Main function zoning (MFZ) is the fundamental system of China's territorial development and protection. Through differentiated functional orientation, the core objectives of MFZ are to guide the orderly flow of resources across regions and optimize the spatial pattern of national development. This study evaluates the impacts, their heterogeneities among four types of zones, and underlying mechanisms of MFZ planning on China's population redistribution using county-level data from the three latest national population censuses and causal inference tests through difference-in-differences models. The results show that: The MFZ effects generally align with policy orientation. Post-implementation, optimized development zones, prioritized development zones, main agricultural zones and key ecological zones experienced declining population growth rates, accelerated population agglomeration, accelerated population decline, and growth-to-decline transition, respectively. Using main agricultural zones as reference, the planning reinforced the population concentration advantages of prioritized development zones. The policy effect exhibits dual heterogeneity across regions and hierarchical levels. Population changes in agricultural and ecological zones mostly complied with planning guidance, whereas only 40% of county-level units within prioritized development zones achieved the policy goal of accelerated agglomeration. National-level prioritized development zones demonstrated approximately twice the impact of provincial-level counterparts. Population migration from restricted development zones to urbanized areas predominantly occurred through cross-regional migration rather than intra-prefecture movements, making the policy goal of "concentrated equilibrium" within prefecture-level cities difficult to achieve. The planning promoted shifts in influencing factors of population redistribution within each type of MFZ. The diminished role of secondary industries and enhanced influence of tertiary sectors in optimized zones, along with weakened economic drivers in ecological zones, aligned with planning objectives. However, industrialization in prioritized development zones failed to significantly boost population agglomeration, while agricultural zones remained profoundly influenced by industrialization and economic development, both deviating from intended planning orientations. The findings suggest that the new-round planning could moderately concentrate urbanized area layouts, clarify development sequences for prioritized development zones, and implement more targeted differentiated governance requirements for restricted development zones.

China's population dynamics have entered a critical phase. Understanding the regional variations in human capital and the characteristics of its evolution is essential for grasping the new demographic normal in China and promoting high-quality population development. This study analyzes the spatial and temporal changes in human capital across China's prefecture-level units from 2000 to 2020, focusing on total human capital and labor force participation based on census and sample data. It also examines the influencing factors and mechanisms driving these changes. The main findings are as follows: (1) China's human capital demonstrates significant improvement, with an annual growth rate of 11.54% for total human capital and 12.47% for labor force participation in human capital from 2000 to 2020. However, the human capital index exhibits a downward trend, primarily due to the effects of demographic changes. (2) In both perspectives, regions with high human capital index values are primarily concentrated in the economically developed areas of eastern China and provincial capitals. (3) Significant regional disparities in China's human capital evolution are evident, with the human capital index showing divergence from 2010 to 2020 and an increasing prominence of labor participation in human capital stock. The growth of human capital in the eastern region remains robust, with provincial capitals and the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions consistently demonstrating the most substantial incremental growth. In contrast, more than half of the small and medium-sized cities experience a decline in human capital growth rates. (4) Socio-economic factors, including economic development and infrastructure, are identified as key drivers of regional human capital. In addition, demographic characteristics and other socio-economic elements influence the spatial and temporal variations in human capital, particularly regarding labor force participation and productivity. This study reveals regional differences in human capital across China and the underlying mechanisms by integrating both population scale and structure. It provides empirical evidence to support the strategy for promoting and implementing high-quality population development.

As China faces challenges such as an aging population, declining birth rates, and regional population imbalances, in the context of transitioning to high-quality and innovation-driven economic development, talent, as a carrier of specialized knowledge and technology, has become a new driving force for enhancing regional productivity, promoting economic growth, and fostering innovation. However, existing research rarely analyzes the driving factors of high-skilled talent and their impact on regional innovation from both spatial and temporal perspectives, making it difficult to fully reveal the spatial non-stationarity of the relationship between high-skilled talent and regional innovation and its changing trends over time. From the perspective of spatio-temporal heterogeneity, this study uses data from the fifth (2000), sixth (2010), and seventh (2020) national population censuses, as well as the 1% national population sample surveys conducted in 2005 and 2015. It constructs a Multiscale Geographically Weighted Poisson Regression (MGWPR) model to investigate the driving factors of the spatial distribution of high-skilled talent and their impact on regional innovation from 2000 to 2020. The main findings are as follows: (1) Both high-skilled talent and regional innovation show a growth trend, with a concentrated distribution in the core cities of urban agglomerations and strong provincial capital cities, and the spatial centers and hotspots of high-skilled talent and regional innovation exhibit a southward shift. (2) Compared to high-skilled talent, the spatial imbalance of regional innovation is more pronounced, but the spatial spillover effect is relatively weak. (3) The spatial distribution of talent is influenced by both regional economic development factors, such as income levels and industrial advantages, and local quality factors, such as healthcare, basic education, and air quality. The high-value areas of income levels have shifted from the southern to the northeastern regions, while the high-value areas of industrial advantages have shown an opposite trend. The impact of healthcare and basic education has transformed into a "dual-core" pattern, centered around the Yangtze River Delta and Pearl River Delta urban agglomerations or the Yangtze River Delta and Chengdu-Chongqing urban agglomerations. (4) High-skilled talent significantly drives regional innovation, with the average marginal impact generally increasing over time, although the marginal effect in high-value areas shows a weakening trend and has gradually shifted from northwestern, northeastern, and eastern regions to southern regions. The study's conclusions have important implications for implementing the national talent strategy, promoting high-quality development, and advancing innovation-driven growth in the new era.

