As the “ageing echo” of the “Baby Boom”, the “Ageing Boom” has not yet received sufficient scholarly attention. Since the founding of the People's Republic of China, the nation has experienced four distinct “Baby Boom” cohorts, which are projected to transform into “Ageing Boom” during the periods 2010-2018, 2022-2036, 2041-2054, and 2071-2079 respectively. Each “Ageing Boom” represents a peak in the growth of the new elderly population, and its cumulative and superimposed effects will drive China's ageing population through phased transformations characterized by sequential stages: ascent, peak, buffer, and decline. China's demographic ageing under the “Ageing Boom” paradigm exhibits both distinctive and general characteristics: a historically unprecedented population scale, asynchronous growth in size and proportion, ageing of the age structure, balancing of the gender structure, and steadily rising survival rates. Focusing on the ongoing second “Ageing Boom”, projections indicate that approximately 330 million individuals will enter old age, with urban residents and population with at least secondary education constituting the majority of this elderly cohort. These demographic shifts present dual implications—harboring developmental opportunities while simultaneously posing practical challenges that require prudent consideration and proactive responses.

Under the background of negative population growth, whether the scale of China's floating population will continue to grow and how its structure will change in the future are related to the construction goals of relevant social systems. Based on the data of the fifth, sixth, and seventh national population censuses, age structure standardization and decomposition methods are used to analyze the relative role of structure effect, scale effect, and intensity effect in the change of China's floating population in the past 20 years, and population projections are used to examine the future trend of the floating population. Research has found that the increase in the size of China's floating population in the past 20 years is mainly due to scale and intensity effects. Among them, the increase in the size of the urban floating population is mainly due to scale effects, while the increase in the rural floating population is mainly due to intensity effects; The structure effect has always had a negative impact, with the negative effect in the last 10 years being greater than that in the first 10 years. This negative structure effect shows similar behavior among different types of floating populations. Assuming the age-specific migration rate remains unchanged, the size of China's floating population will continue to decline in the future, and its age and urban-rural structure will undergo significant changes. The structure effect can be regarded as an intrinsic factor of natural growth, and the study of China's floating population must be situated within the context of negative population growth and population ageing.

Based on data from population censuses and a specialized survey, this paper systematically examines the trends, characteristics, and determinants of late marriage and non-marriage in China from 1990 to 2020. The findings reveal that the size of the never-married population aged 30 and above has been growing since 1990, with a larger absolute increase among men but a faster growth rate among women. The urban never-married population surpassed the rural never-married population after 2010. Among the never-married population aged 30 and above, those with a junior high school education constitute the largest increment, while those with a college degree or higher exhibit the fastest growth rate. In addition, the lifelong never-married population has grown, with men outnumbering women and in rural areas exceeding in urban areas. While the largest increment occurred among those with lower educational attainment, recent years have witnessed accelerated increases in both the size and prevalence of non-marriage among highly educated groups. Further analysis reveals that never-married people aged 30 and above exhibit low marriage intentions and demonstrate passive dating behaviors, which are likely to further depress marriage rates and elevate late marriage and non-marriage rates. Structural mismatches in marriage markets and economic pressures are the predominant factors influencing marital decision-making. The study proposes marital support policies focusing on expanding partner-matching networks and reducing the economic costs of marriage.

Diverging from patterns observed in developed countries, China has experienced a uniquely rapid fertility decline occurring at an earlier socioeconomic development level than would be predicted. Based on data from a special survey on groups who remain unmarried, childless, or have few children in China, this study identifies three distinctive factors contributing to China's ultra-low fertility: childcare challenges, education anxiety, and housing cost burden. Against the backdrop of a rapid “refamilialization” of caregiving responsibilities, the nurturing capacity of families has weakened while parental obligations have intensified. The intersection of traditional values emphasizing education and an intensely competitive selection system subjects parents to growing pressure and responsibilities in their children's education. Meanwhile, population concentration, unequal housing renter-owner rights, and inadequate housing security have sustained high housing costs. These factors significantly increase the financial, time, and psychological costs associated with raising children, resulting in suppressed fertility intentions. Effective pronatalist policies should remove barriers to fertility decision-making and target these three constraints unique to China's childbearing-age population.

