Next Article in Journal
Energy Transition Consumption, Climate Risk Regulation and Economic Well-Being of Rural Households
Previous Article in Journal
Evaluating the Effectiveness of Different Demonstration Models on Agricultural Climate-Smart Technology Adoption: Evidence from China’s Cotton Farmers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 17
Issue 16
10.3390/su17167352
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Ecosystem Services in Northeast China’s Cold Region: A Comprehensive Review of Patterns, Drivers, and Policy Responses

by
Xiaomeng Guo
1,
Chuang Yang
1,
Zilong Wang
1 and
Li Wang
2,*
1
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, China
2
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7352; https://doi.org/10.3390/su17167352
Submission received: 22 July 2025 / Revised: 3 August 2025 / Accepted: 13 August 2025 / Published: 14 August 2025
Browse Figures
Versions Notes

Abstract

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China.

1. Introduction

Ecosystem services (ESs) refer to the ecological properties, functions, or processes that directly or indirectly contribute to human well-being—namely, the benefits people derive from well-functioning ecosystems [1,2]. The currently widely adopted classification of ESs is based on the Millennium Ecosystem Assessment (MEA) framework, supplemented by quantifiable accounting approaches. This scheme typically includes four perspectives: provisioning services (e.g., food production, water yield), regulating services (e.g., climate regulation, water purification, soil conservation), supporting services (e.g., biodiversity, primary productivity), and cultural services (e.g., aesthetics, recreation) [3,4]. In recent years, as global environmental problems have intensified and sustainable development goals have advanced, ESs have emerged as a key research focus. Serving as a vital link between humans and nature, and between ecosystems and socioeconomic systems, they have attracted growing attention across multiple disciplines, including ecology, geography, environmental science, and economics [5]. Human activities are exerting increasing pressure on natural systems, largely driven by the pursuit of material growth. These impacts are reflected in the widespread substitution or transformation of ecosystem types, substantial interference with ecosystem structure and function, and the excessive use of ESs. As a result, the study of ESs has become increasingly urgent. In particular, a systematic understanding of their spatial patterns, temporal dynamics, and the mechanisms underlying trade-offs and synergies is of both theoretical and practical importance for safeguarding ecological security, optimizing land use, improving human environmental quality, and advancing the goals of carbon peaking and carbon neutrality [6,7].
ESs exhibit significant spatial heterogeneity, with their types, intensities, and temporal dynamics varying considerably across regions. These differences are largely shaped by the interplay of natural geographic conditions, ecosystem types, and the intensity of human activities, resulting in clear regional patterns [8]. On the one hand, natural factors such as climate, hydrology, topography, and soil conditions shape the spatial foundation of ecological processes, influencing both ecosystem productivity and regulatory capacity across regions. On the other hand, different ecosystem types—such as forests, wetlands, and agricultural lands—possess distinct structures and functions, which, in turn, lead to variations in the services they provide, including carbon sequestration, water regulation, and biodiversity conservation [9]. In addition, the intensity and mode of human activities (e.g., land development, agriculture, urban expansion) can also profoundly reshape regional ES patterns [10,11]. Therefore, identifying and understanding the characteristics and evolutionary patterns of ESs at different regional scales has become an important foundation for ecosystem management and policy development.
Compared with temperate and tropical regions, Northeast China—characterized as a cold ecoregion—is particularly sensitive to climate warming and land-use change due to its distinct climate, hydrology, soil, and vegetation structures. This sensitivity is reflected in pronounced fluctuations in ESs and complex interrelationships among them [12]. Ecosystems in cold regions tend to exhibit heightened sensitivity to external disturbances, such as climate warming and human development, resulting in more complex patterns of service coupling and trade-offs. These characteristics make Northeast China a particularly representative and pressing case for ES research, meriting systematic and in-depth investigation. The region is experiencing steady economic recovery and is home to a wide variety of ecosystems, including forests, grasslands, wetlands, and farmlands. It serves not only as a major national grain-producing base and ecological security barrier, but also as a key area for several national initiatives, such as the Black Soil Conservation Project, the Northeast Revitalization Strategy, and the Ecological Redline Protection Program. Owing to its long, harsh winters, extensive permafrost zones, fertile black soil plains, and characteristic cold-temperate forests, the region supports a broad array of ESs: provisioning services (e.g., food and water resources), regulating services (e.g., carbon storage, water retention), supporting services (e.g., soil conservation, biodiversity maintenance), and cultural services linked to agro-cultural heritage and eco-tourism [13,14,15]. As a critical region for national food security, carbon neutrality goals, and ecological protection, Northeast China plays a pivotal role in supporting key national strategies. Northeast China encompass ecological system variations. For instance, Sanjiang Plain in eastern Heilongjiang is characterized by vast wetlands and paddy fields, shaped by large-scale agricultural reclamation and intensive water use policies [16]. Greater and Lesser Khingan Mountains are dominated by temperate coniferous and mixed forests, where ecological protection and logging bans influence land management [17]. Meanwhile, the Songnen Plain presents a mixed area of saline-alkaline grasslands, dry farmlands, and urban expansion zones, with associated ecological degradation and fragmented governance [18]. These regional variations result in markedly different ESs and management priorities, ranging from water retention and biodiversity conservation in mountainous reserves to provisioning services and pollution control in intensively cultivated plains. Recognizing such heterogeneity is critical for the development of nuanced ES assessment frameworks and spatially differentiated policy responses. Therefore, a comprehensive analysis of ES types, trade-offs, synergies, and their driving mechanisms in this cold region holds significant theoretical and practical value. Such efforts are essential for enhancing the scientific basis of regional ecological management and advancing sustainable development in cold environments.
This review systematically examines the current state of ES research in Northeast China, focusing on service characteristics, trade-offs and synergies, driving mechanisms, and modeling advancements. In contrast to previous studies that are often limited in thematic scope or geographical focus, this review offers a comprehensive synthesis by integrating multiple ES categories, conducting cross-scale comparisons, and incorporating diverse modeling approaches. Its novelty lies in bridging spatial–temporal patterns of ESs with policy and management responses, thereby providing a more holistic framework to support the coordinated governance of multifunctional ecosystems in cold regions.

2. Materials and Methods

To systematically trace the developmental stages, core issues, and key methodologies in ES research in Northeast China (Figure 1), this study employs a systematic literature review, combining qualitative content synthesis. Although ESs have been widely studied across various regions and ecological contexts, this review narrows its focus to empirical, data-based studies conducted specifically in Northeast China.

2.1. Literature Acquisition and Data Sources

To ensure a rigorous and transparent literature review process, this study followed a systematic review protocol referencing the PRISMA framework [19,20]. Literature was retrieved from two major databases—China National Knowledge Infrastructure (CNKI) and Web of Science—covering the period from 1997 to 2025. The keywords used included the following: “ecosystem services”, “Northeast China”, “trade-offs and synergies”, “spatiotemporal patterns”, “driving factors”, and “policy”.

2.2. Literature Screening and Classification Methods

Only peer-reviewed journal articles were considered, and literature languages were restricted to Chinese and English. Initial retrieval yielded 129 publications, which were screened based on title and abstract, followed by full-text assessment. Then, we rigorously screened studies that performed quantitative analyses of ESs with clearly defined spatial scopes and region-specific ES estimations. As a result, 56 methodologically sound and thematically relevant studies were included in the final review. The inclusion criteria were as follows: (1) the study explicitly targeted Northeast China, including various spatial scales such as watersheds, provinces, cities, or counties; (2) the research addressed at least one of the following themes: ES type identification, spatiotemporal pattern analysis, trade-off and synergy relationships, driving mechanisms, or regulatory strategies, and must include quantitative metrics for at least one type of ES; (3) the study employed ecological modeling tools or provided policy-oriented insights or recommendations; (4) excluded were non-empirical policy commentaries, conference abstracts, and instructional materials. Literature quality was ensured by giving preference to papers published in SCI-indexed journals, core CSSCI journals, or highly cited works. The full review and screening process is illustrated in Figure 2.
Based on research focus, the selected literature was further categorized into five thematic modules: (1) ES types and their spatiotemporal patterns; (2) trade-offs and synergies; (3) identification of driving factors; (4) regulatory pathways and policy evaluation; (5) emerging research trends and future directions.

2.3. Analytical Framework

To support a more systematic analysis, this review develops a three-dimensional evaluation framework encompassing temporal, spatial, and service type dimensions: (1) Temporal dimension: Based on publication year, the literature is divided into three phases—an exploratory stage (1997–2010), a developmental stage (2011–2017), and a stage of comprehensive deepening (2018 onward)—to capture the evolution of research trends over time; (2) Spatial dimension: studies are categorized by spatial scale, including provincial level, watershed level, and city or county level; (3) Service-type dimension: ESs are classified into four major categories—provisioning, regulating, supporting, and cultural services. For each study, the number of service types assessed, their spatial coverage, and the frequency of coupling are recorded and analyzed. In addition, this study reviews commonly used methods for identifying relationships among ESs to clarify the dominant methodological pathways in current research.
This framework serves as the structural basis for our systematic review, enabling a clear categorization of the literature across time, space, and service typology. Although it does not derive from a specific theoretical model, this multi-dimensional approach allows for a coherent synthesis of empirical findings and highlights the evolution and diversity of ES research in Northeast China.

3. Results and Discussion

This section presents a thematic synthesis of findings drawn from 56 empirical studies on ESs in Northeast China. Rather than reporting original data, we integrate patterns, relationships, and methodologies identified across the literature. The synthesis is organized around four major themes: (1) spatiotemporal characteristics of ESs; (2) trade-offs and synergies; (3) driving mechanisms; and (4) policy and regulatory pathways: (5) comparative analysis.

