Based on the theoretical mechanism analysis of FDI, regional innovation, and green economic efficiency, this article uses China’s provincial panel data to calculate the provincial green economic efficiency level based on the three-stage DEA method and uses the system GMM model, intermediary effect model, and threshold model to empirically test the specific effects and transmission paths of FDI on the efficiency of the green economy. Research shows that FDI is one of the important factors that promote the improvement of green economic efficiency. Subregional tests have found that FDI has a significant regional heterogeneity in promoting the efficiency of the green economy. The mediation effect test found that the mediation effect of regional innovation is significant, and FDI can significantly promote the growth of green economic efficiency through regional innovation. The threshold effect analysis found that there are significant and effective double thresholds for regional economic levels, and the impact of FDI on green economic efficiency is heterogeneous within different threshold intervals. The research conclusions provide new inspiration for China to allocate FDI more rationally and efficiently under the new development pattern.

This paper uses WIOD data to calculate and analyze the dominant comparative advantage of Chinese manufacturing global value chain (GVC) based on the WWZ method and empirically studies the influence of digitization on the competitiveness of manufacturing GVC. The main findings are as follows: (1) The competitiveness of Chinese manufacturing GVC has been improved as a whole. The GVC competitiveness of different types of industries is quite different: GVC in middle- and low-knowledge-intensive industries have the highest competitiveness, while those with middle- and high-knowledge-intensive industries have the lowest competitiveness and apparent shortboard industries. (2) Digitization is conducive for enhancing the competitiveness of manufacturing GVC. In terms of industries, digitization has a significant positive effect on the GVC competitiveness of middle and high-knowledge-intensive industries but not on low knowledge ones. As for the sources of digital input, the positive effect is more outstanding. Also, it is more remarkable when it comes to the software and information technology service industry. (3) As for the mechanism of action, labor productivity is an effective way to improve the manufacturing GVC competitiveness digitally. Finally, after a robustness test, the positive effect of the digital input remains robust.

Based on the panel data of 30 provinces (except Tibet) in China from 2012 to 2019, this paper uses the ML index method and spatial econometric model to evaluate the impact and spillover effect of innovation investment on environmental total factor productivity. The study found the following conclusions. (1) Environmental total factor productivity showed a fluctuating upward trend as a whole, and the driving force of environmental total factor productivity growth mainly came from technological progress. (2) There is global spatial correlation and positive spatial spillover effect in regional environmental total factor productivity. There is a certain spatial correlation in provincial environmental total factor productivity, and the improvement of environmental total factor productivity in this region will promote the improvement of environmental total factor productivity in surrounding areas. (3) Innovation investment, economic development, and foreign investment level play a significant role in promoting the improvement of environmental total factor productivity, and energy structure and human capital level have a negative impact on the improvement of environmental total factor productivity. (4) Innovation investment, economic development, human capital, and foreign investment have positive spillover effects on the improvement of environmental total factor productivity in the surrounding areas, while the energy structure shows negative spillover effects.

Based on the sample data from 2005 to 2019, this paper calculates the poverty nature of contiguous destitute areas through FGT index and its decomposition and systematically analyzes the impact of economic growth, inequality, and population change on poverty change. From the decomposition results of poverty change, we can see that, first, economic growth, inequality, and population change have different impacts on poverty change in counties and rural areas, and inequality and population mobility have widened the gap between them; second, population factor has always played a key role in the change of poverty, and the deceleration of population growth has a more significant impact on poverty change; third, the impact of the mobility on the poverty change of the counties is different from that of the rural areas. Accordingly, the paper puts forward some countermeasures and suggestions, such as promoting the organic connection between rural revitalization and poverty alleviation, speeding up rural governance, and promoting the process of urbanization.