sustainability

Article Population and Economic Projections in the Yangtze River Basin Based on Shared Socioeconomic Pathways

Min Zhu 1 , Zengxin Zhang 1,2,* , Bin Zhu 1, Rui Kong 1, Fengying Zhang 1, Jiaxi Tian 1 and Tong Jiang 3 1 Joint Innovation Center for Modern Forestry Studies, College of Forestry, Nanjing Forestry University, Nanjing 210037, China; [email protected] (M.Z.); [email protected] (B.Z.); [email protected] (R.K.); [email protected] (F.Z.); [email protected] (J.T.) 2 College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China 3 School of Geographical Sciences, Nanjing University of Information Sciences & Technology, Nanjing 210044, China; [email protected] * Correspondence: [email protected]; Tel.: +86-25-8542-8963



Received: 18 February 2020; Accepted: 17 May 2020; Published: 20 May 2020


Abstract: The shared socioeconomic pathways (SSPs) were designed to project future socioeconomic developments as they might unfold in the absence of explicit additional policies and measures to limit climate forcing or to enhance adaptive capacity. Based on the sixth national population census and the third economic census data of China in 2010, this paper projects the population and economic conditions of the Yangtze River basin from 2010 to 2100 under the SSPs. The results showed that: (1) the population growth rate in most areas of the Yangtze River basin will decrease from 2021 to 2100. The population of the eastern Sichuan Province will decrease obviously, while it will increase obviously in Shanghai during this period. The population of the Yangtze River basin will decline from 2010 to 2100 under the SSPs except for SSP3; (2) The GDP (Gross Domestic Product) in most regions will increase by more than CNY 30 billion (Chinese Yuan) compared with 2010 and the total GDP will continue to rise after 2020; (3) The population of the three major urban agglomerations will decrease from 2020 to 2100. However, the GDP of the three major urban agglomerations will increase year by year, among which the YRDUA (Yangtze River Delta Urban Agglomeration) has obvious economic advantages. The GDP growth rate will maintain above 6% in 2020 under different SSPs, and then the growth rate will slow down or stall, even with negative growth in SSP1 and SSP4; (4) The GDP Per of the Yangtze River basin shows growth under different SSPs and it will maintain a growth rate of 6–9% until 2020. While the average annual growth rate of the SSP5 will be about 2.56% at the end of the 21st century, and it will remain at about 1% under other scenarios. This paper provides a scientific basis for the study of future population and socioeconomic changes and climate predictions for quantifying disaster risks.

Keywords: Yangtze River basin; population; GDP; shared socioeconomic pathways

1. Introduction The continuous growth of the global population and economy has brought unprecedented pressure on the climate environment [1–3]. Scenarios are used to help people understand the long-term consequences of short-term decisions, and scholars can explore future possibilities in the context of future uncertainty. This is an important part of climate change research and assessment [4]. Under this background, the socioeconomic scenario based on population and economic prediction has gradually become the core area of climate change research [5]. Describing the link between radiation forcing

Sustainability 2020, 12, 4202; doi:10.3390/su12104202 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 4202 2 of 21 and socio-economic development plays an important role in the impact assessment of climate change and related climate policy formulation [6]. The population growth rate in developed countries has been slow or even negative since entering the 21st century; moreover, the population growth rate in most developing countries decreases year by year. The negative effects caused by labor shortage and aging, along with some poverty and employment difficulties in the national economic development, have attracted the attention of all countries in the world [7]. At present, in the context of global warming, combining population and economic development with the formulation of policies for the mitigation and adaptation of climate change, the study of future population and socio-economic changes is as important as the quantification of disaster risk by climate prediction. The Shared Socioeconomic Pathways (SSPs) were developed in 2010 to build on the RCPs (Representative Concentration Pathways), which was used to understand potential climate change in the future through new socio-economic scenarios [8–11]. Many scholars described the SSPs as the reference path of scientific substitution features in the process of socio-ecological systems at the global or large regional levels in the 21st century, and spanning the time scale of the whole century [9,12]. The SSPs constructed on the basis of RCPs contain both radiative forcing characteristics and assumptions about future socio-economic development [9]. The SSPs can be set from the perspective adaptation and mitigation of the future social and economic challenges, which can be divided into five paths representing [9,13]. Apart from long-term population and socio-economic prediction, the SSPs can also be applied to predict the uncertainty of future climate change on the water shortage in a river basin [14]. The significance lies in reflecting the challenges of adaptation and mitigation in different socio-economies [15,16]. The simulation results of future emissions and land-use changes under SSPs have been applied to the Scenario MIP (Scenario Model Intercomparison Project) [17]. The Scenario MIP is an important part of the Sixth Coupled Model Comparison Program (CMIP6), which will provide multi-model climate prediction results under the framework of RCPs-SSPs and provide important scientific evidence [18–20]. For example, ISI-MIP (the Inter-Sectoral Impact Model Intercomparison Project), based on RCPs-SSPs, simulates the possible impacts of climate change on water supply, human health, agricultural production, ecosystems, etc., in a unified framework across sectors and across scales [21]. Recently, most of the researches directly used the global SSPs database, or used scenario data down-scaled to smaller areas. The detailed development of the ball SSPs story line at the national/sub-national and sector level is not fully considered, and it is difficult to meet the scenario needs of the regional conference department research [17]. For example, the current research on China is mainly divided into two categories: climate change impact assessment and SSPs element estimation. The previous researches are focused on the national level; however, there are fewer studies in subregions (at the subnational level) [17]. For example, Chen et al. estimated the number of Chinese provincial population, gender, age, education level and other structural information under five socio-economic development paths from 2010 to 2100, and found that China’s population would peak between 2027 and 2034 [22]. Pan et al. analyzed the impact of major economic impact factors on Chinese economic development under the SSPs and they found that the dominant factor affecting economic development has changed from capital input to total factor productivity [23]. Huang et al. pointed out that China’s population is expected to keep growing in 2025–2035. As a result of the change in fertility policy, the peak population will be 1.39 billion to 1.42 billion in different social scenarios [24]. Samir KC and Wolfgang Lutz reported that the difference between the population is 1.5 billion between the SSP3 and SSP1 pathways by the middle of the century [25]. Marian Leimbach made predictions based on Shared Socioeconomic Pathways (SSPs) and found that the global per capita GDP will grow at a rate of 1% to 2.8% per year during 2010–2100. [26]. Moreover, the SSPs are also used to assess the impact of climate change on agriculture and water resources [27,28]. For example, Hanasaki et al. [29] proposed a new global water scarcity assessment method, which can be used to determine the areas vulnerable to water shortage and analyze the time and degree of water shortage. Sustainability 2020, 12, 4202 3 of 21

SustainabilityRelying 2020 on, 12 the, x FOR development PEER REVIEW of the Yangtze River basin, the Yangtze River Economic Belt occupies3 of 21 an important strategic position in China. The construction and development of the Yangtze River RiverEconomic Economic Zoon isZoon a major is a strategymajor strategy for China’s for Chin regionala’s regional development development and opening and opening up to the up outside to the worldoutside to world promote to promote the further the development further development of China’s of economy China’s econom [30]. They existing[30]. The literature existing takesliterature into takesaccount into the account national the development national development and regional and di regifferences,onal differences, and combines and combines the latest the census latest data census and datapolicies and to policies estimate to theestimate population, the population, GDP and GDP urbanization and urbanization level of Chineselevel of Chinese provinces provinces under the under SSP thescenarios, SSP scenarios, but forbasin but for scale basin is stillscale relatively is still relatively rare. Based rare. on Based the important on the important strategic strategic position position over the Yangtzeover the RiverYangtze basin River of China, basin itof is China, of great it significanceis of great significance to project the to future project economy the future and economy population and of populationthe Yangtze of River the basinYangtze under River diff basinerent under SSPs scenario different [31 SSPs–34]. scenario It can also [31–34]. provide It can a reliable also provide reference a reliablefor exploring reference the population for exploring and economic the population problems inand the economic sustainable problems development in the of the sustainable ecological developmenteconomic system of the in ecological other areas economic in the world. system in other areas in the world.

2. Materials Materials and and Methods

2.1. Study Study Area Area The location map of the Yangtze River basin is shown in FigureFigure1 1.. TheThe YangtzeYangtze RiverRiver isis thethe largest riverriver inin ChinaChina andand thethe thirdthird largest largest river river in in the the world, world, accounting accounting for for 35.1% 35.1% of of the the total total flow flow in inChina China [35 [35].]. The The river river originates originates from from the the Qinghai-Tibet Qinghai-Tibet plateau plateau and flowsand flows from from west west to east. to Theeast. main The 2 mainstream stream of the of Yangtze the Yangtze River River covers covers an area an of area 1.8 of million 1.8 million km , accountingkm2, accounting for 18.8% for 18.8% of the of land the land area, area,36% of36% the of population the population and 40% and of 40% the GDPof the [36 GDP]. According [36]. According to the survey, to the thesurvey, Yangtze the RiverYangtze basin River has basinthe largest has the number largest of number towns of and towns population and population in the world, in the and world, the mostand the complete most complete industrial industrial system. system.The Yangtze The Yangtze River occupies River occupies an important an import positionant position in regional in regional development. development.

35°N (b) ±

Nanjing Wuhan Shanghai Chengdu 30°N Hangzhou Chongqing Nanchang Changsha Legend Mainriver 25°N DN (Average light intensity) 0440880 3 15 40 Km

100°E 110°E 120°E Figure 1. LocationLocation map map of of the the Yangtze Yangtze River basin (( a),), Urban Urban agglomeration; agglomeration; ( b), Night light average intensity map).

There are three major urban agglomeration in the Yangtze River basin, including Chengdu- Chongqing Urban Agglomeration (CYUA), Middle Yangtze Reaches Urban Agglomeration (MYRUA) and Yangtze River Delta Urban Agglomeration (YRDUA) (Table 1).

