Evaluation of the Synergistic Interactions Between Socioeconomic Development and Ecosystem Services in China’s Hebei Province: A Perspective from the SDGs

Abstract

Coordinating ecosystem services (ESs) and socioeconomic development is crucial for sustainability. This study examined Hebei Province, China, a representative region within the Beijing–Tianjin–Hebei area with diverse ecosystems and sharp developmental contrasts. A comprehensive evaluation framework aligned with the Sustainable Development Goals (SDGs) was developed, integrating economic, social, and ES dimensions. The entropy weight-TOPSIS method was used for overall assessment, and self-organizing maps (SOM) were employed to analyze spatial coupling relationships. The results indicate that social and economic indicators often exhibit synergistic effects, whereas trade-offs dominate the relationship between socioeconomic indicators and ecosystem services. In the multidimensional coordinated development zone is coordinated, all three dimensions display integrated progress. Socioeconomic development in Hebei Province shows a “multi-sphere” pattern of spatial expansion, while ecosystem services reveal distinct “mountain–plain” contrasts. Ecological functions have undergone significant transformations across prefecture from 2005 to 2020, with the region as a whole demonstrating coordinated development—characterized by relatively stable ecosystem services alongside gradual improvement in the low socioeconomic development zone. This study clarifies the synergistic mechanisms between regional development and ES, providing a theoretical and methodological basis for differentiated sustainable development strategies under the SDG framework.

1. Introduction

The SDGs, introduced by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development, constitute a comprehensive global framework for guiding development efforts [1,2]. They seek to mobilize nations toward the collective achievement of 17 core objectives by 2030, encompassing poverty eradication, ecological protection, the promotion of social equity, and the realization of peaceful and prosperous societies. To further advance the practical implementation of SDGs, the concept of pragmatic sustainability has gained increasing attention in recent years [3,4]. An expanding body of research has begun to explore feasible pathways toward sustainability from the perspectives of governance systems [5], energy transitions [6], and environmental management [7]. The realization of pragmatic sustainability depends on the scientific quantification of sustainability progress and the timely adjustment of policies and management strategies based on monitoring outcomes [8,9]. Within this process, economic growth, social progress, and ecological protection constitute the core dimensions for assessing regional sustainability and serve as the fundamental framework for constructing a pragmatic sustainability evaluation system [10]. As one of the fastest-growing and most populous countries, China faces significant challenges in reconciling socio-economic advancement with ecological sustainability [11], while also serving as a critical empirical case for informing global pathways toward sustainability. In this context, the quest for a dynamic balance between economic development, social progress, and ecological protection amid rapid industrialization and urbanization has emerged as a prominent research frontier, drawing substantial scholarly attention worldwide.

The interrelationships among economic development, social progress, and ecological sustainability are often examined through the dual lenses of trade-offs and synergies. In the early stages of economic growth, widening inequality constrains long-term development, though coordinated regional advancement can later alleviate these disparities and foster alignment between social well-being and economic performance [12,13,14]. The interaction between economic development and ecological outcomes is frequently explained by the Environmental Kuznets Curve (EKC), which describes an inverted U-shaped trajectory wherein ecological degradation intensifies with initial economic expansion but is gradually mitigated as economies adopt greener practices and sustainable innovations [15,16,17], Similarly, while rising social demands for resources are commonly associated with ecological degradation, a synergistic feedback mechanism has been theorized, whereby ecological restoration enhances social well-being, and improved social well-being, in turn, reinforces ecological protection [18,19,20,21].

Recent studies have increasingly focused on methods for identifying trade-offs and synergies among social progress, economic development, and ecological sustainability, examining their driving factors, spatial heterogeneity, and implications for policy and governance. A central approach has been the evaluation of the Coupling Coordination Degree (CCD) of socio-economic and ecological systems across multiple spatial scales, including nations [22,23], river basins [24,25], urban agglomeration [26], and provinces or prefecture-level cities [27]. These assessments capture the degree of consistency in development levels and growth rates among the three systems. Empirical analyses of 30 Chinese provinces [22] and resource-based cities in Northeast China [28] indicate that most regions remain at low to moderate levels of socio-ecological coordination, with some exhibiting declining CCD, reflecting delayed industrial restructuring alongside mounting ecological pressures. The Social–Economic–Resource–Environment CCD across prefectures in Hebei Province remain at relatively low levels [29]. Xu et al. [30] emphasize that the tension between socio-economic development and the ecological environment in Hebei necessitates further industrial restructuring to promote improvement.

To further reveal feedback dynamics and spatial heterogeneity, methods such as geographically weighted regression [31], structural equation modeling [32], and multilevel variable analysis [33] have been employed. Findings highlight characteristic spatial patterns of coordination, including “low in the northwest and high in the southeast” [22,34], “higher in urban than rural regions” [35], and “lower in mountainous areas than in plains” [36]. largely shaped by terrain, transportation, and related factors [35]. Building on these insights, researchers have proposed long-term strategies for region-specific collaborative development, emphasizing governance optimization, ecological compensation, and livelihood transformation [37,38]. Such strategies provide both theoretical foundations and practical pathways for advancing ecological civilization and achieving regional sustainable development.

