Spatiotemporal evolution characteristics and trend prediction of the fertility level of women of childbearing age in China

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Methods Statistical data related to fertility were collected from 2008 to 2022 for China and 31 provincial administrative regions (except Hong Kong, Macao and Taiwan). Statistical descriptions of the fertility situation were made in both spatial and temporal dimensions to understand its spatial and temporal distribution; the future trend of fertility was predicted by using the ARIMA projection model of time series analysis, and the spatial autocorrelation analysis was used to explore its spatial aggregation characteristics. Results From 2008 to 2022, the fertility level of women of childbearing age in China has been in a long-term depression and there is a trend of continued decline, with the phenomenon of delayed childbearing evident in the population of childbearing age. The spatial pattern shows a gradual decrease from south to north.China, as well as the three major regions of the East, Centre and West, will reach their lowest point in 2023, followed by a slow recovery and a gradual stabilisation in the following decade, but will still be at a relatively low level of fertility as a whole;while in the Northeast, fertility levels will continue to decline and will be in a state of negative growth in 2036. There is a positive spatial autocorrelation of fertility levels across provinces,and the characteristics of the spatial agglomeration of fertility levels vary from region to region. Conclusion The results of this study show that the fertility level of women of childbearing age in China continues to be low, and is expected to remain at a very low fertility level for a long time to come. Individualised recommendations are made for future development trends in different regions to create a fertility-friendly social environment and promote long-term balanced population development. Fertility level Forecast Spatial and temporal evolution Spatial aggregation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Introduction Population is the most basic resource of a country or region, and fertility level, as one of the important indicators of demographic research, is of great significance for demographic change, social and economic development, and the formulation of social policies. The current extremely low fertility level is the greatest risk and challenge to China's development. Data from the National Bureau of Statistics show that in 2022, China's birth rate will drop to 6.77 per thousand, the death rate will be 7.37 per thousand, and the natural growth rate will drop to -0.60 per thousand, and our country is already in the ‘three lows’ stage of population with a low birth rate, a low death rate, and a low natural growth rate.Since the beginning of the 21st century, China's fertility rate has shown a downward trend. According to the national census data, the total fertility rate was 1.22 in 2000 and dropped to 1.18 in 2010. In response to this phenomenon, China successively introduced the “selective two-child” and “comprehensive two-child” policies in 2013 and 2016, but the actual effect is not satisfactory. According to data from the seventh national population census, China's total fertility rate is 1.3, which is lower than the international fertility warning line of 1.5, and is considered to be at risk of falling into a low fertility rate. At the same time, the age structure of China's population has also undergone a major transformation, with the scale of the working-age population declining and the degree of ageing further deepening. For the reason, the Chinese government decided to further optimise its fertility policy, and in 2021,the State Council issued “The Decision on Optimising Fertility Policy to Promote the Long-Term Balanced Development of Population”, making a major decision on ‘implementing the three-child policy and complementary support measures’. The National Population Development Strategy Study states that in order to maintain the long-term coordinated development of population and economy and society, China's total fertility rate should be maintained at around 1.8 in the next 30 years. It is particularly urgent and important to find ways to reasonably raise China's fertility level and to cope with changes in the demographic structure of the population. As fertility levels continue to be low around the world, the research on fertility rate and fertility policy has received extensive attention from scholars at home and abroad.Some scholars predict that low fertility will become a global crisis and will reach ultra-low fertility levels in the next 20 years [1] . Previous studies have shown that fertility is affected by a variety of factors, mainly including changes in fertility policies, increased economic uncertainty, increased educational attainment, large-scale entry of women into the labour market, and changes in gender roles [2, 3] . It has also been shown that migrant populations and ethnic disparities are the main factors contributing to the fertility gap across countries [4] . A study of fertility differences among 402 regions in Germany showed that regional factors are highly correlated with fertility, with low population density, relatively spacious housing and low unemployment significantly increasing fertility in the region [5] . This is confirmed by studies of differences in female fertility in the United States, where fertility rates are higher in small areas than in large cities [6] . Domestic scholars have focused more on urban-rural differences in fertility rate and the trend of rural family fertility,and have obtained fruitful research results [7, 8] . Despite the large number of studies on fertility by modern scholars, however, few studies has examined the spatial correlation and regional variability of fertility levels from a spatial perspective. Studies based on time series to predict the population size are on the high side, and there is little literature to make corresponding countermeasures for different regions. In summary, the fertility level of China's population has continued to decline and the degree of population aging has been deepening,while changes in the fertility status of women of childbearing age groups are conducive to the adjustment of the population structure, thus promoting the long-term balanced development of China's population. This paper analyses the characteristics of the spatial and temporal evolution of fertility levels and the future development trends in China and 31 provincial-level administrative regions from a dual perspective of time and space, based on demographic data from 2008 to 2022, using time series analysis and exploratory spatial analysis with a view to improving China's fertility levels and providing references and suggestions for the formulation of future population policies and balanced social development. Materials and methods Data source The data in this paper are mainly derived from the Chinese Population Census Yearbook and the China Statistical Yearbook, and 31 provincial-level administrative regions except Hong Kong, Macao and Taiwan are selected as the study areas. Demographic data related to fertility in China and the 31 provincial administrative regions from 2008 to 2022 were obtained for analysis. The vector map data of China used in this study were downloaded from the National Basic Geographic Information Center, and the subsequent analysis was based on the map data without any modification of the base map. Statistical analysis The analysis method and procedure for solving the research problem are as follows. SPSS 26.0 and ArcGIS 10.8 programs were used for data handling and model analysis. First, statistical descriptions of the fertility situation were made in both spatial and temporal dimensions to understand its spatial and temporal distribution;Second, the future trend of fertility was predicted by using the ARIMA projection model of time series analysis, and third, the spatial autocorrelation analysis was used to explore its spatial aggregation characteristics. Statistical model time series analysis The ARIMA model is a widely used model in time series analysis, and is known as the Autoregressive Moving Average Model. The basic idea is to convert the non-stationary time series into stationary time series, and then establish an ARMA model for the stationary time series. The formula is as follows: ARIMA(p, d, q) . Where AR stands for autoregression, and p is the order of autoregression; MA stands for moving average, q is the number of moving average terms; d is the number of differences made when the time series becomes stationary. Spatial aggregation analysis Global spatial autocorrelation analysis The global Moran's I index was used to analyze the spatial distribution, correlation and statistical significance of fertility as a whole, so as to judge whether fertility was spatially clustered. Moran's I index formula is as follows: $$\:I=\frac{n}{{\sum\:}_{i=1}^{n}{\sum\:}_{j=1}^{n}{w}_{ij}}*\frac{{\sum\:}_{i=1}^{n}{\sum\:}_{j=1}^{n}{w}_{ij}({x}_{i}-\stackrel{-}{x})({x}_{j}-\stackrel{-}{x})}{{\sum\:}_{i=1}^{n}{({x}_{i}-\stackrel{-}{x})}^{2}}$$ where n is the total number of spatial units in the study area, x i is the actual observation on the ith spatial region, x j is the actual observation on the jth spatial region, \(\:\stackrel{-}{x\:}\) is the mean value of the whole space x ; w ij is the spatial weight matrix of the ith and jth spatial regions (as a reflection of the neighbourhood of each spatial region, i and j represent the rows and columns of the matrix, respectively), and in this study, we adopt the neighbourhood spatial weight matrix, when two spatial units i and j are adjacent to each other, then w ij= 1, otherwise w ij= 0. The distance measurement method adopts the Euclidean distance. Local spatial autocorrelation analysis Local Moran's I index and LISA aggregation maps were used to study the local regional characteristics of fertility, to identify the exact aggregation areas and to determine the aggregation patterns. The local Moran's I index expression is given below: $$\:{I}_{j}=\frac{{n}^{2}\left({x}_{j}-\stackrel{-}{x}\right){\sum\:}_{i=1}^{n}{w}_{ij}({x}_{i}-\stackrel{-}{x})}{{\sum\:}_{i=1}^{n}{({x}_{i}-\stackrel{-}{x})}^{2}}$$ Its hypothesis testing is the same as the global Moran ' s I index, the local Moran's I index is able to analyse the relationship between the study area and the surrounding neighbouring areas, it classifies the relationship between the study spatial unit and the surrounding areas into four types, including two positively correlated aggregation modes, high-high aggregation and low-low aggregation, and two negatively correlated aggregation modes, high-low aggregation and low-high aggregation. Result Overview of the total fertility rate in China Data on the total fertility rate of China and 31 provinces, municipalities and autonomous regions in 2010 and 2020 were obtained from China's population census data and the percentage change was calculated, and the results are shown in Figure 1. As can be seen from the figure above, China's total fertility rate was 1.18 in 2010, with only three provinces, Guangxi, Guizhou and Xinjiang, having total fertility rates above 1.5, while first-tier cities such as Beijing, Tianjin and Shanghai and the three northeastern provinces were all below 1.0.In 2020, China's total fertility rate will be 1.3, and only seven provinces will have a total fertility rate greater than 1.5, with Tibet, Guangxi, and Guizhou provinces having relatively high fertility levels, with total fertility rates of 1.93, 1.94, and 2.12 respectively, while Beijing, Tianjin, and Shanghai, as well as the three northeastern provinces, will remain below 1.0, and China will still be at an ultra-low fertility level.Comparing the distribution of fertility levels in China and 31 provinces, municipalities and autonomous regions in 2010 and 2020, the total fertility rate will rebound slightly in 2020, but it will decline most severely in Xinjiang, from 1.53 in 2010 to 1.06 in 2020, a decline of about 31 per cent; and it will increase most rapidly in Tibet, from 1.05 in 2010 to 1.93 in 2020, an increase of about 84 per cent. The increase is about 84 per cent. Age-specific fertility profile of women of childbearing age Census data show that in 2020 the fertility rate for 15-19 year olds was 6.07 per 1,000, an increase from the previous two early childbearing ages, and that the fertility rate for women of childbearing age 20-24 was 55.22 per 1,000, a 14.25 per cent decrease from 2010. The fertility rate for women of childbearing age is 98.98 per 1,000 live births in the 25-29 age group, and 190.94 per 1,000 live births for women of childbearing age aged 25-39, which is a significant increase from 2010, especially for women aged 30-34, which is 36.43 per cent and 19.21 per cent higher than in 2000 and 2010 respectively. The fertility rate for persons aged 40-49 declined from 2010, with the rate for persons aged 45-49 declining by 3.07 per cent from 2010. As shown in figure 2. Overview of the general fertility rate in China The number of women of childbearing age from the national census and the number of births in previous years were used to calculate the country's general fertility rate for the past 15 years. And according to the country's economic layout, the Mainland is divided into four segments: East, Middle, West and Northeast. In this paper, the data of each administrative region are summed up and analysed year by year according to the scope of the four regions. The results of the analyses and the time series charts are shown in Table 1 and Figure 3. Table 1 . General fertility rate of women of childbearing age in the four regions of China, by year in per cent Year Northeast Eastern Middle Western Nationwide 2008 28.35 37.68 45.12 48.22 45.81 2009 27.39 38.36 45.48 47.96 46.01 2010 29.56 38.61 45.61 47.11 45.35 2011 26.14 38.58 45.42 46.68 45.68 2012 26.50 41.02 46.99 47.72 46.56 2013 25.40 39.89 47.30 47.94 46.71 2014 28.10 43.01 48.59 48.81 48.07 2015 24.80 40.56 47.50 49.32 47.15 2016 25.27 47.26 49.76 50.41 50.86 2017 26.39 49.51 51.17 53.00 49.09 2018 25.65 43.58 47.02 50.03 43.40 2019 24.72 41.85 44.50 47.77 41.73 2020 17.12 33.11 35.35 40.47 34.20 2021 15.93 29.75 30.37 35.94 30.26 2022 14.16 26.59 27.34 32.77 27.23 As can be seen from Figure 3, China's fertility level is highest in the western region, second highest in the middle region, and lowest in the north-eastern region. Fertility in the western region has been slowly declining since 2008, slowly increasing after 2011 and reaching its highest value in 2017, with a declining trend in the western region after 2017 and a significant drop in fertility after 2019;Fertility in the Central Region as a whole showed a slow upward trend from 2008-2017, with slight decreases in 2011 and 2015, and a maximum in 2017, and a downward trend in the Central Region after 2017; Fertility in the Eastern Region as a whole