Narrow Results

Free trade zone (FTZ) in China has been demonstrating remarkable achievements since its establishment, yet its effects on the environment in the zones cannot be ignored. However, there is still a lack of research on the impact of the quasi-natural experiment in the China pilot FTZ on China’s environment. Based on this, this paper uses panel data from 196 cities in China from 2010 to 2017 and uses the propensity score matching and difference in difference (PSM-DID) model to empirically test the environmental effects of the establishment of the FTZ. The result shows that there is an obvious causal relationship between the establishment of the FTZ and environmental quality. The establishment of the FTZ has exacerbated the environmental pollution problem in the pilot zone. Through a series of robustness tests, it is concluded that the estimated results of the benchmark model are robust. However, after a further study on whether the effect of the FTZ on environment is time varying, it was found that the effects of the FTZ on the environmental pollution in the test zones gradually weakened over time, which means that with the gradual maturity of China’s free trade pilot zone, the positive effect on environmental improvement will gradually highlight.

Artificial intelligence (AI) is the most significant technological revolution since we entered the 21st century. It has become a new focus of public attention and international competition. Industrial integration with AI technology not only brings vast opportunities for transformation and upgrading of enterprises but also has an impact on employment structure. Focusing on the fusion of the manufacturing industry integrating AI, we analyze the integration progress of AI and segmented manufacturing industries, describe a supply-and-demand situation of labor market with different skills, and discuss the impact of AI technology on manufacturing employment theoretically. Then we construct the propensity score matching–difference-in-difference model, divide intelligent manufacturing enterprises into various categories, and inspect the influences on the employment structure of different segmented manufacturing enterprises before and after integrating AI technology. Finally, we put forward efficient methods of transformation and upgrading of manufacturing enterprises and practical suggestions to solve problems on employment structure.