However, existing studies have largely concentrated on overall coupling measurements at broad spatial scales, while paying insufficient attention to the spatial interrelationships, structural dependencies, and interactional characteristics of internal indicators within the three systems. Pragmatic sustainability requires a fine-grained understanding of internal spatial dynamics [39], as the absence of spatially explicit interaction analysis can cause decision-makers to overlook regional heterogeneity and misjudge local constraints, ultimately undermining the formulation of SDG-oriented targeted interventions [40]. Given that both ecosystem service assessments and SDG indicator analyses are inherently multidimensional, integrative approaches are required to better capture the trade-offs and synergies among indicators. To address this gap, the present study introduces the concept of the “ecosystem service bundles” and extends it into an “SDGs bundles” framework. These bundles denote recurring spatial–temporal combinations of ecosystem services, which help to reveal patterns of synergy, trade-off, and spatial aggregation across services [41]. Methods for identifying such bundles include principal component analysis (PCA) [42], cluster analysis [41], spatial autocorrelation [43], and SOM [44], with SOM offering particular advantages in handling high-dimensional data, reducing human interference, and identifying the optimal number of clusters.

Hebei Province—situated at the core of the Beijing–Tianjin–Hebei coordinated development region—has diverse topography and complex urban functions [45]. From the perspective of the urban sustainability index [46], prefecture in the central region of Hebei Province are relatively stable, while those in the northern region are comparatively lower. Yang et al. [47] reported that Shijiazhuang exhibits the highest sustainable development capacity, whereas Chengde demonstrates a comparatively lower capacity relative to other regions. These observations indicate distinct regional characteristics in the sustainability evaluation of Hebei Province, and the CCD is shifting from a low–moderate coupling stage toward a moderate–high stage [48]. Accordingly, investigating governance pathways for multi-system coordination at geospatial and regional levels in Hebei carries substantial practical significance.

This study examines the localized implementation of the SDGs at the regional scale. Grounded in the theoretical framework of coordinated development across social progress, economic growth, and ecological sustainability, it constructs an SDG assessment framework for Hebei Province through the integration of spatial indicators. By introducing the concept of the “bundles” and employing the SOM method, the study seeks to reveal the coupling interactions and spatial clustering patterns among multidimensional indicators across the three systems. This approach not only enhances the precision of geospatial identification and the representational capacity of spatial interactions but also extends the analytical capabilities of traditional coupling measurement methods. In doing so, it deepens the understanding of multi-system interaction mechanisms and provides both theoretical underpinnings and practical pathways for advancing multi-objective coordinated governance.

2. Materials and Methods

2.1. Study Area

Hebei Province, situated within one of China’s three major urban agglomerations—the Circum-Bohai Sea Region—extends between E 113°27′–119°50′ and N 36°05′–42°40′. Encompassing and encircling the municipalities of Beijing and Tianjin, it serves as the core province of the Beijing–Tianjin–Hebei metropolitan area. The province is characterized by a diverse urban landscape, including heavy industrial bases such as Tangshan and Handan, underdeveloped prefecture such as Hengshui and Cangzhou, ecological service-oriented prefectures such as Chengde and Zhangjiakou, and functional relocation hubs such as Langfang and Baoding that accommodate the transfer of non-capital functions from Beijing (Figure 1). Amid rapid industrialization and urbanization, Hebei has faced increasingly severe resource and ecological constraints, marked by heightened pressures on ecosystem services and the dual challenge of pursuing economic transformation while advancing green and sustainable development.

2.2. Data Sources and Processing

This study utilized multi-source datasets spanning the period 2005–2020, encompassing Digital Elevation Model (DEM), soil, land use, precipitation, evapotranspiration, depth to bedrock, river and road networks, Points of Interest (POI), socioeconomic statistics, and water resources. The majority of these datasets were acquired from authoritative national scientific data centers, supplemented by official statistical yearbooks and government bulletins, thereby ensuring both reliability and consistency. All raster datasets were resampled to a uniform spatial resolution of 1 km, and all spatial datasets were projected into the WGS 1984 UTM Zone 50N coordinate system (

Table 1. Data source.

Data	Resolution	Time	Data Source
DEM	30 m	2020	National Earth System Science Data Center (GEOdata)
Chian Soil Database	1 km	2008	Global Soil Database (https://nesdc.org.cn/)
Annual Precipitation	1 km	2005–2020	National Qinghai–Tibet Plateau Science Data Center (https://data.tpdc.ac.cn)
Evapotranspiration	1 km	2005–2020
Land Use Data	30 m/1 km	2005–2020	Resource and Environment Science Data Center (RESDC)
Depth to bedrock map	1 km	2020	https://doi.org/10.1038/s41597-019-0345-6
River and Road		2020	National Administration of Surveying, Mapping and Geoinformation (NASG)
POI		2005–2020	Gaode, Baidu
Socioeconomic statistics	Prefecture	2005–2020	Hebei Statistical Yearbook, China Urban Statistical Yearbook
Total Water Resources	Province	2005–2020	Hebei Water Resources Bulletin

).

