Population Projections and Sustainable Development Challenges in China

Abstract

The essence of population forecasting lies in the understanding and grasp of population development patterns. The ability to predict population trends scientifically has become a crucial tool for advancing the sustainable socio-economic development of a nation. Traditional studies predominantly employ mathematical models to project future population trends, yet few have endeavored to capture both the linear and nonlinear characteristics of population changes in their predictions. This study employs an ARIMA-LSTM coupling model, enhanced through Bootstrapping, to forecast the population scale and structural trends at the national, provincial, and municipal levels from 2021 to 2035.The research indicates that the population size of China will continue to show a negative growth trend in the future.By 2035, the total population will shrink to 1.314 billion, and 80.9% of the urban population will face contraction. In the future, China's population age structure will face multiple challenges such as severe aging, severe low birth rate, and contraction of the labor force size.By 2035, the proportion of people aged 65 and above will exceed 30%, the proportion of people aged 0–14 will be lower than 14%, and the proportion of the 15–64 years old working-age population will drop to around 54%.The age structure type will shift from a young growth type and a mature stable type to an unbalanced type and an aging decline type. In the future, the urban-rural structure of China's population will show a migration of the population from rural areas and small and medium-sized cities to core urban agglomerations and megacities.The net inflow of urban population and the net outflow of rural population will grow simultaneously.By 2035, the urban population will increase to 1.022 billion, and the rural population will shrink to 292 million.

Key words:

Population prediction

Population size

Population structure

Bootstrapping-ARIMA-LSTM coupled model

1. Introduction

The problems of low birth rate, aging population and regional population differentiation worldwide have become increasingly prominent, and have become one of the major strategic challenges facing global sustainable development(McNicoll 2025).In recent years, global fertility rates have generally declined. This trend first emerged in European countries that underwent population transformation, and gradually spread to major economies in Asia, North America, and other regions, directly leading to the global population growth rate dropping below 1%(Massimo 2021).According to the latest prediction by the United Nations, although the world's population will reach a peak of approximately 10.3 billion in the mid-2080s, it will gradually decrease at a rate of about 1 million per year thereafter(The UN Population Division 2024).At the regional level, the issue of population decline is particularly severe. Europe serves as a typical example, with over 20% of its regions experiencing continuous negative population growth between 1990 and 2010. Meanwhile, East Asian countries such as South Korea and Japan have fallen into structural decline due to "excess low birth rates". These changes have constrained economic growth, exacerbated social security burdens, intensified regional development disparities, and profoundly impacted the global process of sustainable development. Against this backdrop, population forecasting has become a core concern for scholars worldwide(Xu et al.,2024;Temple and Wilson 2025).

As a populous country, China has witnessed changes in its population size and structure(Li et al.,2025).Population is the foundation of a country's development. Its changes directly affect the growth of the national economy, the stability of society, and sustainable development. Scientifically grasping the new characteristics and challenges of China's population development, formulating long-term strategic plans, and promoting balanced population development have become a major issue that needs to be urgently addressed at present(Shan et al.,2024).However, currently our country is facing two major challenges brought about by negative population growth and structural changes in the population. According to data released by the National Bureau of Statistics, at the end of 2024, the total population of the country was 1.408 billion, a decrease of 1.39 million compared to the previous year. The total population has been experiencing negative growth for three consecutive years. At the same time, phenomena such as a decrease in the birth rate, aging population, shrinking labor force, and regional population differentiation have become increasingly prominent(Sang et al.,2024;Yu et al.,2022).Therefore, making scientific predictions about future population trends is not only a necessary measure to address the challenges brought about by the current demographic changes, but also a crucial factor in ensuring that the strategic goal of basically achieving socialist modernization is achieved by 2035.

The core of population forecasting lies in conducting dynamic simulations of population size and structure to scientifically address the risks of future population development imbalances.In recent years,numerous research institutions and experts both at home and abroad have carried out a large number of predictive studies on population trends(Li et al.,2022;Yu et al.,2023).Previous studies mainly focused on the following aspects:First,the methods for population forecasting have become diversified. Traditional mathematical models (such as cohort component method, Logistic model,Leslie matrix) focus on quantifying the relationships between population age structure and dynamic factors such as fertility rate and mortality rate. However,their accuracy is highly dependent on parameter assumptions. For instance,the UN's prediction of China's population in 2024 differed from the actual population by 12.62 million people(Gupta et al.,2025;Rosenbaum and Fronhofer 2023); the Grey System Theory GM(1, 1) model is suitable for small sample data,but it has relatively large errors for long-term predictions(Pang et al.,2025); Traditional econometric models (such as ARIMA,Lee-Carter) have high requirements for data stationarity and are difficult to capture sudden changes like the sharp decline in fertility rates(Wang et al.,2025); The system dynamics model starts from individual behaviors or system feedback mechanisms and is only applicable to the analysis of urban-rural population migration(Guo et al.,2022);furthermore,machine learning and artificial intelligence models (such as LSTM, GM-LSTM, XGBoost) enhance the prediction accuracy of complex patterns through nonlinear fitting, but they rely on large amounts of data and computing resources(Wang et al.,2021).Second,population forecasting research has expanded from the traditional macroscopic scale to the mesoscopic scale. Macroscopic research mainly focuses on national and regional levels, concentrating on changes in the total population. While mesoscopic research focuses on the population dynamics of urban groups within urban agglomerations and metropolitan areas, revealing more detailed characteristics of population mobility. Although mesoscopic research has gradually gained attention, research at the prefecture-level city scale is still relatively scarce. The population development at this scale is influenced by national and regional macro policies, and also exhibits different spatial change characteristics compared to the mesoscopic scale of urban agglomerations and metropolitan areas(Reia et al.,2022;Tan et al.,2022).Thirdly, traditional population forecasting mainly focuses on the study of a single dimension such as population size, structure, distribution, and their dynamic evolution patterns. Although current population forecasting research has laid an important foundation for scientifically understanding population change trends and formulating policies, its limitations are becoming increasingly evident. As population dynamics become increasingly complex, future population forecasting will place greater emphasis on comprehensive analysis of multiple dimensions such as population size, age structure, urban-rural differences, and spatial distribution, thereby revealing the patterns of population change more accurately(Láng-Ritter et al.,2025).

