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1. Introduction

Owing to escalating global urbanization, the proportion of the global urban population is projected to rise to 68% by 2050, up from 56% in 2021 [1]. Urbanization exerts multidimensional pressures on ecosystems, resulting in the degradation of ecosystem functions, a loss of ecosystem services (ESs), and a decline in their value [2,3], thereby jeopardizing regional sustainable development [4]. The Millennium Ecosystem Assessment (MA) report indicates that over 60% of global ESs are declining, and this trend is unlikely to be effectively reversed in the next 50 years [5]. Therefore, it is imperative to enhance our understanding of the evolution of ESs during the urbanization process, as well as the coupling coordination relationship and interactions between urbanization and ESs. This understanding is pivotal in alleviating the disruption of ESs induced by urbanization and fostering the high-quality and coordinated development of urbanization and ESs.

ESs encompass the tangible or intangible benefits derived from the structures, functions, and processes within ecosystems [6,7]. The ecosystem service value (ESV) serves as a key indicator for measuring ESs [8]. Various monetary and non-monetary methodologies have been suggested for assessing ESs, typically categorized into two groups: raw data-based approaches and unit value-based approaches [9]. Raw data-based methodologies utilize ecological process models and data on ecological components to compute ESV [10]. However, they are mainly applicable to small-scale areas or individual ESs [11,12], and their relevance and specificity render a comprehensive assessment of ESV challenging. The unit value-based approach allows for a thorough evaluation of ESV by considering the comparative potential of diverse ecosystems. It is more suitable for ecological asset assessment at large spatial scales [13,14,15]. Costanza et al. [7] initially proposed the equivalent factor method for evaluating ESV. However, this method does not entirely align with the actual ecosystem situation in China [16]. Therefore, Xie et al. [17,18,19] adapted the equivalent factor method by conducting a questionnaire survey involving 200 ecologists and established an ecosystem service valuation system suitable for China. However, this assessment method neglects the amount of biomass in the ecosystem. Correcting the ESV by utilizing the plant net primary productivity (NPP) index can yield more accurate assessment results [20]. Urbanization exerts a pivotal force on ecosystem service trade-offs and synergies [21], supply and demand [22], and value assessment [23]. However, most previous studies ignored other socio-economic indicators and focused only on one specific dimension of urbanization (e.g., land use change, urbanization rate, or nighttime lighting data) and were unable to evaluate urbanization comprehensively, holistically, and scientifically from a multidimensional perspective.

Urbanization and ESs evolve separately and interact with each other. Based on systemic thinking, they can be regarded as two independent but interrelated systems, interacting and influencing each other through their own natural and human elements, with complex coupling coordination relationships and interactions. Scholars have carried out abundant studies on the complex relationships between urbanization and ESs [24,25,26,27,28]. Domestic and international scholars primarily adopt the grey correlation degree model [29], the pressure-state-response (PSR) model [30], and the coupling coordination degree (CCD) model [31] to study the coupling coordination relationship between urbanization and ecosystems. Among these, the CCD model can comprehensively analyze the nonlinear relationship between the two systems and has been widely used to analyze the coordination effect between urbanization and ESs. However, most studies have only explored the linkages between urbanization and ESs from a system-level perspective and lack in-depth exploration of the coupling coordination relationship between urbanization and ESs at the subsystem level [31,32]. Additionally, the CCD model cannot effectively analyze the interaction and influence between urbanization and ESs from the perspective of spatiotemporal heterogeneity. Therefore, spatiotemporal models must be introduced to provide a more refined interpretation of the characteristics and intensity of the interactions between urbanization and ESs. Existing studies on the interaction between urbanization and ESs have mainly used the system dynamics (SD) model [33], the STIRPAT model [34], the geographical detector technique [35], the OLS least squares method [36], the geographically weighted regression (GWR) model [37,38], the spatiotemporal geographically weighted regression (GTWR) model [39], and other methods. Among these, the GTWR model introduces the temporal dimension into the geographically weighted regression model [40], effectively addressing the traditional GWR model’s insufficient consideration of “time–space” non-stationarity and multiscale effects, as well as the problem of poor fit, thereby better explaining the spatiotemporal heterogeneity of the variables and the dependent variable [41]. It provides an important analytical tool for solving the problem of limited samples of cross-sectional data and addressing “time–space” non-stationarity. Many scholars have demonstrated the effectiveness of the GTWR model [42,43]. However, most of these studies focus on the overall impact of urbanization on ESs while neglecting the impact of urbanization on ESs at the subsystem level and the impact of ESs on urbanization at the subsystem level [44,45,46,47]. In short, there is a paucity of research on the coupling coordination relationship between urbanization and ESs at both system and subsystem levels, as well as on the spatiotemporal heterogeneity of their interactions. This oversight hampers our capacity to devise tailored policies that effectively synchronize ecological conservation endeavors with urbanization.

Given the aforementioned research deficiencies, the study developed a comprehensive three-dimensional urbanization index evaluation system based on demographic, spatial, and economic subsystems. The study employed the NPP-modified equivalent factor method to calculate ESV, examined the spatiotemporal evolution characteristics of urbanization and ESV by combining with spatial autocorrelation analysis, and utilized the CCD model and the GTWR model to analyze the complex coupling coordination relationship between urbanization and ESs and the spatiotemporal heterogeneity of their interactions at both the system and subsystem levels from 1985 to 2021 in the Songhua River Basin (SRB). The objective of this study is to furnish a scientific foundation and novel perspectives for coordinating ecological protection and the high-quality development of urbanization in the SRB and other similar basins, with regional representativeness and long-term strategic significance.

2. Materials and Methods

2.1. The Study Area

The SRB, located between 41°42′ N and 51°38′ N latitude and 119°52′ E and 132°31′ E longitude, is situated in the north-central part of Northeast China (Figure 1a). Covering a basin area of 55.46 × 10⁴ km², it is the third largest basin in China after the Yangtze River and the Yellow River [48]. This basin encompasses the northeastern forest belt within China’s “Two ecological barriers and three shelters” (TEBTS), a strategic ecological security pattern, and incorporates five nationally important ecological function zones, principally contributing to water conservation and biodiversity protection [49]. It holds strategic importance in regulating the water cycle and local climate of Northeast Asia, comprehensively revitalizing Northeast China, and safeguarding the country’s ecological security [50]. Due to its abundant natural resources, energy, and mineral resources, the SRB serves as a significant industrial, forestry, animal husbandry, and food production base in China. The study area encompasses 27 prefecture-level cities spanning four provinces: Heilongjiang, Jilin, Liaoning, and the Inner Mongolia Autonomous Region. It includes most of the resource-based, old, industrial cities in northeast China based on administrative units (Figure 1b). The SRB lies in the cold-temperate zone, illustrated by a gradual rise in elevation from the Songnen Plain in the central area to the mountain system located at the border (Figure 1c). The region experiences a distinct continental monsoon climate, characterized by low annual average temperatures and an uneven spatiotemporal distribution of precipitation. The spatial distribution of the annual average net primary productivity (NPP) of vegetation exhibited an uneven distribution pattern of “high in the southeast, low in the southwest, high in the surrounding area, and low in the middle of the country” (Figure 1d). Land use in the SRB exhibited clear spatial distribution patterns, with farmland predominantly situated in the Songnen Plain and the Sanjiang Plain, forestland mainly concentrated in the western and eastern regions, and grassland sporadically scattered across the central and southwestern regions (Figure 1e). The SRB, with its high forest cover and large per capita arable land, bears significant responsibility for the pattern of national food security and ecological security [51], ensuring that “food production increases” and “ecological barriers are not destroyed” will be the main focuses of its development. Between 1985 and 2021, the per capita GDP of the SRB increased from 0.12 × 10⁴ to 4.74 × 10⁴ CNY, and the urban population surged from 6.12 × 10⁷ to 7.09 × 10⁷, marked by a rise in the urban population proportion from 51.18% to 55.37%. The rapid socio-economic development has markedly heightened the level of urbanization. However, as the demographic structure has aged, the industrial structure has heavily relied on resource-based industries, and some of these industries continue to decline [52]. The rapid expansion of urban space in the SRB over the past 30 years has diminished the ESs and ecological carrying capacity of the basin [53]. The black soil in the SRB has deteriorated [54], and the grassland has experienced degradation and salinization [55]. Furthermore, there have been issues such as lake wetland degradation and shrinkage [56], frequent floods and droughts [57], and other emerging ecological problems that have posed a challenge to the SRB’s high-quality development. Previous studies on the SRB have mainly focused on climate change, habitat quality, pollution assessment, and land cover change, with fewer studies on the dynamic monitoring and quantification of ESV over long time series, the complex coupling coordination relationship between urbanization and ESs, and the spatiotemporal heterogeneity of their interactions.

