Geographical Types and Driving Mechanisms of Rural Population Aging–Weakening in the Yellow River Basin

Abstract

Population aging–weakening has become a critical constraint on rural sustainability in China’s Yellow River Basin (YRB), posing substantial challenges to ecological conservation and high-quality development. This study develops a multidimensional evaluation framework categorizing rural aging–weakening into four typologies: general development type (GDT), shallow aging–weakening type (SAT), medium aging–weakening type (MAT), and deep aging–weakening type (DAT). Then, the XGBoost model is used to assess the factors influencing the spatial diversity of aging–weakening types in the rural population at different spatial and temporal scales. The key findings reveal the following: (1) The proportion of aging–weakening areas increased from 65% (2000) to 72% (2020), exhibiting distinct regional trajectories. Upper reaches demonstrate severe manifestations (34% combined MAT/DAT in 2020), contrasting with middle reaches dominated by GDT/SAT (>80%). Lower reaches show accelerated deterioration (MAT/DAT surged from 10% to 31%). (2) Spatial differentiation primarily arises from terrain-habitat conditions, industrial capacity, urbanization, and agricultural income. While most factors maintained stable directional effects, agricultural income transitioned from positive to negative correlation post-2010. Upper/middle reaches are predominantly influenced by geographical environment, with the role of socioeconomic factors gradually increasing. Lower reaches exhibit stronger economic–environmental interactions. (3) This research provides actionable insights for differentiated regional strategies: upper reaches require ecological migration programs, middle areas need industrial transition support, while lower regions demand coordinated economic–environmental governance. Our typological framework offers methodological advancements for assessing demographic challenges in vulnerable watersheds, with implications extending to similar developing regions globally.

1. Introduction

The longstanding dual urban–rural system and city-centered development strategy in China have increased the demand for rural resources and production in urban areas [1,2,3]. This shift has weakened rural communities, leading to challenges such as population decline, a shrinking youth demographic alongside a growing aging population, reduced fertility rates, and brain drain [4,5,6,7]. These factors create significant obstacles to comprehensive rural revitalization and modernization. In the ecologically delicate YRB, the urgent challenges posed by slow socioeconomic development and stark urban–rural disparities are further exacerbated by the rapid aging and decline of rural populations. This demographic crisis has emerged as a formidable barrier to both ecological conservation and sustainable, high-quality development. Data from 2000 to 2020 reveal a concerning trend: the proportion of children aged 0–14 in rural counties has decreased by 7.3%, while the elderly population (aged 65 and older) has surged by 10.6% [8]. Concurrently, the working-age population (ages 15–64) has contracted by 1.4%, and the number of women of childbearing age has plummeted by 9.9%. This profound demographic shift is not just a statistic; it significantly undermines resource utilization, stifles industrial transformation, hampers ecological protection efforts, and threatens the very fabric of cultural preservation in the basin’s rural communities. It is imperative that we conduct a comprehensive assessment of the spatial and temporal patterns, evolutionary mechanisms, and multidimensional impacts of aging rural populations in the YRB. Such an analysis is crucial for crafting targeted and effective revitalization strategies that can safeguard both the environment and the communities that rely on it.

