Measuring the Coordinated Development of Urban Agglomerations from the Perspective of New Quality Productive Forces: Evidence from the Beijing–Tianjin–Hebei Region

Abstract

New quality productive forces are increasingly recognized as important drivers of coordinated regional development, with urban agglomerations acting as key vehicles for their spatial implementation. Based on the theory of new quality productive forces, this study takes the 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration as its research subjects, spanning the period from 2005 to 2023, and constructs a four-dimensional evaluation index system for new quality productive forces covering economic, social, ecological, and technological dimensions. It employs the entropy method to determine indicator weights and calculate development indices for each dimension and utilizes a coupling coordination model to measure the overall and subsystem-level coordination by analyzing their spatiotemporal evolution characteristics. The results indicate a steady upward trend in the overall coordination level, progressing from a low level to an intermediate level, with the state of coordination continuously improving; spatial differentiation is significant, forming a gradient development pattern centered on Beijing, with marked disparities in coordination levels among cities. Subsystem analysis reveals an imbalanced synergy structure: while economic and ecological synergy levels are relatively high, the coupling and synergy between science and technology and the economy and society remain prominent weaknesses. Most cities in Hebei Province lack sufficient scientific and technological innovation capabilities, resulting in a weak supportive role for economic and social development. Based on these findings, this study proposes policy recommendations such as establishing a regional innovation community, promoting the integration of factor markets, and strengthening collaborative governance of the ecological environment, with the aim of leveraging new quality productive forces to drive a qualitative leap in the coordinated development of the BTH urban agglomeration.

1. Introduction

With the accelerating advancement of a new round of global scientific and technological revolution and industrial transformation, China’s economy has entered a crucial stage of transition from rapid growth to high-quality development [1]. In this context, the concept of “new quality productive forces” has become a central theoretical and practical guide. The term was initially proposed by Chinese President Xi Jinping during his visit to Heilongjiang Province in 2023 and was further emphasized during the 11th collective study session of the 20th Central Politburo of the Communist Party of China in 2024. Transcending the constrains of conventional economic growth models, new quality productive forces are driven by innovation and characterized by high technology, high efficiency, and high quality. With a significant increase in total factor productivity as their core hallmark, they represent an advanced form of productivity [2]. By reshaping the structure of productivity and guiding the transformation of development modes, this concept provides a new perspective for understanding how regions can overcome structural imbalances and achieve sustainable development [3].

At the same time, achieving coordinated development across regions has become a central goal of sustainable development strategies [4,5,6]. It emphasizes breaking administrative barriers, optimizing the spatial division of labor, and improving coordination mechanisms to transform regional development from a dispersed and homogeneous pattern to one characterized by agglomeration and integration. Its core principles are highly consistent with the innovation-driven, efficient coordination, and quality improvement connotations of new quality productive forces [7]. Urban agglomerations, as the principal form of new-type urbanization and the key spatial carriers of national economic growth, represent crucial arenas for implementing regional coordinated development theory and cultivating as well as unleashing new quality productive forces [8]. Promoting the coordinated development of urban agglomerations—moving from traditional factor-driven growth to innovation-driven development, from administrative segmentation to market integration, and from homogeneous competition to the division of labor and cooperation—is essentially a process of reconstructing regional production relations, optimizing the efficiency of resource allocation, and enhancing overall regional competitiveness through new quality productive forces [9]. Therefore, scientifically measuring the level of coordinated development within urban agglomerations and deeply coupling it with the theory of new quality productive forces can not only provide an accurate quantitative benchmark for regional coordinated development but also offer clear guidance for the spatial implementation of new quality productive forces.

The BTH urban agglomeration, as the first major national strategy for regional coordinated development in China, undertakes the important mission of building a pioneering demonstration zone for Chinese-style modernization [10]. Since the implementation of the coordinated development strategy, the region has achieved remarkable progress in relieving Beijing of non-capital functions, industrial upgrading and relocation, joint ecological governance, and transportation integration, with the coordination of regional development continuously improving [11]. However, the region still faces several practical challenges, including insufficient coordination between innovation chains and industrial chains, the incomplete removal of barriers in the market-oriented allocation of production factors, persistent regional development disparities, and the need for further improvements in collaborative governance efficiency. From the perspective of new quality productive forces, systematically measuring the coordinated development level of the BTH urban agglomeration and accurately identifying the shortcomings and core driving mechanisms in the coordination process are of great theoretical and practical significance. These efforts will help promote the region’s transition from coordinated development to high-quality coordinated development and provide exemplary guidance for the transformation and development of other urban agglomerations across the country [12].

The existing literature has provided important insights into both new quality productive forces and the coordinated development of urban agglomerations [13,14]. On the one hand, studies on new quality productive forces have focused on their conceptual connotations and measurement frameworks, typically emphasizing innovation, efficiency, and sustainability dimensions [15,16]. On the other hand, research on urban agglomeration coordination has widely applied methods such as the coupling coordination model to examine the spatial and temporal patterns of development [17,18]. However, these two strands of research remain largely disconnected. In particular, three limitations can be identified: First, most studies analyze new quality productive forces and coordinated development separately, lacking an integrated analytical framework. Second, existing measurement systems often rely on traditional development indicators and do not fully capture the multidimensional structure of new quality productive forces. Third, empirical analyses at the urban agglomeration level, especially those based on long-term city-level panel data, remain relatively limited.

Based on this background, this study reviews relevant research on the theory of new quality productive forces and the coordinated development of urban agglomerations. Taking into account the development realities of the BTH urban agglomeration, it constructs an evaluation index system for the development of new quality productive forces from four dimensions: economy, society, ecology, and science and technology. The entropy method is employed to determine the weights of each indicator and calculate the comprehensive index of new quality productive forces for the urban agglomeration. Furthermore, a coupling coordination degree model is used to measure the coupling coordination degree of new quality productive force development within the BTH urban agglomeration, thereby systematically evaluating the coordinated development status among the internal systems of the region and providing decision-making references for promoting the high-quality coordinated development of the urban agglomeration. The contributions of this study are summarized as follows:

• This study operationalizes the core connotation of new quality productive forces into a four-dimensional evaluation framework consisting of economic, social, ecological, and technological dimensions. It breaks through the limitations of studies that have predominantly measured the coordinated development of urban agglomerations from a single dimension or a traditional industrial structure perspective, thereby providing an actionable theoretical analytical tool for the high-quality development of urban agglomerations.
• Using a long-term panel dataset from 2005 to 2023, this study not only measures the overall level of coordinated development but also innovatively introduces a pairwise coupling analysis among subsystems. It identifies that the structural bottlenecks in the coordinated development of the BTH urban agglomeration lie in developmental technological–economic and technological–social imbalances. This addresses the gap in previous research, which mostly focused on the overall coordination degree while neglecting the structural matching characteristics among internal subsystems.
• This study reveals the mutually reinforcing mechanism between the technological divide and the core–periphery spatial structure. Specifically, the high concentration of technological factors in core cities, combined with insufficient cross-regional mobility, constrains the quality of coordination among subsystems. These findings provide important policy implications for breaking administrative barriers and promoting the efficient cross-regional flow of advanced production factors.