2.2. Data Sources

The years 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2021 were chosen as the cross-sectional years. The data used in this study include administrative boundary data, digital elevation model (DEM) data, land use data, grain production data and annual vegetation net primary productivity (NPP) data used to modify the equivalence factor of ESV, and various types of socio-economic data (e.g., percentage of the nonagricultural population, percentage of employees in the secondary and tertiary sectors, paved road area per capita, percentage of urban built-up area in the total area of a city, GDP per capita, total retail sales of consumer goods per capita, etc.) used to measure urbanization. All raw spatial raster data were converted to uniform projected coordinates (Krasovsky_1940_Albers) and a spatial resolution of 100 m × 100 m after resampling and projection transformation in ArcGIS 10.8. The details and sources of the data are shown in Table 1.

2.3. Methods

2.3.1. Evaluation of Urbanization

Urbanization is multidimensional and heterogeneous. It encompasses a range of complex spatial evolution processes, including urban demographic agglomeration, economic development, and urban land expansion [58]. The core element of urbanization is the increase in the urban demographic ratio, with capital agglomeration serving as its driving force. The alteration in the land use structure and land use mode represents the spatial manifestation of urbanization. Establishing an evaluation index system is crucial to measuring urbanization. Since demographic, economic, and spatial urbanization are coordinated and mutually supportive, the comprehensive index method better reflects the overall characteristics and level of urbanization compared to the single-index approach based on a single dimension of land use or population.

This study referenced existing research findings [59,60]. The assessment encompassed the comprehensive urbanization index across three dimensions: demographic, spatial distribution, and economic indicators. Three primary indicators (demographic urbanization index (DUI), spatial urbanization index (SUI), and economic urbanization index (EUI)) were chosen to assess the urbanization index (UI). Meanwhile, based on the influencing factors of comprehensive urbanization, the characteristics of regional development, and the scientific and acquisition feasibility of data acquisition, nine secondary indicators, which are widely used to reflect China’s urbanization process, were selected to evaluate each urbanization index. To mitigate the impact of diverse quantitative data on the comprehensive assessment, this study initially standardized the raw data values of the aforementioned indicators (see Equation (1)). Subsequently, it applied the entropy weight method to distribute weights to each indicator, drawing upon the information entropy obtained from the dispersion of these indicators [61]. This approach aimed to uphold objectivity and precision. Finally, the comprehensive urbanization index was calculated using the linear weighted sum method and integrated into a comprehensive index (see Equation (2)). This process formed the SRB urbanization comprehensive evaluation index (Table 2).

(1) ${U^{'}}_{i j} = \frac{U_{i j} - m i n (U_{j})}{m a x (U_{j}) - m i n (U_{j})}$

Here, U′_ij represents the standardized value of urbanization indicator j (i.e., DUI, SUI, and EUI); U_ij denotes the raw value of urbanization indicator j in year I; max (U_j) and min (U_j) are the maximum and minimum values of urbanization indicator j across all years, respectively.

(2) ${U I}_{i} = \sum_{j = 1}^{3} {U^{'}}_{i j} \times w_{j}$

where UI_i is the composite urbanization index in year i and w_j is the weight of urbanization indicator j.

2.3.2. Evaluation of Ecosystem Services Value

Various scholars have classified ecosystems in diverse ways. In this study, ESs were categorized into four main categories and nine subcategories based on the principles of comprehensiveness (including multiple types of ESs), dominant function (expressing the natural environmental traits of the study region), feasibility, and existing classifications proposed by the Millennium Ecosystem Assessment [5], Costanza et al. [7], and Xie et al. [18]. This classification was integrated with the geographic and environmental characteristics of the SRB, along with studies conducted in northeast China [62]. The application of the equivalent factor method to compute ESV in the SRB involved three important aspects: first, the determination of the value of a standardized equivalent factor in the SRB; second, the construction of a table of ESV per unit area in the SRB; and third, the classification of land use and the determination of the area of each type of land use. The standard equivalent factor of ESV was determined based on the annual yield per hectare of the farmland ecosystem, which represented the contribution to ESs. For different land types, the corresponding ecological value equivalent could be calculated, and subsequently, the land area could be used to evaluate the ESV. The equation for determining the value of a standardized equivalent factor is as follows:

(3) $D = 1 / 7 \times \sum_{f = 1}^{s} P_{f} \times Q_{f}$

where D represents the value of an equivalent factor—specifically, ESV per unit area of farmland; 1/7 signifies that the economic worth of the equivalent factor of ESV in the SRB equals 1/7 of the average grain yield’s market value. This implies that the economic value offered by natural ecosystems, which do not require artificial inputs, amounts to 1/7 of the economic worth of the grain production services per unit area of existing farmland. Additionally, f denotes the main grain species in the SRB, encompassing rice, wheat, and corn; s stands for the number of crop species; P_f signifies the grain yield per unit area of crop f, calculated by dividing the yield of crop f by the arable area of crop f; and Q_f represents the grain price of crop f. To ameliorate the ramifications of fluctuations in grain prices and inflation regarding the estimation outcomes, the grain price was set as the average price of the three major grain species of the SRB in 2021. By substituting into Equation (3), the standard equivalent factor in the SRB was determined to be 2204.41 CNY/hm².

Equations (4) and (5) were utilized to determine the ESV per unit area in the SRB. The “Ecosystem service equivalent value per unit area” proposed by Xie et al. presents the unit value of ecosystem service in China based on the national average state. However, the actual ESV in the SRB was contingent upon the biomass quantity within its ecosystem. To address the heterogeneity, complexity, and dynamics of ESV, this study chose plant net primary productivity (NPP) as the SRB biomass correction factor [63]. NPP reflected the organic matter production capacity of vegetation communities in the natural environment. In conjunction with the grain yield correction method, the ESV coefficient per unit area underwent adjustment to better synchronize the outcomes with the ecological characteristics of the SRB. The calculation equation is as follows:

(4) ${V C}_{i k} = D \times M_{i k} \times N$

(5) $N = \frac{{N P P}_{s}}{{N P P}_{c}}$

where VC_ik represents the modified ESV per unit area (CNY/hm²) of the ith ecosystem service provided by land use type k in the ecosystem; M_ik denotes the equivalence coefficient of the ith ecosystem service provided by the different land use types k; N is the adjustment coefficient; NPP_s represents the net primary productivity of vegetation in the SRB; and NPP_c represents the national net primary productivity of vegetation. NPP_s is the NPP in the SRB, while NPP_c is the NPP in the entire country. Taking into account the attributes of land resources in the SRB and the prevailing circumstances, coupled with China’s land use classification system, Current Land Use Classification [64], the land utilization data were reclassified into seven distinct categories: farmland, forestland, grassland, water body, barren land, wetland, and construction land using ArcGIS 10.8. The average regulation coefficient N value of the SRB from 1985 to 2021 was calculated to be 0.97. From the VC results, the table of ESV per unit area of the SRB was obtained (Table 3), and the individual ESV and total ESV were calculated in the following equations:

(6) ${E S V}_{i} = \sum_{k = 1}^{n} A_{k} \times {V C}_{i k}$

(7) $E S V = \sum_{i = 1}^{m} {E S V}_{i}$

where ESV_i and ESV are the ESV subtype and total ESV, respectively. A_k denotes the area of land use type k, and m signifies the number of cities.

2.3.3. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis enables the assessment of aggregation and dispersion effects within regional units [65]. To further explore whether there are certain patterns or correlations within the spatiotemporal relationships between urbanization and ESV that interact at global and local scales, this study utilized the GeoDa 1.22 [66]. We used spatial autocorrelation analysis, which included single-variable spatial autocorrelation as well as bivariate spatial autocorrelation analyses (both global and local). The calculation equations are as follows.

(8) $I = \frac{\sum_{i = 1}^{n} \sum_{j = 1}^{n} W_{i j} (x_{i} - \bar{x}) (y_{j} - \bar{y})}{S^{2} \sum_{i = 1}^{n} \sum_{j = 1}^{n} W_{i j}}$

In the equation, I represents the global bivariate Moran’s I, which signifies the comprehensive correlation between urbanization and ESV at the global scale, with a value range of [−1, 1]. When I > 0, it signifies a positive spatial correlation, with a stronger correlation represented by larger values. When I < 0, it signifies a negative spatial correlation, with greater spatial variability reflected in larger values; I = 0 suggests that the spatial distribution pattern tends to be random, with no spatial correlation. Here, n = 27; W_ij denotes the spatial weighting matrix; x_i and y_j represent the urbanization and ESV of different cities, respectively; and S² represents the variance of all samples.