Current research on rural demographics, both domestically and internationally, has focused on critical issues including population loss, rural hollowing, aging populations, and gender disparities in women’s employment. These can be described as follows: (1) Rural Depopulation and Hollowing: Since 2000, China has witnessed a dramatic decline in its rural population, particularly in the eastern coastal regions, traditional agricultural zones in the center, and peri-urban villages [9,10,11,12]. This alarming trend has not only led to a persistent outflow of residents but has also caused a systemic degradation of rural functions. The repercussions are severe, with transformation rates faltering and vast tracts of land being abandoned [13,14]. In the YRB, the situation is equally concerning, as official statistics reveal a staggering reduction of 45.62 million rural residents from 2000 to 2020. The most pronounced losses are evident in the middle and lower reaches of the basin, resulting in a proliferation of hollowed-out villages that illustrate the stark reality facing rural communities today [14,15]. (2) Rural Aging: Studies reveal that rural areas exhibit an older population structure compared to their urban counterparts [16], with over 80% of China’s rural counties now classified as aging societies, predominantly distributed northwest of the Hu Huanyong Line and in the Greater Khingan Range region [17]. This demographic shift stems directly from low fertility rates, increased longevity, and outmigration, while socioeconomic development constitutes its fundamental driver [18,19,20,21]. Accelerated rural aging has precipitated the “graying” of agricultural labor forces [22], a weakening economic structure in households [23], and heightened risks of farmland abandonment and non-grain conversion [24,25], collectively exerting substantial adverse impacts on rural socioeconomic development [26,27,28]. Concurrently, emerging scholars examine the marginalization of elderly rural residents as a vulnerable group through the lens of healthy aging, alongside systemic pressures on pension systems and corresponding policy implications [29,30]. Multidimensional policy interventions—including the restructuring of public systems and the revitalization of the role of the family—are increasingly advocated as essential pathways to address aging-induced challenges [31,32]. (3) Rural Women’s Employment and Gender Disparities: Massey and Little argue that space is socially constructed, with human habitats being deeply gendered environments [33,34]. In particular, rural areas often link femininity to natural and pastoral landscapes, which reinforces the male gaze and upholds patriarchal dominance over rural and wilderness spaces [35,36]. However, urbanization, digitalization, and modernization have created more off-farm employment opportunities for rural women, improving their social status and empowerment [37,38]. Despite these advancements narrowing gender gaps, challenges remain [39,40,41]. Deeply rooted rural ideologies and competitive labor markets continue to expose rural women to work–family conflicts [42,43,44,45], occupational segregation into contingent roles, and increased economic precarity among older female cohorts [46,47,48]. Ironically, the rise in female employment participation can contribute to rural depopulation by undermining traditional household roles [49,50], lowering fertility rates, and accelerating demographic transitions. This situation highlights the complex trade-offs involved in rural gender equity initiatives.

Recent research has made significant progress in understanding rural population decline, its causes, and the socioeconomic impacts on marginalized groups. This work has substantially advanced our knowledge of rural demographic transitions. However, most studies tend to focus on isolated aspects of rural population dynamics, often overlooking the combined phenomenon known as “population aging and weakening”, his study introduces the concept of “population aging–weakening”, which encompasses factors such as rural population loss, aging, low fertility, and population burden. In the context of globalization, marketization, and rapid urbanization, rural areas are experiencing population loss, which leads to an accelerated decline of the countryside [51,52]. This situation poses significant challenges to the sustainable development of these rural regions. As a result, identifying the issues faced by rural populations in the Yellow River Basin—along with understanding regional differences, the mechanisms behind these changes, and their impacts on the socio-economic and ecological environments—has become an important area of research. It proposes an analytical framework that includes an indicator system with three dimensions: demographic structure, development dynamics, and reproductive potential. This system aims to assess the level of rural population aging–weakening and classify its geographical types. Additionally, the study employs the XGBoost model, a machine learning algorithm, to identify the factors influencing rural population aging–weakening and to explore the mechanisms behind its development in the YRB. The analysis focuses on resources, environmental factors, economic development, and social change, with the goal of providing scientific guidance for the sustainable development of rural populations in the YRB and for the modernization of rural areas.

This study aims to evaluate the geographic characteristics of rural population aging and decline in the YRB, while also analyzing their spatial and temporal differences, as well as the influencing factors, utilizing the XGBoost model. The main components of the study include: (1) the development of an evaluation index system for rural population aging and decline, alongside the identification of geographic categories of rural population aging within the Yellow River Basin; (2) an exploration of the spatial–temporal variations in these geographic categories; and (3) the application of the SHAP interpretation package based on the XGBoost model to determine and rank the importance of the influencing factors. Overall, the integration of qualitative and quantitative methods for classifying the geographic types of rural population aging and decline, along with the identification of influencing factors through the XGBoost model, demonstrates commendable effectiveness in this study.

The paper is organized as follows: The Section 2 establishes a theoretical analytical framework for understanding rural population aging and decline, while the Section 3 provides an overview of the research area, including data sources and research methodology. Section 4 presents the study’s findings, Section 5 offers a discussion, and Section 6 concludes the paper.

2. Theoretical Analysis Framework

Population aging–weakening refers to the process in which a specific geographical area experiences a sustained increase in the proportion of individuals with decreased resource acquisition capabilities or reliance on others for livelihood support, due to various internal or external factors [53,54]. This phenomenon manifests through three main dimensions: the aging of the population structure, inadequate demographic growth rates, and declining reproductive potential. The transition of the rural population is a direct manifestation of the evolution of the rural territorial system and results from the interaction of multiple factors within the urban and rural territorial systems. Among these factors, the geographical environment serves as the fundamental driving force for rural demographic transition, while economic development plays a key role, and social change acts as a direct influencing factor.

