Impacts of Population Multifactor Changes on Economic Development: The Case of China's Liaoning Coastal Economic Zone

Impacts of Population Multifactor Changes on Economic Development: The Case of China's Liaoning Coastal Economic Zone | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impacts of Population Multifactor Changes on Economic Development: The Case of China's Liaoning Coastal Economic Zone DongXia Zhao, GuangYuan Bai, AXing Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9056444/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The driving effects of population factors on economic development are more from the combined forces of multiple population factors than from individual factors. This paper examines the impacts of the changes in population factors from a multi-factor synergy perspective using the Liaoning Coastal Economic Belt of China as the study area over the period of 2007 through 2017. The factors used include population quantity, structure, quality, and migration. The Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) were combined to explore this multi-factor synergy. The results showed that: 1) Population factors drove economic development not through the independent action of single factors, but through core-factor synergy and auxiliary-factor complementation. The most effective driving path consisted of dual-core drivers: change in education level and change in age structure, combined with population quantity change complemented by migration. 2) The importance of population factors was clearly ranked. Change in education level was the primary dominant factor, followed by change in age structure. The driving effect of population migration was stronger than that of natural population change. 3) From the temporal dimension, the population driving effect went through three stages: a golden period dominated by population quantity, a transition period with weakened quantity-driven effects, and a quality-driven period centered on population mobility and quality. 4) From the spatial dimension, the impact of the same population factor combination showed significant regional differences. It formed a multi-level intensity gradient of "high-medium-low" relating to the spatial differentiation of combination effects due to changes of these multi population factors over space. Through this multi-factor analysis approach, this paper shows the inner working of multi-factor driving mechanism of population factors on economic development. The findings on the inner working of the changes in population factors on economic development provide support for economic management through population policy during the era of rapid changes in population. Social science/Development studies Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Figures Figure 1 Figure 2 1. Introduction Since the beginning of the twenty-first century, all countries in the world have experienced dramatic demographic changes, such as a continuous decline in fertility, unusually active population mobility and migration, and a deepening degree of ageing, which are not only an important feature of the demographic development of the global society in the era of the second demographic transition but also an inevitable result of economic globalisation in an open environment. Numerous studies have shown that population change is no longer the change of a single element, but the interaction of multiple elements to change together (Keyfitz & Caswell,2005; Esin Ickin et al.,2025). For example, in addition to institutional and policy influences, the decline in fertility has been accompanied by an increase in the level of education, which has contributed to a delay in the age of first marriage, and the reconfiguration of population distribution due to mobile migration (United Nations,2024;Li et al.,2024;Luo et al.,2025). The aging of the population is not only due to longer life expectancy, but more importantly to the fact that fewer newborns and the emigration of a young workforce have changed the local demographics (Chen,2020;Ao and Chang,2020). The movement of population (talent) affects both the population quantity and quality of the places of emigration and immigration, and may exacerbate the imbalance of global population aging. The result is that such multifactorial changes in population will impact economic development differently, compared to a single factor. Therefore, as the trend of population change continues to increase, active research into the changes in multiple elements of the population changes and the various dynamics of the impact on the economy will not only help to understand the current status and problems of population development from a holistic perspective, but also provide a scientific basis for decision-making to optimise regional population policies, and enhance the vitality of the local economy. At present, research on impacts of population change on regional economy mainly focuses on four aspects from the perspective of single factors. The first focuses on the perspective of natural population change, specifically delving into the influence of fertility variations on regional economic development (Guo and Tian,2019; Bruno Arpino et al.,2015). The declining fertility rate that induced trends of fewer children and an aging population have reduced the size of the working-age population in regions, directly leading to a shortage of labor supply. This has increased labor costs for enterprises and weakened the vitality of economic development. Meanwhile, the increase in the elderly dependency ratio has compelled regions to allocate more fiscal resources to social security fields such as pension systems and healthcare (Bloom et al.,1998;Wang,2018). This reallocation displaces productive investments in education and technological R&D, thereby constraining the long-term economic growth potential (Nargund,2009; Acemoglu and Restrepo,2017). Consequently, persistent low fertility rates may form a negative feedback loop characterized by labor shortage-intensified aging-economic pressure-decreased willingness to bear children-further labor shortage-increased elderly care burden (Lutz et al.,2008). The line of argument posits that low fertility rates fundamentally disrupt the momentum of natural population growth, thereby constraining economic expansion. However, the positive regulatory impacts of non-natural factors—such as population mobility and migration—on economic development was not taken into account. The second dimension pertains to the perspective of mechanical population change, specifically examining the impact of population mobility on regional economic development. Drawing on data from China's Seventh National Population Census (National Bureau of Statistics of China,2021), scholars have documented a striking increase in the floating population, rising from 221.43 million in 2010 to 375.82 million in 2020, with a compound annual growth rate of 5.43% (Ma,2022). Empirical analysis reveals a nonlinear relationship: when a region's net inflow rate exceeds 3%, a 1-percentage-point increase in population mobility correlates with a 0.8-percentage-point excess GDP growth. Conversely, when net outflow rates surpass 2%, each additional percentage point of population outflow is associated with a 0.6-percentage-point decline in economic growth (Glaeser and Gottlieb,2009;Liu,2018). This dynamic underscores that while migration may temporarily alleviate localized labor shortages, it perpetuates a reciprocal growth-decline dynamic across regions (Sun et al.,2023). For instance, population agglomeration in China's eastern urban clusters exhibits a "siphon effect," whereas outflows from western and northern provinces trigger a "shrinkage effect" (Glaeser,2011;Chen et al.,2019;Gu and Shen, 2020). These perspectives primarily focus on the impact of population mobility on regional economic disparities while the transformation of local demographic structures following migration, which exacerbates aging and sub-replacement fertility issues and thereby influences economic development, was not included. The third analytical lens focuses on the dimension of demographic structural elements, specifically examining how changes in the degree of aging influence regional economic development. The elderly population represents a demographic segment that has withdrawn from occupational productive activities and predominantly assumes the role of "consumer-only" agents within the economic system (Longhi et al.,2008; Maestas et al.,2023). As the proportion of the elderly population within the total population increases, it implies a higher share of output being allocated to this demographic. Consequently, the proportion of economic output available for productive investment decreases relatively, and the labor supply also declines (Maestas et al.,2023;Zorlu and van Gent,2024). Consequently, this situation impedes capital accumulation, diminishes the potential output level of the economy, intensifies the pressure on social security and public services, and ultimately constrains the high-quality and sustainable development of the region. This perspective posits that aging and a low fertility rate are bound to impede economic development (Maity and Sinha,2020; Zhang et al.,2023). Evidently, it overlooks the fact that high-quality, highly skilled talent, whose influence is not constrained by the age structure, has emerged as a pivotal force driving sustained economic growth. Such talent, regardless of age, possesses the capabilities to foster innovation, enhance productivity, and optimize resource allocation. Fourthly, from the perspective of changes in population quality, it refers to the impact of the educational attainment level of the population on the regional economic development. Education serves as the primary means of improving the quality of the labor force (Hanusek and Woessmann,2020). A sound educational level is conducive to alleviating the decline in the absolute quantity of labor factor inputs caused by population aging, and promoting the enhancement of labor productivity and the labor participation rate. Specifically, education equips workers with advanced knowledge and skills, enabling them to adapt to technological advancements and changes in the labor market (Becker G,1964; Psacharopoulos and Patrinos, 2018 ). This not only helps to offset the negative impact of a shrinking workforce due to an aging population but also stimulates the improvement of labor efficiency and the expansion of the labor participation scale, thereby contributing to the stable operation and sustainable development of the economy (Bloom et al.,2010;Hanusek and Woessmann,2020;Wang,W., et al.,2023). Therefore, during the period of high-quality development, the transition from the "demographic dividend" to the "talent dividend" serves as an endogenous driving force for the sustainable development of the regional economy. Specifically, this transformation is crucial as it shifts the focus from simply relying on the large quantity of the labor force to fully leveraging the high quality and diverse capabilities of talents. This perspective posits that changes in the quality of the population constitute the core driving force stimulating economic development. Above all, it emphasizes that the enhancement of the overall quality of the population, encompassing aspects such as education, skills, and health, plays a pivotal role in fostering technological innovation, improving production efficiency, and promoting the optimization of economic structures. In summary, most of the existing research findings consider that the impact of population change on the economy is dominated by a single factor. For instance, the population elements that drive economic development are mainly the quantity of the labor force, or the increment of the migrant population from outside, or the relatively low level of population aging within the population structure, or the presence of a high-quality population. However, in reality, population change is a dynamic process involving the interaction of multiple elements, that is the interplay of various factors such as population quantity, structure, quality, and mobility. Therefore, when exploring the impact of population change on economic development, it is necessary to shift to a perspective driven by multiple elements, particularly the interplay of these factors. This paper examines the impacts on population change on the economic development from this perspective. 2. Study design and methods 2.1. Study area and data 2.1.1 Study area The study area is the Liaoning Coastal Economic Belt, in the Northeastern part of China (Fig. 1 ). The population dynamics in this region are characterized by the coexistence of low fertility, high-level aging, persistent population out-migration, and regional development differentiation. For instance, by the end of 2023, the average aging rate in this region had reached 21.1%, significantly exceeding the national average of 15.4%. Meanwhile, traditional industries have been shrinking (with electricity consumption in heavy industry dropping by 0.88%), emerging industries have been disconnected (with strategic emerging industries accounting for 16.1% of the total), and investment confidence has been insufficient (with the growth rate of foreign capital being lower than the national average). Therefore, Its economic development is facing dual structural constraints of "aging before affluence" and "declining fertility before prosperity", forming a typical feedback mechanism of "low fertility – labor outflow – industrial ecosystem recession". From what is described above, the study area is a good representation of nearly 24 developing nations that have entered phases of ultra-low fertility and hyper-aging, particularly in the inherent contradictions between population dynamics and economic development. With a per capita GDP of $ 13,000 (data from 2023) and a 65 + age cohort constituting 18.7% of the total population, it mirrors the predicament of countries like Greece (per capita GDP of $ 21,000; aging rate of 22.8%) and Bulgaria ( $ 11,000; 21.3%). These economies, typically in the mid-industrialization stage, confront a precipitous decline in labor supply. In the Northeast Coastal Economic Belt, the working-age population (aged 15–64) has contracted at an annual rate of 2.3% over the past decade, aligning closely with Serbia's 2.1% shrinkage during the same period. Additionally, traditional heavy industries account for 41.8% of its industrial structure, echoing Bulgaria's reliance on manufacturing. This international comparability renders the region an ideal case study for examining the ramifications of demographic dividend exhaustion on middle-income economies. 2.1.2 Variables Population change stems from changes in four elements: quantity, mobility, structure, and quality. In this study, natural change is used to represent changes in population quantity, migration change in population mobility change, age change in population structure change, and change in education level representing population quality change. These four factors were used as the independent (explanatory) variable and the per capita Gross Domestic Product (GDP) was selected as the dependent variable characterizing the level of economic development. Specifically, first, we use the natural population growth rate to characterize natural changes, and judge whether the study area is in a stage of population growth, stability, or decline based on the quantitative difference between the number of births and deaths. Second, we use the net population migration rate to characterize population mobility and observe the population distribution status through population inflows and outflows. Third, we use the old-age dependency ratio to characterize changes in the population age structure, which reflects the dependency burden of the working-age population on the elderly population and indicates the degree of population aging in the region. Fourth, we use the proportion of the population with or above upper secondary education in the total population to characterize changes in the population's education level, which reflects the overall quality of the population. In addition, although factors such as industrial structure, residents' living standards, public financial expenditure, and technological innovation do not directly fall under the category of "population change factors", they can regulate the intensity and direction of the impact of population factors on the economy. As indispensable "environmental variables" in the multi-factor-driven framework, they need to be incorporated into the analysis to account for interference, and thus we take these factors as control variables. Information on these variables is shown in Table 1 . Table 1 Descriptive statistics of variables Type of variable Name of variable Explanation Notation Mean Min Max Dependent variable Level of economic development GDP per capita (natural logarithm) REO 10.76 9.62 11.78 Explanatory variables Natural change of population Natural rate of population growth(‰) NVP -0.30 -16.50 5.07 Migration change of population Net migration rate(‰) MCP -0.66 -20.44 9.05 Population age structure changes Age dependency ratio(%) ADC 53.41 41.01 71.09 Population education level changes The proportion of students with a high school education or above in the total population(%) PEC 4.14 2.52 7.67 Control variables Industrial structure The proportion of the sum of the gross domestic product of primary and tertiary industries in the gross regional product(%) IS 53.7 27.5 75.72 Residents' living standard Per capita disposable income (natural logarithm) RLS 9.83 8.95 10.52 Public finance expenditure General public budget expenditures of local governments (natural logarithm) PFE 5.36 3.87 6.99 Technological innovation Total number of invention patents granted by region (natural logarithm) TI 4.68 2.30 8.33 The data for the explanatory (independent) variables and the dependent variable are obtained from the Statistical Yearbook of Liaoning Province, China City Statistical Yearbook ( https://www.yearbookchina.co- -m), as well as the statistical yearbooks and statistical bulletins on national economic and social development of all prefecture-level cities within the region, covering the period from 2007 to 2022 (National Bureau of Statistics of China, 2008–2023; 2007–2022). To ensure data accuracy, we cleaned the raw data by removing missing values and duplicate records, and identifying and correcting outliers (Wooldridge,2013). The gross regional product, natural population growth rate and the number of students enrolled in high school and above are all original data available in statistical yearbooks. The net population migration rate was calculated as the difference between the population immigration rate and emigration rate published in the statistical yearbooks. The old-age dependency ratio is measured as the proportion of the population aged 65 and above to the working-age population aged 15–64, using data from the statistical yearbooks. All the above are standard methods in demographic research (Preston et al., 2001 ;Ye et al.,2021;United Nations Population Division, 2022 ). In addition, to ensure data consistency and accuracy, we also cross-referenced and rechecked the data of each indicator with the relevant data published by the National Bureau of Statistics of China ( https://www.stats.gov.cn ). Among the control variables, the data on patent grants are obtained from the official website of the China National Intellectual Property Administration ( https://www.cnipa.gov.cn ). The data for indicators such as the gross domestic product (GDP) of the primary and tertiary industries, the per capita disposable income of urban and rural residents, and the general public budget expenditure of local finance all come from the statistical yearbooks and statistical bulletins from which the population data were obtained. This is a commonly adopted methodology in studies related to China's urban and economic development (Liu et al., 2024 ). 