Quantifying the Relationship Between Blue–Green Landscape Spatial Patterns and Carbon Storage: A Case Study of theZhengzhou Metropolitan Area

Abstract

Against the backdrop of global warming and the urgent demand for sustainable development, blue–green spaces (BGSs) play a vital role in carbon reduction and sequestration, yet the multi-scale spatial mechanisms by which blue–green space patterns (BGSPs) regulate carbon storage (CS) remain unclear. Taking the Zhengzhou Metropolitan Area as the study area, this research clarifies the BGSP-CS correlations at both class and landscape levels and quantifies their spatial interaction mechanisms, providing scientific support for integrated BGS planning that aligns with sustainable development objectives. Using the InVEST model coupled with regional carbon density correction, the total CS of the area is estimated at 1112.27 × 10⁶ t. Spearman’s correlation analysis shows that at the class level, area–edge and shape complexity indicators (e.g., Landscape Shape Index, LSI: r = −0.427) are negatively correlated with CS, while connectivity indicators exert no significant effect. At the landscape level, Shannon’s Diversity Index (SHDI: r = −0.635) and area–edge indicators inhibit CS, whereas Shannon’s Evenness Index (SHEI: r = 0.602), Largest Patch Index (LPI: r = 0.618) and shape complexity indicators exert positive effects. A comparative analysis of three regression models reveals that the multi-scale geographically weighted regression (MGWR) model outperforms the ordinary least squares (OLS) and geographically weighted regression (GWR) models, with R² values of 0.505 (class level) and 0.484 (landscape level). It effectively captures the “west–strong and east–weak” spatial heterogeneity of BGSP impacts on CS. This study identifies key BGSP indicators regulating CS and their spatial mechanisms, providing scientific support for integrated BGS planning, regional carbon sink enhancement, the achievement of “dual carbon” goals, and the promotion of sustainable development in metropolitan areas. Future research may optimize model parameters through field surveys and explore the coupling mechanism between BGSPs, land surface temperature and CS to better align BGS management with sustainable development agendas.

1. Introduction

Human activities have exacerbated global warming [1], posing a severe threat to global sustainable development and prompting the implementation of international and domestic measures focused on climate action and ecological protection. Urban areas are a major factor contributing to climate change, as cities, occupying less than 2% of the Earth’s surface, consume 78% of the world’s energy and emit over 60% of greenhouse gases. As a result, cities and metropolitan areas are crucial carriers for achieving sustainable development and “dual carbon” goals. Blue–green spaces (BGSs), consisting of green plant areas and blue water bodies, form an interconnected whole that not only serves as the foundation for urban ecological networks but also plays a significant role in enhancing urban resilience and reducing carbon emissions [2]. Their landscape patterns are closely linked to carbon sink capacity, which has become a focus of current research. Existing studies on BGSs and carbon sinks have formed two main research strands: one focusing on the carbon sequestration capacity of single BGS components (e.g., forests, rivers), and the other exploring the synergistic carbon sink effects of integrated BGS systems. However, there is still no consensus on the optimal landscape configuration of BGSs to maximize carbon sink efficiency, and most studies lack comparative analysis across different urbanization levels [3,4]. As a complex and diverse ecosystem, BGS provides multiple ecological benefits, such as stormwater regulation [5], urban heat island mitigation [6], and carbon sequestration enhancement [7]. Green spaces include agricultural land, mountains, forests, grasslands, parks, and other vegetated areas, while blue spaces encompass rivers, lakes, reservoirs, ponds, beaches, and other water bodies [8]. Landscape patterns generally refer to their spatial arrangements, reflecting the interactions among different landscape patches, corridors, and matrices. In this study, BGSP is divided into two analytical dimensions: internal pattern (class level, structural characteristics of a single BGS type) and overall pattern (landscape level, comprehensive structure of all BGS types). These patterns not only embody the heterogeneity of landscapes but also represent the results of various ecological processes at different scales. In the context of the “dual carbon” goals, urban BGSs serve as a critical means for carbon reduction. Specifically, the integration of BGSs into urban development has been increasingly emphasized to strengthen carbon storage (CS) capacity, aligning with the country’s focus on ecological civilization and low-carbon urbanization. Accordingly, this study is highly aligned with such development trends, as it explores the link between BGS landscape patterns and carbon storage capacity to provide practical insights for Chinese cities. Research suggests that green spaces are primary carriers of carbon sequestration, capable of absorbing one-quarter of urban carbon dioxide emissions annually [9]. Simultaneously, BGS serves as a significant carbon sink, absorbing and storing carbon.

CS mainly includes aboveground biomass CS, belowground biomass CS, soil organic CS, and dead organic matter CS [10]. These are important indicators of urban ecosystem service functions. Accurately estimating CS within a region and exploring its spatial distribution patterns and influencing factors is crucial for enhancing and managing the carbon sink function of urban ecosystems. Traditional methods for estimating CS, such as soil type analysis, life zones, plot surveys, and biomass measurements, are relatively accurate and widely used [11]. However, these methods are resource-intensive, requiring substantial human effort, materials, and time. Additionally, they are not well-suited for studying long-term, large-scale changes in CS and their influencing mechanisms. Model-based CS estimation methods can effectively address the shortcomings of traditional methods. These methods offer advantages such as ease of operation, wide applicability, extensive information, and cost-effectiveness [12], making them suitable for large-scale CS estimation. They can be divided into process-based and non-process-based modeling methods. Process-based modeling methods excel in simulating small areas but require numerous parameters, limiting their applicability in larger regions [13]. In contrast, non-process-based modeling methods require fewer parameters and are thus more suitable for large-scale studies [11]. The InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) model is one of the most widely used non-process-based models, primarily driven by land use data. It can effectively assess CS across various ecosystem types based on large-scale land use changes [14].