Fertility support policies are essential for addressing low fertility rates, and it is crucial to promptly evaluate their effects on fertility. Based on the data from a specialized survey in China, this study adopts the Logit model to examine the impact of expected policy effects on fertility plans across various parental statuses and their mechanisms. The results show that positive expectations of policy effects significantly promote fertility plans. Paid parental leave and universal childcare are particularly effective for married but childless individuals. Paid parental leave and childcare subsidies also support fertility plans among parents with one child. The impacts of expected policy effects are heterogeneous across marital statuses and socioeconomic backgrounds. A positive fertility concept positively moderates the impact of policy effects. Favorable expectations for multiple policies can generate synergistic effects in promoting fertility plans. The findings suggest that we should publicize fertility support policies, enhance policy targeting, highlight the power of culture, and improve policy synergy.

Based on the 2021 survey data on fertility and childcare service intentions of childbearing-age parents, the study found that the sharing model of family childrearing responsibility is mainly divided into two categories, namely the “male as breadwinner” model and the “dual earner-dual carer” model. Compared to the “male as breadwinner” model, the “dual earner-dual carer” model can significantly reduces the intention to have another child of childbearing-age gruop. The paid maternity leave policy (time-support policy) exerts a negative moderating effect, enhancing the negative impact of the “dual earner-dual carer” model on the intention to have another child, while the tax reduction policy (economic-support policy) plays the opposite effect, weakening the negative impact of the “dual earner-dual carer” model on the intention to have another child. The childcare services (service-support policy) have not produced significant effects. These findings suggest that the government should optimize the fertility support policies by fully considering the sharing model of family childrearing responsibility, in order to reduce childrearing pressure and promote fertility intention through reasonable division of childrearing responsibility among different subjects.

In recent years, the Chinese marriage market has witnessed emerging trends of high divorce rates and an increasing number of divorces. Remarriage, as a personal choice for divorced adults, has become an increasingly significant marital status that cannot be overlooked. Based on the theoretical framework of assortative mating that incorporates preferences and opportunity structures, this study explores the logic of assortative mating in remarriage in China. By merging eight waves of data that meets research requirements from the Chinese General Social Survey (CGSS) between 2006 and 2021 and the 2010 China Family Panel Studies (CFPS) data, this paper investigates the matching characteristics and group differences in age and educational assortative mating in remarriage compared to first marriage. The findings indicate that age and educational heterogamy are the dominant patterns in remarriage, with gender differences in age assortative mating being significantly greater than those in educational assortative mating. The “older husband-younger wife” marriage pattern overwhelmingly dominates all remarriage age assortative mating patterns among men, and this advantage strengthens as men's remarriage age increases. The increased educational heterogeneity in remarriage is reflected in both men and women partnering with those of lower educational levels. However, the impact of educational level on educational assortative mating in remarriage varies by gender.

Based on data from the national censuses and other sources, this paper examines the characteristics and trends of cross-county migration in border counties from 2000 to 2020, and explores the long-term and periodic factors influencing out-migration and in-migration in border counties. Since 2000, both the out-migration and in-migration rates in border counties have increased, with population decline driven by net outflows becoming the dominant trend. In the long term, the urban wage levels and medical conditions are the main drivers of cross-county out-migration, while the proportion of non-agricultural industries, urbanization rates, and preferential policies are the key factors attracting people to border counties. Out-migration rates in border counties were initially influenced by transportation infrastructure and educational attainment, whereas they are currently affected by socio-economic factors and living conditions. Meanwhile, cross-county in-migration rates were also initially influenced by transportation infrastructure but later became related to preferential policies, medical conditions, and urbanization rates. It is necessary to significantly improve the living conditions and socioeconomic development in border counties suffering from net outflows and population decline, and to enhance economic growth and talent attraction in border counties with net inflows. Most importantly, promoting two-way mobility among diverse ethnic groups in border areas.