In the digital age, network flow has profoundly altered both production and daily life, presenting new opportunities and challenges for places as well. The flow economy serves as a significant carrier of new quality productivity and a vital means to drive place development. Developing new quality productivity tailored to local conditions requires a key focus on the coupling relationship between flow and place. This study fo- cuses on the relationship between flow and place, summarizing the impact of flow on place development trends. It analyzes the restructuring of place by flow in spatial, emotional, social, and economic dimensions, constructing a schematic representation of the relationship between flow and place development. Three types of coupling between flow and place are delineated: strong coupling, weak coupling, and decoupling. Based on this analysis, suggestions for collaborative governance are proposed. Geography needs to enhance its interdisciplin- ary integration with other fields, delving deeper into the mutual shaping and construction processes and mech- anisms between flow and place. This will facilitate the innovation of place theories and serve the national strategy of modern governance. This endeavor seeks to provide a new theoretical foundation, practical strategies, and experiential insights for the localized development of new quality productivity, thereby promot- ing the sustainable development of the tourism industry with stability and long-term vision. Simultaneously, it aims to offer a fresh perspective for expanding and deepening research on the relationship between flow and place.

Extreme disasters such as floods caused by climate change seriously affect the sustainable develop- ment of rural areas. This article focuses on the vulnerability of rural disasters, taking 418 villages or communit- ies in Xun County, Henan Province as the research object. From three dimensions of exposure, sensitivity, and adaptability, an indicator system is constructed to comprehensively evaluate the vulnerability of rural areas un- der the impact of floods. Research shows that: 1) The average vulnerability of rural disasters in Junxian County is 0.466 (out of 1), which is at a lower than average level. The disaster exposure (0.72) and adaptability (0.76) are relatively high, while the sensitivity (0.45) is relatively low. There are spatial differences in disaster resili- ence performance among different townships. 2) Areas closer to rivers and with low-lying terrain naturally have higher exposure levels, and are more likely to suffer from flood impacts earlier than other areas, and tend to face greater disaster losses. At this point, a good early warning mechanism and preventive measures in the pre disaster stage can effectively reduce the vulnerability of rural disasters by improving adaptability. 3) When a flood occurs, the public is more inclined to protect personal property before the disaster poses a threat to per- sonal safety. This is reflected in the fact that the proportion of understanding and responding measures in each township is generally higher than the proportion of actually following orders. At this point, on-site guidance plays an irreplaceable key role in the face of disaster impacts. Based on the above research results, the article proposes policy recommendations to reduce rural disaster vulnerability and enhance rural disaster resilience.

Establishing and facilitating high-quality talent flows is a crucial way to China’s international talent cultivation and intellectual-attraction country construction. Drawing on international student mobility data among 39 countries including OECD countries and China in 2019, this research constructed a weighted and directed international talent mobility network, and employed social network analysis to explore its spatial patterns and influencing mechanisms. 1) International student exchanges were quite tight among the 39 countries, forming 4 visible regional communities, i.e., the North American and Asia-Pacific community, the South America and European community, the Nordic and Baltic community, and the Czechoslovakian community. China and the United States were centers of international talent exchange. Moreover, China, the United Kingdom, Germany, the United States and France were also the gateways and hubs of international talent flows. 2) The patterns of international student outflows and inflows of 39 countries were asymmetric. The outflows of Chinese students accounted for nearly half of the total, while the outflows of OECD countries were relatively balanced. The United States, the United Kingdom and Australia ranked the top 3 in international student attraction, followed by China with 7.17% international student inflows. 3) The impact of openness degree, education quality, economic level on international student mobility all suggested sender and receiver effects. The proximity in geography, language and culture, and commodity trade could promote the bi-directional talent flows. The structure dependence effect was an important driver to the evolution of international student mobility network, with the reciprocity, preferential attachment and transitive closure effects being most pronounced, which could somewhat substitute the effects of exogenous force. There is still much room for the improvement of the talent mobility among China and OECD countries. It can be optimized from the perspectives of the country’s endowments, multi-dimensional proximity, and network structures, to promote a more flat and diversified patterns of talent mobility among these countries.

Optimizing land use objectives offers effective tools for judicious resource allocation. Simulating future land use and carbon stock changes is vital for formulating regional sustainability policies and enhancing terrestrial ecosystem carbon storage. This paper analyzed the spatial and temporal differences in land use and carbon storage from 2000 to 2020, predicted the spatial and temporal differences in land use changes and car-bon storage under four development patterns, namely, “natural development pattern (BAU), urban develop-ment pattern (RED), cultivated land protection pattern (CPS), and ecological protection pattern (EPS)” in 2030 by constructing the coupled land use model (PLUS-InVEST), and estimated the economic value of the carbon storage by combining the formula of compounded present value and compounded terminal value in Lanzhou City from 2000 to 2030. The results showed that land use changes were dominated by the decrease of grass-land area and the increase of building land area in Lanzhou City from 2000 to 2020, with a loss of 61.77×10⁴t of carbon storage during the 20-year period. With the exception of the EPS, in which the carbon storage in-creased by 5.09 ×10⁴t, all other scenarios showed different degrees of carbon loss compared to the 2020, with the largest loss of carbon in RED at 56.46×10⁴t. In this study, the economic value of carbon storage increased by 10.3×10⁸yuan in Lanzhou City from 2000 to 2020 through the compound present value method, which is mainly attributed to the significant increase in carbon price on the time scale. Compared to the economic value of carbon storage in 2020, the EPS has the highest economic value of carbon storage at 34.58×10⁸yuan, which is the optimal development model for the study area. This study has important practical significance for the low carbon development of land resources and scientific decision-making of ecosystem management in Lanzhou.

Return migration has become a notable socio-economic trend in the new stage of China's urbanization, and the analysis of its driving mechanism has received extensive academic attention. As a micro behavior, the return of migrants is not only affected by personal and family factors, but also closely related to external environment. However, existing studies have focused on the role of individual factors, but not enough research has been conducted on the relationship between regional contexts and return migration. We introduce a gradient boosting decision tree model in the field of machine learning, based on the data from the 2017 China Migrants Dynamic Survey, with the return intention as the response variable and the regional contexts—Both in the place of origin and destination—As well as migrants' personal and household factors as the explanatory variables, focusing on the non-linear influence of the regional contexts on the return migration intentions and the threshold effect. The results show that: 1) The total contribution of the local contexts of the place of place of the origin and destination to the intentions of the migrants to return is 44.1%, which is an important factor influencing the return intentions, and the contributions of the two places is roughly equal. Among these, medical and health resources and air pollution are extremely important in both places. In addition, economic growth in the place of origin is also important for the return intention of migrants, while the climatic condition in the place of destination is more important; 2) There are both non-linear and linear relationships between local contextual factors and migrants' intention to return. Among them, medical and health resources, basic education resources, air pollution have obvious non-linear effects on the return intention, while economic growth and temperature conditions have mainly linear effects; 3) The influence of individual factors on return intention is mainly nonlinear effect. There is an irregular U-shaped relationship between age, migration duration and return intention, and the non-linear influence of household income is more complex. There is an obvious threshold effect between household housing expenditure and return intention, and a negative correlation between migrant's education level and return intention. This study incorporates the local contexts of the place of origin and destination into the analytical framework for the mechanism of return migration, identifies the relative importance of the local context and individual characteristics of the two places on the return intention of the migrants, and reveals the specificity and complexity of internal return migration in China, which contributes to deepening the research on migration in the new era and provides scientific reference for policy makers.