2.2. Methodologies To reveal the combined impact of demographic changes on economic development, we have selected the Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) methods for data analysis. From a qualitative comparative perspective, fsQCA identifies the combinations of demographic change factors that exert a significant driving effect on economic development. This serves as a basis for the subsequent factor selection in GTWR. GTWR, in turn, further quantifies the differences in the impact intensity of these variables across different spaces and over different time periods, addressing the question of "when, where, and which configuration factors have a stronger impact". These combination of methods allows us to reveals configurational effects of multi-dimensional demographic factors and spatiotemporal hdynamics. 2.2.1 Fuzzy Set Qualitative Comparative Analysis Fuzzy Set Qualitative Comparative Analysis (fsQCA) quantitatively evaluates the strength of the association between conditional combinations and outcomes. It does this by calculating consistency and coverage. Consistency measures how well a conditional combination aligns with an outcome. Coverage measures the scope of outcomes explained by a specific conditional combination (Kraus et al.,2018). Specifically, consistency serves two purposes. First, it can measure how well a population factor combination aligns with "economic development". This helps answer the question: Can the combination stably promote economic development? Second, it allows comparison of consistency across different population factor combinations. This reveals which combinations have more stable cumulative effects. The coverage measures the scope of economic development explained by a population factor combination. It answers the question: how many regions with high economic development can the combination explain? In practical research, consistency and coverage need be used together to quantify the cumulative effects of different combinations. The formulas are as follows: $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Consistency({X}_{i}\le\:{Y}_{i})=\frac{\sum\:min({X}_{i}，{Y}_{i})}{\sum\:{X}_{i}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:（1）\:$$ $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:Coverage({X}_{i}\le\:{Y}_{i})=\frac{\sum\:min({X}_{i}，{Y}_{i})}{\sum\:{Y}_{i}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:（2）$$ Where: \(\:\:{X}_{i}\) is the membership degree in the combination; \(\:\:{Y}_{i}\) is the membership degree in the result. The consistency and coverage ranges are (0,1). In addition, with the use of three-valued anchor method (setting 95%, 50%, and 5% as full membership, cross point, and full non-membership, respectively), it allows for distinguishing between core conditional variables that play a key role and peripheral conditional variables that serve an auxiliary function. The core question, "whether the combination of multiple population factor changes exerts multi-dimensional impact effects on the economy to different degrees", lies in identifying key elements of multiple population factors, combinatorial paths, and impact effects of multiple population factors. FsQCA can effectively answer this question through the following analysis. First, it takes multiple population factors (such as old-age dependency ratio, population size, population mobility, etc.) as conditional variables. It takes economic development indicators as outcome variables.Then, it directly analyzes the impact of different combinations of population factors (not individual factors) on economic development. In this way, it can accurately capture the superimposition effect. Second, through configuration analysis, it reveals the interactive relationships between population factors. For instance, it clarifies whether it is the combination of "high old-age dependency ratio + high labor quality" or "population outflow + low fertility rate" that affects economic development, and identifies the mechanisms of synergy or inhibition between factors. Third, it screens effective paths based on consistency and coverage, and presents multiple equivalent paths through which "the combination of multiple population factors affects economic development" (e.g., Path 1: Population aging + high human capital→economic transformation; Path 2: Population agglomeration + low dependency ratio→economic growth), explaining the impact differences under different contexts. Fourth, by distinguishing between core conditional variables and peripheral conditional variables, it identifies the key elements that play a decisive role in economic development within the combination of population factors (e.g., "human capital level" may be a core condition) and those that only serve an auxiliary role (e.g., "population density" may be a peripheral condition), clarifying the focus of intervention. Finally, through the above analyses, it directly responds to the question "whether the combination of multiple population factor changes exerts multi-dimensional impact effects on the economy to different degrees". Specifically, the number of paths, the differences in consistency/coverage among various paths, and the variations in core elements prove that the impact of the superimposition of population factors has "degree differences" (reflected by coverage and consistency) and "dimension differences" (reflected by different paths and different core elements). 2.2.2 Geographically and Temporally Weighted Regression Geographically and Temporally Weighted Regression (GTWR) integrates spatial heterogeneity and temporal dynamics into a unified analytical framework. Specifically, by dynamically quantifying the impact intensity and interactive relationships of multi-dimensional demographic factors across different spatial and temporal dimensions, this model can decompose the combined effect of these factors on economic development. It thus provides core technical support for subsequent analyses (Fotheringham, 2015). In terms of the spatial dimension, GTWR assigns differentiated weights to different geographic units and dynamically calculates regression coefficients for each region. This allows to clearly identify which combinations of demographic factors have a stronger combined impact in which regions. In terms of the temporal dimension, by analyzing changes in regression coefficients over time series, GTWR can further assess the temporal dynamic characteristics of these superimposed impacts. Based on the above analytical logic of dual spatial and temporal dimensions, GTWR uses spatio-temporal dynamic coefficients to indirectly quantify the combined intensity of multi-factor interactions. Ultimately, it clarifies when, where, and which interactive combinations of demographic factors have the most significant effect on economic development, forming a causal inference chain (Fotheringham et al., 2017 ). The general formula of GTWR is given as follows: $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:{Y}_{i}={\beta\:}_{0}{(u}_{i}，{v}_{i}{，t}_{i})+{\sum\:}_{k=1}^{P}{\beta\:}_{k}{(u}_{i}，{v}_{i}{，t}_{i}){X}_{ik}+{\epsilon\:}_{i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:（3）$$ Where: \(\:{\text{Y}}_{\text{i}}\) is the observed value; \(\:{(\text{u}}_{\text{i}}，{\text{v}}_{\text{i}})\) is the latitude and longitude coordinates of the i-th sample point; \(\:{(\text{u}}_{\text{i}}，{\text{v}}_{\text{i}}，{\text{t}}_{\text{i}})\) is the spatio-temporal coordinate of the i-th observation point; \(\:{\:{\beta\:}}_{0}({\text{u}}_{\text{i}}，{\text{v}}_{\text{i}}，{\text{t}}_{\text{i}})\) represents the spatio-temporal intercept term of observation point i; \(\:{\:{\beta\:}}_{\text{k}}({\text{u}}_{\text{i}}，{\text{v}}_{\text{i}}，{\text{t}}_{\text{i}})\) represents the regression coefficient of the k th explanatory variable at observation point i; P is the total number of variables; \(\:{\:\text{X}}_{\text{i}\text{k}}\) is the value of the k th independent variable at the i-th observation point; \(\:{{\epsilon\:}}_{\text{i}}\) is the residual term corresponding to the sample points. 3. Results 3.1. Population Multifactor Changes on Economic Development The results on impacts of population multifactor changes on economic development are presented in two elements: the combined impacts and the action paths of population factors. From the perspective of combined impacts, the consistency coefficient was used to judge the stability of the overlapping effects produced by different population factor combinations and identify which combinations can continuously affect economic development. From the perspective of action paths, the coverage coefficient was used to explain the applicable scope of such stable combinations. Core condition variables and marginal condition variables were identified to examine the interactive relationships among multiple population factors and to reveal dynamic impact mechanism. 3.1.1 The combined effect of multiple population elements Table 2 presents the consistency results showing the effects of different combinations of the four population variables (factors), namely natural change, migration change, education change and age structure change. These population combination effects were obtained under the control of four external factors ( Industrial Structure, Residents' Living Standard, Public Finance Expenditure, and Technological Innovation ). The consistency values of all combintations range from 0.9842 to 0.7147. When the consistency value is higher than 0.7, the combination is considered to have a consistent impact (Ragin, 2008 ). Thus, the overall level is acceptably high in consistency. Among them, 72.7% of the commbinations have a consistency exceeding 0.8. This indicates that the combination of population factors in the study area has a consistent causal explanatory power for regional economic development. Clearly, when more population factors are included in a combination, the consistency value gets higher except Combination 2 which will be explained below. Table 2 Results of overlapping combinations of multiple population elements Serial No. Population Factor Included Control Variable Pri Consistency 1 *Natural Change, *Migration Change, *Age Structure Change, *Education Change Yes 0.9810 2 *Migration Change, *Age Structure Change, *Education Change Yes 0.9842 3 *Natural Change, *Age Structure Change, *Education Change Yes 0.9655 4 *Natural Change, *Migration Change, *Age Structure Change Yes 0.8648 5 *Natural Change, *Migration Change, *Education Change Yes 0.8456 6 *Age Structure Change, *Education Change Yes 0.8710 7 *Migration Change, *Age Structure Change Yes 0.8706 8 *Natural Change, *Education Change Yes 0.8489 9 *Migration Change, *Education Change Yes 0.8288 10 *Natural Change,*Age Structure Change Yes 0.7763 11 *Natural Change, *Migration Change Yes 0.7147 The different effects of different combinations of population factors are also apparent in Table 2 . Among the three-factor combinations, two combinations have high consistency values greater than 0.9, which suggests that these two combinations have the strongest stability of impact on economic development. They are Migration Change, Age Structure Change, Education Change (Combination 2, 0.9842, in fact it is the highest) and Natural Change, Age Structure Change, Education Change (Configuration 3, 0.9655). Their common characteristics are both configurations include Age Structure Change and Education Change , forming the "dual-core" combination of population factors driving economic development. The only difference is that one has Migration Change and the other contains Natural Change . In fact, these two are reflecting another important aspect of population changes on economic development, that is, population change. Together, these combinations manifest the three key elements of population effects on economic development, that is, population size, age structure, and education level. The fact that Combination 2 is the highest in consistency value, even higher than the four factor combination, further attests that the persistent role of these three aspects. From the two-factor combination perspective, the overall consistency values of the two-factor combinations are understandably lower than those of the three-factor core combinations. However, analysis of the two factor combinations shows thar four combinations have consistency values above 0.8. They are Age Structure Change , Education Change (Combination 6), Migration Change , Age Structure Change (Combination 7), Natural Change, Education Change (Combination 8), and Migration Change , Education Change (Combination 9). A common feature of these combinations is that each includes two of the three key aspects stated above. It also shows that Education Change is the predominant factor and that any combination involving education resulted in higher consistency values (Combinations 6, 8 and 9). Age Structure Change is an important factor after Education Change , then followed by Migration Change with Natural Change being the least important in the combinations. This order of importance in combinations is easily understood. It is well known that education level is one of the most important factors in raising the quality of labor force, in turn, playing key role in economic development (Petrakis and Stamatakis,2002;Qin and Xu,2024). Age structure is another aspect reflecting the quality of labor force. Among the two factors relating to population size change, it is easy to understand that natural change is a less important, compared to migration, due to the impact of the not long ago one-child policy which significantly limited the natural growth of population in China. In addition, migration not only increases the population size, but also brings quality of labor, thus, being more important in population size change than the natural growth. This order of importance also helps to explain that Combination 10 has a lower consistency value compared to the other combinations covering two of the three key aspects, because it combines a weaker population size element, Natural Change , with Age Structure Change , a second important population factor. From the perspective of the overall stability of population factors' impact on economic development, age structure, education level and population size change are the core dimensions of stable impact. Among them, population age structure and education level form the foundation of stable impact combinations. Their superposition and synergy are the key to forming high-stability impacts. If combined with population size change, it will drive economic development in a sustained and stable manner. It should be noted that when incorporating factors reflecting population size change into the combination, choosing population migration change rather than natural population change can significantly improve the stability of the combination's impact on the economy. This is because migration change can not only alter population size, but also improve labor quality. Its driving effect on the economy is stronger than that of natural change.This feature is particularly obvious in regions with low birthrate. Therefore, in the analysis of the superposition and combination of population factors, we need to pay attention to the differentiated effects of ombinations with the same number of factors. 