Spatiotemporal Relationship Between Carbon Metabolism and Ecosystem Service Value in the Rural Production–Living–Ecological Space of Northeast China’s Black Soil Region: A Case Study of Bin County

Abstract

Amid global climate challenges and an urgent need for ecological protection, the northeastern black soil region—one of the world’s remaining “three major black soil regions”—confronts significant tensions between agricultural economic development and land ecological protection, threatening national food security. Based on the “production–ecology–life” (PLE) classification system, this study established a dual-dimensional evaluation for carbon metabolism and ESV in horizontal and vertical dimensions. The horizontal flow of carbon and ESV was traced across different ecosystems, while the spatial and temporal dynamics of carbon metabolism and ESV were analyzed vertically. Spatial autocorrelation analyses were employed to examine the interaction patterns between carbon metabolism and ESV. The findings reveal that (1) cropland production space remains the dominant spatial type, exhibiting fluctuating patterns in the size of other spatial types, with a notable reduction in water ecological space. (2) From 2000 to 2020, high-value carbon metabolism density areas were primarily concentrated in the central region, while low-value areas gradually decreased in size. Cropland production space and urban living space served as key compartments and dominant pathways for carbon flow transfer in the two periods, respectively. (3) The total ecosystem service value (ESV) showed a downward trend, decreasing by CNY 1.432 billion from 2000 to 2020. The spatial distribution pattern indicates high values in the center and northwest, contrasting with lower values in the southeast. The flow of ecological value from forest ecological space to cropland production space represents the main loss pathway. (4) A significant negative correlation exists between carbon metabolism density and ESV, with areas of high correlation predominantly centered around cropland production space. This study provides a scientific foundation for addressing the challenges facing the black soil region, achieving synergistic resource use in pursuit of carbon neutrality, and constructing a more low-carbon and sustainable spatial pattern.

1. Introduction

Carbon emissions are currently one of the most significant contributors to global climate change, leading to increasing environmental concerns [1]. Tracking regional carbon inflows and outflows from a metabolic perspective can effectively quantify the strength of carbon cycle flows between compartments of regional ecosystems, thereby identifying key nodes for carbon reduction and enhancement [2]. As significant carbon reservoirs within the carbon cycle, terrestrial ecosystems are crucial in combating global climate change and achieving regional carbon neutrality. These ecosystems provide goods and services to humans directly and indirectly, and their value is reflected in ecosystem service values (ESVs) [3].

Imbalances in carbon metabolism not only directly contribute to climate change but also indirectly affect ecosystem services through various pathways. For instance, excessive carbon emissions exacerbate climate change and disrupt climate patterns, which, in turn, destabilize ecosystems, reduce carbon sequestration capacity, and weaken the effectiveness of carbon sinks, ultimately diminishing the value of ecosystem services [4]. Conversely, healthy ecosystems can effectively absorb carbon dioxide and enhance carbon storage capacity, thereby mitigating the impacts of climate change. The economic value of their carbon sequestration services can be quantified through the ecosystem service value (ESV) [5]. It is important to note that the relationship between carbon metabolism and ecosystem service value is not limited to carbon sequestration; it is also closely linked to the evolution of PLE space. Urbanization and changes in agricultural land use are often accompanied by alterations in land cover and ecosystem types, which directly influence the sources and intensity of carbon metabolism while also changing the type and spatial distribution of ecosystem services [6]. The reduction in ecological space may lead to an imbalance in carbon metabolism, resulting in increased carbon emissions and the degradation of carbon storage functions, thus lowering the value of ecosystem services. Therefore, carbon metabolism and ecosystem service value interact and influence each other, with healthy ecosystems regulating carbon metabolism through their carbon sink functions [7]. To achieve the “dual-carbon” goal, in-depth studies of the spatial and temporal relationship between these factors provide a theoretical foundation for the development of more scientifically informed environmental policies and carbon reduction strategies [8].

Carbon is the material basis for various human socio-economic activities and a product of emissions. It is a crucial indicator of the coupled development and utilization of regional water, soil, and energy resources. Carbon metabolism refers to the flow of carbon between the biosphere, atmosphere, and urban systems, encompassing both carbon emission and absorption processes [9,10]. Current academic research on carbon metabolism primarily focuses on land use types with differing carbon sink functions at the urban level. Accounting methods vary based on their specific applications [11]; among these, carbon emission factor methods, model estimation methods, and remote sensing and mapping estimation methods have become the predominant approaches in the relevant research [11,12]. This study primarily focuses on the characteristics of the spatial distribution of carbon metabolism [13], the processes of carbon flow [14,15,16], the underlying driving mechanisms [17], and scenario modeling [18].

Building on the UN Millennium Ecosystem Assessment’s research on ecosystem valuation and ecological well-being, Constanza and other scholars have delved deeper into the value of environmental services [19]. Researchers predominantly employ the equivalent factor method, the benefit transfer method, and the ecosystem service value (ESV) index method to calculate ESV in the context of land use change. Research has concentrated on the spatiotemporal patterns of ecosystem service values (ESVs) [20], simulation predictions [21], trade-offs and synergistic relationships among ESVs [3,22], and the spatiotemporal correlations of ESVs [23]. In recent years, scholars have increasingly focused on the correlation between ESV and carbon emissions [24,25]. Frequent anthropogenic activities significantly affect the sustainable provision of ecosystem services. Research findings have revealed a significant spatial correlation between carbon emissions and ecosystem service value (ESV). However, most studies have focused on provincial and urban scales, with limited attention to local spatial scales within the national territory. In the context of China’s unique national conditions, it is particularly important to investigate the spatial relationship between carbon metabolism and the ESV at the county scale.

The report of the 18th National Congress of the Communist Party of China (CPC) introduced the concept of shaping a beautiful ecological space, an intensive and efficient production space, and a livable and moderate PLE space as part of the construction of a beautiful China. The term ‘PLE space’ refers to the following three interconnected space types: production, living, and ecological spaces [26]. The division of PLE space is not only a representation of the current situation but also emphasizes the evolution of spatial patterns over time. By analyzing the temporal and spatial dynamics of PLE spaces, it becomes possible to reveal the conversion relationships between different space types, the shrinkage or expansion of ecological spaces, and the dynamic changes in production and living spaces. This approach is crucial for understanding the impact of land use changes on carbon emissions and carbon sequestration [15]. Furthermore, the PLE space classification system considers not only economic development and production needs but also emphasizes the balance between social well-being and ecological protection [27]. PLE space can systematically encompass all land functions and coordinate the diverse needs of society, the economy, and the environment, thus promoting integrated management. This comprehensive perspective holds significant practical value for decision making in carbon emission management, ecological restoration, and resource allocation.

Even though relevant research has produced rich results and the theoretical foundations have been increasingly improved, existing studies have mainly focused on the driving mechanisms, spatial and temporal characteristics, and changes in patterns of carbon emissions in large-scale regions. There is a lack of in-depth exploration of the flow of carbon in small-scale areas and its relationship with land use under the ‘Ecology–Production–Life’ spatial system. In particular, there is a lack of integrated research on the process of carbon metabolism and the ecosystem value of land use in PLE spaces. Neglecting the PLE space classification system in studies of carbon metabolism and ecosystem service value introduces several limitations. The PLE space framework offers an integrated, cross-sectoral, and cross-disciplinary perspective that enables a comprehensive assessment of the synergistic effects between carbon metabolism and ecosystem services. It avoids the constraints imposed by relying on a single land use classification. Furthermore, the PLE space framework enhances the analysis of carbon emission reduction and ecological protection strategies across different regions while revealing the complex relationship between human activities and ecosystem functions, particularly during land use changes and urbanization. Therefore, studies that do not incorporate the PLE space framework are unable to fully capture the dynamic interactions between carbon metabolism and ecosystem services. Furthermore, the single carbon emission factor method commonly employed by research institutes has inherent limitations, failing to fully capture land use changes and carbon flows within ecosystems. This approach primarily relies on fixed emission factors and overlooks multiple sources of carbon emissions, including agriculture, energy consumption, livestock respiration, and straw burning. To address these limitations, research should integrate multiple accounting pathways to enhance the accuracy of carbon emission assessments.