3.1. Synthesized Patterns and Temporal Evolution of ESs in Northeast China

In recent years, research on ES types and spatial scales in Northeast China has continued to deepen. Following literature collection, screening, and classification of service types, preliminary statistical results were obtained. In terms of research scale, most existing studies are concentrated at the provincial, urban, and county levels but are gradually expanding to more management-relevant spatial units such as watersheds, ecological zones, and urban agglomerations. Among these, provincial-scale studies are particularly effective in informing macro-level strategies for the balanced management of ESs [21,22,23], and at municipal and county scales, the transition from “trade-offs” to “synergies” among different ESs can be more precisely observed and characterized [24,25]. Using administrative units as the research scale is well aligned with the formulation of management objectives in China. However, from the perspective of natural spatial attributes, an increasing number of studies have focused on watershed-scale analyses of ESs, including case studies in the Rao River Basin, the Ashi River Basin, and the Sanjiang Plain [26,27,28], and aligning administrative and watershed units offers a more integrated basis for setting ecological management goals that reflect both natural processes and human development needs. In terms of temporal scale, most studies adopt a time window of the past two decades (e.g., 2000–2020) to analyze the evolution of ESs, using remote sensing data and ecological models such as InVEST, CASA, and RUSLE. However, these analyses often rely on discrete time points at 5–10 year intervals rather than continuous time series, leading to limited temporal resolution. Only a few studies have conducted long-term, continuous assessments [29,30]. Some studies have further extended their scope to scenario based prediction. Most of these adopt InVEST coupled with the PLUS model to simulate carbon storage under alternative future scenarios. For instance, Gao et al. [31] simulated land use changes and corresponding carbon storage in Heilongjiang Province for 2030–2050 under the SSP-RCP framework of CMIP6 by integrating the System Dynamics (SD) model, PLUS, and InVEST. Similarly, Li et al. [21] applied the InVEST-PLUS model framework to simulate spatial land use policy scenarios in Liaoning Province and projected carbon storage for the year 2050. However, these studies tend to focus on a single ES and emphasize the influence of driving factors.
In terms of service types, most studies in Northeast China address mainstream ESs such as food production, water yield, soil conservation, carbon sequestration, habitat quality, and supply–demand matching. These largely cover the three primary categories of provisioning, regulating, and supporting services. Among these, regulating services have received particular attention, reflecting the region’s sensitivity to ecological stability and functional recovery under its inherently vulnerable cold-zone conditions. Although the MEA framework includes four main categories of ESs, most existing studies in Northeast China’s cold regions have focused on provisioning and regulating services. This is largely due to the availability of quantitative data and direct relevance to economic and environmental management. In contrast, cultural services, such as recreation, aesthetic value, and education, are often difficult to quantify. Therefore, limited research attention has been paid to culture services, reflecting both methodological challenges and disciplinary biases. Future studies should aim to fill these gaps through interdisciplinary approaches that incorporate social survey methods and process-based ecological modeling.
Empirical findings also indicate that national policies, such as the Grain-for-Green Program and black soil protection initiatives, have contributed to improvements in ESs in Northeast China [32,33]. Regulating services have generally shown increasing trends, although their spatial distribution remains uneven—being more favorable in the southern part of Northeast China and in hilly areas, compared to the north and intensively cultivated plains. Overall, current research on ESs in Northeast China has formed a technical framework characterized by multi-scale spatial analysis, phased time series monitoring, and multi-model simulation. This framework provides a valuable knowledge base for understanding service interactions, predicting future trends, and supporting ecological decision-making. Nevertheless, challenges remain, including limited spatial scale diversity, uneven regional representation, and a tendency to overemphasize certain service types. Addressing these issues will require further development of cross-regional comparisons and expanded scenario analyses (Table 1).

3.2. Integrated Evidence on Trade-Offs and Synergies Among ESs

Identifying trade-offs and synergies among ESs is fundamental to understanding the evolution of regional ecological functions and informing resource management strategies. As shown in Table 1, existing studies in Northeast China can be broadly classified into two categories: single-service studies and integrated multi-service studies. Each research paradigm has its strengths and limitations in terms of analytical depth and comprehensiveness. Single-service studies tend to offer more detailed insights into individual service dynamics, but they often fall short in capturing systemic relationships. In the Northeast region, some studies have focused on individual services such as carbon storage, habitat quality, water yield, and water availability [22,29,70]. The strength of this type of research lies in its ability to offer detailed insights into the evolution mechanisms of individual ESs, their driving factors, and corresponding management strategies. It is particularly well suited for ecological management practices with clearly defined goals, such as water source protection zones or high-yield agricultural areas [45,63]. However, a key limitation of single-service studies is their inability to identify potential trade-offs or synergies with other services, thereby overlooking ecosystem integrity and system-level feedbacks. As a result, many studies have shifted toward multi-service analyses, which offer a broader understanding of overall service patterns. Nonetheless, these studies face challenges in uncovering the underlying mechanisms driving trade-offs and synergies. In recent years, researchers have increasingly selected three or more ESs for joint analysis, using quantitative modeling and multivariate statistical methods to examine their interactions. This type of research offers a more comprehensive understanding of the interactions among regional ESs and supports decision-making aimed at balancing regional development with ecological protection [74,75]. A “trade-off” between ESs refers to a situation where the enhancement of one service occurs at the expense of another, whereas “synergy” implies that multiple services can be improved simultaneously under the same set of driving conditions. The relationships reflect positive and negative correlations between services as a result of ecological processes, resource allocation, or human intervention, and are a key basis for understanding the diversity of ecosystem functions and optimizing their management [74]. As a result, the quantitative assessment of trade-offs and synergies among ESs has become a key focus in recent research. A variety of methods have been applied to reveal interactions among different services, primarily targeting their identification at both overall and spatial scales. A summary of the mainstream quantitative methods used in current ESs research is presented in Table 2, including correlation analysis, geographically weighted regression, regression modeling, spatial autocorrelation, cluster analysis, principal component analysis, and the Geodetector model. These methods vary in their ability to capture spatial, temporal, and interaction dynamics among services. In empirical research on Northeast China, overall scale analyses are predominantly based on correlation techniques, such as Pearson and Spearman correlation, which can reveal overall directional relationships between services but are limited in capturing spatial heterogeneity and underlying drivers. In contrast, spatial scale analyses often rely on geographically weighted regression (GWR), which is more effective in identifying spatial variation and exploring potential driving mechanisms. Among spatial-scale analyses, GWR is the most commonly used method because it captures variations in the strength and direction of service relationships across different geographic units. In contrast, other quantitative methods such as root mean square error (RMSE), the trade-off/synergy degree index (TSD), and the coupling coordination degree (CCD) have seen limited application in the Northeast region, which restricts a deeper understanding of the complexity and dynamics of ES interactions in this area. As shown in Table 3, each method has specific advantages and limitations regarding spatial applicability, interpretability, and analytical depth [76]. In addition to traditional statistical methods, recent studies have begun incorporating more advanced analytical frameworks. For instance, structural equation modeling (SEM) allows researchers to explore latent relationships and infer potential causal mechanisms among multiple ES variables and their drivers [77]. This approach is particularly useful in disentangling direct and indirect effects within complex socioecological systems. Furthermore, machine learning (ML) algorithms have demonstrated strong performance in predicting ES distributions, identifying key drivers, and modeling nonlinear, high-dimensional relationships [78]. These methods are increasingly employed in spatially explicit ES assessments, especially when dealing with large or heterogeneous datasets. Future research should further integrate SEM and ML models to enhance interpretability, generalizability, and decision-support potential in ES research, particularly in cold and data-scarce regions.
Future research on ESs in Northeast China should advance in several key areas. First, greater emphasis should be placed on methodological integration and comparative validation, combining overall scale and spatial scale approaches to enhance diagnostic capacity and better reflect the ecological characteristics of the region. Second, the applicability of methods should be expanded across different administrative levels, ecosystem types, and data dimensions to improve contextual adaptability. Third, it is essential to strengthen the connection between methodological outputs and policy-making by applying the results of trade-off and synergy analyses in practical ecological management processes, such as ecological redline delineation and territorial spatial planning.
From the perspective of implications in spatial resolution, spatial resolution is a fundamental yet often underappreciated factor in the assessment of ESs. It directly influences the precision with which spatial patterns can be identified, the robustness of model simulations, and the relevance of the results for policy and management decisions. Studies reviewed in this article commonly employ remote sensing data with resolutions ranging from 30 m to 1 km, and spatial units such as county boundaries, watershed, or grids. Fine resolution data enable the identification of localized ES hotspots or conflict zones, such as soil conservation areas vulnerable to farmland expansion, which are often smoothed or entirely missed at coarser scales. This is particularly relevant in heterogeneous landscapes such as Northeast China, where ecological functions vary markedly over short distances. Moreover, models such as InVEST, GWR, and Geodetector are sensitive to spatial resolution, especially in how they delineate service supply and demand areas, assess spatial autocorrelation, or estimate variable interactions. Previous studies have demonstrated that coarser spatial resolutions may blur critical spatial gradients and even exaggerate spillover effects, thereby influencing the interpretation of trade-offs and synergies among ESs [86]. Spatial resolution has implications for scale mismatches between ecological processes and administrative units. While policies are often implemented at municipal or county levels, ES processes such as water regulation or carbon sequestration do not follow political boundaries. If spatial analyses use resolutions that misalign with governance scales, policy design and ecological compensation mechanisms may suffer from inefficiencies or misallocations. The selection of spatial resolution reflects trade-offs between computational feasibility, data availability, and management objectives. High-resolution analysis improves local relevance but increases data volume and processing costs [87]. Scale dependence is also evident in ES studies. For example, synergistic relationships are more frequently identified at the county level, whereas trade-offs are more commonly observed at broader scales such as the provincial level. In a case study of Binxian, water conservation and NPP exhibited a strong synergy. In contrast, in large-scale agricultural regions such as the Songnen Plain, food production and soil conservation services showed significant trade-offs [25,36]. In this review, we systematically identified and organized the current trade-offs and synergies among typical ESs in Northeast China (Table 3). The analysis shows that synergies are primarily found in areas dominated by natural ecosystems, such as regions with high forest cover or dense grasslands on sloped terrain. In contrast, trade-offs are more frequently observed in zones characterized by intensive agriculture and a high proportion of arable land, particularly in services associated with food production. These differences are mainly driven by variations in land use types, the intensity of policy interventions, natural conditions, and stages of socioeconomic development. For instance, in the agricultural regions of Heilongjiang, high-intensity cultivation often results in structural conflicts between provisioning and regulating services. Conversely, in the forested areas of the Changbai Mountains, ecological restoration efforts have led to simultaneous improvements in multiple services.