In the overall strategy for achieving carbon neutrality, many countries are enhancing their efforts to reduce and store carbon in urban areas. Adjusting the morphological structure and layout of BGS has become an important means of reducing carbon emissions and increasing CS in the era of stock. The Fragstats program-based landscape pattern quantification method [15] has been widely applied to assess BGS impacts on climate change. Ran et al. [16] found that the high fragmentation and reduced connectivity of BGS increase urban vulnerability to climate change impacts. Han [17] studied morphological changes in green spaces in South Korea and their effects on CS, revealing that the reduction of green space area and patch fragmentation are associated with decreased carbon sequestration. Yuan et al. [18] demonstrated that increasing the area and connectivity of BGS benefits carbon sink capacity, while separation and shape indices negatively impact this capacity. By quantifying various landscape indices of different types of BGSs, they provided a basis for optimizing BGS systems. For example, a study by Zhang [19] highlighted the spatiotemporal impacts of BGSs on carbon emissions in China’s Yangtze River Delta. Three aspects of existing research need deepening. First, existing studies on BGS carbon sinks mostly focus on single green or blue space types, lacking systematic comparisons between the internal patterns of individual BGS patches (class level) and the overall patterns of integrated BGS systems (landscape level), leading to an incomplete understanding of BGS’s multi-scale carbon storage regulation [20]. Second, while spatial statistical methods like GWR have been applied to analyze the BGS-CS relationship, the MGWR model is underutilized to explore the scale-dependent effects of landscape pattern indices [21]. Third, existing BGS optimization strategies are mostly universal, lacking integration with the spatial heterogeneity of BGS patterns’ impacts on CS, restricting their precision and practical value in regional planning [3]. Overall, existing studies mainly focus on the relationship between green spaces and carbon sinks, with limited research on the inter-regional correlations of BGS morphology and layout with CS, especially considering their spatial heterogeneity. Traditional regression analysis methods have been widely used to explore the relationship between BGS and CS. However, these methods assume a uniform relationship among all observation points, overlooking heterogeneity in geographical space, which may lead to inaccuracies and limitations in interpretation of results. To overcome this limitation, spatial statistical methods such as geographic weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) have been developed. GWR introduces a spatial weight matrix to conduct regression analysis at each geographical location, capturing the local relationships between variables [22]. This approach enables researchers to reveal regional differences in the impact of BGSPs on carbon sinks. However, GWR is limited by its reliance on a single spatial scale for analysis. MGWR addresses this limitation by assigning different spatial scales to different explanatory variables, allowing for a more precise capture of the complex spatial relationships and enhancing the accuracy and interpretability. This method is commonly used to address the spatial influence of factors [23]. This study innovatively adopts the MGWR model to analyze the scale-dependent effects of BGSPs on CS, and combines the class–landscape scale dual-dimensional pattern quantification method to establish a theoretical framework for carbon sink regulation of BGSs suitable for northern plain-type metropolitan areas, addressing the limitations of single-method approaches and insufficient generalizability in existing research. Therefore, this study utilized the InVEST model to estimate CS within the research area and quantified the landscape patterns of urban BGS. We further analyzed the spatial heterogeneity of the effects of different explanatory variables at different spatial scales using both GWR and MGWR. With the aim of optimizing urban BGS to CS in urban ecosystems, the specific research objectives are as follows: (1) to quantify the correlation between the overall and internal landscape patterns of BGS and CS in the Zhengzhou Metropolitan Area, along with key indicators; (2) to analyze the spatial heterogeneity of the impacts of different explanatory variables at different spatial scales using MGWR and other methods; and (3) to propose planning methods aimed at enhancing CS through improved urban BGS, providing references for urban planning and resource management practices.

2. Materials and Methods

2.1. Study Area

The Zhengzhou Metropolitan Area, located in central China in the middle and lower reaches of the Yellow River, is an urban functional region composed of closely connected cities such as Zhengzhou, Kaifeng, Xuchang, Xinxiang, Jiaozuo, Luoyang, Pingdingshan, and Luohe. In June 2024, the “Zhengzhou Metropolitan Area Development Plan” was officially released. As the tenth national-level metropolitan area in China, Zhengzhou features a predominantly plain topography. As shown in Figure 1, the varying levels of development among cities within the Zhengzhou Metropolitan Area have led to distinct landscape patterns. Undergoing a rapid urbanization transition, it has diverse BGSs and distinct gradient differentiation of BGSPs, serving as an ideal carrier for analyzing the correlation mechanism between BGSPs and CS at class and landscape levels. As the core of Henan Province, the relationship between CS and the landscape patterns of urban BGSs in the Zhengzhou metropolitan area is of significant importance for achieving the “dual carbon” goals.

2.2. Data Sources

The 30 m spatial resolution land use data for the Zhengzhou Metropolitan Area in 2022 was obtained from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (CAS). Based on the definition and classification of BGSs [23], the ArcMap reclassification tool in ArcGIS 10.8 was used to classify arable land, forest land, and grassland as green spaces, water bodies as blue spaces, and construction land and unused land as non-BGSs. This resulted in a distribution map of urban BGS, as presented in Figure 2, which served as the foundational dataset for further BGSP analysis.

Carbon density data are a crucial input for the InVEST model. In this study, a carbon density correction method was used to determine the carbon density of the study area. First, comprehensive carbon density data for the entire country were obtained by referencing relevant studies [24,25,26,27], as shown in

Table 1. Carbon density of various parts of different land use types in China (t·hm⁻²).