As fundamental units of urban and rural development, counties are characterized by extensive agricultural activities and significant ecosystem service values [28]. Their land use is primarily composed of farmland, forests, and wetlands, which play a vital role in carbon sequestration and emissions reduction. Effective carbon emission management at the county level should integrate energy optimization with ecosystem services to enhance the carbon absorption capacity of agricultural and natural ecosystems [29]. By delineating ecological functional zones and implementing differentiated strategies, a balance between agricultural production and ecological conservation can be achieved, fostering sustainable low-carbon development. In recent years, Bin County has introduced various ecological protection policies to advance green development and environmental sustainability. As part of Harbin Municipal Government’s 14th Five-Year Plan for Ecological and Environmental Protection, Bin County has actively participated in initiatives targeting key areas such as soil pollution prevention and control, groundwater protection, and rural ecological and environmental management.

Therefore, building on existing research, this study establishes a spatial classification system of ‘Ecology–Production–Life’ (PLE) in Bin County and comprehensively evaluates carbon metabolism and the ecosystem service value (ESV) in both horizontal and vertical dimensions (Figure 1). The vertical assessment focuses on accounting for carbon metabolism and ESV quantities in unit areas simultaneously, revealing both factors’ spatial and temporal evolution characteristics. Conversely, horizontal measurements more effectively track the flow of carbon and ESV between different ecosystems throughout the study period. The study proposes, for the first time, a dual-dimensional evaluation framework, which provides a framework for the in-depth analyses of carbon metabolism and ESV in both vertical and horizontal directions, as opposed to the traditional method that focuses only on changes in carbon metabolism or ESV in a single dimension. This method is used to study the flow of regional substances in different ecosystems in different directions, especially in the agricultural counties in the black soil area. It can solve the dual problems of its economic development and ecological protection to address the limitations of previous research methods.

This study constructs a dual-dimensional evaluation for carbon metabolism and ecosystem service value (ESV) based on the “Ecology–Production–Life” (PLE) classification framework, analyzing their spatial and temporal dynamics in both horizontal and vertical dimensions. The flow of carbon and ESV across different ecosystems is quantified to reveal their spatial and temporal evolution, while spatial autocorrelation analysis is employed to explore the interaction mechanisms between carbon metabolism and ESV. The comprehensive assessment model integrates vertical and horizontal dimensions to provide a holistic understanding of carbon metabolism and ESV. Additionally, future strategies for sustainable development in Bin County are proposed for different sub-regions, offering a scientific basis and policy recommendations for promoting ecological sustainability in agricultural counties within the black soil region, with Bin County as a representative case.

2. Materials and Methods

2.1. Study Area

Bin County, situated in the southern part of Heilongjiang Province, covers 3843 km². Its geographical coordinates range from 126°55′41″ to 128°19′17″ (east longitude) and from 45°30′37″ to 46°01′20″ (north latitude). Renowned as the eastern suburban garden of Harbin City, Bin County is rich in resources. However, the rapid economic development in the region has exacerbated the conflict between agricultural economic growth and land ecological protection. Inappropriate developmental practices have significantly increased carbon emissions and environmental degradation. Agricultural production is a significant industry in Bin County, and as societal demands have grown, crop yields have risen correspondingly. This increase has been accompanied by a dramatic increase in the use of pesticides and fertilizers and heightened intensity in developing and cultivating agricultural land. Unfortunately, the absence of rational planning and regulatory policies has resulted in an imbalance in the carbon dynamics of Bin County. Therefore, exploring the spatial and temporal evolution of carbon metabolism and ecosystem service values across the PLE space in Bin County and their intercorrelation is crucial in better understanding the ecological impacts in the region (Figure 2).

This study’s selection of Bin County, a typical agricultural county, is significant and representative. (1) As a prominent agricultural county in the black soil region, Bin County has undergone extensive reclamation and utilization, resulting in frequent ecological and environmental issues. Consequently, there is an urgent need for a scientifically informed approach to sustainable development. (2) Using the Harbin Municipal Government’s ‘14th Five-Year Plan’ for ecological environmental protection as a policy guideline, it was pointed out that Bin County should carry out ecological protection work in areas such as rural ecological environment management. (3) The northeast black soil region is one of the only remaining ‘three major black soil regions’ in the world, which is known as the ‘Giant Panda of Arable Land’ because of its fertile soil and high food production capacity. As a typical county in the black soil area, Bin County has apparent advantages in cropland resources. However, Bin County is facing the dilemma of green development, and its research results can provide reference and guidance for other similar regions in the world, as well as new research perspectives and suggestions for managers formulating policies to protect the black soil area.

2.2. Data Sources

The data for this study include both spatial and statistical data. Detailed information on the data used in this study is provided in Table 1.

2.3. Methods

2.3.1. Production–Ecology–Life Classification System and Carbon Metabolism–ESV Dual-Dimensional Evaluation Framework

The case selected for analysis in this research is Bin County, located in Harbin City, where the distinction among PLE space is based on the core functional elements of land types [30]. Since different land types embody varying degrees of ecological, production, and living functions, the differentiation of the PLE space should rely on the primary functions of the land. For instance, cropland production is considered its core function as a cornerstone of national food security [31]. While cropland also possesses ecological functions, such as providing regulatory benefits through extensive agricultural planting, this study classifies cropland as cropland production space, emphasizing its primary role. Conversely, the ecological function of grassland is typically regarded as its core function, with the production function manifesting in animal husbandry activities. The ecological space of grassland is delineated according to its core function category and includes water bodies and woodland. Additionally, construction land is categorized into urban and rural living spaces to meet the study’s requirements, with other construction land being grouped with urban land due to its minimal area. Therefore, concerning the existing classification standards, a “Production–Life–Ecology” classification system for Bin County is constructed based on the scientific requirements [32] (Table 2).

The research methodology employed in this study is illustrated in Figure 3. A spatial classification system of ‘ecology–agriculture–urban’ in Bin County is the foundation for this research, which utilizes land use vector data from Bin County between 2010 and 2020. A land transfer matrix is calculated based on land use/cover change using ArcGIS spatial analysis tools, facilitating the spatial visualization of land transfer scenarios. Carbon metabolism and ecosystem service value (ESV) are assessed comprehensively through a dual-dimensional evaluation. The horizontal dimension relies on the land use transfer matrix, enabling the effective tracking of carbon and ESV flows across different ecosystems via a carbon flow model and an ecosystem value gain/loss model. The vertical dimension evaluates the amount of carbon metabolism and ESV per unit area. Carbon metabolism is determined by calculating carbon absorption and emissions, while ESV is derived using the value per unit area equivalent factor method. The evolution of carbon metabolism density and ESV is then visualized through spatial analysis tools. Finally, spatial interactions between carbon metabolism and ESV are analyzed using bivariate autocorrelation to examine the spatial distribution of various clustering patterns, thereby identifying areas of environmental pressure and ecological potential within the study area.

2.3.2. Accounting for Carbon Metabolism in PLE Space

Regional carbon metabolism encompasses both carbon uptake and emission processes. This study primarily addresses the carbon fluxes associated with the following seven spatial land types: cropland, forest land, grassland, water bodies, urban land, rural land, and other land. The specific research methods are detailed in Table 3 [33,34].

• (1). Estimation of carbon absorption

According to the Bin County PLE space classification system, cropland production space can contribute to carbon absorption and the various types of ecological space that function as carbon sinks. In this context, the ecological space employs the land use carbon sink coefficient method to calculate carbon absorption, as expressed by the following formula:

(1)$E={\displaystyle \sum {\lambda }_i}={\displaystyle \sum S_i}\cdot {\delta }_i$

where E represents the total carbon emission (t); λi denotes the carbon emission (or carbon absorption) generated by the i th land use (t); Si refers to the land area (hm²) associated with the i th land use, which includes various types of ecological space; and δi is the carbon emission (or absorption) coefficient of the i th land use (t/hm²).

Given that the primary land type of agricultural production space is cropland, the indirect estimation method for carbon absorption through crop growth is employed [23]. In the calculation process, the main crops selected for this study, reflecting the actual conditions in Bin County, are rice, maize, and beans. The calculation formula is as follows:

(2)$C_d=C_f\cdot D_w=C_f\cdot {\displaystyle \frac{Y_w}{H_i}}$

where C_d represents the carbon absorption of cropland; C_f denotes the carbon absorption rate of different crops; Y_w indicates the economic yield of various crops; D_w refers to the biological yield of other crops; and H_i is the economic coefficient of the i th crop [35] (Table 4).