The rest of the paper is organized as follows: Section 2 reviews the relevant literature and constructs the theoretical framework. Section 3 introduces the research area, methods, and data. Section 4 analyzes the spatiotemporal evolution of the overall coordinated development, while Section 5 delves into the internal synergistic structure by examining the pairwise coupling of subsystems. Section 6 provides a comprehensive discussion. Finally, Section 7 summarizes the conclusions and proposes policy recommendations.

2. Literature Review and Theoretical Framework

2.1. Literature Review

Productivity remains the fundamental driver of sustained socio-economic development [19]. Historically, traditional productivity models relied heavily on capital expansion, labor, and natural resource consumption, which are primarily characterized by factor-driven growth and scale expansion [20]. While this model facilitated rapid economic growth during specific periods, it has increasingly exposed structural vulnerabilities. These include tightening environmental constraints, inefficient factor allocation, industrial homogeneity, regional imbalances, and unsustainable growth trajectories, rendering the model inadequate for the demands of modern high-quality development [21]. As China transitions from a phase of rapid expansion to one of high-quality development, the traditional factor input model confronts both the ecological limits of resource-carrying capacity and the economic limits of diminishing marginal returns. Consequently, structural transformation and the cultivation of new growth drivers have become central to achieving high-quality regional development [22]. Achieving this transformation requires a decisive shift from extensive resource consumption and scale expansion toward technological innovation, improved efficiency, green and low-carbon development, and coordinated openness [23]. This paradigm shift is underpinned by the convergence of three theoretical frameworks: innovation-driven development, high-quality development, and sustainable development. Innovation-driven development theory posits technological breakthroughs, knowledge accumulation, and research and development investment as the primary engines for reshaping economic growth [24]. High-quality development theory emphasizes total factor productivity (TFP) improvements, prioritizing structural optimization and efficiency over quantitative expansion [25]. Furthermore, sustainable development theory highlights resource and environmental boundaries, advocating for green production, low-carbon transitions, and the alignment of socio-economic and ecological benefits [26]. From both theoretical and practical perspectives, overcoming the path dependence of traditional growth necessitates a dynamic system led by innovation centered on TFP enhancement and supported by optimized resource allocation [27]. The concept of new quality productive forces provides a novel theoretical lens for this transition. Unlike traditional productivity, it emphasizes innovation-led growth, technological breakthroughs, and advanced industrial upgrading. The existing literature generally defines new quality productive forces as an advanced form of productivity distinguished by high-technology, high-efficiency, and green, low-carbon development, with improved TFP as its core hallmark [13]. Accordingly, considerable scholarly effort has focused on employing comprehensive evaluation methods to construct indicator systems that quantitatively assess the regional development levels of these new quality productive forces.

Methods of quantifying new quality productive forces have garnered significant scholarly attention, with existing research predominantly employing comprehensive evaluation methods to construct indicator systems for regional assessments [15]. In terms of indicator design, different studies generally establish multi-level and multidimensional evaluation frameworks based on dimensions such as new quality laborers, new quality means of labor, and new quality objects of labor while incorporating factors including human capital, energy utilization, digital infrastructure, and the ecological environment [28]. Wang et al. (2025) approach the issue from the perspectives of infrastructure development and technological research and development (R&D) investment, selecting indicators such as the ratio of highway mileage to GDP, the number of broadband internet access ports per capita, and robot installation density to evaluate the level of regional new quality productive forces [29]. On this basis, Hu and Liu (2024) further incorporate industrial coordination into the evaluation system to capture the structural linkage characteristics of new quality productive force development [30]. Xu (2025) evaluates new quality productive forces based on five dimensions: digitalization, coordination, greenness, openness, and sharing [31]. In addition, some studies have extended the analytical boundaries of new quality productive forces from the perspectives of public services and cultural resource allocation. For example, Wu and Mao (2026) explore the logical relationship between new quality productive forces and the high-quality development of libraries [32], and Yu (2025) incorporates indicators such as public library collections into the measurement of technological cultivation capacity in order to enrich the evaluation system [33]. Overall, most existing studies have selected indicators of economic development, social sharing, the ecological environment, and scientific and technological innovation, thereby laying a relatively solid foundation for the quantitative study of new quality productive forces. However, several limitations remain in the current literature on new quality productive forces. Most studies still take provinces or individual cities as their main units of analysis, while relatively few focus specifically on urban agglomerations as the core spatial unit. Moreover, existing research has mainly concentrated on measuring the development level of new quality productive forces and comparing regional differences, while quantitative analyses of their relationship with the coordinated development of multiple subsystems within urban agglomerations, including the economy, society, ecology, and science and technology, remain relatively limited.

Regional synergistic development promotes the transition from fragmented competition to collaborative interaction by facilitating factor mobility, optimizing functional divisions, and improving collaborative mechanisms. This transition aligns closely with the core tenets of new quality productive forces, namely innovation-driven growth, efficient coordination, and quality enhancement [34]. As regional synergistic development strategies advance, research on urban agglomerations has deepened. Scholars commonly employ spatial autocorrelation analysis, modified gravity models, the rank-size rule, network analysis, and composite system coordination models to measure urban agglomeration synergy across multiple dimensions, including spatial structure, economic linkages, and system coupling [35]. Empirical applications of these methods are extensive. Zeng (2021) constructed an indicator system based on coordinated development theory to assess and compare the coordination capacity of 41 cities in the Yangtze River Delta [36]. Similarly, coupling coordination models have evaluated the population–water–ecology–economy nexus in the Tianshan Mountains [37] and composite industrial subsystems in Chongqing [38]. Wang and Li (2017) have explored regional synergy primarily through the lens of spatiotemporal differentiation in new urbanization [39]. Despite these methodological advancements, the integration of new quality productive force theory into the study of urban agglomerations remains critically insufficient. The existing literature has yet to systematically re-examine the evolutionary logic, momentum sources, and optimization pathways of agglomeration synergy from this novel theoretical perspective. While nascent research has explored the coupling relationship between new quality productive forces and regional development across 30 Chinese provinces, Gang and Zhao (2025)’s investigations are largely confined to macro-level provincial analyses [40]. They lack an in-depth focus on urban agglomerations, which are the most relevant spatial units for policy practice and economic organization. Specifically, targeted research on the BTH urban agglomeration is notably scarce. As a vital national-level agglomeration, this region is a primary spatial carrier for relieving Beijing’s non-capital functions and driving high-quality development in northern China. Although elevating the synergistic development of the BTH region to a national strategy has fostered differentiated functional divisions, the area continues to grapple with the uneven allocation of innovation resources, weak industrial linkages, and inadequate coordination in ecological governance [41]. Consequently, investigating the synergistic development of the BTH urban agglomeration through the lens of new quality productive forces holds profound theoretical significance and practical value for overcoming these regional bottlenecks.