3.3. Literature-Based Analysis of Driving Factors Influencing ESs

The evolution of ESs is shaped by both natural processes and human activities. Identifying and understanding the underlying driving mechanisms is essential for analyzing patterns of change and formulating targeted management strategies. In ES research, the concept of “drivers” extends beyond the identification of variables; it also involves explaining change, uncovering mechanisms, predicting future trends, and supporting decision-making. In many cases, these drivers also serve as input variables in ES modeling. In Northeast China, recent studies have begun to establish a framework of key factors influencing the supply and demand of ESs across multiple dimensions, including natural, social, land use, policy, and climate-related factors. The present review summarizes the main drivers identified in current research on this region (Table 4). These driving factors are typically treated as independent variables and are analyzed for their relationships with ES outcomes (as dependent variables) using a range of qualitative and quantitative methods. These methods mainly include the following: (1) correlation analysis, which examines whether there is a relationship between variables; and (2) regression modeling, which assesses which variables have the greatest influence. At the local spatial scale, most studies in Northeast China have applied correlation analysis, GWR, and Geodetector models to explore spatial heterogeneity and variable influence strength, particularly at the county or sub-regional level. With the advancement of machine learning, studies in other regions have begun to use techniques such as random forests and gradient boosting to capture non-linear relationships and assess the relative importance of driving factors [88,89].
Current research on the driving mechanisms of ESs mainly relies on quantitative approaches such as Geodetectors, spatial regression models, GWR/GTWR, and machine learning. These methods have contributed to significant progress in understanding the spatial heterogeneity of ESs and the underlying influence of various drivers. The findings are particularly valuable in identifying key areas of influence and potential zones of synergy in the evolution of ESs across Northeast China, offering important insights for regional ecological governance [90]. However, considering the unique ecological and socioeconomic context of Northeast China, existing research still shows several notable limitations. In the reviewed studies, the selection of driving factors remains largely focused on broad categories such as natural factors, climatic factors, land use, and socioeconomic factors (Table 5). However, in order to better reflect the distinctive agroecological context of Northeast China, we also highlight key policy and management, such as ecological redlines, land zoning policies, nature reserve designation, and reforestation programs. These region-specific factors help capture the interactions between ecological processes and anthropogenic management more accurately. This tendency toward the “generalization of variables” reduces both the explanatory power and contextual relevance of models when applied to the specific conditions of Northeast China. Although the region experiences distinct seasons, a cold climate with a pronounced freeze-up period, and ecosystems that are highly sensitive to climatic variability, most studies still treat climate factors as static control variables and rarely model their interactions with regional ecological processes. Moreover, many existing analyses rely on correlation or contribution-based approaches, which fail to capture nonlinear interactions between variables, making it difficult to uncover the true causal mechanisms behind changes in ESs. In particular, few studies have explored compound ecological effects, and the potential of machine learning for capturing complex dynamics remains underutilized. In addition, most current research on driving mechanisms remains at the level of static analysis and lacks systematic modeling across time series, policy scenarios, and future development pathways, limiting its capacity to support predictive analysis and informed policy design at the regional scale. Going forward, it is essential to develop a more comprehensive system of driving factors tailored to the unique characteristics of Northeast China. This includes integrating agricultural resource indicators, policy implementation metrics, seasonal climate dynamics, and ecological process responses, while adopting modeling approaches that combine geospatial simulations with scenario-based policy analysis. Such efforts will support a shift from descriptive spatial analysis toward process-based modeling and strategy development, helping ES research in the Northeast cold region advance toward greater integration, dynamism, and foresight.

3.4. Review of Regulatory Strategies and Policy Responses in the Context of ESs

Building on the identification of ES types, their spatial and temporal patterns, and the trade-offs and synergies among them, the development of well-informed and scientifically grounded regulation strategies has become central to supporting the healthy functioning of regional ecosystems. In recent years, the focus of ES research has gradually shifted from merely mapping service patterns to actively exploring strategies for intervention and regulation, particularly in Northeast China. With the ongoing implementation of China’s national ecological civilization strategy, both academic research and policy practices related to ES regulation have continued to advance and deepen [36,37,58]. At present, existing studies usually formulate regulation programs for ESs from three technical paths: (1) In regional classification and management based on the spatial distribution characteristics of ESs, tools such as InVEST, ES cluster, and GTWR are commonly used to identify high-value service areas, zones of potential conflict, and regions experiencing service degradation. These areas are then categorized and managed according to their spatial and temporal dynamics. For example, Hang et al. [91] also employed clustering and regional classification techniques to divide the Songnen Plain into four categories: core advantageous areas, stable maintenance areas, transitional adjustment areas, and high-risk areas. This zoning framework offers valuable insights for planning agricultural and ecological interventions across regions. (2) Optimization and regulation pathways based on the matching between ES supply and demand have gradually developed into a systematic methodology in research on Northeast China. For example, spatial matching analyses using outputs from the InVEST model are commonly used to compare service supply with human demand, while mismatched areas are identified through correlation analysis and spatial clustering. These approaches help inform ecological compensation mechanisms and guide strategies for land use optimization [12,53]. Jin et al. [55] suggested incorporating mismatch indicators into the decision-making framework for constructing ecological value chains, in order to support regional management and the design of compensation policies. (3) To compare the response trends of ESs under different policy intervention pathways by constructing scenario-based simulation and prediction models, thereby providing multiple options to support informed decision-making, Jiang et al. [92] applied multi-objective planning (MOP) and the PLUS model to simulate spatial variations in ES values (water yield, soil conservation, and carbon storage) under different scenarios in Heilongjiang Province from 2000 to 2035. This approach enabled a comparative assessment of policy pathways, such as ecological priority versus economic priority, to support more precise analysis and decision-making. As a key external driver of ESs, policy interventions play a significant moderating role in Northeast China. In recent years, both central and local governments have introduced a range of policies aimed at ecological protection and agricultural management. These include the Returning Farmland to Forests Project, the Black Soil Protection and Utilization Plan, the Northeast Ecological Barrier Construction Initiative, the Green Agriculture Development Policy, and the Ecological Red Line Delineation Program. By adjusting land use structures, limiting high-intensity development, and promoting environmentally friendly production practices, these policies have contributed to enhancing the region’s ecological regulatory functions and the sustainable provision of ESs. Figure 3 illustrates the relationships between major ecological policies, their applicable regions, implementation timelines, ecological objectives, and associated ESs in Northeast China.
Northeast China, particularly the Songnen and Sanjiang Plains, plays a central role in national food security as a primary grain-producing region. Accordingly, food production is a dominant provisioning ES assessed in the existing literature [93,94]. Multiple studies indicate that land use intensification, black soil degradation, and climate variability have significantly influenced crop yields and agricultural sustainability. National and provincial policies, such as the Black Soil Protection and Utilization Plan, have been implemented to enhance soil fertility and secure long-term food supply. However, spatial heterogeneity remains evident, with lower productivity in marginal lands or ecologically sensitive areas.
Despite the gradual improvement of the policy framework, current ecological policies still face several shortcomings in both design and implementation. First, most policy formulations are based on administrative boundaries or functional zoning, with limited alignment to ES patterns and their dynamic responses. As a result, the spatial heterogeneity of ESs is often poorly reflected in policy planning. Second, many policies tend to prioritize provisioning services—such as food production and arable land preservation—while placing insufficient emphasis on regulating and cultural services. This imbalance can undermine the overall functionality and resilience of ESs. For example, in the context of black soil conservation, excessive emphasis on farmland’s production function may overlook its important roles as a habitat and a carbon sink [95]. Third, most current policy implementations lack a dynamic evaluation mechanism based on ES feedback, making it difficult to effectively monitor the process and outcomes of service responses after policies are enacted, which, in turn, hampers timely policy adjustment and improvement. Liu et al. [73] pointed out that although ecological restoration projects have been carried out in the northeastern forest areas, the absence of a follow-up dynamic feedback mechanism has limited the long-term stability of restoration outcomes and reduced management responsiveness. Fourth, the phenomenon of “disconnection” between ES research and policy formulation still exists. Although a substantial body of academic work has been produced on service identification, trade-off analysis, and driving mechanisms, its application in concrete policy areas, such as ecological compensation, territorial spatial planning, and agricultural support, remains limited. A cohesive pathway linking scientific research, policy design, and governance implementation has yet to be effectively established. Zhang et al. [96] noted that although ES research in China has developed a wide range of models, the integration of scientific findings into practical spatial planning and policy implementation remains limited and has yet to be effectively translated into action. Therefore, future ES regulation strategies in Northeast China should place greater emphasis on bridging scientific spatial analysis with real-world policy practices. On the one hand, an ecological management framework that is both differentiated and spatially zoned should be developed based on the spatial and temporal heterogeneity of ESs, advancing the establishment of a regional governance model oriented toward ESs. On the other hand, a comprehensive assessment and feedback system focused on service synergies should be introduced, incorporating tools such as scenario forecasting, targeted interventions, and policy simulations to facilitate the shift from static control to dynamic regulation. Meanwhile, mechanisms for translating scientific research into policy should be strengthened, promoting the effective integration of ES theories with policy frameworks to support the coordinated and sustainable development of both the ecological environment and the socioeconomic system in Northeast China.
While existing policy frameworks such as the National Key Ecological Function Zoning, the Grain-to-Green Program, and ecological redline delineations have clearly contributed to the conservation and restoration of ecosystem functions, their direct and indirect impacts on ES remain underexplored. For example, the implementation of natural forest protection policies in the Greater Khingan region has significantly enhanced regulating services such as carbon sequestration and water conservation, but at the cost of reduced provisioning services like timber production [17]. Similarly, the expansion of ecological compensation programs in the Sanjiang Plain has improved wetland restoration and biodiversity support, yet challenges remain in balancing food production goals with ecological targets [16]. These examples suggest that policies are not only drivers of ES changes, but also responses to ES degradation signals. Future ES-oriented policy design should consider spatial heterogeneity, ecosystem trade-offs, and long-term adaptive feedbacks to maximize multifunctionality. A dynamic policy–ES coupling framework is thus essential for aligning conservation effectiveness with socioeconomic development goals.
In summary, this review’s novelty lies in its ability to unify ES research from disparate fields, identify trade-off regularities in Northeast China, and outline actionable insights for policy design, which are seldom addressed jointly in earlier reviews. Compared with previous literature, this review highlights several novel aspects. First, it expands the traditional pattern-function perspective by incorporating policy responses as a key component of ES analysis. Second, it integrates quantitative trend analysis with policy text interpretation, offering a multidimensional understanding of ES evolution. Third, this study proposes a regionally-adapted policy-research framework tailored to cold-region ESs, which can be extended to other high-latitude or ecologically vulnerable regions. These contributions collectively enhance the theoretical depth and practical relevance of ES research in cold regions.