• (2). Estimation of carbon emissions

Carbon emissions from agricultural production space, urban living space, and rural living space are accounted for separately. Considering the actual conditions in Bin County and the availability of data, the spatial carbon emissions from agricultural production are primarily calculated based on the agricultural inputs used in production activities. These inputs include fertilizer application, agricultural machinery, the use of agricultural films, and agricultural irrigation as follows:

(3)$E_a=C\cdot k_c+M\cdot k_M+W\cdot k_w+F\cdot k_f+G\cdot k_g$

(4)$M=S\cdot D$

where E_a represents the carbon emission from cropland; C denotes the total amount of fertilizer applied; M is the cultivated area of agricultural land; W represents the total power of agricultural machinery; F indicates the amount of agricultural film used; G is the irrigated area of agricultural land; S refers to the total area of agricultural land; and D is the replanting index. The different emission factors corresponding to various agricultural activities are provided in Table 5 [36].

Carbon emissions from urban living spaces are primarily calculated based on urban residents’ respiratory carbon emissions and energy-related carbon emissions. The respiratory carbon emissions of urban residents are determined by multiplying the urban population by the human respiratory carbon emission coefficients [37]. Energy carbon emissions are calculated using the following formula [38,39]:

(5)$E_t={\displaystyle \sum E_{ti}}={\displaystyle \sum E_{ni}}\cdot {\theta }_i\cdot f_i$

(6)$C{E}_t=P_c/P_s\cdot E_t$

where E_t represents the carbon emissions from construction land; E_ti denotes the carbon emissions from various fossil energy sources; E_ni is the consumption of different fossil energy sources; θ_i is the conversion coefficient of various fossil energy sources to standard coal; and f_i is the carbon emission coefficient of various fossil energy sources, with the conversion coefficient for standard coal provided in Table 6. Additionally, CE_t refers to the total carbon emissions of the county; P_c represents the total population density within the county; and P_s is the total population within the provincial area.

Carbon sources from rural living spaces are primarily calculated based on the carbon emissions from straw combustion and the respiration of rural residents. The calculation formula is as follows:

(7)$C_s={\displaystyle \sum _{i=1}^n{P}_i}\cdot S_c\cdot {\theta }_i\cdot a\cdot b$

where n represents the crop type (species); P_i is the yield of the $\mathrm{i}$th crop (kg); S_c is the grain-to-straw ratio of the i th crop; θ_i is the carbon emission coefficient for straw combustion of the i th crop; a is the ratio of open burning of straw, with a value of 0.165; and b is the efficiency of straw combustion, with a value of 0.8 [40].

• (3). Carbon metabolism density and carbon flow modeling

Based on the accounting of carbon emissions and absorption processes across various types of space, carbon fluxes on the vertical scale are established through the overall spatial cross-section at a single point in time. Consequently, the carbon fluxes of the PLE space are calculated by aggregating the total carbon emissions and carbon absorption within these spaces. In accordance with previous research methods [41,42], net carbon emission per unit area is defined as net carbon metabolism density. To quantify the carbon metabolism capacity of different spaces and to measure the resulting carbon fluxes and their directional changes within the PLE space, this study employs the difference in carbon metabolism density alongside the area of PLE space transfer. The calculation formula is as follows:

(8)$C_t=C_e+C_a$

(9)$C={\displaystyle \sum _{i=1}^n\frac{S_i{P}_i}{S}}$

where C_t represents the total carbon flux in the PLE space; C_e denotes the carbon emission in the PLE space; C_a is the carbon absorption in the PLE space; S_i refers to the area of each type of space i within the grid; and P_i is the net carbon emission in each space. Additionally, C is the density of carbon metabolism; S represents the total area of the grid; S_i denotes the area corresponding to each type of PLE space i within the grid; and P_i indicates the carbon flux from each space.

(10)$f_{ij}=\Delta W\cdot \Delta S$

(11)$\Delta W=W_j-W_i={\displaystyle \frac{V_j}{S_j}}-{\displaystyle \frac{V_i}{S_i}}$

here, f_ij represents the carbon flow from site j to site i (kg C); ΔW denotes the difference in carbon metabolism density (kg C/hm²); ΔS is the area transferred from site j to site i (hm²); W_i and Wj are the densities of net carbon flow in the horizontal direction at sites i and j (kg C/hm²); V_i and V_j are the net carbon flows in the vertical direction at sites i and j (kg C); and S_i and S_j are the areas (hm²) of sites i and j. If f_ij > 0, it indicates a positive carbon transfer that contributes to the balance of carbon metabolism; if f_ij < 0, it signifies a negative carbon transfer that exacerbates the imbalance in carbon metabolism.

2.3.3. Estimation of the Value of Ecosystem Services in PLE Space

• (1). Correction for ESV per unit area

Revision of the economic value of food yields. The average grain production in Bin County from 2000 to 2020 was 3898.67 kg/hm², and the unit price of grain was based on the average price in Bin County for 2020, which was 2.52 CNY/kg. This reference was employed to determine the equivalence of other ecosystem services by utilizing the economic value of one equivalent as one-seventh of the market value of the average grain yield in the current year [43]. The calculation formula is as follows:

(12)$D={\displaystyle \frac{1}{7}}\cdot {\displaystyle {\sum }_{i=1}^n(\frac{P_i\cdot G_i}{S_i}})$

where D denotes the standard equivalent ecosystem service value of PLE space in Bin County (CNY/ha); P_i represents the unit price of the i th crop in the current year (CNY); G_i indicates the annual production of the i th crop; and S_i signifies the planted area of the i th crop.

Land use type adjustment. In Bin County, the cropland production space is dryland and paddy farming, and the average value of these two equivalence factors is taken as the base equivalence. The forest ecological space in Bin County mainly consists of coniferous forests, mixed coniferous and broad forests, broad-leaved forests, shrub forests, etc., and the average of the four equivalence factors of these four woodland types is taken as the base equivalence. The ecological space of grassland in Bin County mainly refers to scrubland, grassland, and meadow, and the average value of the three equivalence factors is taken as the basis of its equivalence. The water ecological space in Bin County mainly refers to wetlands, and the wetland equivalent factor was used. The primary land type of living space in Bin County is construction land, which has a negative impact on the ecosystem due to the massive use of water resources and the production of pollutants that are harmful to humans. Based on previous research, the hydrological regulation of urban and rural living spaces is assigned a value of −7.55. The ecological space of unused land in Bin County mainly consists of desert and bare land, and the average value of these two equivalent factors was used [44].

Correction for regional coefficients of variation. Due to geographical and environmental condition variations across different regions, biomass levels also differ; higher biomass generally indicates stronger ecosystem service functions. In light of the specific circumstances of the study area, this research employs the net primary production potential instead of biomass to adjust the equivalent factor. The particular calculation formula [45] is as follows:

(13)$Q_t={\displaystyle \frac{NPP}{NP{P}_{mean}}}$

(14)$NPP=3000\left[1-e^{-0.0009695\left(V-20\right)}\right]$

(15)$V={\displaystyle \frac{1.05R}{\sqrt{1+{\left(1+1.05/L\right)}^2}}}$

(16)$L=3000+25t+0.05t^3$

where Q_t represents the regional coefficient of variation in year t; NPP denotes the net primary production potential of natural vegetation (t/(hm²·a)); NPP_mean indicates the average net primary productivity across all types of vegetation; V is the actual annual evapotranspiration (mm); L is the average yearly evapotranspiration (mm); t refers to the average annual temperature (°C); and R is the average yearly precipitation (mm).

The average regulation coefficient of PLE space in Bin County from 2000 to 2020 is calculated to be 0.88.

• (2). Determination of the value of ecosystem services per unit area

The coefficient of ecosystem service value per unit area was obtained after correcting for the economic value of grain yield, land use type, and regional variation coefficient, as presented in Table 7.

• (3). Models for measuring the value of ecosystem services

Based on the actual conditions in Bin County and incorporating the standard equivalence and value coefficients derived from the calculations outlined in the above formula, the ecosystem service value of PLE space in Bin County can be determined using the following formulas [46]:

(17)$ESV={\displaystyle \sum A_x}\cdot V{C}_x$

(18)$ES{V}_f={\displaystyle \sum A_x}\cdot V{C}_{fx}$

where ESV represents the value of ecosystem services; A_x denotes the area of the land category; VC_x indicates the value coefficient; ESV_f refers to the value of the f th ecosystem service function; and VC_fx is the value coefficient of the f th ecosystem service function.