Land Use Type	Aboveground Carbon Storage	Belowground Carbon Storage	Soil Organic Carbon Storage
Cropland	5.7	80.7	108.4
Forest	42.4	115.9	158.8
Grassland	35.3	86.5	99.9
Water	3	0	0
Construction land	2.5	0	78
Unused land	1.3	0	31.4

.

The national and Henan Province average temperature and precipitation data used in this study were sourced from the National Meteorological Information Center–China Meteorological Data Service Center (https://data.cma.cn/).

2.3. Data Analysis Procedures

This study comprised four main steps—data collection and processing, data calculation, data analysis, and optimization recommendations—as illustrated in the following flowchart (Figure 3).

2.4. CS Calculation in the Study Area

In this study, the CS within the study area was estimated using the carbon module of the InVEST model. The total CS was divided into four basic carbon pools: aboveground biomass carbon pool, belowground biomass carbon pool, soil organic carbon pool, and dead organic matter carbon pool. The formula is as follows:

$$C_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}i}=\sum _{j=1}^n{A}_{ij}\times \left(C_{aj}+C_{bj}+C_{cj}+C_{dj}\right)$$

(1)

where $C_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}i}$ represents the total CS (t); $A_{ij}$ is the area of each land use type j (hm²); $C_{aj}$, $C_{bj}$, $C_{cj}$, and $C_{dj}$ are the aboveground biomass carbon density, belowground biomass carbon density, soil carbon density, and dead organic matter carbon density (t·hm²) corresponding to each land use type j; and n is the number of land use types. Soil organic carbon density in the large-scale study area is controlled by multiple factors (e.g., soil texture, topography, human activities) beyond precipitation, and systematic in situ sampling across the region is restricted by manpower and material resources; the subsequent regional correction process effectively offsets the model deviation, thus ensuring simulation reliability.

2.5. Carbon Density Correction

Carbon density, defined as the CS per unit of land use area, is a crucial input parameter in the CS calculation of the InVEST model. Carbon density varies with changes in climate, soil properties, and land use types. Regional differences in these factors can significantly affect the final CS calculation, necessitating a correction. Both aboveground and belowground carbon densities are positively correlated with annual precipitation and average annual temperature, while soil carbon density is positively correlated with annual precipitation. The carbon density for various land use types in the Zhengzhou Metropolitan Area was corrected using the methods proposed by Alam [26], Giardina [24], and Chen Guangshui [27], as shown in Equations (2)–(4).

$$C_{SP}=3.3968\times P+3996.1 \left(R^2=0.11\right)$$

(2)

$$C_{BP}=6.798e^{0.0054P}\left(R^2=0.70\right)$$

(3)

$$C_{BT}=28\times T+398 \left(R^2=0.47,P<0.01\right)$$

(4)

where $C_{SP}$ represents the soil carbon density (t·hm²), calculated based on the average annual precipitation; $C_{BP}$ and $C_{BT}$ represent the biomass carbon densities (t·hm²), calculated based on the average annual precipitation and average annual temperature; $P$ is the average annual precipitation (mm); and $T$ is the average annual temperature (°C).

By substituting the average annual precipitation and temperature values for Henan Province and China into the above formulas (where the average annual temperature in Henan Province in 2022 was 15.8 °C, and the average annual precipitation was 621.7 mm; the average annual temperature in China in 2022 was 10.5 °C, and the average annual precipitation was 606.1 mm), the correction coefficient can be obtained. The carbon density data for Henan Province is then calculated by multiplying the national carbon density data by the correction coefficient. The specific formula is as follows:

$$K_{BP}={\displaystyle \frac{C_{BP}^{\prime }}{C_{BP}^{″}}}$$

(5)

$$K_{BT}={\displaystyle \frac{C_{BT}^{\prime }}{C_{BT}^{″}}}$$

(6)

$$K_B={\displaystyle \frac{K_{BP}}{K_{BT}}}$$

(7)

$$K_S={\displaystyle \frac{C_{SP}^{\prime }}{C_{SP}^{″}}}$$

(8)

where $K_B$ and $K_S$ are the soil carbon density correction coefficient and biomass carbon density correction coefficient for Henan Province, respectively.$K_{BP}$ and $K_{BT}$ are the correction coefficients for the precipitation and temperature factors affecting biomass carbon density. $C_{BP}^{\prime }$ is the biomass carbon density value for Henan Province, calculated based on average annual precipitation. $C_{BT}^{\prime }$ is the biomass carbon density value for Henan Province, calculated based on the average annual temperature. $C_{SP}^{\prime }$ is the soil carbon density value for Henan Province, calculated based on average annual precipitation. $C_{BP}^{″}$ is the biomass carbon density value for China, calculated based on average annual precipitation. $C_{BT}^{″}$ is the biomass carbon density value for China, calculated based on the average annual temperature. $C_{SP}^{″}$ is the soil carbon density value for China, calculated based on average annual precipitation. After calculating the carbon density correction coefficients and applying them to the national carbon density values, the carbon density values for Henan Province are obtained (

Table 2. Revised carbon intensity parameter of Henan Province (t·hm⁻²).

	Land Use Type	Aboveground Carbon Storage	Belowground Carbon Storage	Soil Organic Carbon Storage
1	Cropland	5.75	81.41	111.23
2	Forest	42.77	116.91	116.47
3	Grassland	35.61	87.26	101.58
4	Water	3.03	0	0
5	Construction land	2.52	0	79.31
6	Unused land	1.31	0	31.93

).