showed an upward trend from 2008-2017, with slight decreases in 2011, 2013 and 2015, and the fastest rate of increase in 2016, the reached its highest value in 2017, fertility continued to decline after 2017, and the fertility level in the East is comparable to that of the Central Region in 2022;Fertility in the Northeast has been on an overall downward trend for 15 years, with peaks in 2010 and 2014 and the largest decline in 2020, but still below the national average. The national fertility rate has been slowly increasing since 2008, reaching a peak in 2016, and then declining steeply after 2016, with the largest decreases in 2018 and 2020, and the overall level is between that of the Central and Eastern regions, with the three levels being comparable after 2020. Characteristics of the spatial distribution of fertility The spatial distribution of fertility is mapped according to the fertility of each region in 2008-2022. The results, as shown in Figure 4, indicate that fertility rate exhibits an uneven spatial distribution, but the spatial distribution of fertility is similar in each region in each year, with high-fertility areas distributed mainly in the western and southern provinces, municipalities and autonomous regions, mainly Xinjiang, Tibet, Qinghai, Guangxi, Sichuan and its neighbouring provinces; and low-fertility areas distributed mainly in the Low-fertility areas are mainly distributed in the northeastern region and eastern coastal provinces and autonomous regions, such as Heilongjiang, Jilin, Liaoning, and Zhejiang, Shanghai and other provinces. And during the period of 2008-2017, the colour of each region gradually becomes darker, indicating that the fertility rate of all regions in the country tends to be stable and shows a slow growth trend; while the colour of each region gradually becomes lighter after 2018, indicating that the fertility rate generally decreases and reaches the lowest value in 2022. Time Series Prediction National Fertility Level Projections A time series model was established based on the data of fertility rate of women of childbearing age in China for a total of 15 years from 2008 to 2022. According to the results of ADF test, P =0.005<0.05, indicating that the series is a smooth series. The autocorrelation coefficient plot and partial correlation coefficient plot of the national fertility rate are obtained, as shown in Figure 5. Since the time series itself is a smooth series, no differencing is required and the data is directly modelled and predicted using an ARMA model, an autoregressive moving average model.The general form of the ARMA model is ARMA(p, q), where p denotes the order of the autoregressive part and q denotes the order of the moving average part. The model results in ARMA(1,3) based on the AIC information criterion to find the optimal parameters. The ARIMA model requires that the residuals of the model are free of autocorrelation, i.e., the model residuals are white noise. The residual test of ARMA (1,3) model using Ljung-Box Q statistic, P =0.939＞0.05, which indicates that the residual series of the fitted model is a white noise series, and R 2 =0.84, which indicates that the model performs well, and the model basically meets the requirements. Therefore, the ARMA (1,3) model was selected as the optimal time series model of fertility in China. The model is used to forecast China's fertility rate from 2023 to 2050, and the forecast value, 95% confidence upper limit and 95% confidence lower limit, and the trend graph are obtained, and the specific results are shown in Table 2 and Figure 6. Table 2 . Projections of National Fertility Rate, 2023-2050 Year 2008 ... 2022 2023 2025 2030 2035 2040 2045 2050 National Fertility Rate 45.81 ... 27.23 Predicted value 29.30 26.18 28.22 35.20 38.05 39.22 39.70 39.89 95 per cent upper confidence limit 30.17 36.95 51.52 55.32 56.64 57.15 57.35 95 per cent lower confidence limit 22.19 19.49 18.87 20.78 21.79 22.24 22.43 As shown in Figure 6, according to the prediction results of ARMA (1,3) model, China's fertility level will reach its lowest value in 2023, and the fertility rate will continue to increase from 2023 to 2050, and it is expected that the fertility rate will increase at a faster rate in the next ten years, and then level off after 2035, but the overall level will still be at a relatively low level. It can provide a reference basis for the formulation of relevant population issues and population policies in China. Projections of fertility levels in the north-eastern region A time series model was established based on the fertility data of the Northeast region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, P =0.965＞0.05, indicating that the series is a non-stationary series. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the northeastern regional fertility series, which yields P=0.001＜0.05, indicating that the series is a smooth series. The plots of autocorrelation coefficient and partial correlation coefficient after first-order differencing are drawn, as shown in Figure 7. Since the first order difference is applied to the series, d=1. Based on the AIC information criterion, the AIC minimum model is optimal to find the optimal parameters. The model results in ARIMA (0,1,0). Using Ljung-Box Q statistic for residual test of ARIMA (0,1,0) model, P =0.550＞0.05, R 2 =0.7, which indicates that the residual sequence of the fitted model is a white noise sequence, and the fitted model performs well, and the model basically meets the requirements. Therefore, the ARIMA (0,1,0) model was selected as the optimal time series model for fertility in the Northeast China. The model was used to predict the fertility level in the Northeast China from 2023 to 2050, the specific results are shown in Table 3 and Figure 8. Table 3 . Statistical Table of Fertility Rate projections in Northeast China from 2023 to 2050 Year 2008 ... 2022 2023 2025 2030 2035 2040 2045 2050 Fertility Rate in the Northeast Region 28.35 ... 14.16 Predicted value 14.92 13.15 11.12 6.05 0.98 -4.08 -9.15 -14.22 95 per cent upper confidence limit 18.05 19.61 19.92 18.67 16.72 14.37 11.73 95 per cent lower confidence limit 8.24 2.62 -7.82 -16.70 -24.89 -32.67 -40.17 As shown in Figure 8, according to the prediction results of the ARIMA (0,1,0) model, the fertility level in Northeast China will continue to decline and enter a negative growth state in 2036. Projections of fertility levels in the Eastern Region A time series model was established based on the fertility data of the eastern region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, P =0.181＞0.05, indicating that the series is a non-stationary series. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the fertility rate series in the eastern region, with P =0.056＞0.05, indicating that the series is still non-stationary. Continuing to second-order differencing of the series, the ADF test on the second-order differenced series of fertility rate in the Eastern Region shows that P =0.000<0.05, indicating that the series is smooth at this point. The plots of autocorrelation coefficient and partial correlation coefficient as shown in Figure 9. Based on the AIC information criterion, the optimal parameters are found. The model result is AR (1). The residual test of AR (1) model using Ljung-Box Q statistic shows that: the p-value of Q6 is 0.311, More than 0.05, then the original hypothesis can't be rejected at the significance level of 0.05, which indicates that the residual series of the fitted model is a white noise series; R 2 =0.591, the fitted model performs well, and the model basically meets the requirements. Therefore, the AR (1) model is selected as the optimal time series model of fertility in the eastern region of China. The model was used to project fertility levels in the eastern region for the period 2023-2050, and the results are shown in Table 4 and Figure 10. Table 4 . Statistical Table of Fertility Rate Projections in the Eastern China, 2023 — 2050 Year 2008 ... 2022 2023 2025 2030 2035 2040 2045 2050 Fertility Rate in the Eastern Region 37.68 ... 26.59 Predicted value 30.92 28.31 30.9 34.34 35.67 36.18 36.38 36.46 95 per cent upper confidence limit 35.57 41.55 46.94 48.54 49.09 49.29 49.37 95 per cent lower confidence limit 21.04 20.25 21.74 22.81 23.28 23.47 23.55 As shown in Figure 10, according to the projection results of the AR(1) model, the fertility level in the eastern region of the country will slowly increase from 2024, and the expected fertility rate in the eastern region will level off after 2035. Projections of fertility levels in the central region A time series model was established based on the fertility data of the central region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, P =0.288＞0.05, indicating that the series is non-stationary. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the central region fertility series, which is P =0.765＞0.05, indicating that the series is still non-stationary. Continuing to second-order differencing of the series, the ADF test of the second-order differenced series of fertility rate in the Central Region shows that P =0.000<0.05, which means that the series is smooth at this point. Autocorrelation coefficient and partial correlation coefficient of fertility in the central region are shown in Figure 11. The model results were ARIMA (2,0,0) based on the AIC information criterion to find the optimal parameters. Using Ljung-Box Q statistic to test the residuals of ARIMA (2,0,0) model, the result of Q statistic shows that P =0.542＞0.05, Q6 does not present significance at the level, and the hypothesis that the residuals of the model are white noise series can not be rejected, at the same time, the model's goodness of fit R² is 0.814, the model performance is excellent, and the model basically meets the requirements. Therefore, the ARIMA (2,0,0) model was selected as the optimal time series model for fertility in the central region of China. Using this model to predict the fertility level in the central region from 2023 to 2050, the specific results are shown in Table 5 and Figure 12. Table 5 . Statistical Table of Fertility Rate Projections in the Central Region, 2023 — 2050 Year 2008 ... 2022 2023 2025 2030 2035 2040 2045 2050 Fertility rate in the Central Region 45.12 ... 27.34 Predicted value 28.87 27.58 33.24 44.28 42.38 41.50 41.92 41.93 95 per cent upper confidence limit 32.49 45.07 59.51 57.81 56.95 57.37 57.38 95 per cent lower confidence limit 22.66 21.41 29.05 26.95 26.05 26.46 26.47 As shown in Figure 12, according to the projection results of the ARIMA (2,0,0) model, the fertility level in the central region of China will continue to rise from 2023 to 2031, peak around 2031, and then decline slightly, but generally stabilise. Projections of fertility levels in the western region A time series model was established based on the fertility data of the western region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, P =0.000<0.05, indicating that the series is a smooth series. The autocorrelation coefficient plot and partial correlation coefficient plot of the fertility rate in the western region are obtained, as shown in Figure 13. Based on the AIC information criterion, the model results were AR (2). Using Ljung-Box Q statistic to test the residuals of AR (2) model, the Q statistic results show that P =0.535＞0.05, Q6 does not present significance at the level, and the hypothesis that the residuals of the model are white noise series can not be rejected, at the same time, the model's goodness of fit R²=0.856, the model performs well, and the model basically meets the requirements. Therefore, the AR (2) model was selected as the optimal time series model of fertility in the western region of China. Using this model to predict the fertility rate in the western region from 2023 to 2050, the predicted values and predicted trend graphs are obtained, and the specific results are shown in Table 6 and Figure 14. Table 6 . Statistical Table of Fertility Rate Projections in the Western Region, 2023 — 2050 Year 2008 ... 2022 2023 2025 2030 2035 2040 2045 2050 Fertility Rate in the Western Region 48.22 ... 32.77 Predicted value 34.35 32.76 38.65 49.38 44.34 44.85 45.63 45.09 95 per cent upper confidence limit 36.58 48.12 61.18 56.70 57.27 58.06 57.53 95 per cent lower confidence limit 28.94 29.19 37.58 31.97 32.43 33.19 32.66 As shown in Figure 14, according to the results of the AR(2) model, the fertility rate in the western region of China is expected to rise linearly after 2023 and peak in 2030; then it will show a slow decline, and the fertility level in the western region of China will gradually rebound and level off after 2040. Analysis of spatial aggregation of fertility rates Global spatial autocorrelation analyses of fertility rates The results of the analysis are shown in Table 7, the Moran's I value of the national fertility rate of women of childbearing age in each year from 2008 to 2022 is greater than 0, and its value is between 0.174-0.374, and the global Moran's I value of the fertility rate is the smallest in 2008 at 0.174, and the largest in 2018 at 0.374; the p-value of global Moran's I value of fertility rate in each year is less than 0.05, and the result is statistically significant. It suggests that the fertility rate of women of childbearing age in each year has significant spatial aggregation, and there is a global spatial positive correlation. This suggests that spatial factors should be taken into account when studying the fertility rate of women of childbearing age across the country. Table 7. Results of the spatial global autocorrelation analysis of the fertility rate of women of childbearing age at the national level, 2008-2022 Year Moran’s I Z P 2008 0.174 1.888 0.030 2009 0.182 1.958 0.025 2010 0.232 2.435 0.007 2011 0.209 2.204 0.014 2012 0.267 2.750 0.003 2013 0.260 2.682 0.004 2014 0.248 2.559 0.005 2015 0.350 3.488 0.000 2016 0.297 3.061 0.001 2017 0.314 3.193 0.001 2018 0.374 3.725 0.000 2019 0.328 3.299 0.000 2020 0.280 2.915 0.002 2021 0.219 2.359 0.009 2022 0.231 2.518 0.006 Local spatial autocorrelation analysis of fertility rates The local Moran's I index visualisation distribution is shown in Figure 15, where red, pink, blue and light blue represent high-high aggregation, high-low aggregation, low-low aggregation and low-high aggregation that are statistically significant at the 0.05 significance level, respectively.There were 9 high-high aggregation areas in 2019, 8 high-high aggregation areas in 2018, 6 high-high aggregation areas in 2015, 5 high-high aggregation areas in both 2010 and 2020, 4 high-high aggregation areas in 2012, 2013 and 2017, and 3 high - high aggregation areas in 2011.There are two high-high agglomeration areas in each of 2008, 2009, 2014 and 2021, and one high-high agglomeration area in each of 2016 and 2022;6 low-low agglomeration areas in both 2021 and 2022, 5 low-low agglomeration areas in 2010, 4 low-low agglomeration areas in both 2008-2013 and 2015-2020, and 3 low-low agglomeration areas in 2014;There are two