(9) $I_{i} = Z_{i} \sum_{j = 1}^{n} W_{i j} Z_{j}$

where I_i represents the local bivariate Moran’s I. Z_i and Z_j denote the normalized values of urbanization and ESV observed in spatial units i and j, respectively. The LISA cluster maps can yield four distinct clustering patterns: high–high, high–low, low–high, and low–low. High–high indicates high values of urbanization and a high ESV, while low–low indicates a low urbanization index and low ESV; both patterns are spatially positively correlated. Conversely, low–high and high–low are spatially negatively correlated.

2.3.4. The Coupling Coordination Degree Model

To enhance clarity regarding the coupling coordination relationship of urbanization and ESs at the both system and subsystem levels in the SRB, this study utilized the coupling coordination degree (CCD) model to quantitatively assess the coordination degree of the interactive coupling between ESs and urbanization. Initially, the coupling degree (C) between urbanization and ESs was calculated using Equation (10).

(10) $C = \sqrt{(U_{1} \times U_{2}) / {((U_{1} + U_{2}) / 2)}^{2}}$

Here, C represents the level of coupling between urbanization and ESV, which reflects the degree of interaction between the systems [67]. The value of C varies between 0 and 1, with a higher value of C indicating a stronger interaction between urbanization and ESV. U₁ and U₂ represent the integrated assessment values of the urbanization index and ESV in the study area, respectively. Both U₁ and U₂ are normalized. It should be noted that high coupling values differ from high levels of coupling. Coupling only reflects the level of correlation between systems rather than indicating the level of coordinated development; the actual development of these two systems may be poor, but the level of coupling is high. Therefore, the degree of coordination between urbanization and ESV as well as the degree of coupling coordination were measured using Equations (11) and (12), based on the coupling degree analysis.

(11) $T = α U_{1} + β U_{2}$

(12) $D = \sqrt{C \times T}$

In the equation, T encapsulates the overarching influence and extent of coordination among the subsystems, assessing the extent of beneficial coupling among the coupling relationships within the systems. It represents the healthy development of the system and mirrors the caliber of coordination. [68]. α and β denote the weighting coefficients of the two systems. Given that current research indicates equal importance between urbanization and ESs, the values of the α group and β group are both set to 0.5. D indicates the coupling coordination degree (CCD) of the urbanization and ESV [69], ranging from 0 to 1, reflecting the dynamic trend of the subsystem from disorder and incoherence towards orderly coordination.

Drawing from previous studies [70,71], we categorized the CCD between urbanization and ESV into five levels: seriously unbalanced (0.0–0.2), moderately unbalanced (0.2–0.4), basically balanced (0.4–0.6), moderately balanced (0.6–0.8), and highly balanced (0.8–1.0). To further elucidate the intrinsic connections between urbanization and ESV, each type of CCD was further classified into three subtypes: urbanization lagging, systematically balanced, and ESV lagging (Table 4).

2.3.5. The Spatiotemporal Geographically Weighted Regression Model

This study used the GTWR model to explore the spatiotemporal heterogeneity of urbanization and ESs at both the system and subsystem levels in the SRB. The GTWR model was calculated using Equation (13).

(13) $Y_{i} = β_{0} (u_{i}, v_{i}, t_{i}) + \sum_{k = 1}^{p} β_{k} (u_{i}, v_{i}, t_{i}) X_{i k} + ε_{i}$

In the equation, Y_i denotes the explanatory variables in the ith sample city; (u_i, v_i, t_i) denotes the spatiotemporal coordinates of the ith sample city; μ_i, v_i, and t_i are the longitude, latitude, and time of the ith sample city, respectively; β₀ (u_i, v_i, t_i) denotes the regression intercept, i.e., the constant term in the model, of the ith sample city; X_ik denotes the regression coefficients of the kth explanatory variable in the ith sample city; p denotes the number of explanatory variables; β_k (u_i, v_i, t_i) denotes the regression coefficient of the kth explanatory variable in the ith sample city; and ε_i is the residual term of the model.

The GTWR model employs regression coefficients to indicate the effect of each factor on the research system. A regression coefficient β greater than 0 indicates a positive correlation with the explanatory variables, i.e., a facilitating effect, while a coefficient less than 0 indicates an inhibiting effect. The greater the absolute value of the regression coefficient β, the greater the intensity of the effect. This study’s GTWR model analysis was primarily based on ArcGIS 10.8, using AICc optimization settings, and the GTWR plug-in developed by Huang et al. [40].

3. Results

3.1. Spatiotemporal Characteristics of Urbanization

Figure 2 displays the changes in various urbanization indicators of the SRB. From 1985 to 2021, the level of urbanization in the SRB changed dramatically, with the UI increasing from 0.09 in 1985 to 0.34 in 2021. From 1985 to 2005, the UI in the SRB experienced slow growth. Starting around 2005, aided by the implementation of the Northeast Revitalization Strategy, urbanization within the SRB embarked upon a phase of accelerated advancement, with the rate of urbanization increasing significantly. The growth rate of UI slowed down from 2015 to 2021, indicating that urbanization in the SRB entered a phase of deceleration, focusing on improving quality and stability after a period of prosperity. The DUI, SUI, and EUI increased by 0.08, 0.22, and 0.33, respectively, during the study period. Among them, SUI maintained a stable growth trend; however, EUI showed a significant growth trend until 2021 and declined in 2021 due to China’s strict blockade and quarantine measures during the COVID-19 epidemic, severely disrupting business activities in the SRB and across China. The DUI experienced slow growth throughout the study period, significantly lagging behind SUI and EUI, which aligns with the observed phenomenon of “industry–city separation” in the basin. Since the initiation of the Northeast Revitalization Strategy, urban economies within the SRB have shown signs of development, accompanied by a continuous expansion in urban spatial dimensions. However, challenges persist in resource-dependent, old, industrial cities, characterized by outdated property rights systems and industrial structures. These factors have hampered industrial growth vitality and have failed to adequately attract population concentration. Consequently, significant population outflows have ensued, contributing to sluggish overall population growth within the region. Based on the weights of the three primary urbanization indicators, SUI and EUI significantly contributed to the UI (0.84), signifying a substantial shift in urbanization within the SRB from rapid population agglomeration to a high-quality growth model driven by spatial expansion and economic development.

The spatial arrangement of urbanization within the SRB demonstrated significant heterogeneity, characterized by an uneven spatial pattern of “high in the middle–low in the surroundings”, with the core city of the Harbin-Changchun City Cluster (HCCC) serving as the center of gravity. The temporal evolution demonstrated the Matthew effect, wherein “the strong are always stronger and the weak are always weaker” (Figure 3). The UI of all cities in the SRB increased significantly from 1985 to 2021. However, notable regional disparities existed in the rate of urbanization expansion, with urbanization progressing more rapidly in the city clusters located in the central part of the region, characterized by a stronger developmental foundation. Conversely, urbanization advanced at a slower pace in the northwestern eco-functional area, southeastern mountainous area, and ecologically fragile area in the western part of the region, which had a weaker developmental foundation. Based on the LISA cluster and significance analysis, the high–high and low–low agglomerations of the urbanization in the SRB exhibited relatively stable spatial patterns. The high–high agglomerations were primarily concentrated in the HCCC, including cities such as Daqing, Harbin, and Changchun. Local variations in spatial patterns demonstrated the time-varying characteristics of high–high agglomerations, which expanded and contracted over time. The low–low agglomerations were predominantly situated in the municipalities of the Inner Mongolia Autonomous Region. Overall, the rate of urbanization decreased from central cities and economically developed areas towards the periphery, consequently augmenting the spatial heterogeneity of urbanization.

3.2. Spatiotemporal Characteristics of Land Use and ESV

The main land use types in the SRB were farmland and forestland, which accounted for 45.90% (25.46 × 10⁴ km²) and 42.49% (23.57 × 10⁴ km²) of the basin area, respectively. Following them were grassland and construction land, comprising 6.07% (3.36 × 10⁴ km²) and 3.74% (2.07 × 10⁴ km²) of the basin area, respectively. Conversely, the area of water bodies and barren land was relatively small, constituting 1.39% (0.77 × 10⁴ km²) and 0.40% (0.22 × 10⁴ km²), respectively. Wetland accounted for the smallest proportion, at 0.02% (0.01 × 10⁴ km²). With the advancement of urbanization and the implementation of land improvement projects in the SRB, there was an increasing trend in the area of farmland and construction land, while the area of grassland, forestland, wetland, and barren land showed a decreasing trend from 1985 to 2021. The farmland area increased the most, with a cumulative increase of 1.56 × 10⁴ km², representing an average rate of 6.54%, followed by construction land, with an increase of 1.20 × 10⁴ km². The grassland, forestland, wetland, and barren land areas decreased, with decreases of 1.47 × 10⁴ km², 1.02 × 10⁴ km², 0.15 × 10⁴ km², and 0.12 × 10⁴ km², respectively (Figure 4). This suggests that due to China’s agricultural development policy aimed at enhancing food security, local governments and agricultural producers in the SRB continued to convert ecological land into farmland, leading to serious deforestation and land clearing. Meanwhile, economic development and urbanization have precipitated a rise in the extent of construction land, resulting in the encroachment of other land areas. However, no change in the water body was evident during the same period.