The geographical environment serves as the foundational factor for rural population evolution. Favorable climate and topography, adequate water resources, and fertile land constitute essential conditions for population agglomeration [55]. In areas with inferior resource environments, such as arid deserts, alpine mountainous regions, and humid tropical forests, the basic ecological requirements for human survival—water, soil, air, and living conditions—remain suboptimal, characterized by lower life expectancy and constrained population mobility, resulting in relatively stable demographic patterns. However, improved transportation infrastructure in high-altitude regions has recently facilitated more frequent rural population movements. Conversely, regions with fertile land, ecologically favorable habitats, and abundant water resources typically exhibit dense population concentrations, though they demonstrate greater volatility due to relatively unimpeded mobility.

The Yellow River traverses three topographic terraces: its upper reaches flow through the Qinghai–Tibet Plateau, characterized by high elevation, limited precipitation, and barren land; the middle course passes through the Taihang Mountains and Loess Plateau, featuring moderately high elevation, relatively low rainfall, and poor soil quality; and the lower reaches meander through the North China Plain, with low elevation, abundant precipitation, and fertile alluvial soil. This pronounced regional disparity in resource environments across the YRB has engendered significant geographical variations in the aging–weakening of rural populations and their developmental trajectories, driving factors, and associated challenges.

Economic development is the fundamental driver of rural population aging–weakening. Capital intensification displaces labor, as evidenced by China’s thriving capital markets since 2000, which have channeled substantial investments into agricultural modernization. The rapid advancement of irrigation infrastructure and agricultural machinery has dramatically reduced labor demands in farming, exacerbating the rural labor surplus. Concurrently, with massive inflows of foreign capital, southeastern coastal regions and major urban centers have leveraged advantages in geographical factors and policy to become industrial hubs, triggering large-scale migration of rural youth to these areas. Meanwhile, enhanced agricultural productivity and industrialization have steadily increased rural incomes, fundamentally transforming traditional reproductive concepts and family values, manifesting in rationalized approaches to reproduction [19].

Spanning eastern, central, and western zones, the YRB exhibits inherent regional disparities in agricultural productivity, industrialization levels, and farmer incomes, consequently manifesting distinct spatial patterns in population mobility and ideological orientations. These variations inevitably produce differentiated intensities and driving mechanisms of rural population aging across regions.

Social change operates as the proximate force shaping rural population aging. Urbanization, demographic mobility, healthcare advancements, and cultural shifts collectively exert profound impacts on regional population dynamics. Accelerated urbanization post-2000 has precipitated massive rural-to-urban migration, generating concurrent challenges: rural depopulation, labor drain, aging demographics, and left-behind children/women. County towns have become primary migration destinations, benefiting from policy support, transportation advantages, and a concentration of higher-quality educational, medical, and employment resources. However, elderly populations often remain in rural areas due to difficulties in urban adaptation and deep-rooted attachment to their land [56]. Furthermore, limited local employment drives skilled laborers to developed regions, while women and the elderly become confined to agricultural production or domestic care due to mobility constraints and market segregation, intensifying rural aging and human capital depletion. Improved healthcare, while extending life expectancy, paradoxically elevates elderly population ratios amid declining fertility rates and labor exodus, thereby compounding rural demographic aging [19].

Rural demographic transition is an ecological process influenced by both environmental conditions and economic factors such as agricultural productivity, industrialization, and living standards, along with social forces including urbanization, migration, and healthcare development, creating a complex causal framework. (Figure 1).

3. Study Area and Methodology

3.1. Study Area and Data

The Yellow River originates from the northern slopes of the Bayan Har Mountains on the Qinghai–Tibet Plateau, traversing nine provincial-level regions (Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan, and Shandong) in a characteristic “几” course. The basin encompasses 588 county-level administrative units (416 counties/cities/banners and 172 urban districts) (Figure 2). From 2000 to 2020, the rural population in the YRB decreased by approximately 45.62 million, with significant reductions in those of labor age (32.41 million), women (22.98 million), and women of reproductive age (20.16 million). These demographic declines have precipitated critical challenges—labor drain, population aging, and human capital depletion—severely constraining ecological conservation and high-quality development in the basin.

Given that urban districts predominantly consist of non-agricultural populations and developed land, this study focuses on the 416 non-urban county-level units. After excluding 13 administrative regions with incomplete data (e.g., Aba Tibetan and Qiang Autonomous Prefecture), the final analytical framework comprises 402 county-level units. Rural population data were derived from the Fifth (2000), Sixth (2010), and Seventh (2020) National Population Censuses of provincial authorities within the basin. Data on vector administrative division, topography, and vegetation cover were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. Socioeconomic factors were sourced from the statistical yearbooks (2001, 2011, 2021) of the nine provinces/autonomous regions in the basin.

