. Strat. Manage. J. 27, 131–150. doi: 10.1002/smj.507

CrossRef Full Text | Google Scholar

Leiponen, A., and Helfat, C. E. (2010). Innovation objectives, knowledge sources, and the benefits of breadth. Strategic Manage. J. 31, 224–236. doi: 10.1002/smj.807

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, D., and Zeng, T. (2020). Are China's intensive pollution industries greening? An analysis based on green innovation efficiency. J. Clean. Prod. 259, 120901. doi: 10.1016/j.jclepro.2020.120901

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, L., Lei, L., and Han, D. (2018). Regional green innovation efficiency in high-end manufacturing. J. Coast. Res. 82, 280–287. doi: 10.2112/si82-040.1

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, Q., Guo, J. J., Liu, W., Yue, X. G., Duarte, N., and Pereira, C. (2020). How knowledge acquisition diversity affects innovation performance during the technological catch-up in emerging economies: a moderated inverse u-shape relationship. Sustainability 12, 945. doi: 10.3390/su12030945

CrossRef Full Text | Google Scholar

Li, T., Liang, L., and Han, D. (2018). Research on the efficiency of green technology innovation in China's provincial high-end manufacturing industry based on the RAGA-PP-SFA Model. Math. Probl. Eng. 2018, 9463707. doi: 10.1155/2018/9463707

CrossRef Full Text | Google Scholar

Li, X. (2011). Sources of external technology, absorptive capacity, and innovation capability in chinese state-owned high-tech enterprises. World Dev. 39, 1240–1248. doi: 10.1016/j.worlddev.2010.05.011

CrossRef Full Text | Google Scholar

Lin, T. X., Wu, Z. H., and Yang, J. J. (2023). The evaluation of innovation efficiency of China's high-tech manufacturing industry based on the analysis of the three-stage network DEA-Malmquist model. Prod. Plan. Control 21, 189. doi: 10.1080/09537287.2023.2165189

CrossRef Full Text | Google Scholar

Liu, C., Gao, X., Ma, W., and Chen, X. (2020). Research on Regional Differences and Influencing Factors of Green Technology Innovation Efficiency of China's High-Tech Industry. J. Comput. Appl. Math. 369, 112597. doi: 10.1016/j.cam.2019.112597

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, G., Ding, X., Peng, D. X., and Hao-Chun Chuang, H. (2018). Addressing endogeneity in operations management research: recent developments, common problems, and directions for future research. J. Oper. Manag. 64, 53–64. doi: 10.1016/j.jom.2018.10.001

CrossRef Full Text | Google Scholar

Luo, Q., Miao, C., Sun, L., Meng, X., and Duan, M. (2019). Efficiency evaluation of green technology innovation of China's strategic emerging industries: an empirical analysis based on malmquist-data envelopment analysis index. J. Clean. Prod. 238, 117782. doi: 10.1016/j.jclepro.2019.117782

CrossRef Full Text | Google Scholar

Luo, Y. S., Salman, M., and Lu, Z. N. (2021). Heterogeneous impacts of environmental regulations and foreign direct investment on green innovation across different regions in China. Sci. Total. Environ. 759, 143744. doi: 10.1016/j.scitotenv.2020.143744

PubMed Abstract | CrossRef Full Text | Google Scholar

Peng, F., Zhang, X., and Zhou, S. (2022). The Role of Foreign technology transfer in improving environmental efficiency: empirical evidence from China's high-tech industry. Front. Environ. Sci. 10, 855427. doi: 10.3389/fenvs.2022.855427

CrossRef Full Text | Google Scholar

Peng, F., Zhou, Y. Z., and Zhou, S. Z. (2018). The Impact of Technology Transfer on the Efficiency of High-Tech Industries from an Environmental Perspective. Wuhan: Wuhan University Press.

Google Scholar

Peters, G. P., and Hertwich, E. G. (2006). Pollution embodied in trade: the norwegian case. Global Environ. Chang. 16, 379–387. doi: 10.1016/j.gloenvcha.2006.03.001

CrossRef Full Text | Google Scholar

Qian, F., Hong, J., Hou, B., and Fang, T. (2022). Technology transfer channels and innovation efficiency: empirical evidence from chinese manufacturing industries. IEEE T. Eng. Manage. 69, 2426–2438. doi: 10.1109/TEM.2020.3014731

CrossRef Full Text | Google Scholar

Sari, D. W., Khalifah, N. A., and Suyanto, S. (2016). The spillover effects of foreign direct investment on the firms' productivity performances. J. Prod. Anal. 46, 199–233. doi: 10.1007/s11123-016-0484-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, J., and Qin, F. (2021). Analyzing the effect of local government competition on green total factor productivity from the market segmentation perspective in China—evidence from a three-stage DEA model. Front. Environ. Sci. 9, 763945. doi: 10.3389/fenvs.2021.763945

CrossRef Full Text | Google Scholar

Tavana, M., Mirzagoltabar, H., Mirhedayatian, S. M., Farzipoor Saen, R., and Azadi, M. (2013). A new network epsilon-based DEA model for supply chain performance evaluation. Comput. Ind. Eng. 66, 501–513. doi: 10.1016/j.cie.2013.07.016