3.5. Cross-Regional Comparison of ESs

To provide a more comprehensive understanding of ES dynamics in Northeast China, this study draws on comparative insights from three ecologically and socioeconomically distinct regions: the Loess Plateau, the Yangtze River Basin, and the Canadian permafrost zone. These regions were selected for their representative environmental conditions and contrasting land management histories. The Loess Plateau exemplifies a typical case of ecological degradation followed by intensive, policy-driven restoration efforts [97]. The Yangtze River Basin, as China’s most densely populated and economically active watershed, faces significant ecological pressures and has developed more integrated governance approaches to ES management [98]. In contrast, the permafrost zone offers a cold-region international reference system, where ecosystem functioning is predominantly shaped by permafrost dynamics and climate feedbacks rather than direct land-use change [99]. Taken together, these cases allow for a multidimensional comparison of ES types, trade-off patterns, influencing drivers, and institutional responses under varying environmental and policy contexts. Table 6 provides a synthesized comparison to highlight regional divergences in ES priorities, assessment approaches, and governance mechanisms, offering a foundation for contextualized ES management strategies in Northeast China.
In Northeast China, ES assessments emphasize provisioning services such as food production and regulating services like carbon storage, commonly quantified using InVEST or statistical correlations. In contrast, studies in the Loess Plateau focus heavily on soil and water conservation, windbreak/sand fixation, biodiversity, and carbon sequestration following ecological engineering, typically using remote sensing based value estimation frameworks [100]. In the Yangtze River Basin, multi-service evaluation frameworks are employed, often integrating hydrological regulation, climate regulation, provisioning, and cultural services via hotspot and spatial econometric methods [101]. By comparison, in the permafrost zone, regulating services such as carbon sequestration, hydrological buffering, and climate regulation are dominant due to the vast carbon stored in frozen soils and the ecosystem’s role in climate feedback mechanisms [102]. This highlights a methodological divergence from Chinese studies, which more often depend on land use proxies and ES valuation coefficients. Together, these variations reflect how differing ecological conditions and management objectives influence both the emphasis of ES categories and the methodological pathways used for their assessment.
Trade-off/synergy relationships differ markedly regionally. In Northeast China, ES trade-offs mainly occur between provisioning services and regulating services such as carbon storage and soil conservation, especially in black-soil farmlands. In the Loess Plateau, trade-offs are exacerbated between hillside food cultivation and soil water conservation functions; studies report significant erosion and soil loss resulting from provisioning service prioritization [103]. For the Yangtze River Basin, trade-offs like water yield versus carbon storage and synergies between habitat quality and water regulation have been spatially analyzed, showing stronger regulation-service dominance [101]. Trade-offs in the permafrost region primarily stem from permafrost degradation and thawing processes, which diminish carbon sequestration capacity and disrupt hydrological regimes [104]. Driving factors also differ across regions. In Northeast China, primary ES drivers include NDVI, land use intensity, black soil extent, elevation, and cropping inputs. Meanwhile, in the Loess Plateau, the impact of ecological restoration (Grain-for-Green) is significant—land-use change variables such as reclaiming farmland to forest/grassland directly influence ES capacity index. In the Yangtze River Basin, driving models incorporate climatic variables (precipitation, temperature) combined with socioeconomic variables and land use, evaluated via hotspot and panel regression analyses. In the permafrost zone, however, the dominant drivers are less anthropogenic and more cryospheric in nature. These include permafrost stability, ground ice content, soil temperature regimes, and thaw depth variability. These comparisons reveal that both the nature of ES trade-offs and their underlying drivers are deeply context-specific, shaped by biophysical constraints, land use intensity, and regionally tailored ecological policies.
Comparative policy approaches are region-specific. Northeast China policies include black soil conservation, ecological redlines, and cropland zoning; however, implementation fragmentation remains an issue. The Loess Plateau has benefited from government-led ecological restoration through Grain-for-Green, resulting in significant land cover improvement and ES enhancement. Meanwhile, in the Yangtze River Basin, a combination of mechanisms, ecosystem compensation, and basin-scale ES accounting supports more mature policy–science integration. By contrast, ecosystem governance in permafrost regions often involves co-management frameworks that integrate indigenous knowledge, local community participation, and long-term scientific monitoring. These contrasts highlight the necessity for flexible, context-aware policy frameworks that align ecological priorities with local capacities and stakeholder engagement.

4. Conclusions and Future Expectations

As a representative region of China’s cold ecosystems, Northeast China has gradually developed a relatively systematic research framework for ES, covering service identification, analysis of trade-offs and synergies, exploration of driving factors, and discussion of regulatory pathways. These studies have contributed to building regional ecological security and promoting sustainable development. However, existing research still faces several limitations in theoretical innovation, methodological depth, regional relevance, and policy application. Future studies should aim to be more systematic, forward-looking, and practice-oriented. Key directions for future research include the following: (1) Strengthening the understanding of ecological processes and service mechanisms in cold regions: Most current studies focus on static spatial patterns, while the role of cold-region processes, such as freeze–thaw cycles, snow cover, and seasonal hydrology, in shaping ESs is still underexplored. Future work should focus on the mechanisms behind service generation in integrated ecosystems such as forests, wetlands, and farmlands. Simulation models that reflect cold-region ecological processes, combined with temperature sensitivity analysis and monitoring of freeze–thaw dynamics, are needed to improve scientific explanations of service evolution. (2) Future research should enhance the integration of multi-source data and promote dynamic scenario simulation: Existing assessments often rely on static data and lack the capacity to simulate long-term changes or evaluate future policy impacts. To address this, the coupling of ecological models (e.g., InVEST, FLUS, PLUS, and System Dynamics) with machine learning techniques (e.g., Random Forest, XGBoost, SHAP) and big data platforms is recommended. These approaches can improve the accuracy and adaptability of ES simulations under complex and evolving scenarios. Moreover, adopting advanced quantitative methods such as Partial Least Squares Structural Equation Modeling (PLS-SEM) could help capture the interactions among natural, social, and policy-related factors. PLS-SEM is particularly advantageous for analyzing non-normally distributed data and for modeling latent variables with multiple indicators. Its use in ES research can enhance our understanding of trade-offs, synergies, and spatial heterogeneity, while providing stronger empirical support for targeted policy interventions in cold regions. (3) Exploring the causal mechanisms and cross-scale interactions of trade-offs and synergies: Many studies still rely on correlation-based methods, which are not sufficient to reveal complex causal relationships and feedback loops. Future research should combine methods such as GTWR and structural equation modeling to identify the interaction pathways of ESs, their spatial heterogeneity, and threshold effects under both natural and human influences. (4) Developing service-oriented regulatory strategies and policy evaluation systems: Current regulatory strategies are often based on service distribution but lack integration with spatial planning and real-world policy tools. Future work should combine ES assessment results with land use planning, ecological compensation mechanisms, black soil protection, and green agriculture policies. It is also important to build policy assessment tools that are dynamic, adaptable, and simulation-based, forming an integrated system of “identification-simulation-assessment-feedback”. (5) Enhancing the understanding of supply–demand matching and its impact on well-being: In Northeast China, there is often a mismatch between high ES supply and relatively low actual demand or use. Future research should focus on analyzing supply–demand relationships by considering factors such as population distribution, land use, and socioeconomic conditions. Identifying the causes of mismatches will help develop targeted ecological compensation strategies and green infrastructure plans to strengthen the role of ESs in supporting human well-being.