3.2. Indicator System for Assessing the Factors Influencing Rural Population Aging–Weakening

Building upon the preceding analysis and existing research, this study constructs an evaluation index system for rural population aging–weakening through three dimensions: population structure, population development dynamics, and population reproduction potential. Population structure directly manifests demographic aging–weakening, measured by indicators including aging, number of young people, and gender composition. Elevated proportions of populations aged 65+ (senior dependency), 0–14 (juvenile dependency), and females correlate with intensified structural aging–weakening. Population development dynamics reflect a region’s capacity for socioeconomic productivity, operationalized through illiteracy scale, labor force size, and educational attainment. Higher illiteracy rates, smaller labor pools, and lower educational levels indicate weaker developmental momentum, exacerbating population aging–weakening. Population reproduction potential gauges regional demographic growth capacity, where stronger intrinsic growth capabilities (e.g., fertility rates, prime reproductive-age female cohorts, dependency ratios) correspond to milder aging–weakening (

Table 1. Indicator system for assessing rural population aging–weakening and its influencing factors (drawing by the author).

Variable	Subsystems	Indicator	Meaning of Indicator
Population aging–weakening		Aging	Percentage of population aged 65 and above (%)
	Population structure	Number of children	Percentage of children aged 0–14 (%)
		Percentage of women	Female population/total population (%)
	Population dynamics	Scale of illiteracy	Percentage of illiterates aged 15 and over (%)
		Workforce Scale	Percentage of resident working-age population (%)
		Quality of population (in terms of educational attainment)	Proportion of population with a specialist degree or higher (%)
	Population reproduction potential	Fertility	Fertility rate (%)
		Women of childbearing age	Percentage of women aged 15–49 (%)
		Dependency ratio	65 and over/15–64 (%)
Influencing factors	Geographical environment	Topographic relief (X1)	Average elevation (m)
		Habitat (X2)	Vegetation cover
		Agricultural resources (X3)	Per capita cultivated land area (mu/person)
	Economic development	Agricultural productivity (X4)	Per capita value added in the primary industry (CNY 10,000 per capita)
		Industrialization (X5)	Per capita industrial value added (CNY 10,000 per capita)
		Farmer income (X6)	Per capita disposable income of rural residents (CNY 10,000 per capita)
	Social change	Urbanization (X7)	Proportion of urban population at the county level (%)
		Demographic mobility (X8)	Loss of permanent rural population (person)
		Healthcare advancements (X9)	Hospital Beds per 10,000 people

).

Fundamentally, rural population aging–weakening emerges from evolving human–environment interactions, shaped by the geographical environment, economic development, and societal transformations. More specifically, these factors can be described as follows: geographical environment: topography, livability conditions, and agricultural resources; economic development: agricultural productivity, industrialization level, and rural income; and societal transformation: urbanization rate, outmigration intensity, and healthcare accessibility. Accordingly, this study selects nine indicators to construct an influencing factor system for the YRB: mean elevation, vegetation coverage, per capita arable land area, per capita primary industry output, per capita industrial value-added, rural disposable income per capita, urbanization rate at county level, rural resident population change rate, and number of hospital beds per 10,000 residents. These collectively capture the multidimensional drivers of rural demographic transition in the basin.

3.3. XGBoost Model and SHAP-Based Interpretability

The XGBoost model is an integrated decision tree-based learning algorithm designed to improve the prediction accuracy of a model by constructing multiple weak classifiers and combining their predictions. XGBoost employs a gradient-boosting framework to fit the residuals by iteratively adding new decision trees to produce a regression tree. The greatest advantage of the SHAP value is that it can reflect the influence of the characteristics in each sample; it also shows the positivity and negativity of the influence. This method is used in this study to analyze the direction and intensity of the role of factors influencing the aging–weakening of the rural population. Among them, rural population aging–weakening is the dependent variable, and topography, survival environment, agricultural resources, production efficiency, industrial level, farmer income, urbanization rate, population migration, and medical conditions are the independent variables.

