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Technology imports, independent innovation, and China’s green economic efficiency: an analysis based on spatial and mediating effect

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Abstract

Environmental quality and economic growth are important factors that need to be balanced for sustainable development, especially in developing countries where technology is relatively backward. Many studies have shown that technology imports may be beneficial to economic growth, but once the resources and environment are taken into consideration, the role of technology imports becomes blurred. Based on provincial panel data of China from 2004 to 2019, this paper investigates the influence mechanism of domestic and foreign technology imports on the green economic efficiency (GEE) of 30 provinces in China. There are two main conclusions: First, GEE is spatially related and the impact of technology imports on GEE has a significant spillover effect. Besides, the relationship between technology imports and GEE is non-linear, both in terms of direct and indirect effects. Second, independent innovation plays an important role in the influence mechanism of technology imports on GEE. As the level of independent innovation increased, the impact of technology imports on GEE turns from negative to positive, and it is strengthened through the channel of “transfer-absorption-diffusion-re-innovation.” In this regard, some measures should be taken to enhance the role of technology imports in improving GEE.

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The datasets can be obtained from the National Bureau of Statistic of China.

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Contributions

ZX proposed the idea, conceived the framework, wrote the original, and revised manuscript. YH collected data. YL proposed the methodology and revised the manuscript.

Corresponding author

Correspondence to Lin Yang.

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The authors declare no competing interests.

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Responsible Editor: Eyup Dogan

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Appendix

Appendix

Before the empirical analysis, we performed the unit root test and multicollinearity test on panel data to avoid spurious regression. Table 11 shows that both the LLC test and the ADF-Fisher test reject the null hypothesis, indicating that the panel sequence is stable. And the variance inflation factor (VIF) test in Table 12 shows that the maximum VIF value is 2.87, and the VIF < 10 is acceptable (Hair et al. 1995). So, we can assume that there is no severe multicollinearity between variables.

Table 11 Results of unit root test
Table 12 Results of multicollinearity test

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Zheng, X., Yu, H. & Yang, L. 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 (2022). https://doi.org/10.1007/s11356-021-17499-y

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  • DOI: https://doi.org/10.1007/s11356-021-17499-y

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