Analysis of the Coupling Coordination between the Ecosystem Service Value and Urbanization in the Circum-Bohai-Sea Region and Its Obstacle Factors

Abstract

In light of the tremendous pressure for improving urbanization levels and expanding construction land on ecosystems, regional sustainable development is premised on the coordinated development of the ecological environment and urbanization. This paper focuses on the Circum-Bohai-Sea Region and assesses the ecosystem service value (ESV) and urbanization level using the equivalence factor and entropy-weighted TOPSIS methods. Based on these assessments, it examines the coupling coordination between the ESV and urbanization as well as the obstacles to this coordination, utilizing the coupling coordination degree model, spatial autocorrelation model, and obstacle model. The results revealed several key findings. First, the Circum-Bohai-Sea Region experienced a continuous improvement in the urbanization level from 2005 to 2020, which presented a “center–periphery” spatial pattern. Overall, the ESV exhibited an N-shaped trend, namely, initially increasing, then decreasing, and then rapidly increasing. The ESV per unit of area exhibited a spatial difference, with the boundary of Baoding and Cangzhou as the dividing line. The ESV per unit of area in the northern parts was higher than that in the southern parts. Second, the degree of the coupling coordination between the ESV and urbanization exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. It also exhibited significant spatial heterogeneity, with the degree of the coupling coordination in the northern parts being higher than that in the southern parts and that in the central parts being higher than those in the eastern and western parts. Additionally, the ESV showed a significant spatially positive correlation with urbanization. H-H clusters were predominantly found in central and southern Liaoning Province, while L-L clusters were primarily located in southern Hebei Province and western Shandong Province. Third, the obstacle factors remained stable during the study period. The top five obstacles in the ESV system were the water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. Moreover, the top five obstacles in the urbanization system were the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values.

1. Introduction

Ecosystem services refer to the various benefits mankind may gain from natural ecosystems, which serve as the foundation of human existence and social and economic development [1]. However, 60% of the ecosystems worldwide are degrading or have already degraded because of human activities [2]. According to research, urbanization is a primary cause of ecosystem degradation [3,4]. Urbanization is an important indicator of a country’s development level [5]. Over the past decades, rapid urbanization has had a far-reaching impact on Earth’s ecosystems, despite its significant contributions to social civilization progress and economic growth [6]. In China, a country with the world’s highest rate of urbanization, rapid expansion of urban land has transformed the structure, functions, and evolutionary processes of ecosystems, leading to changes in ecosystem services and regional capabilities of sustainable development [7]. Given this context, a scientific understanding of the coupling coordination relationship between urbanization and ecosystem services, as well as its key influencing factors, will provide an important reference for formulating policy recommendations to strengthen regional sustainable development capabilities.

The concept of ecosystem services can be traced back to the 1960s, originally termed as “environmental services” [8] or, as described by Westman, “natural services” [9]. The term “ecosystem services”, or “ESs”, was formally coined in 1982 [10]. In 2005, for the first time, the United Nations’ Millennium Ecosystem Assessment (MA) classified ecosystem services into four major categories, namely, provisioning services, regulatory services, supporting services, and cultural services [11]. Since then, ecosystem services, as a bond between human society and natural ecosystems [12], have received increasing attention in the academic community. Scholars in various disciplines have broadened the concept [13] and connotation [14] of ecosystem services from different perspectives through systematic analyses of their functions, value evaluation [15], and examining the relationships among ecosystem services [16]. In terms of the ESV evaluation, various methods have been utilized, including value quantity evaluation and material quality evaluation [17,18], to develop evaluation models, such as ARIES [19], SoLVES [20], and InVEST [21]. These models were further used to evaluate the service values of different types of ecosystems, such as grasslands, oceans, and water systems, at both large scales [22] (global and national) and small scales [23,24] (regional, provincial, and municipal) [25]. Regarding the relationships among ecosystem services, methods such as mapping overlay analysis [26], statistical analysis [27], and scenario analysis [28] were primarily employed to explore the tradeoffs, synergy, and irrelevant relationships between ecosystem services. Other scholars have proposed another type of relationship in recent years, namely, the constraint relationship [29].

Scholars have conducted extensive studies in recent years on urbanization as a significant influencing factor of ecosystem services. Existing studies primarily focus on the following three aspects: first, evaluating the adverse effects of rapid urbanization on ecosystems [30]; second, studying the relationship between urbanization and ecosystem services, which may exhibit a positive correlation, negative correlation, or an inverted U-shaped relationship [31], and subsequently exploring possible paths of the coordinated development of urbanization and ecosystems [32]; third, revealing the mechanism of interactions between urbanization and ecosystems [33]. Although previous studies have demonstrated the intricate relationship between urbanization and ecosystem services, the following areas still warrant further in-depth investigation: First, the majority of the prior studies were focused on the impact of urbanization development on the ESV but seldom discussed the coupling coordination relationship between the ESV and urbanization. Existing studies primarily delve into the spatiotemporal characteristics of the coupling coordination degree but pay less attention to the obstacle factors that affect their coupling coordination degree. Second, there are few papers detailing the coupling coordination relationship between urbanization and the ESV in urban agglomerations, which are the primary executors of new urbanization and an important carrier of modernization construction. Therefore, future studies should focus on developing effective methods to quantify the spatiotemporal coupling relationship between multiple ESVs and urbanization for urban agglomerations to provide more effective strategies for sustainable urbanization and ecosystem protection.