Assuming that the ith sample is ${\mathrm{x}}_{\mathrm{i}}$, the jth feature of the ith sample is ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}$, the model’s predicted value for this sample is $\widehat{{\mathrm{y}}_{\mathrm{i}}}$, and the baseline for the entire model (usually the mean of the target variable across all samples) is ${\mathrm{y}}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$, then the SHAP value obeys Equation (1) [42,43]:

$$\widehat{{\mathrm{y}}_{\mathrm{i}}}={\mathrm{y}}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}+\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}1}\right)+\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}2}\right)+...+\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)$$

(1)

where $\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right)$ is the SHAP value of ${\mathrm{x}}_{\mathrm{i}\mathrm{j}}$. Intuitively, $\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}1}\right)$ is the value of the contribution of the first feature in the ith sample to the final predicted value ${\mathrm{y}}_{\mathrm{i}}$. When $\mathrm{f}\left({\mathrm{x}}_{\mathrm{i}1}\right)>0$, it means that the feature enhances the predicted value and has a positive effect; when the relationship is in the opposite direction, it means that the feature makes the predicted value lower and has a negative effect.

4. Results

4.1. Overall Characteristics of Rural Population Aging–Weakening

The entropy weight method is used to quantitatively evaluate the nine indicators of rural population aging–weakening in the YRB counties. The ArcGis natural breakpoint method is applied to categorize the levels of rural population aging–weakening into four types: general development type (GDT), shallow aging–weakening type (SAT), medium aging–weakening type (MAT), and deep aging–weakening type (DAT).

Figure 3 illustrates the cumulative changes in the proportions of various types of aging–weakening among the rural population in the YRB. Overall, the types within this population have changed significantly, with a notable trend toward aging–weakening, although the overall degree remains relatively low. Specifically, the general development type showed a fluctuating downward trend, from 35% in 2000 to 28% in 2020; the mild aging–weakening type presented a fluctuating and rapidly increasing trend, from 36% in 2000 to 53% in 2020; the moderate type exhibited a declining trend, dropping from 13% in 2000 to 212% in 2010, followed by a significant decrease to 10% in 2020; and the severe type demonstrated a continuous decline from 16% in 2000 to 9% in 2020.

4.2. Regional Differences in the Type of Population Aging–Weakening

Figure 4 illustrates the distinct regional differences in changes to rural population aging–weakening types across the upper, middle, and lower reaches of the YRB from 2000 to 2020. Specifically, in the upper reaches of the YRB, the proportion of the general development type steadily declined, shrinking from 21.6% in 2000 to 13.6% in 2020. In contrast, mild aging–weakening gradually grew, from 21.6% in 2000 to 52.8% in 2020, an increase of 31.2 percent. Meanwhile, the moderate type initially surged from 16% in 2000 to 33.6% in 2010 but then dropped sharply to 9.6% by 2020. The severe type underwent a rapid decline, with its proportion reducing from 40.8% in 2000 to 24% in 2020. Overall, rural population aging–weakening in the upper reaches of the YRB remained relatively severe in this period, but the general trend was improving.

In the middle reaches of the Yellow River, the proportion of the general development type had been on the rise, increasing from 43.7% in 2000 to 51.9% in 2020. The mild aging–weakening type initially declined from 37.2% in 2000 to 26.8% in 2010, before increasing to 45.4% by 2020. Meanwhile, the proportion of the moderate type exhibited a fluctuating downward trend, rising from 12.6% in 2000 to 17.5% in 2010, before plummeting to 2.2% in 2020. Similarly, the severe type had an unstable downward trend, increasing slightly from 6.6% in 2000 to 8.2% in 2010, and then decreasing sharply to just 0.5% in 2020. Overall, the degree of aging–weakening in the middle region remained relatively mild, and it gradually evolved toward the general development and mild aging–weakening types.

In the lower reaches of the Yellow River, the proportion of the general development type showed a rapid overall decline, from 39.4% in 2000 to 1.1% in 2020. The proportion of the mild aging–weakening type generally increased, rising from 51.1% in 2000 to 68.1% in 2020. The moderate type followed a steady upward trend, expanding from 8.5% to 24.5% over this period; similarly, the severe type showed continuous growth, climbing from 1.1% in 2000 to 6.4% in 2020. Overall, rural population aging in the lower reaches of the Yellow River had been gradually intensifying, reflecting a clear shift towards both mild and moderate aging types.