The relationship between the value of ecosystem services and their intensity is as follows:

(19)$\overline{ESV}={\displaystyle \frac{ESV}{S}}$

where ESV denotes the ecosystem service value, $\overline{ESV}$ is the ecosystem service value intensity, and S is the grid area.

• (4). Ecosystem service value flow gain/loss modeling

To reflect the spatial transfer flow of ecosystem service value and the direction of quantitative change while revealing the internal gains and losses of ecosystem service value [47], this research applies a value gain and loss model based on the land use transfer matrix. This approach quantifies ecosystem service value gains and losses within the study area. The calculation formula is as follows:

(20)$P_{ij}=\left(V{C}_i-V{C}_j\right)\cdot A_{ij}$

where P_ij represents the ecosystem service value gain or loss resulting from the conversion of land use type i to type j (CNY); VC_i and VC_j are the ecosystem service value coefficients for land types i and j, respectively; and A_ij denotes the area of land use type i converted to type j (hm²).

2.3.4. Bivariate Spatial Autocorrelation Analysis

Bivariate spatial autocorrelation refers to the clustering of the high and low values of two variables in space, which indicates whether the two variables are correlated spatially and assesses the degree of spatial autocorrelation [48]. The bivariate global Moran index quantifies the level of autocorrelation between the two variables within the specified space. The bivariate global Moran index is calculated as follows:

(21)$I_a={\displaystyle \frac{W_{ij}{Z}_1{Z}_2}{n}}$

(22)$Z_1={\displaystyle \frac{x_k^i-\overline{x_k}}{{\sigma }_k}}$

(23)$Z_2={\displaystyle \frac{x_r^i-\overline{x_r}}{{\sigma }_r}}$

where I_a represents the bivariate global Moran’s index; Z₁ and Z₂ are the standardized values of element i and element j for the k and r attributes, respectively; σ_k and σ_r denote the variances of the k and r attributes, respectively; W_ij is the weight matrix of the elements; and n is the number of study subjects.

The bivariate local Moran’s index assesses the degree of association between two variables within a regional space. It is calculated as follows:

(24)$I_b=Z_1{\displaystyle {\sum }_{j=1}^n}{W}_{ij}{Z}_2$

where I_b represents the bivariate local Moran’s index, W_ij is the weight matrix of the elements, and n is the number of study objects.

3. Results Analysis

3.1. Analysis of the Evolution of Spatial and Temporal Patterns in PLE Space

In this study, land use information for Bin County in Harbin City was processed using ArcGIS, utilizing national land use data from 2000, 2010, and 2020. The data were classified into seven types of production–living–ecological (PLE) space (Figure 4). The results indicate that between 2000 and 2020, land use types in Bin County were predominantly characterized by cropland production space and forest ecological space, with the overall area remaining relatively stable and exhibiting minor local changes. Specifically, the area of agricultural production space experienced a slight decline from 2000 to 2010, followed by a continued decrease from 2010 to 2020. Forest ecological space, primarily concentrated in the southeast, remained stable overall. The area of grassland ecological space saw a significant increase from 2000 to 2010 but decreased in the northwest from 2010 to 2020. Additionally, the water ecological space remained stable during 2000–2010 but experienced a significant decline from 2010 to 2020, negatively impacting the overall ecosystem. Urban living space is concentrated in township centers, indicating population concentration, while rural living space, though more significant, is irregularly distributed, showing little change over the study period.

By combining the PLE space transfer heat map (Figure 5) with the Sankey diagram (Figure 6), it is evident that between 2000 and 2010, cropland production space in Bin County primarily flowed into forest ecological space and rural residents’ living space, covering areas of 99.53 km² and 46.77 km², respectively. Concurrently, forest ecological space converted to cropland production space, with an area of 125.87 km², indicating a gradual increase in the demand for agricultural land. Additionally, the change in grassland primarily originated from cropland production space, with an area of 37.16 km². In contrast, the area transferred out was relatively small, reflecting the local management unit’s focus on ecological protection. Rural living space transformed 51.19 km² into cropland production space, whereas cropland also transferred 46.77 km² into rural living space. The transfer in and out of areas was roughly equivalent, indicating that the development of agricultural production and residents’ living conditions has remained stable.

From 2010 to 2020, cropland production space in Bin County primarily flowed into forest ecological space and rural residents’ living space, covering 124.06 km² and 48.52 km², respectively. Meanwhile, forest ecological space shifted to cropland production space by 79.19 km². Compared to the previous period, the area flowing into forest ecological space increased by 1.24 times, while the area of forest ecological space converting to cropland production space decreased by 1.58 times. During this period, grassland ecological space mainly transitioned to other ecological spaces and cropland production space, with areas of 17.42 km² and 14.34 km², respectively. Compared to the previous stage, the outflow area of the grassland ecological space showed a significant increase, highlighting that the importance of grassland ecological protection has yet to receive sufficient attention. Additionally, the inflow area from rural living space to cropland production space decreased from 51.19 km² to 43.71 km², and land transfers to various types of ecological space also declined. The conversion to forest ecological space increased from 0.66 km² to 1.27 km². This suggests that government and management have begun to focus on ecological protection despite the impact of urban development on production space.

From Table 8, an analysis of the attitude toward individual PLE spaces in Bin County reveals that the attitude towards other ecological spaces from 2000 to 2020 is 9.34%. This is followed by grassland ecological space and urban living space, with attitudes of 7.11% and 4.62%, respectively. The most significant adverse change in land use attitude during this period occurred in water ecological space, which experienced a decrease of 15.15%. This was followed by cropland production space, which experienced a slight negative change of −0.21%.

Significant differences in the mobility of the three types of PLE space were observed during the study period. Grassland ecological space exhibited the highest mobility, at 40.90%, followed by other ecological spaces and urban living spaces, with mobilities of 11.87% and 2.48%, respectively, between 2000 and 2010. Meanwhile, different types of space displayed negative changes. In the subsequent period from 2010 to 2020, the motivation for other ecological spaces rose to the forefront, with urban living space following at 2.01%. Conversely, water body ecological space experienced the most significant negative change, while grassland ecological space and cropland production space also demonstrated downward trends. The shrinkage of water body ecological space was particularly drastic during this period.

In the context of Bin County, this change is closely related to implementing the policy of returning cropland to forest and grassland. Between 2000 and 2010, the motivation of grassland ecological space was as high as 40.90 percent, indicating that the grassland ecological area achieved significant growth during this period. From 2010 to 2020, the combined motivation of other ecological spaces also increased, indicating that environmental projects were still being promoted. Nevertheless, the dynamics of water ecological space show a negative change, suggesting that although environmental management has achieved initial results, we still need to pay attention to the existing problems to achieve the integrated and coordinated development of urban and rural areas and natural ecology.

3.2. Analysis of the Spatial and Temporal Evolution of Carbon Metabolism in PLE Space

• (1). Carbon metabolism calculation results

By using land use data from Bin County, the carbon emissions and carbon absorption of PLE space over the past 20 years were estimated, with the results presented in Table 4 and Table 5. The data reveal that carbon emissions from urban residents’ living spaces from 2000 to 2020 consistently represented a dominant share of total carbon emissions, with an increasing proportion each year. Specifically, the emissions were 204.2 × 10⁴ t C, 654.1 × 10⁴ t C, and 607.1 × 10⁴ t C, contributing 69.83%, 72.99%, and 90.44% of the total carbon emissions, respectively. As shown in Table 9, the study area exhibited a trend of total carbon emissions that increased and declined from 2000 to 2020. The peak was reached in 2010, with total emissions in Bin County at 896.16 × 10⁴ t C, an increase of 3.07 times compared to 292.41 × 10⁴ t C in 2000. After this peak, total emissions began to decline, with emissions in 2020 recorded at 671.25 × 10⁴ t C, reflecting a decrease of 224.90 × 10⁴ t C compared to 2010. Regarding carbon sinks, the total carbon sinks in Bin County in 2000, 2010, and 2020 were 42.75 × 10⁴ t C, 107.94 × 10⁴ t C, and 124.80 × 10⁴ t C, respectively. While the total carbon sink in Bin County shows an increasing trend, it primarily comprises contributions from cropland, forest, grassland, water, and other ecological spaces. Notably, the carbon sink from cropland land production space accounted for 82.35%, 93.28%, and 94.18% of the total carbon sink in those years. The carbon sinks from other ecological spaces, such as grasslands and water bodies, decreased after peaking in 2010.