2.6. Quantification of BGSPs

The landscape pattern of the blue–green system refers to the spatial configuration of urban blue spaces, green spaces, and non-BGSs at various scales. Landscape scales are quantitative indicators reflecting the characteristics of the urban blue–green system and can be divided into three levels: patch level, class level, and landscape level [28]. Patch level primarily reflects the characteristics of individual patches, forming the basis for calculating other landscape-level indicators and focusing on the specific attributes and morphology of individual patches. Class level reflects the structural characteristics of different patch types, examining the distribution and attributes of various patch types within the landscape, analyzing their interactions and relationships, as well as their contributions to the overall functionality of the landscape (

Table 3. Class level of the blue–green spatial pattern.

Category	Metrics	Abbreviations	Formula	Descriptions
Area–edge	Percentage of Landscape	PLAND	$PLAND={\sum }_{j=1}^n{a}_{ij}$	The proportion of a specific patch type within the entire landscape
	Edge Density	ED	$ED=[{\sum }_{j=1}^{n_i}{e}_{ij}/A]\times 1000$	The length of edges per unit area in a landscape
Shape complexity	Landscape Shape Index	LSI	$LSI={\displaystyle \frac{0.25{\sum }_{k=1}^m{e}_{ik}}{\sqrt{A}}}$	The ratio between the actual landscape edge length and the assumed minimum edge length
	Area-Weighted Patch Fractal Dimension	FRAC-AM	$FRAC-AM={\sum }_{i=1}^m{\sum }_{j=1}^n\left({\displaystyle \frac{2\ln \left(0.25P_{ij}\right)}{\ln \left(a_{ij}\right)}}\left({\displaystyle \frac{a_{ij}}{A}}\right)\right)$	The degree of shape complexity of patches in a landscape
Aggregation	Aggregation Index	AI	$AI=\left[{\sum }_{i=1}^m\left({\displaystyle \frac{g_{ii}}{max\to g_{ii}}}\right)P_i\right]\times 100$	The aggregation or clumping of patches in a landscape
	Landscape Division Index	DIVISION	$DIVISION=\left(1-{\sum }_{i=1}^m{\sum }_{j=1}^n\left({\displaystyle \frac{a_{ij}}{A}}\right)\right)$	The degree to which a landscape is subdivided into separate patches
Connectivity	Connectance Index	CONNECT	$CONNECT=\left[{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{j\ne k}^n{e}_{ijk}}{{\sum }_{i=1}^m\left(\frac{n_i\left(n_i-1\right)}{2}\right)}}\times \left(100\right)\right]$	The degree of connectivity between patches in a landscape

). Landscape level reflects the overall structural characteristics of the landscape, focusing on the composition, spatial configuration, and dynamic changes in the entire landscape system. It is used to assess the heterogeneity, diversity, fragmentation degree, and spatial relationships between landscape elements, which are crucial for understanding the ecological processes and functions of the landscape [29] (

Table 4. Landscape level of the blue–green spatial pattern.

Category	Metrics	Abbreviations	Formula	Descriptions
Area–edge	Edge Density	ED	$ED=[{\sum }_{j=1}^{n_i}{e}_{ij}/A]\times 1000$	The length of edges per unit area in a landscape
	Largest Patch Index	LPI	$LPI={\displaystyle \frac{max\left(a_{ij}\right)}{A}}$	The length of edges per unit area in a landscape
Shape complexity	Patch Cohesion Index	CONHESION	$CONHESION=$ $\left[1-{\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^m{P}_{ij}}{{\sum }_{i=1}^n{\sum }_{j=1}^m{P}_{ij}\sqrt{a_{ij}}}}\right]\times {\left[1-{\displaystyle \frac{1}{\sqrt{z}}}\right]}^{-1}\times \left(100\right)$	The physical connectedness of patches within a landscape
	Contagion Index	CONTAG	$CONTAG=1+{\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^m{P}_{ij}\ln \left(P_{ij}\right)}{2\ln \left(n\right)}}$	The degree to which different patch types are aggregated or clumped in a landscape.
Connectivity	Connectance Index	CONNECT	$CONNECT=\left[{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{j\ne k}^n{e}_{ijk}}{{\sum }_{i=1}^m\left(\frac{n_i\left(n_i-1\right)}{2}\right)}}\times \left(100\right)\right]$	The degree of connectivity between patches in a landscape
Diversity	Shannon’s Diversity Index	SHDI	$SHDI={\displaystyle \frac{-{\sum }_{i=1}^m\left(P_i\times \ln P_i\right)}{\ln m}}$	The diversity of patch types within a landscape
	Shannon’s Evenness Index	SHEI	$SHEI={\displaystyle \frac{SHDI}{\ln m}}$	The evenness of the distribution of patch types within a landscape

). In this study, the moving window method was used to assess landscape pattern indicators, allowing for the characterization of spatial dynamic changes in these indicators. This method considers scale effects [18]. The internal and overall patterns of BGSs are quantified separately at the class level and landscape level. The selected indicators are categorized into five types: area–edge, shape complexity, aggregation, connectivity, and diversity [18]. The parameter settings were configured to align with ecological processes and ensure quantification accuracy: the moving window size was set to 500 m × 500 m, matching the sample point spacing and the dominant patch scale of local blue–green spaces to effectively capture vegetation growth and soil carbon accumulation processes; the Gaussian kernel function was adopted for MGWR analysis to fit the continuous spatial heterogeneity of carbon storage and reduce extreme value interference; the raster resolution was uniformly set to 30 m, consistent with the input land use data, to accurately identify small blue–green space patches such as urban pocket parks and linear rivers; and bandwidth optimization was implemented via Akaike Information Criterion (AICc) minimization to determine the optimal bandwidth for each explanatory variable, thereby enhancing the model’s capacity to reflect spatial non-stationarity between blue–green space patterns and carbon storage.