high - low aggregation areas in both 2008-2013 and 2015-2019, and one high - low aggregation area in both 2014 and 2020;Three low-high aggregation areas existed in 2009, 2010, 2012, 2013, 2020 and 2021; two low-high aggregation areas existed in 2008, 2011, 2015 and 2022; and one low-high aggregation area existed in 2016-2019. Summarise the frequency of aggregation types in each spatial unit from 2008 to 2022. The areas showing a high-tohigh aggregation distribution are mainly concentrated in some provinces, municipalities and autonomous regions in the south, with Guangxi showing the highest frequency of aggregation at 13 times, followed by Hainan, Guizhou and Hunan, which had 11, 9 and 7 aggregations, respectively; Tibet and Yunnan, which had 4 aggregations; Chongqing, which had 3 aggregations; Guangdong, Qinghai and Sichuan, which had 2 aggregations; and Jiangxi, which had 1 aggregation;Areas showing low-low aggregation distribution were mainly concentrated in some northern provinces, municipalities and autonomous regions, with Heilongjiang, Jilin and Liaoning showing low-low aggregation distribution every year, followed by Inner Mongolia aggregated 14 times, Hebei and Shandong aggregated 2 times, and Shanxi aggregated 1 time;The regions with low-high aggregation distributions were mainly Sichuan, Chongqing, Guangdong and Xinjiang, which aggregated 10 times, 9 times, 6 times and 5 times, respectively; the regions with high-low aggregation distributions were Shandong and Hebei, which aggregated 13 times and 12 times, respectively.See Table 8 and Figure 16 for details. Table 8. Summary of LISA analyses of fertility rates of women of childbearing age by region, 2008-2022 Region High-high aggregation High-low aggregation Low-high aggregation Low-low aggregation Heilongjiang 0 0 0 15 Xinjiang 0 0 5 0 Shanxi 0 0 0 1 Ningxia 0 0 0 0 Tibet 4 0 0 0 Shandong 0 13 0 2 Henan 0 0 0 0 Jiangsu 0 0 0 0 Anhui 0 0 0 0 Hubei 0 0 0 0 Zhejiang 0 0 0 0 Jiangxi 1 0 0 0 Hunan 7 0 0 0 Yunnan 4 0 0 0 Guizhou 9 0 0 0 Fujian 0 0 0 0 Guangxi 13 0 0 0 Guangdong 2 0 6 0 Hainan 11 0 0 0 Jilin 0 0 0 15 Liaoning 0 0 0 15 Tianjin 0 0 0 0 Qinghai 2 0 0 0 Shaanxi 0 0 0 0 Inner Mongolia 0 0 0 14 Chongqing 3 0 9 0 Hebei 0 12 0 2 Shanghai 0 0 0 0 Beijing 0 0 0 0 Sichuan 2 0 10 0 Gansu 0 0 0 0 Discussion Based on China's demographic statistics from 2008 to 2022, this study analyses the spatial and temporal evolution of fertility levels and future development trends in China and 31 provincial-level administrative regions using time-series analysis and spatial exploratory analysis, and finds, firstly, that the fertility levels of Chinese women of childbearing age have been in the doldrums for a long period of time and continue to decline, and the phenomenon of postponement of childbirth among the population of childbearing age is obvious.Fertility levels in all Chinese provinces (autonomous regions and municipalities directly under the central government) have declined significantly after 2017, and the spatial pattern shows a gradual decrease from south to north. This is consistent with the findings of previous studies [9-11] . The reason for this is that the decades-long one-child policy implemented in China has led to an aging population structure and declining fertility levels The number of women of childbearing age has decreased, and although the policy has been adjusted, the effects of the past continue. With the rapid development of China's economy, urbanisation has accelerated, the cost of living has risen, and the cost of education and healthcare has increased, resulting in young couples being reluctant to have children or delaying childbearing [12] . In addition, socio-environmental factors are even more influential, with studies confirming the statistical association between ambient particulate matter and fertility rate in China and suggesting that poor air quality may be contributing to childlessness in China [13, 14] . And pandemics of Infectious Diseases, the Impact of COVID-19, and women of childbearing age are concerned that neococcal infection during pregnancy may be associated with pregnancy complications, adverse pregnancy outcomes, and abnormal growth and development of offspring [15] . This may be the direct cause of the sudden drop in fertility in the last two years. The second is a projection of future trends in fertility levels at the national level and in the four major economic regions. The results of the study show that the fertility rate of women of childbearing age in China will reach its lowest value in 2023, and that the fertility rate will continue to rebound from 2023 to 2050, with a faster rate of rebound expected in the next 10 years and a gradual levelling off after 2035. However, the country as a whole is still at a low fertility level. It has been reported that if the fertility rate is below the replacement level for a long period of time, the population will inevitably experience negative growth, which will not be altered by the current demographic changes, and the demographic changes will only have a certain impact on the time needed to reach the negative population growth [16] . According to the results of our time-series projections, fertility levels in the Northeast will continue to decline and will enter a state of negative growth in 2036. The Northeast region has a relatively early economic development and a serious aging population. The proportion of elderly people is relatively high, while the number of young people is decreasing, and this imbalance in the demographic structure directly affects the fertility rate. Modern scholars generally believe that the causes of low fertility in the Northeast region are not only cultural attitudes, but also economic and social development, the implementation of fertility policies and population migration are the most fundamental factors, and these factors interact with each other to continue to play an impact [17] . And the demographic problems brought about by declining fertility are an important factor limiting the economic vitality and inhibiting economic growth in the Northeast. As a result of this cycle, some scholars believe that the Northeast region has gradually entered the ‘low fertility trap’ and it is difficult to get out of it [18] . Fertility projections for the eastern region of China show that fertility in the eastern region will slowly rebound and gradually level off from 2023 to 2050. The study found that the fertility level in the eastern region of China is only higher than that in the northeastern region and lower than the national average; the total fertility rate in some megacities, such as Beijing and Shanghai, is lower than 1.0, which is a very low fertility level. Some studies have shown that the eastern region is driven by the ‘cost (average price of housing) - security’ effect [19] . Gu Baochang's study found that women's education level has the most obvious effect on fertility in a region [20] . The eastern region has a high level of economic development, a higher cost of living, and women generally receive higher education and have more advanced concepts of childbearing, so the fertility level in this region is lower. However, at the same time, the education level of women of childbearing age and the regional medical level in the eastern region have obvious advantages over the other three regions. Fertility levels in China's central and western regions will continue to rise from 2023 to 2030, peaking around 2030 and then declining slightly, with fertility generally levelling off from 2045 to 2050. Some scholars believe that the working-age population in the central and western regions is burdened by a larger non-working-age population, resulting in a less strong desire to have children and a decline in the regional fertility rate [21, 22] . In addition, the divorce rate is higher in the central region, and the level of female education and economic development also have a negative impact on the fertility rate in the central region. In the western region, the economic development is fast but not as high as in the developed regions, so the effects of the local economic development level, divorce rate and female education level on the fertility rate are not as obvious as those in the eastern and central regions, and there has been no big change in the fertility concepts of the ethnic minorities in recent years, so the fertility rate of the western region belongs to a higher position in the national scale. Finally, spatial aggregation analyses show that there is a positive spatial autocorrelation of fertility levels in China's provinces, suggesting that high-value provincial units are able to exert a strong radiation effect on neighbouring provinces. The characteristics of spatial agglomeration of fertility levels vary from region to region, with some areas in the south of the country, such as Guangxi, Guizhou, Hunan and Hainan, experiencing a long-standing ‘high-high’ agglomeration of fertility levels;In the northern regions of Heilongjiang, Jilin, Liaoning and Inner Mongolia, there has been a long-term ‘low-low’ agglomeration of fertility levels;Long-term ‘low-high’ agglomeration of fertility levels in Sichuan, Chongqing, Guangdong and Xinjiang; Long-term ‘high-low’ agglomeration of fertility levels in Shandong and Hebei. At the national level, the phenomenon of low fertility is becoming increasingly serious, but at the provincial level, there are areas of high fertility concentration in South-West and South China. Whether this pattern of ‘high-high’ agglomeration of fertility levels will continue to spread in the future requires further study. The evolution of the ‘low-low’ pattern of agglomeration shows a spreading phenomenon. Since 2008, the central area of the ‘low-low’ agglomeration pattern has started in Heilongjiang, Jilin and Liaoning and has evolved over time, radiating from the north-east to the northern part of the country, centred on Inner Mongolia, and then to the central part of the country, centred on Hebei, with an expanding area of low fertility levels. The ‘low-low’ cluster is currently concentrated in the northern part of the country, and whether it will continue to spread to the central part of the country in the future will require continued attention and research. Recommendations Formulating a reasonable fertility policy and supporting measures China can learn from international experience to formulate a fertility policy that both meets the needs of national development and stimulates the people's desire to have children, as well as to increase the level of policy support and fine-tune the design of the policy [23-25] . This includes comprehensively considering the needs of families throughout their life cycle, such as support for marriage, housing, and family work balance, and creating a fertility-friendly social environment. Maternity protection can be further enriched through measures such as granting maternity subsidies, increasing the beneficiary period of paid maternity leave for women, and legislating for fathers' right to take leave to provide full-course protection from prenatal to postnatal periods. Focusing on regional differences and adopting targeted policies a. The Northeast Region: Attracting and retaining a young population and raising the income level of residents through industrial restructuring, optimising employment policies, and encouraging innovation and entrepreneurship; supplemented by measures such as maternity insurance and maternity subsidies; and, in addition, reducing the proportion of women of childbearing age who are not in a marriage by controlling the divorce rate, so as to increase the level of childbearing in the Northeast China region. b. The Eastern Region: economically developed regions such as Beijing and Shanghai can take advantage of their economic development to boost fertility support policies, and introduce classified guidelines that focus on the government and share the costs of childbirth between enterprises and individuals, thus reducing the costs of childbirth, parenthood, and education for residents. c. The Central Region: Maternity insurance coverage should be expanded, and the medical environment and standard of care should be improved. d.The Western Region: The implementation of the ‘One Belt, One Road’ strategy will promote the economic development of the western region, improve the living conditions of local residents, and accelerate the process of modernisation. Conclusion In this study, we examined the changes in China’s fertility rate and analyzed them statistically in both spatial and temporal dimensions. The results show that from 2008 to 2022, China's population fertility level has been in a long-term depression and there is a trend of continued decline, and the phenomenon of delayed childbearing is obvious among people of childbearing age. The spatial pattern shows a gradual decrease from south to north. There is a positive spatial autocorrelation of fertility levels across provinces, suggesting that high-fertility provinces are able to exert a strong radiation effect on neighbouring provinces. Therefore, individualised recommendations are made for future development trends in different regions to create a fertility-friendly social environment and promote long-term balanced population development. Declarations Acknowledgements We acknowledge the National Bureau of Statistics of China to collect and provide national data available to the public for use in surveys and research. Authors’ contributions Hua Yiwei, Wang Lin, Wang Wenzhen, Wen Yaling and Liu Xiaochun contributed to the conceptualization and design of the study. Hua Yiwei and Wang Lin designed the methodology, managed the sofware used and contributed to the collection, analysis, and interpretation of the data. Hua Yiwei, Wang Lin, Wang Wenzhen and Wen Yaling accessed and validated the underlying data. Hua Yiwei, Wang Lin, Wang Wenzhen, Wen Yaling and Liu Xiaochun led the data visualization. Hua Yiwei and Wang Lin wrote the original draf of the manuscript. All authors reviewed and edited the manuscript and approved the final version. Liu Xiaochun was responsible for general supervision as joint corresponding authors. Funding No fund. Availability of data and materials The data that support the fndings of this study are available from the corresponding author upon reasonable request. Human Ethics and Consent to Participate declarations The human data in this paper are selected from the public data of China Statistical Yearbook of the National Bureau of Statistics of China. Because the data does not include any information that could identify individuals, ethical approval is not necessary. Because this study was based on routine data collection, the informed consent requirement was waived. Competing interests The authors declare that they have no competing interests. References Spears Dean, Vyas Sangita, Weston Gage, and Geruso Michael. 2024. Long-term population projections: Scenarios of low or rebounding fertility. PloS one , 19(4), e0298190. https://doi.org/10.1371/journal.pone.0298190 Eroğlu Kafiye, Koruk Fatma, Koruk İbrahim, Çelik Kezban, Güner Perihan, and Kiliçli Ayşegül. 