• (2). Characterization of the spatial distribution of carbon metabolism density

The carbon flux for each land use within the PLE space in Bin County was determined by calculating the carbon absorption and emissions associated with each land use type. By utilizing land use data, ArcGIS 10.7 software was employed to classify the carbon metabolism density values for Bin County from 2000 to 2020 with natural breaks. This classification resulted in the following five levels: lower, low–medium, medium, high, and extreme. The spatial distribution of carbon metabolism density in the PLE space for the years 2000, 2010, and 2020 was, thus, mapped (Figure 7). Notably, areas with extreme carbon metabolism density values are primarily concentrated in the central region of Bin County. These regions mainly rely on agricultural production and human activities as the primary sources of carbon emissions. The spatial area of cropland is concentrated and widely distributed, making it the primary contributor to carbon emissions and resulting in the highest carbon metabolism density values. In contrast, areas with lower carbon metabolism density values are predominantly located in the peripheral regions of the county, particularly within ecologically sustainable forest spaces. However, these areas have begun to decrease in size year after year, following a brief period of modest growth. Between 2000 and 2020, only minimal changes were observed in low-to-medium, medium, and extreme carbon metabolism density regions, with the overall number of regions remaining stable. The distribution of extreme and medium regions primarily surrounds high carbon metabolism density zones, exhibiting a diffuse trend and indicating the potential for transformation into low carbon metabolism density classes. Therefore, carbon reduction and greening strategies could be implemented in these areas to support future low-carbon development goals.

• (3). Carbon flow process analysis

According to the carbon metabolism density and land use transfer matrix, the carbon flow in the horizontal direction of various types of PLE space land in Bin County from 2000 to 2020 was obtained, as shown in Figure 8.

Based on the Sankey map of horizontal carbon flow in PLE space in Bin County (Figure 9), three primary carbon flow pathways were identified from 2000 to 2010, all indicating negative carbon flow. These include shifts from cropland production to urban living space and rural living space to urban living space, with transfer amounts of 188.77 × 10⁴ t C and 54.97 × 10⁴ t C, in that order. In this period, cropland production and forest land ecological space were the main carbon flow output compartments, while rural and urban living spaces were the primary input compartments. Positive carbon flow between 2000 and 2010 was primarily observed from urban living space to cropland production space and from cropland production space to forest ecological space, with transfer amounts of 8.4 × 10⁴ t C and 1.5 × 10⁴ t C, correspondingly. From 2010 to 2020, two main carbon flow pathways were identified as follows: cropland production space with negative carbon flow shifted to urban living space, and cropland production space with positive carbon flow shifted to forest land ecological space, with transfer amounts of 210.43 × 10⁴ t C and 6.7 × 10⁴ t C, in sequence. Compared to the previous period, interactions among carbon metabolism compartments were stronger between 2010 and 2020, marked by an increase in overall carbon transfer and a diversification of carbon flow pathways. The positive carbon metabolism compartments also contributed more significantly to positive carbon flows.

As shown in Table 10, the net carbon transfer in Bin County between 2000 and 2020 was consistently negative. This indicates that land use changes have adversely affected the region’s carbon balance, resulting in an imbalanced carbon metabolism. The land conversion was more significant when shifting from negative to positive carbon metabolism compartments. From 2000 to 2010, positive carbon transfer was the lowest, amounting to only 11.60 × 10⁴ t C. The primary source of this positive carbon transfer was the shift from urban living space to cropland production space, which accounted for 8.44 × 10⁴ t C, marking it as the critical compartment and pathway for positive carbon transfer. The amount of negative carbon transfer was more than 22 times greater than that of positive carbon transfer. The shift in cropland production space to urban living space resulted in a negative carbon transfer of 188.77 × 10⁴ t C, contributing 72.77%, with the net carbon transfer reaching a maximum of 247.82 × 10⁴ t C. The land use changes driving negative carbon transfer during both periods primarily stemmed from this shift. This indicates that the high carbon emissions from urban living spaces are a significant factor obstructing the balance of regional carbon metabolism, peaking at 210.43 × 10⁴ t C from 2010 to 2020, which accounted for 91.87% of the harmful carbon transfer.

3.3. Analysis of the Spatial and Temporal Evolution of Ecosystem Service Values (ESV) in PLE Space

• (1). ESV Calculation Results

As shown in Table 11, the total ecosystem service value of the PLE space in Bin County decreased from 2000 to 2020, with a reduction of CNY 1.432 billion. The ecological service values for 2000, 2010, and 2020 were CNY 6.984 billion, CNY 5.227 billion, and CNY 5.419 billion, respectively, indicating a decline in overall ecosystem service value. The values for the ecological water space and cropland production space experienced the most significant reductions, amounting to CNY 1.475 billion and CNY 26 million, respectively. This phenomenon primarily results from the diminished values of production and ecological spaces. As the urban population increases, the demand for urban land rises, leading to a reduction in the areas of cropland and water bodies, which subsequently affects the overall value of ecosystem services.

The primary contributor to the ecological value of PLE space in Bin County is forest ecological space, with its contribution rate increasing from 59.56% to 60.77%. Despite the decline in overall ecosystem service value (ESV), its positive impact on the ecosystem remains significant. The contribution rate of cropland production space rose to 23.23% between 2000 and 2010. However, during 2010–2020, although the contribution rate continued to increase, the total ESV decreased, indicating the weakening of its ecological service capacity. The contribution of grassland ecological space increased throughout the study period, though its overall impact was minimal. In contrast, water ecological space experienced a significant decline in contribution rate from 2000 to 2010, accompanied by a decrease in ESV. This suggests that degradation has impaired its ecological function. Urban and rural living spaces both showed negative contribution rates, highlighting their limited role in providing ecosystem services. Meanwhile, the contribution rate of other ecological space, though relatively small, exhibited a slow growth trend, indicating potential for further ecological value. Overall, the value of ecosystem services in the PLE space of Bin County has decreased over these 20 years, indicating increasingly severe ecological issues that require urgent attention.

• (2). Characterization of the spatial distribution of ecosystem service values (ESVs)

To analyze the spatial distribution and changes in ecosystem service values (ESVs) in Bin County from 2000 to 2020, this study employed ArcGIS software to classify the ESVs into the following five levels based on natural breaks: low, low to medium, medium, high, and extreme. The spatial distribution of PLE space ESVs in Bin County for 2000, 2010, and 2020 was then determined (Figure 10).

The overall layout of ESV distribution in Bin County remained relatively consistent between 2000 and 2010. The central and western regions predominantly exhibited low-to-medium ESVs, mainly due to the extensive distribution of cropland production space in these areas, which has a relatively low ecological service value. By 2020, the most noticeable increase in lower ESVs occurred in Minhe Township, where watershed regions were transformed into unused land between 2010 and 2020, contributing to a decline in overall ecosystem service value. In summary, the spatial distribution pattern of ecosystem service values (ESVs) in the PLE space of Bin County from 2000 to 2020 was characterized by higher values in the northwest and central regions, while the southeast exhibited lower values. This pattern remained consistent over the two decades. Furthermore, the area classified as having extreme ESVs in the PLE space significantly diminished, reducing the overall ecosystem service value and highlighting the urgent need to address ecological issues.

• (3). Analysis of the flow of ecosystem service value (ESV) gains and losses

This study employs the ecosystem service value flow gain–loss model to analyze the impact of changes in the PLE space pattern on ecosystem service values. This method quantitatively assesses the flow of ecosystem service values among different land uses due to transfers between them. Utilizing land use transfer data for PLE space in Bin County and the ecosystem service values associated with each type of PLE space, the flow of ecosystem service value gains and losses during each period was calculated.

In this model, the horizontal axis represents the changes in value for the transferred areas of each PLE space land use type. In contrast, the vertical axis reflects the changes in value for the regions replenished for each PLE space land use type. This approach enables a comprehensive understanding of how land use dynamics influence the overall ecosystem service values in Bin County (Figure 11).