2.7. Data Analysis

2.7.1. Sample Point Generation

In this study, 3000 sample points were randomly generated within the Zhengzhou Metropolitan Area using ArcGIS 10.4, ensuring that no sample points were located within a 500 m radius of each other. This 500 m minimum spacing matches the 500 m × 500 m moving window for BGSP quantification, adapts to the 100–500 m dominant patch scale of local BGS, and ensures sample independence by avoiding spatial overlap between adjacent sampling units (Figure 4). The landscape pattern indices and CS values were then extracted and associated with the corresponding sample points using the extraction tool. The extracted data were compiled into an attribute table linking the landscape pattern indices and CS in the study area. This table was subsequently exported to Excel for further analysis.

2.7.2. Correlation Analysis

In this study, SPSS 26.0 software was used to conduct a Spearman correlation analysis of landscape patterns and CS of urban BGSs in the study area at both the class level and landscape level. The correlation coefficient R represents the strength of the relationship between BGSP indices and carbon storage data, ranging from −1 to 1. An R value between −1 and 0 indicates a negative correlation. Specifically, an absolute value of the correlation coefficient R greater than 0.5 indicates a strong correlation; a value between 0.5 and 0.3 indicates a moderate correlation; a value between 0.3 and 0.1 indicates a weak correlation; and an R value less than 0.1 is generally considered to indicate weak or no correlation [30].

2.7.3. Regression Analysis

This study employed three regression methods—Ordinary Least Squares (OLS), GWR, and MGWR—to investigate the relationship between urban BGSP indices and CS.

OLS is a classic linear regression method used to analyze the impact of one or more independent variables on a dependent variable [31]. GWR builds upon the classic linear regression model by accounting for the influence of spatial relationships on the regression model [32]. However, GWR is limited by the assumption that all variables share the same optimal bandwidth, typically reflecting the average of the best bandwidth across all independent variables. In contrast, MGWR allows each independent variable to have its own bandwidth, thereby addressing the limitations of GWR. Compared to traditional GWR, MGWR enables each variable to exhibit distinct spatial smoothing levels, addressing the limitations of the GWR model [16], more accurately represents the true and useful spatial processes, and results in better model performance.

In this study, the performance of the three regression methods (OLS, GWR, and MGWR) is compared using R², Adj. R², and the corrected AICc. A higher R² or adjusted R² value and a lower AICc indicate an improved regression model.

3. Results

3.1. Correlation Quantification Between BGSP Indices and CS

As shown in Figure 5, based on the specific CS values calculated by the InVEST model, we computed the CS of each grid within the study area, with the total CS of the Zhengzhou Metropolitan Area determined to be approximately 1112.27 × 10⁶ t. Carbon storage high-value areas concentrate in the forested regions of southern Luoyang and western Pingdingshan, while low-value areas are in the built-up zones of Zhengzhou and Xuchang, showing significant spatial differentiation. The results of the quantified BGSPs are presented in Figure 6 and Figure 7.

Table 5. Spearman’s correlations between CS and class-level indices. Note: ** p < 0.01 (two-tailed).

	Indicators	PLAND	LSI	FRAC_AM	ED	DIVISION	CONNECT	AI
CS	Spearman	−0.129 **	−0.427 **	−0.297 **	−0.344 **	0.132 **	−0.199 **	−0.084 **
	Sig.	0.000	0.000	0.000	0.000	0.000	0.000	0.000

shows that at the class level, the correlations between BGSP indices and carbon storage (CS) exhibit multidimensional variations. In the area–edge dimension, PLAND shows a weak negative correlation with CS (∣R∣ = 0.129, p < 0.001). This might be because cropland, as a dominant low-carbon vegetation patch type, suppresses overall carbon storage when its area expands; ED demonstrates a moderate negative correlation (∣R∣ = 0.331, p < 0.001); and high edge density exacerbates landscape fragmentation, triggering microclimate changes and the loss of core habitats, thereby hindering the growth of high-carbon vegetation. Notably, the inhibitory effect of LSI (|R| = 0.427) on CS is stronger than that of FRAC_AM (|R| = 0.297). More intact and stable ecological spaces are more conducive to carbon storage; FRAC_AM indicates a weak negative correlation (∣R∣ = 0.297, p < 0.001). This suggests that complex and fragmented patch boundaries are highly susceptible to urban encroachment. In the aggregation dimension, CONNECT has weak negative correlations with CS (∣R∣ = 0.199, p < 0.001); DIVISION has weak negative correlations with CS (∣R∣ = 0.132, p < 0.001). This indicates that high separation reduces connectivity between patches in the landscape, hinders the flow of matter and energy between ecosystems, and impacts carbon storage, while AI shows no correlation.

Table 6. Spearman’s correlations between CS and landscape-level indices. Note: ** p < 0.01 (two-tailed).