2021. Women's reproductive behaviour and perspectives on fertility, and their modifying factors, in a Turkish province with a high fertility rate. The European journal of contraception & reproductive health care : the official journal of the European Society of Contraception , 26 (2), 139–147. https://doi.org/10.1080/13625187.2020.1857355 Askew Ian, Maggwa Ndugga, and Obare Francis. 2017. Fertility transitions in ghana and kenya: trends, determinants, and implications for policy and programs. Population and Development Review , 43: 289-307. https://doi.org/10.1111/padr.12010 Pattaro Serena, Vanderbloemen Laura and Minton Jon. 2020. Visualizing fertility trends for 45 countries using composite lattice plots. Demographic Research , 42: 689-712. https://doi.org/10.4054/DemRes.2020.42.23 Bujard Martin, and Scheller Melanie. 2017. Impact of regional factors on cohort fertility: new estimations at the district level in germany. Comparative Population Studies , 42, 55-88. https://doi.org/10.12765/CPoS-2017-07 Li, Xiaoyin, and Winters John V. 2024. Fertility divergence across large and small areas. Growth and Change , 55 (2): https://doi.org/10.1111/grow.12720 Liang Tonggui. 2021. The Two Blindness in the Research on the Fertility Level of Migrants and a Re-examination on the Fertility Level. Population & Economics (05): 95-110.(in Chinese with English abstract) Liu Jianping, and Li Zhifen. 2021. Analysis on the Changing Trend and Reasons of Rural Family Fertility Level——Social Survey Based on 1683 Families in Gansu Province. The World of Survey and Research (06): 65-75. DOI:10.13778/j.cnki.11-3705/c.2021.06.008. (in Chinese with English abstract) Zhang Ju, and Yin Qin. 2024. Research on the Spatiotemporal Evolution of China's Population Fertility Level and Socio-Economic Influencing Factors. Productivity Research (04): 17-23. DOI:10.19374/j.cnki.14-1145/f.2024.04.012. (in Chinese with English abstract) Li Long, Jin Guangzhao, Lai Xiaozhen, Jing Rize, and Zhu He. 2024. A reassessment of trends and rural-urban/regional differences in the total fertility rate in China, 2000-2020: analyses of the 2020 national census data. Scientific reports , 14(1), 8601. https://doi.org/10.1038/s41598-024-59177-2 Li Hong-Tian, Tang Jin-Ling, and Qiao Jie. 2024. China's declining fertility rate. BMJ (Clinical research ed.), 385, q1000. https://doi.org/10.1136/bmj.q1000 Wang Yuanyuan, Kong Fei, Fu Yu, and Qiao Jie. 2024. How can China tackle its declining fertility rate?. BMJ (Clinical research ed.), 386, e078635. https://doi.org/10.1136/bmj-2023-078635 Xue Tao, and Zhu Tong. 2018. Increment of ambient exposure to fine particles and the reduced human fertility rate in China, 2000-2010. The Science of the total environment, 642, 497–504. https://doi.org/10.1016/j.scitotenv.2018.06.075 Xue Tao, and Zhang Qiang. 2018. Associating ambient exposure to fine particles and human fertility rates in China. Environmental pollution (Barking, Essex : 1987), 235, 497–504. https://doi.org/10.1016/j.envpol.2018.01.009 Wedner-Ross Shona, Schippert Cordula, and Von Versen-Höynck Frauke. 2022. The impact of the COVID-19 pandemic on women seeking fertility treatment: the patient's perspective. Archives of gynecology and obstetrics, 305(6), 1615–1624. https://doi.org/10.1007/s00404-021-06379-y Moscone Francesco, Madia Joan E, Nicodemo Catia, An Jong-Chol, and Lee Changkeun. 2024. Addressing fiscal uncertainty: proposing policy pathways for enhancing economic growth and fertility rates in south korea. Research in Economics, 78 (3): 100975-. https://doi.org/10.1016/j.rie.2024.100975 Zhang Jinwei, Ding Shuzhen, and Hu Xijian. 2022. Analysis of spatial and temporal impact differences of birth rate in mainland China. Scientific reports, 12(1), 17396. https://doi.org/10.1038/s41598-022-22403-w Wang Mengqiao. 2019. A retrospective and predictive study of fertility rates in China from 2003 to 2018. Heliyon, 5(3), e01460. https://doi.org/10.1016/j.heliyon.2019.e01460 Jiang Maomin. 2024. Multi-factor Driving Effects and Paths to Improvement of Fertility Levels in China. Statistics & Decision, 40(11): 81-85. DOI:10.13546/j.cnki.tjyjc.2024.11.014. (in Chinese) Gu Baochang, Hou Jiawei, and Wu Nan. 2020. Why Is China's TFR so Low?---Interplay between Postponement and Recuperation. Population & Economics(01): 49-62. (in Chinese with English abstract) Shen Jingyi. 2020. Study on the spatial differentiation of fertility in China's provinces and Its influencing factors. (Doctoral dissertation, Jiangxi University of Finance and Economics). (in Chinese with English abstract) DOI:10.27224/d.cnki.gnmdu.2022.000121. Zhu Mengjie. 2022. China's ferility space evolution and influence factors of research. (Master's degree thesis, Inner Mongolia University). DOI:10.27224/d.cnki.gnmdu.2022.000121. (in Chinese with English abstract) Atongu Simon Ferguson, Aninanya Gifty Apiung, and Howard Natasha. 2024. Factors associated with initial AstraZeneca vaccine knowledge, attitudes, and uptake among hospital nurses: A cross-sectional study in Ghana's Upper East region. PLOS global public health, 4(2), e0002674. https://doi.org/10.1371/journal.pgph.0002674 Makarski Krzysztof, Tyrowicz Joanna, and Malec Magda. 2019. Fiscal and Welfare Effects of Raised Fertility in Poland: Overlapping Generations Model Estimates. Population and Development Review (4), 795-818. https://doi.org/10.1111/padr.12297 Sági Judit, and Lentner Csaba. 2018. Certain aspects of family policy incentives for childbearing—a hungarian study with an international outlook. Sustainability, 10:3976-3976. https://doi.org/10.3390/su10113976 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5301682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373144400,"identity":"2b6bc29b-319c-458f-a56e-a8600fc28faf","order_by":0,"name":"Yiwei Hua","email":"","orcid":"","institution":"Academy of Medical Sciences, Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiwei","middleName":"","lastName":"Hua","suffix":""},{"id":373144401,"identity":"e3b496a4-d629-4f1d-964f-33004dcd112c","order_by":1,"name":"Lin Wang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, 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8","display":"","copyAsset":false,"role":"figure","size":43887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in fertility projections for the Eastern Region\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/5de41fe76c15067f6693a68f.jpg"},{"id":68226026,"identity":"a3f864c5-43b1-4348-b235-544e3c0e2935","added_by":"auto","created_at":"2024-11-05 03:33:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":485879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of autocorrelation coefficients and partial correlation coefficients for fertility in the Eastern 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11","display":"","copyAsset":false,"role":"figure","size":504354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutocorrelation coefficient and partial correlation coefficient of fertility in the central region.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/a4719c3e2a98895d7fabec88.png"},{"id":68226416,"identity":"ab65f718-3746-4bde-955d-7f34dffd9adf","added_by":"auto","created_at":"2024-11-05 03:41:08","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":37182,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in fertility projections for the Central Region\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/1618a39ebbe505c54054dc03.jpg"},{"id":68226420,"identity":"03db82ac-b9d2-44e4-b3d3-dcbcbbd5b3e1","added_by":"auto","created_at":"2024-11-05 03:41:09","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":475844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutocorrelation and partial correlation coefficients of fertility in the western region.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/61dc77c8fbf237490e74785f.png"},{"id":68226036,"identity":"c7cffbcb-bf69-4050-88f3-c473398645b3","added_by":"auto","created_at":"2024-11-05 03:33:09","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":41678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProjected fertility trends in the western region\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/dfdb7b82ca2cbee0a3c0852b.jpg"},{"id":68226418,"identity":"9a048a17-2279-484c-9bfa-0a1c172b25c0","added_by":"auto","created_at":"2024-11-05 03:41:08","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":4292444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLISA chart of fertility rate of women of childbearing age in the country, 2008-2022\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Review No. GS (2023) 2767]\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure15.png","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/d6cf3eb975a494f8f6fd7e93.png"},{"id":68226035,"identity":"30b92f2b-4b7d-4c07-aff8-8cd88beca65b","added_by":"auto","created_at":"2024-11-05 03:33:08","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":2818617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency distribution of fertility LISA clusters, 2008-2022 [Review No. GS (2023) 2767].\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure16.png","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/f224804579699f25038e13ff.png"},{"id":68412554,"identity":"f2ac3bbc-cf6a-43a6-983c-bd72eb55133d","added_by":"auto","created_at":"2024-11-07 04:17:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14855909,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5301682/v1/0279547d-3fe6-4d9f-ab28-90ee4819accd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal evolution characteristics and trend prediction of the fertility level of women of childbearing age in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation is the most basic resource of a country or region, and fertility level, as one of the important indicators of demographic research, is of great significance for demographic change, social and economic development, and the formulation of social policies. The current extremely low fertility level is the greatest risk and challenge to China's development. Data from the National Bureau of Statistics show that in 2022, China's birth rate will drop to 6.77 per thousand, the death rate will be 7.37 per thousand, and the natural growth rate will drop to -0.60 per thousand, and our country is already in the \u0026lsquo;three lows\u0026rsquo; stage of population with a low birth rate, a low death rate, and a low natural growth rate.Since the beginning of the 21st century, China's fertility rate has shown a downward trend. According to the national census data, the total fertility rate was 1.22 in 2000 and dropped to 1.18 in 2010. In response to this phenomenon, China successively introduced the \u0026ldquo;selective two-child\u0026rdquo; and \u0026ldquo;comprehensive two-child\u0026rdquo; policies in 2013 and 2016, but the actual effect is not satisfactory. According to data from the seventh national population census, China's total fertility rate is 1.3, which is lower than the international fertility warning line of 1.5, and is considered to be at risk of falling into a low fertility rate. At the same time, the age structure of China's population has also undergone a major transformation, with the scale of the working-age population declining and the degree of ageing further deepening. For the reason, the Chinese government decided to further optimise its fertility policy, and in 2021,the State Council issued \u0026ldquo;The Decision on Optimising Fertility Policy to Promote the Long-Term Balanced Development of Population\u0026rdquo;, making a major decision on \u0026lsquo;implementing the three-child policy and complementary support measures\u0026rsquo;. The National Population Development Strategy Study states that in order to maintain the long-term coordinated development of population and economy and society, China's total fertility rate should be maintained at around 1.8 in the next 30 years. It is particularly urgent and important to find ways to reasonably raise China's fertility level and to cope with changes in the demographic structure of the population.\u003c/p\u003e \u003cp\u003eAs fertility levels continue to be low around the world, the research on fertility rate and fertility policy has received extensive attention from scholars at home and abroad.Some scholars predict that low fertility will become a global crisis and will reach ultra-low fertility levels in the next 20 years\u003csup\u003e[1]\u003c/sup\u003e. Previous studies have shown that fertility is affected by a variety of factors, mainly including changes in fertility policies, increased economic uncertainty, increased educational attainment, large-scale entry of women into the labour market, and changes in gender roles\u003csup\u003e[2, 3]\u003c/sup\u003e. It has also been shown that migrant populations and ethnic disparities are the main factors contributing to the fertility gap across countries\u003csup\u003e[4]\u003c/sup\u003e. A study of fertility differences among 402 regions in Germany showed that regional factors are highly correlated with fertility, with low population density, relatively spacious housing and low unemployment significantly increasing fertility in the region\u003csup\u003e[5]\u003c/sup\u003e. This is confirmed by studies of differences in female fertility in the United States, where fertility rates are higher in small areas than in large cities\u003csup\u003e[6]\u003c/sup\u003e. Domestic scholars have focused more on urban-rural differences in fertility rate and the trend of rural family fertility,and have obtained fruitful research results\u003csup\u003e[7, 8]\u003c/sup\u003e. Despite the large number of studies on fertility by modern scholars, however, few studies has examined the spatial correlation and regional variability of fertility levels from a spatial perspective. Studies based on time series to predict the population size are on the high side, and there is little literature to make corresponding countermeasures for different regions.\u003c/p\u003e \u003cp\u003eIn summary, the fertility level of China's population has continued to decline and the degree of population aging has been deepening,while changes in the fertility status of women of childbearing age groups are conducive to the adjustment of the population structure, thus promoting the long-term balanced development of China's population. This paper analyses the characteristics of the spatial and temporal evolution of fertility levels and the future development trends in China and 31 provincial-level administrative regions from a dual perspective of time and space, based on demographic data from 2008 to 2022, using time series analysis and exploratory spatial analysis with a view to improving China's fertility levels and providing references and suggestions for the formulation of future population policies and balanced social development.