2.3. Methods

2.3.1. Indicator System Construction

In developing the indicator framework, this study adopts the principle of coordinated development across society, economy, and ecology. Considering Hebei Province’s status as an agricultural region, alongside its dual tasks of industrial transformation and ecological restoration, a systematic, multidimensional, and quantifiable framework comprising 28 indicators was established. The framework encompasses 12 social indicators, 12 economic indicators, and 4 ecosystem service indicators (

Table 2. SDGs-based Socio-Economic Ecosystem Services Evaluation System of indicators and their weights. Note: “+” denotes positive indicators, while “−” denotes negative indicators.

DIMENSION	INDICATORS	CORRESPONDING SDG INDICATORS	ATTRIBUTE	WEIGHTS
SOCIAL INDICATORS	Total power of agricultural machinery	SDG 2.a: Increase investment in infrastructure and technology to enhance agricultural productivity	+	0.080
	Year-end number of livestock	SDG 2.3: Output per labor unit by size of agricultural, pastoral, and forestry enterprises	+	0.082
	Rural fertilizer application	SDG 2.3: Sustainable agriculture	−	0.033
	Total sown area of crops	SDG 2.3: Improve agricultural productivity	+	0.027
	Grain yield	SDG 2.4: Establish sustainable food production systems to increase productivity and output	+	0.032
	Number of doctors per 10,000 people	SDG 3.c: Availability and distribution of health workers	+	0.137
	Number of hospital beds per 10,000 people	SDG 3.8: Access to effective medical services and universal health coverage	+	0.135
	Government expenditure on education	SDG 4.1: Achieve free, equitable, and quality primary and secondary education	+	0.024
	Number of students enrolled in universities	SDG 4.8: Access to quality higher education	+	0.178
	Effectively irrigated farmland area	SDG 6.4: Improve water-use efficiency	+	0.068
	Green space area	SDG 11.2 Sustainable transportation systems.	+	0.127
	Road area	SDG 11.7: Provide safe, inclusive, and accessible green spaces	+	0.077
ECONOMICAL INDICATORS	GDP	SDG 1.1: Eliminate poverty	+	0.119
	Sewage treatment volume	SDG 6.a: Capacity building for water and sanitation-related activities	−	0.001
	Science and technology expenditure	SDG 8.3: Promote innovative enterprises	+	0.104
	Primary industry GDP	SDG 8.4: Improve the quality of economic growth	+	0.034
	Tertiary industry GDP	SDG 8.4: Improve the quality of economic growth	+	0.141
	National fixed asset investment	SDG 8.4: Improve the quality of economic growth	+	0.011
	Average wage of employed workers	SDG 8.5: Achieve full and productive employment	+	0.117
	Secondary industry GDP	SDG 9.2: Increase industry’s share of employment and GDP	+	0.163
	Industrial smoke and dust emissions	SDG 9.4: Adopt clean and environmentally sound technologies and processes	−	0.001
	Industrial SO2 emissions	SDG 9.4: Adopt clean and environmentally sound technologies and processes	−	0.001
	Total retail sales of consumer goods	SDG 12.2: Sustainable consumption and production patterns	+	0.178
	Actual use of foreign investment	SDG 17.1: Promote international cooperation	+	0.133
ECOSYSTEM SERVICE INDICATORS	Water yield	SDG 6.6: Protect and restore water-related ecosystems	+	0.001
	Carbon storage	SDG 13.2: Integrate climate change measures into national policies	+	0.267
	Soil retention	SDG 15.3: Combat desertification and restore degraded land and soil	+	0.235
	Habitat quality	SDG 15.5: Take urgent action to reduce degradation of natural habitats	+	0.497

).

The social development dimension emphasizes basic livelihood security and agricultural productivity, incorporating indicators that reflect fundamental agricultural conditions, such as grain yield and total sown area. Indicators including total power of agricultural machinery and annual livestock numbers capture production efficiency and the level of animal husbandry in rural areas, while fertilizer application intensity reflects the potential ecological pressures arising from farming practices. Healthcare and education indicators—such as the number of hospital beds and physicians per 10,000 people, government expenditure on education, and university enrollment—represent levels of health security and educational equity. The extent of effectively irrigated farmland indicates the capacity for efficient water resource utilization, whereas road area and urban green space serve as measures of living environment quality in terms of infrastructure provision and residential livability.

The economic development dimension encompasses both the overall level of economic productivity and the structural composition of economic growth. Core indicators such as absolute GDP, national fixed asset investment, and total retail sales of consumer goods capture the scale and dynamism of economic activity. The average wage of employed workers and the actual utilization of foreign investment reflect the degree of economic openness and the quality of employment opportunities. With respect to industrial structure, the contributions of the primary, secondary, and tertiary sectors to GDP illustrate the configuration and transformation of the industrial system. Expenditure on science and technology highlights the pivotal role of innovation in driving high-quality development. At the same time, indicators of industrial pollutant discharge—such as industrial smoke and dust emissions and industrial SO₂ emissions—serve to evaluate the trade-off between industrial productivity and environmental performance. Finally, sewage treatment volume reflects regional capacity for ecological management and pollution control.

The ecosystem service dimension highlights ecological regulation and sustainability. Key indicators include carbon storage [49], habitat quality [50], soil retention [51], and water yield [52], which collectively provide a comprehensive representation of ecosystem services and their contribution to sustainable regional development as referenced in previous research [53,54]. In this study, these ecosystem service indicators were quantified using the InVEST model, with provided computational procedures available in the official user guide (https://naturalcapitalproject.stanford.edu/software/invest, accessed on 10 September 2025).