Throughout the period from 1985 to 2021, the total ESV in the SRB experienced considerable change, decreasing from 2091.42 × 10⁷ CNY in 1985 to 2002.44 × 10⁷ CNY in 2021, representing a decrease of 81.61 × 10⁷ CNY. During this time frame, the total ESV exhibited a sharp decrease from 1985 to 2000, followed by a slow increase year by year from 2000 to 2010, but remained on a decreasing trend from 2010 to 2021 (Table 5). The years 2000 and 2010 are significant time points for changes in ESV in the SRB, which are closely related to changes in land use transition patterns. Since 1985, large-scale deforestation activities in the SRB have significantly expanded the amount of potential farmland, resulting in significant decreases in forestland and grassland, thereby having major impacts on the ecosystem. Given this background, food security has once again emerged as a core concern of the Chinese government. In 2010, despite the execution of the grain capacity building project, the increase in ESV attributable to the expanding area of farmland was not sufficient to compensate for the decrease attributed to the reduction in the area of other ecological land. Consequently, the ESV persisted in showing a decreasing trend after 2010. Following 2015, with the implementation of the national food security strategy and the establishment of high-standard farmland, the rate of decline in ESV markedly decelerated.

Regarding individual ESs, each of the four types exhibited different degrees of decline during the study period. The order of each ES value from highest to lowest was regulating service > support service > provision service > cultural service. This indicated that the greater forest cover in the SRB rendered regulating service the core function of ecosystem service and determined the overall trend of ESV. From the perspective of subtypes for ESs, aside from food production (FP), which increased during the study period, the value of other services exhibited varying degrees of decreasing trends, with the water regulation (WR) function experiencing the largest value loss (up to 15.17 × 10⁷ CNY). This suggested that the increase in the cultivated land area in the SRB contributed to the increase in food production (FP). Nevertheless, the proliferation of farmland and the expansion of construction land encroached upon additional land spaces, resulting in the degradation of other functions of the ecosystem in the SRB, such as soil conservation (SC) and climate regulation (CR). Thus, the decrease in ESV in the SRB was primarily attributed to the expansion of farmland and construction land, resulting in the loss of forestland and grassland.

Spatially, the dispersion of ESV within the SRB showcased pronounced spatial heterogeneity (Figure 5). High-value ESV predominantly occurred in the northwestern part of the SRB, which was notably abundant in ecological resources as an important forest resource reserve area in China. Being located at the border of China, where development is restricted, resulted in higher ESVs in this region. Conversely, a low-value ESV was prevalent in the Songnen Plain, located in the central part of the SRB. Being the main grain-producing area in China, this region experienced a high population concentration and agricultural production activities. The conversion from dryland to rice to boost grain production resulted in high water demand, leading to the loss and degradation of wetlands and grasslands. As a result, the ESV remained low. Regarding spatial evolution, the ESV displayed a general degradation trend at the municipal level over the study period. During this time, 85.19% of cities witnessed a decline in ESV, with variations in the degree of decline from city to city. Among them, the ESV in Heihe City decreased the most, by 19.42 × 10⁷ CNY, which was closely related to environmental problems such as regional soil and water loss caused by agricultural over-reclamation, forestland over-logging, grassland over-grazing, and mine over-mining in the process of rapid urbanization. The results from the LISA cluster and significance indicated that there was minimal alteration observed in the spatial dispersion of ESV within the SRB over time, but the overall spatial distribution pattern remained stable. The high–high agglomeration areas were mainly distributed in northeastern Inner Mongolia and northwestern Heilongjiang Province, characterized by high vegetation cover and rich biodiversity, while the areas exhibiting low–low agglomeration were predominantly situated in the southern region of Jilin Province, where industrial production activities were more prevalent. The disturbance of the landscape by human activities further intensified the spatial heterogeneity of the distribution of ESV in the SRB.

3.3. The Coupling Coordination Relationship between Urbanization and ESs

3.3.1. The Temporal Differentiation Characteristics of the CCD in the SRB

After revealing the spatiotemporal evolution characteristics of urbanization and ESs, the CCD model was employed to analyze the coupling coordination relationship between urbanization and ESs at both the system and subsystem levels in the SRB (Figure 6). The results indicated that the coupling coordination degree (CCD) between urbanization and ESs at both the system and subsystem levels showed an increasing trend in the SRB from 1985 to 2021, transitioning from the moderately unbalanced to the basically balanced stage. It showed that SRB made significant achievements in improving the coupling coordination relationship between urbanization and ESs. Specifically, the CCD of urbanization and ESs increased from 0.30 to 0.44, representing an increase of 43.16%. At different stages of urban development, variations existed in the intensity and coordination of the interaction between urbanization and ESs, as well as in the development trends. The upward trend of the CCD was relatively flat from 1985 to 2005, and the CCD level increased significantly from 2005 to 2021. In 1985, merely 12% of urban areas in the SRB achieved the CCD indicative of a basically balanced stage. The main reason was the backwardness of economic development in the SRB during this period. Moreover, the industrial composition of the SRB during this period was dominated by heavy industry, resulting in the region being energy-consuming, resource-dependent, and environmentally burdensome. This resulted in the substantial degradation of the ecological environment. By 2005, due to the implementation of policy support from the National Northeast Revitalization Strategy and proactive measures by local governments, the urbanization development level and environmental management capacity of the SRB had rapidly improved. Consequently, 41% of the cities reached the basically balanced stage, and the number of cities with a seriously unbalanced stage dropped to zero. By 2021, with the rise in the ecological civilization as a national strategy, the SRB implemented measures such as returning farmland to forests, restoring grasslands, and remediating polluting enterprises. These ecological construction projects and environmental management efforts yielded remarkable achievements. As a result, 56% of the cities were at the basically balanced stage, and 7% reached the moderately balanced stage.

3.3.2. The Spatial Differentiation Characteristics of the CCD in the SRB

Figure 7 shows the spatial differentiation characteristics of the CCD in the SRB from 1985 to 2021. The results indicated that the CCD between urbanization and ESs improved significantly at both the system and subsystem levels in the SRB, with seriously unbalanced and moderately unbalanced municipalities contracting and basically balanced and moderately balanced municipalities expanding. There was an overall shift from seriously unbalanced and moderately unbalanced to basically balanced, moderately balanced, and even highly balanced. Overall, most cities in the SRB were still in the transitional phase of coupling coordination development. Except for Hulunbuir, where the CCD between economic urbanization and ESs reached the highly balanced stage in 2015, the various types of CCD between urbanization and ESs in most cities were still at the basically balanced and moderately balanced stages by 2021. In addition, there was considerable variation in the CCD between different cities, with marked variations in the spatial distribution. Spatially, the areas with low CCD values in the SRB were mainly concentrated in grassland ecological degradation areas and ecologically fragile areas like Tongliao City, resource-dependent, old, industrial cities such as Hegang, Jiamusi, Jixi, and Tonghua City, as well as highly urbanized areas like Liaoyuan City. Tongliao City, known for agriculture and animal husbandry, is a typical area of grassland ecological degradation and is ecologically fragile. Transitional grazing practices there had resulted in severe degradation, with grassland resources becoming degraded, sandy, and highly salinized. The delayed benefits of grassland ecological management contributed to a relatively low CCD. Concerning resource-dependent, old, industrial cities like Tonghua City and Jixi City, their single industrial structure, high energy consumption, and high emissions have significantly and negatively impacted ecosystem structures, processes, and functions. Consequently, these areas exhibited a relatively low CCD. The areas with high CCD values were mainly concentrated in the Daxing’an and Xiaoxing’an mountains forest ecological function area, the Hulunbuir grassland meadow ecological function area, and the Changbai Mountain forest ecological function area, which are regions with low levels of urbanization, as well as in Harbin, strategically positioned as the “Northeast Asia Regional Center City”. The differing levels of urbanization and ESV development across various cities presented a challenge in promoting the high-quality coordinated development of urbanization and ESs in the SRB.