Population forecasting holds crucial strategic and comprehensive significance for the future development of a country(Shi et al.,2025). Currently, using traditional linear or nonlinear single models, with national, regional, provincial, urban agglomeration, and metropolitan area as spatial units, focusing on population size, structure, and distribution in a single dimension, these studies can grasp the macroscopic trend of population changes, but they are difficult to reveal the differences in population spatial distribution. In this context, by predicting the population size and structure at the prefecture-level city level, it is possible to more accurately identify the differences in resource allocation, social services, and public policies among different cities and regions. Therefore, based on the data from the fifth, sixth, and seventh national population censuses, this paper adopts an ARIMA-LSTM coupled model improved by Bootstrapping to predict the development trends of urban and rural population size and structure at the national, provincial, and municipal scales from 2021 to 2035, providing data support for promoting the high-quality development of the national social economy.

2. Methods and Data Sources

2.1 Methods

2.1.1 Selection of prediction Methodology

The essence of population forecasting lies in the understanding and grasp of population development laws. How to scientifically understand population development laws determines the thinking and methods of population forecasting. The theoretical research on population development laws is the core issue of demography, involving the dynamic evolution of population quantity, structure, and distribution, as well as their interaction with social economy, resources, and environment(Mills and Rahal 2021;Billari 2022).The essence of population forecasting lies in the understanding and grasp of population development laws. How to scientifically understand population development laws determines the thinking and methods of population forecasting. The theoretical research on population development laws is the core issue of demography, involving the dynamic evolution of population quantity, structure, and distribution, as well as their interaction with social economy, resources, and environment(Fronhofer et al.,2024;Murphy 2021;Shen et al.,2021).Second, Marxist population theory emphasizes the social and historical nature of population laws, and holds that population development is constrained by the mode of production(Brook 1988).Our country's family planning policy is based on this theory, which aims to achieve sustainable development by regulating the relationship between population and resources. This theory criticizes the mathematical school for simplifying population to a purely quantitative calculation process, and points out that a comprehensive analysis should be conducted by taking into account social factors such as economy and culture(Ma 1983).The third point is that the theory of population transition emphasizes the process of shifting from high birth rates and high death rates to low birth rates and low death rates(Pavlik 1980).Since the 1990s, China's total fertility rate has dropped below the replacement level, marking the completion of the transformation in the type of population reproduction. Along with the modernization process, the population has shown trends such as aging, low birth rates, and urbanization. Although the population transformation theory has been verified many times and has yielded a lot of experience, it still has certain limitations. When explaining the population development process and phenomena in non-EU and non-US countries and regions, this theory is often not applicable, and insufficient attention is paid to the influence of social, cultural and educational factors(Caldwell 2004).

Under the condition that the established social and economic operation and policy environment maintain a long-term stable state, the evolution of the population within the region usually follows identifiable dynamic patterns(Gertsev et al.,2008).Based on this, the core of population forecasting lies in constructing the study area as a closed system with boundary constraints. By analyzing the intrinsic mechanisms of historical population changes (such as fertility decline curves, mortality U-shaped patterns, age structure inertia, etc.), a parameterized mathematical model is established to predict the future trajectory of population evolution(Koons et al.,2007;Samoletov and Vasiev 2024).However, the demographic development process of our country exhibits a notable "time-space compression" feature, meaning that the population transformation process that took hundreds of years in developed countries was completed within a relatively short period of time(Lin et al.,2025).This form of compressed modernization is manifested as follows: the institutional transition from planned economy to market economy (1978–1992), the industrial revolution from agricultural society to industrial society (1992–2010), and the technological leap from traditional society to digital society (2010–2025). The interaction of these multiple transitions has led to a fundamental restructuring of China's population patterns. At the top is the rapidly expanding elderly population, in the middle is the continuously shrinking working population, and at the bottom is the sluggish fertility base(Huang and Wang 2024).This structural contradiction calls for breaking away from the traditional population forecasting based on the aforementioned theories and methods, and establishing a forecasting approach that can capture both linear and nonlinear changes in the population.

The model assumptions, data quality, and selection of influencing factors of population forecasting methods directly affect the accuracy of the predictions. In recent years, with the development of machine learning technology, the ARIMA-LSTM coupled model has demonstrated significant advantages in the field of time series forecasting. It can simultaneously capture the linear trends and nonlinear characteristics in the time series and enhance the model's robustness by using the Bootstrapping method, and can effectively handle the uncertainties in population changes(Apolloni et al.,2008).

2.1.2 Construction of the prediction model

The ARIMA-LSTM coupled model improved based on Bootstrapping is designed with a hierarchical structure, consisting of three main levels(Wang et al.,2024):

Basic model ARIMA.The ARIMA model combines the characteristics of the autoregressive model (Autoregressive Model, AR), the moving average model (Moving Average Models, MA), and differencing. It is generally denoted as ARIMA (p, d, g), and its formula is:

Among them, p represents the order of the autoregression;q is the order of the moving average;t represents time;Y’_t is the value after d-th difference operation;φ₁,φ₂,...,φ_p is the autoregressive coefficient;θ₁, θ₂, …, θ_q is the moving average coefficient;ε_t,ε_t−1,…,ε_t−p is an error term.