2.3.2. Entropy Weight-TOPSIS Method

By integrating the relative strengths and weaknesses of individual indicators, this study employs an improved entropy weight–TOPSIS method [55], which integrates information entropy–based weighting with the Technique for Order Preference by Similarity to an Ideal Solution. This combined framework offers a rigorous and unbiased evaluation mechanism that has seen extensive use in research on regional development and ecological performance. In the entropy weight procedure, indicator importance is derived from their information entropy, representing the variability or dispersion observed among indicators. The weighted decision matrix generated through this step is subsequently processed using TOPSIS, which establishes ideal and anti-ideal reference solutions for the dimensions of social progress, economic development, and ecosystem services. The relative proximity of each assessment unit to these benchmark solutions is then calculated to facilitate ranking and performance differentiation. By simultaneously accounting for the comparative advantages and disadvantages of each indicator, this method enhances both the discriminative capacity and the reliability of the evaluation results. The corresponding formulas are presented below:

$${\mathrm{e}}_{\mathrm{j}}=-{\displaystyle \frac{1}{\mathrm{l}\mathrm{n}\left(\mathrm{m}\right)}}\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}\mathrm{l}\mathrm{n}\left({\mathrm{p}}_{\mathrm{i}\mathrm{j}}\right)$$

(1)

$${\mathrm{w}}_{\mathrm{j}}={\displaystyle \frac{1-{\mathrm{e}}_{\mathrm{j}}}{\sum _{\mathrm{j}=1}^{\mathrm{n}}(1-{\mathrm{e}}_{\mathrm{j}})}}$$

(2)

$${\mathrm{v}}_{\mathrm{j}}^{+}=\underset{\mathrm{i}}{\mathrm{m}\mathrm{a}\mathrm{x}}({\mathrm{w}}_{\mathrm{j}}\cdot {\mathrm{r}}_{\mathrm{i}\mathrm{j}}),{\mathrm{v}}_{\mathrm{j}}^{-}=\underset{\mathrm{i}}{\mathrm{m}\mathrm{i}\mathrm{n}}({\mathrm{w}}_{\mathrm{j}}\cdot {\mathrm{r}}_{\mathrm{i}\mathrm{j}})$$

(3)

$${\mathrm{D}}_{\mathrm{i}}^{+}=\sqrt{\sum _{\mathrm{j}=1}^{\mathrm{n}}({\mathrm{v}}_{\mathrm{i}\mathrm{j}}-{\mathrm{v}}_{\mathrm{j}}^{+}{)}^2},{\mathrm{D}}_{\mathrm{i}}^{-}=\sqrt{\sum _{\mathrm{j}=1}^{\mathrm{n}}({\mathrm{v}}_{\mathrm{i}\mathrm{j}}-{\mathrm{v}}_{\mathrm{j}}^{-}{)}^2}$$

(4)

$${\mathrm{C}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{D}}_{\mathrm{i}}^{-}}{{\mathrm{D}}_{\mathrm{i}}^{+}+{\mathrm{D}}_{\mathrm{i}}^{-}}},0\le {\mathrm{C}}_{\mathrm{i}}\le 1$$

(5)

In the formula, ${\mathrm{p}}_{\mathrm{i}\mathrm{j}}$ represents the normalized value of the value of indicator $\mathrm{j}$ in dimension $\mathrm{i}$. $\mathrm{m}$ refers to the number of dimensions (social progress, economic development, and ecosystem services), while $\mathrm{n}$ is the number of indicators (12 social progress indicators, 12 economic development indicators, and 4 ecosystem service indicators). ${\mathrm{e}}_{\mathrm{j}}$ is the entropy value of the $\mathrm{j}$ indicator. ${\mathrm{r}}_{\mathrm{i}\mathrm{j}}$ represents the normalized matrix. ${\mathrm{v}}_{\mathrm{j}}^{+}$ and ${\mathrm{v}}_{\mathrm{j}}^{-}$ are the positive and negative ideal solutions for the $\mathrm{j}$ indicator, respectively. ${\mathrm{D}}_{\mathrm{i}}^{+}$ and ${\mathrm{D}}_{\mathrm{i}}^{-}$ represent the distances of object $\mathrm{i}$ from the positive and negative ideal solutions of the dimensions (social progress, economic development, and ecosystem services). ${\mathrm{C}}_{\mathrm{i}}$ indicates the relative closeness coefficient between each dimension and the ideal solution, with a higher value meaning a closer fit to the ideal solution.