Sustainability 2020, 12, 4202 4 of 21

There are three major urban agglomeration in the Yangtze River basin, including Chengdu-Chongqing Urban Agglomeration (CYUA), Middle Yangtze Reaches Urban Agglomeration (MYRUA) and Yangtze River Delta Urban Agglomeration (YRDUA) (Table1).

Table 1. Major Cities in Yangtze River basin Urban Agglomeration.

Number of Population Urban Agglomeration Main Cities GDP (Trillion) Cities (Million) Shanghai Yangtze River Delta Nanjing YRDUA 26 150 12.67 Urban Agglomeration Hangzhou Hefei Wuhan Middle Yangtze Reaches MYRUA Changsha 31 6.0 Urban Agglomeration 121 Nanchang Chengdu-Chongqing Chongqing CYUA 16 91 3.76 Urban Agglomeration Chengdu

2.2. Data The population forecast in this paper uses the age structure, sex ratio, education, fertility, mortality, migration and other indicators from the fifth and sixth national census data. The economic forecast takes the data of total factor productivity, energy, capital, labor in the 2nd and 3rd economic censuses as the original data. The Population–Development–Environment (PDE) model and Cobb–Douglas Economic (Cobb–Douglas) model were used to forecast the population and economy of the Yangtze River basin from 2010 to 2100 [31,33].

2.3. Methods Meng Lingguo and others compared the Linear Regression model, the United Nations model, the average growth rate method, the exponential smoothing method, the average growth rate method, the grey system GM method and the Population–Development–Environment (PDE) model used in the current population prediction [37]. Although the average growth method, average growth rate method, and exponential smoothing method are simple, convenient, and fast to calculate, due to the many factors affecting population growth, the assumption of a new fixed number of people every year is unrealistic. Therefore, the accuracy of the model’s prediction results has been questioned. The premise of Linear Regression method is to assume that the future will follow the historical trend unchanged, but considering the current changes in China’s fertility policy, so this method is also not suitable for this study. The United Nations model is used to predict the proportion of urban population and is not suitable for studying the population trend of the Yangtze River basin. The grey system GM method abandons the statistical inspection process, and its scientific nature is questioned. The PDE model expands the cohort composition prediction method and multi-state life table, and divides the population into different combinations according to age and sex, fertility, and mortality. The calculation formula is easy to understand. There is a lot of space for using this model to study the future total population and population structure change. Population forecast for 2010–2100 adopts the PDE model, taking the sixth national population census in 2010 as the original data, and assuming that the model parameters of future mortality, fertility, education and migration are high, medium and low (Table2). Under the medium assumption, the social and economic level remains at the current level. Combined with the basic national conditions of the current two-child policy, the total population of China will increase to a certain extent. China’s total fertility rate will rise to 1.9 in 2019 and gradually stabilize after dropping to 1.8 after 2020. According to the age-specific mortality rate directly registered in the census, the average life expectancy of China’s population in 2010 will reach 77.95 years, including 75.65 years for men and 80.49 years for women. According to the prediction of the United Nations, the life expectancy of Chinese men will increase from 74.23 years in 2010–2015 to 86.71 years in 2095–2100, while that of women will increase from Sustainability 2020, 12, 4202 5 of 21

77.23 years to 88.18 years [24]. Provincial life expectancy is calculated on the basis of provincial death rates at different ages in 2010, with life expectancy expected to increase by two years per decade before 2050 and drop to one year per decade after that. The parameter setting of migration rate is the same as that in 2010, and the migration population of each province remains unchanged at the current level [32].

Table 2. Parameters of population prediction model under the SSPs [31].

Model Parameter Fertility Mortality Mobility Education SSP1 Low Low Medium High SSP2 Medium Medium Medium Medium SSP3 High High Low Low SSP4 Low Medium Medium Low SSP5 Low Low High High

Under the low/high hypothesis, TFR (Total Fertility Rate) is lower/higher than the medium hypothesis since 2011, 20% lower/higher than the medium hypothesis by 2030, and 25% lower/higher after 2050. Assume that life expectancy is reduced/increased by 1 year under low/high assumptions. Under the low assumption, the migrant population in each province decreased to year by year, while under the high assumption, the final migrant population was twice that of 2010. Lower assumptions suggest, to maintain the current level of education, under the hypothesis of the development of the higher, that the level of education in China will reach the level of South Korea or Singapore by 2050 (Korean elementary school middle school and university enrolment were 1.00, 1.00 and 0.93, respectively), the medium hypothesis—between higher and low assumptions, takes the middle value between the high hypothesis and the low hypothesis [32]. The model expression is as follows [31]: P + = Pt0 (1 D + ) + M + (1) t 1 × − t 1 t 1 Pt+1—t + 1 year old population in a given year, Pt0—Number of ts in the previous year, Dt+1—The death rate of t + 1 year old population in that year, Mt+1—That year t + 1 year old migration population.

X49 Pn = Pt Rt Ft (2) × × t=15

Pn—The number of births in a given year; Pt—Population of t years old at that year; Rt—The proportion of the population aged t was female; Ft—The birth rate at t. The SSP1 scenario presents a sustainable green development path; the challenges of climate change adaptation and mitigation are low. It maps the SRES B1/A1 scenario. Due to the improvement of education and health, the fertility rate and mortality rate are lower, resulting in a reduction in the number of people. The SSP2 scenario represents the intermediate path, facing adaptation and mitigation of moderate climate change. It maps the SRES B2 scenario. The development trend of the world is basically similar to the main trend in recent decades. Parameters such as fertility, mortality, migration and education are all medium-sized assumptions. The SSP3 scenario is partial or inconsistent development, facing high climate change challenges. It maps the SRES A2 scenario—scenarios with high fertility and mortality, and low education. Under the SSP4 scenario, it is mainly focused on adaptation challenges and unbalanced development. It maps the SRES A2 scenario—low fertility countries, low fertility and education, moderate mortality and migration. The SSP5 scenario focuses on mitigating challenges, attaches more importance to the traditional rapid development, and focuses on the rapid development of its own economy and strength—accompanied by low fertility and mortality, and high education [12,24]. Table3 shows the summary of the SSPs. Sustainability 2020, 12, 4202 6 of 21

Table 3. Summary of the SSP narratives [4].

SSP Narratives Sustainability—Green development path (Low climate change SSP1 challenge), low fertility, high life expectancy, moderate migration, high education levels Middle of the Road (Medium climate change challenge) SSP2 Parameters such as fertility, mortality, migration and education are all medium-sized assumptions Regional Rivalry—A Rocky Road (Climate change challenges are higher) SSP3 The education level maintains the current enrollment rate, high fertility, high mortality, low migration Inequality—A Road Divided (Focusing on adapting to challenges, mitigation challenges are low) SSP4 The low fertility countries will have low fertility, medium mortality, medium migration, and low education. Fossil-fueled Development—Taking the Highway (Traditional SSP5 development scenarios focus on mitigating challenges) low fertility and mortality, high education

Over the past decades, many scientific researchers have looked for a function that can represent the output level as a function to the input level, for example, capital, labor, and land. It was found that the Cobb–Douglas model can make the best statistical fit for empirical data on the premise that the elasticity of the constant output to the factor input and the Hicks neutral technique [26,38]. The Cobb–Douglas model is widely used in the systematic study of regional economy due to its simple data form and clear economic significance. It is called the general method of regional system diagnosis and system prediction [39]. The Cobb–Douglas model is adopted to predict China’s economy in 2010–2100 according to the data of the third economic census and the statistical data of 2010 [33]. The Cobb–Douglas model is used to predict the production of industrial systems or large enterprises in countries and regions and to analyze the ways in which production is developed. It takes the form [40,41]:

α 1 α λt Y(t) = K(t) L(t) − T(0)e (3) where Y denotes GDP, K capital stock, α the output elasticity on capital, L labor input, T total factor productivity (TFP) and λ Level of scientific and technological progress. T(0) is TFP in 2010. TFP is calculated by dividing output by the weighted average of labor and capital input. Among these, Labor (L) has three components: working age people (WAP), labor force participation (LFPR) and education (H). X L = H LEPR(q) WAP(q) (4) q × × where WAP distinguishes two age classes q: those aged 15–64 years and those aged 65 and above. In 2010, the LFPR of the population aged 16–64 was about 77.37%, and the LFPR of the population aged 65 and above was about 19%. The factors of labor production are affected by education (H), the calculation formula is as follows [26]:

H = exp(0.134min(M , 4) + 0.101min(max(M 4, 0), 4) + 0.068max(M 8, 0)) (5) 2010 2010 − 2010 − where M2010 denotes Mean years of schooling (MYS) in 2010, China’s MYS in 2010 was approximately 9.04 years. The capital stock (K) reflects the cumulative amount of social and economic capital at a certain point in time. The most common method for measuring basic stock is the perpetual inventory method [33]. Using 1952 as the base year, the perpetual inventory method is used to calculate the capital stock of Sustainability 2020, 12, 4202 7 of 21

China and the provinces (districts/cities) in 2010 at the constant price of the base year [33]. It takes the form: K + = (1 d)Kt + It (6) t 1 − where d denotes Depreciation rate, which is 6.3%; I—Total amount of fixed assets, China’s capital stock at CNY 1.391795 trillion in 2010. As for the assumed value of LFPR of labor force population aged 15–64 under each path of SSPs, the SSP2 is at a medium level, and LFPR will converge to about 0.7 in the future. LFPR converges to 0.7 in the future SSP1. The fast developing SSP5 LFPR converges to about 0.8. For SSP3 with slow development, LFPR will converge to 0.6 in the future. Under the path of SSP4, the growth rate of LFPR is slow, but eventually it will reach the long-term stable state of LFPR at 0.75 [24]. Under the medium assumption of maintaining the current level of science and technology, the average annual growth rate of factor productivity of all people is 0.7%. The low/high hypothesis assumes that the total factor productivity is lower/higher than the medium hypothesis. Under the SSP1–5, the average annual growth rate of factor productivity of all people is different. Under the SSP1, SSP2 and SSP4, it is 0.7%; under the SSP3 and SSP5, it is 0.35% and 1.05%, respectively [33,42]. The elasticity coefficients of capital output were also different in different paths. The SSP1 and SSP5 scenarios developed rapidly, with alpha converging to 0.35 and 0.45, respectively, and the convergence time was 75 years and 250 years. It takes 150 years for convergence to 0.35 and 0.25 under the SSP2 and SSP3 scenarios. Under the SSP4, alpha will converge to 0.3, which takes about 75 years (Table4).