3.4. Spatiotemporal Heterogeneity in the Interaction between Urbanization and ESs

3.4.1. Data Verification

This study employed the GTWR model. To further explore the impacts of various urbanization subsystems on ESs, we analyzed the nine secondary indicators (X1–X9) of the three primary indicators (demographic, spatial, and economic urbanization) as explanatory variables, with ESs as the dependent variable. The spatiotemporal heterogeneity of the impacts of each subsystem on ESs was analyzed. The spatiotemporal heterogeneity of the impacts of each ES subsystem on urbanization in the SRB from 1985 to 2021 was also analyzed, with urbanization as the dependent variable and the four subsystems of ecosystem services (Y1–Y4) as the explanatory variables. Before performing the GTWR model calculations, all variables were standardized to prevent pseudo-regression. Subsequently, SPSS 29 was employed to test the multicollinearity of all standardized variables using regression analysis. The selected subsystems of urbanization and ESs met the variance inflation factor (VIF) criterion of <10 and passed the significance test. Detailed model calculation results and parameters are presented in Table 6. The parameters indicated that the model effectively measured the spatiotemporal heterogeneity of the influence of explanatory variables on dependent variables.

3.4.2. Temporal Heterogeneity in the Interaction between Urbanization and ESs

Figure 8a–i illustrate the temporal evolution of the regression coefficients for the impact of each subsystem of urbanization on ESs. Overall, SRB urbanization exhibited a generally negative impact on ESs. Specifically, the temporal heterogeneity characteristics of each urbanization subsystem’s impact on ESs in the SRB from 1985 to 2021 varied significantly. The positive impact of population density (X1) on ESs gradually decreased and then stabilized; the negative impact of the percentage of the nonagricultural population (X2) on ESs increased; the impact of the percentage of employees in the secondary and tertiary sectors (X3) on ESs gradually changed from positive to negative; the positive impact of the per capita paved road area (X4) on ESs decreased and then increased; the positive impact of the percentage of urban built-up area (X5) on ESs increased; the negative impact of building density (X6) on ESs first decreased and then increased; the impact of GDP per capita (X7) on ESs shifted from positive to negative over time; the impact of per capita total retail sales of consumer goods (X8) on ESs gradually changed from negative to positive; and the negative impact of night light values (X9) on ESs gradually weakened. There were significant differences in the direction and intensity of each urbanization subsystem’s influence on ESs, with varying trends, reflecting the complexity of urbanization’s impact on ESs.

Figure 8j–m illustrate the temporal evolution of the regression coefficients for the impact of each subsystem of ESs on urbanization. The results indicated that the regression coefficients of the impacts of each subsystem of ESs on urbanization in the SRB from 1985 to 2021 were all negative, suggesting that all subsystems of ESs negatively impacted urbanization. After 2005, the absolute value of the regression coefficients decreased over time, indicating a declining intensity of the negative impacts of the subsystems of ESs on urbanization, suggesting significant progress in improving ecosystem quality in the SRB.

3.4.3. Spatial Heterogeneity in the Interaction between Urbanization and ESs

The GTWR model quantified the spatial heterogeneity characteristics of the impacts of various urbanization subsystems on ESs. Using the natural breaks method, the GTWR regression coefficients were classified and visualized. Positive impacts were divided into high positive impact, moderate positive impact, and low positive impact, while negative impacts were categorized into high negative impact, moderate negative impact, and low negative impact, as shown in Figure 9. Overall, urbanization had a generally negative impact on ESs, but the influence of urbanization subsystems on ESs was multifaceted, with varying directions and intensities across different regions. Over the past three decades, the spatial distribution pattern of the impacts of urbanization subsystems on ESs remained relatively stable, except for occasional fluctuations between 2000 and 2005. This pattern exhibited significant north–south and east–west spatial differentiation.

Specifically, the population density (X1) had both positive and negative impacts on ESs, with a balanced proportion of both. Negative impact areas were mainly concentrated in the ecologically fragile regions in the western and eastern parts of the SRB, while positive impact areas were primarily distributed in the high-ESV areas in the north and resource-dependent, old, industrial cities in the south. The percentage of the nonagricultural population (X2) had a negative impact, mainly in the northern SRB, with positive impacts primarily in the southern SRB. The impact of the percentage of employees in the secondary and tertiary sectors (X3) on ESs was mainly characterized by low positive and low negative impacts, with negative-impact areas concentrated in the ecologically fragile regions of the western and eastern SRB. The per capita paved road area (X4) generally had a positive impact on ESs, with the impact strength gradually weakening from north to south. The percentage of the urban built-up area (X5) showed complex spatial heterogeneity, with high-positive-impact areas mainly in the northern SRB. The building density (X6) had a high negative impact mainly in the high-ESV areas in the northern SRB. The areas with positive impacts of GDP per capita (X7) on ESs were mainly concentrated in resource-dependent, old, industrial cities in the southern SRB. The per capita total retail sales of consumer goods (X8) mainly had a low positive impact on ESs. The areas with positive impacts of night light values (X9) on ESs gradually shifted towards the northern SRB.

Additionally, a comparative analysis of the GTWR regression coefficients revealed that the ecosystems in the northwestern SRB were more sensitive and had a higher response to urbanization changes, indicating a necessity for the enhanced protection and improvement of the ecological spaces in this region.

The spatial differentiation characteristics of the impacts of various ESs subsystems on urbanization are illustrated in Figure 10. The results indicated that the spatial patterns of and intensity changes in the impacts of various ESs subsystems on comprehensive urbanization had exhibited similar trends. Over time, the spatial heterogeneity of the impacts of various ESs subsystems on urbanization had increased. From 1985 to 2010, the scope of the negative impacts of various ESs subsystems on comprehensive urbanization had decreased, while the intensity of these impacts had increased. Conversely, the scope and intensity of the positive impacts of various ESs subsystems on urbanization had expanded gradually. From 2010 to 2021, the intensity and spatial patterns of both types of impacts had occasionally fluctuated but had remained generally stable. In resource-dependent, old, industrial cities like Daqing, Baicheng, and Songyuan, the urbanization process experienced prolonged and intense negative impacts from various ESs subsystems. The rapid expansion of urbanization in these cities had caused significant disturbances to the structure and function of ecosystems, resulting in encroachment and degradation. In the vast surrounding areas, forest, grassland, and farmland ecosystems had transformed into artificial or semi-artificial ecosystems, leading to a decline in ESV. This decline in ESV had further constrained urbanization development.

Notably, the spatial distribution pattern of the impacts of the four ESs subsystems on urbanization was closely correlated with the spatial distribution of ESV. In areas with low ESVs, the impacts of ESs subsystems on urbanization had been primarily negative, whereas in areas with high ESVs, the impacts had been primarily positive. This finding indicated that urbanization development had been significantly constrained by the ecological carrying capacity, as higher ESVs had favored urbanization development.

4. Discussion

4.1. The Coupling Coordination Relationship between Urbanization and ESs

This study utilized the CCD model to analyze the coupling coordination relationship between urbanization and ESs in the SRB at both the system and subsystem levels and explored the spatial differentiation characteristics. From 1985 to 2021, the CCD between urbanization and ESs in various cities of SRB showed an upward trend at both the system and subsystem levels. This indicated that after experiencing a period of rough economic growth, the contradiction between the supply and demand of urban construction land in the SRB forced continuous improvements in resource utilization efficiency. Urbanization was promoted through refined and efficient land use, with increased attention to ecological protection, leading to improvements in the CCD level. However, the overall CCD had not yet reached a satisfactory level, indicating considerable room for improvement.

The areas with high CCD values between urbanization and ESs in the SRB were mainly concentrated in ecological function areas and regions with low levels of urbanization. Among them, HulunBuir boasted the highest ESV, but due to its low level of urbanization, the coordination between urbanization and ESs had not yet reached a highly balanced stage [31]. The city of Harbin, being the economic and social center of Northeast China, had actively improved land use efficiency and focused on industrial structure adjustment and layout optimization in response to sustainable development principles. By limiting industries with high environmental costs and a high consumption of raw materials and energy, supporting green industries, and developing urbanization and ecological protection in parallel, Harbin had effectively enhanced the stability and balanced development capability between urbanization and ESs. Despite its high level of urbanization, Harbin had achieved the CCD of a moderately balanced stage.

The areas with low CCD values between urbanization and ESs in the SRB were mainly concentrated in grassland ecological degradation areas, ecologically fragile areas, resource-dependent, old, industrial cities, and highly urbanized areas. Resource-dependent, old, industrial cities face acute problems such as depleted natural resources, severe environmental pollution, and economic growth stagnation, inevitably leading to increased conflicts between urbanization and ESs [32,72]. As the ESV decreases, the pace of urbanization development gradually slows, accompanied by population loss and economic decline, making it increasingly difficult to improve the level of CCD. Resource-dependent, old, industrial cities such as Jiamusi and Hegang are located in the Sanjiang Plain wetland ecological functional area with a low population density and large wetland areas. If the local government effectively enhances environmental management, introduces new production technologies, and optimizes the industrial structure from the source, process, and end-of-pipe emissions, the conflicts between urbanization and ESs could be reduced, potentially improving the CCD level. The city of Tongliao, a grassland ecological degradation area with a large population but low per capita economic indicators, faces dual pressures of ecological environmental protection and socio-economic growth due to underdeveloped secondary and tertiary industries and slower overall urbanization compared to urban agglomerations.