2.3.3. Self-Organizing Map

The SOM clustering method [56] is a classical unsupervised neural network approach that integrates nonlinear dimensionality reduction with topology-preserving capabilities. Through a “competition–cooperation” learning mechanism, SOM projects high-dimensional input data onto a low-dimensional regular grid, ensuring that samples with similar distances in the original space remain spatially adjacent on the grid, thereby facilitating visualization and pattern recognition. The SOM architecture consists of grid nodes defined by position and weight vectors. For each input sample, the network first identifies the best matching unit (BMU) through competitive learning; subsequently, under gradually decreasing learning rates and neighborhood radii, the BMU and its neighboring nodes are iteratively adjusted toward the input sample. This process enables the establishment of a global structure at the initial stage and the refinement of local structures in later stages. Compared with traditional linear dimensionality reduction or direct clustering methods, SOM more effectively preserves topological relationships, captures nonlinear manifold structures, and enhances interpretability. Moreover, with the incorporation of batch or mini-batch training and neighborhood smoothing, SOM demonstrates superior robustness and computational efficiency in processing large-scale datasets and noisy inputs. The specific computational procedure is described as follows:

$$\mathrm{x}=[{\mathrm{x}}_1,{\mathrm{x}}_2,{\mathrm{x}}_3{]}^{\mathrm{T}}$$

(6)

$${\mathrm{w}}_{\mathrm{i}}=[{\mathrm{w}}_{\mathrm{i}1},{\mathrm{w}}_{\mathrm{i}2},{\mathrm{w}}_{\mathrm{i}3}{]}^{\mathrm{T}}$$

(7)

$$\mathrm{c}=\mathrm{a}\mathrm{r}\mathrm{g}\underset{\mathrm{i}}{\mathrm{m}\mathrm{i}\mathrm{n}}\parallel \mathrm{x}-{\mathrm{w}}_{\mathrm{i}}\parallel$$

(8)

$${\mathrm{w}}_{\mathrm{i}}(\mathrm{t}+1)={\mathrm{w}}_{\mathrm{i}}\left(\mathrm{t}\right)+\mathsf{\alpha }\left(\mathrm{t}\right)\cdot {\mathrm{h}}_{\mathrm{c}\mathrm{i}}\left(\mathrm{t}\right)\cdot \left(\mathrm{x}\right(\mathrm{t})-{\mathrm{w}}_{\mathrm{i}}(\mathrm{t}\left)\right)$$

(9)

In the equations, $\mathrm{x}$ denotes an input grid cell (a total of 184,289 grid cells in this study), with $\mathrm{x}=[{\mathrm{x}}_1,{\mathrm{x}}_2,{\mathrm{x}}_3]$ representing the normalized values for the social progress, economic development, and ecosystem service, respectively. ${\mathrm{w}}_{\mathrm{i}}$ denotes the weight vector of neuron $\mathrm{i}$, initialized randomly. The Best Matching Unit (BMU) is $\mathrm{c}=\mathrm{a}\mathrm{r}\mathrm{g}\underset{\mathrm{i}}{\mathrm{m}\mathrm{i}\mathrm{n}}\parallel \mathrm{x}-{\mathrm{w}}_{\mathrm{i}}\parallel$. The neighborhood kernel ${\mathrm{h}}_{\mathrm{c}\mathrm{i}}\left(\mathrm{t}\right)$ is Gaussian, and the learning rate $\mathsf{\alpha }\left(\mathrm{t}\right)$ is initialized at 0.01 and decays over time.

3. Results and Discussion

3.1. Spatiotemporal Distribution Characteristics of the Social, Economic, and Ecosystem Service Dimension

The spatial distribution of social progress in Hebei Province exhibits a “multi-core–belt-shaped” pattern (Figure 2), with high areas in the central districts of Shijiazhuang, Tangshan, and Handan [57], and extending along the high-speed railway corridors of the Beijing–Tianjin–Hebei urban agglomeration. This reflects the stronger capacity of well-connected, resource-rich urban clusters to provide and diffuse social services [58,59]. In contrast, the mountainous and remote counties of northwestern Zhangjiakou, northern Chengde, and northwestern Baoding—characterized by complex terrain, limited transportation accessibility, and insufficient public resource investment—have persistently exhibited lagging levels of social progress. This observation aligns with the findings of Wu et al. [60], who highlighted that geographic limitations are a decisive factor influencing social organization in remote mountainous regions. Temporally, the diffusion intensity of social progress across Hebei Province has shown a steady upward trend throughout the study period. Following 2010, the accelerated rollout of the Beijing–Tianjin–Hebei coordinated development initiative significantly strengthened the public service capacities of prefecture-level cities such as Cangzhou and Hengshui [61]. By 2020, several areas that had previously demonstrated moderate to low levels of social progress—such as southern Tangshan and parts of Handan—showed substantial improvement, overcoming their earlier lagging status. This result is consistent with the conclusions of Gautam et al. [62], which emphasize the positive effects of coordinated development strategies in strengthening public services in peripheral regions.

The spatial distribution of economic development in Hebei Province closely mirrors that of social progress (Figure 3), with high-value areas forming a belt-like pattern along high-speed railway corridors and within the Circum-Bohai Sea Region (Figure 1), reflecting the critical role of transportation networks in fostering regional economic agglomeration [63,64]. Notably, Langfang and Baoding—situated in the core of the Beijing–Tianjin–Hebei coordinated development zone—have experienced significantly faster growth than other regions by undertaking industrial transfers from Beijing and Tianjin, supported by favorable policy frameworks and resource spillover effects [27]. Tangshan, underpinned by its traditional industrial base and port infrastructure, has maintained a relatively high level of economic performance. Furthermore, the port economy has stimulated hinterland development, exemplifying the strategic prioritization of coastal development within the broader trajectory of urbanization [65].