3.1.2 The action path of multiple elements Following Charles C. Ragin (2009), combinations with a consistency threshold above 0.7 are considered to have strong explanatory power. This resulted in 6 core population configuration paths that are considered to have strong effects on regional economic development (Table 3 ). In terms of configuration types and factor combination characteristics, the coverage of these 6 paths ranges from 0.2341 to 0.3155. This shows that population factors can impact economic development through multiple paths. It also confirms that the complexity and diversity of how population factors affect economic development. Table 3 The configuration of population conditions affecting economic development Path Configuration Type Combination Configuration Coverage Path 1 High Migration+High Population Quality Positive Migration Growth+High Educational Level+ Neutral Aging Structure+Neutral Natural Change 0.3155 Path 2 High Population Quantity+ High Population Quality Increased Natural Growth+Positive Migration Growth +Young Aging Structure+High Educational Level 0.3019 Path 3 High Natural Growth+ High Population Quality Increased Natural Growth+High Educational Level +Neutral Aging Structure+Neutral Migration Change 0.2785 Path 4 High Population Quantity +Young Age Structure Increased Natural Growth+Positive Migration Growth +Young Aging Structure+Neutral Educational Level 0.2430 Path 5 Young Age Structure+High Population Quality Young Aging Structure+High Educational Level+ Neutral Natural Change+Neutral Migration Change 0.2387 Path 6 High Natural Growth+High Population Quality+Young Age Structure Increased Natural Growth+Young Aging Structure+ High Educational Level+Neutral Migration Growth 0.2344 In the population configuration paths affecting regional economic development, there is a clear order in the impacts of population change factors on economic development. High educational level again is the primary dominant factor. It participates in 5 paths (Paths 1, 2, 4, 5, and 6) with an average coverage of 0.2783. It is involved in almost all paths leading to high economic development. This shows that population quality is the core driving force for population conditions to promote economic development. A young age structure follows closely as the second most important core factor. It participates in 4 paths (Paths 1, 2, 3, and 5) with an average coverage of 0.2836. It is a core element that can effectively match both high population quantity and high population quality. This reflects the quality and vitality of labor supply among the population quantity change factors, positive migration and natural growth are basic elements for forming high population quantity. However, their impact on economic development only appears in some paths (1, 3, and 6). They also have strong substitutability. For example, regions with negative population growth can compensate by attracting migrating labor. This stabilizes economic development. Both play auxiliary roles. Notably, the importance of migration change is significantly higher than that of natural change. Positive migration can not only expand the population quantity but also introduce high-quality labor. Natural growth only affects the population quantify and has limited effect on improving labor quality. Therefore, migration change has a more prominent impact in the dimension of population quantity compared with natural change. The impact of population factors on economic development is not driven by a single factor independently. Instead, it is achieved through the synergy of core factors and the matching of auxiliary factors. For example, taking "high educational level" as the core factor, it is necessary to match different auxiliary factors (Path 4, Path 5, Path 6). These paths have a similar regional scope of impact on economic growth (coverage value is approximately 0.24). They are respectively suitable for different scenarios, such as strong population absorption capacity, abundant high-quality labor force, and sound natural population growth. If the core role of "high educational level" is weakened, even if the total population size is expanded through the dual effects of natural growth and positive migration growth with labor vitality guaranteed by a younger population structure, the scope of economically growing regions that can be explained will decrease (Path 3). This is due to the lack of synergy between core factors and auxiliary factors. Among these paths, the dual-core drive of "educational change + age structure change", coupled with migration-led complementation of population quantity, forms the most effective paths for population factors to promote economic development. This echoes the findings from the earlier analysis of factor superposition combinations. It confirms the crucial and important role of population quality improvement and labor structure optimization in economic development. 3.2. The Impacts of the Action Path of Multiple Elements on Economic Development Across Different Temporal and Spatial Dimensions The impact of overlapping combinations of multiple population factors on economic development is not spatially and temporally homogeneous, showing significant differences in both temporal evolution and spatial distribution. From a temporal perspective, this heterogeneity is reflected in the dynamic evolutionary characteristics of the impact paths of overlapping population factors, which can be further revealed by analyzing changes in regression coefficients. From a spatial perspective, this heterogeneity is manifested in the obvious differentiation of the impact effects of the same population factor combinations across different regions,and the specific manifestations of such differences can be clearly identified through regional comparative analysis. 3.2.1 Temporal evolution of the impact of multi-factor population combination paths on economic development Based on the aforementioned six types of impact paths of multi-population factor combinations on economic development (see Fig. 2 ), this study selects three key periods based on the time dimension: 2007–2011, 2012–2016, and 2017–2021. It systematically analyzes and summarizes the dynamic evolution and phased characteristics of the multi-factor combination impact paths. The specific results are as follows. During 2007–2011, the combined effect of population quantity factors, dominated by 'natural growth and migration' combination, was distinctly prominent. The synergistic driving force of this factor combination hit its peak. This shows that this stage was the prime time for population quantity to influence the economy. Specifically, the paths involving high population quantity and age structure (Path 1, Path 2, Path 3, Path 5) acted as core drivers. They formed high-impact clusters in areas (marked in red for subfigures in the first column of Fig. 2 ) such as Jinzhou, Huludao, Yingkou and Panjin, with impact intensity generally ranging from 0.7 to 1.0. However, the paths involving population migration and quality (Path 4, Path 6) had weaker effects. They mostly appeared as light yellow and light green low-value zones, with intensities ranging from 0.2 to 0.4. Dalian remained at a medium-to-low level during this stage. This shows that population quality and mobility did not yet play a significant role. The period from 2012 to 2016 marked a turning point (the second column of Fig. 2 ). The impact intensity of all paths showed a downward trend. Among them, the influence driven by natural population factors weakened more significantly, indicating that this stage marked the beginning of a decline phase for the superposition effect of population quantity factors. This stage was clearly marked by a decline in the impact intensity of all paths. The high-value zones (marked as red) basically disappeared. Blue and light green low-value zones (intensity 0.1–0.3) covered more than 80% of the area. The positive driving effect weakened greatly. The negative inhibitory effects expanded significantly. At the same time, this stage also showed characteristics of "factor transition". In the original high-impact areas (Jinzhou, Huludao), the impact intensity of Path 1 and Path 2 dropped from above 0.8 to below 0.3. In Dalian, Path 3 and Path 5 also fell from 0.6 to around 0.4. These changes reflect that the traditional quantity-driven model can no longer meet the new requirements of economic development. The driving role of quality-based factors has not yet formed. From 2017 to 2021, the quantitative-driven model basically ended. It shifted to a quality-driven model centered on high-quality population factors. This stage was marked by the results of this shift. Pathways involving high migration and high population quality (Path 3, Path 4, Path 5) became the new core. They formed a red high-impact zone (intensity 0.8-1.0) in Jinzhou, Dandong, and Dalian (marked in red for subfigures in the third column of Fig. 2 ). This shows that quality-driven factors began to have a strong influence on the economy. In contrast, pathways involving population quantity and natural growth (Path 1, Path 2, Path 6) remained low. Only Dandong had a local orange median zone (intensity 0.5–0.6). This indicates that the influence of traditional quantity-driven factors continued to decline. The overall evolution shows a clear pattern: strong in the early period (2007–2011), weak in the middle period (2012–2016), and extremely weak in the later period (2017–2021). In summary, the driving effects of multiple population factors on the regional economy from 2007 to 2021 in this study area showed a distinct temporal evolution pattern. In the early stage, population quantity factors were the core driver with a prominent and peaked effect. In the middle stage, the superimposed effects of quantity factors declined, the influence of all driving paths weakened comprehensively, showing a transitional feature of multi-factor transformation. In the later stage, the quantity-driven model ended, and the economy officially shifted to a quality-driven model centered on population migration and quality. High-quality population factors became the new core driver, and a fundamental shift took place in the driving logic of population factors on the economy. 3.2.2 Spatial differentiation characteristics of the impact of multi-factor population combination paths on economic development From a spatial perspective, the impacts of the same population factor combinations show significant spatial differences across different regions. Based on the spatial distributionof the six multi-population factor paths (see Fig. 2 ), a comparison of six cities—Jinzhou, Panjin, Huludao, Yingkou, Dandong and Dalian—reveals that such spatial differences are mainly reflected in the impact intensity gradient, area of agglomeration and spatial differentiation patterns. The impact intensity gradients show distinct differences, forming a multi-level pattern of "high-medium-low" clusters. Taking Path 1 ( high population quantity+high population quality ), the path with the strongest initial influence among all paths, as an example, this path showed an obvious three-level intensity gradient across six cities from 2007 to 2011. Huludao, Jinzhou, Panjin and Yingkou were high-intensity areas (marked as red and yellow), with impact factors (standardized regression coefficients) in the ultra-high range of 7.152 to 21.275. Dandong and Dalian were medium-intensity areas (marked as yellow and green), with impact factors ranging from 3.901 to 7.151. The maximum difference in impact factors between high and low-intensity areas reached 17.374, indicating significant regional differences in intensity. Similarly, Path 6 ( High Natural Growth+ High Population Quality ) also presented an intensity gradient of "high in the middle and low in the north and south" during the same period. The impact factors of Panjin and Yingkou (1.9 to 2.9) were more than twice those of Dandong and Dalian (below 1.0). This confirms that the influence intensity of the same factor combination varies substantially across regions. The areas of agglomeration show distinct spatial patterns, forming a differentiated agglomeration pattern of "north-central-south" in the study area. High-impact zones of different population factor combinations show obvious spatial regional preferences. For example, the high-impact zones of Path 1 ( high population quantity+high population quality ) and Path 4 ( High Migration+High Population Quality ) are both concentrated in Jinzhou and Huludao in the north. Central and southern cities are at medium and low impact levels. They form an agglomeration pattern with the north as the core. The high-impact zones of Path 2 ( High Natural Growth+High Population Quality+Young Age Structure ) and Path 6 ( High Natural Growth+Hig h Population Quality ) are focused on Panjin and Yingkou in the central region. Northern cities are medium-impact zones, and southern cities are low-impact zones. Their core agglomeration areas are in sharp contrast to those of the northern agglomeration paths. Path 5 ( Young Age Structure+High Population Quality ) is the only path with southern agglomeration characteristics. Its high-impact zones are concentrated in Dalian and Dandong. Northern and central cities are both at medium and low impact levels, exhibiting. a spatially differentiation pattern with distinction between northern and central areas. The spatial differentiation patterns are distinct, presenting the characteristic of "areal agglomeration- punctiform scattering". The spatial distribution patterns of the same factor combination vary significantly across different regions. In the core northern, central and southern areas with concentrated high-impact zones, the impact effects mostly show a contiguous areal distribution. For instance, the high-impact zones of Path 1 in Jinzhou and Huludao form a continuous areal distribution. In cities outside these core areas, the impact effects are mostly scattered in isolated spots. For example, the impact zone of Path 5 in Jinzhou, a northern city, is only a local spotted area. It does not form a contiguous pattern. In addition, the special Path 3 ( High Population Quantity+Young Age Structure ) presents unique spatial differentiation. It shows a reverse differentiation pattern of "positive in the south and negative in the central region". Dalian and Dandong are spotted areas with positive impact. Panjin and Yingkou are areal areas with negative impacts. This spatial differentiation with coexistence of positive and negative effects further confirms the significant regional heterogeneity of the impact effects of the same population factor combination. In summary, during the study period, the spatial impact pattern of multi-factor population combination paths on economic development shows distinct spatial evolution characteristics. In the early stage, the impact intensity stays at a high-value range. Core clustering spaces are concentrated in western and south-central Liaoning cities. They present a distribution pattern of local high-value clustering. In the middle stage, the overall impact intensity drops to a low-value range. The whole region shows a trend of low-value diffusion. Core clustering areas begin to show the transitional feature of spatial expansion and shift. In the later stage, the impact intensity of core driving paths rebounds to a high-value range. Clustering spaces expand eastward to coastal cities in eastern Liaoning. The distribution pattern turns to a clustering model mainly driven by population quality. The regional spatial topological structure completes the systematic transformation of self-organized optimization and factor restructuring. These characteristics of spatial patterns are the results of the transition from the combined effects of population quantity factors to quality factors over time. 4. Discussion This paper focuses on the relationship between multi-factor population combinations and regional economic development in the study area from 2007 to 2021. It conducts empirical analysis from four dimensions. These dimensions are the stability of factor combinations, the order of importance, the characteristics of temporal evolution, and that of spatial differentiation. The research conclusions obtained not only verify existing academic achievements, but also supplement, expand and deepen the existing cognition. The main research contributions are reflected in the following aspects. First, compared with analyses focusing on the impact of a single population factor on economic development, the multi-factor combination approach can better describe the complex driving mechanism of population factors. It also makes up for the gaps in existing research on factor synergy and differential effects. This study identifies three core demographic factors that affect economic development: population size, age structure, and educational attainment. This is consistent with the consensus of existing demographic economics. That is, demographic characteristics are key drivers of long-term economic growth (Bloom et al.,2003; Barro and Lee,2015; Ronald,2002). Bloom et al. ( 2003 ) emphasized that age structure (demographic dividend) promotes growth through labor supply. Barro ( 2015 ) confirmed that educational attainment (human capital) can improve labor productivity. Ronald (2002) explored the complex relationship between population size and the economy. These studies are important in understanding the impacts of these core factors from a single factor perspective. This study finds that these three core factors can form a stable driving mechanism for economic development when they function in combination. Moreover, multiple demographic factors do not act in isolation. Instead, they have a certain hierarchical and synergistic relationship. For example, "changes in age structure" and "changes in educational attainment" are the basic "dual cores" that constitute highly stable impacts. On this basis, combining with factors reflecting changes in population size (migration or natural growth) can form a more complete driving pathway. In addition, the study finds that under the dimension of population size, there is an asymmetric effect between migration and natural growth. Migration improves the stability of the combination, while natural growth plays a weaker role. These findings add to the existing understanding of the differential effects within similar factors (Lutz et al.,2008;Wang and Li,2018). Second, it is generally recognized that population quality drives economic development better than population quantity. But, there is a lack of systematic quantitative tests on the importance ranking of multiple population factors and the differential effects of quantity changes. This study is consistent with existing research that "population quality has a better driving effect than quantity" (Ding et al.,2022; Lutz et al.,2008). It also confirms through empirical quantification that high education level is the primary leading factor, and age structure is the second core factor. In terms of quantity factors, the importance of population migration change is significantly higher than that of natural population change. This refines the existing understanding of the roles of population factors in economic development. Existing studies have also noticed the heterogeneity of quantity changes and the impacts of migration and natural growth in the stage of population negative growth (Zhang, 2016 ; Liu and Yuan,2020). This study supplements and confirms that positive migration and natural growth have substitutability in the path of economic development. At the same time, this study also shows the differential effects and substitution mechanism between the two. Regions with negative population growth can make up for the quantity gap and stabilize economic development by attracting migrant populations. Moreover, migration has a much stronger driving effect on economic development than natural growth, which only affects quantity, because migration has the dual effects of "expanding size" and "improving quality". These findings improve the understanding of effects of sub-factors of population quantity changes. Third, in temporal studies on the relationship between population and economy, it is generally recognized that population quality and population migration will gradually replace population quantity and will become the core population driving forces for economic development (Huang and Duan,2022; Bloom et al.,2010). The empirical results of this paper further verify and expand on this view. This study, through the analysis of transformation paths, reveals an important evolution turning point and transition stage characteristics of the population-driven model. It shows that the peak period (2007–2011), recession turning point (2012–2016) of the population quantity-driven effect, and the formation period (2017–2021) of the quality-driven effect. It depicts the characteristics of the transition stage, that is, "weakened quantity-driven effect and unformed quality-driven effect". At the same time, it quantifies the impact intensity of each factor combination in different stages. It presents the fundamental transformation of population driving from "quantity-oriented" to "quality-oriented". This paper also responds to the research suggestions put forward by some scholars: "It is necessary to strengthen research on the differentiation of population-driven effects at different time sequences at the regional scale." (Chi and Ventura,2011;Liu et al.,2022). It provides a referenceable analytical framework and empirical reference for research on the coordinated development of population and economy in similar regions. Finally, existing studies have generally confirmed that the economic effects of population factors exhibit significant spatial heterogeneity. They have also shown that the spatial concentration characteristics of population factors are highly coupled with the regional economic development pattern (Yang et al.,2022; Burgi and Gorgulu, 2024 ). Meanwhile, the spatial adaptability between population and economy in the Liaozhongnan region shows obvious urban hierarchy and regional differentiation characteristics. The impact of population factors varies with spatial location (Liang et al.,2024). The empirical results of this study further verify and expand the above understanding. This study analyzes the spatial effects of multi-factor population combinations from three dimensions: impact intensity gradient, concentration area, and spatial differentiation pattern. It identifies that under similar population factor combinations, core economic concentration zones show path-dependent spatial shifts and hierarchical differentiation. It confirms that population quality is the key factor driving the dynamic gradient evolution of spatial agglomeration patterns. The results show that spatial differentiation differs significantly across different population factor combination paths. Some paths exhibit a unique reverse differentiation pattern, while non-core combination paths remain at a low impact gradient. These findings deepen our understanding of the spatial differentiation mechanism of population economic effects from a multi-factor perspective. They also respond to calls for stronger spatial heterogeneity analysis (Chen et al.,2024). The conclusions can provide general empirical references for the coordinated population-economic development in similar regions. 5. Conclusion This paper analyzes the relationship between population factors and economic development, revealing the driving mechanism of multi-factor population changes on regional economy. To this end, this paper employed two methods: Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) to examine the combined impacts of population multifactor changes on economic development. The research shows that the driving effect of population factors on economic development does not rely on the independent role of a single factor. Instead, it is achieved through the combination of core factors and the complement of auxiliary factors. Among them, the dual-core drive of "changes in educational level and changes in age structure", combined with the population size dominated by migration, is one of the more effective paths for population-driven economic growth in the study area. The order of priority for population factor combinations is clear. Changes in educational level are the primary leading factor, followed by changes in age structure. Among population quantity factors, the driving effect of population migration is stronger than that of natural population change. From a temporal perspective, the population driving effect during the research period went through three stages. It started with a golden period dominated by population quantity factors. Then it entered a transition period where the quantity-driven effect weakened. Finally, it turned into a quality-driven period centered on population migration and population quality. From a spatial perspective, due to differences in the spatial realization characteristics of various population factors, the impact of the same population factor combination varies significantly in different regions. In the study area, it has formed a multi-level intensity gradient of "high-medium-low" and a differentiated agglomeration pattern of "north-center-south". The core agglomeration area has gradually expanded from western and central-southern Liaoning to eastern Liaoning coastal areas. The multi-factor combination research method adopted in this paper identifies the configurational effects of multi-factor combinations. It makes up for the deficiencies of traditional research methods in revealing the synergistic effect of factors. It also provides a new analytical perspective for the research on the relationship between population factors and regional economic development. However, in the process of research application, the following points need to be noted. The selection of factor combinations has a significant impact on the stability of economic effects. Different factor combinations will directly affect the robustness and explanatory power of the economic effects of the paths. When core factors and auxiliary factors are reasonably matched, the identification of economic effects is more stable and reliable. This makes the research conclusions convincing. The findings from this study are based on the time interval of 10 years (2007—2017) from a specific study area in the coastal region of Northeastern China. While efforts have been made to select the temporal period and regional coverage as representative as possible, interpretation of the findings is still constrained by the temporal length of the data and the regional coverage. The other aspect of caution is that the control variables currently only contain industrial structure, living standards, fiscal expenditure, technological innovation and in the future can be expanded to include more potential factors such as policies and urban planning. This will help improve the universality and depth of the research findings. Declarations Author Contribution GY.B. and DX.Z. contributed to the conception and design of the study, conducted the data analysis, and wrote the main manuscript text. AX.Z. contributed to the interpretation of the results and critically revised the manuscript for important content. All authors reviewed the manuscript, provided valuable feedback, and approved the final version for submission. All authors agree to be accountable for the integrity and accuracy of the work. Acknowledgement This work was supported by National Natural Science Foundation of China [grant number 42471221]. Data Availability Data Availability StatementAll data supporting the findings of this study are obtained from public and authoritative sources. The primary raw dataset (Raw data.xlsx) is derived from two core sources: (1)the Liaoning Provincial Statistical Yearbook (2007—2022), accessible via the Liaoning Provincial Bureau of Statistics official website (https://tjj.ln.gov.cn/tjj/tjsj/tjnj/index.shtml); (2)the 2007—2022 Statistical Bulletins on National Economic and Social Development of Dalian, Dandong, Jinzhou, Yingkou, Panjin and Huludao, which are publicly available on the Liaoning Provincial Government portal.The calibrated fsQCA dataset (fcQCA Raw data.csv) and the processed GTWR dataset (Raw GTWR.xls) generated during the analysis are attached in the supplementary materials of this article. All data used in this study are publicly available and non-confidential, with no proprietary or restricted data included in the analysis. References Acemoglu D, Restrepo P (2017) Secular Stagnation? The Effect of Aging on Economic Growth in the Age of Automation. 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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-9056444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611735972,"identity":"05f2cb34-8425-4871-b6fc-1abe0de5abc6","order_by":0,"name":"DongXia Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDACCSDmYbCR42dmPviAFC1pxpLtbMkGpGg5nLjhPI+ZAFE65Gc3P3zwtu1w4ubDDGYMDDU20QS1MM45Zmw450y68bbDDGkPGI6l5TYQ0sIskWAmzVNhLQvUctyAseEwYS1sEunfpHkMmBk3NzO2SRClhUciB2SLs+IGZmY24rRISOQUA/2SZixxmI3ZIIEYv8jPSN8IDDFgVPaf//jgQ40NYS2oIIE05aNgFIyCUTAKcAEAVtU6qaMbNHMAAAAASUVORK5CYII=","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":true,"prefix":"","firstName":"DongXia","middleName":"","lastName":"Zhao","suffix":""},{"id":611735976,"identity":"fa6809d0-a45d-4495-a9ac-4ce6b42e007e","order_by":1,"name":"GuangYuan Bai","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"GuangYuan","middleName":"","lastName":"Bai","suffix":""},{"id":611735980,"identity":"b5f87fc4-02bc-4df8-b4d7-22705a6252e1","order_by":2,"name":"AXing Zhu","email":"","orcid":"","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"AXing","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-03-07 07:38:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9056444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9056444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105477343,"identity":"893a0cd3-d1d5-4c49-8ba3-7613711d4362","added_by":"auto","created_at":"2026-03-26 13:07:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7458170,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the Study Area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9056444/v1/8b2d0a3f79bbc0e5b30510d5.jpeg"},{"id":105477341,"identity":"8397aad3-abe2-4f96-bf97-9523b022420b","added_by":"auto","created_at":"2026-03-26 13:07:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209508,"visible":true,"origin":"","legend":"\u003cp\u003eSpatio-temporal differentiation of the impact of multifactorial population changes on economic development in the Liaoning coastal economic zone, 2011-2021\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9056444/v1/47e114203639994d6702d2b6.jpg"},{"id":105729776,"identity":"d209678f-e778-406f-9529-a8379ae28f06","added_by":"auto","created_at":"2026-03-30 11:19:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8625409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9056444/v1/2b01eb1f-3d3d-4e3a-b810-1b1c448506e4.pdf"},{"id":105727953,"identity":"9665c0bc-8dcb-4651-a3e5-6ba73b30b6dd","added_by":"auto","created_at":"2026-03-30 11:06:15","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":43486,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydocuments.zip","url":"https://assets-eu.researchsquare.com/files/rs-9056444/v1/5fef88cbe81695352fa7316b.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of Population Multifactor Changes on Economic Development: The Case of China's Liaoning Coastal Economic Zone","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the beginning of the twenty-first century, all countries in the world have experienced dramatic demographic changes, such as a continuous decline in fertility, unusually active population mobility and migration, and a deepening degree of ageing, which are not only an important feature of the demographic development of the global society in the era of the second demographic transition but also an inevitable result of economic globalisation in an open environment. Numerous studies have shown that population change is no longer the change of a single element, but the interaction of multiple elements to change together (Keyfitz \u0026amp; Caswell,2005; Esin Ickin et al.,2025). For example, in addition to institutional and policy influences, the decline in fertility has been accompanied by an increase in the level of education, which has contributed to a delay in the age of first marriage, and the reconfiguration of population distribution due to mobile migration (United Nations,2024;Li et al.,2024;Luo et al.,2025). The aging of the population is not only due to longer life expectancy, but more importantly to the fact that fewer newborns and the emigration of a young workforce have changed the local demographics (Chen,2020;Ao and Chang,2020). The movement of population (talent) affects both the population quantity and quality of the places of emigration and immigration, and may exacerbate the imbalance of global population aging. The result is that such multifactorial changes in population will impact economic development differently, compared to a single factor. Therefore, as the trend of population change continues to increase, active research into the changes in multiple elements of the population changes and the various dynamics of the impact on the economy will not only help to understand the current status and problems of population development from a holistic perspective, but also provide a scientific basis for decision-making to optimise regional population policies, and enhance the vitality of the local economy.\u003c/p\u003e \u003cp\u003eAt present, research on impacts of population change on regional economy mainly focuses on four aspects from the perspective of single factors. The first focuses on the perspective of natural population change, specifically delving into the influence of fertility variations on regional economic development (Guo and Tian,2019; Bruno Arpino et al.,2015). The declining fertility rate that induced trends of fewer children and an aging population have reduced the size of the working-age population in regions, directly leading to a shortage of labor supply. This has increased labor costs for enterprises and weakened the vitality of economic development. Meanwhile, the increase in the elderly dependency ratio has compelled regions to allocate more fiscal resources to social security fields such as pension systems and healthcare (Bloom et al.,1998;Wang,2018). This reallocation displaces productive investments in education and technological R\u0026amp;D, thereby constraining the long-term economic growth potential (Nargund,2009; Acemoglu and Restrepo,2017). Consequently, persistent low fertility rates may form a negative feedback loop characterized by labor shortage-intensified aging-economic pressure-decreased willingness to bear children-further labor shortage-increased elderly care burden (Lutz et al.,2008). The line of argument posits that low fertility rates fundamentally disrupt the momentum of natural population growth, thereby constraining economic expansion. However, the positive regulatory impacts of non-natural factors\u0026mdash;such as population mobility and migration\u0026mdash;on economic development was not taken into account.