Urbanization and ESs are interdependent and mutually restrictive, with the dynamic changes in their CCD reflecting the amalgamated impacts of various processes, encompassing economic advancement, the urbanization process, alterations in ecosystems, and policy orientations [8,73]. Against the backdrop of “Revitalizing the old industrial base in Northeast China”, the relevant governments in the SRB have adopted a series of policy measures to seek coordinated development paths between urbanization and ecological protection. In addition to considering assessments of environmental carrying capacity and land suitability, it is also worth considering incorporating the CCD development into future national spatial planning.

4.2. Spatiotemporal Heterogeneity in the Interaction between Urbanization and ESs

The spatiotemporal heterogeneity characteristics of the interaction between urbanization and ESs in the SRB from 1985 to 2021 revealed the complexity of the direction and intensity of their influences. The overall impact of various urbanization subsystems on ESs in the SRB was generally negative. Specifically, demographic urbanization and spatial urbanization trends showed a weakening positive impact and a strengthening negative impact on ESs. In the early years, the level of demographic urbanization in the SRB was low. The continuous concentration of the urban population could effectively alleviate issues such as resource consumption and environmental pollution. However, as the diversified demands for food, water, and land resources from demographic urbanization increased, the rapid expansion of cropland and construction land at the expense of ecological land, such as forestland and grassland, placed immense pressure on the carrying capacity of resources and the environment, exacerbating the loss of ESs. Spatial urbanization led to the expansion of construction land and road networks, which increased surface runoff, fragmented habitat patches, disrupted the ecosystem balance, and reduced ESs capacity. With the growth of the regional economy and social productivity in the SRB, residents’ environmental awareness increased, and the demand for a high-quality environment grew. High-pollution, high-emission, and high-energy consumption industries, previously reliant on resource development and infrastructure construction, transitioned toward green development. Concurrently, the government invested more in ecological restoration and protection, adjusting policies to enhance the ecosystem’s resilience against the negative impacts of urbanization, so the negative impact of economic urbanization on ESs gradually weakened. Notably, the shift in trends of urbanization subsystems’ impacts on ESs indicated that the interaction between urbanization subsystems and ESs had critical thresholds. The intensity of urbanization expansion beyond these thresholds suppressed ESs capacity. Furthermore, the interaction between urbanization and ESs was delayed. The decline in ESV did not immediately constrain urbanization development, and optimizing urbanization patterns did not immediately improve ESV [32].

The negative impacts of various ESs subsystems on urbanization in the SRB initially increased and then decreased during the study period, indicating that the disordered expansion of construction land due to rapid urbanization in the past disrupted material cycles and energy flows, weakened the carrying capacity of resources and the environment, fragmented habitat patches, and undermined regional ecological security, thus constraining urbanization development. However, with the implementation of land use policies such as permanent basic farmland and ecological redlines, the optimization of landscape patterns and the enhancement of ecological connectivity in the SRB improved the ecosystem’s resilience to external disturbances, maintained the stability of ESs, and ensured their sustainable supply, thus gradually reducing the negative impacts of ESs on urbanization. The spatial distribution patterns of ESs subsystems’ impacts on urbanization were highly correlated with the spatial distribution patterns of ESV in the SRB. In the western part of the SRB, particularly in the Horqin Grassland ecological functional area of Hinggan League, strategic positioning had restricted large-scale industrial agglomeration and the expansion of built-up areas, leading to continued negative impacts of ESs subsystems on urbanization. With the rise of China’s ecological civilization strategy after 2010, the impacts of ESs subsystems on urbanization in the northwestern SRB had shifted from negative to positive, indicating that the interaction between urbanization and ESs was influenced not only by ecological carrying capacity but also by policy factors [74].

Urbanization exacerbated the demand for land resources and infrastructure through population growth, economic development, and spatial expansion. This led to drastic changes in regional land-use types, structures, and patterns, disrupting ecosystem structure, function, and quality, thereby significantly inhibiting ESs [75,76,77,78,79]. Conversely, ecosystems had a limit to the services they could provide. When the external disturbances from urbanization expansion exceed the carrying capacity of the ecosystem to maintain its ecological balance, the imbalance between the supply and demand of ESs triggers environmental problems. This, in turn, constrains and limits urbanization development through factors such as resource endowment, population displacement, capital competition, and policy intervention, thereby affecting regional coordinated development [80,81,82]. Different types of urbanization influenced each other, indirectly affecting ESs. In summary, there existed complex interactions between urbanization and ESs, with ESs possibly being constrained by other factors such as rainfall [83], topography [84], and soil type [85].

4.3. Policy Implications

This study demonstrated that the coupling coordination relationship and interaction between urbanization and ESs in the SRB exhibited spatiotemporal heterogeneity, with national policies significantly influencing their coordination. Therefore, the government and policymakers in the SRB should consider high-quality coordination as a goal, adjusting urbanization planning and formulating differentiated ecological restoration and environmental development policies based on the characteristics of coupling coordination relationships in different regions. Given this, we propose region-specific suggestions for coordinating urbanization and ESs in the SRB.

For ecological function zones and regions with low levels of urbanization, efforts should focus on mitigating urban expansion impacts on ecosystems. This involves scientifically allocating land resources between ecological and non-ecological uses, reasonably limiting urbanization scale and intensity, and strictly controlling disorderly expansion. Additionally, black soil protection should be strengthened, high-standard farmland construction promoted, and the transition to sustainable agriculture and food diversity enhanced to increase agricultural systems’ resilience and alleviate pressure on other ecosystems [86,87]. Shifts in agricultural water use from extensive to conservation-intensive methods should be encouraged, and the upper limits of groundwater resource utilization in the Songnen Plain and Sanjiang Plain should be strictly observed. Further support for ecological economic shelter forests should be provided through funding and technology to improve reforested lands. Residents should adopt ecological lifestyles, accelerate the development of productive service industries such as eco-leisure tourism, and expand the market for green products and services.

For grassland ecological degradation areas and ecologically fragile zones, efforts should focus on the conservation of grassland and wetland resources to maintain their ecosystem functions. Utilizing the advantage of extensive wetlands in the SRB could improve the wetland protection system, promote the restoration of degraded wetlands, maintain wetland biodiversity, and improve the wetland protection and management mechanism. Simultaneously, under the premise of grassland ecological security, the protection and rational use of grassland resources should be ensured. Basic grassland protection, grass-livestock balance, and rotational grazing systems must be strictly enforced, alongside efforts to restore degraded grasslands, develop ecological restoration technologies, and implement targeted protection and restoration for various degradation types, such as desertification, salinization, and severe degradation. These measures aim to improve overall grassland vegetation cover and restore the lost ecosystem functions.

For resource-dependent, old, industrial cities and highly urbanized areas, efforts should focus on improving land use efficiency and balanced development capacity. Gradual adjustments should be made to the unreasonable land use patterns formed during the early stages of urbanization to mitigate the adverse impacts on ecosystems, optimizing urbanization development patterns to enhance the ecological carrying capacity. Coordinated planning for urbanization development and ecological restoration should be strengthened, emphasizing urban forest construction and biodiversity protection [88]. Maximizing the retention and maintenance of existing forest and lake natural ecosystems’ self-regulation, self-purification, and self-restoration capabilities is crucial to avoiding further habitat loss [89]. Both natural and artificial restoration methods should be used to restore river and lake ecosystems, and the comprehensive control of soil erosion in mining wastelands, sloping farmlands in black soil areas, and erosion gullies should be promoted. Emphasis should be placed on transforming resource-dependent industries, fostering alternative industries, accelerating the completion of resource industry chains, diversifying the single industrial structure, and forming a green industrial system aligned with sustainable development principles. Alongside economic transformation, equal emphasis should be placed on improving urban functions and environmental protection, promoting breakthrough development in both the economy and ecology, and enhancing the sustainable development capacity of resource-dependent, old, industrial cities.

The SRB’s ecosystems involve multiple stakeholders and administrative units, requiring full consideration of the regional coupling coordination characteristics to effectively coordinate the interests of various stakeholders and guide specific ecological activities. Possible interventions in the SRB include establishing effective communication and cooperation mechanisms, developing green ecological corridors to improve ecological connectivity in areas with declining ESs, strengthening the flow of ESs, and continually enhancing ecosystem functions and the ecological carrying capacity of the SRB. Future efforts should focus on coordinating economic and social development, land use, and the ecological environment, enhancing the targeting and feasibility of planning, and promoting urbanization towards a verdant, low-emission, innovation-centric, and synergistic development paradigm.