Building upon these insights, this study investigates the theoretical nexus between new quality productive forces and the synergistic development of urban agglomerations. Focusing on the BTH region as a case study, we construct a multidimensional evaluation framework encompassing economic, social, ecological, and technological subsystems. Furthermore, we analyze the mechanisms and characteristics through which new quality productive forces drive regional synergy. Ultimately, this study aims to provide novel theoretical explanations and robust empirical evidence to support the high-quality, integrated development of the BTH urban agglomeration.

2.2. Theoretical Framework and Mechanism Analysis

Urban agglomeration coordination is essentially a process in which the economic, social, ecological, and technological subsystems evolve from fragmented operation toward coordinated interaction. From the perspective of new quality productive forces, this process no longer relies primarily on the large-scale input of traditional production factors but increasingly depends on new drivers such as technological innovation, optimized factor allocation, industrial transformation and upgrading, and green low-carbon development. New quality productive forces not only provide endogenous momentum for the coordinated development of urban agglomerations but also offer a new pathway for regional integration. Specifically, new quality productive forces promote urban agglomeration coordination through three main mechanisms: The first is the innovation-driven mechanism through which technological breakthroughs, knowledge spillovers, and the diffusion of innovation resources facilitate multi-system interaction. The second is the factor allocation optimization mechanism through which the efficient flow of capital, talent, information, and data improves regional resource allocation efficiency. The third is the industrial upgrading and green transformation mechanism through which the cultivation of emerging industries, the upgrading of traditional industries, and the diffusion of green technologies promote coordinated development across the economic, social, ecological, and technological subsystems.

Based on this, this study constructs an analytical framework of “new quality productive forces–multi-system coordinated development” to characterize the coordinated development level of the BTH urban agglomeration from four dimensions: economy, society, ecology, and technology. By combining the entropy method with the coupling coordination degree model, this study measures and analyzes the coordinated development level and spatiotemporal evolution of 13 cities in the BTH urban agglomeration during 2005–2023. The specific theoretical framework is shown in Figure 1.

3. Research Area, Methods, and Data

3.1. Research Area Summary

This study focuses on the BTH urban agglomeration, which is located in North China and at the core of the Bohai Rim economic zone. The geographical location of the study area is shown in Figure 2. As one of China’s three world-class urban agglomerations, it serves not only as a major engine of economic growth in northern China but also as an important platform for regional coordinated development and the cultivation of new quality productive forces. The region includes the two municipalities of Beijing and Tianjin and 11 prefecture-level cities in Hebei Province, covering approximately 216,000 km² and hosting a permanent population of over 100 million. Characterized by close economic linkages, strong spatial connectivity, and an integrated industrial foundation, the region provides favorable conditions for coordinated development. At the same time, it continues to face challenges such as uneven development, resource and environmental constraints, limited factor mobility, and insufficient functional coordination. These features make the BTH urban agglomeration a representative case for examining the relationship between regional coordinated development and the cultivation of new quality productive forces. A growing body of empirical research has therefore focused on its high-quality development and coordinated governance. For example, Sun (2024) focused on the resilience systems of cities in the BTH urban agglomeration by measuring the resilience levels of different cities within the region and mapping their spatial clustering characteristics [42]. Yu (2022) explored the coupling relationship between urbanization levels and the ecological environment in the urban agglomeration, revealing that, as urbanization continues to rise, both urban metabolic efficiency and ecological environmental pressure worsen [43]. Mu (2024) analyzed the spatial evolution patterns of carbon dioxide emissions in the region, identified the key drivers of carbon emissions and their interactions, and proposed core mechanisms for promoting coordinated carbon reduction in the urban agglomeration [44]. Lin (2025) validated the impact of regional coordination policies on enhancing the resilience levels of the urban agglomeration, confirming that such policies effectively strengthen the ability of cities to withstand and recover from various shocks [45]. Overall, the BTH urban agglomeration has become a key focus in the fields of regional development and coordination, and the related findings provide an important foundation for the research design and empirical analysis in this study.

3.2. Source of Data

The data used in this study are mainly drawn from the China City Statistical Yearbook, the Beijing Statistical Yearbook, the Tianjin Statistical Yearbook, the Hebei Statistical Yearbook, statistical yearbooks of prefecture-level cities in the BTH region, and the Statistical Communiques on National Economic and Social Development. Electricity consumption data are obtained from grid company statistics. Owing to adjustments in statistical coverage and changes in data release practices, the data sources vary slightly across years: earlier data are mainly taken from statistical yearbooks, while more recent data are supplemented by statistical communiques. To ensure consistency and comparability, data from different sources are harmonized and cross-checked. The study period, which spans 2005–2023, was determined by both data availability and continuity; it also covers the implementation of the BTH coordinated development strategy in 2014, thereby allowing the dynamic evolution of regional coordination to be captured. A small amount of missing data for certain years and cities is supplemented using linear interpolation, which is appropriate for relatively smooth time-series data and does not lead to significant deviations from the original trends. In addition, monetary indicators are deflated using the corresponding price indices and converted into constant 2005 prices to improve intertemporal comparability.

3.3. Research Methods

3.3.1. Entropy Method

To reduce the bias of subjective weighting and improve objectivity, this study employs the entropy weight method to determine indicator weights. Based on information entropy theory, the method measures the information content of each indicator through its degree of dispersion. In general, indicators with greater dispersion contain more information, have stronger explanatory power for sample differences and should therefore be assigned higher weights in the composite evaluation. Using panel data from 2005 to 2023, this study adopts a full-sample standardization approach to ensure intertemporal comparability. Specifically, the maximum and minimum values of each indicator are determined based on all observations for the 13 prefecture-level cities in the BTH region over the study period, yielding 247 samples for each indicator, and the raw data are normalized accordingly. This approach captures relative differences across cities and years under a unified benchmark, avoids the intertemporal incomparability caused by year-by-year standardization, and improves the stability and consistency of the evaluation results. The specific calculation steps are as follows.

(1) Indicator normalization

Given the differences in units and scales across indicators, the raw data are first normalized. This study adopts the min–max normalization method to rescale all indicator values to the interval $\left[0,1\right]$.