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data in this paper are mainly derived from the Chinese Population Census Yearbook and the China Statistical Yearbook, and 31 provincial-level administrative regions except Hong Kong, Macao and Taiwan are selected as the study areas. Demographic data related to fertility in China and the 31 provincial administrative regions from 2008 to 2022 were obtained for analysis. The vector map data of China used in this study were downloaded from the National Basic Geographic Information Center, and the subsequent analysis was based on the map data without any modification of the base map.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe analysis method and procedure for solving the research problem are as follows. SPSS 26.0 and ArcGIS 10.8 programs were used for data handling and model analysis. First, statistical descriptions of the fertility situation were made in both spatial and temporal dimensions to understand its spatial and temporal distribution;Second, the future trend of fertility was predicted by using the ARIMA projection model of time series analysis, and third, the spatial autocorrelation analysis was used to explore its spatial aggregation characteristics.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical model\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003etime series analysis\u003c/h2\u003e \u003cp\u003eThe ARIMA model is a widely used model in time series analysis, and is known as the Autoregressive Moving Average Model. The basic idea is to convert the non-stationary time series into stationary time series, and then establish an ARMA model for the stationary time series. The formula is as follows: ARIMA(p, d, q) .\u003c/p\u003e \u003cp\u003eWhere AR stands for autoregression, and p is the order of autoregression; MA stands for moving average, q is the number of moving average terms; d is the number of differences made when the time series becomes stationary.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSpatial aggregation analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGlobal spatial autocorrelation analysis\u003c/h2\u003e \u003cp\u003eThe global \u003cem\u003eMoran's I\u003c/em\u003e index was used to analyze the spatial distribution, correlation and statistical significance of fertility as a whole, so as to judge whether fertility was spatially clustered. \u003cem\u003eMoran's I\u003c/em\u003e index formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:I=\\frac{n}{{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{j=1}^{n}{w}_{ij}}*\\frac{{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{j=1}^{n}{w}_{ij}({x}_{i}-\\stackrel{-}{x})({x}_{j}-\\stackrel{-}{x})}{{\\sum\\:}_{i=1}^{n}{({x}_{i}-\\stackrel{-}{x})}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e is the total number of spatial units in the study area, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the actual observation on the \u003cem\u003eith\u003c/em\u003e spatial region, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the actual observation on the \u003cem\u003ejth\u003c/em\u003e spatial region, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x\\:}\\)\u003c/span\u003e\u003c/span\u003eis the mean value of the whole space \u003cem\u003ex\u003c/em\u003e; \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the spatial weight matrix of the \u003cem\u003eith\u003c/em\u003e and \u003cem\u003ejth\u003c/em\u003e spatial regions (as a reflection of the neighbourhood of each spatial region, \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e represent the rows and columns of the matrix, respectively), and in this study, we adopt the neighbourhood spatial weight matrix, when two spatial units \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e are adjacent to each other, then \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij=\u003c/em\u003e\u003c/sub\u003e 1, otherwise \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij=\u003c/em\u003e\u003c/sub\u003e 0. The distance measurement method adopts the Euclidean distance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLocal spatial autocorrelation analysis\u003c/h3\u003e\n\u003cp\u003eLocal \u003cem\u003eMoran's I\u003c/em\u003e index and LISA aggregation maps were used to study the local regional characteristics of fertility, to identify the exact aggregation areas and to determine the aggregation patterns. The local \u003cem\u003eMoran's I\u003c/em\u003e index expression is given below:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{I}_{j}=\\frac{{n}^{2}\\left({x}_{j}-\\stackrel{-}{x}\\right){\\sum\\:}_{i=1}^{n}{w}_{ij}({x}_{i}-\\stackrel{-}{x})}{{\\sum\\:}_{i=1}^{n}{({x}_{i}-\\stackrel{-}{x})}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIts hypothesis testing is the same as the global \u003cem\u003eMoran ' s I\u003c/em\u003e index, the local \u003cem\u003eMoran's I\u003c/em\u003e index is able to analyse the relationship between the study area and the surrounding neighbouring areas, it classifies the relationship between the study spatial unit and the surrounding areas into four types, including two positively correlated aggregation modes, high-high aggregation and low-low aggregation, and two negatively correlated aggregation modes, high-low aggregation and low-high aggregation.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eOverview of the total fertility rate in China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData on the total fertility rate of China and 31 provinces, municipalities and autonomous regions in 2010 and 2020 were obtained from China\u0026apos;s population census data and the percentage change was calculated, and the results are shown in Figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs can be seen from the figure above, China\u0026apos;s total fertility rate was 1.18 in 2010, with only three provinces, Guangxi, Guizhou and Xinjiang, having total fertility rates above 1.5, while first-tier cities such as Beijing, Tianjin and Shanghai and the three northeastern provinces were all below 1.0.In 2020, China\u0026apos;s total fertility rate will be 1.3, and only seven provinces will have a total fertility rate greater than 1.5, with Tibet, Guangxi, and Guizhou provinces having relatively high fertility levels, with total fertility rates of 1.93, 1.94, and 2.12 respectively, while Beijing, Tianjin, and Shanghai, as well as the three northeastern provinces, will remain below 1.0, and China will still be at an ultra-low fertility level.Comparing the distribution of fertility levels in China and 31 provinces, municipalities and autonomous regions in 2010 and 2020, the total fertility rate will rebound slightly in 2020, but it will decline most severely in Xinjiang, from 1.53 in 2010 to 1.06 in 2020, a decline of about 31 per cent; and it will increase most rapidly in Tibet, from 1.05 in 2010 to 1.93 in 2020, an increase of about 84 per cent. The increase is about 84 per cent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge-specific fertility profile of women of childbearing age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCensus data show that in 2020 the fertility rate for 15-19 year olds was 6.07 per 1,000, an increase from the previous two early childbearing ages, and that the fertility rate for women of childbearing age 20-24 was 55.22 per 1,000, a 14.25 per cent decrease from 2010. The fertility rate for women of childbearing age is 98.98 per 1,000 live births in the 25-29 age group, and 190.94 per 1,000 live births for women of childbearing age aged 25-39, which is a significant increase from 2010, especially for women aged 30-34, which is 36.43 per cent and 19.21 per cent higher than in 2000 and 2010 respectively. The fertility rate for persons aged 40-49 declined from 2010, with the rate for persons aged 45-49 declining by 3.07 per cent from 2010. As shown in figure 2.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of the general fertility rate in China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of women of childbearing age from the national census and the number of births in previous years were used to calculate the country\u0026apos;s general fertility rate for the past 15 years. And according to the country\u0026apos;s economic layout, the Mainland is divided into four segments: East, Middle, West and Northeast. In this paper, the data of each administrative region are summed up and analysed year by year according to the scope of the four regions. The results of the analyses and the time series charts are shown in Table 1 and Figure 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. General fertility rate of women of childbearing age in the four regions of China, by year in per cent\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEastern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWestern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNationwide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e28.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e37.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e48.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e45.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e27.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e38.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e45.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e46.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e29.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e38.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e45.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e45.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e26.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e38.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e45.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e46.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e45.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e26.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e41.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e46.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e46.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e25.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e39.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e46.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e28.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e43.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e48.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e48.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e48.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e24.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e40.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e49.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e47.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e25.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e49.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e50.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e50.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e26.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e49.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e51.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e53.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e49.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e25.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e43.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e50.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e43.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e24.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e41.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e47.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e41.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e33.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e35.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e40.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e34.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e29.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e30.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e35.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e30.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4021%;\"\u003e\n \u003cp\u003e14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e27.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5876%;\"\u003e\n \u003cp\u003e32.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.433%;\"\u003e\n \u003cp\u003e27.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs can be seen from Figure 3, China\u0026apos;s fertility level is highest in the western region, second highest in the middle region, and lowest in the north-eastern region. Fertility in the western region has been slowly declining since 2008, slowly increasing after 2011 and reaching its highest value in 2017, with a declining trend in the western region after 2017 and a significant drop in fertility after 2019;Fertility in the Central Region as a whole showed a slow upward trend from 2008-2017, with slight decreases in 2011 and 2015, and a maximum in 2017, and a downward trend in the Central Region after 2017; Fertility in the Eastern Region as a whole showed an upward trend from 2008-2017, with slight decreases in 2011, 2013 and 2015, and the fastest rate of increase in 2016, the reached its highest value in 2017, fertility continued to decline after 2017, and the fertility level in the East is comparable to that of the Central Region in 2022;Fertility in the Northeast has been on an overall downward trend for 15 years, with peaks in 2010 and 2014 and the largest decline in 2020, but still below the national average. The national fertility rate has been slowly increasing since 2008, reaching a peak in 2016, and then declining steeply after 2016, with the largest decreases in 2018 and 2020, and the overall level is between that of the Central and Eastern regions, with the three levels being comparable after 2020.