CrossRef Full Text | Google Scholar

Tukker, A., de Koning, A., Wood, R., Moll, S., and Bouwmeester, M. C. (2013). Price corrected domestic technology assumption—a method to assess pollution embodied in trade using primary official statistics only. With a case on CO₂ emissions embodied in imports to Europe. Environ Sci. Technol. 47, 1775–1783. doi: 10.1021/es303217f

PubMed Abstract | CrossRef Full Text | Google Scholar

Vujanovic, N., Radosevic, S., Stojcic, N., Hisarciklilar, M., and Hashi, I. (2022). FDI spillover effects on innovation activities of knowledge using and knowledge creating firms: evidence from an emerging economy. Technovation 118, 102512. doi: 10.1016/j.technovation.2022.102512

CrossRef Full Text | Google Scholar

Wang, Q., and Zhao, C. (2021). Dynamic evolution and influencing factors of industrial green total factor energy efficiency in China. Alex. Eng. J. 60, 1929–1937. doi: 10.1016/j.aej.2020.11.040

CrossRef Full Text | Google Scholar

Wang, Q. W., Hang, Y., Sun, L. C., and Zhao, Z. Y. (2016). Two-stage innovation efficiency of new energy enterprises in China: A Non-radial DEA Approach. Technol. Forecast. Soc. 112, 254–261. doi: 10.1016/j.techfore.2016.04.019

CrossRef Full Text | Google Scholar

Wang, Y., Pan, J. F., Pei, R. M., Yi, B. W., and Yang, G. L. (2020). Assessing the technological innovation efficiency of china's high-tech industries with a two-stage network DEA Approach. Socio-Econ. Plan. Sci. 71, 100810. doi: 10.1016/j.seps.2020.100810

CrossRef Full Text | Google Scholar

Wang, Y., Zhao, N., Lei, X., and Long, R. (2021). Green finance innovation and regional green development. Sustainability 13, 8230. doi: 10.3390/su13158230

CrossRef Full Text | Google Scholar

Xu, Q., Li, Y., and Wu, H. (2020). Market mechanism or non-market mechanism, which kind of technology transfer is benefit to innovation capability. J. Ind. Eng. Eng. Manag. 34, 196–206. doi: 10.13587/j.cnki.jieem.2020.04.021

CrossRef Full Text | Google Scholar

Yang, H. C., Li, L. S., and Zhang, F. M. (2020). High-tech industrial agglomeration and green technological innovation performance. Sci. Res. Manage. 41, 99–112. doi: 10.19571/j.cnki.1000-2995.2020.09.010

CrossRef Full Text | Google Scholar

Yang, J., Wang, S., Sun, S., and Zhu, J. (2022). Influence mechanism of high-tech industrial agglomeration on green innovation performance: evidence from China. Sustainability 14, 3187. doi: 10.3390/su14063187

CrossRef Full Text | Google Scholar

Yu, L. P., Li, H. Y., Wang, Z. G., and Duan, Y. L. (2019). technology imports and self-innovation in the context of innovation quality. Int. J. Prod. Econ. 214, 44–52. doi: 10.1016/j.ijpe.2018.11.023

CrossRef Full Text | Google Scholar

Zhang, B., Luo, Y., and Chiu, Y. H. (2019). Efficiency evaluation of China's high-tech industry with a multi-activity network data envelopment analysis approach. Socio-Econ. Plan. Sci. 66, 2–9. doi: 10.1016/j.seps.2018.07.013

CrossRef Full Text | Google Scholar

Zhang, J., Ouyang, Y., Ballesteros-Pérez, P., Li, H., Philbin, S. P., Li, Z., et al. (2021). Understanding the impact of environmental regulations on green technology innovation efficiency in the construction industry. Sust. Cities Soc. 65, 102647. doi: 10.1016/j.scs.2020.102647

CrossRef Full Text | Google Scholar

Zhang, L., Ma, X., Ock, Y. S., and Qing, L. (2022). Research on regional differences and influencing factors of Chinese industrial green technology innovation efficiency based on dagum gini coefficient decomposition. Land 11, 122. doi: 10.3390/land11010122

CrossRef Full Text | Google Scholar

Zheng, X., Yu, H., and Yang, L. (2022). Technology imports, independent innovation, and China's green economic efficiency: an analysis based on spatial and mediating effect. Environ. Sci. Pollut. Res. 29, 36170–36188. doi: 10.1007/s11356-021-17499-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhou, X., Sun, X., Yang, Y., and Chen, Y. (2020). FDI inflows, market fragmentation and their impact on industrial environmental efficiency: an empirical study in China. Pol. J. Environ. Stud. 29, 2969–2980. doi: 10.15244/pjoes/111970

CrossRef Full Text | Google Scholar

Zhu, L., Luo, J., Dong, Q., Zhao, Y., Wang, Y., and Wang, Y. (2021). Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: dynamic change and improvement path. Technol. Forecast. Soc. 170, 120890. doi: 10.1016/j.techfore.2021.120890

CrossRef Full Text | Google Scholar