Author Contributions

Methodology, data analysis, writing—original draft preparation, X.G.; Data collection, C.Y.; Supervision, Z.W.; Supervision, modification, L.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Xiaomeng Guo of FUNDER. The research was supported by the Natural Science Foundation of China (52309042), the Natural Science Foundation of Heilongjiang Province (LH2023E004), and the “Young Talents” Project of Northeast Agricultural University (22QC07).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Costanza, R.; d’Arge, R.; Rudolf, d.G.; Stephen, F.; Monica, G.; Karin, L.; Shahid, N.; Robert, V.O.; Jose, P.; Robert, G.R.; et al. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [Google Scholar] [CrossRef]
  2. Costanza, R.; de Groot, R.; Braat, L.; Kubiszewski, I.; Fioramonti, L.; Sutton, P.; Farber, S.; Grasso, M. Twenty years of ecosystem services: How far have we come and how far do we still need to go? Ecosyst. Serv. 2017, 28, 1–16. [Google Scholar] [CrossRef]
  3. MEA, Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis; Island Press: Washington, DC, USA, 2005. [Google Scholar]
  4. Fu, B.; Zhang, L. Land-use change and ecosystem services: Concepts, methods and progress. Prog. Geogr. 2014, 33, 441–446. (In Chinese) [Google Scholar] [CrossRef]
  5. Zhou, J.; Yang, J.; Tang, Z.; Xue, L.; Zhang, W.; Zhang, J. Driving mechanisms of ecosystem services and their trade-offs and synergies in the transition zone between the Qinghai-Tibet Plateau and the Loess Plateau. Ecol. Indic. 2025, 171, 113148. [Google Scholar] [CrossRef]
  6. Ahmadi Mirghaed, F.; Mohammadzadeh, M.; Salmanmahiny, A.; Mirkarimi, S.H. Decision scenarios using ecosystem services for land allocation optimization across Gharehsoo watershed in northern Iran. Ecol. Indic. 2020, 117, 106645. [Google Scholar] [CrossRef]
  7. Anand, S.; Gupta, S. Provisioning ecosystem services: Multitier bibliometric analysis and visualisation. Environ. Sustain. Indic. 2020, 8, 100081. [Google Scholar] [CrossRef]
  8. Li, Z.; Xia, J.; Deng, X.; Yan, H. Multilevel modelling of impacts of human and natural factors on ecosystem services change in an oasis, Northwest China. Resour. Conserv. Recycl. 2021, 169, 105474. [Google Scholar] [CrossRef]
  9. Huang, L.; Wang, B.; Niu, X.; Gao, P.; Song, Q. Changes in ecosystem services and an analysis of driving factors for China’s Natural Forest Conservation Program. Ecol. Evol. 2019, 9, 3700–3716. [Google Scholar] [CrossRef]
  10. Wang, H.; Liu, G.; Li, Z.; Zhang, L.; Wang, Z. Processes and driving forces for changing vegetation ecosystem services: Insights from the Shaanxi Province of China. Ecol. Indic. 2020, 112, 106105. [Google Scholar] [CrossRef]
  11. Ye, Z.; Gong, J.; Teng, W.; Yang, Z.; Wang, Y.; Xu, T.; Jin, T. Climate change and human activities have resulted in substantial alterations to ecosystem quality within the Yarlung Zangbo River basin. Ecol. Front. 2025, 45, 925–938. [Google Scholar] [CrossRef]
  12. Xiang, H.; Zhang, J.; Mao, D.; Wang, Z.; Qiu, Z.; Yan, H. Identifying spatial similarities and mismatches between supply and demand of ecosystem services for sustainable Northeast China. Ecol. Indic. 2022, 134, 108501. [Google Scholar] [CrossRef]
  13. Yang, S.; Bai, Y.; Alatalo, J.M.; Wang, H.; Jiang, B.; Liu, G.; Chen, J. Spatio-temporal changes in water-related ecosystem services provision and trade-offs with food production. J. Clean. Prod. 2021, 286, 125316. [Google Scholar] [CrossRef]
  14. Li, X.; Huang, C.; Jin, H.; Han, Y.; Kang, S.; Liu, J.; Cai, H.; Hu, T.; Yang, G.; Yu, H.; et al. Spatio-temporal patterns of carbon storage derived using the invest model in Heilongjiang Province, Northeast China. Front. Earth Sci. 2022, 10, 846456. [Google Scholar] [CrossRef]
  15. Wang, S.; Shi, H.; Xu, X.; Huang, L.; Gu, Q.; Liu, H. County zoning and optimization paths for trade-offs and synergies of ecosystem services in Northeast China. Ecol. Indic. 2024, 164, 112044. [Google Scholar] [CrossRef]
  16. Yan, F.; Zhang, S. Ecosystem service decline in response to wetland loss in the Sanjiang Plain, Northeast China. Ecol. Eng. 2019, 130, 117–121. [Google Scholar] [CrossRef]
  17. Jia, G.; Dong, Y.; Zhang, S.; He, X.; Zheng, H.; Guo, Y.; Shen, G.; Chen, W. Spatiotemporal changes of ecosystem service trade-offs under the influence of forest conservation project in Northeast China. Front. Ecol. Evol. 2022, 10, 978145. [Google Scholar] [CrossRef]
  18. Guo, X.; Yang, Y.; Zhang, Y.; Kalantari, M.; Sun, J.; Sun, W.; Guan, G.; Du, G. The spatiotemporal correlation between human activity intensity and the evolution of ecosystem service value in the Songnen Plain, China. Land 2024, 13, 1158. [Google Scholar] [CrossRef]
  19. Booi, S.; Mishi, S.; Andersen, O. Ecosystem services: A systematic review of provisioning and cultural ecosystem services in Estuaries. Sustainability 2022, 14, 7252. [Google Scholar] [CrossRef]
  20. Balzan, M.V.; Sadula, R.; Scalvenzi, L. Assessing ecosystem services supplied by agroecosystems in Mediterranean Europe: A literature review. Land 2020, 9, 245. [Google Scholar] [CrossRef]
  21. Li, P.; Chen, J.; Li, Y.; Wu, W. Using the InVEST-PLUS model to predict and analyze the pattern of ecosystem carbon storage in Liaoning Province, China. Remote Sens. 2023, 15, 4050. [Google Scholar] [CrossRef]
  22. Ren, D.; Cao, A.; Wang, F. Response and multi-scenario prediction of carbon storage and habitat quality to land use in Liaoning Province, China. Sustainability 2023, 15, 4500. [Google Scholar] [CrossRef]
  23. Wang, H.; Wang, W.J.; Liu, Z.; Wang, L.; Zhang, W.; Zou, Y.; Jiang, M. Combined effects of multi-land use decisions and climate change on water-related ecosystem services in Northeast China. J. Environ. Manag. 2022, 315, 115131. [Google Scholar] [CrossRef]
  24. Riao, D.; Zhu, X.; Tong, Z.; Zhang, J.; Wang, A. Study on land use/cover change and ecosystem services in Harbin, China. Sustainability 2020, 12, 6076. [Google Scholar] [CrossRef]
  25. Ning, J.; Shi, D.; Zhou, S.; Xia, Z. Spatial and temporal patterns of ecosystem services and trade-off synergistic relationships in Bin County, Heilongjiang Province. Res. Soil Water Conserv. 2022, 29, 293–300. (In Chinese) [Google Scholar] [CrossRef]
  26. Zhang, L.; Yu, X.; Jiang, M.; Xue, Z.; Lu, X.; Zou, Y. A consistent ecosystem services valuation method based on total economic value and equivalent value factors: A case study in the Sanjiang Plain, Northeast China. Ecol. Complex. 2017, 29, 40–48. [Google Scholar] [CrossRef]
  27. Guo, X.; Wang, L.; Fu, Q.; Ma, F. Ecological function zoning framework for small watershed ecosystem services based on multivariate analysis from a scale perspective. Land 2024, 13, 1030. [Google Scholar] [CrossRef]
  28. Dai, X.; Wang, Y.; Li, X.; Wang, K.; Zhou, J.; Ni, H. Effects of temporal and spatial changes in wetlands on regional carbon storage in the Naoli River Basin, Sanjiang Plain, China. Land 2023, 12, 1300. [Google Scholar] [CrossRef]
  29. Lyu, L.; Jiang, R.; Zheng, D.; Liang, L. Impact of climate change and land use/cover change on water yield in the Liaohe River Basin, Northeast China. J. Arid Land 2025, 17, 182–199. [Google Scholar] [CrossRef]
  30. Cheng, W.; Shen, B.; Xin, X.; Gu, Q.; Guo, T. Spatiotemporal variations of grassland ecosystem service value and its influencing factors in Inner Mongolia, China. Agronomy 2022, 12, 2090. [Google Scholar] [CrossRef]
  31. Gao, F.; Xin, X.; Song, J.; Li, X.; Zhang, L.; Zhang, Y.; Liu, J. Simulation of LUCC dynamics and estimation of carbon stock under different SSP-RCP scenarios in Heilongjiang Province. Land 2023, 12, 1665. [Google Scholar] [CrossRef]
  32. Li, Z.; Fang, H. Modeling the impact of climate change on watershed discharge and sediment yield in the black soil region, northeastern China. Geomorphology 2017, 293, 255–271. [Google Scholar] [CrossRef]
  33. Chu, X.; Zhan, J.; Li, Z.; Zhang, F.; Qi, W. Assessment on forest carbon sequestration in the Three-North Shelterbelt Program region, China. J. Clean. Prod. 2019, 215, 382–389. [Google Scholar] [CrossRef]
  34. Xia, H.; Kong, W.; Zhou, G.; Sun, O.J. Impacts of landscape patterns on water-related ecosystem services under natural restoration in Liaohe River Reserve, China. Sci. Total Environ. 2021, 792, 148290. [Google Scholar] [CrossRef] [PubMed]
  35. Zhu, Q.; Tran, L.T.; Wei, W. Understanding synergistic ecosystem services in China’s Northeast Forest Belt: A blueprint for spatially targeted management. Ecol. Indic. 2024, 166, 112434. [Google Scholar] [CrossRef]
  36. Li, Y.; Cong, Y.; Zhang, J.; Wang, L.; Fei, L. Ecosystem service trade-offs and synergies in a temperate agricultural region in Northeast China. Remote Sens. 2025, 17, 852. [Google Scholar] [CrossRef]
  37. Wang, H.; Wang, W.; Wang, L.; Ma, S.; Liu, Z.; Zhang, W.; Zou, Y.; Jiang, M. Impacts of future climate and land use/cover changes on water-related ecosystem services in Changbai Mountains, Northeast China. Front. Ecol. Evol. 2022, 10, 854497. [Google Scholar] [CrossRef]
  38. Liu, L.; Wang, W.J.; Wang, L.; Cong, Y.; Wu, H. Impacts of multi-land use decisions on temperate forest habitat quality in the Changbai Mountain Region, Northeast China. Ecol. Evol. 2025, 15, e71123. [Google Scholar] [CrossRef]