4.4. Limitations and Future Work

This study analyzed the spatiotemporal evolution characteristics of urbanization and ESs, exploring the complex coupling coordination relationship and spatiotemporal heterogeneity of their interactions. However, it does have some shortcomings. Initially, this study only selected three urbanization indicators—economic, demographic, and spatial—which have some limitations. Future research should explore more scientific methods for measuring urbanization and develop a more comprehensive urbanization assessment indicator system. Second, this study employed a static equivalent factor method to evaluate ESV, which failed to account for the spatiotemporal heterogeneity of ecosystems [90]. Although the standard equivalent factor of ESV was modified in this study according to the SRB’s NPP, the land use type data and the standard equivalent factor of ESV are still affected by data precision and errors in earlier statistical data. Thus, we mitigated the negative impact on the precision of ecosystem service valuation by preserving the proportion of changes between the data in each period through the examination of longitudinal data series. Lastly, the relatively coarse spatial resolution of the urban scale as the spatial study unit in this paper could potentially have overlooked, excluded, or distorted the interaction between urbanization and ESs at different scales. Moving forward, we should embrace a more detailed multi-scale perspective to comprehensively investigate the impact of urbanization on ESs across various scales, their coupling coordination relationships, and scale effects. This approach will facilitate the acquisition of more scientifically rigorous and comprehensive results underpinning future research on urbanization and ESs.

5. Conclusions

This study revealed the spatiotemporal evolution characteristics of urbanization and ESs in the SRB from 1985 to 2021, based on multi-source data analysis. A multidimensional urbanization evaluation index system, combined with the modified equivalence factor method and spatial autocorrelation analysis, facilitated a comprehensive analysis of the spatiotemporal evolution characteristics of urbanization and ESs in the SRB. The innovation of this study lies in its comprehensive perspective, assessing the coupling coordination relationship between urbanization and ESs in the SRB over an extended period at both the system and subsystem levels. Additionally, by employing the GTWR model, the study treated different subsystems of urbanization and ESs as explanatory variables to explore the spatiotemporal heterogeneity characteristics of their interactions. The study yielded the following conclusions:

From 1985 to 2021, the urbanization index in the SRB increased from 0.09 to 0.34. The development of urbanization underwent a significant transition from rapid growth driven by population aggregation to a high-quality growth model dominated by spatial expansion and economic development. During this period, the ESV in the SRB decreased by 88.98 × 10⁷ CNY, with all subtypes of ecosystem services, except for food production services, experiencing a decrease. Both urbanization and ESV exhibited significant spatial heterogeneity in their distributions. The decrease in ESV in the SRB was mainly attributed to the expansion of farmland and construction land, leading to the loss of forestland and grassland.

From 1985 to 2021, the CCD between urbanization and ESs in the SRB at both the system and subsystem levels showed significant improvement. The overall trend transitioned from moderately unbalanced to basically balanced, indicating an improving trajectory. Spatially, the results of this study showed that areas with high CCD values were mainly distributed in ecological functional zones and low-urbanization areas, while areas with low CCD values were primarily located in grassland ecological damage zones, ecologically fragile areas, resource-dependent, old, industrial cities, and highly urbanized areas. Most cities in the SRB remain in the transitional stage of CCD development, with significant differences among cities, posing a challenge to promoting the high-quality, coordinated development of urbanization and ESs in the SRB.

From 1985 to 2021, the impacts and trends of the various urbanization subsystems on ESs in the SRB varied, exhibiting significant spatial heterogeneity in the coefficients but maintaining a relatively stable spatial distribution pattern. The northwestern part of the SRB exhibited a higher response to urbanization changes. Conversely, all ESs subsystems showed a trend of initially strengthening and then weakening negative impacts on urbanization. The spatial distribution patterns of the positive and negative impacts of ESs subsystems on urbanization were closely correlated with the spatial distribution pattern of ESV in the SRB. The interaction between urbanization and ESs is influenced not only by resource endowment and ecological carrying capacity but also by policy and additional factors.

This study, grounded in the basin’s actual conditions, provided a practical guide for promoting high-quality urbanization development and high-level ecological protection in the SRB by elucidating the evolutionary characteristics of urbanization and ecosystem services and their coupling coordination relationship and interaction. The models employed in this study can also be applied to urbanization and ecosystem high-quality coordinated development in other similar basin regions, offering a scientific basis and decision-making reference for future national spatial planning.

Author Contributions

Conceptualization, H.B. and D.X.; methodology, H.B. and Y.C.; software, H.B. and Y.C.; validation, H.B. and S.W.; formal analysis, H.B. and R.C.; investigation, H.B. and J.F.; resources, D.X.; data curation, H.B. and H.Z.; writing—original draft preparation, H.B; writing—review and editing, H.B. and Y.C.; visualization, H.B. and S.X.; supervision, D.X.; project administration, D.X.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data available on request due to restrictions (e.g., privacy, legal or ethical reasons): The data presented in this study are available on request from the corresponding author (accurately indicate status).

Acknowledgments

The authors are particularly grateful to all researchers and institutes for providing data for this study. The authors are also very grateful to the editors and reviewers for their comments and suggestions for improving this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

View Image - Figure 1. The study area. (a) Location of SRB in China; (b) administrative divisions; (c) elevation; (d) spatial distribution of annual average NPP in the SRB; (e) land use type.

Figure 1. The study area. (a) Location of SRB in China; (b) administrative divisions; (c) elevation; (d) spatial distribution of annual average NPP in the SRB; (e) land use type.

Figure 2. Levels of different urbanization indicators.

Figure 3. Spatiotemporal change and LISA cluster of urbanization in the SRB.

Figure 4. Land use dynamics in the SRB from 1985 to 2021.

Figure 5. Spatiotemporal change and LISA cluster of ESV in the SRB.

Figure 6. The temporal changes in the CCD between urbanization and ESs in the SRB from 1985 to 2021.

Figure 7. The spatial differentiation characteristics of CCD in the SRB from 1985 to 2021.

Figure 8. Temporal heterogeneity in the interaction between urbanization and ESs.

Figure 9. Spatial heterogeneity characteristics of the impact of each subsystem of urbanization on ESs in the SRB from 1985 to 2021.

Figure 10. Spatial heterogeneity characteristics of the impact of each subsystem of ESs on urbanization in the SRB from 1985 to 2021.

Table 1

Details and sources for the datasets used in this study.

Data Name	Detail	Data Source
Administrative boundaries	Shapefile, polygon	Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn, accessed on 12 November 2023)
Digital elevation model (DEM)	Rater, 30 m
LULC data	Rater, 30 m	The Annual China Land Cover Dataset (CLCD), produced by Jie Yang and Xin Huang’s team from Wuhan University, based on Landsat images from Google Earth Engine (GEE)
Grain production data	Text, county level	China Yearbook of Agricultural Price Survey
Net primary productivity (NPP)	Rater, 500 m	The “MODIS/006/MOD17A3HGF” dataset on the Google Earth Engine (GEE) cloud computing platform (https://developers.google.cn/earth-engine/, accessed on 15 November 2023)
Socioeconomic data	Text, county level	China City Statistical Yearbook; Statistical Yearbook of four provinces: Heilongjiang, Jilin, Liaoning, and the Inner Mongolia Autonomous Region

Table 2

Comprehensive evaluation index system of the urbanization level.

Primary Index	Weight	Secondary Index	Weight
Demographic urbanization index (DUI)	0.16	Population density (persons km⁻²)	0.10
		Percentage of nonagricultural population (%)	0.04
		Percentage of employees in the secondary and tertiary sectors (%)	0.02
Spatial urbanization index (SUI)	0.35	Paved road area per capita (m²)	0.13
		Percentage of urban built-up area in the total area of a city (%)	0.12
		Building density (%)	0.10
Economic urbanization index (EUI)	0.50	GDP per capita (CNY)	0.19
		Total retail sales of consumer goods per capita (CNY)	0.18
		Night light values (cd m⁻²)	0.13

Table 3

ESV per unit area of different land use types in the SRB (CNY/hm²).