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of the spatial distribution of fertility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of fertility is mapped according to the fertility of each region in 2008-2022. The results, as shown in Figure 4, indicate that fertility rate exhibits an uneven spatial distribution, but the spatial distribution of fertility is similar in each region in each year, with high-fertility areas distributed mainly in the western and southern provinces, municipalities and autonomous regions, mainly Xinjiang, Tibet, Qinghai, Guangxi, Sichuan and its neighbouring provinces; and low-fertility areas distributed mainly in the Low-fertility areas are mainly distributed in the northeastern region and eastern coastal provinces and autonomous regions, such as Heilongjiang, Jilin, Liaoning, and Zhejiang, Shanghai and other provinces. And during the period of 2008-2017, the colour of each region gradually becomes darker, indicating that the fertility rate of all regions in the country tends to be stable and shows a slow growth trend; while the colour of each region gradually becomes lighter after 2018, indicating that the fertility rate generally decreases and reaches the lowest value in 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime Series Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNational Fertility Level Projections\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA time series model was established based on the data of fertility rate of women of childbearing age in China for a total of 15 years from 2008 to 2022. According to the results of ADF test, \u003cem\u003eP\u003c/em\u003e=0.005\u0026lt;0.05, indicating that the series is a smooth series. The autocorrelation coefficient plot and partial correlation coefficient plot of the national fertility rate are obtained, as shown in Figure 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince the time series itself is a smooth series, no differencing is required and the data is directly modelled and predicted using an ARMA model, an autoregressive moving average model.The general form of the ARMA model is ARMA(p, q), where p denotes the order of the autoregressive part and q denotes the order of the moving average part. The model results in ARMA(1,3) based on the AIC information criterion to find the optimal parameters. The ARIMA model requires that the residuals of the model are free of autocorrelation, i.e., the model residuals are white noise. The residual test of ARMA (1,3) model using Ljung-Box Q statistic, \u003cem\u003eP\u003c/em\u003e=0.939＞0.05, which indicates that the residual series of the fitted model is a white noise series, and R\u003csup\u003e2\u003c/sup\u003e=0.84, which indicates that the model performs well, and the model basically meets the requirements. Therefore, the ARMA (1,3) model was selected as the optimal time series model of fertility in China. The model is used to forecast China\u0026apos;s fertility rate from 2023 to 2050, and the forecast value, 95% confidence upper limit and 95% confidence lower limit, and the trend graph are obtained, and the specific results are shown in Table 2 and Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. Projections of National Fertility Rate, 2023-2050\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNational Fertility Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent upper confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent lower confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Figure 6, according to the prediction results of ARMA (1,3) model, China\u0026apos;s fertility level will reach its lowest value in 2023, and the fertility rate will continue to increase from 2023 to 2050, and it is expected that the fertility rate will increase at a faster rate in the next ten years, and then level off after 2035, but the overall level will still be at a relatively low level. It can provide a reference basis for the formulation of relevant population issues and population policies in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjections of fertility levels in the north-eastern region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA time series model was established based on the fertility data of the Northeast region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, \u003cem\u003eP\u003c/em\u003e=0.965＞0.05, indicating that the series is a non-stationary series. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the northeastern regional fertility series, which yields P=0.001＜0.05, indicating that the series is a smooth series. The plots of autocorrelation coefficient and partial correlation coefficient after first-order differencing are drawn, as shown in Figure 7.\u003c/p\u003e\n\u003cp\u003eSince the first order difference is applied to the series, d=1. Based on the AIC information criterion, the AIC minimum model is optimal to find the optimal parameters. The model results in ARIMA (0,1,0). Using Ljung-Box Q statistic for residual test of ARIMA (0,1,0) model, \u003cem\u003eP\u003c/em\u003e=0.550＞0.05, R\u003csup\u003e2\u003c/sup\u003e=0.7, which indicates that the residual sequence of the fitted model is a white noise sequence, and the fitted model performs well, and the model basically meets the requirements. Therefore, the ARIMA (0,1,0) model was selected as the optimal time series model for fertility in the Northeast China. The model was used to predict the fertility level in the Northeast China from 2023 to 2050, the specific results are shown in Table 3 and Figure 8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Statistical Table of Fertility Rate projections in Northeast China from 2023 to 2050\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFertility Rate in the Northeast Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-14.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent upper confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent lower confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-16.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-24.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-32.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-40.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Figure 8, according to the prediction results of the ARIMA (0,1,0) model, the fertility level in Northeast China will continue to decline and enter a negative growth state in 2036.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjections of fertility levels in the Eastern Region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA time series model was established based on the fertility data of the eastern region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, \u003cem\u003eP\u003c/em\u003e=0.181＞0.05, indicating that the series is a non-stationary series. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the fertility rate series in the eastern region, with \u003cem\u003eP\u003c/em\u003e=0.056＞0.05, indicating that the series is still non-stationary. Continuing to second-order differencing of the series, the ADF test on the second-order differenced series of fertility rate in the Eastern Region shows that \u003cem\u003eP\u003c/em\u003e=0.000\u0026lt;0.05, indicating that the series is smooth at this point. The plots of autocorrelation coefficient and partial correlation coefficient as shown in Figure 9.\u003c/p\u003e\n\u003cp\u003eBased on the AIC information criterion, the optimal parameters are found. The model result is AR (1). The residual test of AR (1) model using Ljung-Box Q statistic shows that: the p-value of Q6 is 0.311, More than 0.05, then the original hypothesis can\u0026apos;t be rejected at the significance level of 0.05, which indicates that the residual series of the fitted model is a white noise series; R\u003csup\u003e2\u003c/sup\u003e=0.591, the fitted model performs well, and the model basically meets the requirements. Therefore, the AR (1) model is selected as the optimal time series model of fertility in the eastern region of China. The model was used to project fertility levels in the eastern region for the period 2023-2050, and the results are shown in Table 4 and Figure 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical Table of Fertility Rate Projections in the Eastern China, 2023\u003c/strong\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFertility Rate in the Eastern Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent upper confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent lower confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Figure 10, according to the projection results of the AR(1) model, the fertility level in the eastern region of the country will slowly increase from 2024, and the expected fertility rate in the eastern region will level off after 2035.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjections of fertility levels in the central region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA time series model was established based on the fertility data of the central region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, \u003cem\u003eP\u003c/em\u003e=0.288＞0.05, indicating that the series is non-stationary. For this reason, the first-order difference is applied to the series, and the ADF test is performed on the first-order difference of the central region fertility series, which is \u003cem\u003eP\u003c/em\u003e=0.765＞0.05, indicating that the series is still non-stationary. Continuing to second-order differencing of the series, the ADF test of the second-order differenced series of fertility rate in the Central Region shows that \u003cem\u003eP\u003c/em\u003e=0.000\u0026lt;0.05, which means that the series is smooth at this point. Autocorrelation coefficient and partial correlation coefficient of fertility in the central region are shown in Figure 11.\u003c/p\u003e\n\u003cp\u003eThe model results were ARIMA (2,0,0) based on the AIC information criterion to find the optimal parameters. Using Ljung-Box Q statistic to test the residuals of ARIMA (2,0,0) model, the result of Q statistic shows that \u003cem\u003eP\u003c/em\u003e=0.542＞0.05, Q6 does not present significance at the level, and the hypothesis that the residuals of the model are white noise series can not be rejected, at the same time, the model\u0026apos;s goodness of fit R\u0026sup2; is 0.814, the model performance is excellent, and the model basically meets the requirements. Therefore, the ARIMA (2,0,0) model was selected as the optimal time series model for fertility in the central region of China. Using this model to predict the fertility level in the central region from 2023 to 2050, the specific results are shown in Table 5 and Figure 12.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e. Statistical Table of Fertility Rate Projections in the Central Region, 2023\u003c/strong\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFertility rate in the Central Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePredicted\u0026nbsp;value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent upper confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95 per cent lower confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Figure 12, according to the projection results of the ARIMA (2,0,0) model, the fertility level in the central region of China will continue to rise from 2023 to 2031, peak around 2031, and then decline slightly, but generally stabilise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjections of fertility levels in the western region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA time series model was established based on the fertility data of the western region of China for a total of 15 years from 2008 to 2022. According to the results of ADF test, \u003cem\u003eP\u003c/em\u003e=0.000\u0026lt;0.05, indicating that the series is a smooth series. The autocorrelation coefficient plot and partial correlation coefficient plot of the fertility rate in the western region are obtained, as shown in Figure 13.\u003c/p\u003e\n\u003cp\u003eBased on the AIC information criterion, the model results were AR (2). Using Ljung-Box Q statistic to test the residuals of AR (2) model, the Q statistic results show that \u003cem\u003eP\u003c/em\u003e=0.535＞0.05, Q6 does not present significance at the level, and the hypothesis that the residuals of the model are white noise series can not be rejected, at the same time, the model\u0026apos;s goodness of fit R\u0026sup2;=0.856, the model performs well, and the model basically meets the requirements. Therefore, the AR (2) model was selected as the optimal time series model of fertility in the western region of China. Using this model to predict the fertility rate in the western region from 2023 to 2050, the predicted values and predicted trend graphs are obtained, and the specific results are shown in Table 6 and Figure 14.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical Table of Fertility Rate Projections in the Western Region, 2023\u003c/strong\u003e\u003cstrong\u003e\u0026mdash;\u003c/strong\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.16667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e...\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.8333%;\"\u003e\n \u003cp\u003eFertility Rate in the Western Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e48.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.16667%;\"\u003e\n \u003cp\u003e...\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e32.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.8333%;\"\u003e\n \u003cp\u003ePredicted value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.16667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e34.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e32.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e38.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e49.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e44.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e44.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e45.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e45.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.8333%;\"\u003e\n \u003cp\u003e95 per cent upper confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.16667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e36.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e48.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e61.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e56.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e57.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e58.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e57.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.8333%;\"\u003e\n \u003cp\u003e95 per cent lower confidence limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.16667%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e28.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e29.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e37.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e31.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e32.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e33.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e32.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs shown in Figure 14, according to the results of the AR(2) model, the fertility rate in the western region of China is expected to rise linearly after 2023 and peak in 2030; then it will show a slow decline, and the fertility level in the western region of China will gradually rebound and level off after 2040.