Table 4. Assumptions on the economic projections for SSPs [33].

Economic Parameters SSP1 SSP2 SSP3 SSP4 SSP5 LFP 0.70 0.70 0.60 0.75 0.80 Time of convergence to LFP/year 100 100 100 400 100 TFP annual growth rate/% 0.70 0.70 0.35 0.70 1.05 α 0.35 0.35 0.25 0.30 0.45 Time of convergence to α/year 75 150 150 75 250

At present, in China’s population forecast, the input data for the forecasting models were different. Even the same influencing factors were different due to the statistical caliber of each region. This produces errors in the statistical results of the population of the entire region, leading to inaccurate predictions. Using national census and economic census data, the data are authoritative. Under the assumption that the base period population structure data are accurate and the population change parameters are reasonable. The results of the population forecast can indicate the future population development process. Even if there is a gap between these two assumptions and the actual situation, the results of the population forecast can roughly reflect the trend of the future population process. Domestic scholars evaluated the quality of the sixth census data in 2010. It was found that the under-reporting rate of the population aged 0–9 was 0.75%, the re-reporting rate was 0.55%, and the total error rate was 1.30%. According to the spot check results after the registration phase of the census, the total under-reporting rate of the population was 0.12% and 1.6 million were underreported. The total population published in the 2010 Census Bulletin is credible [43]. In the process of economic forecasting, although factors such as population and education were used as inputs, there was a lack of consideration of the direct impact on urbanization [44]. All of these will have a certain impact on the prediction and put limitations on the model prediction.

3. Results

3.1. Verification of Prediction Results The national bureau of statistics data from 2010 to 2018 were analyzed and compared with the data predicted by the model. Sichuan province, Hubei province and Shanghai city were selected as representative provinces (cities) to verify the statistical data and the estimated results under the SSP2 Sustainability 2020, 12, x FOR PEER REVIEW 8 of 21 Sustainability 2020, 12, 4202 8 of 21 analysis results show that the PDE model can be used in population estimation research in the Yangtze River basin, and that the parameter selection is reasonable. scenario.From The 2010 total to 2018, annual the population average annual of Sichuan GDPs Province, announced Hubei by Province the National and Shanghai Bureau of city Statistics from 2010 of Sichuan,to 2018 announced Hubei, and by Shanghai the National were Bureau CNY of2.82 Statistics trillion, was CNY 81.7 2.75 million, trillion, 58.26 and million CNY 2.43 and 23.94trillion. million, The predictedrespectively. average Under annual the SSP2 GDP scenario, of Sichuan, the annual Hubei averageand Shanghai population from estimates2010 to 2018 for is Sichuan, CNY 2.44 Hubei, trillion, and CNYShanghai 2.36 trillion, from 2010 and to CNY 2018 2.01 were trillion 80.1 million, under the 58.09 SSP2 million, scenario. and 23.6The million,average respectively.annual error Theof the annual two setserror of betweendata is about the forecast 14.0% (Figure data and 2b). the The statistical above results data is show below that 4.0% the (Figure Cobb–Douglas2a). The analysis model resultscan be appliedshow that to the PDEpopulation model estimation can be used over in population the Yangtze estimation River basin, research and the in the parameters Yangtze River set by basin, the modeland that are the more parameter reasonable. selection is reasonable.

(a) Population (b) GDP 9000 4.0 Sichuan Sichuan 8000 3.5 7000 3.0 6000 Hubei

5000 2.5 Hubei Shanghai GDP / (trillion) 4000 Forecast data 2.0 Forecast data Population / (ten thousand) Statistic data 3000 Shanghai 1.5 Statistic data

2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 Year Year

FigureFigure 2. 2. ComparisonComparison of of population population and and GDP GDP statistics statistics for for 2010–2018 2010–2018 with with SSP2 SSP2 scenario scenario forecast forecast resultsresults (( ((aa),), Population; Population; ( (bb),), GDP). GDP).

UsingFrom the 2010 statistical to 2018, data the average of 2010–2018 annual and GDPs the announcedprediction data by the under National the SSP2, Bureau the ofME Statistics (Mean Error),of Sichuan, RMSE Hubei, (Root Mean and Shanghai Squared wereError) CNY and MARE 2.82 trillion, (Mean CNY Absolute 2.75 trillion,Relative andError) CNY of the 2.43 Sichuan trillion. Province,The predicted Hubei average Province annual and GDPShanghai of Sichuan, City data Hubei are anddiscussed. Shanghai As fromcan be 2010 seen to 2018from isthe CNY results 2.44 (Tabletrillion, 5), CNY the ME 2.36 of trillion, the population and CNY of 2.01 all province trillion under and city the is SSP2 between scenario. −0.1–0.02, The average the RMSE annual and MARE error of arethe less two than sets 0.051 of data and is about0.019, 14.0%respectively. (Figure The2b). ME The of above GDP resultsof each show province that theand Cobb–Douglas city is about −0.4, model the RMSEcan be and applied MARE to theare populationabout 1.1 and estimation 0.1, respectively. over the YangtzeIt shows Riverthat the basin, accuracy and the of parametersthe prediction set of by populationthe model areand more GDP reasonable.using the PDE and Cobb–Douglas models meets the requirements in this study. Using the statistical data of 2010–2018 and the prediction data under the SSP2, the ME (Mean Error), RMSE (Root Mean SquaredTable Error) 5. Discussion and MARE on the (Mean accuracy Absolute of simulation Relative models. Error) of the Sichuan Province, Hubei Province and Shanghai City data are discussed. As can be seen from the results (Table5), the ME Cities ME RMSE MARE of the population of all province and city is between 0.1–0.02, the RMSE and MARE are less than POP −0.159− 0.479 0.019 0.051 and 0.019,Sichuan respectively. province The ME of GDP of each province and city is about 0.4, the RMSE and GDP −0.421 1.262 0.140− MARE are about 1.1 and 0.1, respectively. It shows that the accuracy of the prediction of population POP −0.019 0.051 0.003 and GDP usingHubei the province PDE and Cobb–Douglas models meets the requirements in this study. GDP −0.381 1.143 0.122 POP 0.002 0.005 0.013 Shanghai cityTable 5. Discussion on the accuracy of simulation models. GDP −0.333 0.999 0.128 Cities ME RMSE MARE 3.2. The Population and Economic SituationPOP of the Yangtze0.159 River Basin before 0.479 the 2050s 0.019 Sichuan province − GDP 0.421 1.262 0.140 The population of the Yangtze River basin in− 2010 was shown in Figure 3a. It can be found that POP 0.019 0.051 0.003 the total populationHubei province of the Yangtze River basin was− 441 million, accounting for 33.1% of the national GDP 0.381 1.143 0.122 population. The average population density was− 245 people/km2. While the future annual average POP 0.002 0.005 0.013 population Shanghaidensity cityin the Yangtze River basin from 2021 to 2050 will be between 240 and 265 GDP 0.333 0.999 0.128 people/km2 under different SSPs (Figure 3b–f). Compared− with the population in 2010, negative population growth can be found in the CYUA and MYRUA areas under different SSP scenarios (Table 6). Sustainability 2020, 12, x FOR PEER REVIEW 9 of 21 Sustainability 2020, 12, 4202 9 of 21 Table 6. The population growth of the three major urban agglomerations in the Yangtze River basin. Unit: ten thousand.

SSP1 SSP2 SSP3 SSP4 SSP5 3.2. The Population and Economic SituationCYUA of−726 the Yangtze−519 21 River−909 Basin−1053 before the 2050s MYRUA −22 432 1322 −201 −507 The population of the YangtzeYRDUA River basin957 1274 in 2010932 was 719 shown 1635 in Figure3a. It can be found that the total populationThe ofpopulation the Yangtze of the CYUA River areas basin might was decrease 441 obviously million, for accountingalmost all SSPs forexcept 33.1% SSP3. of the national population. TheHowever, average the population population of the densityYRDUA areas was will 245 increase people at diff/kmerent2. SSPs. While Moreover, the future it can be annual average population densityfound inthat the theYangtze population River will increase basin in from the mid-lower 2021 to Yangtze 2050 willRiverbe basin between under the 240 SSP2 and and 265 people/km2 SSP3 scenarios. Among them, the largest population decrease will appear in the CYUA areas, with a under differentpopulation SSPs (Figure decrease3b–f). of about Compared 5 million compared with the with population 2010 under different in 2010, SSPs negative except the populationSSP3 growth can be found inscenario. the CYUA Under the and SSP5 MYRUA scenario, more areas than under 70% of di thefferent areas in SSP the scenariosYangtze River (Table basin have6). negative population growth, and some areas have a population decrease of over 200,000.