4.3. Spatiotemporal Evolution and Regional Differentiation of Rural Population Aging–Weakening in the YRB

Figure 5 shows the spatiotemporal pattern of rural population aging–weakening in the counties of the YRB from 2000 to 2020. The spatial agglomeration characteristics of the aging–weakening types are significant and vary greatly over time. In 2000, the severe aging–weakening type was mainly concentrated in Qinghai and Gansu in the upper reaches of the Yellow River. The moderate type was mainly distributed in the Ordos Plateau in the upper reaches of the Yellow River. In contrast, the mild type was widely spread across the middle and lower reaches, whereas the general development type was relatively scarce. By 2010, several notable shifts had occurred. The areas experiencing severe aging–weakening contracted rapidly, while the moderate type became more concentrated in Qinghai, Gansu, and parts of northern Shaanxi in the upper reaches. At the same time, the mild aging–weakening type began to cluster in both the lower and upper reaches of the Yellow River, while the general development type was predominantly found in the middle and upper reaches. In 2020, areas of severe aging–weakening were clustered in the upper and lower reaches of the Yellow River. In addition, the moderate aging–weakening type became concentrated in Henan and Shandong provinces in the lower reaches and Qinghai and Gansu provinces in the upper reaches. Meanwhile, the mild aging–weakening type remained prevalent in the middle reaches, and the general development type was limited to a few areas in Inner Mongolia in the upper reaches.

In general, the degree of population aging–weakening in the lower reaches of the Yellow River gradually increased, transitioning from mild to moderate, and some counties evolved into severe aging–weakening. In contrast, the middle reaches remained relatively stable, exhibiting a lower degree of aging–weakening. Meanwhile, the upper reaches underwent significant changes, characterized by significant spatial disparities, with most counties experiencing a severe degree of population aging–weakening.

5. Discussion

5.1. Drivers of Rural Population Aging–Weakening in the YRB

Figure 6 presents the regression results of the XGBoost model, illustrating the key factors influencing the aging–weakening of the rural population in the YRB from 2000 to 2020. The findings revealed that topography, population migration, agricultural resources, farmer income, and living environment played a significant role in 2000. Among these, topography, population migration, farmer income, and living environment exerted a positive impact, exacerbating rural population aging–weakening, whereas agricultural resources had a negative effect, which mitigated the trend. By 2010, the key influencing factors had shifted. Topography, production efficiency, urbanization, living environment, and industrialization became the dominant determinants. During this period, topography, production efficiency, and the living environment continued to promote rural aging–weakening, while urbanization and industrialization played a role in slowing down the trend. In 2020, the most influential determinants were topography, industrialization, urbanization, farmer income, and living environment. Topography and the living environment continued to exacerbate the rural population aging–weakening, while industrialization, urbanization, and farmer income helped to alleviate the issue.

Topography has consistently had a negative impact on rural population aging, particularly in higher-altitude areas, which tend to experience more severe effects. Since the 1990s, rapid infrastructure development in China has significantly improved transportation networks, especially in previously isolated mountainous regions. This modernization has profoundly changed traditional rural mindsets, making urban job opportunities, higher wages, and modern lifestyles increasingly attractive to young people. Consequently, rural-to-urban migration has intensified, resulting in a growing proportion of elderly individuals and minors in high-altitude regions, while the working-age population continues to decline. For example, in Gansu and Qinghai provinces, the share of the rural working-age population decreased from 65.9% in 2000 to 64.5% in 2020. During the same period, the proportion of the aging population rose dramatically from 7.0% to 17.6%.

The level of industrial development and urbanization in counties has a significant adverse effect on the aging and decline of the rural population. Importantly, the influence of industrial development has been on the rise, climbing from fifth place in 2000 to second place in 2020. As we entered the 21st century, China swiftly established itself as a global industrial powerhouse, expertly harnessing its abundant resources, labor, and robust infrastructure. This transformation was marked by the modernization of industries in the eastern coastal regions and a strategic shift of industries toward inland areas. Consequently, rapid industrialization has taken hold in the central and western regions, driving substantial growth in agriculture, services, and various other sectors at the county level. This surge has prompted an impressive increase in urbanization rates, fundamentally reshaping the economic landscape.

The impact of farmers’ income on the aging and weakening of the rural population has shifted from positive in 2000 to negative by 2020. This indicates that as farmers’ income increases, the degree of aging among the rural population worsens, contributing to a decline in population. Higher income acts as a primary driving force for farmers’ migration, particularly during periods when rural resources are undervalued. Rural residents are attracted to cities by the prospect of higher wages, leading to a significant influx of people into urban areas. Additionally, influenced by the ‘demonstration effect’, young and middle-aged individuals from rural areas are migrating to cities in greater numbers, further exacerbating the aging and weakening of the rural population. However, with the implementation of the national rural revitalization strategy and initiatives aimed at promoting environmentally friendly living, significant progress has been made in agricultural and rural transformation. The rural landscape has been rejuvenated, and the standard of living for farmers has improved considerably. Consequently, the loss of the rural population has slowed, and many individuals are gradually returning to the countryside, helping to alleviate the trends of aging and weakening.

