Preamble

The Impact and Mechanisms of Population Growth and Decline Differentiation on the Economic Resilience of the Lanzhou-Xining Urban Agglomeration

School of Resources and Environmental Sciences, Lanzhou University, Lanzhou, Gansu
School of Economics, Lanzhou University, Lanzhou, Gansu

Introduction

In the context of global economic fluctuations and regional development disparities, the relationship between demographic shifts and economic stability has become a focal point of academic inquiry. The Lanzhou-Xining (Lan-Xi) urban agglomeration, as a critical strategic node in Western China, faces unique challenges characterized by significant internal differentiation in population growth and decline. Understanding how these demographic dynamics influence urban economic resilience is essential for fostering sustainable regional development and enhancing the ability of cities to withstand external shocks.

Economic resilience refers to the capacity of an urban system to absorb, recover from, and adapt to socio-economic disturbances. Population, as a fundamental factor of production and a primary driver of consumption, plays a dual role in this process. While population growth can provide a "demographic dividend" that bolsters labor supply and market demand, population decline—often accompanied by aging and brain drain—may undermine a city's adaptive capacity and long-term growth potential.

Theoretical Framework and Mechanisms

The impact of population differentiation on economic resilience operates through several key pathways. First, the labor market mechanism suggests that population growth provides a diverse pool of human capital, which is vital for innovation and industrial restructuring during economic downturns. Conversely, shrinking cities often face labor shortages and a mismatch between available skills and market needs, weakening their recovery potential.

Second, the consumption and investment mechanism highlights that population concentration stimulates local demand and infrastructure investment. In the Lan-Xi urban agglomeration, the concentration of population in core cities like Lanzhou and Xining creates agglomeration economies, whereas peripheral cities experiencing population loss may suffer from a "vicious cycle" of declining public services and reduced investment attractiveness.

Third, the innovation and industrial structure mechanism posits that demographic vitality is closely linked to a city's ability to transition toward high-value-added industries. Cities with growing, youthful populations are more likely to adopt new technologies and diversify their economic bases, thereby enhancing their structural resilience against sector-specific shocks.

Data and Methodology

This study utilizes panel data from the cities within the Lan-Xi urban agglomeration. To measure urban economic resilience, we construct a multi-dimensional indicator system encompassing resistance, recovery, and reorganization capabilities. Population differentiation

3. 青藏高原人文环境数据智能实验室，甘肃兰州

In the process of accelerating the construction of a new development pattern and promoting high-quality development, human capital has emerged as the core element of competition between cities. In recent years, the Lan-Xi (Lanzhou-Xining) urban agglomeration has faced severe population loss and insufficient human capital competitiveness, which has directly impacted the urban economic resilience of the region. Selecting typical cities within the Lan-Xi urban agglomeration as the research area, this study utilizes a linear mixed-effects model to explore the impact of population growth and decline differentiation on urban economic resilience from 2011 to 2020, as well as the underlying pathways of these effects. The results indicate that:

Between 2011 and 2020, the urban economic resilience of major cities in the Lan-Xi urban agglomeration generally showed an upward trend, forming a "core-periphery" structure with Lanzhou and Xining as the cores and the remaining cities as the periphery. First, population growth and decline differentiation directly affect the urban economic resilience of major cities in the Lan-Xi urban agglomeration, with population inflow exerting a positive externality on resilience. Second, the degree of industrial structure rationalization does not alter the intensity of the impact of population changes on urban economic resilience; for population-shrinking cities within the Lan-Xi urban agglomeration, initial population loss can promote industrial structure rationalization to a certain extent, but this mode of industrial adjustment is not sustainable. Third, population changes influence the level of local informatization, which in turn affects urban economic resilience; an increase in population is conducive to improving the level of urban informatization, thereby enhancing urban economic resilience. Based on these research findings, the urban economic resilience of major cities in the Lan-Xi urban agglomeration should be improved by promoting coordinated regional development, emphasizing human capital accumulation, accelerating the transformation and upgrading of industrial structures, and prioritizing the synergistic advancement of population policy and informatization construction.

关键词

Population Dynamics; Urban Economic Resilience; Impact Pathways; Lan-Xi Urban Agglomeration

Article Number: [Number]

China's economy has transitioned from a phase of high-speed growth to one of high-quality development. In this new era, General Secretary Xi Jinping has explicitly stated that China's economic development has entered a "new normal" characterized by shifting speeds, structural optimization, and a transition in growth drivers. In the process of accelerating the construction of a new development pattern and promoting high-quality development, human capital serves as a vital source and enduring momentum for economic growth. As China's urbanization enters its middle-to-late stages of development, a massive influx of population and resources is rapidly concentrating in eastern regions, particularly in major cities. This phenomenon has left many cities, especially those in western China, facing significant pressure from population loss. The shortage of local labor and high-tech talent has become a critical factor constraining the sustainable and healthy socio-economic development of certain western cities. Furthermore, this shortage hinders industrial upgrading, creating a distinct "bottleneck" effect.

The term "resilience" originates from the Latin resilio, meaning "to return to the original state." As a branch of resilience, economic resilience is defined as the unique ability of an economic system to maintain stability when facing and adapting to various uncertain shocks, and to further drive its recovery into a more robust stage of development. Building upon the concept of economic resilience, Reggiani creatively proposed the concept of urban economic resilience, defined as "the ability of a regional economic system to return to its pre-disturbance state after suffering interference or damage." The differentiation between population growth and decline has a significant impact on urban economic resilience. Currently, research by domestic and international scholars regarding the relationship between population dynamics and urban economic resilience is still in its infancy, with existing studies focusing primarily on theoretical analysis while lacking dynamic research and regional analysis.

Using data to explore the impact of population agglomeration on urban economic resilience, the research results indicate that population agglomeration enhances urban economic resilience by stimulating consumer demand and promoting [further economic activities].

The Impact and Pathways of Population Differentiation on the Urban Economic Resilience of the Lan-Xi Urban Agglomeration

Population growth has improved urban economic resilience. By establishing a theoretical framework and measurement model to study the impact of population agglomeration on urban economies, research has demonstrated that internal population concentration promotes urban economic growth. High levels of population agglomeration can enhance urban economic resilience, making cities more stable when facing various challenges. Researchers have further discovered that, compared to economic recovery capacity, the growth or contraction of a city's population has a more pronounced impact on its risk resistance. In this process, industrial structural configuration and human capital levels play a positive moderating role in the relationship between population dynamics and urban economic resilience. While a positive correlation generally exists between urban economic resilience and population growth, there are also instances where urban economic resilience diverges from population trends.

Cities in China can be categorized by population expansion. The upper reaches of the Yellow River refer to the basin area above Hekou Town in Tuoketuo County, Hohhot, Inner Mongolia Autonomous Region. Compared to the middle and lower reaches, the upper Yellow River region is home to numerous ethnic groups, remains relatively economically underdeveloped, and faces complex and severe environmental issues. The contradiction between ecological environmental protection and economic development is exceptionally prominent here. The Lanzhou-Xining (Lan-Xi) Urban Agglomeration is a vital growth pole for economic development in the upper Yellow River region. However, due to unbalanced regional development and the concentration of resources in eastern China, most cities within the Lan-Xi Urban Agglomeration have become typical areas of population outflow. Local emigration leads to a massive loss of human capital, which directly affects the industrial transformation and high-quality economic development of the region. This study selects typical cities within the Lan-Xi Urban Agglomeration, including the Hainan Tibetan Autonomous Prefecture, as the research area. By evaluating the degree of population loss in the major cities of the Lan-Xi Urban Agglomeration and determining its impact on urban economic resilience, this research aims to provide theoretical support and policy recommendations for enhancing urban economic resilience and promoting high-quality economic development in the region.

1 研究区概况

Introduction

The Lanzhou-Xining (Lan-Xi) urban agglomeration is an economic belt centered around the cities of Lanzhou and Xining, encompassing various prefectures and cities such as the Linxia Hui Autonomous Prefecture. As a critical inter-provincial economic and cultural hub in Western China, the development of the Lan-Xi urban agglomeration significantly influences the economic trajectories of Gansu and Qinghai provinces. Furthermore, its growth serves as a catalyst for economic expansion in the neighboring regions of Tibet and Xinjiang. The strategic development of this region is of paramount importance for promoting ethnic unity, ensuring national territorial security in the west, and maintaining stability and development across Southwest China.

Research Area and Data Availability

In accordance with the "Lanzhou-Xining Urban Agglomeration Development Plan" and considering the availability of data, this study selects the Lan-Xi urban agglomeration—specifically including the Hainan Tibetan Autonomous Prefecture—as the primary research area. The objective is to analyze the underlying mechanisms through which population fluctuations impact urban economic resilience. By examining these dynamics, this research aims to provide policy optimization and empirical support for local efforts to mitigate population loss, attract high-level talent, and facilitate industrial transformation.

[FIGURE:1]

The base map used in this study was produced based on the standard map (Review Number: GS(2019)1822) obtained from the Standard Map Service website of the Ministry of Natural Resources. The boundaries of the base map have not been modified.

2 数据与方法

The data for this study are primarily derived from the 2011–2021 official releases of the Gansu Statistical Yearbook and the Qinghai Statistical Yearbook. Additionally, this research integrates multi-source information, including economic census materials, departmental annual reports, and specialized survey data provided by statistical departments at the municipal and county levels. This study employs three scenario simulation analyses to examine the impact and pathways of population growth and decline differentiation on the urban economic resilience of the Lanzhou-Xining (Lan-Xi) urban agglomeration.

First, the analysis focuses on the impact of population dynamics on urban economic resilience. In this process, population growth may enhance the risk-resistance capacity of the economic system, whereas population decline may weaken the system's adaptability and recovery potential. Second, the study emphasizes the influence of the degree of industrial structure rationalization within the region on urban economic resilience. Population changes affect the allocation efficiency of production factors and industrial matching by adjusting the supply scale and skill structure of the labor force. The rationalization of the industrial structure is influenced by these dynamics, which in turn may affect urban economic resilience.

Third, the study analyzes how population changes affect urban economic resilience under varying levels of regional informatization. The degree of informatization reflects the level of social development and the effectiveness of social progress. A higher degree of informatization helps cities reduce innovation costs and optimize resource allocation efficiency, potentially enhancing the stability of the urban economy.

Regarding the calculation methods for urban economic resilience, they can generally be categorized into single-variable indicator methods and multi-variable indicator methods. While the single-variable approach is relatively intuitive and yields concise results, it fails to directly reflect or characterize the multi-dimensional nature of urban economic resilience. Consequently, this study constructs a comprehensive evaluation index system for urban economic resilience based on four dimensions: economic stability, economic innovativeness, economic recoverability, and economic liquidity.

1. Economic Stability: Measured by the registered urban unemployment rate, fiscal deficit ratio, and educational expenditure.
2. Economic Innovativeness: Measured by R&D investment, innovation output, and per capita fixed asset investment.
3. Economic Recoverability: Measured by per capita total retail sales of consumer goods, the proportion of employment in private enterprises and self-employment, and the balance of loans from financial institutions.
4. Economic Liquidity: Measured by per capita freight volume.

The construction of these indicators refers to the index systems summarized by Xu Guihua et al. regarding shock recovery capabilities, incorporates relevant indicators of economic liquidity proposed by Song Yuru et al., and integrates the framework of Cai Yongmei et al. Ultimately, 12 secondary indicators were established to comprehensively measure the economic resilience development levels of the nine major cities (including Hainan Tibetan Autonomous Prefecture) in the Lan-Xi urban agglomeration.

Economic stability relates to the degree of coordinated development between a city's total economic demand and total supply. When demand and supply are well-coordinated, urban economic resilience remains relatively stable, allowing the city to exert strong self-coordination capabilities. Internal stability factors, such as steady economic development, low inflation and unemployment rates, and minimal economic fluctuations, encourage the maintenance of robust stability. This study selects the inflation rate, registered urban unemployment rate, and fiscal deficit ratio as the three secondary indicators for measurement. The inflation rate reflects the rate of decline in monetary purchasing power; a lower inflation rate indicates better economic stability (treated as a positive indicator). The registered urban unemployment rate serves as a proxy for the vitality and stability of urban economic development; a lower unemployment rate signifies higher stability. Other indicators include total educational expenditure, patent applications, patent grants, total fixed asset investment, total retail sales of consumer goods, total annual tourist arrivals, $GDP/CPI$, employment in private enterprises, self-employment, and the annual balance of loans from financial institutions. Here, $CPI$ represents the Consumer Price Index; $(+)$ denotes a positive indicator, and $(-)$ denotes a negative indicator.

The fiscal deficit ratio reflects the internal stability of the urban economic system; a lower ratio indicates superior economic stability. Economic innovativeness serves as the core driving force for urban economic development. A city's level of economic innovation is primarily reflected in educational expenditure, R&D investment, and the output of innovation achievements. Improving economic innovativeness is conducive to enhancing urban economic resilience. From the perspective of local educational investment, increased funding can improve educational quality and cultivate a high-quality workforce with specialized knowledge and skills, thereby meeting the needs of industrial upgrading and enhancing industrial competitiveness. High educational investment also helps attract and retain talent, generating talent agglomeration and knowledge spillover effects that strengthen the city's risk resistance. Increased R&D investment plays a crucial role in transforming traditional industries, fostering emerging industries, and promoting the translation of innovation results, serving as a key lever for bolstering resilience. An increase in innovation output promotes the city's overall innovation capacity, ensuring stable development even when facing external shocks such as economic crises.

Economic recoverability refers to the capacity of an urban economic system to withstand shocks and achieve restorative growth when facing new environments or crises, such as financial meltdowns or the COVID-19 pandemic. This capacity is a direct manifestation of robust urban economic resilience. Within the urban economic system, investment levels and economic structures significantly influence this recovery capability. Regarding investment scale and structure, the expansion of investment and the optimization of its structure promote healthy socio-economic development and help hedge against unknown risks. Increased investment levels stimulate growth, acting as a primary engine for the economy. Furthermore, a diversified economic structure enhances the stability and recoverability of the system. This study selects per capita fixed asset investment, per capita total retail sales of consumer goods, and the proportion of private and individual employment as indicators of regional economic recoverability. Per capita fixed asset investment represents the scale of investment; larger values indicate stronger recovery capacity. An increase in per capita retail sales signifies expanding consumer demand and heightened economic activity. Increased tourism expands consumption, promotes investment, and optimizes the economic structure, thereby increasing government revenue and recovery potential. Higher levels of $GDP$ per capita indicate a stronger economic foundation and recovery capability. A higher proportion of private and individual employment suggests stronger innovation, adaptability, and market vitality, which are conducive to restorative growth.

Economic liquidity provides essential support for the urban economic system to resist shocks and achieve resilient recovery. When responding to shocks, a strong capacity for capital absorption forms the foundation of liquidity. In the context of effective risk control, this is reflected in the scale of loan balances from financial institutions. A high loan balance reflects market demand and enhances the system's absorption capacity through capital circulation, thereby improving efficiency. This study uses the balance of loans from financial institutions and freight volume as indicators. Freight volume reflects the scale of urban logistics, mapping both the level of transport infrastructure and economic activity. Higher freight volume indicates more efficient material flow and better economic liquidity.

To calculate the degree of population change, considering the consistency in the statistical definitions of local permanent residents and registered household populations, this study uses the difference between the permanent population and the registered population to measure the extent of population growth or loss. The registered population refers to citizens registered with public security organs at their place of regular residence, reflecting the "intended" population within a jurisdiction. The permanent population refers to those who actually reside in an area for more than six months, including both registered residents and eligible non-registered residents. The difference between these two values reflects whether a population contraction has occurred. The following formula is used to calculate population growth and loss:

S i t = R p o p i t - P p o p i t （ 1 ）

The registered population of year $t$ and the permanent population of year $t$ are used to represent urban population loss and urban population growth, respectively. This study employs a linear mixed-effects model to investigate the impact of population fluctuations in the major cities of the Lanzhou-Xining urban agglomeration on urban economic resilience. Generally, a mixed-effects model consists of two components: fixed effects and random effects. The fixed effects characterize the influence of systemic variables, such as population changes, while the random effects capture the nested structures within the urban agglomeration and other non-systemic influencing factors.

The general form of the linear mixed-effects model is:

Y i = I n t e r c e p t + ∑ x i × β + z i × b i + ε i （ 2 ）

where $i$ represents the city, and the city's economic resilience is the dependent variable. In this model, $\mu_i$ denotes fixed effects, $\epsilon_{it}$ represents random effects, and $\nu_{it}$ is the individual residual term. Based on existing research and the foundational impact of population dynamics, this paper incorporates the degree of industrial structure rationalization as a mediating variable to analyze the underlying influence mechanisms.

Specifically, this study introduces both the degree of industrial structure rationalization and the level of local informatization to analyze how population changes affect urban economic resilience. The rationalization of industrial structure is defined by the balance of proportions between different industries and the degree of inter-industry coordination. Its specific mathematical representation is as follows:

R i t = ∑

i = 1

The degree of industrial structure rationalization during the period; the value-added during the period; the regional Gross Domestic Product (GDP) during the period; the number of employed persons during the period; and the number of employed persons during the period. The proportion of mobile phone users and internet users relative to the urban population is adopted as the indicator to measure the local level of informatization.

I i t = M i t P i t

The level of informatization during a given period is determined by the number of mobile phone users and internet users relative to the total population. This indicator comprehensively reflects the regional level of informatization development by calculating the combined proportion of these two user groups within the total population.

3 结果与分析

Evolutionary Characteristics and Spatial Patterns of Urban Economic Resilience

Analysis of urban economic resilience data for the Lanzhou-Xining (Lan-Xi) urban agglomeration reveals that, between 2010 and 2020, the overall economic resilience of the six primary cities followed an upward trend. However, significant disparities exist between individual cities. As the core hubs of the agglomeration, Lanzhou and Xining exhibited particularly pronounced growth in resilience. Notably, Lanzhou's economic resilience index rose from 2010 to 2020, demonstrating a near-doubling of its capacity. In contrast, other cities characterized by population loss showed more volatile or stagnant resilience levels. For instance, the Hainan Tibetan Autonomous Prefecture maintained a relatively high level of resilience in 2010, experienced a significant decline by 2015, and saw a moderate recovery by 2020.

[FIGURE:1]

The spatial distribution of economic resilience within the Lan-Xi urban agglomeration in 2010, 2015, and 2020 highlights a widening gap between the core and the periphery. Compared to other cities, the two core hubs—Lanzhou and Xining—demonstrate substantial advantages in economic resilience, characterized by stronger stability and innovation capabilities. Conversely, cities experiencing population outflow, such as Dingxi, generally exhibit lower levels of economic resilience. The overall spatial structure of the major cities in the Lan-Xi agglomeration is roughly symmetrical, yet it is dominated by a "dual-core polarization" effect. This creates a distinct gradient where economic resilience decreases progressively from the dual centers toward the surrounding peripheral regions.

The case of the Hainan Tibetan Autonomous Prefecture is particularly illustrative of the region's shifting dynamics. While it possessed higher resilience relative to its peers in 2010, its growth trajectory has since flattened, leading to a gradual marginalization of its economic standing. The economic development of Hainan Prefecture exhibits clear stage-based characteristics: in the early stages, its resilience was bolstered by mineral resource extraction, infrastructure investment, and the tourism industry. However, in recent years, as ecological protection efforts on the Qinghai-Tibet Plateau have intensified, the prefecture has faced multiple constraints regarding mineral exploitation and grassland utilization. The pressure of transitioning away from resource-dependent development models has directly impacted its urban economic resilience.

[TABLE:1]

The significant spatial heterogeneity in economic resilience across the Lan-Xi urban agglomeration reflects an internal state of unbalanced regional development. This spatial differentiation has evolved dynamically, shaped largely by the effects of demographic shifts. The impact of population change on urban economic resilience can be categorized into two primary mechanisms. First, the inflow of population and high-level talent directly increases a city's human capital stock. This expands the labor supply and introduces specialized skills and valuable resources, which in turn enhances local technical proficiency and innovation capacity. Consequently, these inflows strengthen the stability and creative potential of the urban economy. Conversely, sustained population outflow leads to the depletion of human capital, creating a restrictive environment for long-term economic resilience.

The Impact and Mechanisms of Population Growth and Decline on Urban Economic Resilience in the Lanxi Urban Agglomeration

Spatial Patterns and Evolution of Urban Economic Resilience

The spatial pattern and evolution of urban economic resilience in the Lanxi urban agglomeration reveal significant disparities. In regions where local industrial development is constrained, structural changes in the economy often lead to structural unemployment. This further triggers a continuous outflow of labor, which ultimately undermines the stability and recovery potential of the urban economy.

However, for certain areas experiencing population outflow, the loss of labor and human capital presents a complex set of challenges and opportunities. Through income transfers from migrant workers—often referred to as the "remittance effect"—the income levels of local residents may actually see a relative increase, thereby improving social welfare. This return of capital not only helps narrow the income gap among local residents but may also alleviate regional economic imbalances through a consumption-driven stimulus effect.

Empirical Analysis of Population Dynamics on Economic Resilience

To analyze the impact of population changes on urban economic resilience, this study employs a linear mixed-effects model. The model's structure incorporates random effects parameters to account for heterogeneity across both spatial (city) and temporal (year) dimensions. The selection of the model was guided by the Corrected Akaike Information Criterion (AICC) and Log-Likelihood values, which remained consistent across iterations.

Diagnostic tests of the residuals indicate that the linear mixed-effects model satisfies the assumption of a normal distribution. Consequently, this model was selected as the most robust framework for the analysis. All model parameters passed significance tests at the $p < 0.01$ level.

[TABLE:1]

The technical results of the model, where intercepts for cities and years are treated as random effects, are summarized in the following sections. Values provided in parentheses represent the standard errors of the parameter estimates. Symbols $$, $$, and $$ denote significance at the 1%, 5%, and 10% levels, respectively.

The regression coefficients for several key variables are negative, indicating a significant negative correlation with urban economic resilience. In cities experiencing population growth, such as Lanzhou and Xining, the dynamics of economic resilience differ markedly from those in declining regions. The results suggest that while population growth can provide a labor dividend, it also places pressure on urban infrastructure and public services, which may negatively impact resilience if not managed through strategic policy interventions.

Analysis of the Impact Mechanism of Population Change on Urban Economic Resilience

The results indicate that larger magnitudes of population growth exert a more pronounced promotional effect on urban economic resilience. Conversely, in cities experiencing population loss, an increase in the degree of depopulation leads to a gradual decline in economic resilience. The variance between groups is of the same order of magnitude as the range of population change, reflecting significant differences in the explanatory power of random effect terms across different cities. This variation is closely related to factors such as the level of economic development, industrial structural characteristics, and the resource and environmental foundations of different cities.

Analysis of Mediating Mechanisms

Based on the previous scenario simulations, the impact of population change on urban economic resilience primarily operates through the degree of industrial structure rationalization and the level of informatization. To verify the validity of these mediating mechanisms, this study constructs a dual-mediation effect model. The results are detailed in [TABLE:N].

In the baseline model without mediating variables, the direct effect of population change on urban economic resilience is $\beta = 0.452$ (p < 0.01), confirming a direct negative correlation between population decline and urban economic resilience. Notably, after introducing the mediating variables, the regression coefficient of the core explanatory variable decreases to $0.321$, representing a $29\%$ reduction compared to the baseline model. This confirms the existence of mediating effects, consistent with theoretical expectations.

The mediation analysis reveals the following specific pathways:
- Informatization Level: The mediating effect value for the level of informatization is significant. Population change leads to shifts in local informatization levels, which in turn affects urban economic resilience within a certain range. For population-growth cities, the increase in population promotes higher levels of informatization, thereby enhancing economic resilience to some extent. For population-loss cities within urban clusters, population reduction leads to a decrease in information flow and exchange, weakening the city's ability to respond to changes and shocks. The decline in informatization makes urban economic resilience more fragile.
- Industrial Structure Rationalization: The mediation test results show that the mediating role of industrial structure rationalization did not pass the significance test. The introduction of this variable did not significantly alter the estimated impact of population change on urban economic resilience. This suggests that the explanatory variables in the model act independently and each contributes significantly to explaining urban economic resilience.

Regional Differences and Sustainability

For cities like Lanzhou and Xining, population growth is a significant factor directly driving an increase in urban economic resilience. However, changes in the degree of industrial structure rationalization do not significantly manifest a promotional effect in the short term. For other cities, population loss is significantly and negatively correlated with urban economic resilience, a finding similar to the research results of Lu Fenggang.

A distinct finding for population-loss cities within the Lan-Xi (Lanzhou-Xining) urban cluster is that a certain degree of population outflow may temporarily promote industrial structure rationalization. This occurs because population loss forces local industrial enterprises to undergo structural adjustments to adapt to a shrinking labor environment. These adjustments often move toward higher efficiency and lower costs, thereby temporarily improving the rationalization of the industrial structure. However, this type of adjustment driven by population loss is unsustainable and cannot directly offset the broader negative impacts caused by depopulation. The standardized coefficients further indicate that the long-term erosion of human capital eventually undermines the city's capacity to withstand external shocks.

4 结论与启示

The overall trend of urban economic resilience across the typical cities within the Lanxi (Lanzhou-Xining) urban agglomeration has shown a consistent upward trajectory over the past period, as indicated by the standardized coefficients.

The impact of population dynamics on the urban economic resilience of the Lanxi urban agglomeration, along with its underlying mechanisms, reveals significant disparities between cities. A core-periphery structure has emerged, centered on the population-growth cities of Lanzhou and Xining, while the remaining population-loss cities are increasingly marginalized. Among these shrinking cities, the growth rate of urban economic resilience in the Hainan Tibetan Autonomous Prefecture is particularly sluggish. Population fluctuation is a critical factor influencing the economic resilience of major cities in the Lanxi agglomeration, and its effects can be analyzed through several dimensions. Population inflow generates positive externalities for urban economic resilience, enhancing economic stability and innovation capacity. Conversely, population outflow exerts negative externalities, placing significant pressure on the stability and recovery capabilities of urban economies.

While the rationalization of industrial structure was considered as a mediating variable, its influence did not reach a statistically significant level. Instead, the degree of industrial rationalization and population change appear to be independent factors affecting urban economic resilience. Consequently, the inclusion of industrial rationalization in the analysis does not alter the intensity of the impact that population change has on economic resilience. In the population-loss cities of the Lanxi agglomeration, initial stages of depopulation can, to some extent, promote industrial rationalization by driving industrial enterprises toward higher efficiency, lower energy consumption, and lower costs. However, this adjustment driven by population loss lacks sustainability and fails to effectively counteract the broader negative impacts of depopulation.

The degree of informatization plays a certain mediating role in influencing urban economic resilience, though this effect is relatively weak. Changes in regional population cause fluctuations in informatization levels, which in turn affect urban economic resilience within a certain range. When the population increases, the level of informatization rises, thereby strengthening the city's resilience. Conversely, a decrease in population leads to a reduction in information flow and exchange, weakening the city's ability to respond to changes and shocks. This decline in informatization further exacerbates the vulnerability of the urban economic resilience.

To promote coordinated regional development, core cities such as Lanzhou and Xining should avoid the excessive concentration of resources, which can aggravate the plight of peripheral cities. Instead, they should strengthen industrial synergy with surrounding areas by co-constructing industrial parks and sharing upstream and downstream industrial chains. This approach would drive industrial development in peripheral cities and help them overcome stagnation. For population-loss cities, particularly the Hainan Tibetan Autonomous Prefecture, it is essential to cultivate specialized and advantageous industries under the premise of ecological protection. For instance, leveraging local ethnic culture to develop tourism or building brands for unique agricultural products can enhance economic resilience, narrow the gap with core cities, and promote the overall coordination of the Lanxi urban agglomeration.

Emphasis must also be placed on the accumulation of human capital. For population-inflow cities like Lanzhou and Xining, the focus should remain on optimizing the business environment to attract high-end talent and innovative enterprises, while simultaneously improving the supply of public services such as education and healthcare to increase urban carrying capacity. For population-loss cities like Hainan Tibetan Autonomous Prefecture and Dingxi, it is necessary to analyze the root causes of outflow—such as limited job opportunities and low income levels—and implement measures like developing characteristic industries and strengthening regional cooperation to mitigate depopulation. Facilitating cross-regional labor mobility and employment will further enhance urban economic resilience.

Furthermore, accelerating the transformation and upgrading of industrial structures is vital for the healthy and sustainable development of the urban economy. Regarding industrial adjustment, the population-loss cities in the Lanxi agglomeration should avoid over-reliance on the short-term industrial rationalization brought about by depopulation. Governments should introduce policies for sustainable industrial upgrading, such as providing subsidies for technical transformation and encouraging research and innovation to enhance corporate competitiveness and offset the negative impacts of population loss. Finally, the coordinated advancement of population policy and informatization construction is essential. By optimizing population policies to attract talent, a human capital foundation for information development is established. Increasing investment in information infrastructure will raise the level of informatization and bolster urban economic resilience. Particular attention must be paid to the informatization of depopulated areas within the Lanxi agglomeration to prevent a decline in information connectivity from weakening economic resilience. Only through these integrated efforts can the major cities of the Lanxi urban agglomeration become more resilient in the face of various changes and shocks, ultimately achieving sustainable development.

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Influence of Population Agglomeration on Urban Economic Resilience in China

Introduction

The relationship between population distribution and economic stability has become a focal point of modern spatial economics. As China undergoes rapid urbanization, understanding how the concentration of human capital affects the ability of cities to withstand and recover from economic shocks is critical for sustainable development. This study investigates the influence of population agglomeration on urban economic resilience across Chinese cities, employing an empirical framework to analyze the underlying mechanisms and regional variations.

Theoretical Framework

Urban economic resilience refers to the capacity of a city's economic system to absorb, adapt to, and recover from external disturbances, such as financial crises or structural shifts in industry. Population agglomeration—the spatial concentration of people in urban centers—plays a dual role in this process. On one hand, it fosters innovation, enhances labor market matching, and facilitates knowledge spillovers, which can bolster a city's adaptive capacity. On the other hand, excessive concentration may lead to "congestion effects," such as increased living costs and environmental pressures, potentially undermining resilience.

Methodology and Data

To quantify these effects, this research utilizes panel data from Chinese prefecture-level cities. The core model evaluates economic resilience through indicators of output stability and employment recovery. Population agglomeration is measured using population density and urban primacy indices.

The study employs the following general econometric specification:
$$R_{it} = \alpha + \beta Agglo_{it} + \gamma X_{it} + \mu_i + \epsilon_{it}$$
where $R_{it}$ represents the economic resilience of city $i$ at time $t$, $Agglo_{it}$ is the measure of population agglomeration, and $X_{it}$ denotes a vector of control variables including industrial structure, fiscal autonomy, and infrastructure levels.

Empirical Results

The findings suggest a significant non-linear relationship between population agglomeration and urban economic resilience. In the initial stages of concentration, the positive externalities of agglomeration—such as diversified labor pools and specialized supplier networks—significantly enhance a city's ability to navigate economic downturns. However, as density surpasses a certain threshold, the marginal benefits diminish, and the costs of congestion begin to impede recovery speeds.

[TABLE:1]

Furthermore, the impact of population agglomeration is heterogeneous across different regions of China. Eastern coastal cities, characterized by higher levels of market integration and technological advancement, demonstrate a stronger synergy between population density and resilience compared to inland cities.

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Liu S, Li Y, Shen Z, et al. The impact of population agglomeration on economic resilience: Evidence from cities in China[J]. In ternational Review of Economics & Finance, Chen L, Wang X, Hao Y, et al. Population agglomeration, bor rowed size and urban economic growth in China[J]. Cities, , doi:

The Impact of Population Growth and Shrinkage on Urban Economic Resilience: Based on the Moderating Effects of Industrial Structure and Human Capital

Abstract

This study investigates the complex relationship between population dynamics and urban economic resilience, specifically examining how population growth and shrinkage influence a city's ability to withstand and recover from economic shocks. By integrating the moderating roles of industrial structure and human capital, we develop a theoretical framework to explain the divergent paths of urban economic performance. Our analysis suggests that while population shrinkage often poses challenges to economic stability, the presence of a diversified industrial base and high levels of human capital can mitigate these negative effects, potentially fostering a "smart shrinkage" trajectory. Conversely, population growth does not automatically guarantee resilience; its impact is contingent upon the city's capacity to absorb labor into high-value sectors and maintain social infrastructure.

Economic Resilience and Population Shrinkage of Chinese Cities: An Explanation from the Perspectives of Related Diversity and Intercity Connectivity

1. Introduction

In the context of global urbanization and shifting demographic patterns, the phenomenon of "shrinking cities" has emerged as a significant challenge for regional development. In China, while many metropolitan areas continue to expand, a growing number of cities—particularly those reliant on traditional manufacturing or natural resources—are experiencing persistent population decline. This demographic shift raises critical questions regarding urban economic resilience: the capacity of a city to maintain its core functions, adapt to external shocks, and transition toward new growth paths.

Existing literature often views population shrinkage as a precursor to economic decay. However, empirical evidence suggests that some shrinking cities exhibit remarkable resilience, while some growing cities remain vulnerable to economic volatility. This paper argues that the impact of population dynamics on economic resilience is not direct but is mediated and moderated by internal structural characteristics and external relational positions.

2. Theoretical Framework

2.1 Related Diversity and Resilience

The concept of "related diversity" is central to understanding how industrial structures influence resilience. Related diversity refers to the coexistence of industries that share similar knowledge bases or technological requirements. This proximity facilitates knowledge spillovers and labor mobility between sectors. In the face of an economic shock affecting a specific industry, cities with high related diversity can more easily reallocate resources and labor to connected sectors, thereby cushioning the impact and accelerating recovery. For shrinking cities, maintaining related diversity is crucial to preventing a total collapse of the local labor market.

2.2 Intercity Connectivity

Beyond internal characteristics, a city's position within the national and

lated variety and inter - city linkages[J]. Journal of Tongji Universi ⁃

On Urban Development in Less Developed Areas: Perspectives for the Urban Cluster Along the Upper Reaches of the Yellow River

Introduction

The development of urban clusters in impoverished regions presents a unique set of challenges and opportunities within the broader context of national urbanization. This study focuses on the urban-dense areas along the upper reaches of the Yellow River, exploring how these regions can overcome structural barriers to achieve sustainable economic growth and regional integration. By analyzing the spatial distribution, economic foundations, and ecological constraints of these clusters, we can better understand the pathways for development in less developed inland areas.

1. Characteristics of the Yellow River Upper Reach Urban Cluster

The urban cluster along the upper reaches of the Yellow River is characterized by a fragmented spatial structure and a heavy reliance on natural resource extraction. Unlike the mature urban megalopolises on China's eastern coast, these inland clusters often face significant geographical isolation and a lack of robust inter-city connectivity. The economic base is typically dominated by primary and secondary industries, with a relatively underdeveloped service sector. Furthermore, the ecological sensitivity of the Yellow River basin imposes strict limits on industrial expansion and urban sprawl, necessitating a development model that balances economic progress with environmental preservation.

[FIGURE:1]

2. Challenges to Development in Impoverished Urban Belts

Several critical factors hinder the development of urban clusters in these impoverished regions. First, the "siphon effect" of larger regional hubs often drains human capital and investment from smaller satellite towns, leading to uneven growth patterns. Second, the infrastructure gap—particularly in terms of high-speed rail and digital connectivity—remains a significant barrier to regional integration. Third, the institutional framework for cross-administrative cooperation is often weak, resulting in fragmented planning and competition for resources rather than collaborative growth.

[TABLE:1]

3. Strategic Pathways for Growth

To foster development in the upper reaches of the Yellow River, a multi-pronged strategy is required. This includes:

• Strengthening Core Cities: Enhancing the radiation capacity of central cities to drive the development of surrounding rural and semi-urban areas.
• Ecological Prioritization: Implementing "green" industrial policies that leverage the region's renewable energy potential (such as solar and wind) while protecting the Yellow River's ecosystem.
• Infrastructure Connectivity: Prioritizing the construction of integrated transport networks to reduce logistical costs and facilitate the movement of goods and people.
• Policy Support and Innovation: Utilizing targeted fiscal transfers and

ban Planning, 2000 , 24 ( 11 ): 16 - 19 . ]

Introduction

The concept of economic resilience has become a central focus in regional science and economic geography, particularly as global and local economies face increasing volatility. Recent scholarship has sought to quantify and analyze the capacity of various regions to withstand, recover from, and adapt to external shocks. For instance, Yu et al. explored the spatiotemporal variation and inequality of economic resilience across Chinese cities and urban agglomerations, highlighting the uneven distribution of adaptive capacities across the national landscape.

Regional Case Studies and Methodological Approaches

In-depth regional analyses provide more granular insights into the drivers of resilience. Qi, Zhang, and Xu conducted an evaluation of county-level economic resilience in Zhejiang Province, offering a localized perspective on how sub-regional units maintain stability. Similarly, Xu and Haidong focused on the Guangdong-Hong Kong-Macao Greater Bay Area, utilizing measurement techniques to analyze time-series evolution and convergence patterns. Their findings suggest that while resilience levels may vary initially, there are observable trends toward convergence within highly integrated economic zones.

Spatial-Temporal Patterns and Industrial Dynamics

Broadening the scope to a national level, Song et al. measured China's overall economic resilience and analyzed its spatial-temporal evolution. Their research, published in Statistics & Decision, provides a comprehensive framework for understanding how resilience fluctuates over time across different provinces. A critical factor in these fluctuations is the role of industrial structure. Cai, Li, and Meng investigated the relationship between industrial diversity and economic resilience, specifically focusing on the impact of spatial spillovers. Their work suggests that industrial variety not only bolsters a region's internal stability but also generates positive externalities that enhance the resilience of neighboring areas through complex economic linkages.

and economic resilience in China[J]. Journal of Technical Econom ⁃

Research on the Economic Growth Effect of Human Capital Change in Northeast China

Abstract

As the global economy transitions toward a knowledge-based paradigm, human capital has emerged as a core driver of regional economic development. This study focuses on the Northeast region of China, a traditional industrial base currently facing significant structural transitions and demographic challenges. By analyzing the evolution of human capital and its impact on economic growth, this research aims to provide a theoretical and empirical basis for revitalizing the regional economy.

1. Introduction

Human capital, defined as the stock of knowledge, skills, and health embodied in the workforce, is a critical factor in modern economic growth theories. Unlike physical capital, human capital exhibits increasing returns to scale and serves as a catalyst for technological innovation and institutional optimization. In the context of Northeast China, the "rust belt" phenomenon and the subsequent "brain drain" have made the study of human capital dynamics particularly urgent. This paper investigates how changes in the structure and quality of human capital influence the regional GDP and industrial upgrading in the three provinces of Northeast China.

2. Theoretical Framework and Literature Review

The theoretical foundation of this study rests upon endogenous growth models, which suggest that long-term economic growth is primarily determined by forces internal to the economic system, particularly those related to human capital accumulation.

2.1 Endogenous Growth Theory

According to the Lucas model, human capital is the engine of growth. We represent the production function as:
$$Y = A K^{\alpha} (u H)^{1-\alpha} H_{a}^{\gamma}$$
where $Y$ is the total output, $K$ is physical capital, $H$ is the human capital stock, $u$ is the fraction of time devoted to production, and $H_{a}$ represents the external effects of human capital.

2.2 Regional Context

Previous studies \cite{1, 2} have highlighted that while Northeast China possesses a solid foundation of vocational and technical education, the mismatch between labor supply and the demands of emerging industries has hindered growth. The transition from a planned economy to a market-oriented one has also led to significant fluctuations in the regional human capital stock.

3. Empirical Analysis of Human Capital in Northeast China

To quantify the economic growth effect, we utilize panel data from the Liaoning, Jilin, and Heilongjiang provinces.

[TABLE:1]

3.1 Measurement of Human Capital

We employ the perpetual

Shenyang: Li

aoning University, 2022 . ]

Li Xun, Chen Guangyu. Study on the influence of population agglomeration on urban economic resilience: Based on an analysis of prefecture-level cities in China [J/OL]. Frontiers of Science and Technology of Engineering Management. [Available at: net/kcms/detail/.html]. Shi Yufang, Niu Yu. Resilience spatial correlation network and its influencing factors in Guanzhong Plain urban agglomeration [J]. Arid Land Geography. Li Liangang, Zhang Pingyu, Cheng Yu, et al. Spatio-temporal evolution and influencing factors of economic resilience in the Yellow River Basin [J]. Geographical Science. Ren Baoping. From the miracle of China's economic growth to high-quality economic development [J]. China Review of Political Economy. Ding Chenhao, Gao Xin, Yi Qing. Spatiotemporal evolution and influencing factors of economic resilience in three major urban clusters in the Yangtze River economic belt: A perspective based on different city types [J]. Resources and Environment in the Yangtze Basin. Xie Xuanli, Shen Yan, Zhang Haoxing, et al. Can digital finance promote entrepreneurship? Evidence from China [J]. China Economic Quarterly.

Xie Xuanli, Shen Yan, Zhang Haoxing, et al. Can digital finance promote entrepreneurship? Evidence from China [J]. China Economic Quarterly. Shi Fan. Spatiotemporal dynamics and determinants of urban entrepreneurship in China from the perspective of evolutionary economic geography [D]. Study on the impact and mechanism of population growth and decline differentiation on the urban economic resilience of the Lanzhou-Xining urban agglomeration.

Hangzhou: Zhejiang University, 2021 . ]

Industry Income Gap, Industrial Structure Upgrading, and Regional Industrial Positioning

1. Introduction

The widening income gap has become a significant challenge for China's sustainable economic development. Among various dimensions of inequality, the industry income gap is particularly prominent, as it reflects the distribution of labor and capital across different sectors of the economy. Concurrently, China is undergoing a critical phase of industrial structure upgrading, transitioning from labor-intensive manufacturing to high-value-added services and high-tech industries. Understanding the relationship between the industry income gap and industrial structure upgrading is essential for formulating effective regional industrial policies and achieving balanced economic growth. This paper explores how the income gap between industries influences the process of industrial upgrading and how regional industrial positioning can be optimized to mitigate negative effects while promoting economic efficiency.

2. Theoretical Framework and Hypotheses

The relationship between income distribution and industrial structure is bidirectional. On one hand, industrial upgrading changes the demand for different types of labor, thereby affecting the income gap. On the other hand, a significant income gap can influence the mobility of production factors, particularly human capital, which in turn impacts the pace and direction of industrial structural changes.

2.1 The Impact of Income Gap on Factor Mobility

According to classical economic theory, labor moves toward sectors with higher returns. A moderate income gap can serve as an incentive for workers to acquire new skills and move into high-productivity sectors, facilitating industrial upgrading. However, an excessive gap may lead to "brain drain" from essential but lower-paying sectors or create entry barriers that prevent labor from transitioning, thus hindering the overall optimization of the industrial structure.

2.2 Industrial Structure Upgrading and Economic Growth

Industrial structure upgrading refers to the process where the focus of the economy shifts from primary and secondary industries to tertiary industries, and from low-productivity to high-productivity sectors. This process is often measured by the ratio of the service sector to the manufacturing sector or the internal sophistication of the manufacturing industry.

[FIGURE:1]

As shown in [FIGURE:1], the evolution of industrial structure typically follows a non-linear path. In the early stages of development, the income gap may widen as resources concentrate in emerging high-growth sectors. As the economy matures, the diffusion of technology and the implementation of redistributive policies should theoretically narrow this gap.

3. Empirical Analysis

To examine the relationship between the industry income gap and industrial structure upgrading, this study utilizes panel data from various provinces in

Journal of Shanxi University of Finance and Economics, 2021 , 43

Spatiotemporal Differentiation and Driving Mechanism of Urban Shrinkage in China Based on Multi-source Data

Abstract

Under the dual influence of global economic restructuring and the transformation of domestic economic development, "urban shrinkage" has become an objective reality in the process of China's urbanization. This phenomenon poses significant challenges to traditional growth-oriented urban planning and regional development strategies. This study utilizes multi-source data—including demographic, economic, and physical geographic indicators—to systematically analyze the spatiotemporal evolution and driving mechanisms of urban shrinkage in China. By integrating multi-dimensional datasets, we aim to provide a more comprehensive understanding of how and why certain Chinese cities are experiencing contraction while others continue to expand.

1. Introduction

For decades, China's urbanization has been characterized by rapid expansion and high-speed growth. However, as the demographic dividend diminishes and industrial structures undergo profound shifts, an increasing number of cities are facing population loss and economic stagnation. This phenomenon, termed "urban shrinkage," is not merely a localized issue but a systemic challenge that reflects deeper structural changes in the national economy. Understanding the spatiotemporal patterns of this shrinkage is crucial for formulating sustainable urban policies.

[FIGURE:1]

2. Data and Methodology

2.1 Multi-source Data Integration

To capture the complexity of urban shrinkage, this research employs a multi-source data approach. Traditional census data are supplemented with big data sources, including nighttime light (NTL) imagery, mobile phone signaling data, and Point of Interest (POI) information. These datasets allow for a more granular analysis of urban vitality and spatial dynamics than traditional statistical yearbooks alone.

2.2 Identification of Shrinking Cities

Urban shrinkage is defined here as a multi-dimensional process involving population decline, economic slowdown, and physical vacancy. We utilize a composite index to identify shrinking cities, where the shrinkage intensity $S$ for a city $i$ at time $t$ is calculated as:

$$S_{i,t} = \omega_1 \Delta P_{i,t} + \omega_2 \Delta G_{i,t} + \omega_3 \Delta L_{i,t}$$

where $\Delta P_{i,t}$ represents the rate of population change, $\Delta G_{i,t}$ represents the rate of GDP change, and $\Delta L_{i,t}$ represents the change in nighttime light intensity. The weights $\omega_n$ are

based on multi - source data[D]. Ji ’ nan: Shandong Normal Universi ⁃

Has Population Loss Affected Economic Growth in Northeast China?

Based on Measurement Data of Registered Population Loss in Northeast China

Abstract: Population loss has become a critical factor constraining the economic revitalization of Northeast China. This study utilizes registered population data to measure the scale and intensity of population loss in the region and empirically analyzes its impact on economic growth. By constructing an econometric model, we examine the relationship between demographic shifts and regional GDP, considering factors such as human capital, industrial structure, and institutional environment.

1. Introduction

In recent years, the phenomenon of population loss in Northeast China has attracted widespread attention from both academia and policymakers. As a traditional industrial base, the region's economic growth has faced significant downward pressure. Unlike the "demographic dividend" that fueled rapid development in coastal provinces, Northeast China is currently experiencing a "demographic deficit" characterized by low birth rates, an aging population, and, most notably, a continuous outflow of the labor force.

The impact of population loss on economic growth is multifaceted. On one hand, the outflow of the working-age population directly reduces the labor supply; on the other hand, since those who leave are often younger and better educated, this "brain drain" leads to a decline in regional human capital levels and innovation capacity. This paper aims to quantify the extent of this loss using registered population data and evaluate its specific consequences for the economic trajectory of the Northeast.

2. Measurement of Population Loss in Northeast China

To accurately assess the demographic situation, it is essential to distinguish between the permanent resident population and the registered (hukou) population. The discrepancy between these two figures provides a primary indicator of net migration.

[TABLE:1]

As shown in [TABLE:1], the data indicates a persistent trend of net outflow across the three northeastern provinces (Heilongjiang, Jilin, and Liaoning). The scale of loss has expanded from selective migration of high-skilled professionals to a broader migration of the general labor force. This trend is further illustrated in the spatial distribution of population density changes.

[FIGURE:1]

[FIGURE:1] demonstrates that the population loss is not uniform; while provincial capitals maintain some degree of resilience, peripheral industrial cities and border areas show the most significant declines.

3. Empirical Analysis of Economic Impact

3.1 Model Specification

To test the impact of population loss on economic growth, we employ a panel data model. The basic form of the growth equation

分析

Resources and Environment

Abstract

In the context of global climate change and the increasing scarcity of natural resources, the relationship between resource management and environmental sustainability has become a critical focus of academic research. This paper explores the integrated management of resources and the environment, emphasizing the role of technological innovation and policy frameworks in achieving sustainable development. By analyzing current trends in machine learning and deep learning applications within this field, we aim to provide a comprehensive overview of how data-driven approaches can optimize resource allocation and mitigate environmental degradation.

1. Introduction

The intersection of resource utilization and environmental protection represents one of the most significant challenges of the 21st century. As industrialization and urbanization continue to accelerate, the demand for energy, water, and land resources has reached unprecedented levels. This surge in demand often leads to environmental consequences, including biodiversity loss, pollution, and accelerated climate change. To address these issues, it is essential to develop robust strategies that balance economic growth with ecological preservation.

Recent advancements in computational science, particularly in the realms of machine learning and deep learning, have opened new avenues for monitoring and managing environmental resources. These technologies allow for the processing of vast datasets, enabling more accurate predictions of environmental shifts and more efficient management of natural capital.

2. Methodological Framework

To understand the dynamics of resource and environmental systems, we employ a multi-dimensional modeling approach. This involves the integration of physical laws with data-driven algorithms to capture the complexity of ecological interactions.

2.1 Data Integration and Processing

The primary challenge in environmental modeling is the heterogeneity of data sources, ranging from satellite imagery to ground-based sensor networks. We define the state of a resource system at time $t$ as $\mathcal{S}_t$, which is influenced by a set of environmental variables $\mathcal{E}_t$ and human intervention factors $\mathcal{H}_t$. The transition function can be represented as:

$$\mathcal{S}_{t+1} = f(\mathcal{S}_t, \mathcal{E}_t, \mathcal{H}_t) + \epsilon$$

where $\epsilon$ represents the stochastic noise inherent in natural systems. By utilizing deep learning architectures, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units, we can model these temporal dependencies with high precision.

[FIGURE:1]

2.2 Optimization of Resource Allocation

Effective resource management requires the optimization of objective functions that account for both

gyu, Yi Jidong, Gan Jianhou. Benefit of ethnic culture tourism in ⁃

dustry in Yunnan based on method of SSA[J]. China Population, Re sources and Environment, (Suppl.

Quantitative Evaluation and Spatiotemporal Evolution of Regional Talent Policy in China

This study provides a quantitative evaluation of regional talent policies in China and analyzes their spatiotemporal evolution. As human capital becomes a core driver of regional economic development, local governments have increasingly implemented diverse policy instruments to attract and retain high-level talent. By constructing a multidimensional evaluation framework, this research systematically assesses the effectiveness and structural characteristics of these policies across different provinces and cities.

The analysis reveals that China's regional talent policies have undergone significant shifts in focus, moving from general administrative support to more targeted financial incentives and integrated service ecosystems. Spatially, there is a clear "center-periphery" pattern, with coastal regions and major metropolitan hubs demonstrating higher policy maturity and innovation. However, inland regions are rapidly catching up by leveraging comparative advantages and tailoring policies to local industrial needs. The findings suggest that while policy intensity has increased nationwide, the coordination between talent policy and industrial structure remains a critical factor for long-term regional competitiveness.

Analysis of the Spatiotemporal Pattern and Influencing Factors of Industrial Ecology in the Lanxi Urban Agglomeration

This research examines the spatial and temporal differentiation characteristics of industrial ecology within the Lanxi (Lanzhou-Xining) urban agglomeration and identifies the primary factors driving these changes. Industrial ecology, which emphasizes the transition toward sustainable, low-carbon, and circular industrial systems, is vital for the fragile ecological environments of Western China.

[FIGURE:1]

The study utilizes a comprehensive evaluation index system to measure the level of industrial ecology across different administrative units within the agglomeration. The results indicate a general upward trend in industrial ecological efficiency, though significant spatial disparities persist between the core cities of Lanzhou and Xining and their surrounding peripheral areas.

Key driving factors identified include:
- Technological Innovation: The capacity for R&D and the adoption of green technologies are the strongest predictors of ecological progress.
- Environmental Regulation: Stringent local environmental policies effectively push industries toward cleaner production methods.
- Industrial Structure: The transition from heavy resource-based industries to high-tech and service sectors significantly enhances ecological performance.
- Urbanization Level: Higher levels of urbanization provide the necessary infrastructure and human capital to support ecological transitions, though they also pose challenges regarding resource consumption.

[TABLE:1]

The findings provide a scientific basis for policymakers to optimize industrial layouts and promote coordinated, sustainable development within the Lanxi urban agglomeration, ensuring that economic growth

zhou University (Natural Sciences Edition), 2022 , 58 ( 5 ): 668 - 677 . ]

Population Mobility and Regional Economic Disparities

[Qiu Tongwei. Targeted poverty alleviation, population mobility and regional economic gap[J]. Economic Theory and Business Management]

Research on Fiscal Policy Incentives for Developing New Quality Productive Forces According to Local Conditions

[Chen Shi, Shang Hangbiao. Research on fiscal policy incentives for developing new quality productive forces according to local conditions[J]. Journal of Fujian Normal University (Philosophy and Social Sciences Edition)]

Digital Economy and High-Quality Population Development within the Perspective of Chinese Modernization

[Zhu Qiaoling, Wan Chunfang. Digital economy and high-quality population development in the context of Chinese modernization]

Digital economy and high quality population development from the perspective of Chinese path to modernization[J]. Economic Review Journal,

Impact and pathways of population growth-decline differentiation on urban economic resilience in the Lanzhou-Xining urban agglomeration KONG Dezhen LUO Yuxuan MAO Jinhuang WANG Meimei WANG Yifei . College of Earth and Environmental Sciences, Lanzhou University, Lanzhou , Gansu, China; . College of Economics, Lanzhou University, Lanzhou , Gansu, China; . Intelligent Laboratory for Humanistic Environment Data of the Qinghai Xizang Plateau, Lanzhou , Gansu, China ment, human capital has become a key driver of intercity competitiveness. In recent years, the Lanzhou-Xining ur ban agglomeration has experienced significant population decline and insufficient human capital competitiveness, which have directly weakened its urban economic resilience. This study examines six representative cities within the Lanzhou-Xining (Lanxi) urban agglomeration, employing a linear mixed-effects model to analyze the effects and mechanisms of differentiated population growth and decline on urban economic resilience in , and . The findings reveal the following characteristics of urban economic resilience in the Lanzhou-Xining urban agglomeration. ( ) Between , urban economic resilience generally increased across the Lanxi re gion, forming a core-edge structure, with Lanzhou City and Xining City at the core and other cities at the pe riphery. ( ) Variations in population growth significantly affected urban economic resilience, with population in flows exerting positive externalities on regional resilience. ( ) The rationalization of the industrial structure did not moderate the impact of population change on urban economic resilience. In cities experiencing population loss, initial declines facilitated some degree of industrial restructuring; however, this mode of adjustment was not sustainable over time. ( ) Population change also influenced the level of local informatization, which in turn af fected urban economic resilience. Population growth supported the development of urban informatization, there by strengthening urban economic resilience. Based on these findings, it is recommended to promote coordinated regional development, enhance human capital accumulation, accelerate industrial transformation and upgrading, and advance population policy in tandem with informatization initiatives to improve the economic resilience of major cities in the Lanzhou-Xining urban agglomeration. eration