Before applying the above formula, the Augmented Dickey-Fuller (ADF) test needs to be conducted for the unit root test. If there is no unit root in the time series data, no differencing is required; otherwise, the data needs to be differenced step by step until it becomes stable (where d is the order of differencing). Subsequently, the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the stable differenced sequence are plotted to determine the range of values for p and q.Before applying the above formula, it is necessary to determine the final value and the optimal model by using the Bayesian Information Criterion (BIc). Based on this, the predicted sequence {δ_t} and the residual sequence {ε_t} (The difference between the true value and the predicted sequence {δ_t} after the ARIMA model training can be obtained.)

ARIMA-LSTM Coupled Model. The ARIMA-ISTM coupled model is an improved model of the residual sequence{ε_t} of the ARIMA model to capture the nonlinear part of the data.{εt} is used as the input value of the LSTM neural network and the residual sequence is trained to obtain the model residual prediction sequence {ε_t} at the corresponding time t,Thus, the prediction sequence of the coupled model is the combination of the predicted value of the linear part of the ARIMA model and the nonlinear residual of LSTM, which is denoted as:

Among them,{K_t} represents the predicted value of the residuals by the ARIMA-ISTM coupled model. Furthermore, the residual sequence of the ARIMA-ISTM coupled model is denoted as {ε_kt} (The difference between the true value and the predicted sequence {K_t}.

An improved ARIMA-LSTM coupled model based on Bootstrapping. The improved coupled model can not only optimize the residuals but also attempt to open the "black box" by recording the training trajectory.Firstly, the residual sequence {ε_kt}of the ARIMA-LSTM coupled model is randomly sampled with replacement for no less than 1000 times, and each sub-residual sequence is {ε_kt^(b)}

, with Brepresenting the number of samplings.Secondly, the sub-residual sequence is re-trained using the ARIMA-LSTM coupled model, and the true values and predicted value sequences {K’_t}(where {K’_t}∊{K_t}),corresponding to each residual sequence are recorded. Then, the predicted value of each coupled model is recorded as:

2.1.3 Model accuracy verification

In order to test the accuracy of the improved ARIMA-LSTM model based on Bootstrapping (Jin et al.,2025), this study compared the population forecast values from 2021 to 2024 with the actual values. A comprehensive evaluation was conducted using the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination R².

MAE refers to the average of the absolute deviations between the predicted values and the actual values, and it can reflect the effectiveness of the predicted values:

MSE refers to the degree of difference between the predicted value and the actual value, and is used to evaluate the accuracy and variation of the model's predictions:

RMSE is the square root of MSE, and it is used to measure the deviation between the predicted values and the actual values:

The MAPE (Mean Absolute Percentage Error) index is highly sensitive to relative errors and can be used to evaluate the accuracy of the model:

R²describes the explanatory power of the model's predicted values relative to the actual values:

Among them,

represents the true value,

represents the predicted value,

and represents the average of the true values.Generally, the lower the values of MAE, MSE, RMSE and MAPE, the closer the coefficient of determination is to 1, which means the error between the predicted value and the actual value is smaller, and the prediction performance of the model is better.

2.2 Data sources

This study selected 341 spatial units (4 municipalities directly under the Central Government, 293 prefecture-level cities, 7 prefectures, 30 autonomous prefectures, 3 leagues, 4 provincial-level counties) as the research objects, and calculated the population size and structure at the national, provincial and municipal levels in 2035. The data sources mainly include: the data from the fifth, sixth and seventh national population censuses in China, as well as the population data released by various provinces and cities. Specifically, this includes total population, urban permanent population, rural permanent population, the proportion of 0–14 years old population, the proportion of 15–64 years old population, the proportion of 65 years old and above population, the proportion of 15–49 years old women of childbearing age, and the urbanization rate data, etc. This study conducted vectorization processing on the relevant data, and the basic spatial data used the 2024 national administrative division data provided by the National Geographical Information Center (https://cloudcenter.tianditu.gov.cn/administrativeDivision). Based on the statistical table of administrative divisions at the league level and above in the "Administrative Division Simplified Catalogue of the People's Republic of China" for each year, the boundaries of the administrative units that underwent boundary adjustments were merged and split, and the corresponding administrative area population data were recalculated to ensure the consistency of the population data. To eliminate the influence of administrative division adjustments, this study took the prefecture-level cities in the seventh national population census in 2020 as the benchmark, and provided explanations for the data missing and adjusted the names of some prefecture-level cities for the fifth and sixth national censuses.

3. Results

3.1 Population prediction results and tests

The predicted results of China's population size and structure are shown in Fig. 1. First, regarding the population size. After reaching a peak of 1.414 billion people in 2021, China's total population began to shift from long-term growth to continuous negative growth. It is expected to drop to 1.314 billion people by 2035. Second, in terms of age structure. Due to the decrease in the number of women of childbearing age and the low fertility rate, the trend of population reduction dominated by the low birth rate has become increasingly obvious. In 2035, the proportion of people aged 0–14 will be 13.51%, a decrease of 4.50 percentage points compared to 18.00% in 2020, resulting in a reduction of over 75.75 million in the number of children; the labor force size will continue to shrink and its proportion will significantly decrease. In 2035, the proportion of people aged 15–64 will drop from 68.50% to 54.00%, and the labor force size will decrease from 976 million to 710 million. The speed and scale of China's aging process are unprecedented. In 2035, the proportion of people aged 65 and above entering the super-aged society will exceed 30%. Third, in terms of urban-rural structure. China is currently in the final stage of the deceleration development phase of its rapid urbanization process. It is expected that China's urbanization rate will reach 77.85% in 2035, with a significant slowdown in growth rate. The average annual growth rate will decrease from the previous 1.4% to approximately 0.97%, entering the mature stage of urbanization. The proportion of urban and rural populations will reverse from the "six-to-four" ratio in 2020 to the "eight-to-two" ratio.