The differentiation of population growth and decline across regions has emerged as a prominent trend in China's demographic landscape, drawing significant attention from both academia and society. Revealing its patterns and mechanisms from a geographical perspective is of great significance for supporting high-quality population development and promoting balanced regional population development. This paper first proposes a geographical analysis framework for regional population growth and decline based on the spatial and comprehensive attributes of the geographical discipline and relevant classical theories. Within the analytical framework, the spatio-temporal patterns and mechanisms of population growth and decline in prefectural-level units in China from 1982 to 2020 are empirically analyzed using five population censuses from the perspective of compositional structure. The empirical results show that the differentiation of prefectural population growth and decline gradually intensifies since 1990. While natural population growth in most regions remains positive, the number of regions experiencing negative growth has increased over the past decade. The trend of net migration in and out of regions continues to diverge, with the number of regions experiencing net outmigration steadily rising. Regional population growth and decline are jointly determined by natural growth and net migration, with the contribution of net migration continuously increasing. There has been a continuous upsurge in the number of regions experiencing population decline driven by net migration alone, as well as those jointly driven by both natural growth and net migration. The spatial scope of the former has expanded from certain inter-provincial mountainous areas to vast regions in the northeastern, central, and western parts of the country, while the latter is predominantly distributed in Northeast China. Concurrently, areas with population growth have progressively narrowed, confining to eastern provinces, provincial capital cities, Xinjiang, Xizang, and other areas. Regional natural conditions, level of economic and social development, policies, and other factors affect population changes through their impact on natural growth and net migration. The pathways of these various factors are heterogeneous and evolve dynamically over time. This paper concludes with a discussion of the mechanisms behind regional population growth and decline differentiation, as well as the policy implications for promoting balanced regional population development.

A series of regional major strategies has continuously been implemented since the 18th National Congress of the Communist Party of China (2012). The Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations are the core spatial carriers of regional major strategies. In the new situation of decreasing and aging population, the growth of employment and the changes in employment structure in the four major urban agglomerations will have a profound impact on the regional sustainable development. Based on the location quotient model, and spatial autocorrelation analysis, the spatial and temporal patterns of employment size and employment industrial structure in the four major urban agglomerations were examined by using the sectoral population data from the 2000, 2010 and 2020 national population censuses. Comparative analysis was conducted in the context of regional major strategies to explore the regularities of urban agglomeration employment growth and structural transformation. The study finds that: (1) The scale of employment in the four major urban agglomerations continues to grow, but the overall growth rate slows down. The overall pattern of employment distribution remains stable, and the high-value areas of employment growth rate have shifted from the central cities to the surrounding cities of the urban agglomerations, forming a trend of balanced distribution and multi-point support within the urban agglomerations. (2) The trend of advanced and high-tech development in employment structure is obvious, and the distribution of employment types within urban agglomerations presents a clear "core-periphery" pattern. The central city always maintains a structure of "high-tech manufacturing and high-tech service industry", while the employment structure of the cities near the central city of the urban agglomeration is characterized by an alternating distribution of "low-tech manufacturing and high-tech service industry" and "high-tech manufacturing and low-tech service industry". The majority of employment in peripheral cities of urban agglomerations belongs to the structural type of "low-tech manufacturing and low-tech service industry". (3) The continuous growth of the employed population and the advanced and high-tech employment structure have created conditions for the four major urban agglomerations to carry out regional major strategies and participate in global competition. However, urban agglomerations face different challenges. It is essential to strengthen the multiplier effect of high-tech employment, promote the qualitative transformation of non-high-tech employment, and implement differentiated employment attraction policies to foster coordinated development among urban agglomerations and ensure the effective implementation of regional major strategy.