Combining the Sankey diagram of ecological value loss and gain in PLE space in Bin County illustrates each period’s dominant paths and key compartments of ecological value changes (Figure 12). From 2000 to 2020, four main paths of ecological value loss and gain were identified. In both periods, the flow of ecological value from forest ecological space to cropland production space served as the primary loss path, with losses amounting to CNY 265.720 billion in the first period and CNY 157.275 billion in the second. The forest ecological space emerged as a crucial compartment for ecological value gains, with positive flows of ecological value from other spaces to the forest ecological space. The value gains during the two periods were CNY 211.163 billion and CNY 315.816 billion, respectively. Notably, from 2010 to 2020, the ecological value flow path from water body ecological space to unused land ecological space became the second major pathway, resulting in a loss of CNY 91.554 billion, significantly higher than other flow paths. This increase can be attributed to the higher ecological value potential of water body ecological space compared to unused land ecological space, with the loss of land use area contributing to the overall decline in ecological value.

As shown in Table 12, from 2000 to 2010, the ecosystem service value (ESV) gained was CNY 295.667 billion, while the losses amounted to CNY 549.939 billion, resulting in a total net loss of CNY 254.272 billion. The conversion of cropland production space to forest ecological space was the primary source of ESV gain during this period, accounting for 68.98% of the total gain. Conversely, the loss of various types of ecological space, particularly forest ecological space, was the main driver of ESV loss, contributing to approximately 48.32% of the total loss.

From 2010 to 2020, the ESV gain increased to CNY 411.790 billion, while losses decreased to CNY 373.940 billion. This period exhibited a smoother overall value flow compared to the previous decade, resulting in a net gain of CNY 378.50 billion. The ecological value gain and loss patterns remained consistent with the earlier period, primarily involving the interconversion between forest ecological space and cropland production space. Notably, 63.45% of the value gain originated from converting cropland production space to forest ecological space. In comparison, 42.06% of the value loss stemmed from converting forest ecological space to cropland production space, leading to a significant loss of CNY 157.275 billion.

In summary, Bin County, as a typical agricultural region in the northeast black soil area, experiences significant negative impacts on ecological and environmental benefits due to agricultural production practices. While recent ecological protection policies implemented by the government have positively influenced the horizontal flow of ecological value, the total ecosystem service value (ESV) continues to decline. This decline is primarily attributed to the gains in PLE space ESV over the past 20 years, mainly from expanding forest ecological space. However, human activities such as land reclamation and urban expansion have encroached upon other ecological areas, leading to substantial ESV losses.

Although policies such as returning farmland to forests have provided some compensation for the ecological disturbances and damages caused by human activities, the overall benefits have not been sufficient to offset the losses. Consequently, the ESV in the region continues to decline. Moving forward, it is crucial to enhance the protection of forest ecosystems and increase the forested land area, fostering a trajectory of high-quality regional ecosystem development.

3.4. Analysis of the Correlation Between Carbon Metabolism and Ecosystem Service Value in PLE Space

The study utilized GeoDa 1.18 software to analyze the ecosystem service value (ESV) and carbon metabolic density data for Bin County at three key time points, 2000, 2010, and 2020, using a 1 km × 1 km grid cell framework. This analysis treated ESV as the independent variable, while carbon metabolic density values were the dependent variable.

According to Table 13 and Figure 13, the bivariate Moran’s index for Bin County in 2000, 2010, and 2020 was negative, with the specific values of −0.4416, −0.4253, and −0.4830, respectively. The p-values corresponding to these negative Moran’s indices were all less than 0.01, and the absolute z-values exceeded 65. These statistical results indicate a significant negative correlation between ESV and carbon metabolic density in spatial distribution. Specifically, high ESVs were associated with low carbon metabolic intensity, while low ESVs correlated with high carbon metabolic density. This finding suggests that the distribution of carbon metabolic density and ecosystem service value among neighboring units in the PLE space of Bin County is not random or discrete but exhibits significant spatial heterogeneity.

Based on the bivariate LISA plot (Figure 14), the high–low clusters in 2000 and 2010 were predominantly located in the central and northwestern regions of Bin County. This area primarily consists of plains, where the high–low clusters are distributed in a fragmented manner, showing a high degree of spatial coincidence with cropland production. In 2020, the distribution of high and low clusters remained similar to that of the previous two periods; however, the low and high clusters began to expand toward the south and east of Bin County. In these areas, mountains and hills limit agricultural use, resulting in a predominance of forest land associated with lower carbon emission intensity and higher ecosystem service value. Notably, the low–low clustering in 2020 exhibited a significantly larger distribution area compared to the earlier periods. This increase can be attributed to changes in other ecological spaces, primarily unused land, which generates lower carbon emissions and possesses a relatively low ecosystem service value.

In summary, a clear negative spatial correlation between carbon emission intensity and ecosystem service value exists within the PLE space of Bin County. Specifically, when a region’s ecosystem service value significantly increases, when a neighboring areas’ carbon emissions tend to decrease, and vice versa. This phenomenon is rooted in the study’s PLE space categorization methodology, which identifies water and forest ecological spaces with higher ecosystem service values. The key features of these spatial types—such as water bodies and woodlands—serve as crucial conduits for carbon dioxide assimilation into living organisms. In promoting the “dual carbon target”, the involvement of these essential features, woodlands, and water bodies is critical for achieving carbon neutrality. Therefore, enhancing ecosystem service values and reducing carbon emissions should be considered as complementary and simultaneous to ecological protection strategies vital for the region’s sustainable development.

4. Discussion

4.1. Changes in Carbon Metabolism and Regional Ecosystem Service Value Distribution Under Long Time Series and Analysis of Causes

Changes in the spatial patterns of different land types reflect shifts in their primary functions. At the same time, socio-economic activities also influence terrestrial ecosystems’ carbon cycling processes, thereby affecting ecosystem services’ supply capacity. In the northeastern black soil region, the intensification of agricultural activities has directly or indirectly impacted carbon metabolism within the ecosystem. From 2000 to 2020, total carbon emissions in the study area increased significantly, resulting in a growing imbalance in carbon metabolism. The analysis of carbon flow paths reveals that this phenomenon can primarily be attributed to spatial structural changes driven by rapid urbanization, increased agricultural activities, and energy consumption. Consistent with findings from previous studies, many urbanized counties have experienced a sharp rise in carbon emissions due to the expansion of construction land. However, unlike other studies focused on urban agglomerations or development areas, research in agricultural counties within the northeast black soil region indicates that the extensive use of fertilizers, pesticides, and other agricultural inputs in cropland production has led to severe pollution and a marked increase in carbon emissions. This trend poses significant challenges to the sustainable development of agriculture and can have detrimental effects on ecological and environmental benefits. Addressing these issues will be crucial for promoting sustainable agricultural practices and mitigating the ecological impacts of intensified agricultural production.

Total ecosystem service values (ESVs) decreased from 2000 to 2020. Higher ESVs were concentrated in the southern regions of the study area, primarily due to the positive impact of extensive forest cover on ESV intensity. However, a significant loss of ecological value in grassland ecological space occurred between 2010 and 2020. This decline can be attributed to both natural and anthropogenic factors. Natural factors include the windy conditions typical of spring in the northeast black soil region, which can lead to wind erosion and subsequent grassland degradation. Anthropogenic factors encompass excessive cultivation and fertilization practices that may diminish soil fertility in the black soil area, adversely affecting grassland vegetation growth. While recent ecological protection policies implemented by the government have positively impacted the ecosystem value of the northeast black soil region, the overall trend remains downward. In 2023, the Harbin municipal government issued the “14th Five-Year Plan” for ecological environmental protection, introducing a range of policies promoting green development and environmental sustainability. The key measures include enhancing the prevention and control of agricultural surface pollution, encouraging cleaner agricultural practices, and improving the management of livestock and poultry waste. Additionally, the plan emphasizes the construction of rural waste treatment facilities and optimizing domestic sewage treatment systems to address rural ecological challenges. These initiatives provide essential policy guidance for implementing future ecological protection and restoration projects.

In this study, Bin County, located in the northeast black soil region, was employed as a case study to evaluate carbon metabolism and ecosystem service values using the “PLE space” classification system. The research also investigated the global applicability and advantages of governance strategies for agricultural countries. As one of the three major black soil regions globally, the northeast black soil region offers a representative context due to its distinctive agroecological characteristics and land use conflicts. This study demonstrates how Bin County, as a predominantly agricultural region, optimizes the functional configuration of production, living, and ecological spaces through refined zoning strategies, effectively balancing the demands of food production and ecological protection. The strengths of this research approach include the following: (1) Applicability: The zoning governance model for agroecosystems is adaptable to regions with diverse climatic conditions, land use patterns, and agricultural activities. (2) Scalability: The assessment framework integrates carbon metabolism and ecosystem service evaluations, enabling the tracking of carbon flow pathways and value dynamics, making it suitable for ecological and economic analyses across various scales. The findings from the Bin County case study provide actionable strategies for promoting low-carbon development in similar agroecosystems. Furthermore, they offer direct guidance for global cropland management and contribute to achieving carbon neutrality goals.