Fig. 1

China's Population Forecast Results from 2021 to 2035

This study compared the predicted values of the permanent resident population of prefecture-level cities with the actual values of the permanent resident population of each prefecture-level city and each province/provincial capital from 2021 to 2024. As can be seen from Fig. 2 and Fig. 3, the magnitudes of the error ratios fluctuated to some extent, but the fluctuations were not significant. The error rates of the predicted results of the permanent resident population of all prefecture-level cities and provinces/provincial capitals from 2021 to 2024 were all lower than 5.0%.

Fig. 2

Error Ratio of Population Forecast Values to Actual Values for Prefecture-level Cities from 2021 to 2024

Fig. 3

Error Ratio of Provincial Population Forecast Values to Actual Values from 2021 to 2024

The national population forecast results are in line with the contraction trend of the United Nations' "World Population Prospects", but the United Nations overestimated China's population growth. The medium scenario predicts that the population will peak at 14.6 billion in 2031. The "National Population Development Plan (2016–2030)" also overestimated the figure, due to the overestimation of the impact of the universal two-child and three-child policies on the fertility rate. It was assumed that the total fertility rate was between 1.5 and 1.6 in 2015, and it was expected to be 1.8 in 2020 and 2030. Thus, it predicted that China's population would peak at 14.5 billion around 2030, which is a gap of over 60 million compared to the prediction results of this study. To further verify the reliability of the forecast results, this study conducted a comprehensive test of the accuracy of the national permanent resident total population from 2021 to 2024 using MAE, MSE, RMSE, MAPE, and R². As shown in Table 1, all indicators have relatively high accuracy, although there is a certain degree of upward fluctuation in size, but the fluctuation range is not large. Therefore, it can be known that the prediction results of this study are relatively reliable.

Table 1

Accuracy Test of the National Permanent Population
Index	2021	2022	2023	2024
MAE	2.78	4.08	5.26	6.49
MSE	35.98	53.04	64.53	94.25
RMSE	5.97	7.28	8.03	9.71
MAPE	0.008	0.011	0.015	0.018
R²	0.99	0.99	0.99	0.99

3.2 The future trend of China's population size development

In the future, China's total population will show a continuous negative growth trend, and the spatial variation of population changes will be significant. From 2020 to 2035, China's total population decreased from 1.410 billion to 1.314 billion, with an average annual decrease of approximately 6.4 million people. This trend indicates that China has entered the accelerated contraction period of the "post-population transformation" stage, and the natural population growth rate has continued to decline. From a temporal perspective, from 2020 to 2025, the total population showed a trend of first increasing and then gradually decreasing at an average annual rate of 800,000 people; from 2026 to 2030, it entered a rapid contraction period, with an average annual decrease of 5.4 million people, and by 2030, the total population will fall below the 1.4 billion threshold; from 2031 to 2035, it entered an ultra-rapid contraction period, with an average annual decrease of 13 million people, and the total population will be reduced to 1.315 billion people. From a spatial perspective, population distribution shows a significant regional differentiation feature. Economically developed regions and policy-benefiting areas maintain stable growth; while traditional industrial regions in the northeast, central and western regions are facing accelerated population shrinkage.

In the future, the population in Northeast and Northwest China will continue to flow out and further concentrate in core urban agglomerations. Comparing the population growth rates of the four major regions and 19 urban agglomerations in China from 2020 to 2035. According to Fig. 4(a), the total population of the four major regions all showed a downward trend, with an average decrease ranging from 2.15% to 16.05%, ranked in descending order as Northeast region > Central region > Western region > Eastern region. At the same time, further comparing the proportion of each region in the national total population reveals that the proportion of the Northeast region decreased from 6.98% to 6.29%, a decrease of 0.69%; the Central region decreased from 25.83% to 25.39%, a decrease of 0.44%; the Western region decreased from 26.73% to 26.33%, a decrease of 0.40%; only the Eastern region's proportion increased from 39.93% to 41.84%, an increase of 1.91%. The Eastern region, with its economic vitality and public service advantages, remains the main area with net population inflow, and its population proportion continues to increase. The Central region shows a mild population outflow trend due to rural-urban migration and cross-regional mobility, while the Western region's population distribution tends to be stable or even slightly returns, and the Northeast region faces the challenges of accelerated population outflow and a significant decrease in proportion due to lagging industrial structure adjustment. According to Fig. 4(b), although the total population of China decreased from 1.410 billion to 1.314 billion, a decrease of 6.90%, the proportion of the 19 urban agglomerations in the total population showed an overall growth trend, with an increase of 2.65%, especially the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu-Chongqing five urban agglomerations had significant population proportion growth, from 40.80% to 43.19%, an increase of 2.39%, while other urban agglomerations showed a slight increase, with an overall increase of 0.26%. Population and economy have been highly concentrated in the 19 urban agglomerations, and in recent years, they have increasingly concentrated in core urban agglomerations. This concentration trend is still strengthening in the 2035 prediction.