ESs		Farmland	Forestland	Grassland	Wetland	Water Body	Barren Land
Provision service (PSV)	Food production (FP)	2132.66	703.78	917.04	767.76	1130.31	42.65
	Raw materials (RM)	831.74	6355.33	767.76	511.84	746.43	85.31
	Subtotal	2964.40	7059.11	1684.80	1279.60	1876.74	127.96
Regulating service (RSV)	Gas regulation (GR)	1535.52	9213.09	3198.99	5139.71	1087.66	127.96
	Climate regulation (CR)	2068.68	8679.93	3326.95	28,897.55	4393.28	277.25
	Water regulation (WR)	1642.15	8722.58	3241.64	28,662.96	40,030.04	149.29
	Waste disposal (WD)	2964.40	3668.18	2815.11	30,710.32	31,670.01	554.49
	Subtotal	8210.74	30,283.78	12,582.70	93,410.54	77,181.00	1108.98
Support service (SSV)	Soil conservation (SC)	3135.01	8573.30	4777.16	4244.00	874.39	362.55
	Biodiversity maintenance (BM)	2175.31	9618.30	3988.08	7869.52	7315.03	853.06
	Subtotal	5310.33	18,191.60	8765.24	12,113.51	8189.42	1215.62
Cultural service (CSV)	Aesthetic landscape provision (ALP)	362.55	4435.93	1855.41	10,002.18	9469.01	511.84
Total ESV		16,848.02	59,970.42	24,888.15	116,805.83	96,716.17	2964.40

Table 4

The standard of the coupling coordination degree.

Range of D Value	Type of CCD
0.8 < D ≤ 1	Highly balanced
0.6 < D ≤ 0.8	Moderately balanced
0.4 < D ≤ 0.6	Basically balanced
0.2 < D ≤ 0.4	Moderately unbalanced
0 < D ≤ 0.2	Seriously unbalanced

Table 5

ESV changes in the SRB from 1985 to 2021 (↑ indicates an increase in ESV; ↓ indicates a decrease in ESV).

ESs		ESV (10⁷ CNY)
		1985	1990	1995	2000	2005	2010	2015	2021
PSV	FP	73.70	74.05	75.43	75.18	74.28	73.84	74.68	74.85
	RM	180.55	180.77	178.48	176.09	175.98	176.20	174.05	174.13
	Subtotal	254.24	254.82	253.91	251.27	250.26	250.04	248.73	248.98
RSV	GR	280.42	279.73	274.82	271.99	272.47	272.54	268.51	267.90
	CR	287.14	285.41	279.26	275.97	276.10	276.04	272.71	272.18
	WR	304.65	303.04	295.45	288.47	289.16	290.60	289.54	289.48
	WD	204.00	202.66	199.06	194.68	194.24	194.75	196.14	196.25
	Subtotal	1076.20	1070.85	1048.60	1031.10	1031.97	1033.92	1026.90	1025.81
SSV	SC	310.30	309.51	305.55	303.43	303.59	303.14	299.62	298.73
	BM	314.93	314.04	308.66	305.24	305.72	305.83	302.09	301.38
	Subtotal	625.23	623.56	614.21	608.67	609.31	608.97	601.70	600.11
CSV	ALP	135.74	134.83	130.91	128.67	129.31	129.71	127.93	127.53
Total ESV		2091.42	2084.05	2047.62	2019.71	2020.86	2022.64	2005.26	2002.44
ESs		Change rate (%)
		1985–1990	1990–1995	1995–2000	2000–2005	2005–2010	2010–2015	2015–2021	1985–2021
PSV	FP	0.48 ↑	1.86 ↑	−0.33 ↓	−1.20 ↓	−0.59 ↓	1.14 ↑	0.23 ↑	1.57 ↑
	RM	0.12 ↑	−1.27 ↓	−1.34 ↓	−0.06 ↓	0.12 ↑	−1.22 ↓	0.05 ↑	−3.55 ↓
	Subtotal	0.23 ↑	−0.36 ↓	−1.04 ↓	−0.40 ↓	−0.09 ↓	−0.52 ↓	0.10 ↑	−2.07 ↓
RSV	GR	−0.24 ↓	−1.76 ↓	−1.03 ↓	0.18 ↑	0.02 ↑	−1.48 ↓	−0.23 ↓	−4.46 ↓
	CR	−0.60 ↓	−2.15 ↓	−1.18 ↓	0.05 ↑	−0.02 ↓	−1.21 ↓	−0.19 ↓	−5.21 ↓
	WR	−0.53 ↓	−2.50 ↓	−2.36 ↓	0.24 ↑	0.50 ↑	−0.36 ↓	−0.02 ↓	−4.98 ↓
	WD	−0.65 ↓	−1.78 ↓	−2.20 ↓	−0.22 ↓	0.26 ↑	0.72 ↑	0.06 ↑	−3.80 ↓
	Subtotal	−0.50 ↓	−2.08 ↓	−1.67 ↓	0.08 ↑	0.19 ↑	−0.68 ↓	−0.11 ↓	−4.68 ↓
SSV	SC	−0.26 ↓	−1.28 ↓	−0.69 ↓	0.05 ↑	−0.15 ↓	−1.16 ↓	−0.30 ↓	−3.73 ↓
	BM	−0.28 ↓	−1.71 ↓	−1.11 ↓	0.16 ↑	0.03 ↑	−1.22 ↓	−0.23 ↓	−4.30 ↓
	Subtotal	−0.27 ↓	−1.50 ↓	−0.90 ↓	0.11 ↑	−0.06 ↓	−1.19 ↓	−0.26 ↓	−4.02 ↓
CSV	ALP	−0.68 ↓	−2.91 ↓	−1.71 ↓	0.50 ↑	0.31 ↑	−1.37 ↓	−0.31 ↓	−6.05 ↓
Total ESV		−0.35 ↓	−1.75 ↓	−1.36 ↓	0.06 ↑	0.09 ↑	−0.86 ↓	−0.14 ↓	−4.25 ↓

Table 6

Explanation of the GTWR model parameters.

DependentVariable	R²	Bandwidth	ResidualSquares	Sigma	AIC_C	SpatiotemporalDistance Ratio
Urbanization	0.9234	0.1150	0.5331	0.0488	−528.4480	0.5418
Ecosystem Services	0.9782	0.1221	0.1842	0.0287	−658.2960	0.2731

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Rapid urbanization in the Songhua River Basin (SRB), a crucial ecological barrier in China and Northeast Asia, has led to the degradation of ecosystem service functions and a decline in their value, thereby posing a significant threat to regional ecological security. Clarifying the complex coupling coordination relationship between urbanization and ecosystem services (ESs) and identifying the spatiotemporal heterogeneity of their interactions will facilitate the high-quality and coordinated development of urbanization and ESs in the SRB. This study employed a systems approach, treating urbanization and ESs as overarching systems and delineating different aspects of urbanization and ecosystem service functions as subsystems within these systems. The spatiotemporal characteristics of urbanization and the ecosystem service value (ESV) in the SRB from 1985 to 2021 were revealed. The coupling coordination relationship and the spatiotemporal heterogeneity of the interactions between urbanization and ESs in the SRB at both the system and subsystem levels were analyzed using the coupling coordination degree (CCD) model and the spatiotemporal geographically weighted regression (GTWR) model. The findings indicated that during the study period: (1) The urbanization index of SRB rose from 0.09 to 0.34, while the ESV experienced a decrease from 2091.42 × 10⁷ CNY to 2002.44 × 10⁷ CNY. (2) The coupling coordination degree (CCD) between urbanization and ESs in the SRB at both the system and subsystem levels increased significantly, generally transitioning from the moderately unbalanced to the basically balanced stage. Areas with high CCD values were mainly distributed in ecological function areas and low-level urbanized areas, while areas with low CCD values were mainly distributed in grassland ecological degradation areas, ecologically fragile areas, resource-dependent old industrial cities, and highly urbanized areas. (3) The subsystems of urbanization had an overall negative impact on Ess, with varying trends, but the spatial distribution pattern of the interactions remained relatively stable. Conversely, the subsystems of ESs all exhibited a trend of initially strengthening and then weakening their negative impacts on urbanization, and the spatial distribution pattern was highly correlated with the spatial distribution pattern of ESV in the SRB.

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Title

Coupling Coordination Relationship and Spatiotemporal Heterogeneity between Urbanization and Ecosystem Services in the Songhua River Basin

Author

He, Bai¹; Chen, Yuanyuan¹; Wang, Shaohan¹; Chu, Rui¹; Fang, Jiyuan¹; Zhang, Huina¹; Shuhan Xing¹; Wang, Lei¹

; Xu, Dawei¹

¹ College of Landscape Architecture, Northeast Forestry University, Harbin 150040, China; [email protected] (H.B.); ; Key Lab for Garden Plant Germplasm Development & Landscape Eco-Restoration in Cold Regions of Heilongjiang Province, Harbin 150040, China

First page

938

Publication year

2024

Publication date

2024

Publisher

MDPI AG

e-ISSN

2073445X

Source type

Scholarly Journal

Language of publication

English

DOI

https://doi.org/10.3390/land13070938

ProQuest document ID

3084927434

Coupling Coordination Relationship and Spatiotemporal Heterogeneity between Urbanization and Ecosystem Services in the Songhua River Basin

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