Ecosystem services in Hebei Province exhibit a pronounced mountain–plain contrast, with higher values concentrated in the northwest and northeast and lower values in the southeastern and central regions (Figure 4). This spatial pattern is consistent with the distribution trends identified by Du et al. [66] in ecosystem service assessments. Marked differences are also observed across distinct landforms. As noted by Wang et al. [67], mountainous areas—characterized by steep slopes, limited land development, and well-preserved vegetation—demonstrate stronger capacities for water conservation, carbon sequestration, and soil retention. In contrast, southeastern plain prefectures such as Langfang, Cangzhou, and Hengshui (Figure 1) are dominated by intensive human activities, resulting in significant ecosystem fragmentation and dense land use for urban and agricultural purposes. These pressures have substantially diminished their capacity to provide ecosystem services.

3.2. SDGs Bundles’ Features

Using the SOM method, a cluster analysis of SDG indicators at the 1 km × 1 km grid scale in Hebei Province was conducted, identifying five distinct types of multi-system synergistic development zones: the low ecosystem service zone, the multidimensional coordinated development zone, the low socioeconomic development lag zone, the moderate ecosystem service zone and the high ecosystem service zone (

Table 3. SDGs bundles features Table.

SDGs Bundles	Social Progress	Economic Development	Ecosystem Service
Low Ecosystem Service Zone	0.397	0.442	0.147
Multidimensional Coordinated Development Zone	0.170	0.179	0.206
Low Socioeconomic Development Zone	0.076	0.060	0.328
Moderate Ecosystem Service Zone	0.020	0.007	0.353
High Ecosystem Service Zone	0.005	0.002	0.822

). These categories exhibit pronounced differences across the three dimensions of ecosystem services, economic development, and social progress, thereby elucidating the structural composition and coupling relationships of regional multi-system functions.

Within the socio-economic zones—namely the low ecosystem service zone, the multidimensional coordinated development zone, and the low socioeconomic development lag zone—social progress and economic development consistently demonstrate synergistic relationships. However, in both the low ecosystem service zone and the low socioeconomic development zone, these dimensions exhibit weak trade-offs with ecosystem services, whereas the multidimensional coordinated development zone is characterized by balanced advancement across all three dimensions. Specifically, the low ecosystem service zone reflects a pattern in which rapid urbanization occurs at the expense of ecosystem service degradation, resulting in diminished ecological capacity alongside highly concentrated socio-economic development. This outcome corresponds to the widely observed phenomenon of ecological decline under conditions of intensive urban expansion [68,69]. by contrast, the multidimensional coordinated development zone encompasses regions where social progress, economic development, and ecosystem services advance in a harmonized manner. Unlike previous studies that have classified such areas as “urban–rural fringes” or “secondary development zones” [70,71], this typology more accurately captures their comprehensive functionality, providing finer evidence in terms of spatial resolution and functional differentiation. In the low socioeconomic development zone, social progress and economic development lag behind ecosystem services, indicating that these areas remain at an early stage of socio-economic development. Nevertheless, supported by existing ecological protection measures and guided by multidimensional coordinated development, these regions demonstrate potential to transition progressively toward the multidimensional coordinated development zone [72,73].

In contrast to ecosystem service-oriented zones, where both social progress and economic development remain relatively weak (i.e., the moderate ecosystem service zone and the high ecosystem service zone), previous studies have largely identified impoverished or underdeveloped regions on the basis of single economic indicators [74]. By incorporating the ecosystem service dimension, however, this study reveals that although these regions possess favorable ecological conditions, their development is constrained by natural barriers such as limited accessibility and complex topography. Consequently, they exemplify cases in which ecological resources have not been effectively transformed into socio-economic gains [75]. Furthermore, the differentiation between the Moderate and High Ecosystem Service-Oriented Zones establishes a hierarchical framework for more precisely identifying regions endowed with ecological advantages.

3.3. Spatiotemporal Distribution Characteristics of SDGs Bundles

The ecosystem service-oriented zones (i.e., the high and moderate ecosystem service zones) constitute the overall dominant category, followed by the low socioeconomic development zone, the multidimensional coordinated development zone, and the low ecosystem service zone. This hierarchy reflects the strong stability and resilience to external disturbance of regions endowed with favorable ecological conditions [76] (Figure 5). The moderate ecosystem service zone has progressively transitioned into the low socioeconomic development zone under the impetus of initial socio-economic growth, thereby contributing to the expansion of the latter. This trajectory is consistent with the developmental patterns of the social and economic dimensions outlined in Section 3.1. The multidimensional coordinated development zone, shaped by the combined influence of multiple policy interventions—particularly the industrial relocation pressures during the early stages of the Beijing–Tianjin–Hebei coordinated development strategy [54] and the implementation of stringent air pollution control measures [77]—has exhibited reciprocal transitions with the low ecosystem service zone.

Overall, the socio-economic zones—namely the low ecosystem service zone, the multidimensional coordinated development zone, and the low socioeconomic development zone—exhibit a concentric spatial expansion pattern, radiating outward from urban core areas (Figure 6) and forming a distinct center–periphery distribution. These zones are predominantly situated in densely populated plain regions with advanced urbanization, such as the central districts of Shijiazhuang, Cangzhou, and Langfang. Driven by intensive land reclamation, industrial agglomeration, and highly concentrated infrastructure networks, these areas display comparatively limited ecosystem service performance.