Fig. 4

Population Changes in Different Regions from 2020 to 2035

In the future, the population of China will continue to concentrate in megacities and super-large cities. By comparing the changes in the urban permanent resident population size in 2020 and 2035, as shown in Fig. 5, cities have become the main regional units where the population of China is decreasing. Among 341 cities, 80.9% have experienced population decline. The proportion of cities on the southeast side of the "Hu Huanxiong Line" with population decline is significantly higher than that on the northwest side. The proportion of cities in the north with population decline is higher than that in the south region. Only a few core cities such as provincial capitals and eastern coastal areas have a continuously growing population. Specifically, cities with an increase in permanent resident population of over 1 million are Hangzhou, Nanjing, Suzhou, Shenzhen, Wuhan, Xi'an, Taiyuan and Changchun. These cities are all core cities of metropolitan areas. In recent years, they have attracted a large number of people through active new economic industries and relatively lenient talent policies. Meanwhile, cities in ethnic areas on the northwest side have a relatively high natural population growth rate, and the population size of most cities shows an increasing trend. In addition, cities with a decline of more than 20% in population are concentrated in the three northeastern provinces and the inter-provincial border areas. Among them, cities in the inter-provincial border areas of Hebei, Henan, Inner Mongolia, Shanxi, Shaanxi, Gansu, Sichuan and Yunnan have particularly significant population loss. A spatial structure of "core concentration - peripheral contraction" has gradually formed around large cities and their surrounding areas.

Fig. 5

Changes in China's Population Size from 2020 to 2035

3.3 The future trend of China's population age structure

In the future, China will face multiple challenges such as an aging population exceeding 30%, a low birth rate, and a shortage of labor force. According to the forecast (Fig. 6), by 2035, the proportion of people aged 65 and above will reach over 30%, the proportion of people aged 0–14 will be lower than 14%, and the proportion of the working-age population aged 15–65 will decline to around 54%. Referring to the relevant research on age structure classification, from 2020 to 2035, the number of cities with a proportion of 0–14 years old below 10% (severe low birth rate) increased from 14 to 68, the number of cities with a proportion between 10% and 20% (moderate low birth rate) increased from 199 to 239, the number of cities with a proportion between 20% and 30% (mild low birth rate) decreased from 124 to 33, and the number of cities with a proportion over 30% (high birth rate) decreased from 4 to 1; the number of cities with a proportion of 65 years old and above over 7% (not aging) decreased from 24 to 0, the number of cities with a proportion between 7% and 14% (mild aging) decreased from 168 to 3, the number of cities with a proportion between 14% and 20% (moderate aging) decreased from 140 to 21, and the number of cities with a proportion over 20% (severe aging) increased from 9 to 317; in addition, only some cities in Guangdong Province did not experience an aging society.

The age structure of China's population will mainly show a concurrent trend of deepening aging and shrinking labor supply, but the driving effect of aging will be more prominent from 2020 to 2035.In 2020, China's age structure types were mainly classified as the young growth type, mature stable type, unbalanced type, and elderly decline type, accounting for 30.20%, 61.90%, 5.30%, and 2.60% respectively. Among the mature stable type structure, the three combinations of moderate aging-moderate low birth rate (adequate labor force), mild aging-mild low birth rate (adequate labor force), and mild aging-moderate low birth rate (adequate labor force) had the highest proportions, which were 30.79%, 25.81%, and 22.87% respectively. At this time, the overall age structure of China's population was in a "middle-aged" type, with a relatively high proportion of the labor force population, and the aging trend began to emerge. There were a total of 9 cities with severe aging, mainly concentrated in Sichuan Province, Nantong and Taizhou cities in Jiangsu Province, and Ulanqab City in Inner Mongolia Autonomous Region. There were only 6 cities facing labor shortage, mainly distributed in Henan Province, Qinzhou and Yulin cities in Guangxi Zhuang Autonomous Region, and Nantong City in Jiangsu Province. There were 10 cities facing severe low birth rate, distributed in Heilongjiang Province and Jilin Province, and the other 3 cities were Shanghai, Nantong City, and Zhoushan City. In 2035, the proportions of the young growth type, mature stable type, unbalanced type, and elderly decline type were 0.30%, 5.90%, 10.00%, and 83.9% respectively. Among the elderly decline type structure, the two combinations of severe aging-moderate low birth rate (lack of labor force) and severe aging-severe low birth rate (lack of labor force) had the highest proportions, which were 58.65% and 18.77% respectively. At this time, China's age structure will face the dual impact of deep aging and labor force contraction. The proportion of cities without severe age structure problems was only 6.16%, with 13 of them concentrated in Guangdong Province, 5 in Sichuan Province, Qinghai Province, and Yunnan Province (where ethnic minorities live), and the other 3 cities were Xining City, Haikou City, and Sanya City. From the transformation path perspective, from 2020 to 2035, it mainly manifested as the coexistence of deep aging and labor supply contraction, among which the three paths of multi-level synchronous deepening, aging-labor supply contraction dual deepening, and aging single pole deepening had the highest proportions, which were 44.28%, 32.55%, and 11.73% respectively.

Fig. 6

Changes in China's Population Age Structure Types from 2020 to 2035

3.4 The future trend of urban-rural population structure in China

China has a typical dual-urban-rural economic structure. The continuous migration of population from rural areas to urban areas is an important feature of the demographic changes during the industrialization process (Fig. 7). From the perspective of urbanization rate, China's urbanization rate is expected to reach 77.95% by 2035, with a significantly slower growth rate. The average annual growth rate has decreased from the previous 1.4% to approximately 0.97%, indicating that China has entered the mature stage of urbanization. In terms of the size of urban and rural populations, by 2035, the urban population is projected to increase to 1.022 billion, an increase of approximately 120 million compared to 2020, while the rural population is expected to shrink to around 292 million. Regarding urban-rural population mobility, by 2035, there will be a total of 50 megacities and super-metropolitan cities in China, an increase of 12 compared to 2020. Particularly, the number of super-metropolitan cities has increased by 7, indicating that the population growth in different-sized cities will shift from synchronous growth to differentiation. The population will migrate from rural and small cities to first- and second-tier metropolitan areas, while the population growth in small cities will stagnate or even experience net emigration.