	Indicators	SHDI	SHEI	LPI	ED	CONTAG	CONNECT	COHESION
CS	Spearman	−0.635 **	0.602 **	0.618 **	−0.616 **	0.342 **	−0.150 **	0.588 **
	Sig.	0.000	0.000	0.000	0.000	0.000	0.000	0.000

shows that at the landscape level, on the area–edge dimension, the strong negative correlation of ED (|R| = 0.616, p < 0.0001) highlights fragmentation’s inhibitory effect on carbon storage, whereas the strong positive correlation of LPI (|R| = 0.618, p < 0.0001) arises from large contiguous patches (e.g., intact forests) preserving soil carbon pools and stable microclimates. Compared with the class level, the inhibitory effect of ED on CS at the landscape level is significantly enhanced. In the shape complexity dimension, COHESION shows a strong positive correlation with CS (∣R∣ = 0.558, p < 0.0001). This further demonstrates that the synergistic integration of blue–green spaces enhances carbon storage capacity, while CONTAG (∣R∣ = 0.342, p < 0.0001) reflects moderate benefits from aggregated patches resisting disturbances. In the connectivity dimension, CONNECT has a weak correlation with CS (∣R∣ = 0.150, p < 0.0001). In the diversity dimension, the strong negative correlation of SHDI (|R| = 0.635, p < 0.0001) implies that anthropogenic patch diversity degrades carbon sinks, whereas SHEI (|R| = 0.602, p < 0.0001) exhibits a strong positive correlation by balancing patch distributions to enhance functional complementarity (e.g., forest–wetland synergies). Exceptions include CONNECT (|R| = 0.150, p < 0.0001), whose weak negative correlation may stem from pest-driven collapses in monoculture plantations.

3.2. Comparative Analysis of Model Regression Results

The results show that the MGWR model performs better than the other models in several key indicators.

Table 7. Regression results of models.

Level Name	Indicators	OLS	GWR	MGWR
class	R2	0.256	0.468	0.505
	Adj. R2	0.254	0.425	0.447
	AICc	7051.910	6556.764	6535.135
landscape	R2	0.383	0.391	0.484
	Adj. R2	0.183	0.339	0.414
	AICc	7663.153	7307.537	7124.151

shows that at the class level: The R² value of MGWR is 0.505, and the adjusted R² is 0.447, which is higher than that of GWR (with R² = 0.468, adjusted R² = 0.425) and OLS (with R² = 0.256, adjusted R² = 0.254). The AICc of MGWR is 6535.135, lower than that of GWR (AICc = 6453.154) and OLS (AICc = 7051.910). At the landscape level, the R² value of MGWR is 0.484, and the adjusted R² is 0.414, which is higher than that of GWR (with R² = 0.391, adjusted R² = 0.339) and OLS (with R² = 0.183, adjusted R² = 0.181). The AICc of MGWR is 7124.151, lower than that of GWR (AICc = 7307.537) and OLS (AICc = 7663.153).

In summary, these results demonstrate that the MGWR model exhibits superior regression performance compared to the GWR and OLS models, effectively capturing the relationship between BGSP indices and CS.

3.3. Spatial Heterogeneity in the Impact of BGSP on CS

This study selected four landscape pattern indices—LSI, ED, FRAC_AM, and CONNECT—that have a high correlation with CS at the class level and visualized their MGWR coefficients spatially. The specific implementation steps are outlined as follows: First, all regression coefficients corresponding to the multi-scale geographically weighted regression (MGWR) model are calculated; second, the generated calculation results are imported into Excel software to screen out variables suitable for subsequent spatial visualization; third, the natural breaks classification method embedded in ArcGIS 10.8 is adopted to categorize the regression coefficients into five distinct levels, thereby realizing the spatial visualization of the coefficient distribution patterns for each landscape index within the MGWR model.

Figure 8 illustrates the distribution of the coefficient patterns for the landscape indices LSI, ED, FRAC_AM, and CONNECT at the class level in the MGWR model. It can be observed that each index exhibits spatial heterogeneity, with varying degrees of heterogeneity among the indices. LSI (Figure 8a): LSI shows a negative correlation with CS overall, with the coefficient pattern displaying a relatively uniform block-like spatial distribution. The negative impact of LSI is stronger in forest-dominated areas (e.g., Luoyang) than in built-up areas (e.g., Zhengzhou). Higher correlations are found in areas dominated by forest land, while lower correlations are observed in areas dominated by built-up land. ED (Figure 8b): The influence of ED on CS is positive, with the coefficient pattern increasing from southeast to northwest, similar to the elevation gradient. FRAC_AM (Figure 8c): The impact of FRAC_AM on CS is not significant but shows both positive and negative variations. Negative impacts are observed in regions like Xinxiang, Kaifeng, Jiaozuo, Xuchang, and the northeastern part of Zhengzhou, while positive influences are primarily seen in Luoyang, Pingdingshan, and Jiyuan. CONNECT (Figure 8d): The influence of CONNECT on CS is low, but its coefficient pattern is block-like. Overall, CONNECT positively influences the northeastern and southwestern parts of the Zhengzhou Metropolitan Area, while having a negative influence in more central cities such as Zhengzhou and Xuchang.

Using the same method, the coefficients of landscape indices at the landscape level in the MGWR model were visualized. Figure 8 illustrates the distribution of coefficient patterns for landscape indices SHDI, LPI, ED, SHEI, COHESION, and CONTAG in the MGWR model at the landscape level, with each index still exhibiting strong spatial heterogeneity. SHDI (Figure 9a) has a negative impact on CS, with its influence generally displaying a banded distribution. The negative impact of SHDI is stronger in the northwest (e.g., Luoyang, Jiaozuo) than in the southeast (e.g., Xuchang). The highest points are located in the northwest of Luoyang, as well as in Jiaozuo, Jiyuan, and northern Xinxiang, while the lowest points are found in cities such as Xuchang and Luohe. The influence of LPI (Figure 9b) on CS shows little variation with spatial changes but remains relatively low in the overall northeastern direction, particularly in the Xinxiang area. The relationship between ED (Figure 9c) and CS remains negative at the landscape level, with the lowest points located in Xinxiang and Kaifeng. The higher points exhibit a radial distribution, with several banded high-impact areas emanating from Zhengzhou. The influence of SHEI (Figure 9d) on CS is more complex. In the northeastern part of the Zhengzhou Metropolitan Area, it has a negative impact and the highest level of influence, while in other cities, the layout is mostly uniform and negatively influenced, with some areas showing a slight positive impact. Both COHESION (Figure 9e) and CONTAG (Figure 9f) have a relatively small impact on CS, with minimal variation in influence across different spatial areas. Overall, the impact of blue–green space pattern on carbon storage shows a spatial differentiation characteristic of “strong in the west and weak in the east”.