  39. Guo, X.; Wang, L.; Wang, Z.; Fu, Q.; Ma, F. Comparative analysis of dynamic changes and scenario predictions of carbon storage in a small watershed driven by social-natural factors in cold regions. Land Degrad. Dev. 2025, 36, 3134–3149. [Google Scholar] [CrossRef]
  40. Xiang, H.; Jia, M.; Wang, Z.; Li, L.; Mao, D.; Zhang, D.; Cui, G.; Zhu, W. Impacts of land cover changes on ecosystem carbon stocks over the Transboundary Tumen River Basin in Northeast Asia. Chin. Geogr. Sci. 2018, 28, 973–985. [Google Scholar] [CrossRef]
  41. Wang, K.; Li, X.; Lyu, X.; Dang, D.; Cao, W.; Du, Y. Unraveling the complex interconnections between food-energy-water nexus sustainability and the supply-demand of related ecosystem services. J. Environ. Manag. 2024, 370, 122532. [Google Scholar] [CrossRef]
  42. Wang, Z.; Li, Y.; Luo, J.; Liu, F.; Wang, Y.; Li, H. Spatio-temporal evolution of habitat quality in the Nenjiang River Basin, Northeast China. Environ. Ecol. 2020, 2, 19–26. (In Chinese) [Google Scholar]
  43. Zhang, J.; Xiang, H.; Hashimoto, S.; Okuro, T. Observational scale matters for ecosystem services interactions and spatial distributions: A case study of the Ussuri Watershed, China. Sustainability 2021, 13, 10649. [Google Scholar] [CrossRef]
  44. Wu, J.; Li, Y.; Huang, L.; Lu, Z.; Yu, D.; Zhou, L.; Dai, L. Spatiotemporal variation of water yield and its driving factors in Northeast China. Chin. J. Ecol. 2017, 36, 3216–3223. (In Chinese) [Google Scholar] [CrossRef]
  45. Dai, L.; Li, S.; Lewis, B.J.; Wu, J.; Yu, D.; Zhou, W.; Zhou, L.; Wu, S. The influence of land use change on the spatial–temporal variability of habitat quality between 1990 and 2010 in Northeast China. J. For. Res. 2018, 30, 2227–2236. [Google Scholar] [CrossRef]
  46. Yang, Z.; Xu, J.; Feng, X.; Guo, M.; Jin, Y.; Gao, X. Effects of land use change on habitat based on InVEST model in Northeast China. Ecol. Sci. 2018, 37, 139–147. (In Chinese) [Google Scholar] [CrossRef]
  47. Mao, D.; He, X.; Wang, Z.; Tian, Y.; Xiang, H.; Yu, H.; Man, W.; Jia, M.; Ren, C.; Zheng, H. Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. J. Clean. Prod. 2019, 240, 117961. [Google Scholar] [CrossRef]
  48. Zhang, X.; Dong, L.; Huang, Y.; Xu, Y.; Qin, H.; Qiao, Z. Equilibrium relationship between ecosystem service supply and consumption driven by economic development and ecological restoration. Sustainability 2021, 13, 1486. [Google Scholar] [CrossRef]
  49. Wang, J.; Xing, Y.; Chang, X.; Yang, H. Analysis of spatial distribution of ecosystem services and driving factors in Northeast China. Environ. Sci. 2024, 45, 5386–5394. (In Chinese) [Google Scholar] [CrossRef]
  50. Zhang, P.; Shi, J. Slow recovery of natural ecosystems as an important factor restricting regional coordinated development. Ecol. Indic. 2024, 158, 111435. [Google Scholar] [CrossRef]
  51. Tang, J.; Jin, X.; Mu, H.; Yang, S.; He, R.; Li, X.; Wang, W.; Huang, S.; Zu, J.; Wang, H.; et al. Impacts of human footprint on habitat quality and permafrost environment in Northeast China. Ecol. Indic. 2025, 175, 113587. [Google Scholar] [CrossRef]
  52. Li, Q.; Pu, Y.; Gao, W. Spatial correlation analysis and prediction of carbon stock of “Production-living-ecological spaces” in the three northeastern provinces, China. Heliyon 2023, 9, e18923. [Google Scholar] [CrossRef] [PubMed]
  53. An, Z.; Sun, C.; Hao, S. Matching relationship between supply and demand of ecosystem services from the perspective of water-energy-food nexus in Northeast China. Acta Ecol. Sin. 2024, 44, 4170–4186. (In Chinese) [Google Scholar] [CrossRef]
  54. Wan, D.; Liu, J.; Zhao, D. Assessment of carbon storage under different ssp-rcp scenarios in terrestrial ecosystems of Jilin Province, China. Int. J. Environ. Res. Public Health 2023, 20, 3691. [Google Scholar] [CrossRef] [PubMed]
  55. Jin, X.; Wang, J.; Liu, D.; Li, S.; Zhang, Y.; Wang, G. Managing the supply–demand mismatches and potential flows of ecosystem services in Jilin Province, China, from a regional integration perspective. Land 2024, 13, 1504. [Google Scholar] [CrossRef]
  56. Li, H.; Mao, D.; Li, X.; Wang, Z.; Jia, M.; Huang, X.; Xiao, Y.; Xiang, H. Understanding the contrasting effects of policy-driven ecosystem conservation projects in northeastern China. Ecol. Indic. 2022, 135, 108578. [Google Scholar] [CrossRef]
  57. Zhao, H.Q.; Liu, G.; Yang, Z.H.; Liu, X.Q.; Miao, Q.F.; Fu, H.C. Ecosystem services assessment and multi-scenario prediction in Liaoning Province from 2000 to 2020. Environ. Sci. 2024, 45, 4137–4151. (In Chinese) [Google Scholar] [CrossRef]
  58. Wu, Q.; Wang, L.; Wang, T.; Chen, H.; Du, P. Global versus local? a study on the synergistic relationship of ecosystem service trade-offs from multiple perspectives based on ecological restoration zoning of national land space—A case study of Liaoning Province. Appl. Sci. 2024, 14, 10421. [Google Scholar] [CrossRef]
  59. Zhu, X.; Qie, R.; Luo, C.; Zhang, W. Assessment and driving factors of wetland ecosystem service function in Northeast China based on InVEST-PLUS model. Water 2024, 16, 2153. [Google Scholar] [CrossRef]
  60. Dou, H.; Li, X.; Li, S.; Dang, D.; Li, X.; Lyu, X.; Li, M.; Liu, S. Mapping ecosystem services bundles for analyzing spatial trade-offs in inner Mongolia, China. J. Clean. Prod. 2020, 256, 120444. [Google Scholar] [CrossRef]
  61. Han, Z.; Liu, C.; Yan, X.; Li, X.; Wang, X. Coupling coordination and matches in ecosystem services supply-demand for ecological zoning management: A case study of Dalian. Acta Ecol. Sin. 2021, 41, 9064–9075. (In Chinese) [Google Scholar] [CrossRef]
  62. Qu, Y.; Lu, M. Identifying conservation priorities and management strategies based on ecosystem services to improve urban sustainability in Harbin, China. PeerJ 2018, 6, e4597. [Google Scholar] [CrossRef] [PubMed]
  63. Qi, Y.; Hu, Y. Spatial-temporal variation and driving forces of water yield in Harbin City. Bull. Soil Water Conserv. 2023, 43, 294–303. (In Chinese) [Google Scholar] [CrossRef]
  64. Zhang, T.; Hu, Y.; Hu, H. Prediction of land use and habitat quality in Harbin city based on the PLUS- InVEST Model. Environ. Sci. 2024, 48, 4709–4721. (In Chinese) [Google Scholar] [CrossRef]
  65. Qi, Y.; Zhao, C.; Shen, P.; Ren, S.; Hu, Y. Spatiotemporal evolution of soil conservation function value and its driving forces in Harbin city. Bull. Soil Water Conserv. 2024, 44, 357–367. (In Chinese) [Google Scholar] [CrossRef]
  66. Qi, Y.; Shen, P.; Ren, S.; Chen, T.; Hu, Y. Coupling effect of landscape patterns on the spatial and temporal distribution of ecosystem services: A case study in Harbin City, Northeast China. Sci. Rep. 2025, 15, 4606. [Google Scholar] [CrossRef]
  67. Li, T.; Wu, Y.; Pan, Y.; Meng, D. Spatio-temporal variation evaluation of ecosystem health in Harbin from the perspective of water-energy-food nexus. J. Chin. Urban For. 2025, 23, 84–91. (In Chinese) [Google Scholar]
  68. Chang, B.; Chen, B.; Chen, W.; Xu, S.; He, X.; Yao, J.; Huang, Y. Analysis of trade-off and synergy of ecosystem services and driving forces in urban agglomerations in Northern China. Ecol. Indic. 2024, 165, 112210. [Google Scholar] [CrossRef]
  69. Gan, S.; Xiao, Y.; Qin, K.; Liu, J.; Xu, J.; Wang, Y.; Niu, Y.; Huang, M.; Xie, G. Analyzing the interrelationships among various ecosystem services from the perspective of ecosystem service bundles in Shenyang, China. Land 2022, 11, 515. [Google Scholar] [CrossRef]
  70. Li, Y.; Liu, Z.; Li, S.; Li, X. Multi-scenario simulation analysis of land use and carbon storage changes in Changchun city based on flus and invest model. Land 2022, 11, 647. [Google Scholar] [CrossRef]
  71. Cui, F.; Tang, H.; Zhang, Q.; Wang, B.; Dai, L. Integrating ecosystem services supply and demand into optimized management at different scales: A case study in Hulunbuir, China. Ecosyst. Serv. 2019, 39, 100984. [Google Scholar] [CrossRef]
  72. Li, J. Identification of ecosystem services supply and demand and driving factors in Taihu Lake Basin. Environ. Sci. Pollut. Res. 2022, 29, 29735–29745. [Google Scholar] [CrossRef]
  73. Liu, Y.; Xu, G.; Jiang, H. Synergy between ecosystem services and ecosystem health in the forest area of Northeast China. Prog. Geogr. 2015, 34, 761–771. (In Chinese) [Google Scholar] [CrossRef]
  74. Zhang, Q.; Wu, Y.; Zhao, Z. Identification of Harbin ecological function degradation areas based on ecological importance assessment and ecological sensitivity. Sustainability 2024, 16, 6763. [Google Scholar] [CrossRef]
  75. Zhang, S.; Liu, L.; Zhang, L.; Zhuang, Y.; Du, J. An optimization model for carbon capture utilization and storage supply chain: A case study in Northeastern China. Appl. Energy 2018, 231, 194–206. [Google Scholar] [CrossRef]
  76. Cord, A.F.; Bartkowski, B.; Beckmann, M.; Dittrich, A.; Hermans-Neumann, K.; Kaim, A.; Lienhoop, N.; Locher-Krause, K.; Priess, J.; Schröter-Schlaack, C.; et al. Towards systematic analyses of ecosystem service trade-offs and synergies: Main concepts, methods and the road ahead. Ecosyst. Serv. 2017, 28, 264–272. [Google Scholar] [CrossRef]