Fig. 7

Changes in Urban and Rural Population in China from 2020 to 2035

In the future, the population distribution in urban and rural areas of China will take on a "urban dominance" pattern. The net inflow of population in urban areas and the net outflow of population in rural areas will grow simultaneously (Fig. 8). The differentiation in population increase and decrease between urban and rural areas is one of the main manifestations of China's population development trend in the future. In the second half of the urbanization process, the urban population system will accelerate the transformation from a "pyramid shape" to a "spindle shape", which is manifested as an increase in the number of megacities, a contraction of medium-sized cities, and the formation of urban clusters as the core carriers. The population mobility pattern will shift from a single "rural-to-urban" migration to a diversified one, characterized by enhanced cross-city migration, an expansion of the scale of population mobility between cities, the concentration of high-skilled talents in "high-value-for-money" cities, and the migration of the elderly to livable cities; intra-provincial migration will dominate, the proportion of cross-provincial migration will decrease, and intra-provincial inter-city migration will increase, forming a "dualistic" lifestyle model; regional differentiation will intensify, with eastern urban clusters such as the Yangtze River Delta and the Pearl River Delta continuously attracting population, while non-capital cities in the northeastern and western regions face population loss.

Fig. 8

Spatial Distribution of Urban and Rural Population Changes in China from 2020 to 2035

4. Conclusions and Discussion

4.1 Conclusions

This study examines the impacts of population changes on various aspects such as the economy, urban planning, and public services. Utilizing data from the fifth, sixth, and seventh national population censuses, an ARIMA-LSTM coupled model improved by Bootstrapping method was employed to predict the trends of urban and rural population size and structure at the national, provincial, and municipal levels from 2021 to 2035. The main conclusions are as follows:

In the future, the total population of China will continue to decline, and regional disparities will intensify. China will enter a stage of continuous negative population growth. It is projected that by 2035, the total population will decrease to 1.314 billion people. The regional differences in population structure are becoming increasingly significant. Economically developed regions and policy-benefiting areas will still maintain stable growth, especially provinces such as Zhejiang and Guangdong, which are driven by the agglomeration effect of core urban clusters. However, traditional industrial provinces in the northeast and central regions, especially those with depleted resources and single industries, will face the predicament of accelerating population decline. The differentiation of urban levels also exhibits a "pyramid-like" characteristic. Super-large cities such as Beijing, Shanghai, and Chengdu show a phenomenon of population increase first and then decrease. While some newly developed large cities have gathered a large number of people through industrial upgrading.

In the future, China's population age structure will face multiple challenges such as super-aged population, super-low birth rate and labor shortage. By 2035, China will enter the stage of "severe aging" and "severe low birth rate", with the elderly population aged 65 and above accounting for more than 30% of the total population, and the proportion of children population dropping significantly to below 14%. The population in the working age group will decrease from 976 million in 2020 to 710 million. The proportion of young and middle-aged workers will decline, while the proportion of elderly workers will increase. The labor market will face a serious risk of skill mismatch. The elderly dependency ratio will exceed 40%, and the total social dependency ratio will exceed 60%. This will greatly increase the pressure on social security and medical expenditures. At the same time, the population structure type will shift from young growth type and mature stable type to unbalanced and aging decline type, leading to a transformation of consumption structure, a sharp increase in demand in medical care and elderly care fields, and possible suppression of innovation vitality. This trend will pose huge challenges to labor supply, economic growth potential and social security system. Policies will have to shift to delaying retirement and redeveloping human resources to alleviate the decline of the demographic dividend.

(3) In the future, the population distribution in urban and rural areas of China will follow a "urban dominance" pattern, with the net inflow of people in urban areas and the net outflow of people in rural areas growing simultaneously.By 2035, China's urbanization rate will reach 77.95%, with a significantly slower growth rate. The average annual growth rate will decrease from the previous 1.4% to approximately 0.97%, entering a mature stage of urbanization. The urban population will increase to 1.022 billion, while the rural population will drop to approximately 292 million. The urban-rural structure will show significant differences. The urban population system will shift from a "pyramid shape" to a "spindle shape", manifested as an increase in the number of megacities, contraction of medium-sized cities, and the formation of urban agglomerations as the core carriers. Meanwhile, rural areas are facing the challenges of population loss and an accelerating aging problem. The urban-rural inversion phenomenon is prominent, and the aging degree in rural areas is significantly higher than that in urban areas. This imbalance in the urban-rural structure will intensify the pressure on social resource allocation and public services, and policy adjustments are urgently needed to promote coordinated development between urban and rural areas.

4.2 Discussion

According to the population size and structure projections in China, the total population is expected to drop to 1.314 billion by 2035. The phenomenon of low birth rate and aging will intensify, and the scale of labor supply will shrink significantly. The urbanization rate is projected to reach 77.85%, while the rural population will decrease significantly. The population issue has always been a fundamental, overall and strategic issue for the development of a country or region. The regularity, trend and structural changes of the population will have a profound impact on various aspects such as economic and social development, public service provision, and urban planning. In recent years, China's population has shown an accelerating trend of aging, low birth rate and agglomeration. The speed and impact have exceeded the predictions based on experience and expectations from various parties. This includes both many new development opportunities and some challenges(Liu and Liu 2025).