The ecosystem service-oriented zones—comprising the moderate and high categories—are characterized by pronounced geomorphological differentiation. The high ecosystem service-oriented zone is predominantly distributed across the mountainous areas of northeastern Hebei Province (Figure 1), where rugged terrain, limited human disturbance, and well-preserved ecosystems contribute to exceptionally high ecological service value but relatively low levels of economic and social development. By contrast, the moderate ecosystem service-oriented zone is primarily located in the transitional areas between low mountains, hills, and plains in the southwest (Figure 1), where ecological service functions are moderate and development potential remains comparatively constrained.

3.4. Comparison of SDGs Bundles Across Different Prefectures

Marked spatial disparities in SDG performance are evident across prefectures in Hebei Province (Figure 7), shaped primarily by the combined influences of topography, industrial structure, and urban functions [78,79,80]. Agriculture-based such as Hengshui and Cangzhou are dominated by moderate ecosystem service zones, with relatively limited representation of low ecosystem service zones. In several mountainous prefectures, including Baoding, Handan, and Xingtai, high ecosystem service zones account for a substantial proportion of the landscape, reflecting their favorable ecological conditions. Chengde and Zhangjiakou, as typical mountainous prefectures, display the most pronounced ecological service advantages, with high ecosystem service zones predominating, although socio-economic development in these areas remains comparatively constrained. By contrast, the provincial capital Shijiazhuang demonstrates the strongest socio-economic development, reflected in the predominance of low ecosystem service zones. Driven by the development of Beijing and Tianjin, Langfang has seen its rural development level rise [81], with a relatively high proportion of socio-economic zones. Meanwhile, Qinhuangdao and Tangshan, supported by port economies and processes of industrial transformation, experienced similar advancements.

Xu et al. [46] ranked the sustainable development of prefectures in Hebei Province from a socio-economic-environmental perspective, with the results being: Shijiazhuang > Baoding > Tangshan. Yang et al. [47], however, pointed out that Langfang had the highest sustainable development indicators, primarily due to differences in the perspective of the selected evaluation indicators and criteria. Nevertheless, both studies indicate that Zhangjiakou and Chengde rank low in sustainable development evaluations, which corresponds to the findings in this research showing that Zhangjiakou and Chengde have a higher proportion of the ecosystem service-oriented zones. And prefectures with higher rankings are generally those with a higher proportion of the socio-economic zones, indicating a strong correlation between the spatial distribution of different bundles and the sustainable development rankings. Unlike studies that measure urban sustainability through composite assessment values [82,83,84], this research further reveals the strengths and weaknesses of prefectures across different dimensions. It also demonstrates the spatial distribution characteristics of various clusters, providing a more precise evaluation of sustainable development.

Among the prefectures of Hebei Province, Shijiazhuang demonstrates the strongest synergy between socio-economic development and ecosystem services, whereas Chengde and Zhangjiakou exhibit the weakest integration across the three dimensions. This divergence highlights the necessity of strengthening functional complementarity among prefectures to optimize regional development. Moving forward, mountainous prefectures such as Chengde and Zhangjiakou should capitalize on their ecological advantages by promoting green ecological tourism and implementing ecological compensation mechanisms to stimulate socio-economic vitality [85]. Economically advanced prefectures such as Shijiazhuang and Tangshan must intensify ecological restoration and accelerate green transformation initiatives to alleviate mounting environmental pressures. Agriculture-based prefectures, including Hengshui and Cangzhou, could enhance the alignment between ecological protection and economic growth through the promotion of efficient agricultural practices, the expansion of green manufacturing, and the adoption of other sustainable strategies. Meanwhile, coastal prefectures such as Tangshan and Qinhuangdao, reliant on port economies, should prioritize marine ecological protection while fostering the development of sustainable and environmentally responsible trade.

3.5. Comparison of Synergies Between Hebei Province and Other Regions

Since 2000, most regions in China have witnessed an overall enhancement in the coupling coordination between socio-economic development and ecological conditions. Nevertheless, pronounced regional disparities persist, with coordination levels generally higher in the southeastern regions than in the northwest [86]. Similarly to developed regions such as the Yangtze Delta [24] and the Pearl River Delta [87], Hebei Province exhibits a “core–periphery” spatial pattern in both socio-economic development and ecosystem services. Urban areas display significantly higher levels of socio-economic development but relatively lower ecosystem service capacity, whereas mountainous regions with favorable ecological conditions are constrained in economic development by factors such as terrain and limited transportation infrastructure. This spatial differentiation parallels the pattern observed in the Yangtze Delta, where central zones of ecological–economic conflict are surrounded by high-coordination areas, and resembles the gradient differences in the Pearl River Delta, where coordination is stronger in the east and weaker in the west. Furthermore, under the influence of policies such as the Beijing–Tianjin–Hebei coordinated development strategy, the synergy between socio-economic development and ecosystem services in Hebei has shown marked improvement in certain areas. This trend aligns with regional coordination pathways facilitated by policy interventions in the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area.