\u003c/p\u003e \u003cp\u003eThe second dimension pertains to the perspective of mechanical population change, specifically examining the impact of population mobility on regional economic development. Drawing on data from China's Seventh National Population Census (National Bureau of Statistics of China,2021), scholars have documented a striking increase in the floating population, rising from 221.43\u0026nbsp;million in 2010 to 375.82\u0026nbsp;million in 2020, with a compound annual growth rate of 5.43% (Ma,2022). Empirical analysis reveals a nonlinear relationship: when a region's net inflow rate exceeds 3%, a 1-percentage-point increase in population mobility correlates with a 0.8-percentage-point excess GDP growth. Conversely, when net outflow rates surpass 2%, each additional percentage point of population outflow is associated with a 0.6-percentage-point decline in economic growth (Glaeser and Gottlieb,2009;Liu,2018). This dynamic underscores that while migration may temporarily alleviate localized labor shortages, it perpetuates a reciprocal growth-decline dynamic across regions (Sun et al.,2023). For instance, population agglomeration in China's eastern urban clusters exhibits a \"siphon effect,\" whereas outflows from western and northern provinces trigger a \"shrinkage effect\" (Glaeser,2011;Chen et al.,2019;Gu and Shen, 2020). These perspectives primarily focus on the impact of population mobility on regional economic disparities while the transformation of local demographic structures following migration, which exacerbates aging and sub-replacement fertility issues and thereby influences economic development, was not included.\u003c/p\u003e \u003cp\u003eThe third analytical lens focuses on the dimension of demographic structural elements, specifically examining how changes in the degree of aging influence regional economic development. The elderly population represents a demographic segment that has withdrawn from occupational productive activities and predominantly assumes the role of \"consumer-only\" agents within the economic system (Longhi et al.,2008; Maestas et al.,2023). As the proportion of the elderly population within the total population increases, it implies a higher share of output being allocated to this demographic. Consequently, the proportion of economic output available for productive investment decreases relatively, and the labor supply also declines (Maestas et al.,2023;Zorlu and van Gent,2024). Consequently, this situation impedes capital accumulation, diminishes the potential output level of the economy, intensifies the pressure on social security and public services, and ultimately constrains the high-quality and sustainable development of the region. This perspective posits that aging and a low fertility rate are bound to impede economic development (Maity and Sinha,2020; Zhang et al.,2023). Evidently, it overlooks the fact that high-quality, highly skilled talent, whose influence is not constrained by the age structure, has emerged as a pivotal force driving sustained economic growth. Such talent, regardless of age, possesses the capabilities to foster innovation, enhance productivity, and optimize resource allocation.\u003c/p\u003e \u003cp\u003eFourthly, from the perspective of changes in population quality, it refers to the impact of the educational attainment level of the population on the regional economic development. Education serves as the primary means of improving the quality of the labor force (Hanusek and Woessmann,2020). A sound educational level is conducive to alleviating the decline in the absolute quantity of labor factor inputs caused by population aging, and promoting the enhancement of labor productivity and the labor participation rate. Specifically, education equips workers with advanced knowledge and skills, enabling them to adapt to technological advancements and changes in the labor market (Becker G,1964; Psacharopoulos and Patrinos, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This not only helps to offset the negative impact of a shrinking workforce due to an aging population but also stimulates the improvement of labor efficiency and the expansion of the labor participation scale, thereby contributing to the stable operation and sustainable development of the economy (Bloom et al.,2010;Hanusek and Woessmann,2020;Wang,W., et al.,2023). Therefore, during the period of high-quality development, the transition from the \"demographic dividend\" to the \"talent dividend\" serves as an endogenous driving force for the sustainable development of the regional economy. Specifically, this transformation is crucial as it shifts the focus from simply relying on the large quantity of the labor force to fully leveraging the high quality and diverse capabilities of talents. This perspective posits that changes in the quality of the population constitute the core driving force stimulating economic development. Above all, it emphasizes that the enhancement of the overall quality of the population, encompassing aspects such as education, skills, and health, plays a pivotal role in fostering technological innovation, improving production efficiency, and promoting the optimization of economic structures.\u003c/p\u003e \u003cp\u003eIn summary, most of the existing research findings consider that the impact of population change on the economy is dominated by a single factor. For instance, the population elements that drive economic development are mainly the quantity of the labor force, or the increment of the migrant population from outside, or the relatively low level of population aging within the population structure, or the presence of a high-quality population. However, in reality, population change is a dynamic process involving the interaction of multiple elements, that is the interplay of various factors such as population quantity, structure, quality, and mobility. Therefore, when exploring the impact of population change on economic development, it is necessary to shift to a perspective driven by multiple elements, particularly the interplay of these factors. This paper examines the impacts on population change on the economic development from this perspective.\u003c/p\u003e"},{"header":"2. Study design and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area and data\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Study area\u003c/h2\u003e \u003cp\u003eThe study area is the Liaoning Coastal Economic Belt, in the Northeastern part of China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The population dynamics in this region are characterized by the coexistence of low fertility, high-level aging, persistent population out-migration, and regional development differentiation. For instance, by the end of 2023, the average aging rate in this region had reached 21.1%, significantly exceeding the national average of 15.4%. Meanwhile, traditional industries have been shrinking (with electricity consumption in heavy industry dropping by 0.88%), emerging industries have been disconnected (with strategic emerging industries accounting for 16.1% of the total), and investment confidence has been insufficient (with the growth rate of foreign capital being lower than the national average). Therefore, Its economic development is facing dual structural constraints of \"aging before affluence\" and \"declining fertility before prosperity\", forming a typical feedback mechanism of \"low fertility \u0026ndash; labor outflow \u0026ndash; industrial ecosystem recession\".\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom what is described above, the study area is a good representation of nearly 24 developing nations that have entered phases of ultra-low fertility and hyper-aging, particularly in the inherent contradictions between population dynamics and economic development. With a per capita GDP of \u003cspan\u003e$\u003c/span\u003e13,000 (data from 2023) and a 65\u0026thinsp;+\u0026thinsp;age cohort constituting 18.7% of the total population, it mirrors the predicament of countries like Greece (per capita GDP of \u003cspan\u003e$\u003c/span\u003e21,000; aging rate of 22.8%) and Bulgaria (\u003cspan\u003e$\u003c/span\u003e11,000; 21.3%). These economies, typically in the mid-industrialization stage, confront a precipitous decline in labor supply. In the Northeast Coastal Economic Belt, the working-age population (aged 15\u0026ndash;64) has contracted at an annual rate of 2.3% over the past decade, aligning closely with Serbia's 2.1% shrinkage during the same period. Additionally, traditional heavy industries account for 41.8% of its industrial structure, echoing Bulgaria's reliance on manufacturing. This international comparability renders the region an ideal case study for examining the ramifications of demographic dividend exhaustion on middle-income economies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Variables\u003c/h2\u003e \u003cp\u003ePopulation change stems from changes in four elements: quantity, mobility, structure, and quality. In this study, natural change is used to represent changes in population quantity, migration change in population mobility change, age change in population structure change, and change in education level representing population quality change. These four factors were used as the independent (explanatory) variable and the per capita Gross Domestic Product (GDP) was selected as the dependent variable characterizing the level of economic development.\u003c/p\u003e \u003cp\u003eSpecifically, first, we use the natural population growth rate to characterize natural changes, and judge whether the study area is in a stage of population growth, stability, or decline based on the quantitative difference between the number of births and deaths. Second, we use the net population migration rate to characterize population mobility and observe the population distribution status through population inflows and outflows. Third, we use the old-age dependency ratio to characterize changes in the population age structure, which reflects the dependency burden of the working-age population on the elderly population and indicates the degree of population aging in the region. Fourth, we use the proportion of the population with or above upper secondary education in the total population to characterize changes in the population's education level, which reflects the overall quality of the population.\u003c/p\u003e \u003cp\u003eIn addition, although factors such as industrial structure, residents' living standards, public financial expenditure, and technological innovation do not directly fall under the category of \"population change factors\", they can regulate the intensity and direction of the impact of population factors on the economy. As indispensable \"environmental variables\" in the multi-factor-driven framework, they need to be incorporated into the analysis to account for interference, and thus we take these factors as control variables. Information on these variables is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eType of variable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eName of variable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eExplanation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNotation\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMax\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of economic development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita (natural logarithm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eREO\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eExplanatory variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural change of population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural rate of population growth(\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNVP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMigration change of population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNet migration rate(\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMCP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-20.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation age structure changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge dependency ratio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eADC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation education level changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of students with a high school education or above in the total population(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePEC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of the sum of the gross domestic product of primary and tertiary industries in the gross regional product(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eIS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidents' living standard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita disposable income (natural logarithm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRLS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic finance expenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral public budget expenditures of local governments (natural logarithm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of invention patents granted by region (natural logarithm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe data for the explanatory (independent) variables and the dependent variable are obtained from the Statistical Yearbook of Liaoning Province, China City Statistical Yearbook (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.yearbookchina.co-\u003c/span\u003e\u003cspan address=\"https://www.yearbookchina.co-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-m), as well as the statistical yearbooks and statistical bulletins on national economic and social development of all prefecture-level cities within the region, covering the period from 2007 to 2022 (National Bureau of Statistics of China, 2008\u0026ndash;2023; 2007\u0026ndash;2022). To ensure data accuracy, we cleaned the raw data by removing missing values and duplicate records, and identifying and correcting outliers (Wooldridge,2013). The gross regional product, natural population growth rate and the number of students enrolled in high school and above are all original data available in statistical yearbooks. The net population migration rate was calculated as the difference between the population immigration rate and emigration rate published in the statistical yearbooks. The old-age dependency ratio is measured as the proportion of the population aged 65 and above to the working-age population aged 15\u0026ndash;64, using data from the statistical yearbooks. All the above are standard methods in demographic research (Preston et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e;Ye et al.,2021;United Nations Population Division, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, to ensure data consistency and accuracy, we also cross-referenced and rechecked the data of each indicator with the relevant data published by the National Bureau of Statistics of China (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stats.gov.cn\u003c/span\u003e\u003cspan address=\"https://www.stats.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the control variables, the data on patent grants are obtained from the official website of the China National Intellectual Property Administration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cnipa.gov.cn\u003c/span\u003e\u003cspan address=\"https://www.cnipa.gov.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data for indicators such as the gross domestic product (GDP) of the primary and tertiary industries, the per capita disposable income of urban and rural residents, and the general public budget expenditure of local finance all come from the statistical yearbooks and statistical bulletins from which the population data were obtained. This is a commonly adopted methodology in studies related to China's urban and economic development (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Methodologies\u003c/h2\u003e \u003cp\u003eTo reveal the combined impact of demographic changes on economic development, we have selected the Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) methods for data analysis. From a qualitative comparative perspective, fsQCA identifies the combinations of demographic change factors that exert a significant driving effect on economic development. This serves as a basis for the subsequent factor selection in GTWR. GTWR, in turn, further quantifies the differences in the impact intensity of these variables across different spaces and over different time periods, addressing the question of \"when, where, and which configuration factors have a stronger impact\". These combination of methods allows us to reveals configurational effects of multi-dimensional demographic factors and spatiotemporal hdynamics.