The global population structure is undergoing significant changes, and the proportion of China's population in the world will gradually decline(Wang et al.,2021). In terms of total population, in 2024, India's population will reach 1.416 billion, officially surpassing China and becoming the world's largest country in terms of population. According to the United Nations' prediction, by 2050, the proportion of China's population in the world will drop from the current 17% to around 10%, and by 2100, it may further fall below 5%. This change will affect China's international status and global influence. From the perspective of fertility rates, in 2022, the total fertility rates of France, the United States, Brazil, the United Kingdom, Germany, Japan, and South Korea were 1.8, 1.7, 1.6, 1.6, 1.5, 1.3, and 0.7 respectively, while China's total fertility rate was around 1.0. China's total fertility rate ranks the second lowest among major economies globally, only slightly higher than that of South Korea(Xu et al.,2025).In terms of the degree of aging, Japan is the country with the most severe aging problem in the world, with the proportion of people aged 65 and above exceeding 29%; South Korea follows closely, at approximately 18%; European countries such as Italy, Germany, and France also have a relatively high degree of aging, with the proportion of people aged 65 and above ranging from 20% to 25%. Compared to China, whose aging process is much faster than that of developed countries, Japan took 24 years to transition from aging to deep aging, Germany took 40 years, France took 126 years, while China only took 21 years. From the perspective of urbanization development, China's urbanization rate is around 67%, which is close to the world average level, but still has a gap compared to developed countries with an urbanization rate of over 80%(Inoue and Inoue 2024).From the perspective of urbanization speed, China's urbanization process is significantly faster than that of developed countries. It has completed the journey that took developed countries hundreds of years in just a few decades. However, in terms of urbanization quality, there is still a certain gap compared with developed countries(Tang and Wang 2025).

The future changes in China's population will have a profound impact on economic and social development(Mason and Lee 2022).Firstly, the decrease in the total population and the aging problem will lead to a further reduction in the working-age population. By 2035, the working-age population in China will decrease by 256 million compared to 2020, and the proportion of the working-age population will drop by 14.5 percentage points. China's population age structure has shifted from "abundant labor force" to "labor shortage", and it has fallen into a serious population deficit situation. If the increase in labor productivity cannot offset the impact of the reduction in the labor force, the continuous decrease in the working-age population will inevitably restrict economic development and reduce the economic scale(Strulik 2024).Secondly, the rapid decline in population and the aging of society will affect consumption capacity, shrinking the domestic consumption market, and thereby weakening the investment willingness and its role in driving economic growth. Currently, the economic growth model is being transformed, with the aim of mainly promoting economic growth through technological innovation. However, population decline and excessive aging will not facilitate the generation of technological innovation through human mobility, aggregation, and communication. Thus, it will hinder the transformation of the economic growth model and reduce the promotion and contribution of technological innovation to economic growth. In the case of a decrease in the labor force, to make up for the shortage of labor, working hours may be extended. This makes it difficult to maintain a balance between work and life, which is not conducive to increasing the birth rate and may lead to a vicious cycle where the decline in population and aging reinforce each other. The negative impact of population decline and excessive aging on the economy will also cause a negative multiplier effect in the interaction between demand and supply. Once the economic scale begins to shrink, it may fall into the so-called "shrinking spiral". When the "shrinking spiral" has a strong effect, it will result in an increase in the burden of the nation exceeding economic growth, thereby lowering the per capita actual consumption level and affecting the quality and standard of national life. Therefore, population decline and excessive aging will bring about population indebtedness and the shrinking spiral, serving as a brake on economic growth and restricting sustained economic growth(Zhang et al.,2025).Thirdly, the economy serves as the material foundation for social security and government finance. Population decline and an aging population beyond the normal level restrict economic growth, reduce the economic scale, and consequently weaken or even reduce the material foundation supporting social security and government finance. In 2020, China still had a demographic dividend, and social security and employment expenditures accounted for only 2.21% of the total fiscal budget, which was much lower than that of developed countries. By 2035, the demographic dividend has been lost, the economic scale has shrunk, the burden of elderly care has increased, and the existing social security system and fiscal system will be difficult to sustain. The development of population decline and excessive aging not only leads to a decrease in pure savings of families and enterprises, but also causes an expansion of fiscal deficits. Thus, it will have to rely on foreign debts for absorption. The result will be an increase in interest payment burdens and an impact on the international financial market. If the fiscal soundness is not effectively promoted, the risk of fiscal crisis will increase. From the perspective of the relationship between production and consumption, support and being supported, and the relationship between parents and children, in 2020, China could have about 5 people of working age for every 1 elderly person aged 65 and above. However, with the decline in population and excessive aging, the increase in the elderly population and the decrease in the working-age population, by 2035, the number of people supporting one elderly person aged 65 and above will rapidly decrease to 2. It can be imagined that when this "backing the society" situation arrives, it will greatly impact social security mainly based on medical care and care services, further deepening the imbalance between payment and burden. The development of low birth rate and excessive aging leads to a reduction in family size, weakened elderly care function, a large number of elderly people living alone, and a significant increase in the burden of elderly care. Population decline and excessive aging will also cause a reduction in community size and aging of the community population. Community administration will also suffer from a shortage of personnel, thus causing difficulties in community administration and assistance for elderly care. The combination of family and community-based elderly care methods and elderly care services will also fall into difficulties. The future population size and structure will not only be unable to ensure human reproduction, but also make it very difficult to maintain economic vitality and the sustainability of social development(Lobanov et al.,2023).