  77. Leong, R.A.T.; Fung, T.K.; Sachidhanandam, U.; Drillet, Z.; Edwards, P.J.; Richards, D.R. Use of structural equation modeling to explore influences on perceptions of ecosystem services and disservices attributed to birds in Singapore. Ecosyst. Serv. 2020, 46, 101211. [Google Scholar] [CrossRef]
  78. Almeida, B.; David, J.; Campos, F.S.; Cabral, P. Satellite-based machine mearning modelling of ecosystem services indicators: A review and meta-analysis. Appl. Geogr. 2024, 165, 103249. [Google Scholar] [CrossRef]
  79. Dade, M.C.; Mitchell, M.G.E.; McAlpine, C.A.; Rhodes, J.R. Assessing ecosystem service trade-offs and synergies: The need for a more mechanistic approach. Ambio 2019, 48, 1116–1128. [Google Scholar] [CrossRef]
  80. Huang, Y.; Wu, J. Spatial and temporal driving mechanisms of ecosystem service trade-off/synergy in national key urban agglomerations: A case study of the Yangtze River Delta urban agglomeration in China. Ecol. Indic. 2023, 154, 110800. [Google Scholar] [CrossRef]
  81. Martínez-Harms, M.; Balvanera, P. Methods for mapping ecosystem service supply: A review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 2012, 8, 17–25. [Google Scholar] [CrossRef]
  82. Lee, H.; Lautenbach, S. A quantitative review of relationships between ecosystem services. Ecol. Indic. 2016, 66, 340–351. [Google Scholar] [CrossRef]
  83. Huang, F.; Zuo, L.; Gao, J.F.; Jiang, Y.; Du, F.; Zhang, Y. Exploring the driving factors of trade-offs and synergies among ecological functional zones based on ecosystem service bundles. Ecol. Indic. 2023, 146, 109827. [Google Scholar] [CrossRef]
  84. Li, B.; Yang, G.; Wan, R.; Hamilton, D.P.; Wang, X. Unravelling the spatiotemporal trade-offs and synergies among hydrological ecosystem services in a large floodplain lake. Ecol. Indic. 2025, 172, 113255. [Google Scholar] [CrossRef]
  85. Zhao, X.; Sun, W.; Zhang, S.; Yang, Y.; Yang, Y.; Lan, H.; Yu, T.; Li, X.; Xie, Y. How can ecosystem services trade-offs/synergies help select the optimal cultivated land restoration pattern? A study based on multi-scenario simulations. Appl. Geogr. 2025, 177, 103563. [Google Scholar] [CrossRef]
  86. Bagstad, K.J.; Cohen, E.; Ancona, Z.H.; McNulty, S.G.; Sun, G. The sensitivity of ecosystem service models to choices of input data and spatial resolution. Appl. Geogr. 2018, 93, 25–36. [Google Scholar] [CrossRef]
  87. Karimi, J.D.; Corstanje, R.; Harris, J.A. Bundling ecosystem services at a high resolution in the UK: Trade-offs and synergies in urban landscapes. Landsc. Ecol. 2021, 36, 1817–1835. [Google Scholar] [CrossRef]
  88. Li, K.; Zhao, J.; Li, Y.; Lin, Y. Identifying trade-offs and synergies among land use functions using an XGBoost-SHAP model: A case study of Kunming, China. Ecol. Indic. 2025, 172, 113330. [Google Scholar] [CrossRef]
  89. Du, P.; Huai, H.; Wu, X.; Wang, H.; Liu, W.; Tang, X. Using XGBoost-SHAP for understanding the ecosystem services trade-off effects and driving mechanisms in ecologically fragile areas. Front. Plant Sci. 2025, 16, 1552818. [Google Scholar] [CrossRef]
  90. Fu, B.; Su, C.; Wei, Y.; Willett, I.R.; Lü, Y.; Liu, G. Double counting in ecosystem services valuation: Causes and countermeasures. Ecol. Res. 2010, 26, 1–14. [Google Scholar] [CrossRef]
  91. Hang, Y.; Lu, X.; Li, X. Spatiotemporal differentiation characteristics and zoning of cultivated land system resilience in the Songnen Plain. Sustainability 2025, 17, 4314. [Google Scholar] [CrossRef]
  92. Jiang, Y.; Du, G.; Teng, H.; Wang, J.; Li, H. Multi-scenario land use change simulation and spatial response of ecosystem service value in Black Soil Region of Northeast China. Land 2023, 12, 962. [Google Scholar] [CrossRef]
  93. Deng, G.; Liu, Y.; Jiang, H.; Zhu, S.; Wen, Y.; Sheng, L.; Guo, Y. Disentangling the relative and cumulative impacts of diverse policies on food- and water-related ecosystem services and their trade-offs in ecologically fragile areas. J. Clean. Prod. 2024, 447, 141322. [Google Scholar] [CrossRef]
  94. Hanes, R.J.; Gopalakrishnan, V.; Bakshi, B.R. Including nature in the food-energy-water nexus can improve sustainability across multiple ecosystem services. Resour. Conserv. Recycl. 2018, 137, 214–228. [Google Scholar] [CrossRef]
  95. Chen, Y.; Zhao, X.; Wu, S.; Tong, Z. Developing ecological protection redline policy for land use pattern optimization in the typical black soil region of Northeastern China. Front. Environ. Sci. 2024, 12, 1422077. [Google Scholar] [CrossRef]
  96. Zhang, K.; Lin, N.; Xu, D.; Yu, D.; Zou, C. Research advance on ecological security in China: Assessment models and management measures. J. Ecol. Rural. Environ. 2018, 34, 1057–1063. (In Chinese) [Google Scholar] [CrossRef]
  97. Tang, Z.; Zhou, Z.; Wang, D.; Luo, F.; Bai, J.; Fu, Y. Impact of vegetation restoration on ecosystem services in the Loess plateau, a case study in the Jinghe Watershed, China. Ecol. Indic. 2022, 142, 109183. [Google Scholar] [CrossRef]
  98. Zhang, Z.M.; Gao, J.F.; Fan, X.Y.; Lan, Y.; Zhao, M.S. Response of ecosystem services to socioeconomic development in the Yangtze River Basin, China. Ecol. Indic. 2017, 72, 481–493. [Google Scholar] [CrossRef]
  99. Schuur, E.A.G.; McGuire, A.D.; Schädel, C.; Grosse, G.; Harden, J.W.; Hayes, D.J.; Hugelius, G.; Koven, C.D.; Kuhry, P.; Lawrence, D.M.; et al. Climate change and the permafrost carbon feedback. Nature 2015, 520, 171–179. [Google Scholar] [CrossRef] [PubMed]
  100. Lu, Y.; Fu, B.; Feng, X.; Zeng, Y.; Liu, Y.; Chang, R.; Sun, G.; Wu, B. A policy-driven large scale ecological restoration: Quantifying ecosystem services changes in the Loess Plateau of China. PLoS ONE 2012, 7, e31782. [Google Scholar] [CrossRef]
  101. Wang, Z.; Mao, J.; Peng, Y.; Wu, J.; Wang, X.; Su, L. Exploring the driving forces of ecosystem services in the Yangtze River Basin, China. Land 2025, 14, 411. [Google Scholar] [CrossRef]
  102. Schuur, E.A.G.; Abbott, B.W.; Commane, R.; Ernakovich, J.; Euskirchen, E.; Hugelius, G.; Grosse, G.; Jones, M.; Koven, C.D.; Leshyk, V.; et al. Permafrost and climate change: Carbon cycle feedbacks from the Warming Arctic. Annu. Rev. Environ. Resour. 2022, 47, 343–371. [Google Scholar] [CrossRef]
  103. Jiang, J.; Zhao, H.; Zhang, J.; Dong, B. Trade-offs between agricultural production and ecosystem services under different land management scenarios in the Loess Plateau of China. Sci. Rep. 2025, 15, 21385. [Google Scholar] [CrossRef]
  104. Chen, S.Y.; Wei, P.J.; Wu, T.H.; Wu, Q.B.; Luo, F.D. Effect of permafrost degradation on carbon sequestration of alpine ecosystems. Sci. Total Environ. 2023, 899, 165642. [Google Scholar] [CrossRef]
Sustainability 17 07352 g001
Figure 1. Geographical location of the study area.
Figure 1. Geographical location of the study area.
Sustainability 17 07352 g001
Sustainability 17 07352 g002
Figure 2. Flow diagram for systematic reviews.
Figure 2. Flow diagram for systematic reviews.
Sustainability 17 07352 g002
Sustainability 17 07352 g003
Figure 3. Key ecological policies in Northeast China.
Figure 3. Key ecological policies in Northeast China.
Sustainability 17 07352 g003
Table 1. Summary of ES research in Northeast China.
Table 1. Summary of ES research in Northeast China.
Study AreaES TypesScaleYearsRES
WYSRCSNPPFPPPMPOCPHBSPFRHMPAWPLSTRED
Liaohe River Reserve√√ √ natural region2007, 2011, 2015[34]
Northeast Forest Belt√√ √√ √ natural region2005, 2015[35]
Songnen Plain√√ √ √√ natural region2000, 2010, 2020[36]
Changbai Mountain region√√ natural region2020, 2050[37]
Changbai Mountain region √ natural region1990, 2020, 2050[38]
Ashi River basin √ watershed1990, 2000, 2010, 2020, 2030[39]
Tumen River Basin √ watershed1990, 2015[40]
Naoli River Basin√ √ √ watershed2005, 2010, 2015[41]
Naoli River Basin√ watershed1993–2022[29]
Nenjiang River Basin √ watershed1980, 2000, 2015[42]
Naoli River Basin √ watershed1993, 1998, 2003, 2008, 2013, 2018, 2022[28]
Songhua River Basin√√ √ √ watershed2000, 2015[13]
Ussuri Watershed√√√ √ watershed, grid2015[43]
Ashi River Basin√√√ √ √ √ watershed, town, grid1995, 2015[27]
Northeast China√ province1990, 2000, 2010[44]
Northeast China √ province1990, 2000, 2010[45]
Northeast China √ province2005, 2010, 2015[46]
Northeast China√√√ √ √√ province2000, 2015[47]
Northeast China√ √ √ province1995, 2010, 2018[48]
Northeast China√√√ √ province2015[12]
Northeast China√√√ √√ province2020[49]
Northeast China√√√ √ √ province2005, 2010, 2015, 2020[50]
Northeast China √ province2000, 2020[51]
Northeast China√√ province1990, 2020[23]
Heilongjiang, Jilin, Liaoning Provinces √ province1990, 2000, 2010, 2020[52]
Heilongjiang, Jilin, Liaoning Provinces√ √ √ province2000, 2005, 2010, 2015, 2020[53]
Heilongjiang Province √ province1980, 2015[14]
Heilongjiang Province √ province2020, 2030, 2040, 2050[31]
Jilin Province √ province2020, 2030, 2040[54]
Jilin Province√√√ √ √ province2000, 2010, 2020[55]
Liaoning Province√√√ √ province2000, 2015[56]
Liaoning Province √ province2000, 2010, 2020[21]
Liaoning Province √ √ province2010, 2020, 2030[22]
Liaoning Province√√√ province2000, 2005, 2010, 2015, 2020[57]
Liaoning Province√√√ √ √ province2020[58]
Inner Mongolia Province√√ √√√ √ √ √ province2000–2019[30]
Three-North Shelterbelt Program region √ province1990, 1995, 2000, 2005, 2010, 2015[33]