Understanding the transformation process and regional differences of China's low fertility-aging types is of great importance for summarizing the historical experience of modernization with Chinese characteristics, and it is also a foundational task for improving population development strategies with a focus on addressing low fertility and aging. This paper constructs a joint framework of low fertility and aging to define the types of low fertility-aging, and uses population census data from 2000 to 2020 to analyze the spatiotemporal evolution characteristics of China's low fertility-aging types at multiple spatial scales. It also interprets the evolution characteristics and formation logic of typical regional low fertility-aging types. The study finds that: (1) In terms of temporal characteristics, most regions have experienced a transformation in low fertility-aging types over the past two decades, generally following a trajectory from multiple children-not aging, to predominantly low fertility-mild aging, and then to a balanced distribution of low fertility-moderate aging, low fertility-mild aging, and multiple children-mild aging. As the spatial scale becomes more detailed, the types and transformation paths of low fertility-aging tend to become more diverse and dispersed. (2) The spatial pattern of low fertility-aging type transformation exhibits a characteristic of expanding from points to areas: regions such as Jiangsu, Zhejiang, Shanghai, Beijing, and Northeast China are at the forefront of the transformation, followed by other eastern coastal and most central and western provincial-level areas, but most ethnic groups-inhabited areas in northwest and southwest China have not yet begun to transform. At the same time, the Central China Plains and southeast regions show a characteristic of asynchronous transformation in low fertility and aging. (3) Taking the county level as the evaluation unit, and based on local perspectives, the study identifies six typical regional patterns at the national level, including the Northeast, the Yangtze River Delta and Beijing-Tianjin, the Pearl River Delta, the Southeast, the Central China Plains, and the ethnic groups-inhabited areas in the northwest and southwest, by analyzing the commonalities and differences in the transformation process and main driving factors of low fertility-aging types. The conclusions not only provide theoretical references and practical guidance for various regions to deeply optimize national population strategies and improve fertility support policies in a location-specific manner, but also contribute new perspectives and insights to the study of population transformation, promoting the establishment of a community with a shared future for humanity in actively addressing low fertility and aging in developing countries.

As China experiences rapid population aging and the diversification of elderly care, the scale of residential mobility among older adults is rising. This trend poses challenges to individual health, family well-being, and the governance of an aging society. Drawing on existing theories of elderly residential mobility, this paper develops an analytical framework tailored to China's unique institutional and socio-cultural context. Using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2018, this study employs panel logit regression models to examine the mechanisms influencing relocation intentions and behaviors among older adults, with a focus on two key dimensions: relocation conditions and motivations. The main findings are as follows: (1) While the overall relocation rate of older adults in China is comparable to that of developed countries, relocation intentions remain relatively low, with unplanned relocations and abandoned intentions being common. In general, urban residents exhibit higher relocation rates than their rural counterparts. Both relocation intentions and behaviors are more prevalent in larger cities. (2) Residential mobility of older adults in China typically follows a nonlinear decision-making process, influenced by demographic characteristics, socio-economic conditions, family and intergenerational relationships, and life-course events, while health, environmental and institutional factors have minor impacts. (3) The factors affecting relocation intentions differ from those affecting actual relocation behaviors: external environmental features and life-course events strongly shape relocation intentions, while socioeconomic conditions and intergenerational relationships play a critical role in actual relocation behaviors. The transition from intention to behavior is significantly mediated by relocation capability.

The mask serves as an auxiliary technique for constructing the population density random forest model. The previous population density random forest model suffered from issues such as questionable sample quality, ecological fallacy within the model, distortion of the population distribution law, inability to perform test-retest reliability checks, failure to ensure reproducibility and weak replicability, etc. To address these issues and improve the test-retest reliability and criterion validity of the population density random forest model, the specialized masks for points, bands and surfaces have been developed based on the traditional mask system. By incorporating specialized masks, this study standardizes the modeling workflow of population density random forest models, systematically resolving the persistent challenges inherent in conventional approaches. The study shows that a comprehensive mask system consists of control masks (including research area mask, zonal mask, calculative mask, zonal calculative mask, and total sampling frame mask), sampling-related masks (including zonal sampling frame mask and zonal sampling mask), dasymetric mapping masks (including county mask or township mask), and testing masks (including reliability testing mask, validity testing mask, and boundary testing mask). The mask system is a key basis for standardizing the modeling process of the population density random forest model. The complete mask system provides a process-oriented framework for improving the population density random forest model. It not only effectively improves the test-retest reliability and criterion validity of the model, but also ensures the reproducibility of the model at the same point in time and the weak replicability of the model at different points in time. Furthermore, the mask system provides a reliable assistive technology for quantitatively characterizing the spatio-temporal evolution of population distribution and influence mechanism. The comprehensive mask system also provides a systematic and structured solution for standardizing modeling workflow of the spatial disaggregation algorithm applied to other social and economic statistics.