Hebei Province, while experiencing rapid economic growth and industrial transformation, still lags significantly behind more developed regions in terms of achieving synergy among the three dimensions of social progress, economic development, and ecosystem services. The multidimensional coordinated development zones in Hebei display considerable instability, with frequent dynamic shifts between ecological and economic priorities. These zones are highly sensitive to policy interventions and industrial upgrading, yet they lack the sustained coordination and steady upward trajectories observed in regions such as the Yangtze River Delta and the Pearl River Delta. This contrast underscores the more stable and balanced development achieved in these advanced regions. Moreover, the prevalence of unbalanced states—characterized either by “strong ecosystem services but weak economic development” or by “strong economic development but weak ecosystem services”—remains widespread in Hebei. The continued expansion of low ecosystem service zones in economically developed prefectures such as Shijiazhuang mirrors the situation of resource-based prefectures in Northeast China [28]. In certain areas, despite favorable ecological conditions, socio-economic development remains sluggish, and the pace of transformation is constrained. Although the stability of ecosystem services in Hebei is comparatively stronger than that in Northeast China [35], the lack of sufficient ecological compensation mechanisms and green transformation measures has impeded the emergence of pivotal “node effects.” This contrasts with prefectures such as Guangzhou and Shenzhen in the Pearl River Delta [88], which play a critical role in driving coupling coordination through such mechanisms.

Hebei Province remains in the exploratory stage of coordinating socio-economic development with ecosystem services, with its spatial patterns and evolutionary pathways displaying marked periodicity and strong regional differentiation. Looking ahead, it will be essential to integrate regional ecological conditions with the characteristics of local industrial structures, thereby enhancing the synergy between socio-economic growth and ecosystem services in a context-specific manner. By drawing on the experiences of more developed regions in green transformation, ecological compensation, and industrial upgrading, Hebei can progressively advance toward sustainable and coordinated regional development.

4. Conclusions

This study established a multidimensional and comprehensive evaluation framework for SDGs, integrating three core dimensions: social progress, economic development, and ecosystem services. The entropy-weighted TOPSIS method was employed for overall assessment, while the SOM method was applied to the 1 km × 1 km grid of Hebei Province to systematically generate SDG bundles, thereby revealing the synergies between socio-economic development and ecosystem services from 2005 to 2020. The results indicate that the socio-economic dimension exhibits a “multi-core, belt-shaped” spatial pattern, with high-value areas concentrated in city centers and along major transportation corridors. The diffusion of social services is facilitated by convenient transportation networks and the concentration of public resources, whereas economic development has been stimulated by industrial relocation and locational advantages. In contrast, ecosystem services display a spatial pattern of “higher in mountainous areas and lower in plains,” reflecting the combined influences of topography and human activity intensity. The five SDG bundles identified through the SOM method reveal the dynamic evolution of socio-economic development and ecosystem services under the combined effects of urban expansion, industrial relocation, and ecological protection. Socio-economic clusters demonstrate a concentric expansion from urban cores to peripheral areas, while ecosystem service clusters show a pronounced “mountain–plain” differentiation. The evolution of functional types and inter-city spatial disparities is evident, with the overall trend suggesting a developmental trajectory characterized by stable ecological conditions alongside the gradual expansion of low socio-economic development zones.

Guided by SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), Hebei Province should enhance inter-city coordination, functional complementarity, and the efficiency of factor flows to promote balanced improvements in social, economic, and ecosystem services. On the one hand, strengthening industrial coordination and the sharing of public services between urban and rural areas is essential for narrowing development gaps in socially and economically lagging regions. On the other hand, it is crucial to preserve the ecological advantages of mountainous areas by establishing ecological barrier zones and high-value ecosystem service corridors, thereby providing stable ecological support for high-quality regional development. By implementing differentiated and functionally oriented regional strategies based on spatial heterogeneity, Hebei Province can accelerate progress toward the inclusive, coordinated, and ecologically resilient development pathways emphasized by the SDGs.

The SDGs bundle identification method proposed in this study, based on the socio-economic ecosystem services framework, exhibits high operational feasibility and reproducibility. This method quantifies various dimensions of social, economic, and ecosystem services at the kilometer grid scale, identifies distinct SDGs bundles, and thereby reveals synergies among these dimensions. Due to its flexibility and versatility, this framework can be widely applied to other regions, providing a scientific basis for adjusting and optimizing regional governance policies. It assists policymakers in gaining a more precise understanding of the current state of socioeconomic conditions and ecosystem services within a region, offering robust support for achieving sustainable development goals.

Due to data limitations, this study assumes that all socioeconomic data are uniformly distributed within units, considering only the proportionate allocation of grid-level and city-level data. This may introduce some error into the results. Furthermore, the study did not sufficiently consider the comprehensiveness and continuity of indicators when selecting them. Future research can extend this framework to different geographic regions and socioeconomic contexts, conducting comparative analyses across regions and scales to develop more precise, tailored sustainable development strategies for each area. Simultaneously, dynamic evolutionary analysis and forecasting using time-series data can effectively track changes in SDG clusters, providing forward-looking guidance for regional policy adjustments. Furthermore, research should refine the indicator system to comprehensively evaluate sustainable development across regions, particularly when confronting dual challenges of ecological pressures and socioeconomic development. By integrating climate change and carbon neutrality scenarios, it should explore how to balance these dual imperatives, deepen understanding of regional development mechanisms and sustainable pathways, and provide more targeted decision support for advancing high-quality coordinated regional development and sustainable growth.