As a significant economic zone in eastern China, the Circum-Bohai-Sea Region boasts abundant marine, mineral, oil and gas, coal, and tourism resources. It is evolving into the third largest urban economic zone in China after the Yangtze River Delta and the Pearl River Delta [34]. In recent years, the Yangtze River Delta and the Pearl River Delta have become new hotspots for researching ecosystem services. Hu et al. established mathematical models to reveal the patterns of the ESV in the Pearl River Delta [35]; Liu et al. analyzed the trend of changes in the Pearl River Delta [36]. The results indicate that in the process of urbanization, the ecosystem service values of both the Yangtze River Delta and the Pearl River Delta ultimately increased. However, the ecological environment of the Circum-Bohai-Sea Region is facing unprecedented pressure due to population concentration and the rapid development of urbanization and industrialization. In this context, this paper aims to scientifically evaluate the degree of the coupling coordination between urban ecosystem services and urbanization in the Circum-Bohai-Sea Region and elucidate the obstacles to their coupling coordination, thereby providing a reference for the sustainable development of cities in this region.

2. Materials and Methods

2.1. Overview of Research Region

As the only inland sea in China, the Bohai Sea comprises Liaodong Bay, Bohai Bay, Laizhou Bay, the Bohai Strait, and the central basin. The Circum-Bohai-Sea Region encompasses Shandong Province, Liaoning Province, Hebei Province, Beijing City, and Tianjin City along the Bohai Sea, where there is a total of 43 cities. (See Figure 1). As one of the three major marine economic regions in China, the Circum-Bohai-Sea Region boasts abundant ecosystem resources, including estuarine deltas, wetlands, farmland, ponds, salt fields, coastal beaches, and towns. These ecosystems offer a wide range of products and services for the region. However, the rapid advancement of regional urbanization exposes coastal ecosystems to increasing disturbances, threatening the marine environment and the sustainable development of coastal cities.

2.2. Research Methodologies

2.2.1. ESV Evaluation Methods

According to the ESV-equivalent scale developed by Xie et al. [37], ecosystem services offer a total of 11 functions, namely, food production, raw material production, water resource supply, climate regulation, gas regulation, hydrological regulation, environmental purification, soil conservation, nutrient cycling maintenance, biodiversity, and aesthetic landscape. On this basis, it is possible to calculate the ESV per unit of area for various ecosystems in the research region. The above-mentioned equivalent scale was modified according to the formula stipulating that “one standard unit of the ESV-equivalent factor is equivalent to 1/7 of the economic value of the grain produced by 1 hm² of farmland in the current year” [37], and it was based on the average grain yield from 2005 to 2020 and the unit price of grain in 2020 within the research region. After the calculation, it was determined that one standard unit of the ESV in the research region amounted to 2151.24 yuan per square hectometer. Subsequently, the ESV-equivalent weight per unit of area for different ecosystems in the research region was further calculated (

Table 1. System service values per unit of area for different ecosystem types in Bohai Rim region (yuan/hm²).

Classification of Ecosystem Services		Land Use Type
Service Type	Sub-Type	CL	FS	GS	WB	CO	BL
Provisioning services	FP	2059.27	464.65	566.53	1490.87	0.00	18.64
	RMP	456.58	1061.00	834.89	428.63	0.00	55.91
	WS	−2431.98	550.38	462.17	15,449.15	0.00	37.27
Regulating services	GR	1658.59	3503.55	2937.02	1434.96	0.00	204.99
	CR	866.57	10,488.28	7767.44	4267.62	0.00	186.36
	EP	251.58	3020.26	2564.30	10,342.92	0.00	577.71
	HR	2786.07	6005.73	5695.13	190,533.35	0.00	391.35
Supporting services	SR	969.07	4266.38	3578.09	1733.14	0.00	242.27
	MNC	288.86	326.75	268.36	130.45	0.00	18.64
	BP	316.81	3882.48	3250.10	4752.15	0.00	223.63
Cultural services	AL	139.77	1703.32	1431.24	3522.18	0.00	93.18

Based on previous studies [1,18], the value coefficient of the construction land was assumed to be 0; CL, FS, GS, WB, WL, CO, and BL refer to cultivated land, forestland, grassland, water body, construction land, and bare land, respectively; FP, RMP, WS, GR, CR, EP, HR, SR, MNC, BP, and AL refer to food production, raw material production, water supply, gas regulation, climate regulation, environmental purification, hydrological regulation, soil formation and retention, maintenance of nutrient cycling, biodiversity protection, and aesthetic landscape, respectively.