Positive indicators are determined as follows:

$$x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-\min (x_{ij})}{\max (x_{ij})-\min (x_{ij})}}$$

(1)

Negative indicators are determined as follows:

$$x_{ij}^{\prime }={\displaystyle \frac{\max (x_{ij})-x_{ij}}{\max (x_{ij})-\min (x_{ij})}}$$

(2)

where $x_{ij}$ represents the original value of indicator $j$ for city $i$ and $x_{ij}^{\prime }$ represents the normalized value. $\max \left(x_{ij}\right)$ and $\min \left(x_{ij}\right)$ represent the maximum and minimum values of indicator $j$ in the full sample over 2005–2023 (247 observations), respectively.

(2) Calculation of indicator proportions

After the normalization procedure, each indicator is converted into a proportional form to characterize its distribution across cities.

$$P_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\displaystyle {\sum }_{i=1}^n{x}_{ij}^{\prime }}}}$$

(3)

where $P_{ij}$ represents the proportion of indicator $j$ for city $i$. This step transforms the data into a probability distribution for subsequent entropy calculation.

(3) Calculation of information entropy

Information entropy is employed to measure the dispersion of each indicator across samples, and its calculation formula is given as follows:

$$e_j=-k{\displaystyle \sum _{i=1}^n{P}_{ij}\ln (}{P}_{ij})$$

(4)

where $e_j$ represents the information entropy of indicator $j$, $n$ represents the sample size, and $k$ represents the normalization coefficient that ensures $e_j\in [0,1]$. A more even distribution of an indicator across cities corresponds to higher entropy and weaker discriminatory power; by contrast, greater variation corresponds to lower entropy and higher information content.

(4) Calculation of the divergence coefficient

Based on the entropy results, the divergence coefficient of each indicator is calculated as follows:

$$d_j=1-e_j$$

(5)

where $d_j$ represents the information utility of indicator $j$. The larger the value of $d_j$, the greater the variation in the indicator across samples and the stronger its ability to distinguish among them.

(5) Determination of indicator weights

The final indicator weights are obtained by normalizing the divergence coefficients.

$$w_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^m{d}_j}}}$$

(6)

where $w_j$ represents the weight assigned to indicator $j$ and $m$ represents the total number of indicators.

(6) Calculation of the composite score for each subsystem

$$Q_i={\displaystyle {\sum }_{j=1}^m{w}_j}{x}_{ij}^{\prime }$$

(7)

where $Q_i$ represents the composite evaluation score of city $i$, $w_j$ represents the weight of indicator $j$, and $x_{ij}^{\prime }$ represents the standardized value of indicator $j$ for city $i$.

It should be noted that indicator weights are calculated using the full-sample data for 2005–2023 and are kept constant over the entire study period. This fixed-weight approach ensures the intertemporal comparability of the composite index and avoids fluctuations caused by the annual re-estimation of weights. Compared with dynamic weighting, it allows the evaluation results to mainly reflect changes in indicator values rather than changes in weights.

3.3.2. Indicator System for New Quality Productive Forces

According to the existing literature, new quality productive forces are innovation-driven and mainly characterized by technological progress, optimized factor allocation, and industrial upgrading. Accordingly, this study constructs an indicator system using four dimensions: economy, society, ecology, and technology. The economic dimension reflects development quality and structural optimization. The social dimension captures human capital and social conditions. The ecological dimension represents green and sustainable development, and the technological dimension measures innovation capacity and R&D input. Together, these four dimensions constitute a multidimensional evaluation framework for new quality productive forces. The evaluation framework for new quality productive forces in urban agglomerations is presented in

Table 1. Economic coordination development indicator system for the BTH urban agglomeration.

System	Subsystem	Evaluation Indicators	Indicator Attributes	Weight
Evaluation Index System for New Quality Productivity in Urban Agglomerations	Economic	Citywide per capita GDP	Positive indicator	0.22
		Share of the tertiary sector in GDP	Positive indicator	0.13
		Average employee wage	Positive indicator	0.16
		Total social retail sales	Positive indicator	0.49
	Social	Year-end registered urban unemployment rate	Negative indicator	0.10
		Fixed asset investment amount	Positive indicator	0.20
		Internet penetration rate	Positive indicator	0.11
		Deposit balance of financial institutions	Positive indicator	0.59
	Ecological	Green space per capita	Positive indicator	0.34
		Energy consumption intensity	Negative indicator	0.15
		Household waste treatment rate	Positive indicator	0.14
		Annual average concentration of PM2.5	Negative indicator	0.37
	Technological	Number of patents authorized	Positive indicator	0.50
		Share of science and technology expenditures in general budget expenditures	Positive indicator	0.17
		Percentage of university faculty among employed individuals	Positive indicator	0.10
		Public library holdings per 100 people	Positive indicator	0.24

.

The objective weights of the four subsystems, namely the economy, society, ecology, and technology, are denoted by $\alpha$, $\beta$, $\gamma$, and $\chi$, respectively, where $\alpha +\beta +\gamma +\chi =1$. Referring to the findings by He [46] and considering the connotation of new quality productive force development, the dimensional weights are determined as follows: $\alpha =0.3$ for the economic subsystem, $\gamma =0.3$ for the ecological subsystem, $\beta =0.2$ for the social subsystem, and $\chi =0.2$ for the technological subsystem. Here, $f_1$, $f_2$, $f_3$ and $f_4$ represent the development level score for each subsystem, which was calculated via the entropy method.

3.3.3. Coupling Coordination

Coupling coordination originates from an important concept in physics. It refers to a dynamic interconnection where two or more systems interact and constrain each other through the combined effects of internal dynamics and external interactions. This study adopts a coupling coordination degree model to measure the interactive relationships and coordinated development levels among the four major subsystems in cities of the BTH urban agglomeration, namely the economic, social, ecological, and technological subsystems. This approach comprehensively evaluates the overall collaborative development status of the BTH urban cluster.

$$C_t={\displaystyle \frac{4\sqrt[4]{(f_{1i}\cdot f_{2i}\cdot f_{3i}\cdot f_{4i})}}{\left(f_{1i}+f_{2i}+f_{3i}+f_{4i}\right)}}$$

(8)

where $C_t$ represents the coupling degree, ranging from 0 to 1. A higher value of $C_t$ indicates stronger coupling and better coordination among the subsystems. $t=1,2,3,4$ represents the corresponding subsystem, namely the economy, society, ecology, and technology. $C_t=0$ indicates a low level of coupling and a disordered development state, whereas $C_t=1$ indicates the maximum coupling degree and the optimal state of coordinated development. $f_1$, $f_2$, $f_3$, and $f_4$ represent the development levels of the four subsystems measured by the entropy method.