The future changes in China's population will directly affect the supply capacity and allocation efficiency of the public service system, and will subsequently have a profound impact on social governance and the country's basic service guarantee system(Ye et al.,2025).Firstly, population decline and changes in population structure will reduce the efficiency of effective allocation of public service resources. On one hand, the number of school-age children is continuously decreasing, causing "shortage of students" in primary and secondary schools and kindergartens in some areas, resulting in idle educational resources and even leading to the closure or merger of some schools; on the other hand, the continuous concentration of urban population leads to concentrated enrollment demands, making the supply-demand contradiction of high-quality educational resources more prominent in first-tier cities. This "uneven distribution" pattern makes it difficult to achieve balanced allocation of educational public service resources, affecting educational equity and quality improvement(Wu et al.,2020).Secondly, the aging population leads to a structural shortage of healthcare resources. Currently, the healthcare resources in our country are still relatively scarce. The aging population has continuously increased the demand for services such as chronic disease treatment, elderly care, and long-term care, putting pressure on the medical system that previously focused more on "acute disease treatment". Some grassroots community medical institutions lack medical and nursing personnel, and the allocation of resources does not match the actual needs of an aging society, greatly affecting the accessibility and precision of medical resources. At the same time, the decrease in population has led to a decline in the utilization rate of medical facilities in some areas, and the efficiency of fiscal investment has decreased, resulting in resource waste(Higuchi and Watari 2024).Thirdly, when facing uneven population distribution, public transportation, infrastructure, and urban service systems will also encounter supply structure imbalance. The decrease and outflow of population have led to an "excess" of infrastructure in some small cities and rural areas. The maintenance costs remain high, while the number of service recipients keeps decreasing, resulting in a decline in public service efficiency. In large cities with continuous population inflow, traffic congestion, housing shortages, and increasing pressure on public services persist, making urban governance burdens heavy and service satisfaction difficult to improve. This structural imbalance will further weaken the guiding role of public services in optimizing population distribution and affect the overall development quality. Fourthly, the increased fiscal burden will also undermine the sustainability of the public service system. As the dependency ratio rises and the tax base shrinks, the fiscal capacity of local governments is limited, and the growth space for public service investment is squeezed. A vicious cycle of "increased service demand - restricted fiscal supply - decreased service quality" is formed. If the fiscal efficiency cannot be improved through institutional reforms and the service structure cannot be optimized, it will be difficult to ensure the fairness, accessibility, and continuous supply of basic public services in the future, and to weaken the national governance capacity and the foundation of social stability(Wu et al.,2024).

The future changes in China's population will have a profound impact on urban development and resource allocation(Zhang et al.,2023).With the decline in population size and the imbalance in population structure, the urban labor supply is facing increasingly severe challenges. In particular, small cities and county towns may be at a disadvantage in competing for young people, enterprises and capital, thereby exacerbating regional population outflow. Future urban planning and development strategies should place greater emphasis on regional balance and differentiated population distribution, avoiding excessive concentration and overdevelopment, and promoting the coordinated development of large cities and small cities(Kikuchi et al.,2022).During the process of urbanization, when people move to cities, they need to make preparations for infrastructure construction in advance to ensure that transportation, education, medical care and other resources can effectively support the growing urban population. For rural areas where people are leaving, it is necessary to balance the allocation of resources between urban and rural areas and promote the implementation of the rural revitalization strategy to alleviate the social burden brought about by urbanization(Dou et al.,2025;Qi et al.,2022).The changes in population structure pose particularly significant challenges to sustainable development. The decline in the birth rate and the aging population have exacerbated the shortage of per capita resources, the insufficiency of labor force, and the increase in the pressure on public services, thereby affecting the country's sustainable development strategy. At the same time, this change also provides opportunities for the development of emerging fields such as green economy, intelligent industries, and health industries. How to find a balance between economic development and social security will be a key issue for promoting sustainable development in the future(Yang et al.,2024).

Population forecasting is not merely a mathematical or statistical issue; it is also an important part of a country's long-term development strategy. Currently, China's population development is at a critical turning point. Scientific forecasting provides crucial decision-making support for addressing issues such as the decline in the birth rate, aging population, and regional population differentiation, and promoting the long-term balanced development of the population(Anonymous 2024;The UN Population Division 2022).This study is based on the improved ARIMA-LSTM coupled model through Bootstrapping, providing a more accurate tool for population prediction. It can conduct comprehensive analysis of time series and spatial distribution at the prefecture-level city level, and has strong empirical and application value. However, this study still has certain limitations. Firstly, the model mainly relies on domestic statistical data and has not fully considered the dynamic impact of international migration flows on population size and structure. With the intensification of globalization and cross-border labor mobility, international migration has become an important variable affecting regional population growth, structure changes, and labor supply. Future research should incorporate it into the prediction framework(Cameron and Poot 2024;Chen et al.,2025).The research mainly focuses on the level of prefecture-level cities, but has not yet covered more granular regional units such as counties, streets or grids(Zhang and Gibson.2025).Thirdly, although the model integrates the advantages of ARIMA and LSTM, there is still room for improvement in parameter optimization, interpretability, and response to non-linear shocks such as epidemics and economic crises. However, future research can further deepen the application of the model. Firstly, the research scope and time scale can be expanded to cover smaller regional levels, such as county-level, street-level or grid-level, in order to provide scientific basis for more precise policy adjustments. Secondly, external factors such as international migration, cross-regional mobility and policy regulation can be introduced to construct a more comprehensive dynamic population prediction system. Thirdly, by combining big data, remote sensing and Geographic Information System (GIS) technologies, population prediction can be conducted at a higher spatial and temporal resolution. With the continuous advancement of big data and artificial intelligence technologies, how to utilize these emerging technologies to improve the accuracy and interpretability of prediction models will become the core direction of future research.

Competing interests

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Author Contribution

J.C.and X.F.Z. conceived and designed the research. J.C. compiled the data,built the models. All authors performed analyses. J.C.and X.F.Z. wrote the manuscript, while Z.Q.T.and K.Q.J. interpreted the findings and provided feedback on the manuscript drafts.

Data Availability

The population survey data supporting the findings of this study are available at http://www.citypopulation.de/.

Acknowledgement

This research was funded by the National Natural Science Foundation of China, grant number 42371293.

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