ABSTRACT
Latecomer technology imitation and learning is an important driving force to enhance their competitiveness and total factor productivity (TFP). This paper constructs green TFP (GTFP) based on the SBM–Global–Luenberger index, using Chinese provincial panel data from 2003 to 2017. The marginal effects of foreign direct investment (FDI), outward foreign direct investment (OFDI), import and export, and intangible and total international technology spillovers (ITS) on GTFP were assessed. These effects were analysed under the influence of absorption capabilities such as research and development intensity, economic and financial, and openness. A spatial diffusion model based on three diffusion sources in China’s first-tier cities, Beijing, Shanghai, and Guangzhou, was constructed to describe the heterogeneous impacts of ITS on GTFP under the marketisation of diffusion sources. We found: (1) ITS based on exports and OFDI promote economic, imports and FDI are relatively small, independent technological innovation is the dominant source of economic growth. (2) Considering spatial diffusion, the contribution of Beijing’s ITS is relatively weak, and Shanghai’s contribution is stronger. Coastal areas have the largest marginal effect of ITS on GTFP, and ITS do not attenuate as geographic distances increase. (3) The market has a multiplier effect, modifying the relationship between ITS and GTFP.
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Notes
1 Since the measurement of ITS is by provinces, we estimate the ITS of Guangzhou City according to the formula below: Guangzhou’s ITS = Guangdong’s ITS × (Guangzhou’s GDP/Guangdong’s GDP).
2 This article selects G7 and countries with close trade relations with China. Considering the availability of data, we selected the United States, Britain, Germany, France, Japan, Canada, Italy, Australia, Brazil, India, South Korea, South Africa, Argentina, and so on.
3 Proportion of labor expenses and equipment purchase expenses in China's scientific and technological activities.
4 The partial effects of international technology spillovers on GTFP under the marketization process are as follows: , where
and
are the interaction terms and the coefficients of the international technology spillovers, respectively. Substituting the mean value of various international technology spillovers can obtain its bias effect, and the same can be used to obtain other interaction term bias effects.
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Xueli Wang
Xueli Wang is a student pursuing a PhD degree in the Institute for the Development of Central China, Wuhan University, Wuhan, China.
Lei Wang
Lei Wang is a professor of Institute for the Development of Central China, Wuhan University, Wuhan, China. His research interests include urban economics.
Song Wang
Song Wang is a lecturer of School of Business Administration, Northeastern University, Shenyang, China. His research interests include scientific and technological innovation, ecological environment and regional economics.
Fei Fan
Fei Fan is an associate professor in Institute for the Development of Central China, Wuhan University, Wuhan, China, and visiting scholar of the School of Earth and Environmental Sciences (SEES) at the University of Queensland, Brisbane, Australia.
Xiaohua Ye
Xiaohua Ye is a student pursuing a PhD degree in the Economic and Management School, Wuhan University, Wuhan, China.