(a) 2010 (b) SSP1, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N Population (Ten thousand) Population (Ten thousand) 0.5 1 2.5 10 40 80 120 250 500 -20 -10 -5 -1 0 1 5 10 20 50 100 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E (c) SSP2, 2021-2050 average minus 2010 (d) SSP3, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N Population (Ten thousand) Population (Ten thousand)

-20 -10 -5 -1 0 1 5 10 20 50 100 -20 -10 -5 -1 0 1 5 10 20 50 100 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E (e) SSP4, 2021-2050 average minus 2010 (f) SSP5, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N Population (Ten thousand) Population (Ten thousand)

-20 -10 -5 -1 0 1 5 10 20 50 100 -20 -10 -5 -1 0 1 5 10 20 50 100 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E Figure 3. GrowthFigure of 3. populationGrowth of population in Yangtze in Yangtze River River Basin Basin from from 2021 to 2021 2050 compared to 2050 with compared 2010 ((a), with 2010 ((a), Population in 2010; b–f, 2021–2050 average minus 2010). Population in 2010; b–f, 2021–2050 average minus 2010). In 2010, the total GDP of the Yangtze River basin was CNY 12.3 trillion, accounting for 28.9% of Table 6. TheChina’s population GDP. The growthGDP density of thewas threeabout 6.845 major million urban CNY/km agglomerations2 in the Yangtze River in the basin Yangtze in 2010. River basin. Unit: ten thousand.The pattern of the GDP distribution is similar to that of the population, and it mainly concentrated in the YRDUA, MYRUA and CYUA. As a whole, the GDP of the Yangtze River basin shows a growth state. The GDP of the three major urban agglomerations continues to grow under different scenarios. The averageSSP1 annual GDP of the three SSP2 major urban agglomerations SSP3 in 2021–2050 SSP4compared to the SSP5 CYUA increase in 2010726 is shown in Table 7. 519 21 909 1053 − − − − MYRUA 22 432 1322 201 507 − − − YRDUA 957 1274 932 719 1635

The population of the CYUA areas might decrease obviously for almost all SSPs except SSP3. However, the population of the YRDUA areas will increase at different SSPs. Moreover, it can be found that the population will increase in the mid-lower Yangtze River basin under the SSP2 and SSP3 scenarios. Among them, the largest population decrease will appear in the CYUA areas, with a population decrease of about 5 million compared with 2010 under different SSPs except the SSP3 scenario. Under the SSP5 scenario, more than 70% of the areas in the Yangtze River basin have negative population growth, and some areas have a population decrease of over 200,000. In 2010, the total GDP of the Yangtze River basin was CNY 12.3 trillion, accounting for 28.9% of China’s GDP. The GDP density was about 6.845 million CNY/km2 in the Yangtze River basin in 2010. The pattern of the GDP distribution is similar to that of the population, and it mainly concentrated in the YRDUA, MYRUA and CYUA. As a whole, the GDP of the Yangtze River basin shows a growth state. The GDP of the three major urban agglomerations continues to grow under different scenarios. The average annual GDP of the three major urban agglomerations in 2021–2050 compared to the increase in 2010 is shown in Table7. Sustainability 2020, 12, 4202 10 of 21

Table 7. The GDP growth of the three major urban agglomerations in the Yangtze River basin. Unit: trillion.

SSP1 SSP2 SSP3 SSP4 SSP5 CYUA 7.15 6.71 5.91 6.61 7.14 MYRUA 9.91 8.96 7.69 9.10 10.10 YRDUASustainability 2020 28.03, 12, x FOR PEER REVIEW 26.07 22.80 26.5510 of 21 29.07

Table 7. The GDP growth of the three major urban agglomerations in the Yangtze River basin. Unit: trillion. The annual average GDP density of the Yangtze River basin might increase by 25–30 hundred 2 SSP1 SSP2 SSP3 SSP4 SSP5 million CNY/km during the periodCYUA of 2021–2050 7.15 6.71 (Figure 5.91 4b–f).6.61 The7.14 GDP in most regions of the middle and upper reaches will increase byMYRUA more than9.91 CNY8.96 307.69 billion 9.10 during10.10 the period 2021–2050 compared YRDUA 28.03 26.07 22.80 26.55 29.07 with that of 2010.The As annual for the average GDP GDP of density capital of the cities Yangtze such River as basin Chengdu, might increase Wuhan, by 25–30 Changsha, hundred Nanjing and Chongqing, themillion GDP CNY/km of these2 during cities thewill period increase of 2021–2050 by (Figure more 4b–f). than The CNY GDP 100 in most billion. regions While of the the GDP of the middle and upper reaches will increase by more than CNY 30 billion during the period 2021–2050 YRDUA will increasecompared with faster that thanof 2010. the As for MYRUA the GDP of and capital CYUA, cities such for as example,Chengdu, Wuhan, Suzhou Changsha, and Shanghai will increase by CNYNanjing 1 trillion and Chongqing, more than the GDP other of these cities cities in thewill increase middle by and more upperthan CNY Yangtze 100 billion. reaches. While Although the GDP growth inthe Qinghai GDP of the Province YRDUA will will increase not faster exceed than the CNY MYRUA 1 billion, and CYUA, the for GDP example, growth Suzhou rateand in the western Shanghai will increase by CNY 1 trillion more than other cities in the middle and upper Yangtze region of Qinghaireaches. Province Although isthe relatively GDP growth higher. in Qinghai Province will not exceed CNY 1 billion, the GDP growth rate in the western region of Qinghai Province is relatively higher.

(a) 2010 (b) SSP1, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N GDP (Hundred million) GDP (Hundred million) 1 3 10 50 100 200 400 2000 6000 0 5 10 100 300 500 1000 2500 5000 10000 90°E 100°E 110°E 120°E 100°E 110°E 120°E

(c) SSP2, 2021-2050 average minus 2010 (d) SSP3, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N GDP (Hundred million) GDP (Hundred million)

0 5 10 100 300 500 1000 2500 5000 10000 0 5 10 100 300 500 1000 2500 5000 10000 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E

(e) SSP4, 2021-2050 average minus 2010 (f) SSP5, 2021-2050 average minus 2010 35°N 35°N

30°N 30°N

25°N 25°N GDP (Hundred million) GDP (Hundred million) 0 5 10 100 300 500 1000 2500 5000 10000 0 5 10 100 300 500 1000 2500 5000 10000 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E

Figure 4. Growth of economy in Yangtze River basin from 2021 to 2050 compared with 2010 ((a), GDP Figure 4. a Growthin 2010; of ( economyb–f), 2021–2050 in average Yangtze minus River 2010). basin from 2021 to 2050 compared with 2010 (( ), GDP in 2010; (b–f), 2021–2050 average minus 2010). 3.3. Population and Economic Trends for 2010–2100 3.3. Population andThe Economic areal mean Trends population for and 2010–2100 GDP variabilities of the Yangtze River basin from 2010 to 2100 are shown in Figure 5. The total population of the Yangtze River basin has been rising up to the 2020s, The arealand mean then populationfalling during the and period GDP of 2030–2100 variabilities under the of SSPs the except Yangtze SSP3. The River population basin of fromthe 2010 to 2100 are shown in FigureYangtze basin5. The will peak total around population 2022–2026 at of452–559 the million Yangtze under River the SSPs basin except under has SSP3. been This rising up to the difference is mainly caused by different fertility assumptions. The SSP3 scenario was different from 2020s, and thenother falling scenarios, during and the the total period population of 2030–2100in the Yangtze under River basin the genera SSPslly except shows an SSP3. increasing The population of the Yangtze basin will peak around 2022–2026 at 452–559 million under the SSPs except under SSP3. This difference is mainly caused by different fertility assumptions. The SSP3 scenario was different from other scenarios, and the total population in the Yangtze River basin generally shows an increasing trend until 2100, with an increase of 118 million people compared with 2010. By 2050, there might be 78 million people between the SSP3 and SSP5 scenarios, while the population will reach 266 million, 380 million, 559 million, 255 million and 224 million people under the SSP1–SSP5 scenarios in the year of 2100. As can be seen from Figure5b, the GDP of the Yangtze River basin will keep increasing, and the GDP will be 7.33, 8.94, 6.55, 5.98 and 12.21 times that of 2010 by 2100 under the SSP1–5 scenarios. Sustainability 2020, 12, x FOR PEER REVIEW 11 of 21 trend until 2100, with an increase of 118 million people compared with 2010. By 2050, there might be 78 million people between the SSP3 and SSP5 scenarios, while the population will reach 266 million, 380 million, 559 million, 255 million and 224 million people under the SSP1–SSP5 scenarios in the yearSustainability of 2100.2020 As, 12can, 4202 be seen from Figure 5b, the GDP of the Yangtze River basin will keep increasing,11 of 21 and the GDP will be 7.33, 8.94, 6.55, 5.98 and 12.21 times that of 2010 by 2100 under the SSP1–5 scenarios. Since the two-child policy replaced the one-child policy, the population has increased, and toSince some the extent, two-child the problems policy replaced of aging the and one-child labor policy,shortage the have population been solved, has increased, which is and one to of some the reasonsextent, thefor problemsthe continued of aging rise andin GDP. labor shortage have been solved, which is one of the reasons for the continued rise in GDP.