). Finally, the ESV in the research region was calculated using the ESV analysis model proposed by Costanza et al. [1]. The formulae are presented as follows:

$${\mathrm{ESV}}_{\mathrm{a}k}=S_{\mathrm{a}}{\mathrm{VC}}_{\mathrm{a}k}$$

(1)

$${\mathrm{ESV}}_{\mathrm{a}}={\displaystyle \sum _k\left(S_{\mathrm{a}}{\mathrm{VC}}_{ak}\right)}={\displaystyle \sum _k{\mathrm{ESV}}_{\mathrm{a}k}}$$

(2)

$$\mathrm{ESV}={\displaystyle \sum _{\mathrm{a}}{\mathrm{ESV}}_{\mathrm{a}}}$$

(3)

where ESV_ak represents the value of the kth ecosystem service of the type a land in a certain year (yuan), S_a denotes the area of the type a land (hm²), VC_ak refers to the value coefficient of the kth ecosystem service of the type a land (yuan/hm²), ESV_a represents the value of the ecosystem services of the type a land (yuan), and ESV refers to the total value of the ecosystem services in the research region.

2.2.2. Urbanization-Level Evaluation Methods

This article presents a complete assessment indicator system for urbanization levels in the Circum-Bohai-Sea Region (

Table 2. Evaluation system for comprehensive levels of urbanization.

First-Level Indicator	Second-Level Indicator	Attribute	Weight
Population urbanization	X1: Population density (10,000/km2)	+	0.109
	X2: Proportion of employed population in the secondary and tertiary industries	+	0.007
	X3: Proportion of urban population	+	0.052
Economic urbanization	X4: Per capita disposable income of urban residents (yuan)	+	0.114
	X5: Per capita gross regional product (yuan)	+	0.107
	X6: Ratio of secondary industries to tertiary industries	-	0.067
Social urbanization	X7: Number of college students per 10,000 people	+	0.175
	X8: Number of hospital beds per 10,000 people	+	0.067
	X9: Number of health technical personnel per 10,000 people	+	0.102
Land urbanization	X10: Per capita road area (m2)	+	0.088
	X11: Per capita construction land area (m2)	+	0.054
	X12: Per capita park area (m2)	+	0.059

). This system was established from the four perspectives of population growth, economic development, improvement in living standards, and spatial expansion based on the main concepts and connotations of urbanization by referring to related studies [38,39]. The system covered four first-level indicators, i.e., population urbanization, economic urbanization, social urbanization, and land urbanization, as well as 12 s-level indicators (Table 2).

This study measured the urbanization level in the research region using the entropy-weighted TOPSIS method. This method assessed the level of urbanization based on the degree to which the indicator data are close to the positive ideal solution and far from the negative ideal solution, considering the optimal and worst solutions of each indicator as positive and negative ideal solutions, respectively. Refer to studies by Sun et al. [40] for the specific steps.

2.2.3. Coupling Coordination Model

This study developed a model for coupling coordination between the ESV and urbanization and determined five levels of the coordination degree, ranging from high to low, severe incoordination, moderate incoordination, general coordination, moderate coordination, and high coordination [7].

$$C=\sqrt{\frac{\mathrm{PESV}\times \mathrm{CD}}{{\left(\frac{\mathrm{PESV}+\mathrm{CD}}{2}\right)}^2}}$$

(4)

$$T=\alpha \mathrm{PESV}+\beta \mathrm{GDP}$$

(5)

$$D=\sqrt{C\times T}$$

(6)

where C denotes the coupling degree, T represents the comprehensive evaluation index of the ESV and urbanization, PESV represents the ESV per unit of area, CD indicates the overall urbanization level, and α and β and are undetermined weights. As the urbanization level was deemed as being as important as the ecological environment in this paper, α and β are both set at 0.5. D represents the coupling coordination degree. A higher D value implies better coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region.

2.2.4. Spatial Autocorrelation Analysis

In this study, the global spatial autocorrelation index (Moran’s I) and local spatial autocorrelation index were employed to evaluate and verify the spatial correlation of and spatial variation in the degree of the coupling coordination between the ESV and urbanization, respectively. The respective formulae are as follows:

$$I=\frac{{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{w}_{ij}\left(D_i-\overline{D}\right)\left(D_j-\overline{D}\right)}{S^2{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{w}_{ij}}$$

(7)

$$I_i=\frac{\left(D_i-\overline{D}\right){\displaystyle \sum _{j=1}^n}{w}_{ij}\left(D_i-\overline{D}\right)}{S^2}$$

(8)

where I denotes the global Moran’s I index, which indicates the overall spatial correlation and spatial variation, while I_i denotes the local Moran’s I index, which indicates the degree and significance of the spatial variation between region i and its neighboring region (j). Additionally, D_i and D_j represent the coupling coordination indexes of regions i and j, respectively; $\overline{D}$ denotes the average value; n represents the number of research regions; S² represents the variance; and $w_{ij}$ is the spatial weight matrix created based on the distance function.