4. Discussion

4.1. Key Indicators of BGSP Affecting CS

This study explored the relationship between urban BGSP and CS. We utilized OLS, GWR, and MGWR to analyze the effects of BGSP on CS at both global and local levels. The findings indicate that many indicators of urban BGSP are correlated with CS in urban ecosystems. Additionally, the influence of BGSP on CS exhibits spatial heterogeneity, demonstrating varying intensities of impact across different geographic locations. At the class level, PLAND, LSI, FRAC_AM, ED, CONNECT, and AI negatively affect CS. In contrast, DIVISION has a positive effect. At the landscape level, SHDI, ED, and CONNECT have a negative impact on CS, while SHEI, LPI, CONTAG, and COHESION exhibit positive influences. The negative impact of LSI on CS is consistent with Teng’s research [33], which found that the LSI in the Yangtze River Delta urban agglomeration is significantly negatively correlated with the carbon budget. This is mainly because complex patch shapes enhance the edge effect, triggering microclimate fluctuations that inhibit vegetation photosynthesis and weaken carbon fixation efficiency. In Peng’s research [30], it was found that the edge density (ED) and Average Patch Shape Index (SHAPE_MN) are positively correlated with carbon sequestration. This aligns with the directional consistency of partial indicators in this study. The positive effect of DIVISION stems from improved spatial heterogeneity after subdividing oversized patches, which forms diversified habitats to stimulate carbon storage potential. The negative impact of landscape-level SHDI is due to the study scale effect: forests are the main CS contributors, and increasing richness breaks their concentrated distribution and reduces overall CS capacity.

Based on the above causes, priority should be given to simplifying patch boundaries (reducing LSI) to minimize microclimate fluctuations caused by jagged edges, thereby enhancing vegetation photosynthetic efficiency. Concurrently, the establishment of ecological buffer zones can scientifically regulate patch connectivity, balancing the prevention of excessive sprawl with the maintenance of essential material exchange. Furthermore, oversized single patches may be appropriately subdivided (increasing DIVISION) to enhance spatial heterogeneity. This approach preserves core ecological stability while stimulating carbon storage potential through diversified habitats. In overall spatial planning, the focus should center on high-carbon-sink landscape types such as forests and wetlands. Expanding core patch sizes (elevating LPI) can create contiguous dominant areas. Simultaneously, a hierarchical network should be established, characterized by “large patches as anchors and small patches as interconnected nodes,” leveraging ecological corridors to boost material cycling efficiency. Additionally, enhancing patch aggregation (COHESION) and internal connectivity is critical to mitigate human-induced fragmentation of plant communities, ultimately providing a stable spatial framework for carbon storage processes. This study refines the quantitative relationship between BGSP indicators and CS at dual scales (class and landscape), supplementing the deficiency of single-scale research in existing literature and providing a methodological reference for subsequent studies on carbon sink response to landscape pattern changes.

4.2. The Impact of BGS Coupling on CS

At the class level, emphasizing spatial integration, LSI has a negative impact on CS. This indicates that the more complex the shape of landscape patches, the less favorable it is for CS when the area of blue–green space is constant. This finding aligns with the research of Zhang [19], suggesting that simpler shapes in blue–green spaces are more beneficial for CS. Complex patch shapes intensify the edge effect, increasing vegetation’s energy consumption for adapting to microclimate changes and reducing the stability of the soil–water environment, thus inhibiting CS. As shown in Figure 8a, areas with high LSI impacts are primarily located in forested regions, while those with lower impacts are in impervious areas. This is because forests are core CS carriers, and their edge effect directly affects vegetation growth, while impervious surfaces have weak CS capacity. Therefore, prioritizing the integrity of forests in the Zhengzhou Metropolitan Area while reducing LSI is more favorable for CS than focusing solely on BGSs in urban settings. ED positively influences CS, and its impact decreases from the southwest to the northeast within the study area. This is because increased contact area promotes material exchange and accommodates more transitional vegetation with strong carbon sequestration capacity. For the same area, meandering shorelines and additional small water bodies, such as rain gardens and wetlands, are more beneficial for CS. The FRAC_AM of BGSs has a suppressive effect on CS in the northeastern part of the Metropolitan Area (including Xinxiang, Kaifeng, northeastern Zhengzhou, and northeastern Xuchang), while promoting effects are observed in other areas. In the northeast, high agricultural land proportion and flat terrain lead to fragile BGS stability, so complex shapes exacerbate fragmentation; in mountainous areas like Luoyang, a complex-shaped BGS adapts to terrain and enhances CS through niche differentiation. Therefore, in cities like Luoyang and Pingdingshan, increasing the shape complexity of BGSs is advisable, while in Xinxiang and Kaifeng, simplifying the composition of BGSs can jointly enhance CS. The overall impact of CONNECT on CS is not high, but it has a positive influence in the western (Luoyang) and eastern (Xinxiang, Kaifeng) regions of the Zhengzhou Metropolitan Area, while negative impacts are observed in Zhengzhou, Xuchang, and Jiaozuo. The core cause is BGS base conditions: abundant BGS benefits from high connectivity, while scarce and fragmented BGS in built-up areas is adversely affected by excessive connectivity. This indicates that in areas where BGSs are scarce and fragmented (such as those with a high proportion of impervious surfaces), it is beneficial for CS to maintain the area of BGSs while reducing their connectivity and forming a more integrated BGS. In contrast, in areas where BGSs are dominant (such as large forested areas and farmland), enhancing connectivity and forming a more complete BGS is more advantageous for CS. By revealing the heterogeneous impact of BGS coupling on CS under different land use and topographic conditions, this research enriches the theoretical system of urban blue–green space ecological functions and provides a targeted theoretical basis for regional differentiated BGS planning.