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Fuzzy Set Qualitative Comparative Analysis\u003c/h2\u003e \u003cp\u003eFuzzy Set Qualitative Comparative Analysis (fsQCA) quantitatively evaluates the strength of the association between conditional combinations and outcomes. It does this by calculating consistency and coverage. Consistency measures how well a conditional combination aligns with an outcome. Coverage measures the scope of outcomes explained by a specific conditional combination (Kraus et al.,2018). Specifically, consistency serves two purposes. First, it can measure how well a population factor combination aligns with \"economic development\". This helps answer the question: Can the combination stably promote economic development? Second, it allows comparison of consistency across different population factor combinations. This reveals which combinations have more stable cumulative effects.\u003c/p\u003e \u003cp\u003eThe coverage measures the scope of economic development explained by a population factor combination. It answers the question: how many regions with high economic development can the combination explain? In practical research, consistency and coverage need be used together to quantify the cumulative effects of different combinations. The formulas are as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Consistency({X}_{i}\\le\\:{Y}_{i})=\\frac{\\sum\\:min({X}_{i}，{Y}_{i})}{\\sum\\:{X}_{i}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:（1）\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:Coverage({X}_{i}\\le\\:{Y}_{i})=\\frac{\\sum\\:min({X}_{i}，{Y}_{i})}{\\sum\\:{Y}_{i}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:（2）$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the membership degree in the combination; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the membership degree in the result. The consistency and coverage ranges are (0,1).\u003c/p\u003e \u003cp\u003eIn addition, with the use of three-valued anchor method (setting 95%, 50%, and 5% as full membership, cross point, and full non-membership, respectively), it allows for distinguishing between core conditional variables that play a key role and peripheral conditional variables that serve an auxiliary function.\u003c/p\u003e \u003cp\u003eThe core question, \"whether the combination of multiple population factor changes exerts multi-dimensional impact effects on the economy to different degrees\", lies in identifying key elements of multiple population factors, combinatorial paths, and impact effects of multiple population factors. FsQCA can effectively answer this question through the following analysis. First, it takes multiple population factors (such as old-age dependency ratio, population size, population mobility, etc.) as conditional variables. It takes economic development indicators as outcome variables.Then, it directly analyzes the impact of different combinations of population factors (not individual factors) on economic development. In this way, it can accurately capture the superimposition effect.\u003c/p\u003e \u003cp\u003eSecond, through configuration analysis, it reveals the interactive relationships between population factors. For instance, it clarifies whether it is the combination of \"high old-age dependency ratio\u0026thinsp;+\u0026thinsp;high labor quality\" or \"population outflow\u0026thinsp;+\u0026thinsp;low fertility rate\" that affects economic development, and identifies the mechanisms of synergy or inhibition between factors.\u003c/p\u003e \u003cp\u003eThird, it screens effective paths based on consistency and coverage, and presents multiple equivalent paths through which \"the combination of multiple population factors affects economic development\" (e.g., Path 1: Population aging\u0026thinsp;+\u0026thinsp;high human capital\u0026rarr;economic transformation; Path 2: Population agglomeration\u0026thinsp;+\u0026thinsp;low dependency ratio\u0026rarr;economic growth), explaining the impact differences under different contexts.\u003c/p\u003e \u003cp\u003eFourth, by distinguishing between core conditional variables and peripheral conditional variables, it identifies the key elements that play a decisive role in economic development within the combination of population factors (e.g., \"human capital level\" may be a core condition) and those that only serve an auxiliary role (e.g., \"population density\" may be a peripheral condition), clarifying the focus of intervention.\u003c/p\u003e \u003cp\u003eFinally, through the above analyses, it directly responds to the question \"whether the combination of multiple population factor changes exerts multi-dimensional impact effects on the economy to different degrees\". Specifically, the number of paths, the differences in consistency/coverage among various paths, and the variations in core elements prove that the impact of the superimposition of population factors has \"degree differences\" (reflected by coverage and consistency) and \"dimension differences\" (reflected by different paths and different core elements).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Geographically and Temporally Weighted Regression\u003c/h2\u003e \u003cp\u003eGeographically and Temporally Weighted Regression (GTWR) integrates spatial heterogeneity and temporal dynamics into a unified analytical framework. Specifically, by dynamically quantifying the impact intensity and interactive relationships of multi-dimensional demographic factors across different spatial and temporal dimensions, this model can decompose the combined effect of these factors on economic development. It thus provides core technical support for subsequent analyses (Fotheringham, 2015). In terms of the spatial dimension, GTWR assigns differentiated weights to different geographic units and dynamically calculates regression coefficients for each region. This allows to clearly identify which combinations of demographic factors have a stronger combined impact in which regions. In terms of the temporal dimension, by analyzing changes in regression coefficients over time series, GTWR can further assess the temporal dynamic characteristics of these superimposed impacts.\u003c/p\u003e \u003cp\u003eBased on the above analytical logic of dual spatial and temporal dimensions, GTWR uses spatio-temporal dynamic coefficients to indirectly quantify the combined intensity of multi-factor interactions. Ultimately, it clarifies when, where, and which interactive combinations of demographic factors have the most significant effect on economic development, forming a causal inference chain (Fotheringham et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The general formula of GTWR is given as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:{Y}_{i}={\\beta\\:}_{0}{(u}_{i}，{v}_{i}{，t}_{i})+{\\sum\\:}_{k=1}^{P}{\\beta\\:}_{k}{(u}_{i}，{v}_{i}{，t}_{i}){X}_{ik}+{\\epsilon\\:}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:（3）$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is the observed value;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(\\text{u}}_{\\text{i}}，{\\text{v}}_{\\text{i}})\\)\u003c/span\u003e\u003c/span\u003e is the latitude and longitude coordinates of the i-th sample point; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{(\\text{u}}_{\\text{i}}，{\\text{v}}_{\\text{i}}，{\\text{t}}_{\\text{i}})\\)\u003c/span\u003e\u003c/span\u003e is the spatio-temporal coordinate of the i-th observation point;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:{\\beta\\:}}_{0}({\\text{u}}_{\\text{i}}，{\\text{v}}_{\\text{i}}，{\\text{t}}_{\\text{i}})\\)\u003c/span\u003e\u003c/span\u003e represents the spatio-temporal intercept term of observation point i;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:{\\beta\\:}}_{\\text{k}}({\\text{u}}_{\\text{i}}，{\\text{v}}_{\\text{i}}，{\\text{t}}_{\\text{i}})\\)\u003c/span\u003e\u003c/span\u003e represents the regression coefficient of the k\u003csup\u003eth\u003c/sup\u003e explanatory variable at observation point i; P is the total number of variables;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{X}}_{\\text{i}\\text{k}}\\)\u003c/span\u003e\u003c/span\u003e is the value of the k\u003csup\u003eth\u003c/sup\u003e independent variable at the i-th observation point; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\epsilon\\:}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003eis the residual term corresponding to the sample points.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Population Multifactor Changes on Economic Development\u003c/h2\u003e \u003cp\u003eThe results on impacts of population multifactor changes on economic development are presented in two elements: the combined impacts and the action paths of population factors. From the perspective of combined impacts, the consistency coefficient was used to judge the stability of the overlapping effects produced by different population factor combinations and identify which combinations can continuously affect economic development. From the perspective of action paths, the coverage coefficient was used to explain the applicable scope of such stable combinations. Core condition variables and marginal condition variables were identified to examine the interactive relationships among multiple population factors and to reveal dynamic impact mechanism.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 The combined effect of multiple population elements\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the consistency results showing the effects of different combinations of the four population variables (factors), namely natural change, migration change, education change and age structure change. These population combination effects were obtained under the control of four external factors (\u003cem\u003eIndustrial Structure, Residents' Living Standard, Public Finance Expenditure, and Technological Innovation\u003c/em\u003e). The consistency values of all combintations range from 0.9842 to 0.7147. When the consistency value is higher than 0.7, the combination is considered to have a consistent impact (Ragin, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, the overall level is acceptably high in consistency. Among them, 72.7% of the commbinations have a consistency exceeding 0.8. This indicates that the combination of population factors in the study area has a consistent causal explanatory power for regional economic development. Clearly, when more population factors are included in a combination, the consistency value gets higher except Combination 2 which will be explained below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of overlapping combinations of multiple population elements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSerial No.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePopulation Factor Included\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eControl Variable\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePri Consistency\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Migration Change, *Age Structure Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Migration Change, *Age Structure Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Age Structure Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Migration Change, *Age Structure Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Migration Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Age Structure Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Migration Change, *Age Structure Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Migration Change, *Education Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change,*Age Structure Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e*Natural Change, *Migration Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe different effects of different combinations of population factors are also apparent in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the three-factor combinations, two combinations have high consistency values greater than 0.9, which suggests that these two combinations have the strongest stability of impact on economic development. They are \u003cem\u003eMigration Change, Age Structure Change, Education Change\u003c/em\u003e (Combination 2, 0.9842, in fact it is the highest) and \u003cem\u003eNatural Change, Age Structure Change, Education Change\u003c/em\u003e (Configuration 3, 0.9655). Their common characteristics are both configurations include \u003cem\u003eAge Structure Change\u003c/em\u003e and \u003cem\u003eEducation Change\u003c/em\u003e, forming the \"dual-core\" combination of population factors driving economic development. The only difference is that one has \u003cem\u003eMigration Change\u003c/em\u003e and the other contains \u003cem\u003eNatural Change\u003c/em\u003e. In fact, these two are reflecting another important aspect of population changes on economic development, that is, population change. Together, these combinations manifest the three key elements of population effects on economic development, that is, population size, age structure, and education level. The fact that Combination 2 is the highest in consistency value, even higher than the four factor combination, further attests that the persistent role of these three aspects.\u003c/p\u003e \u003cp\u003eFrom the two-factor combination perspective, the overall consistency values of the two-factor combinations are understandably lower than those of the three-factor core combinations. However, analysis of the two factor combinations shows thar four combinations have consistency values above 0.8. They are \u003cem\u003eAge Structure Change\u003c/em\u003e, \u003cem\u003eEducation Change\u003c/em\u003e (Combination 6), \u003cem\u003eMigration Change\u003c/em\u003e, \u003cem\u003eAge Structure Change\u003c/em\u003e (Combination 7), \u003cem\u003eNatural Change, Education Change\u003c/em\u003e (Combination 8), and \u003cem\u003eMigration Change\u003c/em\u003e, \u003cem\u003eEducation Change\u003c/em\u003e (Combination 9). A common feature of these combinations is that each includes two of the three key aspects stated above.\u003c/p\u003e \u003cp\u003eIt also shows that \u003cem\u003eEducation Change\u003c/em\u003e is the predominant factor and that any combination involving education resulted in higher consistency values (Combinations 6, 8 and 9). \u003cem\u003eAge Structure Change\u003c/em\u003e is an important factor after \u003cem\u003eEducation Change\u003c/em\u003e, then followed by \u003cem\u003eMigration Change\u003c/em\u003e with \u003cem\u003eNatural Change\u003c/em\u003e being the least important in the combinations. This order of importance in combinations is easily understood. It is well known that education level is one of the most important factors in raising the quality of labor force, in turn, playing key role in economic development (Petrakis and Stamatakis,2002;Qin and Xu,2024). Age structure is another aspect reflecting the quality of labor force. Among the two factors relating to population size change, it is easy to understand that natural change is a less important, compared to migration, due to the impact of the not long ago one-child policy which significantly limited the natural growth of population in China. In addition, migration not only increases the population size, but also brings quality of labor, thus, being more important in population size change than the natural growth. This order of importance also helps to explain that Combination 10 has a lower consistency value compared to the other combinations covering two of the three key aspects, because it combines a weaker population size element, \u003cem\u003eNatural Change\u003c/em\u003e, with \u003cem\u003eAge Structure Change\u003c/em\u003e, a second important population factor.