2.2.5. Spatial Trend Surface

The spatial trend surface analysis method was adopted to simulate the spatial distribution of and variation in various elements and to examine the spatial differentiation trend of the degree of the coupling coordination between the ESV and urbanization. Trend surface analysis is a method for calculating second-order polynomials based on observed values while maintaining a low sum of squared residuals. Based on this approach, trend surface parameters are estimated, and the scatter plot is projected onto the X-Z- and Y-Z-planes to assess the fitting degree. The positive direction of the X-axis denotes east, while the negative direction denotes west. The positive direction of the Y-axis denotes north, while the negative direction denotes south. The formula is as follows:

$$Z_i\left(X_i,Y_i\right)=F_i\left(X_i,Y_i\right)+{\epsilon }_i$$

(9)

where $Z_i\left(X_i,Y_i\right)$ represents the actual observed values of geographical elements, $\left(X_i,Y_i\right)$ represents planar coordinates, $F_i\left(X_i,Y_i\right)$ denotes the trend surface value to reflect the changing trend of the degree of the coupling coordination, and ${\epsilon }_i$ represents the residual between the actual value and the trend value of the coupling coordination degree.

2.2.6. Obstacle Degree Analysis

To determine the influences of evaluation indicators on the coupling coordination degree, the obstacle degree model was employed to analyze each factor. The formulae are as follows:

$$F_{ij}=1-{x_{ij}}^{\prime }$$

(10)

$$H_{\mathrm{ij}}=\frac{F_{ij}{W}_j}{{\displaystyle \sum _{j=1}^p}\left(F_{ij}{W}_j\right)}\times 100\%$$

(11)

where F_ij denotes the degree of the factor’s deviation, indicating the disparity between the indicator and the ideal state; W_j denotes the factor’s contribution, namely, the weight of a single indicator; ${x_{ij}}^{\prime }$ denotes the standardized value of single evaluation indicators; and H_ij represents the degree of the obstacle that a selected indicator poses to the coupling coordination’s improvement. A higher value of H_ij indicates a greater obstacle to the coupling coordination.

2.3. Data Sources

The land use data for the Circum-Bohai-Sea Region for 2005, 2010, 2015, and 2020, with a resolution of 30 m × 30 m, were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn, (accessed on 1 September 2023)). This dataset includes information on cultivated land, forest land, grassland, water areas, construction land, and unused land. The socioeconomic statistical data were extracted from the 2005–2020 versions of the Data Compilation of Cost and Income of National Agricultural Products, Beijing Statistical Yearbook, Tianjin Statistical Yearbook, Shandong Statistical Yearbook, Hebei Statistical Yearbook, and Liaoning Statistical Yearbook.

3. Results

3.1. Urbanization-Level Evaluation

A box plot of the Circum-Bohai-Sea Region was created based on the urbanization evaluation results (Figure 2). As depicted in Figure 2, the box moves steadily upward over time during the study period, indicating a continuous improvement in the overall urbanization level in the Circum-Bohai-Sea Region. The upper and lower quartiles exhibit an overall increasing trend, despite the increasing height difference between them. This implies that the overall urbanization level of different towns mainly exhibited wave-like growth. Specifically, from 2005 to 2010, there was a rapid increase in the growth rate, with the average annual growth rate reaching 46% over the five-year period. However, from 2010 to 2020, the growth rate gradually slowed down, with the average annual growth rate over the ten-year period decreasing to only 45%, and the gap between different towns gradually broadened. In 2005, the overall urbanization level of 43 cities in the Circum-Bohai-Sea Region ranged mainly between 0.06 and 0.31, while the level in 2020 was mainly between 0.21 and 0.55. The average level also increased by 51.43%, from 0.17 to 0.35. The steady growth in the overall urbanization level in the Circum-Bohai-Sea Region was partly attributed to China’s efforts to develop the region as a key area for accelerating opening up since the 14^th National People’s Congress (NPC). Additionally, in the Outline of the 13^th Five-Year Plan for the National Economic and Social Development, China proposed to accelerate the development of urban agglomerations, build world-class urban agglomerations in the Beijing–Tianjin–Hebei region, enhance the level of open competition among urban agglomerations on the Shandong Peninsula, and develop urban agglomerations in central and southern Liaoning to foster growth poles to support regional development. Against this policy backdrop, the urban agglomerations in the Circum-Bohai-Sea Region experienced a continuous improvement in economic development level during the study period, leading to the constant expansion of urban land and steady growth of the overall urbanization level.

The Jenks natural breaks method was utilized to categorize the urbanization levels of the Circum-Bohai-Sea Region from 2005 to 2020 into five sub-levels (Figure 3). It is evident that distinct regional differences existed in the economic, population, social, land, and overall urbanization levels across the Circum-Bohai-Sea Region during the study period. Regarding the spatial distribution, the overall urbanization level exhibited a distinct “center–periphery” spatial pattern, where the urbanization level of the central prefecture-level cities was significantly higher than that of the peripheral cities in the west and south. All the cities, except Jinan, experienced improvements in their urbanization levels. Additionally, there were noticeable differences in urbanization levels among different provinces. In 2020, three out of the five low-level prefecture-level cities were in Liaoning Province, whereas only two out of the eleven high-level cities were located in Liaoning Province.