The living environment has consistently exerted a positive effect on rural aging, although its impact has been relatively modest. One key indicator of living environment quality is vegetation coverage, which reflects a region’s ecological conditions. In areas with high vegetation coverage, the living environment is superior, agricultural production conditions are good, and the rural population is dense. However, the advancement of globalization, marketization, and industrialization has widened the urban–rural divide. Rural areas have thus increasingly become resource pools for urban economic growth; in particular, rural population resources are rapidly transferred to cities. This has led to a rapid outflow of the rural population, exacerbating rural depopulation and the aging population, and increasing the role of women in agriculture [57]. Over time, these trends have contributed to an accelerated rural aging–weakening process.

5.2. Mechanism of Spatial Differentiation of Rural Population Aging–Weakening in Different Areas of the YRB

As shown in Figure 5, the impact of topographic features on rural population aging varies across different regions of the Yellow River. In the upper and middle reaches, these features have a positive effect, leading to a weakening of the rural population. Conversely, in the lower reaches, the effects are negative, as population aging is more pronounced. This difference is primarily due to the geographical characteristics of these areas. The upper and middle reaches are situated in the first and second terraces of China, characterized by high mountains and deserts. These regions have limited arable land, infertile soils, inadequate transportation options, and lagging economic development, which contribute to a high poverty rate. Additionally, there is a strong demand for residents to seek work elsewhere, resulting in the migration of educated and experienced individuals away from these areas. In contrast, the lower reaches of the Yellow River feature flat, fertile arable land that supports high agricultural productivity. These regions also benefit from better infrastructure, including road transportation. As urbanization and industrialization progress rapidly, there is an increasing trend of rural populations migrating to developed cities. This trend is especially evident in low-lying areas where agricultural production is relatively weak and urbanization and industrialization levels remain low. Consequently, the aging and decline of the rural population are becoming more significant in these regions.

Industrial development and farmer income negatively impact the upper, middle, and lower reaches of the Yellow River. Areas with high industrial levels and farmer incomes experience a lower degree of population aging and decline. This is primarily because a market economy fosters the growth of a commoditized society, where the pursuit of wealth has become the main focus for residents. A high industrial level provides more job opportunities and better wages, which helps retain rural workers and reduces population loss. Additionally, this economic environment promotes investment in education and infrastructure, enhancing human capital at the regional level and mitigating the effects of an aging and declining population.

The survival environment plays a critical role in shaping the dynamics of both the upper and lower reaches of the Yellow River. In the upper reaches, we encounter towering mountains, vast deserts, and a delicate ecological landscape. The few regions that offer favorable living conditions become vital hubs for agriculture and population growth, transforming into key urban centers in the region. However, stark urban–rural disparities drive rural populations to migrate to cities, resulting in significant declines in rural communities and a troubling loss of talent. In stark contrast, the lower reaches boast expansive, fertile plains that are the backbone of traditional agriculture and a primary source of China’s agricultural output. Here, areas with rich vegetation are predominantly dedicated to farming, guided by land protection measures and food security strategies. Yet, efforts toward industrialization and urbanization face substantial obstacles, leading to relatively low socioeconomic development. The result is a troubling exodus of labor and a rapidly aging population. The most profound challenges are evident in the middle reaches, where the survival environment is harsh due to the prevalence of gullies, ravines, and shifting sands. This adverse environment hampers socioeconomic development, compounding the loss of a once-thriving rural population. Addressing these complex issues is essential for ensuring a more balanced and prosperous future for all regions along the Yellow River.

County urbanization shows a negative effect on the aging and weakening population in the upper reaches of the Yellow River, and the intensity of this impact has gradually increased. The upper reaches of the Yellow River are closed and economically underdeveloped, with a low urbanization rate as a whole. A few areas with high urbanization have relatively good economic development and more local jobs, which to a certain extent inhibit population loss; however, in areas with low urbanization, a large number of rural laborers migrate to developed areas with employment opportunities due to the limited job opportunities. Meanwhile, with the development of marketization and industrialization, the impact of county urbanization on population flow continues to increase. The impact of county urbanization on the aging and weakening of the rural population in the Middle Reach of the Yellow River gradually turned from positive in 2010 to negative in 2020, and the intensity of the impact gradually weakened. The middle reaches of the Yellow River are an important energy base in China, and the traditional extractive industry and other low-end labor-intensive industries are dominant, with a greater demand for young and strong laborers and higher wages, exacerbating the loss of the rural population. However, as China continues to implement the rural revitalization strategy, the valorization of rural resources has been emphasized, and the highly urbanized areas are gradually shifting towards urban–rural integration, the effect of urbanization on the aging and weakening of rural populations is gradually evolving into a negative direction, and the degree of influence is becoming weaker.