6 160 (a) Population (b) GDP 5 120

4 80 3

GDP (trillion) 40 2 SSP1 SSP2 SSP3 SSP1 SSP2 SSP3 SSP4 SSP5 SSP4 SSP5

Population (hundred million) 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year

Figure 5. Population and economic trends in the Yangtze River basin under SSPs in 2010–2100 ((a), Figure 5. Population and economic trends in the Yangtze River basin under SSPs in 2010–2100 ((a), Population; (b), GDP). Population; (b), GDP). 3.4. Population and Economic Changes of Three Major Urban Agglomerations in Yangtze River Basin 3.4. Population and Economic Changes of Three Major Urban Agglomerations in Yangtze River Basin There are three major urban agglomerations in the Yangtze River basin, namely the YRDUA (theThere Yangtze are three River major delta), urban MYRUA agglomerations (the middle in the reachesYangtze ofRiver the basin, Yangtze namely River) the YRDUA and CYUA (the Yangtze(the Chengdu-Chongqing River delta), MYRUA urban (the agglomerations).middle reaches of the Figure Yangtze6 shows River) that and the CYUA population (the Chengdu- of the ChongqingYRDUA will urban increase agglomerations). before 2050 Figure and then 6 shows begin that to decreasethe population under of the the di YRDUAfferent SSPs.will increase As for beforethe MYRUA, 2050 and the then population begin to decrease will increase under before the different 2030 and SSPs. then As decrease for the MYRUA, except for the SSP3 population scenario. willThe increase population before of the2030 MYRUA and then will decrease keep increasing except for during SSP3 scenario. the period The of population 2010–2100 of under the MYRUA the SSP3 willscenario. keep However,increasing the during population the period of the CYUAof 2010–2100 will keep under decreasing the SSP3 during scenario. the period However, of 2010–2100 the populationunder the di offf theerent CYUA SSP scenarios. will keep Thedecreasing population during was the 96.65 period million, of 2010–2100 125.92 million under and the 146.93different million SSP scenarios.in the year The of 2010population for the was CYUA, 96.65 MYRUA million, and 125.92 YRDUA, million respectively. and 146.93 Themillion population in the year of theof 2010 YRDUA for thewill CYUA, increase MYRUA by 6–14 and million YRDUA, in 2030 respectively. compared The with po thatpulation of 2010 of the under YRDUA the di willfferent increase SSP scenarios, by 6–14 millionand it will in 2030 increase compared by 1–11 with million that for of the2010 MYRUA under th arease different from the SSP year scenarios, of 2010 toand 2030. it will The increase population by 1–11of CYUA million will for decrease the MYRUA 2–6 million areas fromfrom thethe yearyear ofof 20102010 to to 2030 2030. under The thepopulation different of SSP CYUA scenarios will decreaseexcept for 2–6 SSP3 million scenario. from The the populationyear of 2010 of to the 2030 YRDUA under will the keep different increasing SSP sc untilenarios 2050, except while for the SSP3 other scenario.two urban The agglomerations population of willthe decreaseYRDUA will after keep the year increasing of 2030 until in di ff2050,erent while SSP scenarios.the other two The largesturban agglomerationspopulation amount will ofdecrease the YRDUA after the will year reach of its2030 peak in indifferent 2050 under SSP scenarios. the SSP5 scenario, The largest with population 20 million amountmore than of the in 2010.YRDUA will reach its peak in 2050 under the SSP5 scenario, with 20 million more than in 2010.There are only two scenarios in which the future population will increase by 2100, for example, the populationThere are only of the two YRDUA scenarios under in thewhich SSP5 the scenario future willpopulation increase will by 0.2 increase million by and 2100, it will for increase example, by the53.4 population million for of the the MYRUA YRDUA under under the the SSP3 SSP5 scenario scenario compared will increase with by 2010. 0.2 Formillion example, and it the will population increase byof YRDUA53.4 million under for the the SSP1 MYRUA and SSP4 under scenarios the SSP3 will scenario decrease compared by more than with 35 2010. million. For The example, population the populationof CYUA and of YRDUA MYRUA under will decrease the SSP1 by and more SSP4 than scen 50arios million will under decrease the SSP1, by more SSP4 than and 35 SSP5 million. scenarios. The populationIn 2010, the of GDP CYUA was and CNY MYRUA 7.49, 3.07 will and decrease 2.15 trillion by more for than the YRDUA, 50 million MYRUA under the and SSP1, CYUA SSP4 areas and in SSP5the Yangtze scenarios. River In 2010, basin, the respectively. GDP was CNY Figure 7.49,6b shows3.07 and in 2.15 all scenarios, trillion for the the GDP YRDUA, of the MYRUA three urban and CYUAagglomerations areas in the will Yangtze keep increasing River basin, in the respectively. future (Figure Figure6b). 6b The shows GDP in under all scenarios, the SSP1 scenariothe GDP will of the be threethe highest urban foragglomerations the three urban will agglomerations keep increasing in in 2030. the Thefuture GDP (Figure6b). of the YRDUA The GDP in 2030 under will the increase SSP1 scenarioby CNY will 22 trillion be the comparedhighest for with the thatthree of urban 2010 andagglomerations it will increase in 2030. by CNY The 6–7 GDP trillion of the for YRDUA the CYUA in 2030and thewill MYRUA increase areas,by CNY respectively. 22 trillion Incompared the year with of 2100, that the of GDP2010 ofand the it YRDUAwill increase will beby theCNY largest 6–7 trillionwith CNY for the 120 CYUA trillion and under the the MYRUA SSP5 scenario. areas, respective However,ly. the In the GDP year of MYRUAof 2100, the under GDP the of SSP4 the YRDUA scenario will be higher than that of SSP2 scenario while the GDP of the YRDUA under the SSP4 scenario will be lower than that of SSP2 scenario. The reason for this phenomenon may be that the SSP4 scenario is mainly to adapt to the challenges, and the inherent unbalanced development hinders the development of some regions. In the YRDUA, due to policy and regional advantages, the GDP has great advantages Sustainability 2020, 12, x FOR PEER REVIEW 12 of 21 will be the largest with CNY 120 trillion under the SSP5 scenario. However, the GDP of MYRUA under the SSP4 scenario will be higher than that of SSP2 scenario while the GDP of the YRDUA under the SSP4 scenario will be lower than that of SSP2 scenario. The reason for this phenomenon may be Sustainability 2020, 12, 4202 12 of 21 that the SSP4 scenario is mainly to adapt to the challenges, and the inherent unbalanced development hinders the development of some regions. In the YRDUA, due to policy and regional advantages, the GDPin di ffhaserent great years advantages and different in different scenarios. years It can and be di seenfferent that scenarios. income convergence It can be seen is athat rather income slow convergenceprocess. Because is a rather by 2050, slow the process. GDP of Because CYUA willby 2050, be lower the GDP than of that CYUA of the will MYRUA, be lower especially than that farof thelower MYRUA, than that especially of the YRDUA. far lower The than GDP that under of the the YRDUA. SSP2 scenario The GDP will under be 7.2 timesthe SSP2 (CNY scenario 15.45 trillion),will be 7.29 times times (CNY (CNY 27.66 15.45 trillion) trillion), and 9 10.5times times (CNY (CNY 27.66 78.63 trillion) trillion) and larger 10.5 times in 2100 (CNY than it78.63 was trillion) in 2010 forlarger the inCYUA, 2100 than MYRUA it was and in 2010 YRDUA, for the respectively. CYUA, MYRU TheA diandfference YRDUA, between respectively. the CYUA The anddifference the MYRUA between is thegradually CYUA narrowing, and the MYRUA but the gapis gradually with the YRDUAnarrowing, still exists.but the In gap particular, with the the YRDUA difference still between exists. theIn particular,YRDUA and the other difference urban agglomerationbetween the YRDUA under the and SSP5 other scenario urban is alsoagglomeration large. under the SSP5 scenario is also large. (a) Population 2010 SSP1 2030 2050 2100 2010 SSP2 2030 2050 2100 2010 SSP3 2030

2050 2100 2010 SSP4 2030 2050 2100 2010 SSP5 2030 2050 2100 0 25 50 75 100 125 150 175 CYUA MYRUA YRDUA Until:million (b) GDP 2010 SSP1 2030 2050 2100 2010 SSP2 2030 2050 2100 2010 SSP3 2030

2050 2100 2010 SSP4 2030 2050 2100 2010 SSP5 2030 2050 2100 0 25 50 75 100 125 CYUA MYRUA YRDUA Until:trillion FigureFigure 6. The populationpopulation andand GDP GDP of of the the three three major major urban urban agglomerations agglomerations in the in Yangtzethe Yangtze River River basin. basin.((a) population; ((a) population; (b), GDP). (b), GDP).

FigureFigure 77 shows shows the the population population growth growth rate rate of the of three the three major major urban agglomerationsurban agglomerations in the Yangtze in the YangtzeRiver basin River from basin 2020 from to 2020 2100. to The2100. population The population growth growth rate rate of the of the three three urban urban agglomerations agglomerations in inthe the future future shows shows a declininga declining trend, trend, except except for for the the MYRUA MYRUA under under the the SSP3SSP3 scenario.scenario. Although the the population growth growth rate rate of of the the CYUA shows shows a a declining trend trend from from 2010 2010 to 2025 under the SSP3 scenario,scenario, the the population population under the other other scenarios scenarios will decrease decrease from the year of of 2020. 2020. In In the the MYRUA, MYRUA, there will be a negative population growth after the 2020s under the scenarios other than SSP3 scenario. While the population in the MYRUA has been growing under the SPP3 scenario, under the SSP5 Sustainability 2020, 12, x FOR PEER REVIEW 13 of 21 there will be a negative population growth after the 2020s under the scenarios other than SSP3 scenario. While the population in the MYRUA has been growing under the SPP3 scenario, under the SSP5 scenario, the population of the YRDUA began to show negative growth in 2053, while under the other scenarios, the population will decrease after the 2030s. The population growth rate for CYUA will be around −20‰ under the SSP1 and SSP4 scenarios by the year 2100. The negative population growth rate of SSP2 scenario will slow down after the 2060s, and the population growth rate will be −9.63‰ in 2100. Under the SSP5 scenario, the population growth rate of CYUA will drop almost in a straight line. By 2050, the population growth rate will be −12.28‰ and it will drop to −35.09‰ by 2100. The population reduction rate is relatively fast. The population growth rate of CYUA will begin to show negative growth in 2025 under the SSP3 scenario, but the decrease is relatively small. After the 2070s, the population growth rate will stabilize at around −1‰. The highest and lowest population growth rate will appear in the MYRUA under the SSP5 and SSP3 scenarios, respectively. The difference is that from 2020 to 2100, the population growth rate does not show negative growth for the MYRUA under the SSP3 scenario. The population growth rate will be reduced from 6.29‰ (2020) to 1.27‰ (2054) and then begin to keep increasing from 1.27‰ (2054) to 5.51‰ in 2100. After 2075, the population growth rate in MYRUA will decline faster than before 2075 in the scenario of SSP5. The growth rate of the population will grow slowly after the 2090s. the growth rate of population in MYRUA will increase gradually after dropping to −4.27‰ in 2059 under the SSP2 scenario. The population in MYRUA will increase in a small scale in 2100. The population growth rate of shows decreasing trends under different SSP scenarios for the three urban agglomerations during 2010–2100. Moreover, the population growth rate of the CYUA will be obviously negative during 2010–2100, and it will decrease more obviously than other urban agglomerations. The change of population growth rate is relatively complicated in the YRDUA, and the year of negative population growth will be postponed. The population growth rate before the 2080s will be higher under the SSP5 scenario than other scenarios. The highest population growth rate can be found in the 2080s under the SSP3 scenario, and the population growth rate in 2100 will be around −1.15‰. The population growth rate will start to increase after it drops to −6.28‰ in 2059

Sustainabilityunder the SSP32020, 12scenario., 4202 Before the 2050s, the population growth under SSP1 and SSP2 scenarios13 ofwill 21 not significantly different. After the 2050s, the gap will gradually be widened. The population of SSP1 and SSP2 scenarios will decrease rapidly and the population growth rate will be −4.41‰ and −0.39‰, scenario,respectively, the populationby 2100. The of population the YRDUA growth began rates to show of SSP4 negative and SSP2 growth scenarios in 2053, are while basically under the the same. other scenarios,The population the population growth rate will will decrease increase after after the 2085 2030s. under each scenario.