Economic urbanization exhibited a spatial distribution pattern, with higher levels in the north and lower levels in the south. High-value areas were predominantly located in Beijing, Tianjin, the northern part of the Shandong Peninsula, and the Liaodong Peninsula. Most prefecture-level cities experienced an improvement in the economic urbanization level during the study period, reflecting good momentum in economic development within the Circum-Bohai-Sea Region. Population urbanization exhibited a spatial distribution pattern, with a higher level in the south and a lower level in the north. The overall urbanization level and quality decreased slightly at the end of the study period. This decline was the most significant in Liaoning Province, where 10 prefecture-level cities experienced a decline in the urbanization level, reflecting a severe loss of the local population [41]. Social urbanization exhibited a spatial distribution pattern, with a higher level in the north and a lower level in the south. High-value areas were dispersed irregularly, and 15 prefecture-level cities witnessed improvements in the urbanization level. Among them, Beijing, Shenyang, and Jinan remained cities with the highest levels of social urbanization, benefiting from strong development momentum. Therefore, it is recommended to continuously strengthen the provision of public services and improve people’s well-being. Land urbanization witnessed significant changes in the spatial distribution pattern. The pattern characterized by a higher level in the north and a lower level in the south for 2005 was replaced by a completely opposite model for 2020, in which the southern parts had a higher land urbanization level than the northern parts. The reason for this phenomenon is that in the initial stages of the study, the urban industrial base in the northern region was relatively developed. Driven by industrialization, there was an increasing demand for urban construction and land use, leading to the acceleration of land urbanization. By 2020, with the adjustment of the economic structure, the southern region made significant investments in urban planning and construction. The high-quality urban environment attracted more population migration, thereby driving the land urbanization process. High-value areas were primarily concentrated in Shandong Province and Liaoning Province, where the land urbanization level experienced significant enhancement during the study period. This implied efficient land development and utilization in the Circum-Bohai-Sea Region, attributable to its efforts to focus on tract development for infrastructure construction [42].

3.2. Spatiotemporal Evolution of the ESV

The ESV of the Circum-Bohai-Sea Region from 2005 to 2020 is presented in

Table 3. The values of and changes in ecosystem services in the Bohai Rim region.

ESV	Year	CL	FS	GS	WB	CO	ALL
ESV/109 yuan	2005	203.11	393.08	168.71	397.18	1.04	1163.12
	2010	197.89	406.62	138.82	425.71	0.77	1169.80
	2015	195.71	406.13	138.19	426.32	0.74	1167.09
	2020	192.77	407.86	138.53	514.38	0.83	1254.36
ESV rate of change/%	2005–2010	−2.57	3.44	−17.72	7.18	−25.96	0.57
	2010–2015	−1.10	−0.12	−0.45	0.14	−3.49	−0.23
	2015–2020	−1.50	0.43	0.24	20.66	12.10	7.48
	2005–2020	−5.09	3.76	−17.89	29.51	−19.89	7.84
Area change/km2	2005–2020	−140,535.03	41,896.93	−102,815.28	50,065.02	−100,826.64	/

. It exhibits an N-shaped trend, that is, initially increasing, then declining, and then growing rapidly. Initially, the ESV experienced slow growth, increasing by only 397 million yuan from 2005 to 2015. The ESV’s slow growth can be mainly attributed to the rapid advancement of urbanization within the urban agglomerations of the Circum-Bohai-Sea Region during this period, which led to accelerated resource consumption and a significant reduction in ecological land, such as farmland and grassland. However, after 2015, the ESV witnessed a sharp rise, primarily because of a notable increase in the land area covered by ecosystems with higher ESVs per unit of area, such as forests and waters. The growth in the ESV, therefore, offsets its decline caused by accelerated urbanization progress.

The contributions to the ESV also varied across different land types, with the greatest ESV contribution coming from water, followed by forest land. Unused land contributed the least to the ESV’s growth. Their ranking, in terms of their contributions, is as follows: waters > forest land > cultivated land > grassland > unused land. During the study period, cultivated land, grassland, and unused land experienced varying degrees of decline in the ESV. Among them, unused land exhibited the greatest decline of 19.89%. Other types of land witnessed an increase in the ESV, with waters experiencing the highest increase rate of 29.51%, followed by forest land, with a growth rate of 3.76%. These results suggest that cultivated land and grassland were, to some extent, converted to construction land because of the continuous advancement of urbanization in the Circum-Bohai-Sea Region. Ecological projects, such as “returning farmland to forests” and “returning farmland to lakes”, were effectively implemented, leading to the replenishment of ecosystems with high ESV-equivalent weights, such as forests and waters.