Outmigration has predominantly positive effects on the aging of the rural population in the lower reaches of the Yellow River Basin (YRB), while it shows statistically insignificant impacts in the upper and middle reaches. Population emigration directly contributes to regional population decline. The lower reaches of the Yellow River, situated in the expansive Yellow Huaihai Plain, are a significant agricultural production area where the overall level of industrialization and farmers’ income remain low. Due to better transportation and higher wages, many rural residents, particularly quality young and middle-aged workers, have relocated to more developed areas. This migration trend has resulted in the aging and decline of the rural population.

5.3. Shortcomings and Future Research

In this study, we examined the aging and decline of rural populations in county-level regions along the YRB from three perspectives: demographic structure, developmental dynamics, and reproductive potential. We used machine learning models to quantitatively analyze the factors affecting population aging and decline through three lenses: resource environment, economic development, and social transformation. However, the evaluation index system we established for assessing population aging and decline has some limitations. It overlooks vulnerable groups, such as individuals with disabilities and those at risk of falling back into poverty in rural areas. Additionally, it uses somewhat subjective criteria for categorizing the types of aging and decline, and it does not consider the influence of regional culture, transportation, and technology. In the future, we plan to select typical regions to comprehensively utilize multi-source data, including socioeconomic statistical yearbooks, big data, and field surveys. By incorporating cultural differences, transportation accessibility, agricultural technology, and various industrial types, we aim to better understand the mechanisms behind rural population aging and decline on a micro-scale. Furthermore, our future research will focus on several key areas: the effects of an aging rural population on the transformation of rural industries and farmers’ production methods, the spatial changes in rural society due to population aging and their influence on restructuring models and mechanisms, the impact of these changes on the evolution of the regional ecological environment, and the relationship between these influencing mechanisms and carbon emissions.

6. Conclusions

This study integrates census data, socioeconomic statistics, and remote sensing imagery through the machine learning-based XGBoost algorithm to analyze the spatiotemporal patterns and driving mechanisms of rural population aging–weakening across county-level units in the YRB. The key findings are as follows:

(1) The aging–weakening of the rural population in the YRB has changed significantly over the past two decades, with large spatial differences. On the whole, the proportion of the general development type has decreased, and the shallow aging and weakening type has risen sharply. This reflects that although there is a clear trend of rural population aging and weakening in the counties of the YRB, the degree is still shallow. More specifically, the degree of aging in the upstream area is generally good, but there is a clear polarization of types; the aging in the middle reaches of the region is relatively light and the trend is good, while in the downstream area, the population aging is aggravated, and is gradually shifting to the shallow and moderate types.

(2) The regression analysis conducted using the XGBoost model shows that the aging of the rural population in the YRB is influenced by multiple factors and has changed significantly over time. Specifically, topography and geomorphology have had a continuous positive effect on the aging of the rural population in the YRB, and the intensity of this effect is high. The industrialization and urbanization of the counties have curbed the outflow of the rural population and reduced the aging of the population to some extent by promoting economic growth and creating more employment opportunities. With the implementation of rural revitalization and other favorable “three rural” policies, the impact of farmers’ income has gradually evolved to inhibit the development of rural population aging. The survival environment has a positive but relatively small effect on the aging of the population. In addition, the effects of agricultural productivity, population emigration, and agricultural resources are relatively insignificant.

(3) There are significant differences in the factors influencing the aging of the rural population in different areas of the YRB. Topography and geomorphology have a positive driving effect on the development of the aging and weakening population in the upper and middle reaches of the YRB, while there is a negative inhibitory effect on the downstream areas. Increases in industrial development and farmer income also have an inhibitory effect on the upstream, middle reaches, and downstream areas. The living environment has a positive effect on the upper and downstream areas, while there is an opposite effect on the middle reaches. The influence of county urbanization on the aging and weakening population in the upper and middle reaches changes from positive to negative, but the influence on the downstream area is always negative. Population migration has a more significant effect on the downstream areas, and its effect is relatively significant in the upstream and middle reaches. Finally, population emigration has a relatively significant effect on the downstream area and a relatively insignificant effect on the upstream and midstream areas.