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The rapid global spread of the coronavirus disease 2019 (COVID-19) is now a reality. China has taken urban traffic control measures to prevent and control the epidemic, but this has prevented the flow of people between cities. This study investigates the mechanism of the impact of urban traffic control measures on the intercity population flow in China using the one-way causal measurement method. The results show that the impact of urban traffic control measures on the intercity flow of the population changes with time. Based on this, this study makes scientific suggestions for the government on how to reasonably undertake traffic control measures.

We conducted sentiment classification for news reports during the COVID-19 pandemic on Sino-Japanese relations from Chinese and Japanese media. Based on the Word2vec and Dynamic Topic Models, we analyzed the macroscopic dimension of theme distribution and the semantic change process of “Sino-Japanese” related words. The results indicate that Chinese media reports are mainly neutral, but Japanese ones are mainly negative. The meaning of the terms related to “Sino-Japanese” has not changed significantly, but there are minor differences in the content of reports on the same subject in different periods. During Suga’s administration, the overall stability of Sino-Japanese relations and the continued advancement of pragmatic cooperation are expected to continue.

We combine the Apriori data mining algorithm and smooth cut-point calculation to build a model that uses microscopic individual data to predict fertility behavior. The data of China’s migrant population from 2013 to 2015 are used to predict the reproductive behavior of migrant women. The accuracy of the prediction results is over 84%. The model also quantifies the extent to which the existing characteristics of individuals influence their reproductive behavior. The government can regulate individual fertility behavior based on the quantified scores.

The improvement of a country’s technological innovation level is influenced by the technology spillover of inward foreign direct investment (IFDI) and outward foreign direct investment (OFDI). Based on the Coe and Helpmen’s theory of international capital flow model and one-way causality measure model, this study examines the similarities and dissimilarities between the dynamic effects of IDFI and OFDI on technological innovation in China and Japan to enumerate the differences in the utilization effect of FDI between developed and developing countries. The empirical results show that the one-way causality intensity of IFDI to technological innovation in China is weaker than that in Japan, but the FDI volatility in China is stronger than that in Japan. The one-way causality intensity of OFDI to technological innovation are low both in China and Japan, and the patterns of long-term and short-term effects are not identical. According to the results of our empirical research, we draw the conclusions and proposed suggestions for using IFDI and OFDI in China and Japan.

Based on the panel data of R&D activities of the provincial high-tech industry in China from 1998 to 2014, this paper adopts the spatial weight matrix of different dimensions including geographical distance, technical distance, economic distance, proximity distance, and human capital distance, to construct a spatial econometric model to analyze the knowledge spillover effects of R&D activities through both local and transnational routes. The results show that in the case of spatial auto-correlation of the dependent variables, the results of the spatial panel model are more accurate and reliable than those obtained by the conventional panel model. The spatial coefficients of the spatial econometric model based on five different spatial weight matrices are all very significant, and there is a clear spatial correlation between the R&D activities of high-tech industries in different regions. Labor input and exports have a positive impact on innovation output, but the introduction of technology will hinder independent innovation in China’s high-tech industry, and the impact of capital investment to innovation output is uncertain, as it closely relates to the set of models. In addition, the space knowledge spillover effect through the local approach is larger than that produced by the transnational route.