The Spatio-Temporal Dynamics, Driving Mechanism, and Management Strategies for International Students in China under the Background of the Belt and Road Initiatives

Abstract

The management of international students has become a new challenge that China and most countries in the world must face in the context of the “Belt and Road Initiative” (BRI) and the globalization of higher education. In this paper, we conducted an empirical study on international students in China (ISC) based on a combination of spatial econometric models and analytical methods such as BCG, GeoDetector, and DDCAM, trying to provide a basis for “evidence-based decision-making” by the government in the management of international students and talents. Quantitative analyses revealed significant diversity and spatial effects in the spatio-temporal dynamics of ISCs, with the emergence of star, gazelle, cow, and dog types, suggesting that the scientific management of ISCs requires both local adaptation (spatial differentiation and heterogeneity) and regional collaboration (spatial correlation and autocorrelation). There were many factors affecting the spatio-temporal dynamics of ISCs, and the force of different factors varied considerably, with the key factor more than 0.5 but the auxiliary factor being less than 0.2. Moreover, the factors had significant interaction effects, and a large number of super-interacting factor pairs emerged, with a joint force of more than 0.9, twice the force of a single factor. Generally, the factors had a complex driving mechanism, suggesting that ISC management requires identifying the key and important factors, while attaching emphasis to the synergistic effects among different factors. The recommendation is that China should manage ISCs in spatial zones and introduce differentiated development strategies and policies in different zones. In conclusion, this paper proposes a technical route integrating “dynamic analysis-driving mechanism-policy design” for international students, which is applicable to China, while providing a reference for the management and spatial planning design of international students in other countries in the world.

Evolution Pattern and Spatial Mismatch of Urban Greenspace and Its Impact Mechanism: Evidence from Parkland of Hunan Province

“…In the comparative analysis between GWR and OLS, if the R2 of the former is greater than that of the latter, especially if the difference between the AICc (Akaike Information Criterion, corrected) of the former and the latter is more than 3, it indicates that the fitting effect of GWR is better than that of OLS, and that the inclusion of spatial effects in the regression model significantly improves the precision of the results of the analysis [73]. The equations are as follows [74,75]:…”

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

“…W ij represents the spatial weight matrix between cities in the YRD, where 1 indicates adjacent spaces and 0 indicates non-adjacent spaces; ULL i represents the logistics land of City i in the YRD, ULL is the mean value of urban logistics land in the YRD, and n = 41. Moran'sI and G * i (d) are calculated as follows [55,56]:…”

Research on the Geographical Pattern, Evolution Model, and Driving Mechanism of Carbon Emission Density from Urban Industrial Land in the Yangtze River Economic Belt of China

“…The geographical pattern of carbon emission density of urban industrial land and its change is a complex process, affected by the combined effect of economic scale, industrial structure and industrialization level, government intervention, degree of opening up, scientific and technological levels, and other factors. Based on the research experiences of related scholars, we chose the gross domestic product (GDP) to represent the impact of the scale effect of the urban economy in this paper [51,52], the proportion of the tertiary industry (PTI) and per capita GDP (PCGDP) to represent the industrial structure and the industrialization process [53,54], the fiscal expenditure (FE) to represent the impact of government intervention [55], the international trade goods export (ITGE) and foreign direct investment (FDI) to represent the influence of openness to the outside world and the degree of globalization [56], and the number of patent authorizations (NPA) to represent the impact of innovation [57]. It is notable that the distribution and evolution of carbon density come close to the natural environment, especially the geomorphology [58], but they are not included in the analytical framework of this paper due to the lack of the necessary complete data.…”