Northeast China √√ √ province, city2000, 2010, 2020[59]
Inner Mongolia Province√√√ √√√√√√ √√province, county2010[60]
Northeast China√√ √ province, county, grid2000, 2020[15]
Dalian City√√ √√ √ √ city2015[61]
Harbin City√√ √√ √ √ city\[62]
Harbin City√ city2000, 2010, 2020[63]
Harbin City √ city2000, 2010, 2020[64]
Harbin City √ city2000, 2010, 2020[65]
Harbin City√√√ √ √ √ city2000, 2010, 2020[66]
Harbin City√ √ √ city2000, 2010, 2020[67]
Urban Agglomeration in Liaoning Province√√√ √ √ √ city2000, 2020[68]
Shenyang City√√ √√ √√√√ √ city2000, 2019[69]
Changchun City √ city2010, 2020, 2030[70]
Hulun Buir City√ √ √ √ √ city, county2015[71]
Binxian, Harbin√√√ county2000, 2010, 2020[25]
Arun Banner in Hulun Buir City√√ √ √√ county2005, 2010, 2015, 2018[72]
Wangqing, Jiling Province√√ √ √ √ county\[73]
Abbreviation: RES—references; WY—water yield; SR—soil retention; CS—carbon storage; NPP—net primary productivity; FP—food production; PP—pasture production; MP—meat production; OCP—oil-bearing crops production; HB—habitat quality; SP—sandstorm prevention; FR—flood regulation; HM—humidification; PA—purified air; WP—water purification; LS—landscape aesthetics; TR—travel; ED—education. “√” indicates the specific ES types involved in the study.
Table 2. Comparative application of different methods in analyzing ES trade-offs and synergies.
Table 2. Comparative application of different methods in analyzing ES trade-offs and synergies.
Method TypeBrief DescriptionAdvantagesLimitationsScaleReferences
Correlation Analysis (Pearson, Spearman)Assessing the linear or ordinal correlation between two ES variablesSimple and intuitive; easy to computeCannot reveal causality; reflects only statistical correlationOverall[79,80]
Regression Analysis (e.g., multiple linear, nonlinear models)Building a model that treats a specific service as the dependent variable and the others as independent variablesStrong explanatory power for variablesAssumes linearity; difficult to capture complex nonlinear effectsOverall[81,82]
Principal Component Analysis (PCA)/Factor AnalysisReducing dimensionality and extracting common variance among servicesSuitable for identifying high-dimensional variable structuresWeak interpretability; prone to abstractionOverall[82]
Cluster Analysis (e.g., K-means)Identifies “service bundles” or functional groupsCaptures spatial heterogeneity of servicesCannot provide strength or direction of service relationshipsSpatial[80,83]
Spatial Autocorrelation (Moran’s I)Measures clustering of services in spaceReveals spatial synergy/conflict patternsSensitive to spatial scale, data resolution, and boundary settingsSpatial[84]
Root Mean Square Error (RMSE)Calculating deviation within service units and overall difference, reflecting strength of mismatchIntuitive; suitable for identifying pattern mismatch in trade-offs/synergiesUnidirectional; requires normalization, results may depend on scaleSpatial[17]
TSD (Trade-off/Synergy Degree Index)Using standardized service values to quantify trade-off or synergy levelsIdentifies relationship intensity; suitable for spatially explicit trade-off/synergy mappingSensitive to standardization; mechanism interpretation may be weakSpatial[85]
Geographically Weighted Regression (GWR)Analyzing spatial non-stationarity in service relationshipsSuitable for local effect analysis; reveals spatial variation in driversComplex model; sensitive to multicollinearity and parameter dependenceSpatial[36]
Coupling Coordination Degree (CCD)Assessing coordination between multiple service systems over space or timeReveals interaction strength and synergy balance; suitable for long-term monitoringSensitive to parameter settings; cannot explain mechanismsOverall[61]
Table 3. Relationship types of typical ES pairs in Northeast China.
Table 3. Relationship types of typical ES pairs in Northeast China.
Service PairRelationship TypeStudy AreaMain Driving Factors
Food production vs. Soil retentionTrade-offAgricultural areas in HeilongjiangIntensive cultivation, reduced vegetation cover
NPP vs. Water yieldSynergyBinxian, HarbinHigh forest coverage, accumulation of soil organic matter
Food production vs. NPPTrade-offSongnen PlainExcessive agricultural inputs leading to ecosystem degradation
NPP vs. Habitat qualitySynergyChangbai Mountain regionVegetation restoration, ecological engineering efforts
Water yield vs. Soil retentionSynergyNortheastern ChinaGrassland and shrubland protection on sloped land
Habitat quality vs. Food productionTrade-offWestern LiaoningCropland expansion encroaching on ecologically suitable land
Table 4. Comparative applicability of methods for identifying ES driving mechanisms.
Table 4. Comparative applicability of methods for identifying ES driving mechanisms.
Method NameAdvantagesLimitationsApplicable Scale
Pearson/Correlation AnalysisSimple and easy to implement; suitable for preliminary explorationUnable to reveal spatial or nonlinear relationshipsRegional/Provincial
Principal Component Analysis (PCA)/Cluster AnalysisIdentify service bundles and functional regionsNot suitable for analyzing dynamic evolution processesWatershed/Municipal level
GeodetectorReveals spatial heterogeneity and driving factorsLacks capability for temporal dynamic analysisCounty/Grid level
GWR/GTWR/MGWRConsiders spatial variation and reveals local differencesComputationally complex; requires high-quality input dataCounty/Township level
Machine Learning (RF/XGBoost)Strong in capturing nonlinear relationships; robust variable interpretabilityRequires large training datasets; model has black-box characteristicsApplicable across multiple spatial scales
Table 5. Major categories of common driving factors.
Table 5. Major categories of common driving factors.
Category of Driving FactorsRepresentative VariablesMajor Impact Pathways
Natural FactorsElevation, slope, aspect, NDVI, soil type, soil texture, topographic relief, etc.Determine the fundamental ecological pattern; affect water conservation, soil retention, and NPP distribution
Climatic FactorsPrecipitation, temperature, evapotranspiration, wind speed, humidity, etc.Govern the rate of ecological processes; influence water cycling and vegetation growth
Land Use/Land Cover FactorsCropland ratio, forest coverage, impervious surface area, land use intensity, landscape fragmentation, etc.Define spatial structure; excessive agricultural expansion may lead to trade-offs in regulating services
Socioeconomic FactorsPopulation density, GDP, urbanization rate, nighttime light index, road density, etc.Indicate intensity of human disturbance; reflect structural shifts in ES demands
Policy and Management FactorsEcological redlines, land zoning policies, nature reserve designation, reforestation programsRepresent institutional interventions; mediate or constrain trade-offs and synergies among ESs
Table 6. Comparative overview of ES across four representative regions.
Table 6. Comparative overview of ES across four representative regions.
DimensionsNortheast ChinaLoess PlateauYangtze River BasinPermafrost Zone
Dominant ES TypesFood production, carbon conservation, etc.Soil/water conservation, carbon conservation, biodiversityHydrological regulation, climate, cultural servicesCarbon sequestration, hydrological buffering, climate regulation
Assessment MethodsInVEST, statistical modelsRemote sensing, ES index modelsHotspot analysis, spatial econometric modelsProcess-based modeling, remote sensing
Trade-off/Synergy PatternsProvisioning vs. regulating in black soil farmlandSoil conservation, erosion riskWater yield vs. carbon storageCarbon release vs. ecosystem resilience
Driving FactorsNDVI, elevation, soil type, soil texture, cropping intensity, etc.Ecological restoration, slope gradient, etc.Climate, socioeconomic, land use, etc.Permafrost stability, ground-ice, hydrology, etc.
Policy Management Black soil conservation, ecological redlines, zoningGrain-for-Green, afforestation-driven ES recoveryES-based eco-compensation, basin-level planningIndigenous co-management, permafrost carbon protocols
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.

Share and Cite

MDPI and ACS Style

Guo, X.; Yang, C.; Wang, Z.; Wang, L. Ecosystem Services in Northeast China’s Cold Region: A Comprehensive Review of Patterns, Drivers, and Policy Responses. Sustainability 2025, 17, 7352. https://doi.org/10.3390/su17167352

AMA Style

Guo X, Yang C, Wang Z, Wang L. Ecosystem Services in Northeast China’s Cold Region: A Comprehensive Review of Patterns, Drivers, and Policy Responses. Sustainability. 2025; 17(16):7352. https://doi.org/10.3390/su17167352

Chicago/Turabian Style

Guo, Xiaomeng, Chuang Yang, Zilong Wang, and Li Wang. 2025. "Ecosystem Services in Northeast China’s Cold Region: A Comprehensive Review of Patterns, Drivers, and Policy Responses" Sustainability 17, no. 16: 7352. https://doi.org/10.3390/su17167352

APA Style

Guo, X., Yang, C., Wang, Z., & Wang, L. (2025). Ecosystem Services in Northeast China’s Cold Region: A Comprehensive Review of Patterns, Drivers, and Policy Responses. Sustainability, 17(16), 7352. https://doi.org/10.3390/su17167352

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop