A Study on the Coupling and Coordination of Basic Public Services and Population Development in the Beijing–Tianjin–Hebei Urban Agglomeration Under the Context of Regional Collaborative Development

Abstract

Amid demographic restructuring, analyzing the dynamic interplay between public services and population development is vital for advancing coordinated regional development in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA). This study developed an integrated evaluation framework, applying relative development indices, coupling coordination models, and obstacle analysis to examine the spatiotemporal evolution, coordination dynamics, and key constraints of the public service system and population system development from 2012 to 2023. The findings reveal the following: (1) Coordinated development policies have significantly boosted public service levels in cities near Beijing, whereas growth in Beijing and Tianjin has slowed. (2) Although overall coupling coordination across the BTHUA has improved, a marked core–periphery disparity persists. Beijing maintains high-level coordination, while most Hebei cities remain at marginal coordination levels. (3) The constraints on coordinated development vary substantially: Beijing primarily encounters structural challenges in population dynamics, whereas Tianjin and Hebei face basic infrastructural deficiencies. The study recommends developing a public service delivery system aligned with evolving demographic trends and proposes targeted strategies to optimize regional service structures based on each city’s core challenges.

1. Introduction

Basic public services refer to services provided by the government that meet citizens’ essential needs [1], including education, healthcare, social security, cultural services, infrastructure, and environmental protection [2,3]. Basic public services possess the characteristics of public goods and fall within the scope of government resource allocation, underscoring the government’s responsibility to ensure their equitable provision. This theoretical foundation can be traced back to Adam Smith’s The Wealth of Nations, which discussed the duties of the sovereign [4]. Subsequently, scholars such as Samuelson P.A. [5] and Buchanan, J.M. [6] further explored the fairness of public service provision from economic, political, and institutional perspectives. At present, the ability to deliver basic public services is closely tied to urbanization and overall well-being, serving as a key measure of social equity and sustainable regional development. In 2006, the Sixth Plenary Session of the 16th Central Committee of the Communist Party of China explicitly proposed the gradual realization of the equalization of basic public services, making the equalization of basic public services an important policy objective for China. In 2015, the United Nations General Assembly adopted the 2030 Agenda for Sustainable Development, recognizing equitable access to public services as a core element of the Sustainable Development Goals (SDGs) [7]. The 2022 report of the 20th Communist Party of China National Congress emphasized strengthening the basic public service system to promote greater equity and accessibility. Globally, metropolitan areas often face internal development imbalances, and China has consistently committed to achieving regional coordinated development. The equitable provision of public services is a crucial pathway to reducing regional inequality, facilitating the flow of people and resources, and fostering regional cohesion and overall competitiveness [8,9]. The equitable provision of basic public services has been widely studied across disciplines, with research focusing on assessing service supply levels [10,11], evaluating regional equity [12], and exploring their interrelations with urbanization [13], economic development [14], and related systems.

Public services and population systems can be seen as two mutually interacting, dynamically evolving systems, and a balance in public services requires adjustments within both systems [15]. From the perspective of complex systems theory, the relationship between these two systems is not a simple cause-and-effect one; instead, they influence each other and evolve dynamically [16]. Structural changes in the population alter the demand for public services. According to the National Bureau of Statistics in 2023, China’s urbanization rate reached 66.16%, signaling a transition toward a new phase where urban development priorities shifted from expansion to quality enhancement. Meanwhile, China’s population has begun to decline, accompanied by notable shifts in urban demographics. By the end of 2024, the total population had decreased by 1.39 million to 1.40828 billion, with a natural growth rate of −0.99%. By the end of 2024, individuals aged 60 and over totaled 310.31 million (22.0% of the population), while those aged 65 and above reached 220.23 million (15.6% of the population), reflecting a deepening demographic aging trend [17]. China’s birth rate has declined steadily since 2017, and despite a modest rise in births in 2024, the overall downward trend persists [18]. These rapid demographic shifts have disrupted the equilibrium of public service supply and demand, presenting major challenges to maintaining balance [19]. From the perspective of regional development, the supply level and quality of public services affect the mobility, settlement, and talent attraction of the population. In 1966, Friedman, J.R proposed the existence of a core–periphery structure [20], a pattern that still exists in major metropolitan areas, including the Beijing–Tianjin–Hebei region. The unidirectional flow of resources, including public services, toward the regional core exacerbates inter-regional imbalances. In turn, unequal public services solidify regional disparities and hinder the free flow and comprehensive development of the population.

Many scholars have researched the relationship between population development and public services. Research demonstrates that an aging population significantly increases the demand for healthcare and eldercare services [21]. Declining birth rates reduce demand for education services, and resulting school closures may deepen regional disparities in educational access. Buchanan J M and Tullock G’s public choice theory posits that population education levels are key determinants of local government public expenditure, with more educated individuals demanding higher-quality education and healthcare services [22]. In terms of the dynamic relationship between public services and population development within a region, scholars have primarily explored how public services influence population migration and residential choices. Public service availability in urban areas positively influences regional population migration by attracting and retaining residents [23]. Population migration is influenced by the quality of urban public services, and residential choices are, to some extent, a reflection of service levels [24]. Some scholars also argue that the relationship between public services and population development exhibits temporal characteristics. In the short term, increased public services can boost local employment and consumption opportunities, thereby stemming population outflow [25]; in the long term, public services, particularly health and education, contribute to human capital enhancement, potentially leading to outward migration in search of better development opportunities [26]. The relationship between elderly migration and public service provision is also a research hotspot. The quantity, quality, and accessibility of healthcare services influence elderly migration [27] and promote the social integration of elderly migrants [28]. However, existing research has several limitations. Studies on the balanced provision of regional public services primarily focus on evaluating urban supply equity and identifying influencing factors such as economic development and fiscal expenditure, with insufficient research on their coordinated relationship with dynamic population development. Moreover, research offers limited theoretical insight into the alignment between public service provision and population dynamics, with few studies examining how demographic shifts influence service equilibrium [29]. A few scholars have studied the balance of public service supply and demand in China during its population decline. For instance, Li, X et al. argue that the imbalance between population and public service development at the county level in China leads to public services lagging behind population development [15]. Li, G et al. suggest that under urban shrinkage, basic public services follow a “non-linear inverted U-shaped process, initially promoting and then inhibiting public service levels.” [30]. However, at the regional level, a systematic theoretical framework and empirical analysis are still lacking to explain how structural demographic transitions profoundly affect the equilibrium of public service supply and demand, ultimately constraining the overall process of regional coordinated development.

In summary, demographic shifts affect public service needs, while enhanced service quality and availability shape regional population mobility. Amid China’s aging and declining population, ensuring equitable public service delivery has become increasingly challenging. Analyzing the dynamic interplay between population trends and service provision within an integrated framework is essential for advancing regional equity. Originally developed in physics to assess intersystem association and dependency [31], coupling coordination is now widely applied to analyze interactions among complex systems. Given that public services and population development are two closely linked and dynamically evolving systems, the coupling coordination model can be used to assess the degree of coordinated development across systems over various spatial and temporal scales [32], capturing their interconnectivity, interaction patterns, and resulting dynamics [33]. This study focuses on the Beijing–Tianjin–Hebei urban agglomeration (BTHUA) using panel data from 2012 to 2023 to develop an indicator system evaluating basic public service levels and high-quality population development. It analyzes the coupling coordination between these systems and identifies key influencing factors as illustrated in Figure 1. The study first establishes a multidimensional public service (PS) evaluation indicator system encompassing healthcare, education, culture, social security, and related sectors to comprehensively assess public service provision in the BTHUA. Second, it introduces a population development (PD) evaluation framework to quantitatively assess population size, composition, and quality. Third, a coupling coordination degree (CCD) model is applied to examine the interaction and spatiotemporal evolution between the two systems. Fourth, an obstruction degree model is employed to pinpoint the primary factors constraining the coordinated development of both systems. This study pursues three main objectives: First, to calculate the public service supply index and population development index for analyzing their spatial and temporal evolution. Second, to examine the coupling between public services and population development, identify areas of service deficiency or surplus, and support evidence-based policymaking for optimized regional resource allocation. Third, to uncover the underlying dynamics of their interaction, identify strategies to strengthen coordination, and offer theoretical and policy guidance for advancing system integration and equitable public service access.

2. Research Area, Data Sources, and Research Methods

2.1. Research Area

The BTHUA, one of China’s most populous and economically dynamic metropolitan clusters [34], includes Beijing, Tianjin, and 11 Hebei cities, covering 21.68 million km² (Figure 2). As reported by the National Bureau of Statistics, the BTHUA had a permanent population of 109.428 million in 2023 and a gross domestic product of 10.44421 trillion yuan, representing about 8.54% of the national total. The BTHUA exemplifies regional development imbalances [35], marked by substantial disparities in public service provision across Beijing, Tianjin, and Hebei. Designated as a national strategy in 2014, coordinated development efforts have prioritized equalizing public services to advance regional integration. The government has implemented various policies to promote the transfer of public service resources from Beijing and Tianjin to Hebei. Since 2020, the BTHUA’s population has declined, with a 0.88% decrease in permanent residents by 2023. The BTHUA faces significant demographic challenges, including rapid aging and uneven distribution of skilled talent. By the end of 2023, residents aged 60 and above exceeded 24.78 million, with an aging rate of 2.65% higher than the national average, positioning the region among the most aged in China.

2.2. Data Sources

This study primarily draws on data from the Beijing, Tianjin, and Hebei Statistical Yearbooks (2013–2024), regional statistical reports, and the National Economic and Social Development Statistical Bulletin. Missing or inconsistent data were corrected and supplemented using interpolation techniques.

2.3. Indicator System Construction

Guided by the 14th five-year plan for public services, the 2023 National Basic Public Service Standards, and relevant studies, this paper develops an indicator system grounded in comprehensiveness, objectivity, and data accessibility. The selection of evaluation dimensions and indicators forms the foundation of indicator system construction, with scholars adopting diverse perspectives. For instance, Lucy’s framework assessed the fairness of public service allocation through indicators related to resource input, utilization, outcomes, and impact [10]. Warne measured public services on a per capita basis, including per capita provision, budget, and expenditure [36]. Many scholars have also categorized evaluation dimensions according to basic public service domains. Resident satisfaction surveys are also a crucial tool for gauging public service quality [37,38]. Considering data availability and domain, the public service system (PS) is structured into four primary dimensions: education, culture, healthcare, and social security, along with 22 secondary indicators as shown in

Table 1. Evaluation framework for basic public service (PS) system.

First-Class Indicators	Second-Class Indicators	Effect Direction	Unit	Weight	Supporting Literature
Public Education	Number of secondary schools per 10,000 permanent residents [PS1]	+	Individual	0.0239	[39,40,41,42,43,44,45]
	Number of primary schools per 10,000 permanent residents [PS2]	+	Individual	0.0221
	Teacher-to-student ratio of secondary schools [PS3]	−	%	0.0282
	Teacher-to-student ratio of primary schools [PS4]	−	%	0.0188
	Number of universities per 10,000 permanent residents [PS5]	+	Individual	0.0541
	Education expenditure per capita [PS6]	+	Yuan	0.0349
Public Culture	Number of libraries per 10,000 permanent residents [PS7]	+	Individual	0.0228	[13,40]
	Public library collections per capita [PS8]	+	Books	0.1316
	Public Library Books and Periodical Borrowing Volume per 10,000 permanent residents [PS9]	+	Times	0.0850
	Number of museums per 10,000 permanent residents [PS10]	+	Individual	0.1107
Public Healthcare	Hospital beds per 10,000 permanent residents [PS11]	+	Beds	0.0132	[40,42,43,44]
	Number of practicing physicians per 10,000 permanent residents [PS12]	+	People	0.0352
	Number of medical institutions per 10,000 permanent residents (Including hospitals and primary healthcare facilities) [PS13]	+	Individual	0.0248
	Number of hospitals institutions per 10,000 permanent residents [PS14]	+	Individual	0.0133
	Number of medical institutions visits per capita [PS15]	+	Times	0.0220
	Health expenditure per capita [PS16]	+	Yuan	0.0440
Social Security	Number of beds in civil affairs institutions per 10,000 permanent residents [PS17]	+	Beds	0.0373	[13,15,36,39,45,46]
	Number of beds in elderly care institutions per 10,000 permanent residents [PS18]	+	Beds	0.0322
	Basic pension insurance participation rate [PS19]	+	%	0.0129
	Basic medical insurance participation rate [PS20]	+	%	0.0181
	Unemployment insurance participation rate [PS21]	+	%	0.1402
	Social security expenditure per capita [PS22]	+	Yuan	0.0747

.

The coordinated development of public education, healthcare, and social security is central to public service collaboration within the BTHUA. Public cultural services, in particular, exhibit the most significant disparities between Beijing and Hebei Province. Consequently, research on these domains is essential for assessing the effectiveness of coordinated development policies. The second-class indicators reflect the resource investment and outcomes within each public service domain. For instance, PS6, PS16, and PS22 reflect the intensity of government financial investment [39,40]. PS1 and PS2 are indicators of public education, reflecting the scale of compulsory education, while PS3 and PS4 reflect its quality. PS7 and PS10 indicate the scale of cultural facilities; PS8 and PS9 reflect the richness and utilization rates of cultural resources. The selection of secondary indicators for public healthcare primarily reflects the scale and utilization of medical resources [41,42,43]. PS17 and PS18 reflect the supply capacity of urban elderly care services [36,44]. PS19, PS20, and PS21 measure the coverage and equalization extent of basic social security systems. To test the robustness of the indicator system, this study conducted tests by deleting or replacing some secondary indicators. The results showed consistency in the overall trends of the calculated public service supply index.

The population development system (PD) includes three core dimensions: population size, structure, and quality, measured by eight secondary indicators as shown in

Table 2. Evaluation framework for population development (PD) system.

First-Class Indicators	Second-Class Indicators	Effect Direction	Unit	Weight	Supporting Literature
Population Quantity	Number of permanent residents [PD1]	+	10,000 people	0.2294	[42,47]
	Population density [PD2]	+	People/km2	0.1342
	Natural population growth rate [PD3]	+	%	0.0683
Population Structure	Dependency ratio (Population aged 0–14 and over 65/Total population) [PD4]	−	%	0.0561	[42,47,48]
	Proportion of urban population [PD5]	+	%	0.1097
Population Quality	Proportion of high school and above population [PD6]	+	%	0.1414	[15,48]
	Disposable income per capita [PD7]	+	Yuan	0.1443
	Consumption expenditure per capita [PD8]	+	Yuan	0.1165

. The dependency ratio reflects the burden of different age groups on the working-age population, impacting the social security system’s pressure and the potential for sustainable economic development. Population quality serves as a driving force for high-quality regional development, measured by educational attainment and consumption capacity.

2.4. Research Methods

2.4.1. Entropy Method

Indicator weights represent their relative significance. Common weighting methods include expert judgment, analytic hierarchy process, entropy method, and principal component analysis. This study adopts the entropy method, an objective approach based on information entropy, which determines weights by calculating each indicator’s entropy value, ensuring accuracy and impartiality [49]. To facilitate cross-year and cross-city comparisons, this study applies a time-adjusted entropy method, enhancing the reliability of temporal analyses [50].

The calculation proceeds as follows: let r denote the number of years, m the evaluation indicators, and n the cities. $x_{ijk}$ represents the value of the k-th indicator for year i and city j.

(1) To eliminate dimensional inconsistencies and account for positive and negative indicator differences, the original data are standardized.

For positive indicators, the normalization formula is as follows:

$$x_{ijk}^{\prime }={\displaystyle \frac{x_{ijk}-x_{\mathit{min}k}}{x_{\mathit{max}k}-x_{\mathit{min}k}}}.$$

(1)

The formula for negative indicator standardization is as follows:

$$x_{ijk}^{\prime }={\displaystyle \frac{x_{\mathit{max}k}-x_{ijk}}{x_{\mathit{max}k}-x_{\mathit{min}k}}}$$

(2)

(2) Calculate the weight of the j-th region in the i-th year under the k-th indicator:

$$y_{ijk}={\displaystyle \raisebox{1ex}{$x_{ijk}^{\prime }$}\!\left/ \!\raisebox{-1ex}{${\sum }_i{\sum }_j{x}_{ijk}^{\prime }$}\right.}.$$

(3)

Calculate the entropy value of the k-th indicator:

$$e_k=-{\displaystyle \frac{1}{\theta }}{\sum }_i{\sum }_j{y}_{ijk}ln (y_{ijk}).$$

(4)

Among them, $\mathsf{\theta }=ln(r\ast n)>0$, $e_k>0$.

Calculate information entropy redundancy:

$$g_k=1-e_k.$$

(5)

Calculate the weightings of each indicator:

$$w_k={\displaystyle \frac{g_k}{{\sum }_k{g}_k}}.$$

(6)

2.4.2. System Development Index and Relative Development Index

Using Equation (6), the public service and population development levels are computed, where $S_{ij}$ denotes the development index of city j in year i:

$$S_{ij}={\sum }_k{w}_k{x}_{ijk}^{\prime }.$$

(7)

Next, the relative development of public service and population development is calculated as follows, allowing for classification based on their alignment [42]:

$$R_{ij}={\displaystyle \frac{S_{ij1}}{S_{ij2}}},$$

(8)

where $S_{ij1}$ is the development index of public service, and $S_{ij2}$ is the development index of population development. When $R_{ij}$ ≤ 0.8, public service development lags behind population development; when 0.8 < $R_{ij}$ ≤ 1.2, the two develop in tandem; and when $R_{ij}$ > 1.2, population development lags behind public service development.

2.4.3. Coupling Coordination Degree Model (CCD Model)

Coupling describes the interaction between multiple systems. The coupling degree (CD), ranging from 0 to 1, quantifies the strength of this interaction; values closer to 1 indicate greater interdependence [42]. The formula is as follows:

$$C_{ij}={\left({\displaystyle \frac{S_{ij1}{\times S}_{ij2}}{{\left(\frac{S_{ij1}+S_{ij2}}{2}\right)}^2}}\right)}^{1/2}.$$

(9)

where $C_{ij}$ represents the CD between the public service supply and population development systems for city j in year i. $S_{ij1}$ is the development index of public service, and $S_{ij2}$ is the development index of population.

To assess the overall alignment between public service system and population development, the CCD model extends the CD approach. CCD values, ranging from 0 to 1, reflect the level of coordinated development, with higher values indicating stronger system integration [43]. The CCD is calculated using the following formula:

$$T_{ij}=\alpha S_{ij1}{+\beta S}_{ij1},$$

(10)

$$D_{ij}={\left(C_{ij}\times T_{ij}\right)}^{1/2}.$$

(11)

where $D_{ij}$ denotes the CCD between public service system and population development, while $T$ is the composite coordination index. $\alpha$ and $\beta$ represent the respective weights, set equally at $\alpha$ = $\beta$ = 0.5 in this study. Based on previous research [42,43,44] and contextual considerations, classification criteria for coupling and coordination degrees are established (

Table 3. Criteria for the CD and CCD between the PS and the PD.

Range	Coupling Degree (CD)	Range	Coupling Coordination Degree (CCD)
0 < C ≤ 0.3	Primary Coupling	0 < D ≤ 0.1	Extreme disorder
		0.1 < D ≤ 0.2	Serious disorder
		0.2 < D ≤ 0.3	Moderate disorder
0.3 < C ≤ 0.5	Antagonistic Coupling	0.3 < D ≤ 0.4	Mild disorder
		0.4 < D ≤ 0.5	Nearly disorder
0.5 < C ≤ 0.8	Grinding Coupling	0.5 < D ≤ 0.6	Barely coordination
		0.6 < D ≤ 0.7	Primary coordination
		0.7 < D ≤ 0.8	Intermediate coordination
0.8 < C ≤ 1	High Coupling	0.8 < D ≤ 0.9	Good coordination
		0.9 < D ≤ 1	Quality coordination

).

2.4.4. Obstacle Degree Model

The obstacle degree model is a statistical method used to identify key factors hindering development [51,52,53]. In this study, it is applied to determine the main constraints affecting the coordination between public service and population development, offering insights for targeted improvements. The formula is as follows:

$$O_{ijk}={\displaystyle \frac{I_{ijk}{w}_k}{{\sum }_k{I}_{ijk}{w}_k}},$$

(12)

$$I_{ijk}=1-x_{ijk}^{\prime },$$

(13)

where $O_{ijk}$ is the impedance of the k-th indicator in the j-th city in the i-th year.

3. Results

3.1. Spatio-Temporal Characteristics of Development Levels

3.1.1. Spatial and Temporal Characteristics of the Public Service Level

The overall public service level in the BTHUA has steadily increased, with the supply index rising from 2.929 in 2012 to 5.027 in 2023, reflecting an average annual growth rate of 5.03% (Figure 3). This indicates substantial progress in regional public service development. Beijing and Tianjin have consistently led in service provision. Beijing demonstrates a clear resource advantage, recording a public service index of 0.821 in 2023, which is 1.52 times the Tianjin and 2.47 times the Hebei average. In 2012, Hebei’s average index was 0.189, just 34.19% of Beijing’s, highlighting a substantial disparity. Since 2017, most Hebei cities have experienced accelerated growth in public service supply, with a steady rise in index values. Notable improvements in cities, such as Zhangjiakou and Baoding, suggest that the BTHUA coordinated development strategy has effectively enhanced public service provision. In 2020, the COVID-19 pandemic led to uneven regional development, causing declines in public service index for Beijing and Tianjin and a general slowdown in growth. The influence of the Xiongan New Area became evident, boosting service levels in nearby cities. By 2023, Hebei’s average index rose to 0.333, with an annual growth rate of 5.26%, reducing the gap with Beijing by 6.37 percentage points. Handan, Zhangjiakou, and Baoding showed the most significant progress, with annual growth rates of 7.78%, 7.53%, and 7.48%, respectively.

Applying the natural breakpoint method, a cluster analysis classified the study area into five public service supply levels: high, relatively high, medium, relatively low, and low. Six time points were selected for visualization. In 2012, the BTHUA showed a clear core–periphery pattern, with Beijing as the core, Tianjin as the secondary center, and Hebei cities forming the periphery, reflecting marked spatial disparities. Significant disparities in public service provision existed between northern and southern regions, with the north performing better, except for Shijiazhuang, Hebei’s provincial capital. By 2019, the spatial structure had transformed into a “dual core with multi-tiered periphery”, showing a more refined gradient as southern Hebei cities gradually improved. By 2023, Beijing retained its top-tier core position, while Zhangjiakou and Tangshan advanced to the second tier, forming a secondary high-level hub with Tianjin. The medium-tier region expanded considerably, encompassing the majority of Hebei Province (Figure 4).

3.1.2. Spatial and Temporal Characteristics of the Population Development

From 2012 to 2023, all BTHUA cities experienced growth in population development level. Beijing consistently ranked highest, with its index increasing from 0.762 to 0.937, reflecting an average annual growth rate of 1.89% (Figure 5). This suggests that despite a recent population decline, Beijing continues to attract skilled talent and maintain a favorable demographic structure. Tianjin ranks second, with its index increasing from 0.585 to 0.651 and an average annual growth of 0.97%, though growth began to slow after 2017. Hebei’s overall population development level remains considerably lower than Beijing’s, with a 2023 average index of 0.321, just 34.30% of Beijing’s level. From 2012 to 2019, growth across Hebei cities was gradual, with consistent intercity gaps. In 2020, population trends began to diverge significantly. Cities near Beijing, including Langfang, Chengde, Baoding, and Zhangjiakou, began to accelerate development. Overall, Hebei’s population growth outpaced that of Beijing and Tianjin, with Zhangjiakou and Chengde recording annual growth rates of 5.78% and 4.83%, respectively.

From a spatial perspective, six time points are selected to visualize population development. In 2012, the BTHUA exhibited a distinct hierarchical pattern, with Beijing, Tianjin, and Shijiazhuang leading, while northern Hebei lagged behind. By 2015, the surrounding areas of Beijing and Tianjin showed noticeable improvement, and mid-level regions expanded. Since 2020, the population development gap between northern and southern Hebei has gradually narrowed. Despite significant growth in Zhangjiakou and Chengde, their development levels remain limited. High-level areas formed a continuous C-shaped development belt surrounding the core regions (Figure 6).

3.1.3. Relative Development Analysis

Using Equation (8), the relative development trends of public service and population development were assessed. Most cities showed a fluctuating upward trajectory. Between 2016 and 2018, many transitioned toward coordinated development, particularly those previously lagging in public services. By 2023, only two remained behind, with a 66.67% reduction from 2012. In 2023, six cities achieved coordinated development and five led in public service, marking increases of 50% and 66.67% from 2012, respectively. Beijing’s relative development ranged from 0.789 to 0.951, consistently reflecting coordinated growth. Tianjin improved from 0.539 in 2012 to 0.903 in 2023, shifting from a lagging status to coordinated development after 2018. In 2023, Tangshan, Langfang, and Hengshui demonstrated relatively balanced development, with relative indices of 1.230, 0.815, and 1.380, respectively. Handan and Shijiazhuang lag in public service provision, with indices of 0.759 and 0.710, indicating inadequate service supply. Chengde, Zhangjiakou, and Qinhuangdao, with indices of 1.866, 1.921, and 1.380, respectively, were categorized as having advanced public service development, primarily due to their resource-based or tourism-driven economies (Figure 7).

3.2. Coupling Coordination Analysis

Based on Equation (9), BTHUA cities exhibited CDs between 0.899 and 0.999 from 2012 to 2023, reflecting strong interaction between public service provision and population development. While CCDs varied across cities, an overall trend of steady improvement was evident. From 2012 to 2023, most cities saw a 0.10–0.15 increase in coupling coordination, reflecting enhanced alignment between public service system and population development. Beijing recorded the highest improvement, rising from 0.806 to 0.937, transitioning from moderate to high-quality coordination. Tianjin ranked second, with its coordination index increasing from 0.642 in 2012 to 0.779 in 2023, advancing from basic to moderate coordination. In 2012, most Hebei cities were near a state of imbalance; by 2023, they had improved to marginal or basic coordination levels (Figure 8).

From a spatiotemporal perspective, between 2012 and 2015, progress in coupling coordination across cities was limited, with minimal changes in rankings. Nine cities were near-disharmonious, two barely coordinated, and only Tianjin and Beijing were classified as primary and well-coordinated, respectively. The spatial pattern reflected a central-core structure, with Beijing and Tianjin at the center and generally low coordination levels in surrounding cities. After 2016, CCDs across cities improved steadily, with notable ranking advancements, particularly in areas surrounding Beijing. By 2020, the coordination levels had significantly optimized, including marked progress in southern Hebei cities (Figure 9).

By 2023, nine cities reached basic coordination, two achieved primary coordination, one reached intermediate coordination, and one, Beijing, attained high-quality coordination. While the core–periphery structure persists, regional disparities have narrowed. Beijing remains the central driver of regional development, sustaining strong coordination and exerting a significant spillover effect on adjacent areas. Tianjin ranks second in coupling coordination, forming a central axis with Beijing and shaping a spatial gradient of declining coordination toward the periphery. Influenced by the BTHUA regional strategy and the development of Xiongan New Area, cities such as Zhangjiakou and Baoding have made notable progress, though they remain behind the leading cities.

3.3. Obstacle Factor Analysis

To identify key constraints on the coordinated development of public service and population, the study applied Equations (12) and (13) to compute constraint levels. Results from 2012 (Figure 10) show that, within public service system, cultural services posed the greatest limitation, followed by social security, healthcare, and education. Cultural services, including libraries and museums, had the greatest limiting impact on coordinated development. From 2012 to 2023, the cultural subsystem consistently ranked highest in constraint, highlighting widespread deficiencies across cities. In contrast, Beijing saw a marked reduction in the constraint levels of its social security and healthcare subsystems, indicating significant enhancements in services such as healthcare, elderly care, and insurance. In Hebei cities, healthcare constraints have declined, reflecting improved medical resources. Education system constraints have increased across all cities, notably in Beijing, where a sustained reduction in primary and middle schools led to a 98.65% rise in education-related limitations by 2023 compared to 2012.

In 2012, the main constraints in population development system were population quality, size, and structure. After 2015, limitations related to population quality declined, while those linked to population size began rising from 2019. Notably, population size constraints in Beijing increased from 17.16% in 2012 to 76.40% in 2023, and in Tianjin, from 35.34% to 51.27% over the same period. Since 2019, population structure constraints have declined in most Hebei cities, while Beijing experienced a sharp increase, now 15 times higher than in 2012. The recent drop in birth rates, shrinking population, and accelerated aging are key barriers to coordinated development in megacities.

Regarding secondary indicators (Figure 11,

Table 4. Major obstacles to coordinated development among cities in the BTHUA in 2023.

City	Degree of Obstruction
	PS			PD
	Top-1	Top-2	Top-3	Top-1	Top-2	Top-3
Beijing	PS9	PS1	PS2	PD3	PD4	PD1
Tianjin	PS21	PS10	PS8	PD1	PD7	PD3
Shijiazhuang	PS8	PS21	PS10	PD1	PD7	PD6
Chengde	PS21	PS8	PS10	PD1	PD2	PD7
Zhangjiakou	PS21	PS8	PS10	PD1	PD2	PD7
Qinhuangdao	PS21	PS8	PS10	PD1	PD2	PD7
Tangshan	PS21	PS10	PS8	PD1	PD6	PD2
Langfang	PS21	PS8	PS10	PD1	PD6	PD7
Baoding	PS21	PS8	PS10	PD1	PD6	PD7
Cangzhou	PS21	PS8	PS10	PD1	PD6	PD7
Hengshui	PS21	PS8	PS10	PD1	PD7	PD6
Xingtai	PS21	PS8	PS10	PD1	PD6	PD7
Handan	PS21	PS8	PS10	PD1	PD7	PD6

), the main constraints on coordinated urban development in most cities of Tianjin and Hebei are, in order, as follows: unemployment insurance coverage rate, library collections, and museum count, followed by the number of higher education institutions and per capita health spending. For population indicators, key limitations include the number of permanent residents, per capita disposable income, and the share of residents with at least a high school education. In contrast, Beijing, as the core of the region, faces different constraints such as natural population growth rate, dependency ratio, library circulation, and the number of primary and secondary schools.

4. Discussion and Policy Implications

4.1. Discussion

Public service provision and population development in the BTHUA have steadily improved. Beijing remains the regional hub, leading in both public service resources and population resources, with Tianjin trailing but still significantly behind. From 2016 to 2017, most Hebei cities, especially those near Beijing, entered a phase of accelerated growth, reflecting emerging spatial linkage effects in regional development. After 2020, growth in Beijing and Tianjin slowed, while Zhangjiakou, Baoding, and Langfang experienced marked progress due to the Beijing–Tianjin–Hebei coordination strategy. By 2023, most Hebei cities shifted from lagging to participating in coordinated development, reducing the average public service gap with Beijing by 16% compared to 2012. In terms of spatial structure, the public service provision levels in the BTHUA exhibited a typical core–periphery pattern in 2012. However, since 2020, this spatial structure has undergone a significant transformation, gradually evolving into a corridor-like development model along the Zhangjiakou–Beijing–Langfang–Tianjin axis. This signifies that public service provision is no longer a simple outward radiation from the core but rather demonstrates a trend of concentration and enhancement along specific corridors. For instance, the Winter Olympics provided Zhangjiakou with development opportunities, and based on relative development indices, it has now become a city with advanced public service development. This phenomenon highlights the transformative impact of policy-driven public service resource allocation on regional spatial development. It is also noteworthy that urban population development and public service development in the cities of Hebei are not entirely synchronous. Driven by policy initiatives, northern cities of Hebei have seen their public service levels develop at a faster pace than those in the southern areas. Conversely, southern cities, often characterized as population-dense, exhibit higher levels of population development than the north. Hower, regarding urban population development, growth rates declined after 2020. By 2023, most cities faced negative population growth, with a rising share of elderly residents, indicating entry into a moderately aged society.

The coupling and coordination between public service system and population development system in the BTHUA show clear phased patterns. From 2012 to 2015, development was concentrated in Beijing and Tianjin, while other cities had relatively low coordination levels. Post 2016, coordination steadily improved, and regional gaps diminished. After 2020, the region entered a phase of deeper integration, with notable progress in cities near Beijing. Southern Hebei also saw a substantial rise in coupling and coordination levels. This process reflects the strong impetus of regional coordinated development policies on regional progress and validates the view of the dynamic interplay and evolution between public service and population development systems.

Despite continued improvement in the coupling and coordination of public service system and population development system, regional imbalances persist. Core cities such as Beijing and Tianjin maintain a clear lead, and although Hebei has accelerated progress, closing the gap with these remains challenging in the short term. Currently, with the exception of Shijiazhuang and Tangshan, other cities in Hebei are in a state of minimal coordination. However, unlike previous research [15], the reasons for the suboptimal coordination levels across cities are not uniform. Resource-based and tourist cities in northern Hebei show pre-emptive public service development, yet their population growth remains relatively slow. Conversely, southern Hebei cities generally experience public services lagging behind population development, necessitating further enhancements in supply levels. Barriers to coordinated urban development vary by stage and region, with each city facing unique challenges. In Beijing, the main issue has shifted from insufficient service to structural constraints linked to its transition to high-quality development. Low birth rates and high dependency ratios underscore the need to optimize population structure while maintaining control and ensuring alignment with efficient public service. In Tianjin and most Hebei cities, the main challenge lies in foundational deficiencies, particularly in cultural services and social security. Strengthening basic public service provision is essential to attract and retain residents, thereby improving overall urban capacity.

Since the implementation of the Beijing–Tianjin–Hebei coordinated development strategy, local governments have introduced various policies, including collaborative construction and the establishment of branch institutions, aimed at guiding Beijing’s quality educational, healthcare, and elderly care resources towards areas surrounding Beijing, such as Baoding and Langfang. However, due to implicit barriers like administrative divisions, household registration management, and medical insurance systems, public service resources and populations still face difficulties in achieving bidirectional free flow. The process of guiding population mobility through the development of public service facilities is inherently complex and multifaceted. Research findings indicate that Beijing continues to possess a pronounced advantage in public service resources, with a strong resource agglomeration effect still in play. This effect, coupled with institutional barriers, makes it challenging for the mere construction of physical facilities to fundamentally resolve the mismatch between population needs and public service provision.

4.2. Policy Recommendation

Achieving coordinated development in the BTHUA requires managing the dynamic interplay between population shifts and public service provision. Integrating population strategies with service planning is crucial to align public service provision with demographic trends and regional integration goals.

First, focusing on the areas near Beijing is essential to reinforce core radiation and strengthen institutional guarantees. These cities are pivotal to BTHUA coordinated development, experiencing the most intensive population flows with Beijing. Current collaborative initiatives involving comprehensive hospitals, high-quality primary and secondary schools, and higher education institutions require robust institutional support, including streamlined cross-city medical settlement processes and integrated social security systems. Drawing on international experiences, strategies such as The City Together (2004) and The London Collaborative (2008) in the Greater London area, the European Union’s regional cohesion policies, can offer valuable insights for establishing multi-faceted cross-border cooperation. Similarly, metropolitan areas in the United States, such as the New York metropolitan area and the San Francisco Bay Area, primarily rely on inter-local government cooperation agreements and market-driven guidance. Therefore, the development of the areas near Beijing requires exploring diversified supply models involving the joint participation of market entities and social organizations, thereby broadening the channels for basic public service provision.

Second, harness digital technologies to broaden access to public resources and innovate public service delivery. Given the general scarcity of cultural resources in Hebei Province, the development of digital libraries and museums can offset the lack of physical cultural infrastructure. Similarly, online education and telemedicine can overcome spatial limitations, improve service coverage and quality, and reduce regional disparities.

Third, population structure has emerged as a major barrier to coordinated development in core cities, such as Beijing. Currently, Beijing’s aging population is significantly higher than the national average. In contrast, other major metropolitan areas like Greater London and Tokyo exhibit much lower aging rates compared to their national averages. To optimize the population structure, guiding the allocation of healthcare and elderly care resources towards Baoding and Langfang, and encouraging elderly migration, could be beneficial. Enhancing the quality of basic education and optimizing the spatial layout of primary and secondary schools also contribute to fostering a child-friendly social environment and mitigating the trend of low fertility. The delayed and staggered impact of new births on educational resources at different stages, coupled with fluctuations in school-aged populations, complicates educational resource allocation. Based on birth rate trends, the demand for primary and secondary education is expected to increase initially and then decline, shifting the focus of educational resource allocation towards quality development. Regarding the spatial layout of primary and secondary school resources, it is crucial to establish dynamic monitoring of school-age population numbers for different regions and implement tailored measures. According to changing birth trends, core urban areas in cities like Beijing and Tianjin can enhance the efficiency and quality of existing educational resources through dynamic adjustments to school district boundaries, optimizing resource allocation within educational networks, advancing digital learning, and improving the teacher–student ratio. For key receiving areas such as Langfang, the Xiong’an, and the Yanjiao region of Beijing, it is essential to anticipate population influx by reserving and developing educational land.

Fourth, public services in southern Hebei continue to lag behind population development. From the perspective of regional coordinated development, efforts should focus on enhancing the quantity and quality of higher education institutions, comprehensive hospitals, and institutional elderly care services. By establishing a multi-centric network that integrates regional centers, urban clusters, and grassroots units, and by encouraging cross-regional collaboration, a public service supply system with broad reach and complementary functions can be effectively constructed.

5. Conclusions

This study aimed to systematically analyze the spatiotemporal evolution characteristics of basic public service levels and population development in the BTHUA, explore their dynamic coupling and coordination relationship, identify key impeding factors for coordinated development, and subsequently propose recommendations for improvement. By constructing an indicator system for public services encompassing education, culture, healthcare, and social security, and a population development indicator system that includes population size, structure, and quality, coupled with the application of coupling coordination and impediment factor models, this study has achieved its stated research objectives.

Findings reveal notable progress in both public service delivery and population development during the study period. Spatially, the structure of public service supply has evolved from a core–periphery model to a belt-like development pattern. This transition highlights the crucial role of policy intervention in reshaping regional spatial development and reflects the strengths of China’s centralized governance model. The coupling and coordination levels between public service provision and population development have significantly improved, demonstrating a positive interaction and dynamic optimization. However, significant inter-regional imbalances persist. Beijing’s potent resource aggregation effect, coupled with institutional barriers between cities, impedes bidirectional population flows and limits the effective “spillover” of high-quality public services from Beijing to cities like Baoding and Langfang. For the resource-based cities in northern Hebei, population development has lagged behind. The extent to which this ahead-of-schedule public service development spurs population mobility warrants further investigation. China is entering a new stage of demographic transition. With an aging rate exceeding the national average, the BTHUA faces pronounced structural challenges in its population composition. Demographic shifts pose significant challenges to the public service delivery system and influence population distribution across the region. For example, an aging population increasingly demands more eldercare and social security services, yet areas in Hebei exhibit substantial gaps compared to core cities in social security and culture, impacting the mobility of the elderly. In response, the region urgently develops a population development provision model aligned with demographic shifts, transitioning from scale-oriented expansion to structural optimization in resource allocation.

The coordinated development of the BTHUA is a dynamic process shaped by complex interactions between public service provision and demographic shifts. This study’s contribution lies in applying complex systems theory to the interaction between public services and population development, utilizing the coupling coordination model to quantify the dynamic relationship between these two systems, thus offering a novel perspective for understanding regional development. In terms of empirical contributions, this study traces the evolution of the population development and population system before and after the implementation of the regional coordination strategy, offering essential data for objectively assessing policy outcomes. The study highlights areas with inadequate or excessive population development provision and identifies key barriers to intercity coordination. The findings offer a foundation for targeted government resource allocation, improving overall regional efficiency. In terms of policy contributions, the research findings illuminate the profound impact of structural demographic shifts on the equilibrium of public service supply and demand, as well as the role of institutional barriers in impeding bidirectional resource flows. This provides theoretical support and practical guidance for deepening reforms to overcome regional development imbalances and foster greater integration within the BTHUA.

While this study has made progress in analyzing the coupling and coordination relationship between basic public services and population development in the BTHUA, several limitations persist. First, the study relies solely on city-level macro statistics, which may mask intra-city disparities. Second, constrained by data availability, the temporal analysis of public service development has predominantly focused on “scale” indicators, with limited focus on service efficiency and accessibility. Furthermore, qualitative assessments of public service quality, such as resident satisfaction, were not incorporated. From a research perspective, this study has focused on the interplay between public services and population development. However, regional development is a complex, multi-dimensional system. Factors like economic development levels and transportation infrastructure also exert significant influence on regional progress, forming intricate coupling relationships with public services and population dynamics.

Based on these limitations, future research will be expanded in the following directions. First, select representative case study areas to explore public service provision and population development at the community scale, employing multi-source data and GIS technology. This will be complemented by field research and surveys to gather residents’ feedback on the quality of public service delivery. Second, investigate the coupled relationships among public services, population development, and economic development, or between public services, population development, and transportation infrastructure. This will enable the construction of a more comprehensive interactive model for regional coordinated development. Third, conduct comparative analyses of the BTHUA‘s research findings with other metropolitan areas, both domestically and internationally. This will facilitate a broader understanding of the commonalities and specificities of public services and population development within diverse regional contexts.