\u003c/p\u003e \u003cp\u003eFrom the perspective of the overall stability of population factors' impact on economic development, age structure, education level and population size change are the core dimensions of stable impact. Among them, population age structure and education level form the foundation of stable impact combinations. Their superposition and synergy are the key to forming high-stability impacts. If combined with population size change, it will drive economic development in a sustained and stable manner. It should be noted that when incorporating factors reflecting population size change into the combination, choosing population migration change rather than natural population change can significantly improve the stability of the combination's impact on the economy. This is because migration change can not only alter population size, but also improve labor quality. Its driving effect on the economy is stronger than that of natural change.This feature is particularly obvious in regions with low birthrate. Therefore, in the analysis of the superposition and combination of population factors, we need to pay attention to the differentiated effects of ombinations with the same number of factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 The action path of multiple elements\u003c/h2\u003e \u003cp\u003eFollowing Charles C. Ragin (2009), combinations with a consistency threshold above 0.7 are considered to have strong explanatory power. This resulted in 6 core population configuration paths that are considered to have strong effects on regional economic development (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In terms of configuration types and factor combination characteristics, the coverage of these 6 paths ranges from 0.2341 to 0.3155. This shows that population factors can impact economic development through multiple paths. It also confirms that the complexity and diversity of how population factors affect economic development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe configuration of population conditions affecting economic development\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eConfiguration Type\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCombination Configuration\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCoverage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Migration+High Population Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePositive Migration Growth+High Educational Level+\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNeutral Aging Structure+Neutral Natural Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Population Quantity+ High Population Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIncreased Natural Growth+Positive Migration Growth\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+Young Aging Structure+High Educational Level\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Natural Growth+ High Population Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIncreased Natural Growth+High Educational Level\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+Neutral Aging Structure+Neutral Migration Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Population Quantity\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+Young Age Structure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIncreased Natural Growth+Positive Migration Growth\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+Young Aging Structure+Neutral Educational Level\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eYoung Age Structure+High Population Quality\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYoung Aging Structure+High Educational Level+\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eNeutral Natural Change+Neutral Migration Change\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePath 6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Natural Growth+High\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003ePopulation Quality+Young Age Structure\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIncreased Natural Growth+Young Aging Structure+\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHigh Educational Level+Neutral Migration Growth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the population configuration paths affecting regional economic development, there is a clear order in the impacts of population change factors on economic development. High educational level again is the primary dominant factor. It participates in 5 paths (Paths 1, 2, 4, 5, and 6) with an average coverage of 0.2783. It is involved in almost all paths leading to high economic development. This shows that population quality is the core driving force for population conditions to promote economic development. A young age structure follows closely as the second most important core factor. It participates in 4 paths (Paths 1, 2, 3, and 5) with an average coverage of 0.2836. It is a core element that can effectively match both high population quantity and high population quality. This reflects the quality and vitality of labor supply among the population quantity change factors, positive migration and natural growth are basic elements for forming high population quantity. However, their impact on economic development only appears in some paths (1, 3, and 6). They also have strong substitutability. For example, regions with negative population growth can compensate by attracting migrating labor. This stabilizes economic development. Both play auxiliary roles. Notably, the importance of migration change is significantly higher than that of natural change. Positive migration can not only expand the population quantity but also introduce high-quality labor. Natural growth only affects the population quantify and has limited effect on improving labor quality. Therefore, migration change has a more prominent impact in the dimension of population quantity compared with natural change.\u003c/p\u003e \u003cp\u003eThe impact of population factors on economic development is not driven by a single factor independently. Instead, it is achieved through the synergy of core factors and the matching of auxiliary factors. For example, taking \"high educational level\" as the core factor, it is necessary to match different auxiliary factors (Path 4, Path 5, Path 6). These paths have a similar regional scope of impact on economic growth (coverage value is approximately 0.24). They are respectively suitable for different scenarios, such as strong population absorption capacity, abundant high-quality labor force, and sound natural population growth. If the core role of \"high educational level\" is weakened, even if the total population size is expanded through the dual effects of natural growth and positive migration growth with labor vitality guaranteed by a younger population structure, the scope of economically growing regions that can be explained will decrease (Path 3). This is due to the lack of synergy between core factors and auxiliary factors. Among these paths, the dual-core drive of \"educational change\u0026thinsp;+\u0026thinsp;age structure change\", coupled with migration-led complementation of population quantity, forms the most effective paths for population factors to promote economic development. This echoes the findings from the earlier analysis of factor superposition combinations. It confirms the crucial and important role of population quality improvement and labor structure optimization in economic development.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. The Impacts of the Action Path of Multiple Elements on Economic Development Across Different Temporal and Spatial Dimensions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe impact of overlapping combinations of multiple population factors on economic development is not spatially and temporally homogeneous, showing significant differences in both temporal evolution and spatial distribution. From a temporal perspective, this heterogeneity is reflected in the dynamic evolutionary characteristics of the impact paths of overlapping population factors, which can be further revealed by analyzing changes in regression coefficients. From a spatial perspective, this heterogeneity is manifested in the obvious differentiation of the impact effects of the same population factor combinations across different regions,and the specific manifestations of such differences can be clearly identified through regional comparative analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Temporal evolution of the impact of multi-factor population combination paths on economic development\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the aforementioned six types of impact paths of multi-population factor combinations on economic development (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), this study selects three key periods based on the time dimension: 2007\u0026ndash;2011, 2012\u0026ndash;2016, and 2017\u0026ndash;2021. It systematically analyzes and summarizes the dynamic evolution and phased characteristics of the multi-factor combination impact paths. The specific results are as follows.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring 2007\u0026ndash;2011, the combined effect of population quantity factors, dominated by 'natural growth and migration' combination, was distinctly prominent. The synergistic driving force of this factor combination hit its peak. This shows that this stage was the prime time for population quantity to influence the economy. Specifically, the paths involving high population quantity and age structure (Path 1, Path 2, Path 3, Path 5) acted as core drivers. They formed high-impact clusters in areas (marked in red for subfigures in the first column of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) such as Jinzhou, Huludao, Yingkou and Panjin, with impact intensity generally ranging from 0.7 to 1.0. However, the paths involving population migration and quality (Path 4, Path 6) had weaker effects. They mostly appeared as light yellow and light green low-value zones, with intensities ranging from 0.2 to 0.4. Dalian remained at a medium-to-low level during this stage. This shows that population quality and mobility did not yet play a significant role.\u003c/p\u003e \u003cp\u003eThe period from 2012 to 2016 marked a turning point (the second column of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The impact intensity of all paths showed a downward trend. Among them, the influence driven by natural population factors weakened more significantly, indicating that this stage marked the beginning of a decline phase for the superposition effect of population quantity factors. This stage was clearly marked by a decline in the impact intensity of all paths. The high-value zones (marked as red) basically disappeared. Blue and light green low-value zones (intensity 0.1\u0026ndash;0.3) covered more than 80% of the area. The positive driving effect weakened greatly. The negative inhibitory effects expanded significantly. At the same time, this stage also showed characteristics of \"factor transition\". In the original high-impact areas (Jinzhou, Huludao), the impact intensity of Path 1 and Path 2 dropped from above 0.8 to below 0.3. In Dalian, Path 3 and Path 5 also fell from 0.6 to around 0.4. These changes reflect that the traditional quantity-driven model can no longer meet the new requirements of economic development. The driving role of quality-based factors has not yet formed.\u003c/p\u003e \u003cp\u003eFrom 2017 to 2021, the quantitative-driven model basically ended. It shifted to a quality-driven model centered on high-quality population factors. This stage was marked by the results of this shift. Pathways involving high migration and high population quality (Path 3, Path 4, Path 5) became the new core. They formed a red high-impact zone (intensity 0.8-1.0) in Jinzhou, Dandong, and Dalian (marked in red for subfigures in the third column of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This shows that quality-driven factors began to have a strong influence on the economy. In contrast, pathways involving population quantity and natural growth (Path 1, Path 2, Path 6) remained low. Only Dandong had a local orange median zone (intensity 0.5\u0026ndash;0.6). This indicates that the influence of traditional quantity-driven factors continued to decline. The overall evolution shows a clear pattern: strong in the early period (2007\u0026ndash;2011), weak in the middle period (2012\u0026ndash;2016), and extremely weak in the later period (2017\u0026ndash;2021).\u003c/p\u003e \u003cp\u003eIn summary, the driving effects of multiple population factors on the regional economy from 2007 to 2021 in this study area showed a distinct temporal evolution pattern. In the early stage, population quantity factors were the core driver with a prominent and peaked effect. In the middle stage, the superimposed effects of quantity factors declined, the influence of all driving paths weakened comprehensively, showing a transitional feature of multi-factor transformation. In the later stage, the quantity-driven model ended, and the economy officially shifted to a quality-driven model centered on population migration and quality. High-quality population factors became the new core driver, and a fundamental shift took place in the driving logic of population factors on the economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Spatial differentiation characteristics of the impact of multi-factor population combination paths on economic development\u003c/h2\u003e \u003cp\u003eFrom a spatial perspective, the impacts of the same population factor combinations show significant spatial differences across different regions. Based on the spatial distributionof the six multi-population factor paths (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a comparison of six cities\u0026mdash;Jinzhou, Panjin, Huludao, Yingkou, Dandong and Dalian\u0026mdash;reveals that such spatial differences are mainly reflected in the impact intensity gradient, area of agglomeration and spatial differentiation patterns.\u003c/p\u003e \u003cp\u003eThe impact intensity gradients show distinct differences, forming a multi-level pattern of \"high-medium-low\" clusters. Taking Path 1 (\u003cem\u003ehigh population quantity+high population quality\u003c/em\u003e), the path with the strongest initial influence among all paths, as an example, this path showed an obvious three-level intensity gradient across six cities from 2007 to 2011. Huludao, Jinzhou, Panjin and Yingkou were high-intensity areas (marked as red and yellow), with impact factors (standardized regression coefficients) in the ultra-high range of 7.152 to 21.275. Dandong and Dalian were medium-intensity areas (marked as yellow and green), with impact factors ranging from 3.901 to 7.151. The maximum difference in impact factors between high and low-intensity areas reached 17.374, indicating significant regional differences in intensity. Similarly, Path 6 (\u003cem\u003eHigh Natural Growth+ High Population Quality\u003c/em\u003e) also presented an intensity gradient of \"high in the middle and low in the north and south\" during the same period. The impact factors of Panjin and Yingkou (1.9 to 2.9) were more than twice those of Dandong and Dalian (below 1.0). This confirms that the influence intensity of the same factor combination varies substantially across regions.\u003c/p\u003e \u003cp\u003eThe areas of agglomeration show distinct spatial patterns, forming a differentiated agglomeration pattern of \"north-central-south\" in the study area. High-impact zones of different population factor combinations show obvious spatial regional preferences. For example, the high-impact zones of Path 1 (\u003cem\u003ehigh population quantity+high population quality\u003c/em\u003e) and Path 4 (\u003cem\u003eHigh Migration+High Population Quality\u003c/em\u003e) are both concentrated in Jinzhou and Huludao in the north. Central and southern cities are at medium and low impact levels. They form an agglomeration pattern with the north as the core. The high-impact zones of Path 2 (\u003cem\u003eHigh Natural Growth+High Population Quality+Young Age Structure\u003c/em\u003e) and Path 6 (\u003cem\u003eHigh Natural Growth+Hig\u003c/em\u003eh \u003cem\u003ePopulation Quality\u003c/em\u003e) are focused on Panjin and Yingkou in the central region. Northern cities are medium-impact zones, and southern cities are low-impact zones. Their core agglomeration areas are in sharp contrast to those of the northern agglomeration paths. Path 5 (\u003cem\u003eYoung Age Structure+High Population Quality\u003c/em\u003e) is the only path with southern agglomeration characteristics. Its high-impact zones are concentrated in Dalian and Dandong. Northern and central cities are both at medium and low impact levels, exhibiting. a spatially differentiation pattern with distinction between northern and central areas.\u003c/p\u003e \u003cp\u003eThe spatial differentiation patterns are distinct, presenting the characteristic of \"areal agglomeration- punctiform scattering\". The spatial distribution patterns of the same factor combination vary significantly across different regions. In the core northern, central and southern areas with concentrated high-impact zones, the impact effects mostly show a contiguous areal distribution. For instance, the high-impact zones of Path 1 in Jinzhou and Huludao form a continuous areal distribution. In cities outside these core areas, the impact effects are mostly scattered in isolated spots. For example, the impact zone of Path 5 in Jinzhou, a northern city, is only a local spotted area. It does not form a contiguous pattern. In addition, the special Path 3 (\u003cem\u003eHigh Population Quantity+Young Age Structure\u003c/em\u003e) presents unique spatial differentiation. It shows a reverse differentiation pattern of \"positive in the south and negative in the central region\". Dalian and Dandong are spotted areas with positive impact. Panjin and Yingkou are areal areas with negative impacts. This spatial differentiation with coexistence of positive and negative effects further confirms the significant regional heterogeneity of the impact effects of the same population factor combination.\u003c/p\u003e \u003cp\u003eIn summary, during the study period, the spatial impact pattern of multi-factor population combination paths on economic development shows distinct spatial evolution characteristics. In the early stage, the impact intensity stays at a high-value range. Core clustering spaces are concentrated in western and south-central Liaoning cities. They present a distribution pattern of local high-value clustering. In the middle stage, the overall impact intensity drops to a low-value range. The whole region shows a trend of low-value diffusion. Core clustering areas begin to show the transitional feature of spatial expansion and shift. In the later stage, the impact intensity of core driving paths rebounds to a high-value range. Clustering spaces expand eastward to coastal cities in eastern Liaoning. The distribution pattern turns to a clustering model mainly driven by population quality. The regional spatial topological structure completes the systematic transformation of self-organized optimization and factor restructuring. These characteristics of spatial patterns are the results of the transition from the combined effects of population quantity factors to quality factors over time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis paper focuses on the relationship between multi-factor population combinations and regional economic development in the study area from 2007 to 2021. It conducts empirical analysis from four dimensions. These dimensions are the stability of factor combinations, the order of importance, the characteristics of temporal evolution, and that of spatial differentiation. The research conclusions obtained not only verify existing academic achievements, but also supplement, expand and deepen the existing cognition. The main research contributions are reflected in the following aspects.