4.2. Recommendations for Zonal Carbon Reduction and Sink Enhancement Based on Clustering Model Conditions

Based on the results of the correlation study between carbon metabolism and ecosystem service value of PLE space, it can be found that the potential of ecological value enhancement and environmental pressure is different for different space types. Based on the results of the previous study, the area is divided according to the differences in clustering patterns. Meanwhile, combined with the study area’s natural, economic, and social factors, targeted zoning management measures are formulated to rationally utilize different ecosystems’ ecological efficiency and resource allocation to provide theoretical support for the healthy development of Bin County’s socio-economy (Figure 15).

• (1). Production–living integrated management zone (high–low clustering): This area is characterized by high carbon emission intensity alongside low ecosystem service value. To address this, the comprehensive management of agricultural cropland is essential to ensure the coordinated development of agriculture and ecological protection. This should involve optimizing land use through scientific planning and implementing efficient agricultural production methods. For instance, promoting green agricultural technologies and precision agriculture can enhance the output efficiency of cropland while minimizing negative environmental impacts. Additionally, encouraging farmers to adopt sustainable farming practices is crucial, including reducing the use of chemical fertilizers and pesticides. Promoting organic and ecological agriculture can further enhance agriculture’s environmental service functions. Strengthening soil protection and improvement measures for cropland production is also vital to prevent soil degradation and erosion, ensuring the long-term sustainability of agricultural output. Furthermore, inefficient or degraded cropland should be transitioned to ecological protection areas or subjected to ecological restoration as necessary, thereby improving the comprehensive benefits derived from land resources. By managing agricultural cropland comprehensively and optimizing the balance between ecological and construction land, the sustainable use of land resources in the region can be achieved, promoting the synergistic development of agriculture, ecology, and the economy.

• (2). Production–ecology synergistic development zone (high–high clustering area): This area exhibits high carbon emission intensity alongside a significant value of ecosystem services. The key measures to be implemented include optimizing water resource management, mainly focusing on the irrigation management of paddy fields. The adoption of water-saving irrigation technologies, such as drip and sprinkler irrigation, are essential in reducing water waste, enhancing irrigation efficiency, and lessening the demand for water resources in the context of agricultural mechanization. Improvements in agricultural mechanization should involve promoting energy-efficient machinery and equipment to decrease carbon emissions. Additionally, introducing biomass energy sources, such as biomass pellets and biodiesel, is recommended to replace traditional fuels and reduce carbon emissions. Strengthening the restoration and protection of ecosystems surrounding water bodies is also crucial. Maintaining the integrity and functionality of these ecosystems can enhance their capacity to absorb carbon, contributing to ecological health and climate mitigation efforts.

• (3). Ecological conservation zone (low–high clustering area): This area has a high ecosystem service value and minimal carbon emissions, indicating that current ecological achievements should be consolidated. The key measures include developing eco-industries by introducing eco-agriculture, eco-tourism, and eco-breeding. These initiatives will leverage the region’s rich environmental resources to enhance economic benefits while reducing carbon emissions. Additionally, optimizing the energy structure and land use is essential. Promoting the use of clean energy and reducing reliance on fossil fuels can help lower carbon emissions and protect the ecological environment. Strengthening environmental management and monitoring is also critical; establishing a robust environmental management framework can facilitate the effective monitoring and assessment of the ecological environment. This approach should enable the timely identification and resolution of environmental issues, ensuring the stability and security of ecological land.

• (4). Ecological potential zone (low–low clustering area): This area has low carbon emissions and similarly low ecosystem service value. The main measures to be implemented include ecological restoration and reconstruction efforts, such as afforestation, grassland restoration, and wetland rehabilitation, to improve vegetation cover and enhance carbon storage. Additionally, promoting eco-tourism can capitalize on unused land’s natural landscape and ecological resources, thereby increasing its economic value. This may involve developing eco-tourism attractions and establishing eco-leisure resorts. Constructing ecological parks and green spaces on unutilized land can enhance green space coverage and improve the overall ecological environment. Finally, promoting the use of renewable energy is essential. This can be achieved by establishing renewable energy facilities, such as wind farms and photovoltaic power stations, on unused land to reduce reliance on fossil fuels.

4.3. Limitations and Future Enhancements

Existing research has separately examined carbon metabolism and ecosystem service value (ESV) within horizontal and vertical dimensions at the county scale, confirming the spatial correlation between regional carbon metabolism and ESV. This research employs various statistical methods for data analysis; however, certain limitations persist.

Carbon emission coefficients were selected based on previous studies, which may exhibit regional variations in calculating carbon metabolism data. Future research should adjust the carbon emission coefficient calculation model to reflect these differences. Additionally, while the study utilized the carbon emission coefficient method, more refined accounting models must be developed to address the diversity of carbon emission sources, including agricultural production activities, livestock respiration, and residential respiration. The equivalent factor method was employed to measure the ecosystem service value. Although this approach is suitable for large-scale ESV accounting, it remains somewhat subjective. We hope that future studies will conduct more in-depth research on ESVs at the county level. Researchers should seek more comprehensive and precise data sources to enhance future investigations’ scientific rigor and accuracy.

5. Conclusions

The analysis of changes in the PLE space pattern indicates that the overall area of PLE space in Bin County, located in the northeast black soil region, remained relatively stable during the study period, with only minor local changes. Transformations primarily shifted from production and living spaces to production spaces. This shift can be attributed to economic development, which has caused an increase in the demand for agricultural land in Bin County. Meanwhile, a decline in ecological spaces, such as grasslands and water bodies, reflects the insufficient emphasis on ecological importance.

The analysis of the spatial and temporal evolution of carbon metabolism reveals that, despite a decreasing trend in carbon emissions, the net carbon transfer between compartments was negative. Additionally, the density of carbon metabolism exhibited a pattern of high values in the central areas and lower values in the surrounding regions. These findings suggest that agricultural production and human activities have disrupted carbon metabolism in Bin County, necessitating future regulation with a focus on low-carbon development strategies.

The spatial and temporal evolution of ecosystem service value (ESV) demonstrated a distribution pattern characterized by higher values in the central and western regions and lower values in the southeastern part during the study period. While the flow of ecological value had a positive influence, the overall decline in ESV was associated with policies such as farmland-to-forest conversions. However, anthropogenic activities, including over-cultivation and grazing, have caused significant disturbances and the degradation of ecosystems.

The clustering characteristics of carbon metabolism and ESV in Bin County revealed significant negative spatial correlations. The imbalance in carbon metabolism caused by changes in the PLE space pattern was directly reflected in the decline in ecosystem service value. Thus, enhancing ecosystem service value and regulating regional carbon metabolism should be pursued in tandem as complementary ecological protection strategies.

Author Contributions

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

Data Availability Statement

https://www.resdc.cn/Datalist1.aspx?FieldTyepID=1,3, accessed on 10 September 2024; http://www.chinabx.gov.cn/hebbx/tjxxgk/tongji.shtml, accessed on 3 October 2024.

Acknowledgments

We are grateful to the editors and reviewers for their valuable comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Spatial carbon metabolism and ESV horizontal and vertical transfer processes.

Figure 2. (a) Location map of Heilongjiang province. (b) Location map of Harbin city. (c) Location map of Bin county. (d) Map of the research area.

Figure 3. Research methodology flow chart.

Figure 4. Spatial and temporal distribution of PLE in Bin County from 2000 to 2020.

Figure 5. Heat map of land use transfer in Bin County from 2000 to 2020.

Figure 6. Sankey diagram of land use transfer in PLE space in Bin County from 2000 to 2020.

Figure 7. Spatial distribution map of carbon metabolism density in Bin County from 2000 to 2020.

Figure 8. Heat map of carbon flow in Bin County from 2000 to 2020.

Figure 9. Sankey diagram of carbon flows in PLE space in Bin County from 2000 to 2020.

Figure 10. Spatial distribution map of ecosystem service value intensity in Bin County from 2000 to 2020.

Figure 11. Heat map of ESV gains and losses in Bin County from 2000 to 2020.