The ESV per unit of land area (PESV) can more accurately reflect the quality of ecosystem service functions in a city [43]. ArcGIS 10.8 software was used in this study to visualize the PESVs of the Circum-Bohai-Sea Region from 2005 to 2020, as shown in Figure 4. The results revealed that an obvious spatial distribution pattern was established in the Circum-Bohai-Sea Region because of variations in the land use structure, although there were no significant changes in the spatial distribution of the PESV during the study period. With the boundary of Baoding and Cangzhou as the dividing line, the northern parts, particularly, the Liaodong Peninsula and Tianjin City, exhibited generally higher PESVs. The main reason lay in the extensive forest coverage and the wide variety of ecosystems, such as forests and wetlands, in the Liaodong region (e.g., Daling River Estuary Wetland and Xiaoling River Estuary Wetland in Jinzhou, Liaoning, and Tianjin Hangu Coastal Wetland). Consequently, this led to a higher ESV in this region. The regions to the south of Baoding and Cangzhou had relatively lower PESVs because of a vulnerable ecological environment with more grasslands and less forest land, which weakened their ecosystem service capabilities.

3.3. Degree of the Coupling Coordination between the ESV and Urbanization

3.3.1. Temporal Variation

The degree of the coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region was categorized into five types, and ArcGIS software was then used to draw spatial distribution maps. Additionally, the number of prefecture-level cities with varying coupling coordination levels was determined (

Table 4. Statistics on the number of coordination levels between the ESV and urbanization in the Bohai Rim region.

Coupling Coordination Level	2005		2010		2015		2020
	Number	Percentage/%	Number	Percentage/%	Number	Percentage/%	Number	Percentage/%
Severe incoordination	3	6.98	3	6.98	3	6.98	2	4.65
Moderate incoordination	6	13.95	3	6.98	3	6.98	5	11.63
General coordination	20	46.51	15	34.88	13	30.23	16	37.21
Moderate coordination	12	27.91	19	44.19	21	48.84	18	41.86
High coordination	2	4.65	3	6.98	3	6.98	2	4.65

). It is evident that the degree of the coordination between the ESV and urbanization in the Circum-Bohai-Sea Region exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. In 2005, nine prefecture-level cities in the Circum-Bohai-Sea Region were in an uncoordinated state, while thirty-four were in a coordinated state. The number of uncoordinated prefecture-level cities gradually decreased over time. By 2015, the number of uncoordinated prefecture-level cities in the Circum-Bohai-Sea Region had decreased to five, while the number of coordinated ones rose to thirty-seven. In 2020, the coordination degree decreased, with the number of uncoordinated prefecture-level cities rising to seven, of which two were added to the list of moderately uncoordinated cities. Furthermore, the number of coordinated prefecture-level cities declined to thirty-six, with a reduction of three moderately coordinated cities and one highly coordinated city.

3.3.2. Temporal Evolution Trend

The analysis of spatial trend changes can contribute to a comprehensive understanding of the spatial pattern evolution of the degree of the coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region. The trend-surface fitting results for the degree of the coupling coordination between the ESV and urbanization are depicted in Figure 5. The X arrow represents the due east direction; the Y arrow, the due north direction; and the Z arrow, the coupling coordination degree. During the study period, the degree of the coupling coordination between the ESV and urbanization generally exhibited a spatial pattern where “northern parts were higher than southern parts, and central parts were higher than eastern and western parts”, which remained stable during this period. Differences were observed in the trend line changes in different directions. For instance, the trend surfaces in both the north–south and east–west directions appeared steep in 2005, indicating a clear spatial difference in the coupling coordination degree across both directions within the region during that year. The trend surface in the east–west direction flattened over time, which implied a gradual reduction in the disparity in the coupling coordination degree in this direction. In contrast, the trend surface in the north–south direction gradually evolved to an inverted U-shaped line, indicating a higher coupling coordination degree in the central parts compared to both the southern and northern parts.

3.3.3. Analysis of Spatial Agglomeration Evolution

Geoda 1.22 software was used to conduct Moran’s I tests for spatial correlations. The results indicated that the Moran’s I index of the coupling coordination degree in the Circum-Bohai-Sea Region was significant at 1% during the study period. Additionally, the spatial correlation strengthened continuously over time. Further analysis involved the creation of LISA clustering maps of the coupling coordination degree (Figure 6), revealing a generally stable spatial pattern across different areas throughout the study period. H-H clusters were concentrated in cities, such as Liaoyang and Anshan, in central and southern Liaoning. These cities boast favorable resource endowments, longer histories of industrialization, and higher levels of urban development [44]. L-L clusters were mainly located in cities in southern Henan and western Shandong, including Hengshui, Xingtai, Handan, and Liaocheng. In these cities, a poor industrial structure hindered economic and social development as well as environmental quality improvement. Furthermore, because of the lack of definite advantages in city tiers, urbanization levels, and location conditions [45], their urbanizations developed at the cost of the ecological environment. Coordinated development between urbanization and ecological services was, thus, challenging to achieve in these regions. Fewer H-L clusters and L-H clusters were found during the study period. Specifically, H-L clusters were mainly distributed in southern Shandong and gradually spread southward. This indicates that these clusters had a higher coupling coordination degree, whereas their surrounding cities exhibited a lower coupling coordination degree. L-H clusters were primarily distributed in Hebei Province, moving from the vicinity of Zhangjiakou in the west to Langfang in the east. Similarly, this indicated a lower coupling coordination degree in these regions and a higher coupling coordination degree in surrounding cities.