\u003c/p\u003e \u003cp\u003eFirst, compared with analyses focusing on the impact of a single population factor on economic development, the multi-factor combination approach can better describe the complex driving mechanism of population factors. It also makes up for the gaps in existing research on factor synergy and differential effects. This study identifies three core demographic factors that affect economic development: population size, age structure, and educational attainment. This is consistent with the consensus of existing demographic economics. That is, demographic characteristics are key drivers of long-term economic growth (Bloom et al.,2003; Barro and Lee,2015; Ronald,2002). Bloom et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) emphasized that age structure (demographic dividend) promotes growth through labor supply. Barro (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) confirmed that educational attainment (human capital) can improve labor productivity. Ronald (2002) explored the complex relationship between population size and the economy. These studies are important in understanding the impacts of these core factors from a single factor perspective. This study finds that these three core factors can form a stable driving mechanism for economic development when they function in combination. Moreover, multiple demographic factors do not act in isolation. Instead, they have a certain hierarchical and synergistic relationship. For example, \"changes in age structure\" and \"changes in educational attainment\" are the basic \"dual cores\" that constitute highly stable impacts. On this basis, combining with factors reflecting changes in population size (migration or natural growth) can form a more complete driving pathway. In addition, the study finds that under the dimension of population size, there is an asymmetric effect between migration and natural growth. Migration improves the stability of the combination, while natural growth plays a weaker role. These findings add to the existing understanding of the differential effects within similar factors (Lutz et al.,2008;Wang and Li,2018).\u003c/p\u003e \u003cp\u003eSecond, it is generally recognized that population quality drives economic development better than population quantity. But, there is a lack of systematic quantitative tests on the importance ranking of multiple population factors and the differential effects of quantity changes. This study is consistent with existing research that \"population quality has a better driving effect than quantity\" (Ding et al.,2022; Lutz et al.,2008). It also confirms through empirical quantification that high education level is the primary leading factor, and age structure is the second core factor. In terms of quantity factors, the importance of population migration change is significantly higher than that of natural population change. This refines the existing understanding of the roles of population factors in economic development. Existing studies have also noticed the heterogeneity of quantity changes and the impacts of migration and natural growth in the stage of population negative growth (Zhang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu and Yuan,2020). This study supplements and confirms that positive migration and natural growth have substitutability in the path of economic development. At the same time, this study also shows the differential effects and substitution mechanism between the two. Regions with negative population growth can make up for the quantity gap and stabilize economic development by attracting migrant populations. Moreover, migration has a much stronger driving effect on economic development than natural growth, which only affects quantity, because migration has the dual effects of \"expanding size\" and \"improving quality\". These findings improve the understanding of effects of sub-factors of population quantity changes.\u003c/p\u003e \u003cp\u003eThird, in temporal studies on the relationship between population and economy, it is generally recognized that population quality and population migration will gradually replace population quantity and will become the core population driving forces for economic development (Huang and Duan,2022; Bloom et al.,2010). The empirical results of this paper further verify and expand on this view. This study, through the analysis of transformation paths, reveals an important evolution turning point and transition stage characteristics of the population-driven model. It shows that the peak period (2007\u0026ndash;2011), recession turning point (2012\u0026ndash;2016) of the population quantity-driven effect, and the formation period (2017\u0026ndash;2021) of the quality-driven effect. It depicts the characteristics of the transition stage, that is, \"weakened quantity-driven effect and unformed quality-driven effect\". At the same time, it quantifies the impact intensity of each factor combination in different stages. It presents the fundamental transformation of population driving from \"quantity-oriented\" to \"quality-oriented\". This paper also responds to the research suggestions put forward by some scholars: \"It is necessary to strengthen research on the differentiation of population-driven effects at different time sequences at the regional scale.\" (Chi and Ventura,2011;Liu et al.,2022). It provides a referenceable analytical framework and empirical reference for research on the coordinated development of population and economy in similar regions.\u003c/p\u003e \u003cp\u003eFinally, existing studies have generally confirmed that the economic effects of population factors exhibit significant spatial heterogeneity. They have also shown that the spatial concentration characteristics of population factors are highly coupled with the regional economic development pattern (Yang et al.,2022; Burgi and Gorgulu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, the spatial adaptability between population and economy in the Liaozhongnan region shows obvious urban hierarchy and regional differentiation characteristics. The impact of population factors varies with spatial location (Liang et al.,2024). The empirical results of this study further verify and expand the above understanding.\u003c/p\u003e \u003cp\u003eThis study analyzes the spatial effects of multi-factor population combinations from three dimensions: impact intensity gradient, concentration area, and spatial differentiation pattern. It identifies that under similar population factor combinations, core economic concentration zones show path-dependent spatial shifts and hierarchical differentiation. It confirms that population quality is the key factor driving the dynamic gradient evolution of spatial agglomeration patterns. The results show that spatial differentiation differs significantly across different population factor combination paths. Some paths exhibit a unique reverse differentiation pattern, while non-core combination paths remain at a low impact gradient. These findings deepen our understanding of the spatial differentiation mechanism of population economic effects from a multi-factor perspective. They also respond to calls for stronger spatial heterogeneity analysis (Chen et al.,2024). The conclusions can provide general empirical references for the coordinated population-economic development in similar regions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis paper analyzes the relationship between population factors and economic development, revealing the driving mechanism of multi-factor population changes on regional economy. To this end, this paper employed two methods: Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) to examine the combined impacts of population multifactor changes on economic development.\u003c/p\u003e \u003cp\u003eThe research shows that the driving effect of population factors on economic development does not rely on the independent role of a single factor. Instead, it is achieved through the combination of core factors and the complement of auxiliary factors. Among them, the dual-core drive of \"changes in educational level and changes in age structure\", combined with the population size dominated by migration, is one of the more effective paths for population-driven economic growth in the study area. The order of priority for population factor combinations is clear. Changes in educational level are the primary leading factor, followed by changes in age structure. Among population quantity factors, the driving effect of population migration is stronger than that of natural population change. From a temporal perspective, the population driving effect during the research period went through three stages. It started with a golden period dominated by population quantity factors. Then it entered a transition period where the quantity-driven effect weakened. Finally, it turned into a quality-driven period centered on population migration and population quality. From a spatial perspective, due to differences in the spatial realization characteristics of various population factors, the impact of the same population factor combination varies significantly in different regions. In the study area, it has formed a multi-level intensity gradient of \"high-medium-low\" and a differentiated agglomeration pattern of \"north-center-south\". The core agglomeration area has gradually expanded from western and central-southern Liaoning to eastern Liaoning coastal areas.\u003c/p\u003e \u003cp\u003eThe multi-factor combination research method adopted in this paper identifies the configurational effects of multi-factor combinations. It makes up for the deficiencies of traditional research methods in revealing the synergistic effect of factors. It also provides a new analytical perspective for the research on the relationship between population factors and regional economic development. However, in the process of research application, the following points need to be noted. The selection of factor combinations has a significant impact on the stability of economic effects. Different factor combinations will directly affect the robustness and explanatory power of the economic effects of the paths. When core factors and auxiliary factors are reasonably matched, the identification of economic effects is more stable and reliable. This makes the research conclusions convincing.\u003c/p\u003e \u003cp\u003eThe findings from this study are based on the time interval of 10 years (2007\u0026mdash;2017) from a specific study area in the coastal region of Northeastern China. While efforts have been made to select the temporal period and regional coverage as representative as possible, interpretation of the findings is still constrained by the temporal length of the data and the regional coverage. The other aspect of caution is that the control variables currently only contain industrial structure, living standards, fiscal expenditure, technological innovation and in the future can be expanded to include more potential factors such as policies and urban planning. This will help improve the universality and depth of the research findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGY.B. and DX.Z. contributed to the conception and design of the study, conducted the data analysis, and wrote the main manuscript text. AX.Z. contributed to the interpretation of the results and critically revised the manuscript for important content. All authors reviewed the manuscript, provided valuable feedback, and approved the final version for submission. All authors agree to be accountable for the integrity and accuracy of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by National Natural Science Foundation of China [grant number 42471221].\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability StatementAll data supporting the findings of this study are obtained from public and authoritative sources. The primary raw dataset (Raw data.xlsx) is derived from two core sources: (1)the Liaoning Provincial Statistical Yearbook (2007\u0026mdash;2022), accessible via the Liaoning Provincial Bureau of Statistics official website (https://tjj.ln.gov.cn/tjj/tjsj/tjnj/index.shtml); (2)the 2007\u0026mdash;2022 Statistical Bulletins on National Economic and Social Development of Dalian, Dandong, Jinzhou, Yingkou, Panjin and Huludao, which are publicly available on the Liaoning Provincial Government portal.The calibrated fsQCA dataset (fcQCA Raw data.csv) and the processed GTWR dataset (Raw GTWR.xls) generated during the analysis are attached in the supplementary materials of this article. All data used in this study are publicly available and non-confidential, with no proprietary or restricted data included in the analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Restrepo P (2017) Secular Stagnation? 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Int Migrat Rev 58(2):734\u0026ndash;763\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9056444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9056444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe driving effects of population factors on economic development are more from the combined forces of multiple population factors than from individual factors. This paper examines the impacts of the changes in population factors from a multi-factor synergy perspective using the Liaoning Coastal Economic Belt of China as the study area over the period of 2007 through 2017. The factors used include population quantity, structure, quality, and migration. The Fuzzy Set Qualitative Comparative Analysis (fsQCA) and Geographically and Temporally Weighted Regression (GTWR) were combined to explore this multi-factor synergy. The results showed that: 1) Population factors drove economic development not through the independent action of single factors, but through core-factor synergy and auxiliary-factor complementation. The most effective driving path consisted of dual-core drivers: change in education level and change in age structure, combined with population quantity change complemented by migration. 2) The importance of population factors was clearly ranked. Change in education level was the primary dominant factor, followed by change in age structure. The driving effect of population migration was stronger than that of natural population change. 3) From the temporal dimension, the population driving effect went through three stages: a golden period dominated by population quantity, a transition period with weakened quantity-driven effects, and a quality-driven period centered on population mobility and quality. 4) From the spatial dimension, the impact of the same population factor combination showed significant regional differences. It formed a multi-level intensity gradient of \"high-medium-low\" relating to the spatial differentiation of combination effects due to changes of these multi population factors over space. Through this multi-factor analysis approach, this paper shows the inner working of multi-factor driving mechanism of population factors on economic development. The findings on the inner working of the changes in population factors on economic development provide support for economic management through population policy during the era of rapid changes in population.\u003c/p\u003e","manuscriptTitle":"Impacts of Population Multifactor Changes on Economic Development: The Case of China's Liaoning Coastal Economic Zone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:06:44","doi":"10.21203/rs.3.rs-9056444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T13:51:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T14:40:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T15:32:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34527015827488239361184678552893405774","date":"2026-03-27T05:01:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276473086586701580086865261321939503397","date":"2026-03-24T12:57:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T11:30:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T11:18:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-24T10:56:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T11:59:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-03-20T11:13:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a7df4fd0-c02f-4a83-972c-ec8a8cdc48ef","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65088175,"name":"Social science/Development studies"},{"id":65088176,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65088177,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":65088178,"name":"Scientific community and society/Geography"},{"id":65088179,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-05-05T11:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:06:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9056444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9056444","identity":"rs-9056444","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}