4.3. Spatial Heterogeneity in the Impact of the Overall BGSP on CS

At the landscape level, emphasizing the overall configuration of BGS, SHDI negatively impacts CS, showing a decreasing trend from northwest to southeast. While higher richness is generally believed to lead to greater carbon sinks, this study presents a different perspective, mainly due to the study scale and landscape composition: the research area is dominated by forests, and increasing SHDI introduces low-carbon-sink landscapes, thereby reducing overall CS. This also indicates that prioritizing the integrity of large green spaces, such as forest parks and nature reserves, is more beneficial for CS. SHEI predominantly negatively influences CS, with the strongest impact observed in areas like Xinxiang, while other regions show little spatial variation. In contrast, research by Cao et al. [34] indicates that SHDI can have alternating positive and negative effects on CS. This is because Xinxiang’s landscape is dominated by agricultural land and built-up areas, and high SHEI reduces the proportion of core carbon sink patches, leading to scattered carbon sequestration processes. LPI reflects the impact of the proportion of BGSs on CS, overall having a promoting effect. The distribution of high impact levels is relatively scattered, mainly concentrated in the central, southern, and western regions of the Zhengzhou Metropolitan Area, suggesting that increasing the proportion of BGSs in these areas can achieve optimal CS enhancement. This is because increasing the core BGS proportion forms stable carbon sink centers and enhances sequestration stability. At the landscape level, ED has a suppressive effect on CS, which differs from its effect at the class level. This suggests that when BGSs are considered as a whole, a more gradual transition between natural areas and impervious surfaces is more conducive to CS. In contrast, when the internal composition of BGSs is more complex, it favors CS. This necessitates differentiated planning and design strategies for urban development based on varying BGS conditions. COHESION and CONTAG reveal the impact of the aggregation characteristics of patch types in landscape patterns on CS. As shown in Figure 8a,b, the clustered distribution of BGSs can exert a certain suppressive effect on CS, which contrasts with previous studies [34]. However, it also indicates that compared to isolated distributions, the synergy of BGSs can lead to greater CS accumulation. The suppressive effect may come from intra-patch vegetation competition, while the synergy effect stems from enhanced ecosystem stability. This highlights the need to emphasize the integrated development and coordinated planning of BGSs in urban planning. Overall, this study takes the Zhengzhou Metropolitan Area as a case, systematically explores the multi-scale impact of BGSP and BGS coupling on CS and its spatial heterogeneity, which fills the gap in research on carbon sink effects of blue–green space patterns in northern urban agglomerations. The research results not only enrich the academic achievements in the field of urban ecological carbon sink but also provide a scientific basis for the integration of blue–green space planning and “dual carbon” goals, promoting the practical transformation of ecological landscape research results.

4.4. Limitations and Prospects

This study utilized the InVEST model to estimate CS. While this model offers advantages such as low consumption and ease of calculation, it also has limitations, including relatively low accuracy and the assumption of identical CS for the same land use type. These limitations can affect the precision of regression analysis. Moreover, this study classified cultivated land into green spaces following a unified standard, without exploring the impacts of alternative classification schemes on BGSP-CS correlations. Future research could further refine CS estimates by combining field surveys with modeling approaches. It could also conduct sensitivity analyses of different cultivated land classification scenarios to verify the robustness of the conclusions. Additionally, it is generally believed that simply adjusting the structural morphology from a physical standpoint is insufficient to promote the growth of BGSs and, consequently, the accumulation of biomass to increase urban CS. However, changes in the structure of BGSs can enhance their interaction with natural environments, affecting aspects such as ventilation and light exposure, which, in turn, influence vegetation. Some studies have indicated that alterations in BGSPs can impact land surface temperature (LST) [35], while other research has confirmed that LST has a significant influence on carbon sinks [36]. This suggests an intrinsic mechanism by which BGSPs affect CS. This study has initially explored the spatial mechanisms by which BGSPs influence CS. Future research may delve deeper into the underlying factors affecting these impacts.

5. Conclusions

• This study clarified the spatial relationship and influencing mechanisms between urban BGSPs and CS, and quantified their spatial coupling effect, with a focus on supporting sustainable development and the “dual carbon” goals. From the perspective of optimizing BGSPs to enhance the CS function of urban ecosystems, it provides scientific insights for the planning and layout of BGSPs, contributing to the systematic optimization of green space spatial patterns, regional sustainable development, and the achievement of “dual carbon” goals. Based on the above analysis, the following core conclusions are drawn: BGSPs are significantly correlated with CS, with scale-dependent effects. At the class level, area–edge and shape complexity indicators (e.g., LSI, r = −0.427) inhibit CS; at the landscape level, SHDI (r = −0.635) suppresses CS, while SHEI (r = 0.602) and LPI (r = 0.618) notably promote it.
• The MGWR model outperforms the OLS and GWR models, with R² values of 0.505 (class level) and 0.484 (landscape level), and accurately captures the “west–strong, east–weak” spatial heterogeneity of BGSP impacts on CS.
• Optimizing key BGSP indicators—simplifying patch boundaries, expanding core carbon sink patches, and constructing hierarchical ecological networks—provides a scientific basis for boosting regional carbon sinks and advancing the “dual carbon” goals.