Figure 12. Sankey diagram of ecological value gains and losses in PLE space in Bin County.

Figure 13. Bivariate Moran’s index for the production–living–ecological space from 2000 to 2020.

Figure 14. LISA cluster graph of carbon metabolism and ESV in the PLE during 2000–2020.

Figure 15. Development of zoning management measures in the study area based on distinct clustering patterns.

Table 1

Spatial data and statistics required for the research.

Data	Specific Indicators	Brief Introduction	Source
Spatial data	Land use data	Land use data for Bin County in 2000, 2010, and 2020, including the following seven land types: cropland, forest land, grassland, watershed, other land, and urban and rural land	Data Centre for Resource and Environmental Sciences, Chinese Academy of Sciences (DRC-CAAS) (https://www.resdc.cn/, accessed on 23 December 2024)
Statistical data	Agricultural data	Includes areas under crop cultivation, production of major crops, total mechanical power, irrigated areas, central livestock rearing, etc.	“China Energy Yearbook”, “China Urban Statistical Yearbook”, “China County Statistical Yearbook”, and “Bin County Statistics Yearbook” from 2000 to 2020 (https://www.stats.gov.cn/sj/ndsj/, accessed on 23 December 2024)
	Energy data	Includes different industrial energy consumption
	Demographic data	Includes rural resident population and urban resident population

Table source: created by the author.

Table 2

Classification system of PLE space in Bin County.

PLE Space Type Based on PLE Dominant Function			Land Use Classification
First Category	Secondary Category	Code	First Category	Secondary Category
Production space	Cropland production space	C	Cropland	Paddy field, dry land
Ecological space	Forest ecological space	F	Woodland	Wooded land, shrubland, open woodland
	Grassland ecological space	G	Grassland	High- and medium-cover grassland
	Water ecological space	W	Water	Rivers, canals, lakes, reservoirs, ponds, mudflats
	Other ecological Space	Un	Unused land	Marshland, bare land
Living space	Urban living space	Ur	Urban construction land	Townland, other building land
	Rural living space	R	Rural settlement land	Rural settlements

Table 3

Carbon metabolism calculation methodology.

PLE Space Site Types	Carbon Emissions Calculator	Carbon Absorption Calculator	Carbon Emission/Carbon Absorption Factor
Cropland production space	Agricultural activities, livestock respiration	Estimation of carbon absorption in crop growth	—
Forest ecological space	—	Land use carbon sink factor approach	−0.578
Grassland ecological space	—	Land use carbon sink factor approach	−0.021
Water ecological space	—	Land use carbon sink factor approach	−0.252
Other ecological Space	—	Land use carbon sink factor approach	−0.005
Rural living space	Rural residents breathing, straw burning	Indirect estimation of carbon emissions	—
Urban living space	Respiration, energy consumption of urban residents	Indirect estimation of carbon emissions	—

Table 4

Economic coefficient and carbon absorption rate of main crops.

(Farm) Crops	H	Cf
Rice	0.45	0.4144
Maize	0.40	0.4709
Beans	0.34	0.4500

Table 5

Carbon emission factors for each type of agricultural activity.

Activity	Fertilizer Application	Agricultural Machinery	Agricultural Films	Agricultural Cultivation	Agricultural Irrigation
Ratio	0.8956 t/t	0.00018 t C/kW	5.18 kg/kg	0.01647 t C/hm2	0.26648 t C/hm2

Table 6

Standard coal conversion coefficients and carbon emission coefficients of various energy sources.

Energy Type	Standard Coal Conversion Factor(kgce/kg, kgce/106 KJ, kgce/KW·h)	Carbon Emission Factor (tC/t)
Raw coal	0.7143	0.7559
Coke	0.9714	0.855
Crude oil	1.4286	0.5857
Petrol	1.4714	0.5538
Paraffin	1.4714	0.5714
Diesel oil	1.5714	0.5912
Fuel oil	1.4286	0.6185
Liquefied petroleum gas	1.7143	0.5042
Natural gas	1.2143	0.4483
Electricity	0.1229	2.5255

Table 7

ESV coefficients per unit area of the production–living–ecological space (CNY/hm²).

Type of PLE Space	C	F	G	W	Ur	R	Un
ESV coefficients	5543.91	27,645.86	16,931.14	124,604.61	−10,596.59	−10,596.59	912.29

Table 8

Dynamicity of the production–living–ecological space.

Attitude	Type of PLE Space	2000–2010	2010–2020	2000–2020
Homogeneous attitude	C	−0.07%	−0.13%	−0.21%
	F	−0.42%	0.45%	0.01%
	G	40.90%	−3.21%	7.11%
	W	−0.09%	−5.99%	−15.15%
	Ur	5.48%	2.01%	4.62%
	R	−0.41%	0.43%	0.01%
	Un	11.87%	59.52%	9.34%
Integration of dynamic attitudes		0.19%	0.44%	0.43%

Table 9

Carbon emissions and carbon absorption of production–living–ecological space from 2000 to 2020.

Type of PLE Space	2000		2010		2020
	Carbon Emissions/t C	Carbon Absorption/t C	Carbon Emissions/t C	Carbon Absorption/t C	Carbon Emissions/t C	Carbon Absorption/t C
C	838,303.89	352,024.58	2,370,254.82	1,006,794.54	593,803.90	1,175,286.13
W	-	70,452.82	-	67,481.93	-	70,542.97
G	-	30.89	-	157.19	-	106.70
W	-	4953.79	-	4909.89	-	1969.40
Ur	2,042,356.62	-	6,540,966.70	-	6,072,356.61	-
R	43,417.52	-	50,337.53	-	46,356.65	-
Un	-	4.26	-	9.31	-	64.70

Table 10

The dominant carbon transfer in the evolution of spatial patterns.

Exchange Value	2000–2010		2010–2020
	Transitions (104 tC)	Direction	Transitions (104 tC)	Direction
Beneficial carbon flow	11.60	+	171.76	+
Harmful carbon flow	−259.41	−	−229.04	−
Net carbon flow	−247.82	−	−57.28	−
H1	8.44	Ur → C	105.36	Ur → C
Contribution ratio	72.81%	/	61.34%	/
H2	−188.77	C → Ur	−210.43	C → Ur
Contribution ratio	72.77%	/	91.87%	/

Note: “+” represents positive carbon flow direction; “−” represents negative flow direction.

Table 11

Summary table of the value of ecosystem service in PLE during 2010–2020.

Ecosystem	2000		2010		2020		ESV Changes (CNY Billion)
	ESV(CNY Billion)	ESV Contribution Rate (%)	ESV(CNY Billion)	ESV Contribution Rate (%)	ESV(CNY Billion)	ESV Contribution Rate (%)	2000–2010	2010–2020
C	12.68	18.16	12.59	23.23	12.42	22.37	−0.09	−0.17
F	33.70	48.25	32.28	59.56	33.74	60.77	−1.42	1.46
G	0.25	0.36	1.27	2.34	0.86	1.55	1.02	−0.41
W	24.49	35.07	9.33	17.21	9.74	17.54	−15.16	0.41
Ur	−0.08	−0.12	−2.06	−0.24	−0.15	−0.27	−0.05	−0.02
R	−1.21	−1.73	−1.16	−2.14	−1.21	−2.81	0.05	−0.05
Un	0.01	0.01	0.02	0.04	0.12	0.22	0.01	0.1
Total amount	69.84	100.00	52.27	100.00	55.52	100.00	−15.64	1.32

Table 12

The dominant ESV transfer is in the evolution of spatial patterns.

Exchange Value	2000–2010		2010–2020
	Transitions (Billion CNY)	Direction	Transitions (Billion CNY)	Direction
ESV gain	2956.67	+	4117.90	+
ESV loss	−5499.39	−	−3739.40	−
total profit	−2542.72	−	378.50	+
H1	1950.87	C → F	2612.76	C → F
Contribution ratio	65.98%	/	63.45%	/
H2	−2657.20	F → C	−1572.75	F → C
Contribution ratio	48.32%	/	42.06%	/

Note: “+” represents positive carbon flow direction; “−” represents negative flow direction.

Table 13

Global Moran’s index of bivariate spatial autocorrelation in the production–living–ecological space.

Year	Global Moran’ I	p-Value	Z-Score
2000	−0.4416	<0.001	−79.9794
2010	−0.4253	<0.001	−65.4576
2020	−0.4830	<0.001	−74.8719

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