20 20 20 15 SSP1 SSP2 SSP3 CYUA 15 CYUA 15 CYUA 10 MYRUA MYRUA MYRUA 10 YRDUA 5 YRDUA YRDUA 10 0 5 5 -5 0 -10 0 -5

-15 rate (‰) Growth rate (‰) Growth Growth rate (‰) rate (‰) Growth -5 -20 -10

2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Year

10 20 SSP4 15 SSP5 5 CYUA 10 CYUA MYRUA 5 MYRUA 0 YRDUA YRDUA 0 -5 -5 -10 -10 -15 -20 -15 -25 Growth rate (‰) Growth rate (‰) Growth -20 -30 -35

2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Figure 7. The population growth rate of the three major urban agglomerations in the Yangtze River Figure 7. The population growth rate of the three major urban agglomerations in the Yangtze River basin in 2020–2010. basin in 2020–2010. The population growth rate for CYUA will be around 20% under the SSP1 and SSP4 scenarios − by the year 2100. The negative population growth rate of SSP2 scenario will slow down after the 2060s, and the population growth rate will be 9.63% in 2100. Under the SSP5 scenario, the population − growth rate of CYUA will drop almost in a straight line. By 2050, the population growth rate will be 12.28% and it will drop to 35.09% by 2100. The population reduction rate is relatively fast. − − The population growth rate of CYUA will begin to show negative growth in 2025 under the SSP3 scenario, but the decrease is relatively small. After the 2070s, the population growth rate will stabilize at around 1%. − The highest and lowest population growth rate will appear in the MYRUA under the SSP5 and SSP3 scenarios, respectively. The difference is that from 2020 to 2100, the population growth rate does not show negative growth for the MYRUA under the SSP3 scenario. The population growth rate will be reduced from 6.29% (2020) to 1.27% (2054) and then begin to keep increasing from 1.27% (2054) to 5.51% in 2100. After 2075, the population growth rate in MYRUA will decline faster than before 2075 in the scenario of SSP5. The growth rate of the population will grow slowly after the 2090s. the growth rate of population in MYRUA will increase gradually after dropping to 4.27% in 2059 under the SSP2 − scenario. The population in MYRUA will increase in a small scale in 2100. The population growth rate of shows decreasing trends under different SSP scenarios for the three urban agglomerations during 2010–2100. Moreover, the population growth rate of the CYUA will be obviously negative during 2010–2100, and it will decrease more obviously than other urban agglomerations. The change of population growth rate is relatively complicated in the YRDUA, and the year of negative population growth will be postponed. The population growth rate before the 2080s will be higher under the SSP5 scenario than other scenarios. The highest population growth rate can be found in the 2080s under the SSP3 scenario, and the population growth rate in 2100 will be around 1.15%. The population growth − rate will start to increase after it drops to 6.28% in 2059 under the SSP3 scenario. Before the 2050s, − the population growth under SSP1 and SSP2 scenarios will not significantly different. After the 2050s, the gap will gradually be widened. The population of SSP1 and SSP2 scenarios will decrease rapidly and the population growth rate will be 4.41% and 0.39%, respectively, by 2100. The population − − Sustainability 2020, 12, 4202 14 of 21 growth rates of SSP4 and SSP2 scenarios are basically the same. The population growth rate will Sustainability 2020, 12, x FOR PEER REVIEW 14 of 21 increase after 2085 under each scenario. TheThe GDP GDP growth growth rate rate of of the the three three major major urban urban ag agglomerationsglomerations from from 2020 2020 to to 2010 2010 is is shown shown in in FigureFigure 88.. TheThe GDPGDP growthgrowth rate rate of of di differentfferent urban urban agglomerations agglomerations is alsois also diff erent.different. As aAs whole, a whole, the GDP the GDPshows shows a declining a declining trend trend for the for whole the whole Yangtze Yangtze River River basin. basin. The GDP The growthGDP growth of the of YRDUA the YRDUA stable stableunder under different different scenarios. scenarios. The The decline decline of theof the growth growth rate rate in in the the future future is is due due toto thethe aging of of the the societysociety (which (which leads leads to to the the decline decline of of the the labor labor force force participation participation rate rate and and a adecrease decrease in in employment). employment). TheThe GDP GDP growth growth rate rate of of CYUA CYUA is islower lower than than that that of ofthe the MYRUA MYRUA during during the theperiod period of 2020–2100. of 2020–2100. At theAt thebeginning beginning of the of the21st 21st century, century, the the growth growth of ofthe the MYRUA MYRUA fluctuates fluctuates greatly greatly and and will will grow grow slowly slowly underunder SSP1 SSP1 and and SSP5 SSP5 scenarios. scenarios. With With the the passage passage of of time time and and the the level level of of total total factor factor productivity productivity gettinggetting closer closer to to the the level level of ofhigh-income high-income regions, regions, the the GDP GDP growth growth rate rate is continuously is continuously declining. declining. As aAs dividing a dividing point, point, the theGDP GDP growth growth rate rate before before 2050 2050 will will decline decline faster faster than than after after 2050. 2050. Under Under the the SSP1 SSP1 scenario,scenario, the the GDP GDP of of the the CYUA, CYUA, MYRUA MYRUA and and YRDUA YRDUA will will show show negative negative growth growth in in 2087, 2087, 2099 2099 and and 2098,2098, respectively. respectively. Under Under the the SSP4 SSP4 scenario, scenario, CYUA CYUA will will show show negative negative growth growth after after 2065, 2065, while while the the MYRUAMYRUA will will show show negative negative growth growth in in 2075–2085. 2075–2085.

10 10 10 SSP1 SSP2 SSP3 8 8 8 CYUA CYUA CYUA 6 MYRUA MYRUA MYRUA 6 6 YRDUA YRDUA YRDUA 4 4 4 2 2 2 0 Growth rate (%) Growth rate (%) Growth rate (%) rate (%) Growth

-2 0 0 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Year

10 10 SSP4 SSP5 8 8 CYUA CYUA 6 MYRUA MYRUA 6 YRDUA YRDUA 4 4 2 2

Growth rate (%) (%) rate Growth 0 Growth rate (%)

-2 0 2020 2030 2040 2050 2060 2070 2080 2090 2100 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year Figure 8. The GDP growth rate of the three major urban agglomerations in the Yangtze River basin Figure 8. The GDP growth rate of the three major urban agglomerations in the Yangtze River basin in in 2020–2100. 2020–2100. 3.5. Per Capita GDP in Yangtze River Basin from 2010 to 2100 3.5. Per Capita GDP in Yangtze River Basin from 2010 to 2100 The forecast results of per capita GDP in the Yangtze River basin from 2010 to 2100 can be seen in FigureThe9. forecast The per results capita of GDP per shows capita aGDP continuous in the Ya growthngtze River trend basin under from di ff 2010erent to paths. 2100 Undercan be seen SSP2 inand Figure SSP4 9. scenarios,The per capita the peakGDP valueshows willa continuous be CNY 289,400growth trend and CNY under 288,800, different respectively paths. Under in SSP2 2100, andan increaseSSP4 scenarios, of 10.31–10.36 the peak times value compared will be CNY with 2010.289,400 The and growth CNY trend288,800, is obviousrespectively under in SSP12100, andan increaseSSP5 scenarios, of 10.31–10.36 but the times growth compared rate of SSP1 withscenario 2010. The slows growth down trend from is obvious 2050 to 2080, under while SSP1 the and growth SSP5 scenarios,rate of the but SSP5 the scenario growth rate keeps of aSSP1 fast growthscenario rate. slows In down 2100, from the per 2050 capita to 2080, GDP while will bethe CNY growth 672,200, rate ofwhich the SSP5 is 24.07 scenario times keeps that ofa fast 2010. growth Although rate. In the 2100, per the capita per GDPcapita under GDP thewill SSP3be CNY scenario 672,200, shows which an isupward 24.07 times trend, that the of growth 2010. Although rate will slow the per down capi afterta GDP 2040, under and the the per SSP3 capita scenario GDP shows increasing an upward slightly, trend,almost the unchanged. growth rate The will per slow capita down GDP after will 2040, be CNY and the 144,100 per capita in 2100, GDP an increasing increase of slightly, 5.16 times almost over unchanged.2010. In conclusion, The per capita the di ffGDPerence will in be per CNY capita 144,100 GDP underin 2100, di anfferent increase SSP scenariosof 5.16 times before over 2030 2010. is notIn conclusion,significant, andthe difference the difference in per gradually capita appearsGDP un afterder 2050.different The SSP diff erencescenarios between before SSP2 2030 and is SSP4not significant, and the difference gradually appears after 2050. The difference between SSP2 and SSP4 scenarios is not significant. By 2100, the difference between the largest SSP5 scenario and the smallest SSP3 scenario will be CNY 528,100 or more (Figure 9a). According to the per capita GDP growth rate (Figure 9b), under the SSP1-SSP5 scenarios, the average annual growth rate of per capita GDP was about 9% from 2011 to 2018 in the Yangtze River basin. The per capita GDP will still maintain a growth rate of around 6–9% before 2020 under the Sustainability 2020, 12, x FOR PEER REVIEW 15 of 21