However, the coupling degree can only reflect the intensity of interactions among subsystems, failing to capture their overall development level and state of positive coupling. The coordination degree, on the other hand, characterizes the level of positive coupling formed among subsystems through their interactions, effectively reflecting the coordinated development status of urban subsystems. Therefore, it is necessary to further calculate the coordination degree between subsystems.

$$T_t=\alpha f_{1i}+\beta f_{2i}+\gamma f_{3i}+\chi f_{4i}$$

(9)

$$D_t={\left(C_t\times T_t\right)}^{1/4}$$

(10)

where $T_t$ represents the high-quality development index of the BTH urban agglomeration and $D_t$ represents the overall coordination degree of its economic, social, ecological, and technological subsystems. A larger $D_t$ indicates a higher level of coordination among the subsystems.

To further examine the coordination level among urban subsystems in the BTH urban agglomeration, this study extends the coupling coordination model to measure the coupling degree between any two subsystems. Specifically, $C_{y,z}$ represents the coupling degree between subsystem $y$ and subsystem $z$, while $f_y$ and $f_z$ represent the development level indices of the two subsystems as measured by the entropy method.

$$C_{y,z}={\displaystyle \frac{2\sqrt{f_{\mathrm{y}}\cdot f_z}}{f_y+f_z}}$$

(11)

On this basis, the composite coordination index and coupling coordination degree model for subsystems $y$ and $z$ are further constructed as follows:

$$T_{y,z}=Y{f}_y+Z{f}_z$$

(12)

$$D_{y,z}=\sqrt{C_{y,z}\times T_{y,z}}$$

(13)

where $Y$ and $Z$ represent the subsystem weights and satisfy $Y+Z=1$. Drawing on the findings by Li [47] and considering both coordination analysis and fairness between subsystems, this study assigns equal importance to the two subsystems and sets $Y=Z=0.5$. $T_{y,z}$ represents the composite coordination index of subsystems $y$ and $z$, and $D_{y,z}$ represents the coupling coordination degree between them. A larger value indicates a higher level of coordinated development between the two subsystems. Based on the above, the coordination degrees between economy and ecology, economy and technology, society and ecology, society and technology, and ecology and technology can be calculated separately.

4. Measurement of Coordinated Development in the BTH Urban Agglomeration

4.1. Overall Coordination Degree of Cities in the BTH Urban Agglomeration

This study uses a coupling coordination degree model to evaluate the coupling coordination among the economic, social, ecological, and technological subsystems across 13 prefecture-level cities in the BTH urban agglomeration from 2005 to 2023. Based on this analysis, the overall coupling coordination index for each urban system is calculated, and the overall status of the urban systems’ coordinated development is classified and evaluated accordingly. According to the classification criteria of coupling coordination levels, when D < 0.5, the system is considered to be dysfunctional or imbalanced; when 0.5 ≤ D < 0.7, the system is in a low-coordination stage; when 0.7 ≤ D < 0.85, it is in an intermediate-coordination stage; and when D ≥ 0.85, it is in an advanced-coordination stage. Based on these classification standards, the stage characteristics and evolutionary trends of coordinated development among urban systems during the study period can be clearly identified, thereby providing a quantitative basis for analyzing the overall pattern of coordinated development within the BTH urban agglomeration.

From the perspective of the overall development trend, the coupling coordination degree of urban systems in the BTH urban agglomeration exhibited a steady upward trajectory from 2005 to 2023, indicating a continuous improvement in the level of regional coordinated development. The relationships among these subsystems evolved from relatively independent development toward increasingly coordinated interaction, leading to a continuous improvement in the comprehensive coordination capacity of urban systems. Although disparities in development levels exist among different cities, the coupling coordination degrees of most cities show clear upward trends to varying extents. This indicates that notable progress has been achieved in promoting regional coordinated development within the BTH urban agglomeration.

At the city level, as shown in Figure 3, the level of coordinated development within the BTH urban agglomeration exhibits a pronounced gradient differentiation pattern. Among the cities, Beijing consistently maintains a leading position in terms of coordinated development. Its coupling coordination degree remains in the high-level coordination stage throughout the entire study period and demonstrates a steady upward trend. This indicates that Beijing possesses strong comprehensive advantages in economic development, technological innovation, ecological governance, and public social services. The degree of coordination among the subsystems within its urban system is relatively high, enabling Beijing to play a significant leading and driving role in the process of regional coordinated development.

In comparison, cities such as Tianjin, Shijiazhuang, and Tangshan also demonstrate a continuous upward trend in coordinated development, although their development speed and coordination levels remain somewhat lower than those of Beijing. The coupling coordination degrees of these cities generally fall within the moderate-coordination stage during most years and gradually progress toward the high-level coordination stage in the later period of the study. This reflects that the coordination relationships among the subsystems within these urban systems are continuously improving and that their capacity for coordinated development is steadily strengthening.

In addition, cities such as Xingtai, Zhangjiakou, and Chengde exhibit lower levels of coordinated development. Their coupling coordination degrees mainly fluctuate between the low-level coordination stage and the moderate-coordination stage, showing certain phased characteristics of change. Nevertheless, these cities still demonstrate a gradual upward trend overall, suggesting that, with the continued advancement of the regional coordinated development process, the level of coordination among subsystems within their urban systems is gradually improving and that the foundation for coordinated development is steadily being strengthened. Overall, the BTH urban agglomeration has formed a gradient development pattern characterized by core-city leadership and gradual follow-up by surrounding cities.

From the perspective of the policy background and development environment, the continuous improvement in the level of coordinated development within the BTH urban agglomeration is closely related to the ongoing promotion of national strategies for regional coordinated development. In particular, the in-depth implementation of the BTH coordinated development strategy in recent years has produced a series of positive outcomes in areas such as industrial structure adjustment, infrastructure connectivity, collaborative ecological governance, and the sharing of technological innovation resources. These policy measures have, to a certain extent, promoted the rational allocation of regional factor resources and enhanced functional coordination among cities. At the same time, Beijing, as the national political center and technological innovation hub, has generated significant spillover effects on surrounding cities through channels such as technology diffusion, industrial transfer, and the sharing of public resources, thereby further promoting improvements in coordinated development across the region.

Overall, although disparities in the level of coordinated development persist among cities within the BTH urban agglomeration, the long-term evolutionary trend indicates that the overall regional pattern of coordinated development is continuously improving. Intercity cooperation is becoming increasingly close, and the degree of regional integration is steadily deepening. This not only reflects the phased achievements made by the BTH urban agglomeration in advancing regional coordinated development but also lays a solid foundation for further improving regional development mechanisms, narrowing development gaps among cities, and enhancing the overall competitiveness of the urban agglomeration in the future.