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of spatial aggregation of fertility rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal spatial autocorrelation analyses of fertility rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the analysis are shown in Table 7, the \u003cem\u003eMoran\u0026apos;s I\u0026nbsp;\u003c/em\u003evalue of the national fertility rate of women of childbearing age in each year from 2008 to 2022 is greater than 0, and its value is between 0.174-0.374, and the global Moran\u0026apos;s I value of the fertility rate is the smallest in 2008 at 0.174, and the largest in 2018 at 0.374; the p-value of global Moran\u0026apos;s I value of fertility rate in each year is less than 0.05, and the result is statistically significant. It suggests that the fertility rate of women of childbearing age in each year has significant spatial aggregation, and there is a global spatial positive correlation. This suggests that spatial factors should be taken into account when studying the fertility rate of women of childbearing age across the country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Results of the spatial global autocorrelation analysis of the fertility rate of women of childbearing age at the national level, 2008-2022\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMoran\u0026rsquo;s I\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e1.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e1.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e3.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e3.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e3.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e3.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e3.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.4082%;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.6939%;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e2.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.449%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eLocal spatial autocorrelation analysis of fertility rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe local \u003cem\u003eMoran\u0026apos;s I\u003c/em\u003e index visualisation distribution is shown in Figure 15, where red, pink, blue and light blue represent high-high aggregation, high-low aggregation, low-low aggregation and low-high aggregation that are statistically significant at the 0.05 significance level, respectively.There were 9 high-high aggregation areas in 2019, 8 high-high aggregation areas in 2018, 6 high-high aggregation areas in 2015, 5 high-high aggregation areas in both 2010 and 2020, 4 high-high aggregation areas in 2012, 2013 and 2017, and 3 high - high aggregation areas in 2011.There are two high-high agglomeration areas in each of 2008, 2009, 2014 and 2021, and one high-high agglomeration area in each of 2016 and 2022;6 low-low agglomeration areas in both 2021 and 2022, 5 low-low agglomeration areas in 2010, 4 low-low agglomeration areas in both 2008-2013 and 2015-2020, and 3 low-low agglomeration areas in 2014;There are two high - low aggregation areas in both 2008-2013 and 2015-2019, and one high - low aggregation area in both 2014 and 2020;Three low-high aggregation areas existed in 2009, 2010, 2012, 2013, 2020 and 2021; two low-high aggregation areas existed in 2008, 2011, 2015 and 2022; and one low-high aggregation area existed in 2016-2019.\u003c/p\u003e\n\u003cp\u003eSummarise the frequency of aggregation types in each spatial unit from 2008 to 2022.\u003c/p\u003e\n\u003cp\u003eThe areas showing a high-tohigh aggregation distribution are mainly concentrated in some provinces, municipalities and autonomous regions in the south, with Guangxi showing the highest frequency of aggregation at 13 times, followed by Hainan, Guizhou and Hunan, which had 11, 9 and 7 aggregations, respectively; Tibet and Yunnan, which had 4 aggregations; Chongqing, which had 3 aggregations; Guangdong, Qinghai and Sichuan, which had 2 aggregations; and Jiangxi, which had 1 aggregation;Areas showing low-low aggregation distribution were mainly concentrated in some northern provinces, municipalities and autonomous regions, with Heilongjiang, Jilin and Liaoning showing low-low aggregation distribution every year, followed by Inner Mongolia aggregated 14 times, Hebei and Shandong aggregated 2 times, and Shanxi aggregated 1 time;The regions with low-high aggregation distributions were mainly Sichuan, Chongqing, Guangdong and Xinjiang, which aggregated 10 times, 9 times, 6 times and 5 times, respectively; the regions with high-low aggregation distributions were Shandong and Hebei, which aggregated 13 times and 12 times, respectively.See Table 8 and Figure 16 for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8. Summary of LISA analyses of fertility rates of women of childbearing age by region, 2008-2022\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-high aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-low aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-high aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-low aggregation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeilongjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eXinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNingxia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTibet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShandong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHenan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJiangsu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnhui\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHubei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZhejiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJiangxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHunan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYunnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFujian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuangxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuangdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHainan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJilin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLiaoning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTianjin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQinghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShaanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInner Mongolia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChongqing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHebei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSichuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGansu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on China\u0026apos;s demographic statistics from 2008 to 2022, this study analyses the spatial and temporal evolution of fertility levels and future development trends in China and 31 provincial-level administrative regions using time-series analysis and spatial exploratory analysis, and finds, firstly, that the fertility levels of Chinese women of childbearing age have been in the doldrums for a long period of time and continue to decline, and the phenomenon of postponement of childbirth among the population of childbearing age is obvious.Fertility levels in all Chinese provinces (autonomous regions and municipalities directly under the central government) have declined significantly after 2017, and the spatial pattern shows a gradual decrease from south to north. This is consistent with the findings of previous studies\u003csup\u003e[9-11]\u003c/sup\u003e. The reason for this is that the decades-long one-child policy implemented in China has led to an aging population structure and declining fertility levels The number of women of childbearing age has decreased, and although the policy has been adjusted, the effects of the past continue. With the rapid development of China\u0026apos;s economy, urbanisation has accelerated, the cost of living has risen, and the cost of education and healthcare has increased, resulting in young couples being reluctant to have children or delaying childbearing\u003csup\u003e[12]\u003c/sup\u003e. In addition, socio-environmental factors are even more influential, with studies confirming the statistical association between ambient particulate matter and fertility rate in China and suggesting that poor air quality may be contributing to childlessness in China\u003csup\u003e[13, 14]\u003c/sup\u003e. And pandemics of Infectious Diseases, the Impact of COVID-19, and women of childbearing age are concerned that neococcal infection during pregnancy may be associated with pregnancy complications, adverse pregnancy outcomes, and abnormal growth and development of offspring\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e. This may be the direct cause of the sudden drop in fertility in the last two years.\u003c/p\u003e\n\u003cp\u003eThe second is a projection of future trends in fertility levels at the national level and in the four major economic regions. The results of the study show that the fertility rate of women of childbearing age in China will reach its lowest value in 2023, and that the fertility rate will continue to rebound from 2023 to 2050, with a faster rate of rebound expected in the next 10 years and a gradual levelling off after 2035. However, the country as a whole is still at a low fertility level. It has been reported that if the fertility rate is below the replacement level for a long period of time, the population will inevitably experience negative growth, which will not be altered by the current demographic changes, and the demographic changes will only have a certain impact on the time needed to reach the negative population growth\u003csup\u003e[16]\u003c/sup\u003e. According to the results of our time-series projections, fertility levels in the Northeast will continue to decline and will enter a state of negative growth in 2036. The Northeast region has a relatively early economic development and a serious aging population. The proportion of elderly people is relatively high, while the number of young people is decreasing, and this imbalance in the demographic structure directly affects the fertility rate. Modern scholars generally believe that the causes of low fertility in the Northeast region are not only cultural attitudes, but also economic and social development, the implementation of fertility policies and population migration are the most fundamental factors, and these factors interact with each other to continue to play an impact\u003csup\u003e[17]\u003c/sup\u003e. And the demographic problems brought about by declining fertility are an important factor limiting the economic vitality and inhibiting economic growth in the Northeast. As a result of this cycle, some scholars believe that the Northeast region has gradually entered the \u0026lsquo;low fertility trap\u0026rsquo; and it is difficult to get out of it\u003csup\u003e[18]\u003c/sup\u003e. Fertility projections for the eastern region of China show that fertility in the eastern region will slowly rebound and gradually level off from 2023 to 2050. The study found that the fertility level in the eastern region of China is only higher than that in the northeastern region and lower than the national average; the total fertility rate in some megacities, such as Beijing and Shanghai, is lower than 1.0, which is a very low fertility level. Some studies have shown that the eastern region is driven by the \u0026lsquo;cost (average price of housing) - security\u0026rsquo; effect\u003csup\u003e[19]\u003c/sup\u003e. Gu Baochang\u0026apos;s study found that women\u0026apos;s education level has the most obvious effect on fertility in a region\u003csup\u003e[20]\u003c/sup\u003e. The eastern region has a high level of economic development, a higher cost of living, and women generally receive higher education and have more advanced concepts of childbearing, so the fertility level in this region is lower. However, at the same time, the education level of women of childbearing age and the regional medical level in the eastern region have obvious advantages over the other three regions. Fertility levels in China\u0026apos;s central and western regions will continue to rise from 2023 to 2030, peaking around 2030 and then declining slightly, with fertility generally levelling off from 2045 to 2050. Some scholars believe that the working-age population in the central and western regions is burdened by a larger non-working-age population, resulting in a less strong desire to have children and a decline in the regional fertility rate\u003csup\u003e[21, 22]\u003c/sup\u003e. In addition, the divorce rate is higher in the central region, and the level of female education and economic development also have a negative impact on the fertility rate in the central region. In the western region, the economic development is fast but not as high as in the developed regions, so the effects of the local economic development level, divorce rate and female education level on the fertility rate are not as obvious as those in the eastern and central regions, and there has been no big change in the fertility concepts of the ethnic minorities in recent years, so the fertility rate of the western region belongs to a higher position in the national scale.\u003c/p\u003e\n\u003cp\u003eFinally, spatial aggregation analyses show that there is a positive spatial autocorrelation of fertility levels in China\u0026apos;s provinces, suggesting that high-value provincial units are able to exert a strong radiation effect on neighbouring provinces. The characteristics of spatial agglomeration of fertility levels vary from region to region, with some areas in the south of the country, such as Guangxi, Guizhou, Hunan and Hainan, experiencing a long-standing \u0026lsquo;high-high\u0026rsquo; agglomeration of fertility levels;In the northern regions of Heilongjiang, Jilin, Liaoning and Inner Mongolia, there has been a long-term \u0026lsquo;low-low\u0026rsquo; agglomeration of fertility levels;Long-term \u0026lsquo;low-high\u0026rsquo; agglomeration of fertility levels in Sichuan, Chongqing, Guangdong and Xinjiang; Long-term \u0026lsquo;high-low\u0026rsquo; agglomeration of fertility levels in Shandong and Hebei. At the national level, the phenomenon of low fertility is becoming increasingly serious, but at the provincial level, there are areas of high fertility concentration in South-West and South China. Whether this pattern of \u0026lsquo;high-high\u0026rsquo; agglomeration of fertility levels will continue to spread in the future requires further study. The evolution of the \u0026lsquo;low-low\u0026rsquo; pattern of agglomeration shows a spreading phenomenon. Since 2008, the central area of the \u0026lsquo;low-low\u0026rsquo; agglomeration pattern has started in Heilongjiang, Jilin and Liaoning and has evolved over time, radiating from the north-east to the northern part of the country, centred on Inner Mongolia, and then to the central part of the country, centred on Hebei, with an expanding area of low fertility levels. The \u0026lsquo;low-low\u0026rsquo; cluster is currently concentrated in the northern part of the country, and whether it will continue to spread to the central part of the country in the future will require continued attention and research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormulating a reasonable fertility policy and supporting measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChina can learn from international experience to formulate a fertility policy that both meets the needs of national development and stimulates the people\u0026apos;s desire to have children, as well as to increase the level of policy support and fine-tune the design of the policy\u003csup\u003e[23-25]\u003c/sup\u003e. This includes comprehensively considering the needs of families throughout their life cycle, such as support for marriage, housing, and family work balance, and creating a fertility-friendly social environment. Maternity protection can be further enriched through measures such as granting maternity subsidies, increasing the beneficiary period of paid maternity leave for women, and legislating for fathers\u0026apos; right to take leave to provide full-course protection from prenatal to postnatal periods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFocusing on regional differences and adopting targeted policies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. The Northeast Region:\u003c/strong\u003e Attracting and retaining a young population and raising the income level of residents through industrial restructuring, optimising employment policies, and encouraging innovation and entrepreneurship; supplemented by measures such as maternity insurance and maternity subsidies; and, in addition, reducing the proportion of women of childbearing age who are not in a marriage by controlling the divorce rate, so as to increase the level of childbearing in the Northeast China region. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. The Eastern Region:\u003c/strong\u003e economically developed regions such as Beijing and Shanghai can take advantage of their economic development to boost fertility support policies, and introduce classified guidelines that focus on the government and share the costs of childbirth between enterprises and individuals, thus reducing the costs of childbirth, parenthood, and education for residents. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. The Central Region:\u003c/strong\u003e Maternity insurance coverage should be expanded, and the medical environment and standard of care should be improved. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed.The Western Region:\u003c/strong\u003e The implementation of the \u0026lsquo;One Belt, One Road\u0026rsquo; strategy will promote the economic development of the western region, improve the living conditions of local residents, and accelerate the process of modernisation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we examined the changes in China\u0026rsquo;s fertility rate and analyzed them statistically in both spatial and temporal dimensions. The results show that from 2008 to 2022, China's population fertility level has been in a long-term depression and there is a trend of continued decline, and the phenomenon of delayed childbearing is obvious among people of childbearing age. The spatial pattern shows a gradual decrease from south to north. There is a positive spatial autocorrelation of fertility levels across provinces, suggesting that high-fertility provinces are able to exert a strong radiation effect on neighbouring provinces. Therefore, individualised recommendations are made for future development trends in different regions to create a fertility-friendly social environment and promote long-term balanced population development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the National Bureau of Statistics of China to collect and provide national data available to the public for use in surveys and research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHua Yiwei, Wang Lin, Wang Wenzhen, Wen Yaling and Liu Xiaochun contributed to the conceptualization and design of the study. Hua Yiwei and Wang Lin designed the methodology, managed the sofware used and contributed to the collection, analysis, and interpretation of the data. Hua Yiwei, Wang Lin, Wang Wenzhen and Wen Yaling accessed and validated the underlying data. Hua Yiwei, Wang Lin, Wang Wenzhen, Wen Yaling and Liu Xiaochun led the data visualization. Hua Yiwei and Wang Lin wrote the original draf of the manuscript. All authors reviewed and edited the manuscript and approved the final version. Liu Xiaochun was responsible for general supervision as joint corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the fndings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human data in this paper are selected from the public data of China Statistical Yearbook of the National Bureau of Statistics of China. Because the data does not include any information that could identify individuals, ethical approval is not necessary. Because this study was based on routine data collection, the informed consent requirement was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSpears Dean, Vyas Sangita, Weston Gage, and Geruso Michael. 2024. Long-term population projections: Scenarios of low or rebounding fertility.\u003cem\u003e PloS one\u003c/em\u003e, 19(4), e0298190. https://doi.org/10.1371/journal.pone.0298190\u003c/li\u003e\n\u003cli\u003eEroğlu Kafiye, Koruk\u003csup\u003e \u003c/sup\u003eFatma, Koruk İbrahim, \u0026Ccedil;elik Kezban, G\u0026uuml;ner Perihan, and Kili\u0026ccedil;li\u003csup\u003e \u003c/sup\u003eAyşeg\u0026uuml;l. 2021. Women\u0026apos;s reproductive behaviour and perspectives on fertility, and their modifying factors, in a Turkish province with a high fertility rate. \u003cem\u003eThe European journal of contraception \u0026amp; reproductive health care : the official journal of the European Society of Contraception\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 139\u0026ndash;147. https://doi.org/10.1080/13625187.2020.1857355\u003c/li\u003e\n\u003cli\u003eAskew Ian, Maggwa Ndugga, and Obare Francis. 2017. Fertility transitions in ghana and kenya: trends, determinants, and implications for policy and programs. \u003cem\u003ePopulation and Development Review\u003c/em\u003e, 43: 289-307. https://doi.org/10.1111/padr.12010\u003c/li\u003e\n\u003cli\u003ePattaro Serena, Vanderbloemen Laura and Minton Jon. 2020. Visualizing fertility trends for 45 countries using composite lattice plots. \u003cem\u003eDemographic Research\u003c/em\u003e, 42: 689-712. https://doi.org/10.4054/DemRes.2020.42.23\u003c/li\u003e\n\u003cli\u003eBujard Martin, and Scheller Melanie. 2017. Impact of regional factors on cohort fertility: new estimations at the district level in germany. \u003cem\u003eComparative Population Studies\u003c/em\u003e, 42, 55-88. https://doi.org/10.12765/CPoS-2017-07 \u003c/li\u003e\n\u003cli\u003eLi, Xiaoyin, and Winters John V. 2024. Fertility divergence across large and small areas. \u003cem\u003eGrowth and Change\u003c/em\u003e, 55 (2): https://doi.org/10.1111/grow.12720\u003c/li\u003e\n\u003cli\u003eLiang Tonggui. 2021. The Two Blindness in the Research on the Fertility Level of Migrants and a Re-examination on the Fertility Level. \u003cem\u003ePopulation \u0026amp; Economics\u003c/em\u003e (05): 95-110.(in Chinese with English abstract)\u003c/li\u003e\n\u003cli\u003eLiu Jianping, and Li Zhifen. 2021. Analysis on the Changing Trend and Reasons of Rural Family Fertility Level\u0026mdash;\u0026mdash;Social Survey Based on 1683 Families in Gansu Province. \u003cem\u003eThe World of Survey and Research\u003c/em\u003e (06): 65-75. DOI:10.13778/j.cnki.11-3705/c.2021.06.008. (in Chinese with English abstract)\u003c/li\u003e\n\u003cli\u003eZhang Ju, and Yin Qin. 2024. Research on the Spatiotemporal Evolution of China\u0026apos;s Population Fertility Level and Socio-Economic Influencing Factors. \u003cem\u003eProductivity Research \u003c/em\u003e(04): 17-23. DOI:10.19374/j.cnki.14-1145/f.2024.04.012. (in Chinese with English abstract)\u003c/li\u003e\n\u003cli\u003eLi Long, Jin Guangzhao, Lai Xiaozhen, Jing Rize, and Zhu He. 2024. A reassessment of trends and rural-urban/regional differences in the total fertility rate in China, 2000-2020: analyses of the 2020 national census data. \u003cem\u003eScientific reports\u003c/em\u003e, 14(1), 8601. https://doi.org/10.1038/s41598-024-59177-2\u003c/li\u003e\n\u003cli\u003eLi Hong-Tian, Tang Jin-Ling, and Qiao Jie. 2024. China\u0026apos;s declining fertility rate. BMJ (Clinical research ed.), 385, q1000. https://doi.org/10.1136/bmj.q1000\u003c/li\u003e\n\u003cli\u003eWang Yuanyuan, Kong Fei, Fu Yu, and Qiao Jie. 2024. How can China tackle its declining fertility rate?. BMJ (Clinical research ed.), 386, e078635. https://doi.org/10.1136/bmj-2023-078635\u003c/li\u003e\n\u003cli\u003eXue Tao, and Zhu Tong. 2018. Increment of ambient exposure to fine particles and the reduced human fertility rate in China, 2000-2010. The Science of the total environment, 642, 497\u0026ndash;504. https://doi.org/10.1016/j.scitotenv.2018.06.075\u003c/li\u003e\n\u003cli\u003eXue Tao, and Zhang Qiang. 2018. Associating ambient exposure to fine particles and human fertility rates in China. Environmental pollution (Barking, Essex : 1987), 235, 497\u0026ndash;504. https://doi.org/10.1016/j.envpol.2018.01.009\u003c/li\u003e\n\u003cli\u003eWedner-Ross Shona, Schippert Cordula, and Von Versen-H\u0026ouml;ynck Frauke. 2022. The impact of the COVID-19 pandemic on women seeking fertility treatment: the patient\u0026apos;s perspective. Archives of gynecology and obstetrics, 305(6), 1615\u0026ndash;1624. https://doi.org/10.1007/s00404-021-06379-y\u003c/li\u003e\n\u003cli\u003eMoscone Francesco, Madia Joan E, Nicodemo Catia, An Jong-Chol, and Lee Changkeun. 2024. Addressing fiscal uncertainty: proposing policy pathways for enhancing economic growth and fertility rates in south korea. Research in Economics, 78 (3): 100975-. https://doi.org/10.1016/j.rie.2024.100975\u003c/li\u003e\n\u003cli\u003eZhang Jinwei, Ding Shuzhen, and Hu Xijian. 2022. Analysis of spatial and temporal impact differences of birth rate in mainland China. Scientific reports, 12(1), 17396. https://doi.org/10.1038/s41598-022-22403-w\u003c/li\u003e\n\u003cli\u003eWang Mengqiao. 2019. A retrospective and predictive study of fertility rates in China from 2003 to 2018. Heliyon, 5(3), e01460. https://doi.org/10.1016/j.heliyon.2019.e01460 \u003c/li\u003e\n\u003cli\u003eJiang Maomin. 2024. Multi-factor Driving Effects and Paths to Improvement of Fertility Levels in China. Statistics \u0026amp; Decision, 40(11): 81-85. DOI:10.13546/j.cnki.tjyjc.2024.11.014. (in Chinese)\u003c/li\u003e\n\u003cli\u003eGu Baochang, Hou Jiawei, and Wu Nan. 2020. Why Is China\u0026apos;s TFR so Low?---Interplay between Postponement and Recuperation. Population \u0026amp; Economics(01): 49-62. (in Chinese with English abstract)\u003c/li\u003e\n\u003cli\u003eShen Jingyi. 2020. Study on the spatial differentiation of fertility in China\u0026apos;s provinces and Its influencing factors. (Doctoral dissertation, Jiangxi University of Finance and Economics). (in Chinese with English abstract) DOI:10.27224/d.cnki.gnmdu.2022.000121.\u003c/li\u003e\n\u003cli\u003eZhu Mengjie. 2022. China\u0026apos;s ferility space evolution and influence factors of research. (Master\u0026apos;s degree thesis, Inner Mongolia University). DOI:10.27224/d.cnki.gnmdu.2022.000121. (in Chinese with English abstract)\u003c/li\u003e\n\u003cli\u003eAtongu Simon Ferguson, Aninanya Gifty Apiung, and Howard Natasha. 2024. Factors associated with initial AstraZeneca vaccine knowledge, attitudes, and uptake among hospital nurses: A cross-sectional study in Ghana\u0026apos;s Upper East region. PLOS global public health, 4(2), e0002674. https://doi.org/10.1371/journal.pgph.0002674\u003c/li\u003e\n\u003cli\u003eMakarski Krzysztof, Tyrowicz Joanna, and Malec Magda. 2019. Fiscal and Welfare Effects of Raised Fertility in Poland: Overlapping Generations Model Estimates. Population and Development Review (4), 795-818. https://doi.org/10.1111/padr.12297\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;gi Judit, and Lentner Csaba. 2018. Certain aspects of family policy incentives for childbearing\u0026mdash;a hungarian study with an international outlook. Sustainability, 10:3976-3976. https://doi.org/10.3390/su10113976\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fertility level, Forecast, Spatial and temporal evolution, Spatial aggregation","lastPublishedDoi":"10.21203/rs.3.rs-5301682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5301682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe purpose of this study is to understand the temporal trends and spatial distribution characteristics of the fertility rate of women of childbearing age in China and 31 provincial-level administrative regions from 2008 to 2022, and to make projections of future trends in the fertility rate.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eStatistical data related to fertility were collected from 2008 to 2022 for China and 31 provincial administrative regions (except Hong Kong, Macao and Taiwan). Statistical descriptions of the fertility situation were made in both spatial and temporal dimensions to understand its spatial and temporal distribution; the future trend of fertility was predicted by using the ARIMA projection model of time series analysis, and the spatial autocorrelation analysis was used to explore its spatial aggregation characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2008 to 2022, the fertility level of women of childbearing age in China has been in a long-term depression and there is a trend of continued decline, with the phenomenon of delayed childbearing evident in the population of childbearing age. The spatial pattern shows a gradual decrease from south to north.China, as well as the three major regions of the East, Centre and West, will reach their lowest point in 2023, followed by a slow recovery and a gradual stabilisation in the following decade, but will still be at a relatively low level of fertility as a whole;while in the Northeast, fertility levels will continue to decline and will be in a state of negative growth in 2036. There is a positive spatial autocorrelation of fertility levels across provinces,and the characteristics of the spatial agglomeration of fertility levels vary from region to region.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe results of this study show that the fertility level of women of childbearing age in China continues to be low, and is expected to remain at a very low fertility level for a long time to come. Individualised recommendations are made for future development trends in different regions to create a fertility-friendly social environment and promote long-term balanced population development.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal evolution characteristics and trend prediction of the fertility level of women of childbearing age in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 03:33:03","doi":"10.21203/rs.3.rs-5301682/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"078317d2-b87c-4ada-91d1-ee330b32b4bd","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-07T04:09:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-05 03:33:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5301682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5301682","identity":"rs-5301682","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}