SSP1–5 scenarios. Under the SSP1 scenario, the average annual growth rate of per GDP capita will slow down to about 5.1% in the 2020s, and it will remain at about 3.0% in the 2030s, and the annual growth rate will be less than 1% in the 2090s. Under the SSP2 scenario, the average annual growth rate of per GDP capita will be about 4.88% in the 2020s, and will maintain at about 2.18% from 2030 to 2049, and slowdown from 2050 to the end of the 21st century, with an average annual growth rate of about 1.35%. Under the SSP3 scenario, the average annual growth rate of per GDP capita will about 4.22% in the 2020s, will maintain at about 1.79% in the 2030s, and will be relatively slow after 2040. UnderSustainability the SSP42020, 12scenario,, 4202 the average annual growth rate of per GDP capita will about 5.14% in15 the of 21 2020s, and it will begin to slow down after 2050, which is basically consistent with the SSP2 scenario. Under the SSP5 scenario, the annual growth rate is still around 6.7% before 2025, around 4.94% before scenarios is not significant. By 2100, the difference between the largest SSP5 scenario and the smallest 2050, and around 2.56% from 2050 to the end of the 21st century. SSP3 scenario will be CNY 528,100 or more (Figure9a).

70 20 (a) Per capita GDP (b) Growth rate 60 SSP1 SSP2 SSP3 15 SSP1 SSP2 SSP3 50 SSP4 SSP5 SSP4 SSP5 40 10 30

Per capita GDP 20 5 Growth rate (%) (ten thousand (ten thousand RMB) 10

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 Year Year

Figure 9. Changes in per capita GDP and growth rate of Yangtze River from 2010 to 2100 ((a), per capita Figure 9. Changes in per capita GDP and growth rate of Yangtze River from 2010 to 2100 ((a), per GDP; (b), growth rate). capita GDP; (b), growth rate). According to the per capita GDP growth rate (Figure9b), under the SSP1-SSP5 scenarios, theThe average per annualcapita GDP growth of a rate region of per can capita reflect GDP the waseconomic about 9%development from 2011 level to 2018 in ina region. the Yangtze The followingRiver basin. figure The shows per capita the spatial GDP willdistribution still maintain of per a capita growth GDP rate in of the around Yangtze 6–9% River before basin 2020 in under2010 (Figurethe SSP1–5 10a) scenarios.and 2021–2050 Under (Figure the SSP1 10d–f) scenario, compared the average to changes annual in growthper capita rate GDP of per in GDP 2010. capita In 2010, will theslow per down capita to GDP about of 5.1% the inuppe ther, 2020s, middle and and it willlower remain reaches at about of the 3.0% Yangtze in the River 2030s, basin and was the annual CNY 21,300,growth CNY rate 20,700, will be and less CNY than 1%30,600, in the respectively. 2090s. Under Wi theth the SSP2 economic scenario, growth the average and population annual growth decline, rate byof the per 2050s, GDP the capita average will beper about capita 4.88% GDP inof the u 2020s,pper, middle and will and maintain lower reaches at about of 2.18% the Yangtze from 2030 River to basin2049, will and increase slowdown from from 2010. 2050 to the end of the 21st century, with an average annual growth rate of aboutUnder 1.35%. different Under scenarios the SSP3 scenario,(Figure 10d–f), the average there ar annuale differences growth in rate the of per per capita GDP capitaGDP increasing will about over4.22% the in Yangtze the 2020s, River will basin maintain from 2021 at about to 2050. 1.79% With in SSP5 the 2030s, and SSP3 and scenarios, will be relatively the fastest slow and after slowest 2040. economicUnder the development, SSP4 scenario, the the differe averagence annualis obvious. growth The rate per of capita per GDP GDP capita of the will middle about 5.14%and upper in the reaches2020s, andof the it willYangtze begin River to slow will down increase after by 2050, about which CNY is 40,000 basically to CNY consistent 70,000 with compared the SSP2 with scenario. 2010, whileUnder that the in SSP5 the lower scenario, reaches the annual will increase growth by rate about is still CNY around 80,000–110,000. 6.7% before The 2025, per around capita 4.94%GDP of before the upper,2050, andmiddle around and 2.56%lower fromreaches 2050 of tothe the SSP5 end scenario of the 21st is CNY century. 89,800, CNY 90,800 and CNY 139,200, an increaseThe per of CNY capita 68,500, GDP CNY of a70,100 region and can CNY reflect 108,600 the more economic than 2010, development respectively. level The in per a capita region. GDPThe followingof SSP3 scenario figure showsis the lowest, the spatial which distribution is CNY 69,400, of per capitaCNY 63,900 GDP in and the CNY Yangtze 108,300, River respectively, basin in 2010 and(Figure an increase 10a) and of 2021–2050 CNY 48,100, (Figure CNY 10 43,200d–f) compared and CNY to77,700 changes from in 2010. per capitaThe per GDP capita in 2010. GDP Inof 2010,the Yangtzethe per capitaRiver basin GDP ofshows the upper, a growth middle state. and As lower for the reaches per capita of the GDP Yangtze of main River cities basin such was as CNY Shanghai, 21,300, Suzhou,CNY 20,700, Yichang and and CNY Haixi 30,600, Mongolian respectively. and With Tibetan the economicAutonomous growth Prefecture and population in Qinghai decline, Province, by the these2050s, cities the averagewill increase per capita by about GDP CNY of the 300,000–500,000. upper, middle Under and lower the SSP3 reaches scenario, of the the Yangtze per capita River GDP basin ofwill most increase areas in from the 2010.Yangtze River basin will increase by CNY 30,000 to 80,000, and it will increase by about UnderCNY 50,000 different to 100,000 scenarios under (Figure other 10 scenarios.d–f), there are differences in the per capita GDP increasing overUnder the Yangtze different River scenarios, basin from the Haixi 2021 to Mongolian 2050. With and SSP5 Tibetan and SSP3 Autonomous scenarios, Prefecture the fastest andin Qinghai slowest Provinceeconomic has development, the largest increase the difference in per is capita obvious. GDP The in the per western capita GDP region of the of middlethe Yangtze and upper River reachesbasin, andof the its Yangtzeper capita River GDP will growth increase is similar by about to that CNY in 40,000 the eastern to CNY region 70,000 of compared the Yangtze with River 2010, basin. while This that in the lower reaches will increase by about CNY 80,000–110,000. The per capita GDP of the upper, middle and lower reaches of the SSP5 scenario is CNY 89,800, CNY 90,800 and CNY 139,200, an increase of CNY 68,500, CNY 70,100 and CNY 108,600 more than 2010, respectively. The per capita GDP of SSP3 scenario is the lowest, which is CNY 69,400, CNY 63,900 and CNY 108,300, respectively, and an increase of CNY 48,100, CNY 43,200 and CNY 77,700 from 2010. The per capita GDP of the Yangtze River basin shows a growth state. As for the per capita GDP of main cities such as Shanghai, Suzhou, Yichang and Haixi Mongolian and Tibetan Autonomous Prefecture in Qinghai Province, these cities Sustainability 2020, 12, x FOR PEER REVIEW 16 of 21 Sustainability 2020, 12, 4202 16 of 21 may be the reason that the per capita GDP growth on the Yangtze River is slightly higher than that on the Yangtze River. This region is the source of the Yangtze River. It is rich in mineral resources andwill secondary increase by industry about CNY occupies 300,000–500,000. a relatively Underlarge proportion. the SSP3 scenario, In addition, the per natural capita GDPand tourism of most resourcesareas in the are Yangtze abundant. River The basin tertiary will industry increase is by de CNYveloping 30,000 rapidly, to 80,000, the andecon itomy will is increase developing by about fast, andCNY the 50,000 per capita to 100,000 GDP under is high. other scenarios.

(a) 2010 35°N 35°N (b) SSP1, 2021-2050 average minus 2010

30°N 30°N

25°N 25°N Per capita GDP (Ten thousand RMB) Per capita GDP (Ten thousand RMB)

1 1.5 2 2.5 3 4 6 8 15 25 1 3 5 8 10 20 30 40 50 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E

35°N (c) SSP2, 2021-2050 average minus 2010 35°N (d) SSP3, 2021-2050 average minus 2010

30°N 30°N

25°N 25°N Per capita GDP (Ten thousand RMB) Per capita GDP (Ten thousand RMB) 1 3 5 8 10 20 30 40 50 1 3 5 8 10 20 30 40 50 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E

35°N (e) SSP4, 2021-2050 average minus 2010 35°N (f) SSP5, 2021-2050 average minus 2010

30°N 30°N

25°N 25°N Per capita GDP (Ten thousand RMB) Per capita GDP (Ten thousand RMB) 1 3 5 8 10 20 30 40 50 1 3 5 8 10 20 30 40 50 90°E 100°E 110°E 120°E 90°E 100°E 110°E 120°E

FigureFigure 10. 10. GrowthGrowth of per capitacapita GDPGDP inin Yangtze Yangtze River River basin basin from from 2021 2021 to to 2050 2050 over over 2010 2010 ((a), (( 2010;a), 2010;b–f , b2021–2050–f, 2021–2050 average average minus minus 2010). 2010).