4.2. Spatiotemporal Evolution of Coordinated Development in the BTH Urban Agglomeration

4.2.1. Temporal Evolution of Coordinated Development in the BTH Urban Agglomeration

Figure 4 presents the temporal evolution of the overall coordination level of the BTH urban agglomeration from 2005 to 2023. From the perspective of the overall trend, the overall coupling coordination degree of the urban system in the BTH urban agglomeration exhibited a continuous upward trajectory during the period 2005–2023, indicating a steady improvement in the level of regional coordination. The overall coupling coordination degree of the urban systems in the BTH urban agglomeration exhibits a clear pattern of stage-wise evolution. At the beginning of the study period, the overall regional coordination level remained in the low-level coordination stage. Although a certain degree of interaction had already emerged among cities, the foundation for coordinated development was still relatively weak, and the efficiency of factor allocation within the region required further improvement. With the advancement of regional integration and the continuous strengthening of intercity linkages, the coupling coordination degree gradually increased and approached the critical threshold of the moderate-coordination stage. In the later stage of the study period, the overall coordination level of the BTH urban agglomeration achieved a stage breakthrough, and the regional urban system began to enter the moderate-coordination stage. This indicates that the division of urban functions, industrial collaboration, and the flow of resource factors among cities have deepened, significantly enhancing the overall capacity for coordinated regional development.

From the perspective of formation mechanisms, the continuous improvement in the overall coupling coordination degree of the BTH urban agglomeration is closely related to the ongoing advancement of the regional coordinated development strategy. On the one hand, the national government has continuously promoted the implementation of the BTH coordinated development strategy, leading to a gradual improvement in the regional transportation infrastructure network. The development of high-speed railways, highway networks, and an integrated transportation system has significantly strengthened spatial connectivity among cities, providing strong support for factor mobility and industrial collaboration. On the other hand, the relocation of Beijing’s non-capital functions and the continuous adjustment of the regional industrial structure have promoted the gradual optimization of the regional industrial division system. As a result, the level of cooperation among cities in industrial development, resource allocation, and public services has steadily increased. Meanwhile, the continuous improvement of collaborative ecological governance and mechanisms for sharing innovation resources has further strengthened the synergistic interactions within the urban system, thereby driving the steady improvement of the overall coupling coordination degree of the BTH urban agglomeration and facilitating its gradual transition toward a higher level of coordinated development.

Overall, from 2005 to 2023, the overall coupling coordination degree of the BTH urban agglomeration has shown a continuous upward trend, indicating a steady improvement in the level of coordinated development among regional urban systems. However, from a spatial perspective, disparities in coordination levels may still exist among different cities. Therefore, it is necessary to further analyze the spatial distribution characteristics and differentiation patterns of the coupling coordination degree among urban systems within the BTH urban agglomeration in order to more comprehensively reveal the spatial evolution patterns of regional coordinated development.

4.2.2. Spatial Evolution of Coordinated Development in the BTH Urban Agglomeration

To further explore the spatiotemporal evolution characteristics, this study uses ArcGIS 10.8 to map the spatial distribution of the overall coupling coordination degree for the years 2005, 2014, and 2023, as shown in Figure 5.

From the perspective of the overall spatial pattern, the coordinated development level among urban systems in the BTH urban agglomeration exhibits a typical core–periphery structural feature, alongside a distinct spatial gradient distribution pattern. Core cities including Beijing and Tianjin have long maintained relatively high coupling coordination levels in the region, constituting the core growth poles for coordinated development. By contrast, the surrounding cities generally show lower coordination levels, forming a spatial gradient where coordination degrees decline progressively from core areas to peripheral regions. In terms of the overall regional distribution, this spatial pattern also presents a clear trend of higher levels in the north and lower levels in the south. Cities in the northern part of the agglomeration and the Beijing–Tianjin core area have higher coordination levels, while most cities in southern Hebei fall at the lower end of the spectrum. This points to a notable degree of spatial imbalance in coordinated development across the region. Overall, the spatial distribution of coordinated development in the BTH urban agglomeration is characterized by the coexistence of clustered high-value zones and dispersed low-value areas.

From the perspective of spatial evolution mechanisms, the coordinated development level of the BTH urban agglomeration presents a distinct evolutionary path defined by growth pole leadership and spatial diffusion. In the early stage, high-level coordination was mainly concentrated in core cities including Beijing and Tianjin, showing strong polarization effects and growth pole effects in the regional development process. With the continuous improvement of the regional transportation network, the deepening of industrial collaboration, and the increasing cross-regional flow of resource factors, the radiation and driving effects of core cities have gradually strengthened. High-level coordinated development has progressively spread to central Hebei and coastal areas. As a result, the pattern of regional coordinated development has evolved from the initial point-like core agglomeration to a spatial structure featuring core leadership, corridor-based diffusion, and regional linkage. Throughout this process, the dominant effect shaping the regional coordinated development pattern has gradually shifted from polarization to diffusion.

Overall, from 2005 to 2023, the level of coordinated development among urban systems in the BTH urban agglomeration has improved continuously, and the regional pattern of coordinated development has been progressively optimized. Areas with high coordination levels have expanded gradually. However, several peripheral cities remain in the low-coordination phase, and spatial disparities in coordinated development persist across the region. Therefore, future efforts should further strengthen regional factor flow and industrial collaboration to promote the overall improvement of coordinated development within the BTH urban agglomeration.

5. Evolution of the Pairwise Coupling Coordination Degree Among Subsystems in the BTH Urban Agglomeration

Based on the aforementioned calculation method, this study measures the pairwise coordination levels among the four core subsystems, namely the economic, social, ecological, and technological subsystems, within the BTH urban agglomeration.

5.1. Temporal Trends of Pairwise Coupling

Figure 6 illustrates the temporal evolution trajectories of the six pairwise subsystem combinations for the entire BTH urban agglomeration from 2005 to 2023. The coupling coordination level between subsystem pairs in the BTH urban agglomeration remained relatively low from 2005 to 2007 but showed an overall upward trend with clear differences across pairs. From 2007 to 2016, the coupling coordination of most subsystem pairs maintained steady growth. From 2016 to 2023, the coordination levels across all pairs continued to rise, with several pairs presenting a more stable upward trajectory.

Specifically, the synergy levels involving the ecological subsystem (e.g., economic–ecological and social–ecological) demonstrated the most robust and rapid growth. The contradiction between economic development and environmental protection has been significantly alleviated at the regional level. The temporal curves for combinations involving the technological subsystem (such as economic–technological and social–technological) remained relatively flat and positioned at the bottom. Despite a slight upward trend, these pairs stagnated in the low-coordination phase throughout the study period. The core issue behind this pattern lies in the regional imbalance of technological development within the BTH urban cluster. Beyond Beijing and Tianjin, most cities in Hebei Province have weak foundations in technological development. This results in insufficient support and empowerment from technological innovation for economic upgrading and social development, thereby constraining the quality of coordinated development across the entire region.

5.2. Spatial Disparities in Subsystem Coupling

While the temporal trends indicate overall regional progress,

Table 2. Coupling coordination degrees between subsystems of cities in the BTH urban agglomeration.

City	Year	Economic–Social	Economic–Ecological	Economic–Technological	Social–Ecological	Social–Technological	Ecological–Technological
Beijing	2005	0.48	0.61	0.49	0.58	0.46	0.58
Tianjin	2005	0.32	0.47	0.33	0.45	0.31	0.46
Shijiazhuang	2005	0.23	0.40	0.26	0.38	0.24	0.43
Tangshan	2005	0.20	0.41	0.18	0.36	0.16	0.32
Qinhuangdao	2005	0.21	0.45	0.24	0.35	0.19	0.40
Handan	2005	0.19	0.36	0.15	0.36	0.15	0.28
Xingtai	2005	0.10	0.20	0.09	0.28	0.12	0.26
Baoding	2005	0.18	0.33	0.17	0.34	0.18	0.34
Zhangjiakou	2005	0.12	0.32	0.17	0.21	0.11	0.29
Chengde	2005	0.12	0.33	0.14	0.31	0.13	0.36
Cangzhou	2005	0.18	0.31	0.14	0.29	0.13	0.23
Langfang	2005	0.22	0.31	0.19	0.45	0.27	0.38
Hengshui	2005	0.17	0.23	0.10	0.33	0.14	0.20
Beijing	2014	0.74	0.80	0.75	0.75	0.71	0.77
Tianjin	2014	0.57	0.65	0.55	0.61	0.52	0.59
Shijiazhuang	2014	0.42	0.57	0.41	0.53	0.38	0.51
Tangshan	2014	0.40	0.59	0.34	0.52	0.30	0.44
Qinhuangdao	2014	0.34	0.57	0.35	0.50	0.30	0.52
Handan	2014	0.34	0.52	0.29	0.50	0.27	0.42
Xingtai	2014	0.30	0.46	0.24	0.44	0.22	0.35
Baoding	2014	0.32	0.48	0.27	0.45	0.25	0.38
Zhangjiakou	2014	0.29	0.50	0.25	0.44	0.22	0.39
Chengde	2014	0.31	0.54	0.26	0.52	0.25	0.44
Cangzhou	2014	0.34	0.49	0.28	0.44	0.25	0.37
Langfang	2014	0.38	0.49	0.34	0.47	0.32	0.41
Hengshui	2014	0.28	0.45	0.19	0.41	0.18	0.28
Beijing	2023	0.96	0.94	0.97	0.90	0.94	0.92
Tianjin	2023	0.66	0.75	0.65	0.70	0.61	0.69
Shijiazhuang	2023	0.56	0.70	0.54	0.65	0.51	0.62
Tangshan	2023	0.56	0.71	0.49	0.68	0.46	0.59
Qinhuangdao	2023	0.47	0.67	0.42	0.61	0.38	0.55
Handan	2023	0.48	0.64	0.37	0.62	0.36	0.49
Xingtai	2023	0.44	0.61	0.36	0.59	0.35	0.49
Baoding	2023	0.47	0.63	0.42	0.60	0.40	0.53
Zhangjiakou	2023	0.45	0.63	0.36	0.59	0.33	0.47
Chengde	2023	0.44	0.65	0.35	0.61	0.33	0.48
Cangzhou	2023	0.50	0.65	0.40	0.63	0.39	0.50

reveals profound spatial disparities among the 13 cities across the six subsystem dimensions. The following analysis examines the spatiotemporal characteristics based on the six dimensions of pairwise subsystem combinations.

First, a core–periphery structure persists, with Beijing exhibiting clear dominance across all dimensions. Beijing’s coupling coordination degrees were the first to break out of the incoordination stage and have consistently led the region thereafter. By 2023, Beijing’s coordination scores across all six pairwise combinations exceeded 0.90, placing it in the advanced-coordination stage. This reflects Beijing’s ability to convert economic wealth and technological resources into social welfare and ecological improvements. Tianjin functions as the secondary core, with its coordination levels generally ranging between 0.61 and 0.75 in 2023, positioning it within the intermediate-coordination stage.

Second, spatial disparities in ecological-related coordination are gradually narrowing, showing a trend of regional convergence. Driven by the deepening implementation of green development policies and enhanced regional joint pollution control mechanisms, heavy industrial cities in Hebei Province have made significant improvements in the economic–ecological and social–ecological dimensions. For instance, from 2005 to 2023, the economic–ecological coordination scores of Tangshan and Shijiazhuang increased from 0.41 and 0.40 to 0.71 and 0.70, respectively. These cities successfully crossed the threshold into the intermediate-coordination stage, indicating that the severe conflicts between economic growth and ecological carrying capacity in peripheral areas are being effectively mitigated.

Third, the spatial gap in technology-related coordination remains substantial, constituting a key bottleneck for regional integration. The most significant spatial divergence within the BTH urban agglomeration lies in the combinations involving the technological subsystem. While Beijing leverages its highly concentrated innovation resources to maintain scores above 0.90, most cities in Hebei Province exhibit weak innovation capabilities and remain locked in a state of low-level coordination. For example, in 2023, the economic–technological coordination scores for cities like Chengde (0.35) and Xingtai (0.36) were only about one-third of Beijing’s score (0.97). This contrast reveals a severe structural misalignment: peripheral cities lack the absorptive capacity to transform scientific achievements into local industrial upgrading. Consequently, the technological subsystem fails to provide sufficient support to the economic and social systems in Hebei, thereby constraining the overall competitiveness of the urban agglomeration.

6. Discussion

This study constructs a four-dimensional economic–social–ecological–technological framework to evaluate the level of coordinated development in the BTH urban agglomeration. The results indicate that the coupling coordination degree of the BTH urban agglomeration has exhibited a significant upward trend, transitioning from a low-level coordination stage to a medium-level coordination stage. This finding is consistent with previous studies and confirms the positive impact of national regional coordination strategies on regional sustainable development [47,48]. However, a key nuance identified in this study is the marked slowdown in the growth of the coupling coordination degree in recent years. This suggests that, although initial policy interventions have achieved remarkable results, the coordinated development of the BTH region is entering a bottleneck period, with declining momentum for sustained regional growth [49].

Regarding the spatial pattern, this study confirms the existence of a core–periphery structure within the BTH urban agglomeration. Nevertheless, peripheral cities such as Xingtai and Hengshui have experienced relatively rapid growth, demonstrating a significant catch-up effect. In contrast to previous studies that emphasize the widening of regional disparities [42], our findings reveal that peripheral cities have indeed narrowed the gap to some extent by undertaking basic industries and strengthening ecological governance. However, this catch-up effect has not altered the overall core–periphery structure.

Compared with previous studies that primarily focused on the overall coordination level [50,51], the pairwise coupling analysis employed in this study provides a more nuanced understanding of both spatial disparities and the observed slowdown in coordination. The results show that coordination between the economic and ecological subsystems is the strongest, whereas coordination between the technological–economic and technological–social subsystem pairs remains weak. This explains the underlying reason for the overall slowdown: the regional development model has integrated economic growth with environmental protection, but it has failed to translate technological innovation into broad-based economic upgrading and social welfare improvement.

From the perspective of underlying mechanisms, this disconnect in technological transformation can be attributed to the mismatch between the siphon effect of advanced production factors and the limited absorptive capacity of peripheral regions. As the core innovation hub, Beijing has agglomerated a large share of technological resources, as evidenced by its technological coordination scores being nearly three times those of peripheral cities in 2023. However, due to administrative barriers and industrial disparities, technological gains have not effectively spilled over to Hebei. Consequently, while peripheral cities achieve progress in ecological and economic dimensions, they remain trapped in a structural disjunction between technology and economic development. This imbalance significantly constrains the transition of the urban agglomeration toward high-quality and sustainable coordinated development.

This study contributes to the literature on regional coordinated development and sustainability. First, from a theoretical perspective, it extends traditional evaluation methods based on scale expansion and industrial transfer by operationalizing the core essence of new quality productive forces into a four-dimensional framework. This provides a new theoretical tool for understanding the high-quality coordination in urban agglomerations in the new development stage. Second, from a methodological perspective, this study demonstrates that improvements in overall coordination do not necessarily imply structural optimization. By introducing a pairwise subsystem-coupling approach, it uncovers structural imbalances concealed beneath the surface of overall growth. This finding suggests that future research in regional economics should move beyond aggregate indicators and pay greater attention to internal structural alignment and factor matching, thereby providing a more precise basis for identifying development constraints within urban agglomerations.

7. Conclusions and Policy Recommendations

7.1. Conclusions

This study adopts the theoretical framework of new quality productive forces to construct an evaluation index system for urban new quality productive forces encompassing four dimensions: economic, social, ecological, and technological. The entropy method is employed to determine indicator weights and calculate the composite index. Based on this foundation, a coupling coordination degree model is utilized to characterize the level of coordinated development within the BTH urban cluster and its evolutionary patterns. The key findings include the following:

• The overall coordination level has steadily improved, but its growth has slowed down, indicating that coordinated development has entered a critical stage. From 2005 to 2023, the coordination level of the BTH urban agglomeration showed a continuous upward trend, achieving a transition from low-level to moderate coordination. However, the growth of the coupling coordination degree has decelerated, making it increasingly difficult to further enhance regional coordinated development.
• The spatial pattern exhibits a core–periphery structure, while its dynamic evolution presents the dual characteristics of catching up and differentiation. In terms of absolute levels, Beijing consistently maintains a leading position, and the core–periphery gradient gap remains evident. Nevertheless, from a dynamic perspective, the disparity between leading and lagging cities has gradually narrowed. Peripheral cities such as Xingtai and Hengshui have achieved high-speed leaps, demonstrating a significant catch-up effect among peripheral nodes.
• The synergy among subsystems is imbalanced, with technological shortcomings acting as the core constraint. The economic–ecological coordination degree is relatively high and shows a clear upward trend, reflecting the positive results of the green development strategy. In contrast, the coordination degrees between the technology subsystem and both the economic and social subsystems are significantly lower and improving slowly. This has become a key bottleneck restricting the high-quality coordinated development of the BTH urban agglomeration.

7.2. Policy Recommendations

Based on the above conclusions, advancing the coordinated development of the BTH urban agglomeration from horizontal enhancement to qualitative leaps hinges on leveraging new quality productive forces to strengthen the synergistic interaction among innovation-driven development, factor integration, and green transformation. The specific policy recommendations are outlined below:

• To address the mismatch between technological and economic development, it is essential to promote the deep integration of innovation chains and industrial chains across regions. The results indicate that the coupling coordination between technology and the economy remains low and improves more slowly than other subsystem combinations. It is recommended to further relieve Beijing of functions non-essential to its role as the capital and deepen the collaborative model of “R&D in Beijing and Tianjin, transformation in Tianjin and Hebei”. Focusing on the digital and intelligent upgrading needs of Hebei’s traditional manufacturing industry, core technologies should be transferred to build a mechanism for transmission from technological R&D to industrial transformation and ultimately to economic contribution.
• To mitigate core–periphery polarization, it is necessary to innovate mechanisms that promote cross-regional mobility and benefit-sharing among advanced production factors. The findings reveal that the coordinated development of BTH exhibits a core–periphery characteristic, where advanced production factors such as technology and talent are highly concentrated in Beijing but insufficiently diffused outward. Thus, the establishment of a cross-regional benefit-sharing mechanism should be explored. For scientific and technological achievement transformation projects flowing from Beijing to Hebei, a mechanism for returning the incremental tax revenue from industrial transfer should be promoted to reasonably distribute benefits such as employment and tax revenue. Simultaneously, the mutual recognition mechanism of sci-tech innovation vouchers in the BTH region should be deepened to further reduce the institutional transaction costs for enterprises utilizing cross-regional R&D resources.
• To address the weak coupling between technological and social subsystems, equalization of public services should be strengthened to support talent absorption and technology diffusion. The low level of coordination between technology and society indicates that technological progress has not been effectively translated into the optimization of the employment structure and the improvement of public services. Therefore, efforts should be made to extend high-quality educational and medical resources from Beijing and Tianjin to Hebei through mechanisms such as school alliances and medical consortiums. This will enhance the attractiveness of peripheral regions for skilled labor and provide the necessary human capital and social infrastructure to support the effective implementation of technological innovation.

7.3. Limitations and Future Research

Owing to limitations in indicator availability and statistical definition consistency, the measurement framework adopted in this study, which integrates four-dimensional indicators (economic, social, ecological, and technological) with the coupling coordination degree model, can only provide a predominantly descriptive characterization of synergy levels. It is unable to fully identify core dimensions of new quality productive forces such as data elements, as well as the driving mechanisms underlying their synergistic evolution. Future research can extend this work by integrating multi-source data and refining the indicator system. Methods such as spatial econometrics and network analysis can be introduced to identify spatial spillover effects and the underlying driving mechanisms. In addition, robustness tests can be conducted at finer spatial scales or across a wider sample of urban agglomerations.