3.4. Identification of Obstacle Factors for the Degree of the Coupling Coordination between the ESV and Urbanization

This study utilized an obstacle degree model to assess the predominant obstacle factors at both the criterion layer and indicator layer and to further identify the main influencing factors of the coupling coordination between the ESV and urbanization. As can be seen from Figure 7, there was some difference among the criterion-layer obstacle factors in their influence on the coupling coordination degree, which, however, changed little over time. In the urbanization system, social urbanization emerged as the dominant obstacle factor, with its proportion growing from 35% to 37%. Following that was economic urbanization, with its proportion decreasing from 30% to 28%. The difference in the obstacle degree was insignificant between population urbanization and land urbanization, with an average proportion of around 17%. The obstacle degree of the population urbanization increased annually, whereas that of the land urbanization showed a decreasing tendency. In the ESV system, the obstacle degree structure of each criterion-layer factor remained relatively stable. Provisioning services, supporting services, and cultural services were the primary obstacle factors, with an average proportion of about 30%. Regulating services, as another obstacle factor, accounted for a lower proportion, which was further declining.

To identify specific obstacle indicators and assess their criticality, this study extracted the top five obstacle factors from the urbanization and ESV systems within the study area. According to the results shown in

Table 5. Main obstacle factors and their degrees of obstruction.

Year	ESV System/%
	WS	MNC	RMP	AL	FP
2005	11.58	10.84	10.53	10.4	9.82
2010	11.64	10.85	10.55	10.41	9.85
2015	11.62	10.85	10.54	10.4	9.86
2020	11.68	11.03	10.72	10.55	10.03
Year	Urbanization System/%
	Number of college students per 10,000 people	Population density	Number of health technical personnel per 10,000 people	Per capita road area (m2)	Ratio of secondary industries to tertiary industries
2005	21.02	12.60	12.30	10.61	8.08
2010	21.02	12.59	12.30	10.61	8.08
2015	21.01	12.59	12.29	10.61	8.08
2020	20.98	12.57	12.27	10.59	8.07

, different dominant obstacle factors highlight variations among elements within each system. The obstacle factors in the ESV system were ranked from high to low as follows: water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. In the urbanization system, the primary obstacle factors influencing the degree of the coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region included the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values. Each obstacle factor corresponded to a dimension in the criterion layer, which is consistent with the aforementioned results.

4. Discussion

In recent years, the impact of urbanization on ecosystem service functions has garnered increasing attention, with many scholars studying their relationship. The findings indicate that with the advancement of urbanization, different changes have occurred to ecosystem services among cities because of differing development policies and rates of urbanization. Consequently, regional sustainable development has also been affected [46,47]. Therefore, analyzing the coordination between ecosystem services and urbanization is of great significance to mitigate regional differences, optimize regional spatial layouts, achieve equity in urbanization, and promote the healthy development of ecosystems. This study analyzed the degree of the coupling coordination between the ESV and urbanization in cities in the Circum-Bohai-Sea Region, as well as the obstacle factors, using the coupling coordination degree model and the obstacle degree model, respectively. These models play a critical role in resolving the contradiction between these factors and enhancing the ESV. This study can provide a reference for other regions on how to improve ecosystem service functions during rapid urbanization.

4.1. Discussion on the ESV

Ecosystem services are crucial for maintaining ecological security and improving the human quality of life. This study found that the overall trend of the ecosystem service value (ESV) in the Bohai Sea region shows an initial increase, followed by a decrease and then a rapid increase, which is consistent with the findings of previous research by Luo et al. [48]. Early urban expansion in the Bohai Sea region led to the loss of ecosystem services. The decrease in the ecosystem service value caused by the conversion of land for urban construction is the primary factor influencing the ESV [49]. However, research indicates that the implementation of ecological construction projects in certain areas can significantly improve ecosystem services [50] and is a key reason for the rapid growth in the ESV in the Bohai Sea region. Therefore, it is essential to pay attention to ecological environmental management during the process of urbanization and gradually adjust and improve the environmental damage and irrational land use caused by early urbanization to achieve an increase in the value of the ecosystem services.

4.2. Discussion on the Coupling Coordination Degree

Urbanization and the ESV are two sophisticated systems that share an interactive relationship, wherein urbanization exerts both positive and negative impacts on the ESV. Particularly, the negative impact should not be disregarded [51]. These research findings indicate that the degree of the coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region presented an inverted U-shaped trend, initially increasing and then decreasing. This was consistent with previous research conclusions [52]. The enhanced coordination between the two was attributed to the implementation of a series of ecological protection projects in the Circum-Bohai-Sea Region from 2005 to 2015, such as the Bohai Blue Sea Action Plan and the Bohai Environmental Protection Master Plan. Despite increases in the average ESV and urbanization levels after 2015, the gap between different towns gradually widened. Some towns were found to lag behind in ecosystem services and urbanization development. This finally led to a slight decline in the degree of the coordination between them. Therefore, for areas experiencing rapid development of overall urbanization, such as Beijing and Tianjing, attention should be given to the impacts of changes in land use types caused by urbanization on the ecological environment in the future. The pace and extent of urban construction land expansion should be regulated to prevent excessive consumption of land resources and slow down the ESV decline caused by the occupation of other land types by construction activities due to city center radiation. In regions experiencing slow urbanization development, such as Hebei Province and Liaoning Province, they should draw experience from the achievements of areas with rapid economic development, remain committed to industrial transformation and upgrading, and promote high-quality development. They should also comprehensively explore new development models as well as new models of green development and modernization that align with the new era from multiple dimensions and perspectives.