4. DiscussionUnder di fferent scenarios, the Haixi Mongolian and Tibetan Autonomous Prefecture in Qinghai Province has the largest increase in per capita GDP in the western region of the Yangtze River basin, This paper considers the impact on the population and economy after the implementation of the and its per capita GDP growth is similar to that in the eastern region of the Yangtze River basin. two-child policy in China, and the data of the sixth census in 2010. It predicts the population and This may be the reason that the per capita GDP growth on the Yangtze River is slightly higher than that economic growth in the Yangtze River basin under the shared socioeconomic pathways. on the Yangtze River. This region is the source of the Yangtze River. It is rich in mineral resources and The family planning policy can be said to be one of the most important social experiments in secondary industry occupies a relatively large proportion. In addition, natural and tourism resources China in the 20th century. China began to implement the family planning policy in 1978. The fertility are abundant. The tertiary industry is developing rapidly, the economy is developing fast, and the per rate has fallen sharply. The population structure has changed over the decades, and a low population capita GDP is high. growth rate has gradually appeared. Therefore, China’s population is aging very fast and on a large scale4. Discussion [45,46]. After the implementation of the comprehensive two-child policy in 2016, China’s population has This paper considers the impact on the population and economy after the implementation of the increased in a short period. To a certain extent, it has solved the problems of China’s current two-child policy in China, and the data of the sixth census in 2010. It predicts the population and population aging, gender imbalance and labor decline. After the comprehensive implementation of economic growth in the Yangtze River basin under the shared socioeconomic pathways. the two-child policy, it will have a series of impacts on population size, population structure, The family planning policy can be said to be one of the most important social experiments in economic development and environment [47,48]. China in the 20th century. China began to implement the family planning policy in 1978. The fertility The reduction in the working-age population is one of the key factors affecting the economy. rate has fallen sharply. The population structure has changed over the decades, and a low population The increase in the number of people caused by changes in fertility policies can alleviate the shortage growth rate has gradually appeared. Therefore, China’s population is aging very fast and on a large of old motivation and the problem of aging to a certain extent. Although fertility control has been scale [45,46]. fully relaxed, the actual fertility level is not high because it is also affected by other factors, such as Sustainability 2020, 12, 4202 17 of 21

After the implementation of the comprehensive two-child policy in 2016, China’s population has increased in a short period. To a certain extent, it has solved the problems of China’s current population aging, gender imbalance and labor decline. After the comprehensive implementation of the two-child policy, it will have a series of impacts on population size, population structure, economic development and environment [47,48]. The reduction in the working-age population is one of the key factors affecting the economy. The increase in the number of people caused by changes in fertility policies can alleviate the shortage of old motivation and the problem of aging to a certain extent. Although fertility control has been fully relaxed, the actual fertility level is not high because it is also affected by other factors, such as the occupation of the mother, the pressure of raising children, and the lifestyle of the parents [49,50]. The implementation of the two-child policy can alleviate the shortage of old motivation and the problem of aging to a certain extent. Niklas et al. [51] created country-level scenario data consistent with RCPs (urban land use) and SSPs (population) for Africa’s 2000–2100 population projections. The Yangtze River Economic Belt covers half of China, showing obvious characteristics of industrialization, but also showing typical gradient differences and unbalanced development in the east, central and western regions [30]. The Yangtze River Economic Belt emphasizes the path of green development and ecological priority. President Xi Jin-ping held a symposium in Chongqing, emphasizing the need to place the restoration of the Yangtze River ecological environment in a prominent position, focusing on protection rather than large-scale development. In the process of planning and construction, its important ecological status has been constantly emphasized since 5 January 2016. At present, the environmental bearing capacity is close to the limit, the Yangtze River basin and even the whole country are under tremendous pressure. Some researchers have compared the distribution of major lakes in the middle reaches of the Yangtze River 50 years ago by using land use data and remote sensing data. It was found that the diversity of aquatic organisms has decreased significantly. The main causes of these are habitat degradation, large-scale water conservancy projects and construction of protected areas, etc. [52]. With the economic development and the expansion of the city scale, the demand for water resources is gradually increasing. As a continuous external pressure, the population plays an important role in the change of water resource carrying capacity. GDP is a dynamic factor in the economic system. The impact of changes in water resources carrying capacity is clear. Water resources are relatively stable in the short term relative to population changes. Therefore, population development and rapid economic growth will affect the population carrying capacity of water resources in the short term [53]. It can be seen that the environmental and ecological conditions of the Yangtze River basin have been seriously affected by previous human activities, and the climate change within the basin has affected the hydrological conditions of the Yangtze River mainstream [54]. In this case, the concept of green development is needed, which is the objective judgment for national economic development. Contrary to population growth, after the two-child policy is fully opened, in addition to SSP1 and SSP4, other paths and GDP are expected to grow. GDP is growing fast—almost in a straight line growth trend, especially in the SSP5 scenario. The SSP3 scenario is increased, no matter the relatively slow growth rate and total amount, and the SSP3 scenario shows greater GDP than SSP4 scenario in the 2090s. The change in population policy has a great impact on the size of the working-age and elderly populations, which further affects the economic development of the region. As part of China’s new round of reform and opening up, the Yangtze River Economic Belt has implemented a new regional opening-up and development strategy, and is also a leading demonstration zone for ecological civilization construction. The Yangtze River Economic Belt should take the lead and take Shanghai as the leader to drive the overall development of the whole basin and realize the green development strategy. However, the green development is still at the theoretical level, and there is still a long way to go in practice [55]. The long-term population and economic forecast in the Yangtze River basin based on the current situation of population and economy, and the understanding of the population and economy of the Yangtze River basin in the 21st century, can provide a scientific basis Sustainability 2020, 12, 4202 18 of 21 for the adjustment of urban structure and industry in the Yangtze River basin in the future, as well as the better construction of the green development for the Yangtze River Economic Belt. Based on the different paths of SSP, this paper analyzed the characteristics of future population and economic development in the Yangtze River basin. Population and economic development are relatively complicated processes. There is great uncertainty in forecasting the regional population and economic levels of the whole century. Although the implementation of China’s current new population policy and the impact of changes in population policy on changes in the number of working people on economic development have been fully taken into account in the forecast process, major external impacts may occur in the future. There is some uncertainty in the population forecast. The population base uses census data, and there are a certain number of underreports and restatements, which will affect the population forecast. At the same time, there is some uncertainty in the parameter setting during the prediction process. Higher education and medical standards, as well as good living habits, will reduce mortality. A key factor in accurately predicting the future population is fertility. However, due to uncertain factors such as education and urbanization, the total fertility rate is often overestimated, which will affect the accuracy of population forecasts [24]. Urbanization affects population growth by reducing fertility in urban areas [44]. Therefore, it is impossible to predict the impact of larger scale changes. Sudden issues such as wars, natural disasters, the discovery of new resources, and the inability of some rapidly developing provinces and cities to predict population competition may affect long-term forecasts and even permanently affect regional population and economic development. At present, in the past century, there are still uncontrollable factors in China’s political development, technological progress and implementation of low-carbon transition. In addition, economic forecasts also ignore terms of trade, purchasing power parity changes, and environmental impacts on the economy. For sudden major diseases, such as the economic loss caused by the COVID-19 in early 2020, it is unavoidable and unpredictable. Therefore, the forecast of population and economy of the Yangtze River basin in this paper is still uncertain and needs further research and improvement. Different national security programmes face different challenges, and the links between economic activity and these challenges are complex [16]. Therefore, it is necessary to carry out regional population and economic future forecast research in the world.

5. Conclusions Based on the shared socioeconomic pathways, this paper forecasts the population and economy in the Yangtze River basin from 2010 to 2100. The main conclusions drawn from our paper were summarized as follows: (1) The population of the Yangtze River basin shows a negative growth during the period of 2021–2050. It is mainly concentrated in Sichuan Province, Chongqing City, Hubei Province, Hunan Province and Anhui Province. The Eastern Sichuan Province will see the most significant population decrease, and Shanghai the most significant population increase. The GDP will continue to increase in most of the Yangtze River basin, and it will increase by CNY 30 billion compared to 2010 in most regions. (2) During the period of 2010–2100, the population shows a declining trend except for the SSP3 scenario in the Yangtze River basin. The peak population of each path will be 452 million (2022), 460 million (2026), 559 million (2100), 451 million (2021), and 451 million (2020). By the end of the 21st century, the GDP of different paths in the Yangtze River basin will be 7.33, 8.94, 6.55, 5.98, and 12.21 times in 2010, respectively. (3) As a whole, the population of the three major urban agglomerations will decrease in the 21st century. The negative population growth rate for the CYUA and MYRUA will appear in 2020, while it will occur in the 2030s–2040s for the YRDUA. However, the GDP growth rate shows a downward trend and the decline rate will gradually slow down after 2050. (4) The per capita GDP growth rate shows a downward trend after 2020. The growth rate of per capita GDP will be about 1% under the SSP2, SSP3 and SSP4 scenarios by 2100, and it will be that Sustainability 2020, 12, 4202 19 of 21

of about 0.55% and 2.32% under the SSP1 and SSP5 scenarios, respectively. The per capita GDP in the upper-middle reaches of the Yangtze River basin will increase by about CNY 40,000 to 70,000 compared with 2010, while the per capita GDP in the lower reaches will increase by CNY 80,000 to 110,000 during the period of 2021–2050 under different scenarios.

This research provides a reference for the study of future population and socioeconomic changes and climate predictions. It will provide a scientific basis for the adjustment of urban structure and industry in the Yangtze River basin, the better construction of the Yangtze River Economic Belt, and the promotion of green development of the Yangtze River. Meanwhile, it might have some implications for future regional or global population and economic development projections for other areas in the world.

Author Contributions: Z.Z. provided ideas for the paper, T.J. provided data and methods, M.Z., B.Z., F.Z. and R.K. made model predictions and analyzed the data, M.Z. wrote articles, Z.Z. and J.T. revised the language. All authors have read and agreed to the published version of the manuscript. Funding: This paper is financially supported by National Key Research and Development Project of China (Grant no. 2019YFC0409004) and National Natural Science Foundation of China (Grant no. 41971025) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Acknowledgments: We would like to thank Yanjun Wang of Nanjing University of Information Sciences & Technology for providing the calculation method of PDE model and Cobb–Douglas model. We want to thank the editor and reviewers for their valuable advices and suggestions that helped us improve this manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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