4.3. Discussion on Obstacle Factors

With the advancement of urbanization and the expansion of urban areas, human activities have significantly disrupted regional ecological environments. This disruption has led to intensified regional resource disparities and more complex obstacles to the degree of the coupling coordination between the ESV and urbanization in the Circum-Bohai-Sea Region. Urbanization may impact ecosystems’ structures and functions through economic development, population growth, and the expansion of the construction land [53]. Conversely, ecosystems can also hinder urbanization development because of resource availability, environmental conditions, and policy interventions [54]. Identifying the obstacles to the coordinated development of urbanization and the ecological environment is thus crucial for the sustainable development of urban clusters. This study discovered that social urbanization, provisioning services, supporting services, and cultural services are the main obstacle factors, which are consistent with previous findings [55,56]. This means that the degree of the coupling coordination decreased as the level of the social urbanization significantly increased. This because the expansion of the urban infrastructure and the utilization of significant portions of farmland, forest land, and waters by complex transportation networks have adversely impacted the provisioning, supporting, and cultural service functions of ecosystems. Therefore, it is imperative to strengthen the protection and development of land ecosystems with higher ecological value coefficients (e.g., cultivated land and forest land) to fully leverage their service functions. Furthermore, population density, industrial structure, and municipal construction are also crucial for improving the coupling coordination degree. Specifically, population growth causes greater pressures on cities, exhausts urban carrying capacities, and adversely impacts the stability of ecosystem services [57]. Municipal construction is essential for enhancing the living standards of residents, regulating the urban climate, and safeguarding biodiversity. An unreasonable industrial structure can result in environmental pollution and resource depletion, further straining the ecological environment. As a result, reasonably adjusting industrial structures and promoting green development are also key means for achieving the coordinated development of urbanization and ecosystem services.

4.4. Shortcomings and Prospects

The research findings in this paper may offer valuable references for the coordinated development of urbanization and ecosystems in the Circum-Bohai-Sea Region. However, the following shortcomings still exist: First, although the land use data in this paper were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences and could meet the research needs with an accuracy of over 80%, they failed to quantify ecosystem services for urban ecological spaces (e.g., urban green spaces). This omission may impact the evaluation results of the ESV. Enhancing the classification accuracy could be achieved using data with a higher resolution to address this limitation. Second, the ESV in the study area was determined using the equivalence factor method based on the per-unit-of-area value in this paper. Despite its advantages, such as easy data accessibility, operational simplicity, and comprehensive evaluation, certain drawbacks persist, including a high reliance on equivalence factor values and the potential impact of correction methods and parameters on evaluation outcomes [58]. Thus, further research is warranted in this area to address these issues.

5. Conclusions

This study analyzes the spatiotemporal evolution process and identifies obstacle factors affecting the coupling coordination relationship between the ESV and urbanization in the Circum-Bohai-Sea Region from 2005 to 2020. The research methods that were used include entropy-weighted Topsis, the spatial autocorrelation model, and the coupling coordination model. The main research findings are concluded as follows:

(1)

During the study period, the overall level of the urbanization in the Circum-Bohai-Sea Region continuously improved, but the gap between cities gradually widened, resulting in a “center–periphery” spatial pattern. The ESV exhibited an N-shaped trend, characterized by an initial increase, followed by a decrease and then a rapid increase. Waters contributed the most to the ESV’s growth. The ranking of ecosystems in terms of their contributions is as follows: waters > forest land > cultivated land > grassland > unused land. The ESV per unit of area showed a distribution pattern of high in the north and low in the south, with the boundary of Baoding and Cangzhou as the dividing line;

(2)

From a temporal perspective, the degree of the coordination between the ESV and urbanization in the Circum-Bohai-Sea Region exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. From a spatial perspective, there were significant regional differences in the degree of the coupling coordination between the ESV and urbanization. This mainly demonstrates a spatial distribution pattern where “northern parts are higher than southern parts, whereas central parts are higher than eastern and western parts”. H-H clusters were mainly located in central and southern Liaoning, including cities like Shenyang and Anshan, whereas L-L clusters were predominantly found in southern Hebei and western Shandong;

(3)

Regarding the obstacle factors, at the criterion layer, social urbanization, supporting services, and cultural services were the dominant obstacles to the coordinated development of the ESV and urbanization in the Circum-Bohai-Sea Region. At the indicator layer, the top five obstacles within the ESV system were the water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. Additionally, the top five obstacle factors within the urbanization system included the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values.